Paint sludge (PS), a hazardous byproduct of wastewater treatment in paint manufacturing, presents significant environmental, economic, and social challenges when disposed of through conventional methods such as incineration and landfilling. In pursuit of a circular economy approach, this study investigates the feasibility of recycling PS generated by Paint Industry in Sri Lanka into cement-based construction materials by partially replacing natural sand in mortar mixtures. Mortar specimens were prepared with varying PS contents (5, 10, 15 and 20 wt %) and evaluated for compressive strength under different curing conditions, water absorption, thermal behavior, and heavy metal leachability.

Results indicated that incorporating up to 10% PS improved compressive strength by approximately 9% compared to the control, achieving values compliant with Sri Lanka  construction standards (26.17 N/mm²). Higher sludge contents led to strength deterioration due to increased organic and polymeric material presence. Water absorption increased proportionally with PS content, attributed to enhanced porosity. Thermal imaging revealed that blocks with 5% PS exhibited slower heat release, suggesting better insulation potential. Heavy metal leaching tests confirmed that the PS-incorporated blocks remained within acceptable environmental safety limits under varying pH conditions. This research demonstrates that PS can be effectively stabilized within a cementitious matrix, promoting waste utilization while enhancing the mechanical and thermal properties of building materials. This approach supports sustainable construction practices by reducing reliance on raw materials, minimizing landfill disposal, and lowering associated environmental impacts.

Keywords: Paint sludge recycling, Cement mortar, Cement properties, Heavy metal leachability

Unexplained Spike: Investigating the 2022 Road Fatalities in Tasmania

Vili Siale¹ and  Kathirgamalingam Somasundaraswaran²

¹Department of State Growth, Tasmania

²School of Engineering, University of Southern Queensland, Toowoomba, Queensland

Abstract

In 2022, there were 51 road fatalities—more than double the decade average of 21—an outcome that is both alarming and unacceptable. This research project examined the circumstances surrounding these fatalities to identify contributing factors and explore strategies to prevent such tragedies from recurring in the future. The literature review highlighted the increasing number of vehicles and road users as primary contributing factors to road incidents. Based on these findings, several predictive models were developed to better understand and anticipate risk patterns. However, vehicle and road user numbers in Tasmania did not significantly increase in 2022, suggesting that these factors alone cannot account for the sudden spike in road fatalities that year. Data from the U.S. Fatality Analysis Reporting System (FARS) revealed that lane departure crashes were the most common type of fatal incident. This finding aligns with existing literature, which identifies key contributing factors such as driver behaviour, excessive speed, road surface conditions, lighting, and horizontal road alignment. A similar analysis of crash data found that neither road defects nor the commonly cited contributing factors—such as speed, lighting, or road conditions—directly accounted for the 2022 fatalities. This suggests that driver behaviour may have played a more significant role despite the fact that most motorists involved were familiar with the crash locations. The underlying causes of this behavioural component, however, remain unclear.

In addition, given the significant role of the Safe System approach in influencing fatality outcomes, safety audits were conducted at all crash sites. The findings revealed that several locations featured forgiving road environments, which may have mitigated the severity or likelihood of crashes. Interestingly, the majority of these sites were rated as having relatively high safety standards, suggesting that infrastructure alone may not fully account for the fatal outcomes observed.

The research then explored unique events in 2022 that may have influenced driver performance. One such event was the implementation of compulsory COVID-19 vaccinations, which enabled the easing of public health restrictions. While there is no direct evidence linking vaccination to impaired driving, it is possible that broader pandemic-related stressors—combined with increased mobility and a sense of familiarity with the road network—may have contributed to risk-taking behaviours among motorists. These behavioural shifts could have played a role in the rise in road fatalities. Thus, the findings suggest that behavioural changes—possibly influenced by post-pandemic conditions—played a critical role, highlighting the need for deeper exploration of driver psychology and systemic resilience.

Static liquefaction is a major contributor to tailings dam failures. Cone penetration test (CPT) is commonly employed to characterize tailings and evaluate their liquefaction potential. In the mining industry, the state parameter is frequently used to assess static liquefaction, as it effectively captures the mechanical behavior of geomaterials. Nevertheless, accurately determining the state parameter remains a complex and sometimes contentious task. Since the 1980s, numerous methods have been proposed in the literature to evaluate the static liquefaction potential of both natural soils and tailings using the state parameter.

Post-liquefaction strength, defined as the shear strength mobilized during large deformations following liquefaction, is a critical parameter for evaluating the stability of earth structures constructed on or composed of liquefiable soils. Recent failures of tailings dams have prompted mine operators and design engineers to revise design standards to mitigate risks to human life, the environment, and economic assets. As a result, design practices are increasingly adopting a conservative approach that incorporates post-liquefaction shear strength and its ratio, particularly when embankment or foundation materials exhibit brittle behavior, regardless of the specific mechanism that triggers liquefaction.

In this study, four distinct empirical methods were employed to predict the state parameter. The results from these methods were compared with each other and with raw CPT data to evaluate prediction accuracy. At certain depths, discrepancies were observed—some methods indicated dilative behavior, while others suggested contractive behavior. These conflicting predictions can lead design engineers to adopt a more conservative approach. Similarly, the post-liquefaction strength ratio was estimated using various approaches: some relied directly on CPT data, while others used the previously predicted state parameters. Comparisons were made between the strength ratios derived from CPT data and those based on state parameter predictions. Conducting the analyses with the different methods helps the designer understand the potential variability of the material properties and behaviour and can be used to define material strengths in the stability analyses based on an understanding of the risks associated with the earth structures, specifically for mine dams. The inconsistencies observed in these comparisons highlight the need for caution when selecting methods for estimating both the state parameter and the post-liquefaction strength ratio. 

Roundabouts are increasingly being adopted as safer alternatives to unsignalised intersections, and their safety performance has been evaluated in numerous studies. However, despite the growing body of research, analytical methods for accurately estimating the actual safety outcomes of roundabouts remain underdeveloped. This study aims to establish a theoretical foundation by conducting a systematic literature review and meta-analysis to quantify the overall effect of converting traditional intersections into roundabouts based on crash severity.

A systematic review provides a clearly stated, reliable, and up-to-date summary of reported safety effects. This study utilized four major databases ScienceDirect, Scopus, the Transportation Research Board, and Google Scholar to identify relevant articles. Keywords such as “fatal crashes,” “serious injuries,” “property damage only,” and “roundabouts” were employed to guide the search. Articles were shortlisted using defined inclusion and exclusion criteria, with only studies published after 2000 considered for analysis. The final selection comprised 107 studies. Metadata extracted from these studies included publication year, study location, and key research themes. The highest number of publications (14) appeared in 2021. Country-wise analysis shows that Italy (26.6%), the United States (10.5%), and Canada (6.4%) contributed the most to the literature. A keyword co-occurrence analysis revealed dominant themes such as “road safety,” “roundabout,” “roads and streets,” “accident prevention,” “transportation safety,” and “motor transportation,” confirming the study’s thematic alignment.

Furthermore, the meta-analysis utilized reported safety effectiveness as the primary estimator, with many studies employing before-and-after comparisons to derive their conclusions. At this stage, the study presents a methodological framework for evaluating the safety impacts of converting conventional intersections into roundabouts. The incorporation of quality assessment within this framework will ensure that the findings are credible and high-quality with evidence. These insights provide valuable guidance for road designers and transportation policymakers seeking to improve traffic safety through informed infrastructure decisions.

Keywords: Roundabouts, Road Safety, Systematic Review, Meta-Analysis

Employees with proactive personalities tend to take initiative, challenge the status quo, and bring about constructive change. According to Trait Activation Theory, the expression of personality traits is influenced by situational cues. This study explores how servant leadership, which emphasizes empathy, stewardship, and support, activates and enhances the effect of a proactive personality on prosocial motivation an employee's desire to benefit others. While proactive individuals may already be inclined toward positive organizational contributions, the presence of servant leadership may further strengthen their prosocial tendencies. 
 This quantitative, cross-sectional study was conducted among employees in Sri Lanka’s banking sector. A structured, self-administered questionnaire was distributed to 280 employees using convenience sampling, yielding 220 valid responses, validated scales measured proactive personality, prosocial motivation, and servant leadership. Hierarchical regression analysis was performed using SPSS to examine direct and interaction effects. 
 Proactive personality was significantly and positively associated with prosocial motivation (β = 0.47, p < 0.001), indicating that individuals with proactive traits are more inclined to act for the benefit of others. The interaction effect between proactive personality and servant leadership was also significant (β = 0.22, p < 0.01), suggesting that the positive relationship between proactive personality and prosocial motivation is strengthened in the presence of servant leadership. A simple slope analysis showed that the relationship was stronger when servant leadership was high (simple slope = 0.61, p < 0.001) compared to when it was low (simple slope = 0.34, p < 0.01). The overall model accounted for 41% of the variance in prosocial motivation (R² = 0.41).
 Findings support Trait Activation Theory by demonstrating that servant leadership acts as a contextual trigger that enhances the prosocial behavior of proactive employees. Organizations, particularly in high-stakes environments like banking, should foster servant leadership to activate and sustain employees’ intrinsic motivation to help others, thereby promoting a more collaborative and service-oriented workplace culture.

Keywords: Banking Sector; Proactive Personality; Prosocial Motivation; Servant Leadership; Trait Activation Theory; 

Abstract

In Sri Lanka’s apparel sector, employee engagement is vital for consistent productivity and quality. However, workers often face job insecurity due to various factors. Despite these challenges, many employees stay motivated by a sense of responsibility to support their families and contribute to their communities. This study examines the relationship between job insecurity and work engagement in Sri Lanka’s apparel sector, using the Job Demands-Resources (JD-R) theory. It specifically explores the moderating role of prosocial motivation. With limited research on this topic, the study aims to provide insights into how prosocial motivation may mitigate the negative effects of job insecurity on employee engagement. A total of 400 questionnaires were distributed to employees in the apparel sector in Sri Lanka, resulting in 215 usable responses. Data collection was conducted using convenience sampling to select participants from various apparel manufacturing units. The data were analyzed using SPSS, with correlation and regression analyses employed to investigate the relationships between job insecurity and work engagement, as well as the moderating effect of prosocial motivation. The results reveal a significant negative correlation between job insecurity and work engagement (r = -0.42, p < 0.01), indicating that higher job insecurity is associated with lower employee engagement. Additionally, prosocial motivation was found to play a significant moderating role in this relationship (interaction term β = 0.27, p < 0.05). Specifically, employees with higher levels of prosocial motivation were more resilient to the negative effects of job insecurity, maintaining higher levels of work engagement compared to those with lower prosocial motivation. The use of convenience sampling restricts the generalizability of the results, cross-sectional design prevents causal inferences, may change over time, self-reported data introduces potential biases and the study focuses on prosocial motivation but does not examine other potential moderators. The study contributes to Job Demands-Resources (JD-R) theory by emphasizing prosocial motivation as a personal resource that buffers job insecurity’s impact, enhancing employee engagement in challenging environments.

Keywords: Apparel sector, Job Demands-Resources (JD-R) theory, Job insecurity, Prosocial motivation, Work engagement

Abstract

The banking industry plays a vital role in driving Sri Lanka’s economic growth and ensuring financial stability. Professionals in this sector are essential to maintaining operational efficiency, delivering quality customer service, adhering to regulatory standards, and upholding public trust. Despite their critical role, understanding how emotional intelligence influences the relationship between toxic leadership and employee performance is still lacking in the Sri Lankan banking context. Drawing upon Social Exchange Theory (SET), this research explores the impact of toxic leadership on employees’ performance. It particularly focuses on how emotional intelligence may act as a moderating factor, potentially mitigating the negative outcomes associated with toxic leadership behavior. The study employed a quantitative approach using self-distributed questionnaires to gather data from employees in Sri Lanka’s banking sector. A convenience sampling strategy led to 211 valid responses from a total of 400 questionnaires circulated. The collected data were processed and examined using SPSS software, applying both correlation and regression techniques to assess the relationship between toxic leadership and employee performance and to test the moderating effect of emotional intelligence. 

The analysis revealed a statistically significant negative relationship between toxic leadership and employee performance (p < 0.01). Moreover, emotional intelligence was found to significantly moderate this relationship (p < 0.05), indicating that individuals with higher emotional intelligence are more capable of managing the detrimental effects of toxic leadership, thereby preserving their performance levels. These results suggest that emotional intelligence serves as a protective factor, enhancing resilience in adverse leadership conditions. This study underscores the relevance of individual psychological strengths, such as emotional intelligence, in navigating complex organizational dynamics. The findings offer theoretical insights and practical implications for leadership practices and human resource strategies, aiming to enhance employee performance and well-being amid toxic leadership. By applying Social Exchange Theory, the research deepens the understanding of how toxic leadership influences employee outcomes and highlights emotional intelligence as a vital moderating factor. These insights contribute to better leadership development and organizational support structures within the banking sector.

Keywords: Emotional Intelligence, Employee Performance, Social Exchange Theory (SET), Toxic Leadership

The IT Industry in Sri Lanka, propelled by swift innovations and intricate task demands, frequently subjects employees to intensified temporal pressures. In this accelerated context, the proclivity to partake in knowledge hiding, which is defined as deliberately withholding or obscuring information from colleagues, constitutes a significant impediment to collaborative efficacy and organizational learning. Anchored in the Conservation of Resources Theory, which asserts that individuals endeavor to safeguard valuable resources such as time, energy, and cognitive capacity when under duress, this study explores how perceived temporal pressure may catalyse employees to engage in knowledge hiding as a defensive strategy. Additionally, the study examines the moderating influence of prosocial motivation, the intrinsic desire to assist others, to ascertain whether altruistic dispositions can mitigate the detrimental effects of time-related stress on knowledge-sharing practices. A cross-sectional quantitative methodology was employed, utilizing a structured and self-administered questionnaire to collect data from employees affiliated with Sri Lankan IT organizations. A total of 165 valid responses were obtained through convenience sampling. Established and validated measurement scales were utilized to assess perceived time pressure, knowledge hiding, and prosocial motivation. Hierarchical regression analysis was performed using SPSS to evaluate both direct and interaction effects. The findings indicated a statistically significant positive correlation between perceived time pressure and knowledge hiding (β = 0.34, p < 0.001), suggesting that augmented time demands correlate with an increased propensity to conceal information. Importantly, prosocial motivation moderated this relationship (β = -0.19, p < 0.01), signifying that individuals with elevated prosocial motivation were less inclined to engage in knowledge hiding, even amidst substantial time pressures. Simple slope analysis verified that the impact of time pressure on knowledge hiding was diminished among employees with high prosocial motivation (simple slope = 0.18, p < 0.05) in comparison to their counterparts with low prosocial motivation (simple slope = 0.47, p < 0.001). The proposed model accounted for 39% of the variance in knowledge hiding (R² = 0.39). This investigation elucidates that perceived time pressure can substantially foster knowledge hiding within the IT workplace, aligning with the assertions of COR theory that individuals safeguard their resources when confronted with threats. However, employees exhibiting strong prosocial motivation demonstrate resilience against this phenomenon, sustaining knowledge-sharing behaviors despite resource limitations. These findings accentuate the necessity of cultivating prosocial values and nurturing supportive organizational cultures to mitigate the adverse repercussions of workplace stress.

This study explores how interdisciplinary collaboration and problem-based learning (PBL) enhanced students’ analytical and metacognitive skills. Students enrolled in an introductory Human Resource Management course learned concepts like recruitment, training, and negotiation by analyzing their dream jobs, identifying their training needs, and participating in a salary negotiation exercise for their future careers. Each concept was reinforced through structured PBL activities designed to foster deeper understanding and self‐reflective skill development. 

PBL is a pedagogical design that facilitates learning by enabling students to develop viable solutions to defined problems by integrating theory and practice. It aligns with models of writing skill development where repeated experiential assignments help students progress from knowledge‐telling (sharing knowledge through writing) to knowledge‐transformation (understanding how knowledge is conveyed), and ultimately to knowledge‐crafting (understanding how writing is interpreted by others). 

In our course, this metacognitive focus on audience comprehension was supported by an interdisciplinary and global approach to PBL. Management students collaborated with chemistry students on a sustainability-oriented group project, including a field trip and team presentations, to hone skills for cross-disciplinary communication. 

Student reflections were analyzed using LIWC-22 to assess shifts in thinking across four domains: social leadership, global citizenship, work and lifestyle, and experiential learning. Results were modeled using linear mixed-effects analysis to track growth across assignments. The text analysis of 77 students’ reflections across six assignments showed statistically significant upward trends in four key domains: Social Leadership (β=2.94, p<.001), Experiential Thinking (β=1.45, p<.001), Work and Lifestyle Reflection (β=.46, p<.001), and Global Citizenship (β=.15, p=.011). These findings demonstrate students’ growth in reflective and interdisciplinary engagement throughout the curriculum. 

The PBL exercises cultivated increased awareness of global sustainability issues, professional decision-making, and social responsibility. These effects were strengthened by interdisciplinary collaboration with chemistry students and a global classroom field experience. Of the 37 management students who attended the organic vineyard trip, 83% reported increased environmental awareness. Teams incorporating biopesticide research from their chemistry counterparts scored 22% higher on final presentations, demonstrating the value of applied, interdisciplinary learning. 

This study highlights the impact of interdisciplinary collaboration and PBL on students’ analytical and metacognitive development. Integrating HR concepts within sustainable agriculture and fostering collaboration deepened students’ reflective capacities and global sustainability awareness. 
 

Aspiring medical professionals often face challenges in their journey to medical school, including understanding admissions requirements and gaining meaningful clinical experience. This panel discussion and workshop are designed to provide pre-medical students with valuable insights from experienced medical doctors and current medical students. The event will commence with a panel discussion featuring experienced medical practitioners and medical students who will share their personal experiences and insights into the medical school application process. Topics will include crafting strong applications through compelling personal statements, securing impactful letters of recommendation, and engaging in extracurricular activities that demonstrate a commitment to the field. Following the panel, participants will engage in interactive workshops focusing on practical strategies for enhancing their medical school applications. Key topics will include effective MCAT preparation and engaging in meaningful research to strengthen applications. Recognizing the demands of medical education, discussions will also cover strategies for managing academic pressures and maintaining a healthy work-life balance. Expert speakers will provide insights on time management, effective study techniques, and self-care practices essential for success in a rigorous academic environment. The event focuses on building mental toughness and coping mechanisms for aspiring medical students while providing valuable networking opportunities with peers and professionals in the healthcare community. In addition, students will be given information on narrative medicine seminars, planning sessions, and an application prep session organized and conducted by experienced medical professional and career counsellor Dr. Jeya Thayaparan. 

The Internal Combustion Engines (ICE) are the leading contributors for the environmental issues. The Electric Vehicles are the credible alternatives to ICEs which utilize the 80% of the battery efficiency. They have originated in various forms namely, battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and fuel cell electric vehicles (FCEVs) based on their energy sources and power train. The researches pertaining to assess the sustainability of EVs is scarce. Most of them highlighted the Cumulative Energy demand and CO₂ emissions in general. Very similar research was performed in Indonesia with the intension of revealing the economic and environmental pillars only. This research is intended to analyse the EVs from Tripple Bottom Line (Environmental, Economic and Social) perspectives targeting their application in Sri Lanka. The assessments performed the standards specified by standard organizations. A Cradle to Gate approach is used to systematically develop the framework while comparing them with various forms of EVs and ICE. In addition, a Analytic hierarchy process (AHP) is employed in order to establish a criteria for the selection of best alternative. The Environmental impacts were assessed using ReCiPe 2016 (H/A) approach, while Lifecycle costing was performed by Activity Based Costing methods. The Social Impacts were studied by the norms of S-LCA strategies. It is concluded that the HEVs offer a best balance in environmental, economic and social pillars while ICEs being overtaken by other forms of EVs. In conclusion, this emphasizes the application of sustainability matrix in the automotive industry supporting the manufacturers and consumers in choosing the most sustainable transportation. This framework can be further extended for the assessment of other sources of power used in automotive industry as well.

The growing need for secure communication in the digital age has led to the development of advanced encryption techniques that utilize both quantum and post-quantum cryptographic algorithms. This project implements a secure web application using Flask to integrate quantum-secure key exchange protocols, namely BB84 (Quantum Key Distribution) and CRYSTALS-KYBER (Post-Quantum Encryption), to protect sensitive user data. The application encrypts user credentials using the AES-GCM encryption algorithm, with dynamically generated keys obtained through quantum-secure key exchange protocols.

The system offers a registration module that securely stores encrypted user data in an SQLite database and provides real-time attack simulation to test the system's robustness. Upon registering a user, encrypted data such as email and password are stored with the chosen encryption protocol. The application also simulates a Real-Time Left attack that attempts to decrypt encrypted data using the same key exchange protocols, thereby analyzing the vulnerability and performance of the system under potential attacks.

A key feature of the system is the visualization of attack performance, where a graph generated using Matplotlib illustrates the time taken to perform decryption attempts for various algorithms. The BB84 key exchange is implemented using Qiskit’s qasm_simulator to mimic quantum communication between Alice and Bob, while CRYSTALS-KYBER is simulated using securely generated shared secrets with secrets.token_bytes().

The application dynamically measures the success or failure of simulated attacks and stores the results in the database for future reference. This work provides a secure platform that demonstrates the strength of quantum-resistant encryption techniques and allows for real-time analysis of decryption attempts to evaluate the efficiency of different cryptographic algorithms.

The construction of high voltage transmission lines is a key element in electricity infrastructure development in any country. The construction of a new transmission line is a high-cost operation hence the main focus of the design is to transmit the maximum energy between two points within reasonable cost and appropriate safety margins. The construction cost of a transmission line highly depends on the parameters selected for line design such as basic span, wind span, weight span, safety factors, operating temperature, and wind pressure where some of the parameter values are provided by the client and others are based on the geography of the line route.

Sri Lanka also uses the same procedure for transmission line design and the use of unoptimized values for parameters ultimately ended with higher construction costs. Therefore, optimum values for these design parameters should be selected to minimize the weight of the tower which reduces the load on the foundation and end up with a minimum cost for construction. This ultimately led to efficient project execution with less time spend on construction work.

This research study intends to evaluate the values of basic parameters used in transmission line design and propose optimum values to reduce the associated line construction cost. This research mainly focuses on three parameters, weight span, wind pressure, and conductor safety factor because those are the main criteria in the design process. Weight span values were analyzed according to different terrains such as flat and hilly, and wind pressure values were analyzed according to different wind loading zones in Sri Lanka. Also, the conductor safety factor was analyzed. Optimum values for those parameters were proposed to reduce the forces on towers and lines, thereby reducing the future transmission line construction costs owing to material weight.

The proposed optimized values for each design parameter are then applied to the existing 132 kV transmission line in Pannipitiya– Ratmalana which lies on different terrain profiles and different wind zones. The results were validated with PLS-CADD software. Finally reduced tower weights due to optimized wind pressure, weight span, and safety factor are calculated for two different tower types.  Results proved that the initial weight of the tension tower was reduced by 19.61% and the suspension tower by 19% after the optimization.

Wind resource analysis is crucial in designing a sustainable wind turbine, yet site-specific data is often unavailable. In this study, the wind potential at the University of Jaffna’s Kilinochchi campus at 10 m height was investigated using only general data sources - ERA5 reanalysis grids and two nearby weather stations (a rooftop anemometer at 12 m and a renewable energy park mast at 2 m) - without installing instrumentation. Vertical extrapolation was applied via the one-seventh power law to adjust station readings to hub height, then spatial interpolation was performed across four ERA5 grid points using both IDW and simple kriging in a GIS environment. To fill gaps and extend short-term records, Weibull fitting and Markov-chain reconstruction were employed, and random forest bias correction was implemented to minimize systematic deviations. When validated against reference measurements, the final interpolated dataset at 10 m showed RMSE values of 0.3007 m/s for simple kriging and 0.3125 m/s for IDW. Similarly, MAPE values were 41.65% and 44.22%, respectively, indicating that while both methods are comparable, simple kriging performs slightly better for this context. These results affirm that generalized datasets, when bias-corrected and interpolated, can reliably replace costly site-specific measurements for wind resource assessments. However, fixed shear-exponent assumptions may oversimplify vertical wind profiles in complex terrain; the coarse spatial resolution of ERA5 may miss local channeling effects; short-term station records can inadequately capture seasonal variability; and machine-learning bias correction risks overfitting if applied beyond similar climatic regimes. These results affirm that generalized datasets, when bias-corrected and interpolated, can reliably replace costly site-specific measurements for wind resource assessments.

Abstract

This study focuses on the development and assessment of a PV-enabled supercapacitor-assisted uninterruptible power supply for lighting applications. As the global emphasis on renewable energy and energy resilience has increased, efficient, sustainable backup power systems have become critical, particularly in regions facing grid instability and frequent power outages.

The primary objective of this study was to establish a sustainable, reliable, and environmentally friendly power backup system that utilizes solar energy and a supercapacitor bank to ensure continuous lighting. In contrast to traditional batteries, supercapacitors feature rapid charge-discharge functionality, high efficiency, and extended operational lifespans, rendering them well-suited for frequent cycling and transient power interruptions, providing a clean and resilient alternative for lighting in areas with unstable grid conditions.

A literature review revealed key strategies for improving power backup systems, including optimizing efficiency, managing the supercapacitor's discharge process, and addressing power regulation challenges. This project adopts a parallel configuration of the supercapacitor and lighting load, allowing for independent voltage regulation and improved energy utilization. This informed the overall system design and helped maximize performance under dynamic solar conditions.

The SCAUPS system channels solar energy from the PV source through a buck-boost DC-DC converter, which regulates voltage to power the lighting load and charge a parallel connected supercapacitor. This setup allows the supercapacitor to automatically discharge and support the load during low irradiance while maintaining a maximum voltage during high irradiance. The system operates through intelligent switching based on the supercapacitor's real-time voltage, prioritizing the PV source, followed by the supercapacitor, and finally, the grid, minimizing dependence on conventional energy.

The control strategy operates in two dynamic modes. In the primary mode, the photovoltaic source powers the load and charges the supercapacitor. When irradiance drops, the supercapacitor discharges to maintain uninterrupted lighting. The system enters a protective mode if the supercapacitor's voltage falls below a safe threshold. In this mode, the supercapacitor is isolated to prevent deep discharge, the grid powers the load, and the photovoltaic source recharges the supercapacitor. Once recharged, the system reverts to the primary mode.

Experimental testing evaluated the system's real-time responsiveness and stability under controlled irradiance profiles, including constant, slow ramp, and fast ramp. Voltage and current measurements obtained using a power analyser confirmed the system's ability to reliably power the lighting load, effectively balancing inputs from all energy sources. The supercapacitor's rapid charge-discharge capability, long cycle life, and high efficiency proved especially effective during sudden drops in solar power, maintaining voltage within defined threshold limits. The system achieved an average efficiency of 93.6%. Powered by renewable solar energy and supported by a robust energy storage system, the SCAUPS design ensures uninterrupted power delivery with minimal environmental impact. These results validate the system's reliability and responsiveness under dynamic conditions, reinforcing its suitability for deployment in regions with frequent outages. The scalable, eco-friendly, and future-ready SCAUPS solution stands out as an ideal choice for enhancing energy-resilient infrastructure in both urban and rural applications.

Keywords: Lighting Applications, PV, Renewable Energy, SCAUPS, Supercapacitor, Uninterruptible Power Supply.

ZnMn3O4 nanocomposites were prepared using a simple hydrothermal method. The prepared composite was used as a target to grow ZnMn3O4 thin film composite electrodes for electrochemical capacitors via electron beam evaporation method. The ZnMn3O4 is directly grown on gold coated silicon substrates, annealed at 400 °C for 2 hour and used as the electrode material for supercapacitors. The microstructure of the sample is characterized by X-ray diffraction (XRD), Scanning Electron Microscopy (SEM) techniques. The Surface area, pore size and volume of the sample is examined by BET analysis. The elemental binding energies and elemental mapping are studied using X-ray Photoelectron Spectroscopy (XPS) and Energy Dispersive Spectroscopy (EDS) respectively. The supercapacitive performance of the sample investigated by Cyclic Voltammetry (CV), Chronopotentiometry (CP), and Electrochemical Impedance Spectroscopy (EIS) techniques in two different aqueous electrolytes (Li₂SO₄ and KOH). It is demonstrated that the composite film exhibited a high specific capacitance of 498 F/g at a current density of 1 A/g in 1M KOH electrolyte and the capacity retention of 92% even after 4000 cycles.

Wind turbine wakes significantly impact wind farm efficiency by reducing wind speeds and increasing turbulence for downstream turbines, leading to decreased power generation. Understanding and mitigating wake effects are essential for optimizing wind farm layouts and maximizing energy production. This study investigates wind turbine wake dynamics using computational modeling in MATLAB and Simulink. A simple wind turbine model was initially developed and later enhanced with a wake dynamics subsystem to analyze wake recovery distances under different operational conditions. Wind speed data from the Sri Lanka Wind Farm Analysis and Site Selection Assistance report by NREL was used to simulate real-world conditions. The results indicate that higher wind speeds increase wake recovery distances, with an observed 21.6% increase compared to lower speeds. Hydraulic pitch control outperformed ideal pitch control, yielding 3.92% higher power output in Class 1 conditions and showing slight improvements in other classes. Yaw control significantly reduced wake recovery distances, with reductions of 10.77% in Class 1, 10.17% in Class 3, and 10.91% in Class 5 winds, demonstrating its effectiveness in minimizing wake interference. Additionally, the study examined the impact of turbine diameter on wake recovery distance. Increasing the turbine diameter from 80 m to 120 m resulted in a 20% increase in wake recovery distance, while increasing it further to 160 m led to a 40% increase. These findings confirm that larger wind turbines generate longer wakes, requiring careful spacing in wind farm layouts to minimize energy losses due to wake interactions. These results highlight the importance of optimizing turbine spacing, implementing yaw control, and considering turbine size effects in wind farm design. By integrating these wake mitigation strategies, wind farms can enhance power generation efficiency and reduce wake-induced losses. This study emphasizes the value of computational modeling in guiding wind farm optimization, providing insights into turbine configuration, control strategies, and layout planning.

The choice of materials for bio-implants is significantly dependent on their ability to integrate into the human body. For metallic biomaterials, corrosion resistance is crucial because the implant must remain durable and not be rejected by the body. These biomaterials can trigger allergic reactions by releasing toxic ions and particles, such as Ni+, Cr3+, and Co2+, leading to inflammation and increased corrosion in the surrounding tissues. The risk of corrosion increases because these implanted materials are constantly exposed to the bloodstream and aggressive body fluids. This study explores the corrosion properties of medical-grade 316 L stainless steel, which is frequently used in cardiac stents, orthopedic implants, and dental implants. The study utilized electrodischarge treatment (EDT) to apply TiO2 particles to 316 L stainless steel to improve its corrosion resistance. The samples were analyzed using SEM and XRD to link the modified surface to the EDT process parameters. Electrochemical corrosion tests showed that the TiO2-coated sample had a lower corrosion rate of 1.883 mpy than the untreated surface at a rate of 13.729 mpy. The improved corrosion resistance of the TiO2-coated substrate was attributed to the formation of various silicides, carbides, and bioactive compounds. It was also determined that the dielectric, pulse duration, and current were significant factors contributing 23.46%, 20.71%, and 18.64%, respectively, to the bio-favorable surface in the TiO2-EDT medium. The TiO2-EDT surface exhibited dendritic structures and powder particle deposition, which facilitated cell anchoring. Additionally, elemental mapping confirmed the even distribution of titanium particles on the treated surface.

This study presents a comprehensive crystallographic and charge density analysis of antimony Sulfoselenide (Sb₂(S,Se)₃), aiming to reveal its structural properties using X-ray diffraction techniques coupled with Rietveld refinement and Fourier-based electron density analysis using VESTA software. Sb thin films were deposited via RF magnetron sputtering at 100W followed by hot wall deposition of S and Se and post-annealing at 250°C for 2 hours to form crystalline Sb₂(S,Se)₃. X-ray diffraction (XRD) confirms the orthorhombic Pnma phase, with anisotropic lattice expansion upon selenium substitution. Site-specific occupancy refinement reveals that sulfur preferentially occupies sites with smaller coordination geometry, while selenium is accommodated in sites with larger coordination geometry resulting in increased bond lengths and illustrating an ordered chalcogen distribution that directly influences local bonding characteristics. Sb–Se bonds exhibit enhanced covalency and delocalization whereas Sb–S bonds remain shorter and more ionic in nature.3D electron density visualizations simulated using VESTA from Fourier-transformed structure factors, revealed more directional anisotropy in electronic charge distribution across the (322), (213), and (103) crystallographic planes. The (103) plane exhibits the strongest charge localization along Sb–Se bonds indicating dominant conduction pathways, whereas the (322) plane demonstrates the smoothest electron density distribution suggesting minimal charge trapping. Overall charge localization and anisotropic bonding in Sb₂(S,Se)₃  revealed by the charge density maps and crystallography highlights its p-type semiconducting behaviour. This unique structure also imparts strongly directional charge transport along the ribbon axis (a-axis) yielding a favourable band alignment confirming the suitability of Sb₂(S,Se)₃ as an effective p-type absorber in high performance p–n junction photovoltaic devices.

Keywords: Sb₂(S,Se)₃, Charge density distribution, VESTA

*Corresponding Author: [email protected]

Effective surface passivation is critical to suppress non-radiative recombination and improve both the efficiency and stability of wide bandgap perovskite solar cells. In this study, we investigate 4-trifluoromethyl-benzylammonium iodide (TFMBAI) as a molecular passivator for FAPbBr₃ perovskite films through post deposition surface treatment. Morphological analysis via SEM and AFM reveals that films treated with 1 mg/mL TFMBAI exhibit smoother and more uniform surfaces, while higher concentrations lead to increased surface roughness and aggregation.  X-ray diffraction confirms that the perovskite phase is retained after passivation, with slightly increased peak intensity indicating improved crystallinity and possible grain orientation. FTIR spectroscopy provides evidence of specific interactions between the ammonium group in TFMBAI and Pb²⁺, indicating chemical binding at the perovskite surface. Photoluminescence (PL) and time-resolved PL (TRPL) measurements show enhanced emission intensity and extended carrier lifetimes, indicating suppression of non-radiative recombination pathways.  Space-charge limited current (SCLC) analysis further reveals a substantial reduction in trap density for the passivated films.  As a results of these improvements, devices treated with 1 mg/mL TFMBAI exhibit a power conversion efficiency of 7.34%, with a significantly enhanced open-circuit voltage of 1.507 V compared to 1.420 V for unpassivated devices. The passivated devices also exhibit reduced hysteresis and enhanced stability under ambient storage, demonstrating the effectiveness of TFMBAI as a surface passivator for achieving efficient and stable FAPbBr₃ perovskite solar cells.

Growing global population requires increased food production, in which usage of fertilizers is becoming exceedingly high to cater to these agricultural needs. Inherent characteristics of lower nutrient use efficiency (NUE) and the environmental threats conventional fertilizers possess require environmentally benign, effective fertilizer systems for sustainable agriculture. Hydrogels are positioned as an invaluable material to effectively address these challenges, in which synthesize a sodium alginate-carboxymethyl cellulose (SA-CMC) based slow-release fertilizer hydrogel encapsulated with zinc oxide nanoparticles (ZnO NPs), potassium chloride and naphthalene acetic acid (NAA) focusing on flowering and fruiting stages of short-term crop chili was done on this study. ZnO NPs were synthesized via surfactant assisted co-precipitation method and characterized via XRD, SEM and FTIR techniques. Synthesized slow-release fertilizer hydrogels (SRFHs) were characterized using SEM and FTIR techniques. When swelling capacity and biodegradability of the SRFHs were examined, swelling capacity peaked at 202% on day 14 and nearly all the SRFH beads had undergone biodegradation by day 20, with a weight loss percentage of 99.98%, in which these findings were reinforced by optical microscopic pictures. Atomic absorption spectrometry was used to study the release of potassium and zinc nutrients at 24-hour, 7, 14, and 21-day intervals. The results showed that the synthesized SRFH beads exhibited slow-release behavior. UV-VIS spectrophotometry was used to study the naphthalene acetic acid (NAA) hormone's effective encapsulation and release behavior. NAA hormone depicted a higher encapsulation efficiency of 92.53 % with a slow, sustained release profile for the observed duration of 21 days. Kinetics of the exponential phase of NAA release profile which was between 20-450 minutes well matched with the Higuchi model with a R² = 0.97. Plant study was conducted where the parameters of plant height, number of branches, flowers, pods and yield at first plucking were statistically analyzed using ANOVA, where p ≤ 0.05 was considered statistically significant. While positive changes with all parameters were shown, significant increase of flowering and crop yield observed were assigned to the synergistic effect of encapsulated agrochemicals in SRFHs. Release profiles, swelling dynamics and the biodegradability study further proved the applicability of such SRFHs for short-term crop cultivation. With the ability to biodegrade and increase NUE, synthesized SRFHs were attributed as a sustainable alternative for the modern agricultural context.

Keywords: biodegradable, SA-CMC, slow-release fertilizer, hydrogel, NAA hormone, ZnO nanoparticles, sustainable approach, flowering and crop yield, short-term crop 

Enhancing the efficiency and stability of mixed halide perovskite solar cells is vital yet challenging due to inherent defects and photoinduced halide migration, causing phase segregation and performance degradation. To address these issues, we investigate Trimethylenediamine Dihydroiodide (TMDI) as a novel passivation for FA_0.83Cs_0.17Pb(I_0.6Br_0.4)₃ wide bandgap (~1.78 eV) perovskite films. Fourier transform infrared spectroscopy reveals strong molecular interactions between TMDI and PbI₂, specifically through significant shifts in N-H bending vibrations. These shifts indicate strong coordination or hydrogen bonding interactions with undercoordinated Pb²⁺ ions, effectively passivating surface defects that typically serve as non-radiative recombination centres and facilitating halide migration. Consequently, TMDI passivation substantially reduces halide migration, evidenced by a slower photoluminescence (PL) red shift under prolonged illumination and stable absorption characteristics measured via UV-Vis spectroscopy. Additionally, steady-state and time-resolved PL analyses demonstrate enhanced PL intensity and longer carrier lifetimes in TMDI passivated films, confirming reduced trap densities and effective defect mitigation. Consistent with these observations, space-charge-limited current (SCLC) measurements indicate a significant reduction in trap density, further supporting the stabilization of the halide distribution. Solar cells incorporating TMDI passivated FA_0.83Cs_0.17Pb(I_0.6Br_0.4)₃ perovskites exhibit significantly improved performance, achieving a power conversion efficiency of 16.54% and a high open circuit voltage of 1.243 V, compared to 14.11% and 1.135 V for control devices. Moreover, hysteresis is substantially reduced from 9.57% to 3.56% in TMDI-treated devices. These findings highlight the comprehensive role of TMDI in simultaneously addressing defect passivation and halide migration suppression, presenting a promising approach for advancing the stability and efficiency of mixed halide perovskite solar cells.

Keywords: Mixed halide perovskite; Phase segregation; Surface passivation; Halide migration; Solar cell stability

Water pollution due to toxic heavy metals presents significant environmental and health challenges, emphasizing the need for sustainable and cost-effective remediation methods. This presentation discusses the development of an eco-friendly hydrogel created from licorice residue (LR), a pharmaceutical by-product, which is cross-linked with epichlorohydrin (ECH) for efficient heavy metal removal. The hydrogel was synthesized through an alkali/urea dissolution process followed by ECH cross-linking, resulting in a porous three-dimensional network abundant in hydroxyl (-OH) and ether (C-O-C) functional groups. Characterization techniques such as FTIR and SEM confirmed the successful chemical modification and the highly porous structure of the hydrogel. Adsorption tests demonstrated the hydrogel's remarkable capacity for removing Zn²⁺ (234.5 mg/g), Cu²⁺ (161.1 mg/g), and Ni³⁺ (191.5 mg/g), with optimal performance observed at pH levels between 4 and 5. Kinetic studies revealed that the adsorption process follows a pseudo-second-order model, indicating that chemisorption is the rate-limiting step. In contrast, Langmuir isotherms suggest a monolayer adsorption mechanism. Mechanistic investigations revealed that the uptake of metal ions is driven by electrostatic interactions, ion exchange, and coordination bonding with the oxygen-containing functional groups of the hydrogel. This study highlights the potential of transforming agricultural by-products into high-performance adsorbents, paving the way for sustainable wastewater treatment solutions and the valorization of pharmaceutical waste.

Luminescent materials doped with non-rare earth ions such as transition metals and other non-rare earth constituents such as thallium, neodymium, copper, bismuth carbon quantum dots, nanomaterials, etc., exhibit tunable emission spectra, high luminescent efficiency, and enriched stability. (Zhang et al., 2020; Huang et al., 2019). 

Solid-state LED lighting has increasingly focused on the utilization of non-rare earth ion-doped phosphors because of sustainable and cost-effective alternatives to traditional rare earth-based doped phosphors. These phosphors show advanced optical properties of these materials, facilitating their integration into commercial LEDs synthesized by methods such as sol-gel methods, solid-state reactions, and nanostructuring (Sharma et al., 2021). 

These phosphors doped with non-rare earth ions exhibit performance metrics comparable to, or exceeding, those of conventional phosphors doped with rare earth materials, with benefits such as cost reduction, abundance, and environmental friendliness (Zhang et al., 2020), and show promising possibilities for environmentally friendly lighting elucidations. These materials may be used for general illumination purposes, high-resolution display materials, and signaling device materials, addressing the growing demand for sustainable lighting technologies. 

Although there is significant progress, challenges remain in optimizing emission efficiency, achieving thermal stability, and device integration, which is required for large-scale manufacturing (Huang et al., 2019). Therefore, future research is anticipated to focus on novel dopant-host combinations, scalable fabrication processes, and the development of multifunctional phosphor-doped materials to further expand the potential of non-rare earth-based phosphors for solid-state lighting solutions.

 Keywords: Phosphors, Non-Rare Earth Ions, Future Perspectives. 

Abstract 

Research has focused on attempting to replace 2 X 4 construction wood with Polymer Matrix Composites (PMC” s).  In primary load bearing applications, potential advantages commonly expounded by proponents of PMC materials include high specific strength, high specific stiffness, tailorable durability, good fatigue performance, versatile fabrication and lower maintenance costs. Moreover, if PMC”s are used construction for housing they could last for 50 + years, with no need for scrapping or repainting As a result, reinforced polymer composites are being investigated in applications such as rehabilitation and retrofit, and alternative reinforcement for concrete

            However, the question remains, do PMC’s have the same or similar flexural strength /toughness? Three-point flexural testing was performed on wood and Carbon/Kevlar Hybrid sleeving with balsa wood and Divinymat core. The results indicate wood has a flexural strength of 310 MPa and that the PMC’s  a flexural strength of 16 MPa.

A second approach was to examine the-Plane-Strain fracture toughness. Testing was performed on carbon/Kevlar sleeve with Balsa and Divinymat core materials and studied their fracture toughness energy as compared to 2 X 4 wood.  In fracture toughness testing PMC”s do exceed the criteria. Fracture toughness of wood was 0.7 MPa whereas the above PMC gave a fracture toughness of 2.9 MPa

Advancements in sustainable energy systems have elevated hydrogen's role as a clean and high-efficiency energy carrier for future applications. Alkaline water electrolysis presents a promising method for large-scale hydrogen production, yet its widespread adoption remains constrained by the lack of cost-effective, durable, and efficient electrocatalysts for the hydrogen evolution reaction (HER). Precious metal catalysts such as platinum exhibit outstanding HER activity but suffer from high cost and limited abundance. In this context, rare-earth-based compounds offer an underexplored class of materials with unique structural and electronic features that could support efficient electrocatalysis. This study investigates the potential of lanthanum tungstate (La₂(WO₄)₃), neodymium tungstate (Nd₂(WO₄)₃), and samarium tungstate (Sm₂(WO₄)₃) as alternative HER electrocatalysts in alkaline media. These rare-earth tungstate materials were synthesized via a modified ultrasonication assisted coprecipitation method, followed by calcination. Structural characterization through X-ray diffraction (XRD), Scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS) confirmed the tungstate materials formation, rod-like agglomerated morphologies, and homogeneous elemental distribution, indicating high material purity. Superior catalytic activity was carried out in alkaline solution using three electrode system by performing linear sweep voltammetry (LSV), cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and chronoamperometry (CA). Among the three tungstates, La₂(WO₄)₃ demonstrated the lowest overpotential 176 mV at 10 mA/cm² and a favorable Tafel slope 147 mV/dec, indicating more efficient HER kinetics compared to Sm₂(WO₄)₃ and Nd₂(WO₄)₃. This work not only highlights the catalytic potential of rare-earth tungstates in HER under alkaline conditions but also lays the groundwork for further optimization through doping, nano structuring, or hybridization with conductive materials. The findings provide new insights into the lanthanide-based electrocatalysts and expand the material library for green hydrogen production technologies.

This study explores the development and electrochemical properties of monolithic dye-sensitized solar cells (DSSCs) and supercapacitors featuring a novel catalytic layer based on multi-walled carbon nanotube (MWCNT)/titanium dioxide (TiO₂) composites. Commercial MWCNTs were combined with a small quantity of TiO₂, which primarily acted as a binder, to develop composite materials for use as catalytic layers in monolithic DSSCs. The structural and morphological properties of the MWCNT/TiO₂ composites were characterized using X-ray diffraction (XRD) and Raman spectroscopy. XRD analysis confirmed the crystalline nature of TiO₂, predominantly in the anatase phase, while Raman spectroscopy revealed a characteristic D/G band intensity ratio of 1.18, indicating the presence of defect-rich graphitic structures in the MWCNTs. Field-emission scanning electron microscopy (FE-SEM) images demonstrated a uniform distribution of MWCNTs within the TiO₂ matrix, enhancing structural integrity.

Electrochemical performance, evaluated through Nyquist plot analysis, revealed a charge transfer resistance (RCT) of 2.8 kΩ for the MWCNT/TiO₂ composite, which is competitive with carbon-based counter electrodes commonly used in monolithic DSSCs. DSSCs fabricated with the MWCNT/TiO₂ composite catalytic layers achieved a power conversion efficiency of 3.20% under standard illumination conditions, demonstrating promising performance compared to similar monolithic DSSC architectures. The composites performance as supercapacitor electrode was also studied. These findings highlight the MWCNT-based composites, with TiO₂ as a binder, as a cost-effective and scalable alternative for catalytic layers in monolithic DSSCs and as electrodes in supercapacitors.

Municipal Solid Waste Management (MSWM) poses a significant environmental and public health challenge in Sri Lanka, particularly in urban centers such as the Jaffna District. Rapid urbanization, population growth, and inadequate infrastructure have led to the accumulation of unsegregated waste, primarily disposed in open dumping sites such as the Kallundai dumpsite. This practice contributes to environmental degradation, air and water pollution, and land scarcity. The study addresses the urgent need to identify sustainable, economically feasible, and environmentally friendly waste-to-energy (WTE) solutions suitable for the local context. This research focuses on evaluating the current MSW generation, composition, and management practices in Jaffna, and proposes sustainable WTE technology based on anaerobic digestion (AD). The overall objective is to design an effective waste recovery model that can generate renewable energy while minimizing the environmental footprint. Specifically, the study aims to: (1) characterize and quantify MSW in Jaffna, (2) assess the suitability of various technological options for energy recovery, and (3) evaluate the financial feasibility of a selected AD system. The study employed both primary and secondary data collection methods. Waste composition analysis was conducted using the quartering method on samples collected from the Kallundai dumpsite. The findings revealed that approximately 86.25 tons of MSW are generated daily in the Jaffna Municipal Council area, with an average per capita generation of 0.94 kg/day. Organic waste constituted the majority (53%) of the waste stream, including 26%-yard waste and 20% kitchen waste. Other components included paper (12%), polythene (11%), plastic (7%), metal (7%), glass (6%), and other miscellaneous materials (4%). Despite the availability of 25.61 tons/day of recyclable materials, most are discarded, resulting in lost economic potential estimated at LKR 178,000/day. 

A Sustainability Assessment of Technologies (SAT) was performed to evaluate various WTE options including composting, incineration, Refuse-Derived Fuel (RDF), and anaerobic digestion. Through a weighted-point decision-making framework that considered environmental, social, technical, and financial criteria, AD was identified as the most contextually appropriate and cost-effective solution. A decentralized AD system using kitchen waste as feedstock was proposed, with floating drum digesters distributed across seven zones in Jaffna. The system is designed to treat 2,460 kg/day of kitchen waste, yielding approximately 329.3 m³ of biogas daily equivalent to 134 liters/kg sufficient to meet the cooking and lighting needs of around 720 households. Financial analysis affirmed the project's viability, indicating a Benefit-to-Cost ratio of 1.78, a Payback Period of 2.4 years, Net Present Value (NPV) of LKR 17 million, and an Internal Rate of Return (IRR) and Return on Investment (ROI) both exceeding 41%. The study concludes that decentralized AD systems provide a sustainable and scalable MSWM solution for Jaffna and similar regions. It recommends addressing institutional, regulatory, and infrastructural gaps to support broader implementation. The findings offer a replicable model that integrates environmental sustainability with economic returns, contributing to Sri Lanka’s transition toward a circular and green economy.

ARIMAX Modelling of Exploring Determinants Influencing WTI Crude Oil Price

A.R.I. Sasanka¹,

Teaching Assistant, Department of Statistics & Computer Science, Faculty of Science, University of Kelaniya, Sri Lanka.

 A.M.C.H. Attanayake², Ph.D.
 Senior Lecturer,  Department of Statistics & Computer Science, Faculty of Science, University of Kelaniya, Sri Lanka.

Abstract

This research delves into the key macroeconomic and geopolitical determinants influencing the West Texas Intermediate (WTI) crude oil price. Among various benchmarks, WTI serves as a key indicator for global oil pricing, given its strategic and economic significance, Understanding the drivers behind WTI crude oil price movements has become an essential concern for policymakers, investors, and researchers. The key factors which are central to global energy economics and highly sensitive to multiple macroeconomic and geopolitical factors namely, World crude oil supply, World crude oil demand, World GDP growth rate, World Inflation rate and finally, Geopolitical conflicts in oil- supplying regions called OPEC (Organization of the Petroleum Exporting Countries) & Non-OPEC were considered and explored the impact of those selected determinants on the price of WTI crude oil using the ARIMAX (Auto-Regressive Integrated Moving Average with exogenous variables) modeling approach. Monthly data spanning 24 years (2000–2023) were analyzed. After graphical representations and statistical outlier checks, all variables were tested for stationary using the  KPSS, ADF and PP tests. First differencing was applied to all non-stationary variables to achieve stationary. 

The Durbin-Wu-Hausman test was performed to account for the endogeneity of variables and results revealed that World crude oil demand and geopolitical conflicts were endogenous. To overcome this issue three endogeneity corrections called Two-stage least square(2SLS), Lagged variables approach, and the GMM-like approach were applied and then candidate models were identified using ACF and PACF plots. ARIMAX(1,1,1) model estimated using GMM-like approach demonstrated superior performance based on AIC and BIC criteria. The model identified global crude oil supply, global inflation rate, GDP growth rate as statistically significant determinants of WTI crude oil prices, supply exerted negative influence. Conversely, global crude oil demand and geopolitical conflict were statistically insignificant in the final model.

Model diagnostics confirmed that the residuals from selected model exhibited no autocorrelation (Ljung-Box p-value = 0.88) and the residual mean was approximately zero, satisfying the assumption of unbiasedness, affirming the model’s validity for forecasting purposes. However residual analysis indicated the presence of heteroskedasticity and departure from normality, suggesting that further studies incorporating models such as GARCH are recommended to effectively capture volatility clustering and enhance forecasting performance in volatile energy markets.

Keywords: ARIMAX model, Macroeconomic factors, Time series forecasting, WTI crude oil price

Abstract
 Nickel-based layered double hydroxides (NiV-LDHs) have emerged as promising materials for electrochemical energy applications due to their excellent redox activity, structural versatility, and environmental friendliness. In this study, a methanol-assisted synthesis and modification approach is employed to enhance the performance of Ni-LDHs for both the hydrogen evolution reaction (HER) and supercapacitor applications. The as-prepared NiV LDH were coated on Ni foam and used as Electrocatalyst. Field emission Scanning electron microscopy (FESEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) techniques were used for their morphological, structural, and physical characterizations. The methanol treatment effectively modifies the surface structure and electronic environment of Ni-LDHs, leading to an increased electrochemically active surface area, improved conductivity, and enhanced ion transport. Electrochemical evaluations reveal that the methanol-treated Ni-LDH exhibits a significantly reduced overpotential and improved Tafel slope for HER in alkaline media, indicating superior catalytic activity. Furthermore, as an electrode material for supercapacitors, the modified Ni-LDH demonstrates high specific capacitance, excellent rate performance, and outstanding cycling stability. These results highlight the dual functionality of methanol-treated Ni-LDHs and underscore their potential as efficient, low-cost materials for sustainable energy conversion and storage systems.

Carbon nanotubes (CNTs) present their exceptional properties in various energy storage devices, making them invaluable in various industries. In general, chemical vapor deposition (CVD) is one of the most widely used and reliable methods for producing CNTs. In this work, CNTs are synthesized by CVD method by systematically varying the carbon precursor (acetylene) flow rate at 30 sccm, 40 sccm, and 50 sccm to study their effect on the structural, morphological and electrochemical properties for supercapacitor applications. The CNTs are formed on 3D metal foam substrate at constant temperature (700 °C). Morphological studies on SEM and TEM showed distinct variations in tube diameter, density, and orientation with increasing flow rates. Graphitization levels were monitored by Raman spectroscopy, and the 40 sccm sample showed the highest I_D/I_G ratio and, thus, the most favorable defect density for charge storage. This study indicates that the precursor flow rate is a key to controlling the properties of CNTs, which serve as a potential electrode material for high performance in supercapacitors. The symmetric supercapacitor device is fabricated in a coin cell assembly using CNT electrodes and the electrochemical performance is systematically analyzed.

In this study, magnetite (Fe₃O₄) nanomaterials were successfully synthesized using a straightforward co-precipitation method, incorporating both single and co-dopants— pristine Fe₃O₄ (F), Fe₃O₄-copper (FC), Fe₃O₄- rGO (FR), and Fe₃O₄-copper/Carbon (FCR). The synthesized samples were characterized through morphological, optical, FTIR, and structural techniques. X-ray photoelectron spectroscopy (XPS) confirmed the presence of the expected elements and their oxidation states. BET surface area analysis revealed an increase in surface area for the carbon-doped (FR) and co-doped (FCR) Fe₃O₄ samples. Electrochemical performance assessed through chronopotentiometry showed a high specific capacitance of 920 F/g at a current density of 1 A/g. Cyclic voltammetry (CV) further indicated a capacitance of 508.67 F/g in a three-electrode setup using 1 M KOH. A symmetric supercapacitor device was fabricated using the FCR nanocomposite, demonstrating a specific capacitance of 193 F/g, energy density of 26 Wh/Kg, and power density of 1999 W/Kg at 1 A/g. The device also displayed outstanding cycling stability with approximately 90% capacity retention and a coulombic efficiency of 91% over 8000 charge-discharge cycles. 

In this study, anthocyanin and Chlorophyll a natural dyes were extracted from three different colours (red, pink and yellow) flowers in the family of Ixora coccinea and Catharanthus roseus, respectively. Extracted dyes using ethanol and DI-water as solvents were characterized optically and structurally by UV-Visible and Fourier-transform infrared (FTIR) spectroscopies. UV-Visible study revealed that Ixora flower’s ethanol extracts exhibited absorbance corresponding to the anthocyanin molecules and Catharanthus displayed absorbance for Chlorophyll a. However, DI-water extract of all flowers didn’t show any significant absorption peaks. In addition, optical properties of the dye-coated TiO₂ exposed that all ethanol extract sensitized TiO₂ film showed higher absorption than DI-water sensitized TiO₂ and bare TiO₂, among them Ixora flower’s ethanol extract sensitized TiO₂ film exhibited maximum absorption. FTIR spectrum also confirms the presence of anthocyanin and Chlorophyll a in the respective ethanolic flower extracts and attachments of pigment molecules on the TiO₂ surface. Finally, DSSCs were fabricated using all extract sensitized TiO₂ photoanode, / electrolyte and Pt counter electrode and the photovoltaic measurement were carried out under 1 sun illumination with AM 1.5 G filter. Red Ixora flower’s ethanol extract sensitized TiO₂ photoanode-based device displayed the highest power conversion efficiency (PCE) of 0.23%. This remarkable result is attributed to the higher light absorption and low recombination rate. Among the selected flowers, all the optical, structural and electrical properties of the dyes suggested that anthocyanin extract is more prominent dye for DSSCs application compared to Chlorophyll a extract. 

Key words: Ixora coccinea; Catharanthus roseus; Chlorophyll a; anthocyanin, DSSCs; natural dye

Recent progress in the development of binary transition–rare earth metal oxide systems, particularly Co₃O₄-Nd₂O₃ and its carbon-based nanocomposites such as Co₃O₄-Nd₂O₃/rGO and Co₃O₄-Nd₂O₃/g-C₃N₄, has positioned these materials as promising candidates for supercapacitor electrodes. Despite their potential, challenges such as synthesis complexity, particle agglomeration, and limited surface activity—stemming from rapid nucleation, electrostatic interactions, and surface inertness—hinder their electrochemical performance. To address these limitations, nanostructured Co₃O₄-Nd₂O₃ was hybridized with reduced graphene oxide (rGO) and graphitic carbon nitride (g-C₃N₄), yielding Co₃O₄-Nd₂O₃/rGO and Co₃O₄-Nd₂O₃/g-C₃N₄ composites. Among these, the Co₃O₄-Nd₂O₃/rGO composite exhibited outstanding electrochemical performance, including a high specific capacitance of 315 F/g, remarkable thermal stability (98.43%), excellent rate capability, and extended cycling durability. An asymmetric supercapacitor device fabricated using Co₃O₄-Nd₂O₃/rGO as the positive electrode and activated carbon (AC) as the negative electrode in 2 M KOH demonstrated a high energy density of 30.31 Wh/kg, a power density of 374.96 W/kg, and superior stability, retaining 81.05% of its capacitance and 91.78% coulombic efficiency over 15,000 charge–discharge cycles. These findings highlight the effectiveness of hydrothermally synthesized Co₃O₄-Nd₂O₃/rGO//AC as a high-performance electrode material for next-generation energy storage applications.

Green Hydrogen via Solar-driven Water splitting: Advances in TMC/TiO₂ Photocatalyst systems

Sivagowri Shanmugaratnam^1,2*, Punniamoorthy Ravirajan², Yohi Shivatharsiny^3,4, Dhayalan Velauthapillai¹

¹Faculty of Engineering and Science, Western Norway University of Applied Sciences, P.O. Box 7030, 5020 Bergen, Norway

²Clean Energy Research Laboratory, University of Jaffna, Jaffna 40000, Sri Lanka

³Department of Chemistry, University of Jaffna, Jaffna 40000, Sri Lanka

⁴Chemistry & Biochemistry Department, Augustana University, Sioux Falls, SD 57197, USA

Email: [email protected]

Abstract

The increasing demand for green hydrogen is driven by the depletion of fossil fuels and the urgent need for sustainable energy solutions. Green hydrogen is particularly attractive due to its renewable nature, environmental benefits, and cost-effectiveness. Among various production methods, photocatalytic water splitting has emerged as a promising approach for generating green hydrogen using solar energy.

In recent years, a wide range of semiconductor materials has garnered attention for their potential in solar-driven applications. Notably, transition metal chalcogenides (TMCs) have emerged as highly promising photocatalysts, owing to their large surface area, narrow band gaps, and distinctive electronic and optical properties. However, their photocatalytic efficiency is often hindered by rapid charge carrier recombination.

To address this limitation, titanium dioxide (TiO₂)—renowned for its chemical stability and photocatalytic activity—has been combined with TMCs to form nanocomposites. This hybridization facilitates the formation of efficient heterojunctions, which enhance charge carrier separation and transport. As a result, these nanocomposites exhibit improved light absorption, suppressed recombination rates, and significantly enhanced hydrogen evolution under simulated solar irradiation.

This study focuses on the synthesis of TMCs/TiO₂ nanocomposites, with particular emphasis on hydrothermal methods. It explores their structural, optical, and photocatalytic properties and evaluates their performance in hydrogen production from various aqueous systems under extended solar exposure. The findings suggest that low-cost, noble-metal-free TMCs/TiO₂ nanocomposite photocatalysts hold significant potential for efficient solar-to-hydrogen conversion via water splitting.

Key words: Transition metal chalcogenides, photocatalyst, hydrogen, water splitting, TiO₂

A hybrid Mo incorporated ZIF-8 derived Mo₂C/NC nanocomposites were sythesized via a cost effective co-precipitation approach followed by a pyrolysis method. X-ray diffraction analysis was carried out to investigate the crystal structure and phase compositions of the synthesized ZIF-8, Mo/ZIF-8 and derived Mo₂C/NC catalysts. The achieved Mo₂C/NC reveals the hexagonal crystal structure matched with the JCPDS card no 35-0787. According to morphological analysis, ZIF-8 template maintained a rhombic dodecahedron nanostructure, whereas the derived Mo₂C/NC showed a transition to porous nanoparticle morphology. Furthermore, the electrocatalytic performances with respect to the hydrogen evolution reaction (HER) were scrutinized in an alkaline medium using 1M potassium hydroxide (KOH) solution using three electrode process. From the Cyclic voltammetry analysis, the optimized Mo₂C/NC catalyst acquired a large electrochemical active surface area value of 94.75 cm² which accelerates the functional reactive species to driven the HER activity. Also, the low overpotential of 96 mV was required to achieve a current density of 10 mA/cm², and the corresponding Tafel slope of 38.2 mV/dec indicates that the hydrogen evolution reaction proceeds predominantly via the Volmer–Heyrovsky mechanism. Electrochemical Impedance spectroscopy (EIS) was performed to examine the conductivity of the fabricated catalysts. As per that, Mo₂C/NC nanocomposite delivered the low solution resistance value (R_s) of 0.011 Ω and a low charge transfer resistance (R_ct) of 0.609 Ω. In addition, the durability of the optimized Mo₂C/NC catalyst was analyzed by using chronoamperometry test. The study highlights a promising strategy for designing and developing the MOF-derived architectures with enhanced potential in sustainable energy technologies.

Keywords: Hybrid electrocatalyst, Mo/ZIF-8 derived Mo₂C/NC, cost effective strategy, alkaline medium, hydrogen evolution reaction. 

TiO₂-based dye-sensitized solar cells (DSSCs) are cost-effective alternative to silicon-based solar cells. By doping the photoanode material (TiO2), the electronic and light absorption properties of the photoanode can be improved that will enhance the efficiency of DSSCs.  Iron (Fe) was selected as a promising dopant for TiO₂ due to its similar ionic radius to titanium in this study, owing to the limited and sometimes contradictory reports in the literature regarding its effect on DSSC performance. In this work, Fe-doped TiO₂ nanomaterials were synthesized using hydrothermal method by employing Titanium(IV) isopropoxide as the titanium source and anhydrous FeCl₃ as the dopant precursor, with doping concentrations of 2.5, 5.0, 7.5, and 10.0 mmol%. Both Fe-doped and un-doped TiO₂ nanomaterials were separately coated on FTO glass substrates through spray pyrolysis. The structural and optical properties of the synthesized nanomaterials were characterized using X-ray diffraction (XRD) and UV-Visible spectroscopy, respectively. XRD analysis revealed a decrease in the crystallite size upon Fe doping while UV-Visible spectroscopy confirmed enhanced light absorption by the Fe-doped TiO₂ photoanode compared to the un-doped photoanode.

DSSCs were fabricated with the synthesized Fe-doped TiO2 as photoanode, Platinum, N719 dye, and I⁻/I₃⁻ redox couple as counter electrode, sensitizer, and electrolyte, respectively. The photovoltaic performances of the fabricated DSSCs were evaluated under simulated sunlight (100 mWcm⁻², AM 1.5G filter). Among the fabricated devices, the DSSC with 5.0 mmol% Fe-doped TiO₂ photoanode exhibited the highest photovoltaic performance of 8.21%, which was about 12% higher than that of the control device (PCE = 7.36%). This enhancement in PCE of Fe-doped TiO₂ photoanode could be attributed to an increased short circuit current density (J_SC = 17.12 mAcm^-2) resulting from improved light-harvesting capability as confirmed by the UV-Visible spectroscopy, and reduced charge recombination as evidenced by the Electrochemical Impedance Spectroscopy. This study underscores the importance of controlled doping in improving photoanode performance and highlights the potential of iron as an effective dopant for achieving high-efficiency DSSCs.

Keywords: DSSCs, Fe-doped TiO₂, Hydrothermal synthesis, Spray pyrolysis, PV performance

The term "Lean" originates from a Japanese technique known as TPS, which stands for the Toyota Production System. Lean refers to a systematic strategy that focuses on finding and removing waste while simultaneously enhancing processes that bring value. In this study the JIT, total preventive maintenance, total quality management, 5S, and kaizen were identified as the lean practices that majorly used. Consequently, Operational performance refers to an entity's performance in relation to established criteria, including regulatory compliance, waste reduction or elimination, and productivity, among others. Under the operational performance, cost, quality, time, and flexibility are identified as the key dimensions. This study was mainly supported by the resource-based-view (RBV) theory to develop the hypothesis. 

According to the literature, the majority of the studies are focusing on the relationship between lean practices and overall organizational or business performance. On the other hand, lean practices are mainly utilized in the operational context, and this leads scholars to identify the impact of lean practices on operational performance. Further, the majority of studies tested this relationship between lean practices and operational performance in the context of the apparel industry, while keeping a contextual dearth of research in the context of food and beverage companies. On the other hand, the literature argued that lean practices are highly implemented in large-scale organizations in the context of Sri Lanka. Therefore, this study investigates the impact of lean practices on operational performance in the large-scale food and beverage industry in Sri Lanka. 

Therefore, this study aims to investigate the impact of lean practices on the operational performance of large-scale food and beverage companies in Sri Lanka. The procedure of conducting a systematic literature review was used to complete the literature review. Utilizing the Deductive approach of positivist philosophy within the framework of quantitative methodology, this relationship between lean practices and operational performance was tested. Within the context of the food and beverage industry in Sri Lanka, the population of the research consists of all of the large-scale food and beverage companies which adopted or intend to adopt lean practices in Sri Lanka. Therefore, this study used the purposive sampling technique for the sample selection due to the intentional identification of the lean implemented or intended to be implemented in food and beverage companies. Through the use of survey data acquired from executives and above personalities who are working in operational or production-related functions of large-scale food and beverage organizations in Sri Lanka. 

Using survey data, the hypotheses are tested using R Studio 5.0 software. Based on the results of the hypothesis testing, it has been determined that total quality management and kaizen are directly contributing to the improvement of the operational performance while other lean practices such as JIT, total prevention maintenance, and 5S are not directly affect to the operational performance in Sri Lankan large-scale food and beverage organizations. The overall findings of this study improve the understanding of this relationship, while it's limited to Sri Lankan large-scale food and beverage organizations, which future scholars should focus. 

The growing demand for clean and sustainable energy has driven significant advancements in renewable energy technologies, particularly in photovoltaic (PV) systems. As solar energy continues to gain prominence, efficient energy storage and power management become critical. Supercapacitors (SCs), known for their high-power density, rapid charge-discharge capability, and extended lifecycle, have emerged as a promising alternative to conventional batteries in PV-based energy systems. This study investigates the implementation of Maximum Power Point Tracking (MPPT) in PV systems integrated with SCs as the primary energy storage.

An experimental PV setup was developed using a Solar Array Simulator configured to emulate a 10W solar panel. A buck converter was employed for voltage step-down and MPPT control. The energy storage unit consisted of a supercapacitor bank made of six 400F, 2.7V cells connected in series. A parallel-connected electronic load simulated dynamic power consumption patterns. For enhanced real-world applicability, household electrical consumption data were scaled down by a factor of approximately 0.001922, based on a 5202W household load reference to match the 10W lab-scale system. The Constant Voltage (CV) MPPT algorithm was applied under a constant irradiance profile, and household load patterns were implemented in the system to analyze its performance. Furthermore, diverse irradiance profiles generated by a solar array simulator, along with different DC-DC converter configurations, were applied to the source to evaluate system performance under dynamic irradiance and load conditions.

The results demonstrate consistently high MPPT efficiency across all configurations, with a maximum efficiency of 99.97% achieved when using a buck-boost converter combined with an SC-parallel resistor load under triangular ramp conditions. Even under more demanding, variable load profiles, the system maintained an average efficiency of approximately 98.11% continuously, highlighting its strong tracking performance and resilience to sudden load changes. 

The findings underscore the viability of supercapacitors in solar energy applications, especially where rapid load response and high-frequency cycling are required. This research contributes valuable insights for designing high-efficiency, SC-assisted PV systems and lays the groundwork for future studies involving adaptive MPPT algorithms and optimized converter hardware for real-world deployment.

ABO blood group antibodies are considered as naturally occurring antibodies and appear after birth due to environmental factors. Isohemagglutinin titer is used to determine IgM antibody titers among the blood donors. Further the antibody titers are crucial in the process of local antisera production in low-income countries for blood grouping. Notably, blood donors at the National Blood Centre in Sri Lanka exhibited lower anti-A antibody titers compared to anti-B antibody titers. Therefore, the current study aims to investigate the association between anti-A antibody titers and, BMI, demographic and lifestyle related factors among blood group B voluntary donors at the National Blood Centre. 

This cross-sectional and prospective study was carried out in National Blood Centre in Sri Lanka. Group B blood donors were recruited in the study and the demographic data, including age, gender, height, and weight, along with factors related to lifestyle including dietary patterns, physical exercise, alcohol consumption and smoking were gathered via a questionnaire. Social Sciences (SPSS) version 22.0 was used for statistical analysis. Cluster analysis, Fisher Exact test and Chi-square test were used for data analysis. Anti A antibody titers were obtained by using the standard tube technique and the titers ranged between 4 to 1024.The median anti A antibody titer was 32, while titer value of 256 was considered as the critical value in the current study. A total of 340 participants were included in this in which 82 (24.12%) were female donors and 258 (75.88%) were male donors. Most of the male individuals were between 19 to 58 years of age. BMI of the donors was between 16.28 and 39.71. Donors of six ethnic groups namely Malay (0.29%), Indian Tamil (1.47%), Sri Lankan Tamil (1.76%), Sinhalese (94.71%), and Burgher (0.29%) were included in the study. 

 There was a significant association between anti A antibody titers with genders (Chi-square value= 5.053, p=0.025), alcohol consumption (Fisher exact value=9.263, p=0.020), smoking (Chi-square value = 4.533, p=0.033) and the dietary pattern (Fisher exact value =8.651, p=0.013), but not with ethnicity, BMI, age and the physical exercise (p>0.05). 

As per the current study anti-A antibody titer seems to be affected by the gender, alcohol consumption, smoking and the dietary pattern of the blood donors.

Keywords: Anti-A antibody titer, Blood group B, Demographic factors, BMI, Lifestyle factors

Background: Non-communicable diseases (NCDs) account for 90% of deaths in Canada, with physical inactivity being a key modifiable risk factor. Immigrants face unique challenges in meeting physical activity (PA) guidelines due to cultural, socioeconomic, and environmental factors.

Purpose: This study explores barriers and facilitators influencing PA levels among immigrants globally and in Canada to inform policies and programs that promote equitable health outcomes.

Methods: A mixed-methods approach was used, comprising: (1) a literature review of 62 global studies on immigrant PA barriers and facilitators; (2) stakeholder engagement through interviews with immigrant students, researchers, and healthcare professionals; and (3) secondary data analysis of the 2019–2020 Canadian Community Health Survey (CCHS). Theoretical Domains Framework (TDF) guided factor mapping, and logistic regression models identified predictors of meeting PA guidelines.

Results: Key barriers identified included cultural norms, financial constraints, and environmental factors. Stakeholder interviews highlighted intersecting challenges such as language barriers and compounded obstacles for women and disabled immigrants. CCHS analysis identified predictors of meeting PA guidelines, including sex, visible minority status, perceived health, active transportation, and recreational PA. Immigrants were found to have greater vulnerabilities compared to Canadian-born populations.

Recommendations: Strategies include a federal tax credit for PA costs, enhancements to physical education curricula, multilingual municipal surveys for localized program design, and culturally responsive initiatives such as women-only gym hours.

Conclusion: Tailored, evidence-based interventions are essential to address disparities in PA engagement among immigrants. These efforts aim to improve health equity and reduce NCD burdens across Canada.

ABSTRACT 

Background: Over the past ten years, the prevalence of syphilis has been rising, making it a global public health concern. The disease, caused by Treponema pallidum, can lead to severe complications if untreated. This study aims to investigate the prevalence and distribution of Syphilis in the Polonnaruwa District, Sri Lanka, from 2013 to 2023.

Methods: A retrospective study was conducted using data from confirmed syphilis cases in all nine Medical Officer of Health (MOH) areas of the Polonnaruwa District, including Thamankaduwa, Welikanda, Aralaganvila, Bakamoona, Aththanakadawala, Galamuna, Lankapura, Dehiaththakandiya, and Diyabeduma. Data were collected from sexual health clinics, antenatal clinics, and other medical facilities. Descriptive statistics were used to determine the prevalence and demographic distribution of syphilis. The frequency and distribution of syphilis cases were determined by year, age, sex, and geographical area.

Results: The syphilis prevalence remained under 0.01% of the district population. However gradual increase in the number of cases was observed over the last five years, with the highest number of cases recorded in the year 2023. Thamankaduwa MOH area had the highest syphilis prevalence, while the 25-34 age group exhibited the largest number of positive cases, predominantly among males.

Conclusion: This study assesses the prevalence and demographic distribution of syphilis in Polonnaruwa District, offering valuable data for public health planning. In order to lower transmission and enhance disease control, these findings will support the development of targeted screening and highlight the necessity of concentrated public health interventions in high-risk populations and geographic locations.

Keywords: Syphilis, Prevalence, Demographic Factors, Polonnaruwa, Sri Lanka, Retrospective Study

Introduction: During the pregnancy period vaginal discharge is common clinical concern. In the third trimester abnormal discharge is often associated with infections caused by pathogens like Candida spp., Group B Streptococcus and Gram-negative bacilli. This is one of the significant clinical concerns, since they can lead to adverse pregnancy outcomes such as premature rupture of membrane, preterm birth, low birth weight, and neonatal infections. Accurate diagnosis and timely management of such infections are important to minimize the complications and improve both maternal and neonatal health. This study investigates the prevalence and types of pathogens associated with vaginal discharge in the third trimester of pregnancy and the implications for maternal and neonatal health. 

Objective: Present study aims to investigate the prevalence of clinically significant isolates in vaginal swabs obtained from third-trimester pregnant women attending the tertiary care hospital in Sri Lanka. 

Methodology: This study is a descriptive cross-sectional study, conducted at German Sri Lanka Friendship Hospital for Women, Sri Lanka from March to September 2024. Third trimester pregnant women who were admitted to the hospital with symptomatic vaginal discharge included in the study. Laboratory investigations were carried out at the department of Microbiology at the hospital. A total of 358 vaginal swab specimens were collected from pregnant women who satisfied the inclusion criteria. Significant isolates have been identified by their colony morphology, lactose fermenting patterns and biochemical tests results. Antibiotic susceptibility test patterns and disc inhibition zone diameter has been measured daily and recorded in electronic spread sheets (MS Excel) after performing the laboratory investigations.

Results: The mean age of this study sample is 28 years old. The prevalence of infective vaginal discharge during 3rd trimester is 80.3 % (n=252). Candida species (40.76% n=128) had the highest prevalence followed by Group B streptococcus spp.(27.07% n=85). For the Gram-negative bacilli species Imipenem and Amikacin (100%) reported as highest sensitive antibiotic while Ampicillin reported with 75.7% (n=28) resistance. S. aureus reported as 100% susceptibility for Vancomycin. Methicillin Resistant Staphylococcus aureus 4.1 %( n=13) detected. Streptococcus group B spp has showed 100% sensitivity for Penicillin and 98.8% for Ampicillin. No significant relationship between age groups and growth rates has identified. There was no significant relationship between gestational diabetes mellitus and candidial vaginal infections has identified in current study. 

Conclusion: Candida species and the Group B Streptococcus spp. found to be the most prevalent vaginal isolate during third trimester pregnancy. The age between 25-33 years identified as highest risk group found to have highest vaginal infections during pregnancy. 

Key words: Prevalence, vaginal infections, Candida albicans, 3rd trimester pregnancy, vaginal discharge, laboratory investigations.

Phage therapy is re-emerging as a viable alternative to antibiotics, particularly against multidrug-resistant (MDR) bacterial pathogens. However, the rapid development of phage resistance remains a major obstacle to its sustained clinical success. This study investigates the phenotypic consequences of phage resistance in a hypermucoviscous, hypervirulent clinical isolate of Klebsiella pneumoniae (Kleb_53). A lytic phage, designated ‘Disc,’ was isolated from sewage and demonstrated robust environmental stability, remaining viable across a broad temperature (-20 to 60°C) and pH (3 to 11) range. Phage Disc showed efficient infection dynamics, with a 15-minute adsorption time, a 30-minute latent period, and a high burst size of ~354 virions per infected cell. Upon exposure to the phage, resistant variants of Kleb_53 rapidly emerged. These variants exhibited diverse colony morphologies—smooth, rough, and small forms—accompanied by a noticeable reduction in mucoviscosity, as confirmed by string, aggregation, and wetness assays. Alterations in plaque morphology between wild-type and resistant strains further indicated significant phenotypic shifts. Some phage-resistant variants showed partial re-sensitization to antibiotics such as meropenem and third-generation cephalosporins. However, this apparent benefit was counterbalanced by an increase in biofilm formation, which may contribute to persistent infections and treatment failure. These findings highlight the complex trade-offs that accompany phage resistance in hypervirulent K. pneumoniae, including changes in virulence, antibiotic susceptibility, and biofilm formation. The dynamic interplay between phages and bacterial hosts underscores the need for a deeper understanding of resistance mechanisms. This knowledge is crucial for designing effective phage-based strategies, such as tailored phage cocktails or resistance-mitigating adjuvants, to enhance therapeutic efficacy and reduce the risk of resistance in clinical settings.

Introduction: Expectorated sputum is the commonly received specimen to microbiology laboratory to diagnose LRTIs. Sputum Gram stain and culture are the two important tests done to identify significant pathogens. Quantitative culture is considered as the ‘gold standard’ due to misinterpretation of upper respiratory tract commensal bacteria as pathogens in semi-quantitative culture method.

Objectives: This study was conducted to determine the diagnostic value of sputum Gram stain, to compare semi quantitative and quantitative culture methods and to assess the susceptibility pattern of pathogenic bacteria in patients treated at Central Chest Clinic.

Methodology: A cross-sectional study was carried out for 4 months’ duration in 316 patients with LRTIs. Quality of the specimens were graded by Murray and Washington criteria. Sputum Gram stain was assessed for the predominant morphotype and evaluated its usefulness. All samples were subjected to both semi-quantitative and quantitative culture methods. Significant bacterial pathogens were identified to the species level and antibiotic susceptibility was tested. Interviewer-administered questionnaire was used to collect sociodemographic and clinical details. 

Results: Macroscopically purulent samples were significantly found in microscopically good quality specimens (Grade 3,4 and 5).

Sputum Gram stain had good specificity (93%) and NPV (88%) to the culture. The sensitivity was 60% and PPV was 75%. Direct Gram stain results had substantial agreement with culture (K=0.658).

There was no significant difference in pathogen isolation between the two culture methods. Semi quantitative culture had good sensitivity (92%) and specificity (95%) with 86% and 97% PPV and NPV respectively. Both culture methods were in almost perfect agreement (K=0.848).

Gram negative bacilli were the commonly isolated as pathogen compared to typical respiratory pathogens. Most (36%) of the infections were caused by Pseudomonas aeruginosa. Multidrug resistant organisms were rarely isolated in this population.

Conclusion: Both macroscopic appearance and microscopic grading of sputum are important in obtaining reliable results. Direct Gram stain of good quality specimens has shown significant diagnostic value in patients with LRTIs. Significant difference was not seen between two culture methods in isolation of bacterial pathogens.

Key words: Sputum, Gram stain, Quantitative

Cannabis Legalization and Canadian Youth: A Pilot Study Examining Health Outcomes, Public Misconceptions, and Policy Gaps

Eilya Parsa BSc (Hon)1, Mariana Thangaraja MD (Resident Trainee) 2, Jeyasakthy Thayaparan3, MD, FRCPC, ABIM 

1 McMaster University, McMaster University (Psychiatric Resident), St. Joseph’s Healthcare, Hamilton2,  Section of Geriatrics, Department of Medicine, Mackenzie Health Hospital3

This pilot study aims to examine the impacts of cannabis legalization on Canadian youth aged 16 to 25, with a focus on health outcomes, social perceptions, and gaps in policy. Since the legalization of recreational cannabis in Canada in 2018, its use among youth has become increasingly normalized. While legalization was intended to reduce youth access and promote safety, recent research proves that these goals have not been fully realized. Data from the 2024 Canadian Cannabis Survey indicate that cannabis use remains widespread among Canadian youth and young adults, with 43% of individuals aged 16–19 and 48% of those aged 20–24 reporting non-medical cannabis use within the past year. Post-legalization trends show continued growth in cannabis-related harms, including impaired driving, which remains one of the leading causes of injury and death in this age group after alcohol consumption. The perception that legal equals safe has become prevalent, both among youth and older individuals, despite growing research highlighting the harmful effects of cannabis on mental and cognitive health. 

To explore this issue, a mixed-methods pilot study is being conducted using an anonymous, self-reported questionnaire distributed through Google Forms. The survey collects information on cannabis usage patterns, age of initiation, awareness of associated health risks, and perceptions shaped by legalization. The Cannabis Use Disorder Identification Test – Revised (CUDIT-R) is being utilized for the development of questions related to problematic cannabis use. The CUDIT-R enhances the questionnaire’s ability to assess the frequency, intensity, and consequences of cannabis use, thereby improving the reliability and accuracy of the collected data.This method allows us to gather authentic responses from a diverse group of youth across Canada and assess how well-informed they are about the potential harms of cannabis, including anxiety, depression, cognitive impairment, psychosis, and impaired driving. These effects, particularly concerning frontal lobe development, are especially relevant during adolescence and early adulthood, yet often underrecognized.

Our study will examine national trends, including Ontario's emergency department data, which showed a nearly fivefold increase before legalization. Additionally, we will explore the effects of marijuana use during pregnancy, highlighting severe impacts on infants and further increases post-legalization. Preliminary research demonstrates a concerning gap between public health objectives and actual outcomes. These findings suggest a troubling disconnect between public health goals and real-world outcomes.

Ultimately, this study seeks to address the growing divide between the myth and reality of cannabis use in Canada. While legalization has provided a regulatory structure, it has also contributed to a public narrative that downplays risk, particularly among young users. Through this research, we hope to inform future public health messaging, encourage youth-specific education, and advocate for a policy reassessment.

Keywords: Cannabis Legalization, Youth Health, Public Perception, Impaired Driving, Policy Reassessment

In recent years, there has been a significant rise in the consumption of short-form video formats, particularly the popular app TikTok. TikTok allows users to create and view content that is often under a minute long, allowing users to be exposed to several different types of content and topics within a short amount of time, which often becomes addictive. Statistics show that in Canada, the majority of TikTok users are adolescents and young adults, with 43% of Canadian users being within the age range 18-29. TikTok is especially popular among individuals aged 13-25. In this age range, teenagers and young adults’ brains are at a critical stage of development, as the adolescent brain does not stop developing until age 25. TikTok’s algorithm provides constant digital stimulation to the brain, which can lead to negative cognitive effects, such as weakening the brain’s ability to focus on longer tasks and having a shorter attention span. Furthermore, frequent usage of the app may have other impacts on users’ physical health, financial habits, and may also have an emotional impact on users. Physically, users may experience issues such as neck strain and hearing damage due to the app’s reliance on audio-heavy content. Financially, the app promotes overconsumption of a wide range of products, even allowing users to purchase products through the app itself. Emotionally, users may become socially isolated from friends and family through the app’s addictive nature. This study aims to examine the cognitive, physical, financial, and emotional impacts of TikTok on adolescents and young adults aged 13-25. 

To examine these effects, an anonymous Google Forms survey will be distributed among individuals aged 13-25. This survey will first allow individuals to categorize themselves into age groups and occupations. The survey will collect data on the user’s daily TikTok usage, sleep patterns, physical symptoms (such as neck and eye strain), financial behaviour, as well as self-reported effects on attention span. Furthermore, the survey will use a modified version of the Game Addiction Scale (GAS), a standardized tool originally used to identify potential addiction to gaming. This modified version of the scale will help determine potential TikTok addiction in users. This anonymous survey will allow users to reflect on their habits and experiences with TikTok comfortably and provide insight into how the app affects cognitive, physical, financial, and emotional aspects of users’ lives. 

Overall, this study will set up a foundation to investigate how TikTok negatively affects the cognitive and behavioural development of adolescents and young adults whose brains are not yet fully developed. The anonymous survey will help identify key areas of concern, such as attention span, physical and emotional health, and consumption habits, and will allow policymakers to create policies and guidelines aimed at promoting healthier consumption of digital media for both youth and young adults.

Keywords: TikTok, Social Media, Consumption, Digital Media Use, Cognitive Development

Due to the shift to online events, the rise of social media, and increased social anxiety, high school students have been experiencing increased rates of loneliness. To cope with such negative feelings, some students have engaged in unhealthy emotional eating practices. While low social engagement does not equate to loneliness, social isolation can contribute to increased levels of perceived loneliness among high school students. Recent studies have found that inducing social isolation in youth is linked to brain patterns similar to those associated with hunger. Moreover, they have identified that nearly 1 in 4 youth aged 15 to 24 years have always or often felt lonely. This pilot study strives to analyze the relationship between loneliness and unhealthy eating habits among high school students aged 13 to 18, its connection to the gut-brain axis biological communication system, as well as the success of current services that aim to decrease these negative feelings.

To examine this issue, we are conducting a mixed-methods pilot study using an

anonymous, self-reported questionnaire through Google Forms. This survey records data on high school students’ levels of perceived loneliness using 8-10 questions selected from the Revised UCLA Loneliness Scale (RULS), levels of social engagement (both face-to-face and online interactions), overconsumption of unhealthy food choices, and willingness to engage in increased face-to-face social activities. This methodology will provide valuable insight into high school students’ emotions to allow their subjective loneliness categorizations to be standardized. Moreover these tools will provide information about their unhealthy eating habits, as well as the current face-to-face activities that are most popular among this demographic.

Moreover, we will assess Canadian trends and look for patterns regarding the strength of this association and whether it differs based on attributes like gender. Previous studies have indicated a stronger involvement of negative emotions on emotional eating in females, although this difference is small and not apparent throughout all age groups of high school students.

The results of this study will aim to highlight the relationship between negative emotions like loneliness and the rise of emotional eating in teenagers. This research will allow for a more complete understanding of this relationship, which can be used to provide resources that sufficiently help high school students decrease their experiences of loneliness.

Keywords: Mental Health, High School Students, Emotional Eating, Unhealthy Eating, Loneliness.

University students increasingly face mental health challenges due to academic pressures,

including financial strain, competitive environments, and intense workloads. While awareness of

mental health issues and access to university resources have expanded, systemic gaps in

institutional support and lingering misconceptions continue to hinder effective intervention—

issues exacerbated by the impact of the COVID-19 pandemic. According to the Healthy Minds

Study, which collected data from 373 campuses nationwide, more than 60% of college students

met the criteria for at least one mental health concern during the 2020–2021 academic year,

reflecting the heightened distress many experienced amid pandemic-related disruptions. This

pilot study aims to comprehensively analyze the mental health landscape among university

students aged 18 to 30, exploring patterns of psychological distress, specifically anxiety,

depression, and burnout. Additionally, it seeks to identify perceived shortcomings in institutional

policies that affect students' ability to access mental health resources, such as stigma, insufficient

funding, limited professional support, and inefficiencies in institutional outreach.

To investigate these issues, we are conducting a mixed-methods pilot study utilizing an

anonymous, self-reported questionnaire distributed via online platforms (e.g., Google Forms)

comparing university students to high school students. The survey gathers data on participants'

experiences with mental health, particularly burnout, anxiety, and depression, their awareness of

available support systems and their usage of university-provided services. To assess mental

health experiences, the questionnaire integrates items adapted from established scales, including

the General Health Questionnaire (GHQ), the Anxiety Symptoms Questionnaire (ASQ), the

Center for Epidemiologic Studies Depression Scale (CES-D), and the Maslach Burnout

Inventory (MBI). This approach enables a thorough evaluation of gaps between institutional

policies and student well-being, while also measuring the effectiveness of existing university

mental health initiatives.

Furthermore, we will analyze national trends, including student mental health reports

while at university institutions and service utilization statistics. Preliminary research indicates a

sharp rise in university-related mental health concerns, with increasing numbers of students

seeking psychological support. However, barriers such as financial constraints, appointment

shortages, and lack of culturally competent care remain a barrier for students.

This study seeks to bridge the gap between policy and lived experiences by identifying

key weaknesses in university mental health frameworks. Through this research, we hope to

advocate for policy improvements at the institutional level to better support the needs of

students.

Keywords: Mental Health, University Students, Institutional Gaps, Anxiety, Depression

Acinetobacter baumannii, a major cause of hospital-acquired infections, particularly in ICUs, causes ventilator-associated pneumonia, septicaemia, and wound infections. The pattern of extensive resistance exhibited by A. baumannii against antibiotics, including carbapenems, limits treatment alternatives and contributes to poor patient outcomes. The persistence of A. baumannii in healthcare environments is further enhanced by its ability to form biofilms on medical devices. As resistance continues to rise, bacteriophage therapy has emerged as a promising solution. Bacteriophages offer targeted antibacterial activity and biofilm disruption abilities, presenting a viable alternative to antibiotics in the management of multidrug-resistant A. baumannii infections. In this study, a lytic bacteriophage Ab_3 obtained from West Bengal sewage sample was characterized and analyzed for its anti-biofilm activity. Ab_3 was observed to have small, round, pinpointed plaques. The lytic activity of Ab_3 was persistent for 8 out of 40 isolates tested. Ab_3 was stable from pH 1 to 11 and temperatures -80ºC to 65ºC. On performing the life cycle, Ab_3 had an adsorption rate of 85% within 6 minutes, a latency period of 20 minutes, with a burst size of 34 phages/cell. Time-kill assay confirmed the optimum activity at MOI 0.001 and lower. The host bacteria were moderate biofilm producers, with Ab_3 having a significant biofilm reduction activity. Biofilm inhibition was most effective in 1.12×10¹⁷ PFU/ml, followed by MOI 0.001, both not having much difference in reduction, whereas in MOI 0.0001, there was a decline in the anti-biofilm activity. Thus, it is concluded that the characterized A. baumannii phage Ab_3 has the potential to be a promising alternative for drug-resistant strains of A. baumannii in therapeutic applications.

Key words: Acinetobacter baumannii, drug resistance, phage therapy, biofilm formation, anti-biofilm activity.

Anemia has emerged as a serious global health concern. Although anemia affects people
 everywhere, especially developing countries have the highest prevalence of the condition. In order to evaluate the prevalence of anemia among males in the Maskeliya area, the study was planned. The data were collected from the selected laboratories for a period of one year. The relevant parameters to the study title, such as HB, RBC, MCV, and MCHC, were recorded and statistical analysis was performed via SPSS. It showed that 15.36% of anemic males were identified, and it was on a decreasing trend compared to previous years. The 61-70 male age categories have a high anemic prevalence. Among them, 21.9% were found at the mild anemic level, while 2.1% and 1% were found at the moderate and severe anemic levels, respectively. The age categories of 21–30 and 31–40 had the lowest number in all stages of anemia. Tamils were found to have a high (85.4%) prevalence of anemia compared to other ethnic groups, while Muslims were low in prevalence (3.1%). May showed the highest prevalence of anemia among males, while July and October showed the lowest prevalence of anemia. Further, the anemic trend was increasing in pattern from January to May, and from June to December, it was declining. According to the present findings, the anemic males had high, normal, and low levels of RBC, MCV, MCHC, and MCH, and the reason for the differences should be investigated further. The prevalence of anemia was considerable among males in the Maskeliya area, based on investigation reports performed in 2023. There were significant correlations found between the age category, ethnicity, and season.

KEYWORDS: Anemia, Nutritional status, RBC indices, Estate sector

Nanomedicine is an interdisciplinary method that combines biotechnology, information technology, and nanotechnology in one frame. Personalized medication delivery systems, tissue engineering-based regenerative medicine, and patient-specific implants are further made more effective with three dimensional printing technology. The development of bio fabrication technologies is crucial for creating patient-specific medical devices and improving healthcare, while also offering economic opportunities in the global medical device market. The applications of 3D printing in medicine span various domains, including surgical planning and simulation, where patient-specific models enhance accuracy and safety; prosthetics and orthotics, offering customized and cost-effective solutions; and medical education, providing realistic anatomical models for training.  The combination of nanoparticles and 3D printing technology has the potential to transform the healthcare sector by enabling more precise, customized, and efficient medical treatments. This review explores 3D printing technology and integration of nanoparticles in enhancing 3D printing applications for tissue engineering, drug delivery systems, medical imaging, disease treatment and diagnosis. Healthcare applications are experiencing significant improvements in effectiveness, accuracy, and adaptability, attributed to the unique properties of nanoparticles, i.e. small size, more surface area, and high reactivity. Bio-printing or bio-fabrication utilises techniques such as autonomous self-assembly, biomimicry etc. that advances fields like tissue engineering, and cancer treatment, offering significant potential for creating personalized healthcare solutions. Advancements in bio-fabrication technologies promise to enhance medical treatments and patient outcomes globally. The paper highlights the latest developments, obstacles, and potential routes in combining nanotechnology and 3D printing together, illustrating its capacity to revolutionize healthcare by providing highly tailored and efficient treatment alternatives.

Microplastics (MPs) which are plastic particles with dimensions less than 5 mm, have emerged as a widespread environmental and health concern due to pervasive plastic usage and inadequate handling and pollutant management. While numerous studies have focused on environmental contamination, the effect and release of MPs into food and feeding equipment, those used for infants, remain underexplored. Especially in Sri Lanka, where bottle feeding is still practiced despite a strong breastfeeding culture.

This research study addresses the knowledge gap by quantifying MPs released from infant feeding bottles (IFBs) via everyday use, frequent washing, and boiling. The three most common IFB brands were selected, representing three different environments and the quality of the brand. MPs were extracted into hot water and infant formula (IF) from the nipple with conditions mimicking routine consumption. An effective process for digesting the food matrix was optimized as alkaline-oxidative digestion using 4 M KOH, 50% H₂O₂ and Ethanol. The method was optimized and validated to extract MPs with a high recovery of 87.67 ± 1.53%, able to detect by spiking the sample with 100 particles of polyethylene (PE) (300 µm) MPs. The Limit of Detection (LOD) was 3 particles/ 100 ml. Experiments revealed an apparent increase in MP discharge following successive washing and boiling cycles. Brand C exhibited the highest MP emission, whereas Brand A exhibited the lowest, indicating differences in manufacturing quality and material stability.

MPs detection was done by analyzing stained filter papers with 1% Nile red via a stereo trinocular microscope under a blue light source (420 - 470 nm). MP particle counts in water increased from 1.18 ± 0.59 to 7.45 ± 0.34 particles/100 ml for Brand A and from 3.33 ± 0.34 to 15.49 ± 0.90 particles/100 ml for Brand C after 90 washes. In IF, MPs release was much higher, from 4.17 ± 1.44 to 35.00 ± 0.00 particles/100 ml for Brand A and from 9.17 ± 1.44 to 77.50 ± 2.50 particles/100 ml for Brand C. Silicone nipples released MPs ranging 1.00 ± 1.00 to 14.67 ± 1.53 from Brand A, 1.67 ± 0.58 to 12.67 ± 1.53 from Brand B and 2.33 ± 1.15 to 24.67 ± 1.53 from Brand C, indicating significantly smaller count than polypropylene (PP) bottle containers. The study focused on the risk of infants being exposed to increased MP content by frequently using IFBs.

Key Words: Microplastics, Contamination, Infant Feeding Bottles, Infant formula.

Multidrug-resistant Klebsiella pneumoniae (MDR K. pneumoniae) is a significant member of the ESKAPE group of pathogens and a leading cause of healthcare-associated infections. High levels of antibiotic resistance in the upper respiratory tracts of children under five are particularly concerning, especially in neonatal and pediatric intensive care units, where such infections can lead to serious complications and increased mortality. The emergence of MDR K. pneumoniae strains, which frequently resist a broad spectrum of antibiotics including carbapenems, β-lactams, cephalosporins, and fluoroquinolones, is creating an urgent healthcare crisis characterized by prolonged hospital stays and escalating costs. As conventional antibiotics become ineffective, bacteriophages—viruses that specifically target and destroy bacteria—are proving to be a powerful alternative. In this study, we isolated a lytic bacteriophage named Meta from sewage and rigorously tested it against clinical MDR K. pneumoniae strains collected from children. Phage Meta successfully lysed 5 out of 7 isolates, demonstrating impressive stability across a wide range of pH levels (3 to 11) and temperatures (-80°C to 60°C), as well as in the presence of chloroform.  Phage Meta also exhibited effective lytic activity at various multiplicities of infection (MOIs ranging from 0.001 to 100), with 94% adsorption to host cells within 9 minutes, a latent period of 10 minutes, and a burst size of 34 phages per cell. These findings highlights phage Meta as a promising therapy for treating MDR K. pneumoniae infections, especially in vulnerable pediatric populations.

Pseudomonas aeruginosa, a multidrug-resistant (MDR) bacterium that causes severe nosocomial infections, poses a substantial problem in hospital settings since it is resistant to standard treatments. Bacteriophage treatment has emerged as a possible option, utilizing naturally occurring viruses that target and kill bacterial cells. The isolation and characterization of lytic phages from environmental sources have revealed their therapeutic potential, particularly PAB_01 with wide host range of 16 out of 39 isolates, exhibiting high efficiency of plating, stable in range of pH 3-11 and temperature -80°C to 45°C with quick adsorption rate of 4 minutes, latent period- 5 minutes and high burst sizes- 100 phages per cell. Effective reduction of bacterial load when treated with varied dosages of phages from MOI 0.0001 to MOI 100 in time kill analysis assay. Biofilm clearance by the phage showed moderate concentration of the phage is optimal. The biofilm inhibition assay curtailed biofilm formation at lower MOIs (0.1 and 0.01). As antibiotic resistance continues to endanger global health, phage treatment provides a realistic and novel approach to treating chronic and drug-resistant P. aeruginosa infections. With further study and development, it has the potential to supplement or possibly revolutionize conventional treatment strategies for infectious diseases.

Microplastics (MPs) have gained growing attention as emerging environmental and health hazards, especially for infants, who are uniquely vulnerable due to their immature detoxification systems and high food intake relative to body weight. Recent studies have identified MPs in infant feces and breast milk, indicating direct exposure through feeding equipment and food preparation practices. This study aimed to assess and compare the release of MPs from commonly used infant food preparatory containers subjected to repeated washing cycles, thereby evaluating early-life microplastic exposure through food-contact materials.

The main objective was to quantify and compare the release of MPs among five widely used brands of infant food containers after 0 to 90 washing cycles. A validated alkaline-oxidative digestion method with density separation was developed and employed to isolate MPs from plant-based infant food matrices. Each container was subjected to simulated household practices, including 15-cycle interval washings, food loading, mechanical chopping, and 30-minute contact time. Digestion involved treating 5 g of food sample with 4 mol dm⁻³ KOH and 50% H₂O₂, followed by overnight incubation and centrifugation at 3000 rpm for 15 minutes. The digestate was filtered using 0.2 µm glass microfiber filter paper, stained with Nile Red, and observed under a stereo trinocular microscope under white and blue light. Method validation included blank testing, triplicate reproducibility, and spiking with 100 polyethylene MPs (300 µm), yielding a recovery rate of 83 ± 0.82% and a detection limit of 3 particles/g.

Results demonstrated that brand and number of washing cycles significantly influenced MP release (p < 0.001). Brand E exhibited the highest MP release, increasing from 4 ± 0.47 to 22 ± 1.63 particles over 90 cycles, while Brand C remained consistently low with only 1 ± 0.47 particles at the end. Two-way ANOVA revealed significant main and interaction effects between brand and washing cycles, with degradation trends varying by material type. Most brands showed increased MP shedding after 45 cycles, reflecting progressive surface damage from repeated thermal and mechanical stress.

The study highlighted substantial differences in material durability across brands. Brands with inferior resistance to repeated use may contribute to higher MP ingestion in infants, raising serious public health concerns. These findings reinforce the importance of selecting safer food-contact materials and reducing the long-term use of plastic containers in infant care.

In conclusion, this research confirms that commonly used infant food containers release MPs during repeated use, with variability based on brand and material quality. The validated digestion method proved effective for food-based MP detection and can be reliable for future food safety assessments. Given growing evidence of MP presence in infant bodies, immediate action is needed to promote safer alternatives like glass or stainless steel and increase caregiver awareness. The study contributes valuable insight into a silent yet significant route of early-life MP exposure and its relevance to long-term health.

Keywords: Microplastics (MPs), Infant food containers, Washing cycles, public health, Polymer degradation

Rice enriched with vitamin E, a nutrient known for its diverse health benefits, including antioxidant, anticancer, hypolipidemic, nephroprotective, neuroprotective, antihypertensive, and anti-inflammatory properties. Vitamin E consists of eight naturally occurring compounds α, β, γ, and δ forms of both tocopherols and tocotrienols collectively known as tocochromanols. Globally, only a limited number of studies have examined the complete vitamin E profile of rice varieties. In Sri Lanka, despite rice being the primary dietary staple, research on the vitamin E composition of local rice varieties is extremely limited.

This study evaluated the tocochromanol contents in 15 new-improved rice varieties (NIR) widely cultivated in Sri Lanka and the selected rice varieties (RVs) were At 307, At 308, At 311, At 362, Bg 300, Bg 352, Bg 358, Bg 360, Bg 366, Bg 379-2, Bg 403, Bg 450, Bg 94-1, Bw 272-6b and Bw 367. The tocochromanols were extracted from rice brans (n=3) and analyzed using a high-performance liquid chromatography coupled with a fluorescence detector. Results were reported on a dry weight basis for whole grain rice and the data were statistically analyzed using SPSS version 20. 

Results revealed that tocochromanol contents significantly (P<0.05) varied among the studied RVs. The α-tocopherol, β-tocopherol, γ-tocopherol, δ-tocopherol, α-tocotrienol, γ-tocotrienol and δ-tocotrienol contents were ranged from 485.0-1042.1, 38.4–55.3, 751.2–927.9, 62.0-75.1, 369.9–577.3, 2143.8-2462.1 and 45.8–81.5 µg/100 g respectively. Additionally, the total tocopherol and tocotrienol contents varied between 1356.3-1990.2 and 2611.3-2919.2 µg/100 g respectively. Among the studied RVs, γ-tocotrienol, was the most prevalent, followed by γ-tocopherol, α-tocopherol and α-tocotrienol. The Bg 352 was significantly high in α-tocopherol (1042.1 µg/100 g) and β-tocopherol (55.3 µg/100 g) while Bg 450 was significantly high in γ-tocopherol (927.9 µg/100 g) and δ-tocopherol (75.1 µg/100 g). The highest α-tocotrienol (577.3 µg/100 g) and δ-tocotrienol (81.5 µg/100 g) contents were found in Bg 300. Additionally, the highest total tocopherol (1990.2 µg/100 g) and as well as vitamin E (4870.5 µg/100 g) contents were exhibited in Bg 352.

In conclusion, the studied NIR varieties of Sri Lanka are primarily rich in γ-tocotrienol, followed by γ-tocopherol, α-tocopherol, and α-tocotrienol, with smaller amounts of δ-tocopherol, δ-tocotrienol, and β-tocopherol. Among the rice varieties examined, Bg 352, Bg 450, and Bg 300 showed the highest levels of various tocochromanols, suggesting they may offer significant health benefits associated with vitamin E and tocochromanols.

Keywords: Vitamin E, Tocochromanols, New-Improved Rice, Tocopherols, Tocotrienols

Homocysteine is an amino acid involved in metabolic pathways that depend on specific enzymes and vitamin cofactors. A deficiency or dysfunction in these components can elevate plasma homocysteine levels, leading to hyperhomocysteinemia, which is associated with endothelial damage, vascular stiffness, renal impairment, and increased oxidative stress, all of which raise the risk of cardiovascular diseases.

In pediatric patients, monitoring homocysteine levels can aid in the early detection of cardiovascular and metabolic risks. Plasma homocysteine could also serve as a preliminary screening tool for assessing disease risk in pediatric populations.

The objectives of this study were to investigate the correlation between plasma homocysteine levels and various biochemical parameters, including random blood sugar (RBS), serum cholesterol, and serum creatinine, as well as to examine the association between selected clinical conditions and demographic data with plasma homocysteine levels among pediatric patients at Lady Ridgeway Hospital (LRH).

This cross-sectional analytical study included 190 pediatric patients (75 females and 115 males), aged 2 months to 16 years, admitted to LRH with cardiovascular, ophthalmological, neurological, genetic, developmental, or nutritional disorders. Plasma homocysteine levels were measured using the Cobas C111 biochemistry analyzer. Data analysis was conducted using SPSS version 26.

Plasma homocysteine, RBS, serum cholesterol, and serum creatinine levels were not normally distributed, while BMI showed a near-normal distribution. Significant moderate positive correlations were found between plasma homocysteine levels and RBS (r = 0.579), as well as serum cholesterol (r = 0.479). A weak correlation was observed between homocysteine levels and serum creatinine (r = 0.393).

The distribution of patients by disease type was as follows: 4.4% with cardiovascular diseases, 28.3% with developmental and genetic disorders, 9.4% with ophthalmological disorders, 23.5% with neurological disorders, and 34.6% with metabolic and nutritional disorders. Although there appears to be a trend toward an association between homocysteine levels and disease type, it was not statistically significant based on the Pearson chi-square test. Further investigation may be warranted.

No significant correlation was found between gender and homocysteine levels.

This study highlights the potential role of homocysteine as a valuable biomarker in pediatric risk assessment and health management strategies. Early detection through homocysteine monitoring, particularly in patients with elevated RBS, cholesterol, and creatinine levels, allows for timely interventions such as dietary modifications and clinical management, which may reduce the likelihood of long-term complications. The data generated from this research provides valuable insights that can inform future studies with larger cohorts and deepen the understanding of homocysteine’s role in pediatric health.

Key words: biochemical parameters, demographic data, disease conditions, homocysteine, hyperhomocysteinemia,

Abstract

International medical graduates (IMGs) face unique challenges when adapting to clinical practice within the NHS. These challenges include differences in healthcare systems, electronic records and prescription systems, clinical protocols, and communication styles. To support this transition, a bi-annual simulation-based teaching session was introduced in 2023 for IMGs newly joining NHS Lothian. The February 2025 session incorporated a high-fidelity simulation station, communication skills stations, and an introduction to SimConverse, an AI-driven communication training platform.

This poster presents findings from a pilot evaluation using pre- and post-session questionnaires (five and six respondents, respectively), which included Likert scale questions assessing confidence in clinical scenarios, familiarity with local guidelines, awareness of available support, and communication skills.

Results indicate an overall increase in self-reported confidence across all domains following the session. Participants particularly valued the realism of the high-fidelity simulation and the opportunity to practice communication skills in a safe environment. The introduction to SimConverse was well received, with several participants expressing interest in further AI-driven communication training. Limitations include the small sample size, the unpredictability of attendees’ specialties, and potential response bias.

These findings suggest that targeted simulation-based induction can positively impact the confidence and preparedness of IMGs transitioning into the NHS. Given the benefits observed, similar induction sessions should be considered in other NHS regions to better support IMGs nationwide. Further evaluation with larger cohorts and longitudinal follow-up is recommended to assess the long-term impact and inform future curriculum development.

Key Words: international medical graduates, simulation, high-fidelity, NHS, communication skills, medical education, SimConverse

Intimate Partner Domestic Violence (IPDV) is a prevalent issue involving physical, emotional, sexual, psychological, and financial abuse in intimate relationships. While this topic is typically examined in adult contexts, children and teenagers are often silent witnesses to such violence in their homes. In Canada, a significant number of youth are exposed to IPV between parents or caregivers, which can lead to both immediate emotional distress and long-term behavioural, cognitive, and health challenges. The Government of Canada has showcased since 2008 that exposure to intimate partner violence (IPV) accounted for 34% of confirmed child protection cases in Canada.

This research project aims to raise awareness among teenagers by helping them recognize early signs of IPDV, particularly by observing and reflecting on unhealthy relationship patterns they may witness in their home environments. One major reason is the normalization of domestic violence in certain cultures, where it is not always recognized as a form of abuse. This cultural lens can prevent victims and witnesses from identifying violent behaviour as harmful, reducing the likelihood of seeking help. Additionally, systemic barriers within the legal system contribute to underreporting. Many perpetrators are not charged due to a lack of evidence or witness testimony, and obtaining a restraining order is often a lengthy and complicated process. These challenges highlight the need for more accessible, preventive interventions, particularly those aimed at youth. Overall, the study emphasizes prevention over reaction. To achieve this, we propose an approach using the STaT (Slapped, Threatened, and Throw) screening tool, a simple, three-question method designed to identify experiences of violence. Due to its simplicity and accessibility, STaT is particularly effective for use with adolescents and can be adapted to help them recognize similar abusive patterns in family settings.

The study will be carried out through an anonymous Google Forms survey, allowing high school students and any teenager or youth living in Ontario, Canada, to respond openly and safely. The survey will gather information about their awareness of IPV, their ability to identify warning signs, and their perceptions of available support systems. By using tools like STaT in a sensitive, age-appropriate format, this project aims to empower youth to become more aware of the signs of IPV, encourage early intervention, and contribute to long-term prevention strategies that support healthy relationships and family wellbeing. Ultimately, the research highlights that prevention through education and early recognition must be prioritized if we are to reduce the long-term impacts of IPV and protect vulnerable individuals before harm occurs.

Keywords:

Intimate Partner Violence, Youth Awareness, STaT Screening Tool, Prevention, Family Violence

Microsponges: A Novel Platform for Drug Delivery – Comprehensive review

Dr. Shraddha Pande^1*

1 Assistant Professor, Department of Physics L.A.D. & Smt. R.P. College for Women, Nagpur Maharashtra 440010,India

*Correspondence: [email protected]

Abstract 

Microsponges are an emerging and innovative drug delivery system (DDS) that has attracted considerable interest in recent years. These microscopic, porous, sponge-like particles offer a promising approach for the controlled and sustained release of active pharmaceutical ingredients (APIs). This review presents a comprehensive overview of microsponges as a novel drug delivery platform, covering their composition, preparation methods, and characterization techniques. Additionally, it explores their application across various administration routes, including topical, oral, and transdermal. Microsponges offer numerous advantages, such as enhanced bioavailability, minimized side effects, and improved patient compliance. The current challenges and prospectus in use of microsponges were discussed in this paper. Microsponges are designed to deliver APIs efficiently to the targeted site in controlled doses. Microsponges are highly cross-linked, porous polymeric microspheres designed to entrap and gradually release therapeutic agents onto the skin over an extended period. Their effectiveness largely depends on their pore size, which influences the rate of drug release. These systems can provide sustained drug delivery for up to 12 hours.This delivery system enables prolonged drug release with decreased irritation, better patient tolerance, and enhanced thermal, physical, and chemical stability. Microsponges can be prepared using various techniques, such as emulsion or suspension polymerization within a liquid-liquid system. They can encapsulate a wide range of drugs and be incorporated into different formulations, including creams, powders, gels, and lotions. While traditional topical preparations often come with drawbacks like unpleasant odor, greasiness, and skin irritation—and may not achieve systemic circulation—these issues are effectively addressed by the microsponge delivery system. As a result, microsponges enhance therapeutic efficacy and reduce adverse effects.

Key words: Microsponges, Quasi emulsion methods, drug delivery

Abstract: The COVID-19 pandemic began about five years ago and its impact on our society continues to remain prevalent. Pandemic restrictions, specifically quarantine periods, reduced opportunities for in-person socialization across the globe. These led to increased screen time where virtual interactions and an increase in technology became a primary form of interaction and entertainment. These changes could have had major impacts on the formation of cognitive and social skills in children and adolescents (ages 5-14), as their development is heavily shaped by peer interaction and environmental surroundings. This literature review aims to examine whether the social isolation experienced during the pandemic, along with increased technology use, is associated with a decline in social skill development among youth. As pandemic restrictions varied between regions, this literature review examines studies from Canada, the United States of America, Spain, Malaysia, India, Peru as well as broader global analyses not limited to a single country. Furthermore, this review comprises studies that vary in data collection methodologies, such as surveys and participant-provided qualitative updates. The findings of increased screen time and reduced in-person socialization identify an avenue for future research on whether removal of pandemic restrictions reverse these adverse effects on social skills in impacted youth. This review also illuminates the need for strategies to improve social skills in youth that were excessively online during the pandemic. 

Keywords: pandemic, COVID-19, children, adolescent, youth, social skills, screen time

Introduction

Breast cancer patients who undergo axillary surgery may develop post-operative lymphoedema as a complication which can cause significant life challenges due to physical discomfort, functional limitations, disruptions to daily activities, lifestyle changes, and significant psychosocial and emotional challenges. Our study focuses on the quality of life (QoL) in patients who have developed lymphedema following axillary surgery.

 Methodology

This is a retrospective descriptive cohort study with an analytical component, conducted at the oncology clinics of Tellippalai Trail Cancer Hospital. The study period spanned from January 2025 to April 2025. The study population consisted of consenting patients who had undergone surgery for breast cancer at least six months prior to data collection. Lymphedema was defined as a ≥2 cm difference in mid-arm circumference between the affected and unaffected limbs. A total of 59 participants with lymphedema were included in the analysis, which was performed using SPSS version 26. Chi-square test, Fisher’s exact test, and univariate analysis were used to determine associations between variables. A p-value of <0.05 was considered statistically significant.

 Results

Among 59 individuals (mean age =59.9 years) with lymphoedema, 45.8% (n=26) reported reduced QoL, while 54.2% (n=33) did not. Of those with diminished QoL, 53.85% (n=14) experienced general limitations in daily functioning. Others cited specific issues such as childcare difficulties, travel discomfort, and work-related pain or swelling. Additionally, 19.23%(n=5) reported psychological distress due to swelling. QoL by age group showed that all 3 participants under 40 (3/3), 46.15% aged 40–50 (6/13), 40% aged 50–60 (4/10), and 39.4% over 60 (13/33) reported reduced QoL. Fisher’s exact test (p = 0.281) indicated no statistically significant association between QoL and age. Among unemployed participants, 41.51% (22/53) reported reduced QoL, compared to 66.67% (4/6) of employed individuals, with no statistically significant association (p = 0.390) seen between QoL and employment status using Fisher’s exact test. In addition, 51.6% (16/31) of those who had surgery on their dominant hand and 35.7% (10/28) of those with surgery on the non-dominant hand reported reduced QoL. Univariate analysis showed no statistically significant association (p = 0.226) between hand dominance and QoL.

 Conclusions 

Nearly half of the breast cancer patients with post-operative lymphoedema reported a reduced quality of life with general and specific complaints. When compared to studies conducted in Western populations, the prevalence of psychosocial complaints in our population is notably lower. Impact on QoL was not significantly associated with age, employment status, or hand dominance, differing from some Western researches. This may be due to cultural differences in illness perception, lower reporting of psychosocial distress, varied lifestyle expectations, or differences in healthcare access and coping mechanisms. These findings underscore the need for targeted support to address challenges affecting QoL and invite discussion on how geographic and cultural differences shape these concerns.

Key words: Lymphoedema, QoL, Demographical factors, Dominant hand

Modelling Tuberculosis Prevalence in Western Province of Sri Lanka through ARMA Modelling
 

Yasmi Rajapaksha¹

Teaching Assistant in Department of Statistics & Computer Science, University of Kelaniya, 
 Sri Lanka.

Chaditha Attanayake², Ph.D.
 Senior Lecturer in Department of Statistics & Computer Science, University of Kelaniya, 
 Sri Lanka.

Abstract

Tuberculosis (TB) continues to be a critical global health issue, particularly in low- and middle-income countries where social and environmental challenges contribute to its spread. In Sri-Lanka, the Western Province which comprises the districts of Colombo, Gampaha, and Kalutara consistently reports the highest number of TB cases. The combination of high population density accelerated urban development, and limited access to equitable healthcare services in this region increases its susceptibility to infectious disease outbreaks. Despite ongoing efforts in disease control, a major research gap exists in predictive analytics that can provide timely forecasts of TB trends at the regional level. Addressing this gap, the current study focuses on building a forecasting model for weekly TB incidence in the Western Province using time series analysis. The dataset used for this study consists of weekly reported TB cases from the year 2021 to 2024. 
 
 This study involves the application of univariate time series modeling to understand TB incidence. An initial exploratory analysis of the data was conducted to assess patterns such as trend and seasonality. The data appeared relatively stable, with no significant seasonal or long-term patterns evident. Given this, the study moved forward with time series techniques that could model short-term fluctuations effectively. Three candidate Auto-Regressive Moving Average (ARMA) models; ARMA(1,1), ARMA(1,2), and ARMA(1,3), were fitted and evaluated. Based on minimum Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) measures these models were selected among several models. All three candidate models, residual diagnostics showed no autocorrelation and the violations of homoscedasticity and normality assumptions, suggesting data variability capture might be improved.
 
The findings will support as a foundation for short-term planning and resource allocation. Although the data did not exhibit strong seasonal or long-term trends, the models captured short-term variations effectively, highlighting their utility in detecting potential case surges. As a recommendation for future research, models capable of addressing time-varying volatility such as Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models should be explored. These models may enhance the ability to handle irregular fluctuations in disease data, which are common in real-world epidemiological contexts. Overall, the study lays a foundation for extending forecasting methods to other communicable diseases, particularly in regions with similar socio-economic and healthcare challenges.
 
 Keywords: ARMA model, Time Series Analysis, Tuberculosis, Western Province.

BACKGROUND

Lymphoedema remains a well-known long term complication following breast cancer surgery when there is axillary lymph node intervention. Previous literature has identified several risk factors associated with the development of lymphoedema postoperatively. However, such studies are limited in the Sri Lankan context. In this study we sought to assess the association between the total number of lymph nodes dissected and lymph node positivity status with the development of lymphoedema.

METHODOLOGY

A retrospective descriptive cohort study with an analytical component was conducted among 248 patients who underwent axillary surgery at Tellipalai Trail Cancer Hospital between May 2024 and May 2025. Patients without consent or with surgery within the previous 6 months were excluded. Data were collected using extraction sheets and mid-arm circumferences were measured using anatomical landmarks. Lymphoedema was diagnosed based on a ≥2 cm difference between arms. Data were analysed using SPSS version 29. Normality was tested with Shapiro-Wilk, and associations were assessed using the Mann-Whitney U test (p < 0.05).

RESULTS

Among the 248 breast cancer patients (mean age = 58.88 years, SD = 11.843, range = 28–83 years), 23.8% (n = 59) developed post-operative lymphoedema. The Shapiro-Wilk test confirmed that the distributions of both the total number of lymph nodes removed and the number of positive lymph nodes were non-normal (p < 0.05). Therefore, the non-parametric Mann-Whitney U test was employed. Patients who developed lymphoedema had a significantly higher number of lymph nodes removed (mean rank = 143.34) compared to those without lymphoedema (mean rank = 118.62, U = 4464, Z = -2.297, p = 0.022, r = 0.146). The median number of lymph nodes removed was 12.00 (IQR: 8.00–17.00). However, there was no statistically significant difference in the number of positive lymph nodes between the two groups (U = 5155, Z = -0.947, p = 0.344, r = 0.060). The median number of positive lymph nodes was 0.00 (IQR: 0.00–3.00).

CONCLUSION-

The total number of lymph nodes removed is significantly associated with the development of lymphoedema whereas the positive lymph node count is not significantly associated. The findings align with global evidence implicating extensive axillary dissection as a key risk factor for lymphoedema, reinforcing the need for judicious lymph node removal protocols. Patients undergoing removal of a higher number of lymph nodes should be closely monitored postoperatively for early signs of lymphoedema to allow for prompt intervention. Future research should aim to adjust for potential confounding factors such as body mass index (BMI), adjuvant radiation therapy and chemotherapy to better understand the multifactorial nature of lymphoedema development. Additionally, the development of evidence-based guidelines for risk stratification and the implementation of preventive strategies like physiotherapy in high-risk patients are recommended to minimize long-term complications.

KEYWORDS- Breast Cancer, Axillary surgery, Lymph node dissection, Nodal positivity, Postoperative lymphoedema

Prebiotic potential of Lactobacillus acidophilus (LA 5) on traditional rice variety, “Pachchaperumal” with proven functional prospects

Dilini Jayawardana

Food Technology Section, Modern Research and Development Complex, Industrial Technology Institute, Sri Lanka

 Upeka Rajawardana, Ph.D.

Food Technology Section, Modern Research and Development Complex, Industrial Technology Institute, Sri Lanka

Theja Herath, Ph.D.

Food Technology Section, Modern Research and Development Complex, Industrial Technology Institute, Sri Lanka.

Chandrika Nanayakkara, Ph.D.

Department of Plant Sciences, Faculty of Science, University of Colombo, Sri Lanka

Madara Samaranayake

Food Technology Section, Modern Research and Development Complex, Industrial Technology Institute, Sri Lanka

Savidya Liyanage

Food Technology Section, Modern Research and Development Complex, Industrial Technology Institute, Sri Lanka

Abstract

Cereal-based probiotic functional foods are increasingly in demand among consumers seeking to improve gut health and overall quality of life. In 2002, the Food and Agriculture Organization (FAO) of the United Nations and the World Health Organization (WHO) defined probiotics as “live microorganisms that, when administered in adequate amounts, confer a health benefit on the host” and Generally Regarded as Safe (GRAS).  To achieve desired health benefits, it is essential to contain viable probiotics >10⁶ Colony Forming Units per gram or milliliter (CFU/g or ml) in the end product.  The Sri Lankan traditional rice (Oryza sativa) variety, Pachchaperumal, known for its high resistant starch and dietary fiber content, may provide a suitable medium for the growth of probiotics.

To evaluate the prebiotic potential, sterilized rice slurry was inoculated with 2% (w/w) freeze-dried Lactobacillus acidophilus (La-5) culture and incubated at 44 °C for 10 h. Viable probiotic counts, along with selected physicochemical and compositional parameters, were assessed in both fermented and unfermented slurries using standard protocols.

The viable cell count of the fermented slurry was 8.00 ± 0.67 log₁₀CFU/g. Significant differences (P < 0.05) were observed between fermented and unfermented slurries in terms of acidity (expressed as % lactic acid by mass), pH, and viscosity. The fermented slurry exhibited an acidity of 0.16 ± 0.02%, a pH of 4.74 ± 0.26, and a viscosity of 1635.03 ± 3.75 cP (centipoise), while the corresponding values for the unfermented slurry were 0.07 ± 0.01%, 6.13 ± 0.00, and 793.10 ± 4.89 cP, respectively. These differences are attributed to lactic acid production by Lactobacillus acidophilus (La-5) during fermentation. The acidic environment promotes the production of exopolysaccharides, contributing to the increased viscosity of the fermented slurry. The protein and fat contents (expressed as percentages on a dry weight basis) of the fermented slurry (6.25 ± 0.14% and 3.90 ± 0.26%) were significantly reduced (P < 0.05) compared to the unfermented slurry (6.61 ± 0.21% and 3.24 ± 0.37%). These reductions may be attributed to the proteolytic and lipolytic activities of Lactobacillus acidophilus (La-5) during fermentation. The viable cell count and the observed variations in physicochemical parameters confirm that Pachchaperumal rice slurry is a suitable substrate with prebiotic potential to support the growth of Lactobacillus acidophilus (La-5).

In conclusion, the Pachchaperumal rice variety shows potential for the development of synbiotic products aimed at promoting health and well-being.

Keywords: Pachchaperumal, Lactobacillus acidophilus, Fermentation, Prebiotic potential

Proteus mirabilis is a gram-negative bacillus commonly associated with chronic catheter-associated urinary tract infections. It poses a significant public health challenge due to its crystalline biofilm-forming ability, urease production, and antibiotic resistance, contributing to high morbidity and mortality rates. This study aimed to develop a urinary catheter surface coated with curcumin-modified silver nanoparticles (curcumin-AgNPs) to reduce P. mirabilis-induced catheter encrustation, inhibiting crystalline biofilm formation.

An in vitro bladder model (BM), which represents the human bladder, was designed and validated. Body temperature was maintained using circulating water at 37 °C, and artificial urine medium (AUM) was supplied at a constant flow rate of 0.7mL/min. Catheterized four BMs were connected in series.BM1-2 contained quality control strain of P. mirabilis in AUM (positive control), an un-inoculated AUM (negative control), and BM3-4 contained biofilm-forming P. mirabilis clinical strains (n=6). The efficacy of curcumin-AgNPs coated catheters was assessed by comparing the results with uncoated catheters using catheterized BMs (BM1-4) over 2,7,14 days, and until catheter blockage. After each run, crystalline biofilm formation on catheters was evaluated using atomic absorption spectroscopy (AAS) and scanning electron microscopy (SEM). Data were analyzed using Bayesian hierarchical model. 

Successful coating of the catheter surface with curcumin-AgNPs was confirmed using Fourier-transform infrared spectroscopy (FTIR), atomic force microscopy (AFM), and scanning electron microscopy-energy-dispersive X-ray analysis (SEM-EDX). In the bladder model experiment with the coated catheters, the Ca²⁺concentrations increased progressively over time, with mean values of 69.88±8.56 mg/L on day 2, 326.23±20.44 mg/L on day 7, and 595.92±63.56 mg/L on day 14. Following catheter blockage, the Ca²⁺concentration further increased to 1065.99±47.56 mg/L. In contrast, the bladder model run with uncoated catheters exhibited markedly higher Ca²⁺levels at all time points, with mean concentrations of 174.90 ± 71.31 mg/L on day 2, 591.22±208.23 mg/L on day 7, and 982.85 ± 178.42 mg/L on day 14. At the time of catheter blockage, the Ca²⁺concentration had risen to 1176.56 ± 95.42 mg/L. A similar trend was observed for Mg²⁺concentrations, with consistently lower values in the bladder model runs with coated catheters compared to uncoated catheters. In the coated model, the mean Mg²⁺concentration was 0.18±0.06 mg/L on day 2, increasing to 7.15±4.11 mg/L on day 7, 57.17±17.34 mg/L on day 14, and 118.79±22.11 mg/L following the catheter blockage. In the uncoated model, Mg²⁺levels were significantly higher, with mean values of 1.77±0.59 mg/L on day 2, 18.56±4.93 mg/L on day 7, 111.18±22.06 mg/L on day 14, and 135.21±21.27 mg/L at the time of catheter blockage (repeated measures ANOVA;p=0.000). This observed reduction in Ca²⁺ and Mg²⁺concentrations indicates a decrease in crystalline biofilm formation by P. mirabilis, which was further confirmed by SEM analysis. Bayesian Gamma regression demonstrated significantly lower Ca²⁺ and Mg²⁺deposition in coated catheters compared to uncoated ones at days 2,7 and 14. The study revealed that curcumin-AgNP-coated catheters were effective in reducing the crystalline biofilm formation by P. mirabilis. 

The transition from theoretical pre-med courses taken in the classroom to clinical practice may be jarring to many students. Traditional classroom learning, while laying the foundations utilized in the clinical setting, does not properly account for the unpredictable nature of real-time patient care. This presentation explores the experience gained while working as a medical scribe in the emergency department across the Cincinnati TriHealth hospital system as well as the St. Elizabeth hospital system in Northern Kentucky.

Working as a medical scribe involves the documentation of patient encounters using health record systems, through electronic software, specifically the Epic Hyperspace software. While initially appearing to require a vast knowledge of medical terminology, the medical scribing profession instead helps to introduce medical concepts to scribes shift after shift, making it an excellent entry into the clinical profession. Although AI scribing appears to gain a larger and larger foothold in the scribing environment, traditional scribing still reigns supreme due to the human judgement required to better interpret physicians’ communications and understand clinical context. The learning curve for this field is steep, progressing from struggling with basic abbreviations such as “BP 142/88, HR 92,” etc. to more confidently documenting cases involving multiple comorbidities and an array of possible differential diagnoses.

The clinical cases cover a variety of situations commonly encountered in the emergency department, including trauma from motor vehicle collisions and seemingly innocuous abdominal pains that may be caused by serious conditions like appendicitis.

The educational value of medical scribing extends beyond mere exposure to clinical environments. This role develops critical skills essential for other healthcare professions, such as a registered nurse or a physician.

This presentation demonstrates how experiential learning through medical scribing provides invaluable preparation for medical school or other germane medical clinical practice.

One of the most advantageous natural compounds utilized worldwide is curcumin, often known as curcuma longa or turmeric, because of its strong pharmacodynamic qualities with minimal side effects. Curcumin has been utilized both prophylactically and therapeutically as a nutraceutical and dietary supplement. Additionally, it has also been used for cosmetic purposes. The low bioavailability of Curcumin, on the other hand, has long minimized its use in healthcare. As a result, research on curcumin's metabolites has recently gained prominence, and several studies have been conducted to improve curcumin utilization. Curcumin or curcuminoid metabolites also exhibit biological activity that is equivalent to or superior to that of their precursor, according to past and current studies. The current study aimed to establish novel neuroprotective activities of Tetrahydrocurcumin (a key curcumin metabolite). To elucidate and validate the neuroprotective properties of Tetrahydrocurcumin along with potential neuroprotective mechanisms, both in-silico and in-vitro studies were carried out. The effect of tetrahydrocurcumin on the viability of hippocampus and dopaminergic neurons was demonstrated using HT-22 (hippocampal neurons) and N27 (dopaminergic neurons). Additionally, to study the neuroprotective mechanisms, markers of oxidative stress, mitochondrial function, inflammation, and apoptosis were examined. Tetrahydrocurcumin demonstrated significant neuroprotection on both hippocampal and dopaminergic neurons. The neuroprotection was attributed to its antioxidant and anti-apoptotic actions. Furthermore, RNA sequencing was performed to validate the neuroprotective effects. Thus, Tetrahydrocurcumin can be an impactful therapeutic natural bioactive metabolite for preventing, reducing the rate of progression, and treating neurodegenerative pathologies.

[Introduction]
Mitophagy, the selective degradation of mitochondria via autophagy, is essential for maintaining mitochondrial quality control and cellular homeostasis. We previously identified a conserved defect in stress-induced mitophagy across multiple disease models—including burn injury, sepsis, and oxidative stress—and proposed the “Red Queen” model of cytoskeletal equilibrium as a unifying mechanism. In this model, inspired by Lewis Carroll’s Through the Looking-Glass, the “Red Queen’s race” reflects the continuous need for microtubule (MT) synthesis to match demand under stress. Breakdown of this adaptive equilibrium results in mitophagy failure, ROS accumulation, and muscle wasting. However, the universality and druggability of this mechanism remain insufficiently explored. Furthermore, unlike conventional approaches that target upstream inducers of autophagy/mitophagy (e.g., rapamycin or AICAR), our strategy focuses on rescuing a downstream bottleneck—cytoskeletal stagnation—representing a fundamentally distinct therapeutic concept.

[Methods]
C2C12 myocytes expressing fluorescent reporters for mitophagy (mito-QC, mito-Kaede), autophagosomes (GFP-LC3), mitochondrial morphology, and MT dynamics (mClover3-tubulin, EB1-GFP) were subjected to oxidative (H₂O₂), inflammatory (LPS), or burn-serum-induced stress. MT polymerization was tracked via EB1 comets; mitophagy flux and maturation were assessed using mito-QC ratiometry and mito-Kaede photo-conversion with time-lapse analysis. A panel of candidate drugs—including trehalose and SGLT2 inhibitors—was screened for rescue effects.

[Results]
Across all stress conditions, mitophagy induction was impaired, with significant suppression of MT polymerization and defective mitophagosome trafficking/maturation upon CCCP stimulation. Notably, trehalose and SGLT2 inhibitors restored MT dynamics, enhanced mitophagy flux, reduced ROS, and improved myotube diameter and fusion index in vitro. Partial rescue of muscle integrity was also observed in vivo in burn-injured models.

[Discussion]
Our findings define the “Red Queen breakdown” as a stress-convergent failure of cytoskeletal adaptability that underlies mitophagy resistance. Unlike rapamycin or AICAR, which enhance upstream autophagy induction, targeting the Red Queen mechanism directly restored flux and functional outcomes. This suggests a novel class of mitophagy-correcting drugs acting on the cytoskeletal level.

[Conclusion]
We identified promising drug candidates that reverse stress-induced cytoskeletal dysfunction and restore mitophagy. Our preclinical data suggest that correcting Red Queen breakdown offers a unifying and actionable strategy for treating muscle wasting, cachexia, and sarcopenia.

Despite increasing recognition of the psychological and physiologic effects of trauma on health outcomes, the majority of pre-medical training still prioritizes technical proficiency at the expense of trauma-informed care. This teaching style perpetuates clinician bias, misconception, and systemic disparities, especially for historically marginalized populations.

This paper argues for the inclusion of trauma-informed practice in pre-medical undergraduate curricula based on the existing literature in the fields of neurobiology, health equity, and curriculum development. Based on the evidence from peer-reviewed studies on Adverse Childhood Experiences (ACEs), doctor-patient relationships, and medical bias, I discuss how early exposure to trauma-informed models enhances empathy, diagnostic accuracy, and patient outcomes.

The article cites case studies of medical schools that have introduced trauma-informed care in later stages of education and makes the case that this type of approach needs to be modified earlier—at the foundational pre-med years. I suggest a conceptual model integrating narrative medicine, neuroscience, and reflective practice to enable future physicians to appreciate the impact of trauma on patient behavior, somatic symptoms, and adherence. Finally, this study adds to the continued redefinition of what it is to be a competent and empathic physician in the 21st century as becoming more exposed to diverse populations and diseases becomes increasingly important. By incorporating trauma-informed practice into the very first years of medical school, we can cultivate clinicians who are not just scientifically competent but emotionally aware of the unseen injuries that their patients bear.

Keywords: trauma-informed care, pre-med education, medical equity, ACEs, empathy, health disparities, medical curriculum reform

The social media revolution has totally transformed the way teenagers access health information—frequently subjecting them to medical misinformation on key issues such as reproductive health, mental illness, and vaccines. Although there are digital literacy campaigns, there are very few interventions based on the neuroscience of the developing teenage brain, creating an imbalance in the effectiveness of education in critically assessing medical claims.

This article presents a neuroscience-based adolescent health communication development model founded on recent advances in cognitive development, risk perception, and affect processing research. Based on research in prefrontal cortex development, reward system, and peer influence, I argue that conventional public health messaging does not consider how adolescents process uncertainty, credibility, and emotionally relevant information.

The model I propose identifies age-specific approaches that utilize narrative, peer education, and multimedia modalities suited to adolescents' cognitive styles. I also discuss how misinformation disproportionately impacts marginalized youth, perpetuating disparities in access to empowering, accurate health information. By placing neurodevelopment at the center of public health education planning, we can create stronger and more critical future generations of consumers of health—while also preparing future doctors with strategies for connecting with young patients more empathetically and effectively.

Keywords: adolescent brain, health communication, misinformation, neuroscience, public health education, risk perception, youth empowerment

The analysis of utilization of emergency department visits offers vital information regarding the reasons for emergency department visits and the demographic characteristics of individuals who utilize the emergency department frequently. Identifying these patterns enables healthcare administrators and policymakers to understand the population characteristics of emergency department visits and their common reasons. Understanding patterns in emergency department visits can also inform the optimization of hospital resource allocation for emergency departments and other hospital services.

By analyzing recently released public use data sets by the National Center for Health Statistics, this study examines patient demographics and the principal diagnosis during the emergency department visit of individuals seeking care at emergency departments during the years 2020 and 2021.

The research further looks at the patterns of emergency department visits without hospital admission (treat-and-release) and hospital admission.

The key demographic characteristics examined include age, categorized into 17 intervals of 5-year periods, and the patient's sex, classified as male or female. This information is evaluated in conjunction with the first-listed diagnosis in the emergency department, which enables the identification of the demographic group that uses the most emergency department services for a specific health condition.

The study examines mortality patterns of various diagnosed conditions during emergency department use over the year, indicating which disease was the deadliest during the 12-month span.

By using AI visualization tools and data mining techniques, a clearer understanding of the most common reasons for emergency department utilization during the year across different demographic groups can be established. This study utilizes various visualization methods, including bar graphs that organize data by age groups and gender, which enhances the identification of trends among these demographic groups.

This research can be used in the emergency department and healthcare policy development. Furthermore, identifying the characteristics of the demography most commonly utilized by emergency departments can assist with designing and developing programs specifically tailored to these populations to increase health literacy and awareness of health conditions. These targeted interventions can decrease emergency department crowding and reduce the overall healthcare costs related to emergency care use.

Ornamental fish farming is highly reliant on quality fish feed. Fishmeal, being a major ingredient in the fish diet formulations, causes high price to commercial fish feeds due to its limited availability. Hence, there is an increasing trend in investigating the suitability of alternative protein sources to replace expensive fishmeal in fish diets without compromising the fish growth. The objective of this study was to evaluate the effect of brewer’s spent grains (BSG) as a partial replacement of fishmeal in the diet of goldfish (Carassius auratus). Four isonitrogenous diets (40% crude protein) containing 0%, 8%, 16%, and 26% BSG were prepared by adjusting the inclusion levels of fishmeal, soybean meal, and other non-protein ingredients. After a three-day acclimatization, 120 C. auratus with an average initial body weight of 1.13 ± 0.06 g were stocked into 60 L concrete tanks at the rate of 10 fish per tank. Each experimental diet was randomly assigned to three tanks as replicates. The fish were fed with the experimental diets twice daily (08:00 am and 03:00 pm) for 62 days. At the end of the experiment, there were no significant differences (P > 0.05) in daily feed intake (6.9 ± 0.8 % BW/day) and feed conversion ratio (2.4 ± 0.2) among the treatments. Diet with 16% BSG significantly increased (P < 0.05) the length gain (97.8 ± 2.1%) while 8% BSG diet significantly improved (P < 0.05) the weight gain (301.2 ± 9.4%) and specific growth rate (3.5 ± 0.1) of fish compared to other diets. Although condition factor was significantly higher (P < 0.05) in fish fed 0% dietary BSG, it was more than one across the treated fishes (1.8 – 2.5). These findings suggested that the diets of goldfish could be prepared by incorporating 8 – 16 % BSG by reducing the amount of expensive fishmeal in the diet without compromising the fish growth.

Sri Lanka harbors five wild rice species namely Oryza nivara, Oryza rufipogon, Oryza eichingeri, Oryza granulata, and Oryza rhizomatis within the genus Oryza, which comprises 22 wild relatives worldwide These species are taxonomically related to domesticated rice cultivars but thrive in natural wild environments. Some have been studied for their potential in rice crop improvement to ensure global food security. Among them, Oryza rhizomatis is a rare, endemic wild rice species found exclusively in Sri Lanka, known for its unique genetic and morphological traits. However, no studies have been conducted at national or international levels on physicochemical properties of the Oryza rhizomatis species, which are directly relevance to grain quality of rice. Therefore, this study evaluated selected physicochemical properties of Oryza rhizomatis. The analysis included grain length (n=5), width (n=5), length to width ratio (n=5), size (n=5), shape (n=5), color (n=5) and gelatinization temperature (n=6) studied using internationally accepted standard protocols. Results showed that the grain length, width and length to width ratio of Oryza rhizomatis were 4.51±0.03 mm, 1.71±0.04 mm and 2.63±0.00 respectively. The size of the grains was short while shape was bold. The grain color was orange-red, with L*, a* and b* values were 39.59 ±1.14, 9.55±0.20 and 17.35±0.50 respectively. Further, Oryza rhizomatis demonstrated high gelatinization temperature (74.5 to 80 ℃). In conclusion, the Oryza rhizomatis, an endemic wild rice species in Sri Lanka, showed desirable physicochemical properties.

Key Words: Physicochemical properties, Oryza rhizomatis, Wild rice, Endemic species in Sri Lanka 

In Sri Lanka, five wild Oryza species have been identified namely O. nivara, O. rufipogon, O. eichingeri, O. granulata, and O. rhizomatis. These wild rice species represent an underutilized reservoir of genetic diversity that holds significant potential to enhance the different attributes of cultivated rice. Among them, Oryza rhizomatis is particularly noteworthy as it is endemic to Sri Lanka and remains largely untapped. This study aims to investigate the antioxidant properties of Oryza rhizomatis. Samples were collected from its natural habitat, Anamaduwa, Puthalam, Sri Lanka. Freeze-dried 70% ethanolic extracts of whole grains were evaluated for total polyphenolic content (TPC; n=6), total flavonoid content (TFC; n=3), ferric reducing antioxidant power (FRAP; n=3), and DPPH radical scavenging activity (n=3) employing high-throughput technique. Results showed that TPC and FRAP of Oryza rhizomatis were 6.62 ± 0.29 mg gallic acid equivalents (GAEs)/g of sample and 6.26 ± 0.36 mg trolox equivalents (TEs)/g of sample respectively. For DPPH radical scavenging activity, Oryza rhizomatis showed dose dependent activity with IC₅₀ value of 127.62 ± 2.33 µg/mL. Further, trolox equivalent value for DPPH radical scavenging activity was 13.71 ±  0.31 µg/mL. TFC was not detected at studied concentrations. In conclusion, Oryza rhizomatis had phenolic antioxidants and anti-oxidant activity is mediated by radical scavenging mechanisms and reducing capacity. 

Keywords: Antioxidant properties, Oryza rhizomatis, endemic wild Oryza species, whole grains

The oil-trapped sludge (OTS) produced by automobile service stations is an emerging environmental concern. Phytoremediation is a promising technology which is widely used to treat the contaminants released into the environment. The present study assessed the oil degradation potential of OTS-contaminated soil by Crotalaria retusa. The OTS was collected from different automobile service stations in the municipal council area in Gampaha, Western Province in Sri Lanka randomly, and mixed homogeneously to make a composite sample. The experimental treatments involved different concentrations of OTS, which were mixed using composite sludge and uncontaminated soil to obtain 5000 mg/kg (0.5% w/w), 10,000 mg/kg (1% w/w), 15,000 mg/kg (1.5% w/w), 20,000 mg/kg (2% w/w), 25,000 mg/kg (2.5% w/w), and 30,000 mg/kg (3% w/w) concentrations. Four kilograms of prepared soil were placed in plastic pots of 17 cm diameter and 22 cm height, each planted with three seeds and regularly watered. The experiment was triplicated and exposed to sunlight for 120 days. A parallel seedless pot series was also maintained as positive controls. The gravimetric method was conducted to determine the percentage of oil degradation of each concentration at 30-day intervals. 

The results indicated a time-dependent increase in each concentration of the percentage of oil degradation in OTS. In 0.5% w/w concentration, 28.41±0.41%, 40.18±1.18%, 57.82±3.82%, and 63.7±4.71 % were degraded after 30, 60, 90, and 120 days, respectively. According to the results of the 1% w/w concentration, it showed a similar pattern in oil degradation, which has 24.00±3.00 %, 39.00±2.00%, 49.00±2.00%, and 52.33±3.33% at 30, 60, 90, and 120 days, respectively. All the other 4 concentrations also showed a high percentage of oil degradation after 120 days compared to the 30-day degradation. For instance, 1.5% w/w reported the 14.33±3.06% in 30 days and 43.00±2.00% after 120 days. Similarly, in 2 %w/w, 13.29±0.29% degradation was observed in 30 days, and 34.71±0.71% was observed after 120 days. Overall, a very low percentage of oil degradation was observed in 2.5% w/w and 3% w/w concentration, even after 120 days, which were 27.43±0.43% and 28.17±0.17%, respectively. The results represented a concentration-dependent decline in percentage oil degradation. Based on the results, the toxic effect of OTS on C. retusa at high concentrations was explained. However, a progressive increase in oil degradation denotes that phytoremediation requires an adaptation period to breakdown hydrocarbons present at different contamination levels. The finding of the present study explains the potential of C. retusa to remediate OTS-contaminated soil as an eco-friendly alternative for mitigating OTS contamination in soil. 

Plankton communities serve as sensitive bio-indicators of ecological changes in aquatic ecosystems. This study investigates phytoplankton diversity and associated environmental parameters in the Manalkaadu dune slacks, located in the dry dune area of Jaffna, northern Sri Lanka. These dune slacks function as seasonal wetlands, characterized by fluctuating water levels and salinity regimes. 

Four dune slacks (A–D) across the Katkowalam and Manalkaadu regions were selected, and monthly sampling was conducted from January to June 2024.  Plankton samples were collected in the early morning (approximately 06:00) by filtering 20 L of water through a 20 µm mesh plankton net. The subsample was immediately preserved with Lugol’s iodine solution for laboratory analysis. Species identification and enumeration were performed using a Sedgewick-Rafter counting chamber under a light microscope. 

Simultaneously, in situ measurements of total dissolved solids (TDS), electrical conductivity, pH, temperature, and salinity were conducted using standard field methods. Additionally, triplicate 100 mL water samples from each site were collected for laboratory analysis of nitrate-nitrogen (NO₃⁻-N), nitrite-nitrogen (NO₂⁻-N), and phosphate (PO₄³⁻ P), and concentrations reported in parts per billion(ppb). 

A total of 55 phytoplankton species, representing 40 families and eight phyla, were identified across the study sites. Chlorophyta was the most diverse phylum, accounting for 49% of the total genera, followed by Cyanobacteria and Gyrista. Phytoplankton diversity varied spatially, with slack C exhibiting the highest species richness (up to 46 taxa), followed by slacks B, D, and A. Ubiquitous genera included Ankistrodesmus, Chroococcus, Coelastrum, Oscillatoria, Navicula, and Scenedesmus. In contrast, certain taxa, such as Peridinium and Frustulia, were restricted to specific sites. 

The Shannon–Wiener diversity index (H′) ranged from 1.25 to 3.20 across sites and sampling months, with the highest diversity typically observed in April. Evenness value ranged from 0.41 to 0.95, with the highest observed in April. Species richness ranged from 16 to 35, with slacks A and C showing the highest richness overall, and the months of May and June showing the highest richness temporally. 

Environmental parameters showed substantial site- and month-wise variation. Temperature remained consistent at 29.1°C across slacks. pH varied between 6.8(slack C) to 7.5 (slack B), Salinity ranged from 1.0ppt (slack D) to 3.9ppt (slack A), TDS ranged from 216.7mg/l (slack D) to 1117.9mg/l (slack A). Conductivity ranged between 378.8µS/cm (slack D) to 2165.8µS/cm (slack A ). Nutrient concentrations varied as follows: nitrate ranged from 3.085 ppb (slack A) to 5.982 ppb (slack C); nitrite from 0.102 ppb (slack C) to 0.154 ppb (slack D); and phosphate from 11.131 ppb (slack A) to 19.582 ppb (slack D). 

These findings provide baseline ecological data for an understudied habitat type in Sri Lanka and highlight the dynamic nature of phytoplankton communities concerning local environmental variability.

Water availability is essential for plant growth, and drought poses a significant threat to crop productivity. This study examines the effects of triticum aestivum’s (winter wheat) drought resistance through measurements of shoot length and biomass production across three water treatment levels including control watering and two drought conditions that received water either every other day or every third day. The results suggest that biomass declined in both drought treatment levels but the most significant impact occurred only with severe drought conditions alone. Plants respond to moderate drought conditions by growing their shoots instead of maintaining biomass production but severe water shortage creates growth restrictions for wheat plants. Future research should explore root system responses and genetic adaptations to improve drought tolerance in wheat.

ABSTRACT

Groundwater is the only portable water source in the Jaffna peninsula, Sri Lanka. However, the simultaneous existence of naturally occurring minerals and anthropogenic pollutants deteriorates groundwater quality in the peninsula. Without a proper remediation, the groundwater, including water used for drinking purposes, contains multiple contaminants: organic, inorganic, and microbial in levels that exceed Sri Lankan and World Health Organization (WHO) drinking water standards. This critical situation affects the usability of available groundwater and induces potential human health threats. For this study, research articles published between 1983 and 2020 were searched and gathered through Google Scholar, Research Gate, MDPI, JSTOR, and Elsevier. The search was separated into two parts such as ‘Groundwater Contamination’ and ‘Remediation Techniques’, to find the best articles. Additionally, a PRISMA flow chart was utilized to select the best articles. Consequently, out of 216 articles, 53 were selected for groundwater contamination, and out of 90 articles, 15 were selected for groundwater remediation techniques for the Jaffna peninsula. This study found that research studies at various localities regarding the most egregious contaminant from 1983 to 2020 have shown that the Nitrate-N, salinity, iron, microbial, calcium and magnesium, and oil and grease content in the groundwater have increased over this period in the Jaffna peninsula. Meanwhile, the groundwater in Vadamartachi, Valikamam, Jaffna, and Nallur areas are polluted by multiple co-occurring contaminants such as elevated levels of Nitrate-N, water-borne pathogens, and water-hardness. As a result of this, boiling water at 100°C as a remedy could worsen the Nitrate level in the above-mentioned areas. Although comprehensive studies focus on the particular contaminant types, this current review addresses and explores the multiple co-occurring contaminants with the use of the QGIS Software and the influence of the significant contributing factors such as anthropogenic activities, seasonality, and geology in the groundwater of the peninsula. Moreover, recommended remediation options, potential applications, and the importance of selecting the most suitable remediation practice are discussed. 

Key words: Groundwater Contamination, Jaffna Peninsula, Nitrate, Co-occurring Contaminants, Remediation Techniques.  

The purpose of this research was to identify Sustainable Human Resource Management Practices (SHRMPs) that enhance worker productivity, focusing on the mediating role of Quality of Work Life (QWL) in Sri Lanka's tea plantations. The study surveyed 298 employees selected through a stratified sampling technique from eight tea factories in the Southern Province. The tea industry, a cornerstone of Sri Lanka’s economy, faces significant challenges, including declining productivity, labor scarcity, and rising production costs. In response, sustainable HRM practices, such as Human Care Practices (HCPs) and Knowledge Management Practices (KMPs), have emerged as strategic approaches to boost worker productivity. This research employed a mixed-methods approach, collecting data through a paper-based questionnaire distributed among tea plantation workers. One of the unique findings of this study is that the implementation of sustainable HRM practices significantly improves the quality of work life (QWL), which subsequently enhances worker productivity. The results indicate that when workers experience better QWL through supportive HRM practices, their productivity levels increase, contributing positively to the overall performance of the tea industry. These findings provide valuable insights for policymakers and industry stakeholders, highlighting the critical need to integrate sustainability into HRM practices. By focusing on the well-being of workers and fostering an environment conducive to productivity, the tea industry can address its current challenges while ensuring long-term social and economic benefits. This research underscores the importance of adopting a sustainable approach to HRM, which not only benefits individual workers but also strengthens the industry's resilience and competitiveness in a challenging economic landscape.

Keywords: Human Care Practices, Knowledge Management Practices, Quality of Work-Life, Worker Productivity.

Quality seedlings are vital for productive coconut cultivation. Polybagged coconut seedlings are ideal planting materials as they are superior in quality to conventional bare-rooted seedlings. The recommended potting mixture of polybag consists of topsoil, cow dung, and coir dust in a 1:2:3 ratio, featuring a significant amount of coir dust. However, the high export potential of coir dust has rendered it a limited and scarce resource in Sri Lanka. The present study has identified alternative materials as substitutes for coir dust in the potting mixture of coconut polybag seedlings. Three-month-old coconut seedlings of variety CRIC 60 with similar sprout length were established in polybags filled with five different media; T1: coir dust, T2: sawdust, T3: half-burned paddy husk, T4: paddy husk and T5: paddy straw in combination with soil: cow dung in 3:1:2 ratio. The experiment was laid out in a Randomized Complete Block Design with three replicates. Days taken for the emergence of the first leaf, morphological characteristics of the seedlings, relative chlorophyll content of leaves, and soil properties were recorded. Data were analyzed using the Analysis of Variance procedure in R software. Stem girth, seedling height, length and width of leaves, and relative chlorophyll content of leaves were not significantly (p>0.05) different among treatments two months after transplanting. Soil pH and electrical conductivity of potting mixtures were in the desirable range in all treatments. In conclusion, freely available sawdust, paddy straw, and paddy husk can effectively be incorporated into the potting mixture of coconut polybag seedlings as a low-cost substitute for coir dust. Soil nutrient analysis is suggested before any recommendation.

Keywords: Morphological characters, Paddy husk, Paddy straw, Sawdust, Soil properties

Heavy metal toxicity has now become a pressing global concern due to rapid industrialisation and agricultural development, potentially leading to contamination of the food chain that negatively impacts both plant growth and human health. Our study aimed to investigate the effects of chromium and lead on the growth of the medicinal plant Terminalia arjuna in a hydroponic system. We applied five different treatments: concentrations of Cr³⁺ (1 mg/L & 5 mg/L) and Pb²⁺ (1 mg/L & 5 mg/L), along with a control that contained no heavy metals. Our results revealed that both heavy metal stresses significantly (P<0.05) reduced the seedlings' morphological parameters (root and shoot length) and physiological parameters (Net Photosynthesis Rate, Stomatal Conductance, and Membrane Stability Index). We also observed a significant (P<0.05) decline in leaf chlorophyll and carotenoid content under heavy metal stress, while proline, hydrogen peroxide (H₂O₂), and malondialdehyde (MDA) increased significantly (P<0.05) compared to the control. Furthermore, the translocation ratio (TR) of both metals suggests absorption in the three plant parts: root > stem > leaves. The metals in the root parts were transported less to the stem but more mobilized to leaves when available in the stem parts. Overall, this study demonstrates that heavy metals have a detrimental effect on the growth of Terminalia arjuna. Therefore, the accumulation of heavy metals in this medicinal plant poses a significant risk to ecosystems. Moreover, further study is imperative, particularly focusing on effective phytoremediation strategies for heavy metal toxicity in various medicinal plants, ensuring the precise source and translocation mechanisms are assessed.

Camellia sinensis, more commonly known as the tea plant, is grown and drunk in many parts of the world. From Africa to Asia, various varieties, cultivars, and clones are bought, sold, and experimented upon in the pursuit of better plant survival and greater yields. For the last half-century research has been conducted on the plant by various institutions, yielding new clones with special properties and allowing for more efficient irrigation and fertilization. Strides have been made, elucidating fundamental growth mechanisms, genes, and climatological influences, but despite this there remain a multitude of uncertainties - things known neither to the general public nor to those on the forefront of this research. Among these unknowns are the specific conditions required for the plant to grow and survive, as well as the best-suited clones and protective measures to bring it to new cultivation areas. Further to this, the relationship between established tea regions and their current and future climates is still poorly understood. To fill these gaps, this research investigates the geographic and climatic limitations of Camellia sinensis cultivation, with a focus on quantifying both optimal growth conditions and tolerance thresholds for variables such as drought and temperature. By synthesizing data from prominent studies and climate records, this study identifies the key environmental factors influencing tea production, including soil type, precipitation patterns, terrain slope, and seasonal temperature ranges. A comparative analysis of two major tea-growing regions, Xishuangbanna and Lincang, as well as Welland, Ontario is conducted to evaluate their suitability for tea cultivation under current and projected climate conditions. Special attention is paid to the potential for cultivating tea in Ontario, Canada, using soil classifications, climatological norms, and plant hardiness zones, alongside consideration of protective measures and cultivar-specific resilience. Results suggest that while traditional regions in China maintain suitable conditions, climate change may both expand and endanger their productivity due to fungal pathogens and increased variability. Conversely, Ontario shows marginal promise for limited tea cultivation, particularly in warmer zones like Welland, though challenges related to winter cold and shorter growing seasons persist. Cultivar selection and protective agronomic practices could enhance viability. This study also highlights the need for continued research on climate adaptability, genetic variation among cultivars, and the evolving geography of tea production in the context of global climate change.  

Key words: Tea, Camellia sinensis, Climate change, Cultivars, Clones, Varieties, Temperature, Precipitation, Soil, Drought, Growth, Distribution 

Anthropogenic marine debris (AMD) currently represents one of the foremost global environmental challenges. Although the consequences of AMD on beaches are widely recognized both globally and locally, mangroves have received comparatively less attention. Despite the Jaffna peninsula hosting the second-largest mangrove forest in Sri Lanka, the impact of AMD accumulation on its mangroves has not yet been assessed. Therefore, this research aims to evaluate the composition and distribution of AMD in selected mangroves of the Jaffna peninsula. Two locations (Mandaithivu and Araly) featuring abundant mangrove patches dominated by A. marina were selected, with two sites sampled at each location. A comprehensive approach using systematic random sampling was employed for sample collection. The belt transect method was utilized for sampling, with a 100 m transect laid out at each site and five quadrats placed at 20 m intervals.

Classification and source identification of AMD were carried out under the guidelines of NOAA. Samples were broadly categorized into five groups: plastic, glass, metal, wood, and paper. The health status of the studied mangrove ecosystems was assessed using four indices: Clean Coast Index (CCI), General Index (GI), Plastic Abundance Index (PAI), and Hazardous Items Index (HII). 

The mean item density and weight density of debris were 0.1 ± 0.05 items/m² and 1.74 ± 1.33 g/m² (±95% confidence interval), respectively. The results revealed that plastic debris constituted the most prevalent type of AMD (86%), with single-use plastics being the most common category.  Entangled debris within mangrove pneumatophores was frequently observed in the study areas. The primary source of AMD was dumping (81.3%). Based on the scores obtained for CCI and GI, both locations were classified as ‘dirty.’ According to the scores for PAI, Mandaithivu was identified as an area with a very high abundance of plastic debris, while Araly was designated as an area with a high abundance of plastic. In terms of HII scores, Mandaithivu was categorized as having little to no hazardous debris items over a large area, whereas Araly was classified as having a considerable number of hazardous debris items. To mitigate the further accumulation of AMD in the studied mangrove habitats, it is recommended to implement comprehensive legislation and regulatory frameworks, develop efficient waste management systems, enhance public awareness initiatives targeting local communities and school-aged children, and advocate for plastic waste recycling.

Keywords: Anthropogenic Marine Debris, Jaffna Peninsula, Mangroves, Plastic, Pneumatophores

BIO-TURBIDITY REDUCTION POTENTIAL OF SOIL-ISOLATED BACTERIAL CONSORTIA ACROSS DIVERSE WASTEWATER MATRICES

K.G.S. Madhushani ^1,2,3*, T.M.M.P.S. Bandara^3,4

¹Department of Applied Chemistry and Environmental Science, RMIT University, Melbourne, Australia

²Post Graduate Institute of Science, University of Peradeniya, Sri Lanka

³Department of Bio-science, Faculty of Applied Science, University of Vavuniya, Sri Lanka

⁴Central Engineering Services Limited, Colombo, Sri Lanka.

*[email protected]

ABSTRACT

The escalating discharge of wastewater from diverse anthropogenic sources dictates sustainable and eco-friendly treatment approaches to safeguard environment and public health.             Conventional chemical approaches for turbidity reduction often pose secondary environmental hazards, prompting the need for biologically based alternatives. The objective of this study was to assess the turbidity mitigation potential of soil-isolated bacteria across eight distinct wastewater types including agricultural runoff (AG), aquaculture effluent (AQ), automobile wastewater (AU), canteen discharge (CT), dairy effluent (DE), greywater (GW), household discharge (HD), and poultry effluents (PE). Bacterial strains were isolated from soil using the spread plate technique and characterized based on colony morphology and basic biochemical properties. Three dominant bacterial isolates were identified; a Gram-positive, catalase-negative Coccus sp. (cluster-forming, circular-shaped), and two Bacillus sp. (small and long rod-shaped). A laboratory scale experiment was conducted under a Completely Randomized Design (CRD) comprising seven treatments (T₁–T₆ with microbial applications and T₇ as negative control), each replicated three times for all eight wastewater types. The treatments included; T₁ (rod-shaped Bacillus sp.), T₂ (Cocci-shaped bacterium sp.), T₃ (small rod-shaped Bacillus sp.), T₄ (T₁ + T₂), Tr₅ (T₁ + T₃), T₆ (T₂ + T₃), and T₇ (negative control without microbial inoculum). Significant turbidity reductions (P < 0.05) were observed in AG (P = 0.000), AQ (P = 0.001), AU (P = 0.000), HD (P = 0.000), and PE (P = 0.001), indicating strong bacterial efficacy. In contrast, no significant reductions were detected in CT (P = 0.045), DE (P = 0.615), and GW (P = 0.087) likely due to the presence of resistant recalcitrant organic or chemical compositions. Among the treatments, T₃ (small rod-shaped Bacillus sp.) consistently achieved the highest turbidity reduction across the responsive wastewater types, whereas combination treatments (T₄–T₆) exhibited moderate effectiveness. These findings highlight the potential of specific soil-based bacterial strains, particularly small rod-shaped Bacillus sp. as an eco-friendly agent for turbidity mitigation in certain wastewater streams.  Further investigations, including molecular identification of the isolates and pilot-scale studies, are recommended to validate their application in real-world wastewater treatment systems.

Keywords:

Soil isolated bacteria, Bacillus sp., Cocci sp., Turbidity reduction, Wastewater treatment

In a rapidly evolving world, university graduates must demonstrate adaptability, resilience, and a lifelong commitment to learning. Traditional classroom instruction alone is no longer sufficient to cultivate these competencies. How can we motivate the students to actively engage, learn, and adapt in a university setting? In the realm of higher education, fostering student success requires moving beyond traditional classroom methods to cultivate these qualities. This session explores how integrating community-engaged learning into undergraduate education can bridge the gap between academic theory and real-world application. Research has shown that individuals are driven by purpose, thus a comprehensive approach that integrates impactful pedagogical practices with community engagement initiatives can effectively prepare undergraduates for the dynamic workforce. This pedagogical research aims to illustrate two distinct workplace scenarios through community partnership projects in Urban Climatology course, where students were exposed to different paradigms to apply theory into practice. While both projects equipped students with the necessary knowledge, skills, and competencies to excel in their academic and professional pursuits, they offered unique perspectives. In one scenario, students learned to challenge stereotypes to better serve the community, while in another, they embraced the workplace norms to enhance their service delivery. This dichotomy highlights the importance of adapting to workplace culture for resilience in any job. Students’ feedback from these community-engaged learning experiences revealed differing preferences between in-person and remote placements, raising important questions about the future of experiential learning in post-pandemic academic settings. This raises questions about the future of experiential learning, a cornerstone of many university courses that bridge classroom theory with real-world practice. The research also reflects the importance of the role of instructors in designing impactful learning environments that foster academic success, personal development, and meaningful community impact.

One of the more pressing environmental challenges today is the contamination of groundwater, rivers and lakes with heavy metals, primarily driven by the production of industrial waste, acid rain and consumer products. Copper (Cu(II)) and nickel (Ni(II)) are examples of heavy metals that are highly toxic to humans when they are present at high concentrations. Excessive exposure and accumulation of Cu(II) and Ni(II) can lead to serious health issues, including Alzheimer’s disease, dermatitis and carcinogenesis. These heavy metals are also resistant to environmental degradation and tend to persist in aquatic environments, increasing the risk of bioaccumulation within living organisms and ultimately imposing harmful effects on plants, aquatic life and human health. Therefore, the removal of heavy metals from water is important in preserving the environment and maintaining human health. Among the available water treatment technologies, adsorption is considered one of the most efficient and practical methods due to its low cost, simplicity and effectiveness in removing heavy metal contaminants. Biomass-based adsorbents (biosorbents) have recently emerged as a promising alternative for heavy metal removal due to their exceptional water affinity, swelling behavior, high porosity and improved mechanical stability. The functional groups present on the biopolymer units of the biosorbents, such as carboxylic acid (-COOH) and hydroxyl (-OH), in addition to the addition of metal-organic frameworks (MOF) act as complexing agents, binding heavy metal ions and facilitating their removal from aqueous media. In this study, pectin biopolymer was extracted from raw grapefruit peels and was used as a starting material for the synthesis of pectin-hydrogels (PH) and pectin hydrogel – metal organic framework (PHM composite). The hydrogels were characterized by size, Fourier Transform – Infrared Radiation (FT-IR) and Scanning Electron Microscopy (SEM). It was seen that lyophilized hydrogels (dry) were smaller in size (PH 1.67mm and PHM composite 1.05mm) in comparison to the newly synthesized hydrogels (wet) (PH 4.58mm). Furthermore, the adsorption of Cu(II) and Ni(II) was investigated and quantified using Flame Atomic Absorption Spectroscopy (FAAS). Generally, it was seen that as contact time between the metal cation and hydrogel increased, the percent adsorption and adsorption capacity also increased. Cu(II) showed an optimal removal at pH 5, and demonstrated a percent adsorption of 95.11% and an absorption capacity of 97.75mg/g at room temperature by PHs. PHM composites also showed high Cu(II) adsorption, with a maximum absorption capacity of 67.53mg/g and percent adsorption of 94.99% at 150 minutes at room temperature. Finally, Ni(II) adsorption by PH achieved optimal performance after 1 minute of contact time, with a percent adsorption of 92.62% and an absorption capacity of 281.89mg/g at room temperature. Hence, this study demonstrated the successful synthesis of pectin-based hydrogels and their use for the successful removal of Cu(II) and Ni(II) heavy metals from solution. 

Rice is the primary staple food for over half of the global population, with a wide diversity of varieties cultivated worldwide. Various hydrothermal processing techniques such as parboiling, cooking, wet milling, and rice fortification involve grain soaking as a critical step. Therefore, understanding the interaction of rice with water under processing conditions is essential for improving cooking quality, fortification efficiency, and nutritional performance. 

This study evaluated the water absorption characteristics (WAC) of rice kernels during hot soaking at 70 °C, examining three milling stages, including rough rice (RR), brown rice (BR), and polished rice (PR) across 17 Sri Lankan rice varieties, consisting of both traditional and improved types. The selected varieties were categorized into four categories based on grain size and shape (i.e. long-slender, long-medium, intermediate-bold, and short-round). Rice samples were soaked for 5 hours, and the weight measurements were recorded at each 30 minute interval to determine water uptake capacity (WUC), absorption rate, equilibrium moisture content, and moisture sorption coefficient (k). The study aimed to examine the effects of milling stages and soaking time on WAC and compare these parameters across different grain categories.

Results indicated that water absorption followed an exponential asymptotic pattern across all varieties and milling levels. Initial WUC (within 30 minutes) ranged from 9.17–16.97% (RR), 20.01–26.19% (BR), and 18.07–28.55% (PR), while after 5 hours, WUC values were extended to 22.58–44.32% (RR), 23.80–56.56% (BR), and 21.97–185.88% (PR), with polished rice showing the highest variability. Equilibrium moisture content increased with milling degree, ranging from 33.19–50.64% (RR) to 36.59–79.14% (PR), while moisture sorption coefficients ranged from 0.0064 – 0.0848 min⁻¹, depending on rice type and milling level.

Findings highlight that water absorption is primarily influenced by milling degree and rice variety rather than grain size and shape. Rough rice exhibited the lowest initial absorption rate, indicating the husk’s barrier effect. These insights emphasize the importance of milling degree in understanding rice hydration behavior during processing, with significant implications for the efficiency of fortification, cooking quality, and process optimization. Optimizing rice processing based on WAC can lead to more efficient fortification strategies, reduce resource usage (water, energy), and provide valuable data for equipment design and storage planning, particularly in regions where rice is a dietary staple.

Okra (Abelmoschus esculenthus) is a valuable warm seasonal vegetable crop, widely cultivated across tropical and subtropical regions due to its edible pods and nutritional benefits. Okra is very sensitive to fertilizer application patterns, depending on soil fertility, growth stage, and environmental conditions. Proper nutrient management is crucial for optimal yield, but excessive or imbalanced fertilization can harm the plant. Selecting the best fertilizer practices remains a challenge for better yield of Okra. Conventional organic fertilizers and Bio-fertilizers offer sustainable alternatives, but their effectiveness is lower than Chemical fertilizer. This experiment aims to compare the effects of conventional chemical fertilizer practices and eco-friendly fertilizer approaches on the growth and yield of Okra. A plot experiment was carried out using three types of fertilizers and combinations namely, Chemical fertilizer, Modern Organic pellet fertilizer and Biofilm bio-fertilizers.

The experiment was laid out in a randomized complete block design with four replicates having the following treatments: DoA recommended 100% chemical fertilizer(100-CF), LBF recommended 100% organic pellet fertilizer(100-OF), chemical fertilizer 50% + Biofilm biofertilizer(50-CF-BF), organic pellet fertilizer 50% + Biofilm biofertilizer(50-OF-BF), Chemical fertilizer 50% + Organic pellet fertilizer 50%(50-CF-50-OF),Chemical Fertilizer 33% + Organic pellet fertilizer 33% + Biofilm biofertilizer(33-CF-33-OF-BF), no fertilizer-control(NC).  Key agronomic parameters such as plant height, number of leaves, pods and total yield were measured and statistically analyzed using one-way ANOVA and LSD for mean separation.

The findings revealed that different fertilizer practices and combinations had significant effects on growth and yield parameters of Okra over control. Based on the results, CF, 50-CF-50-OF and 33-CF-33-OF-BF treatments showed comparable results for the growth and yield of Okra, suggesting potential for reducing chemical fertilizer dependency. The study concludes that integrated eco-friendly fertilizer practices can effectively support sustainable Okra cultivation without compromising yield, and hence it is recommended. Further studies under field conditions should be conducted to confirm these results.

 Keywords: Biofertilizers, Chemical Fertilizer, Eco-friendly, Organic Pellet, Okra 

Mullets have been classified as part of the economically important species for fisheries and mariculture. Parasites and their infections on mullets can affect the health, reproductive rate, and subsequent economic value of the species. Sri Lanka has put little effort into studying the parasites of mullet, and on a global scale, it is not much better. This study was aimed at checking the parasitic fauna of mullet fishes along with macro-parasitic lesions that infested them. From April to November 2024, Gurunagar market in Jaffna was visited once every two weeks, where a total of twenty mullets (standard length—13 cm to 27 cm and total weight-100 g to 250 g) were purchased. Each fish was checked for external, internal, and macroscopic parasites. Scales, gills, skin, and mucus were checked for ectoparasitic lesions. Endoparasites from intestines, gut contents, blood, eyes, livers, spleens, and hearts were deeply analyzed through dissection. About 23.31% of parasitic infestation was recorded for the sampled fish. Parasitic isopods Aega sp. and Cymothoa sp. were noted in the gill chamber and buccal cavity. Opisthorchis sp., Notocotylus sp., and Nemtobibothrioides histoidii digenic trematodes were identified from intestine and blood smear. Also, acanthocephalans and protozoa were identified. Aega sp. was the most abundant at 30%; the rest of the identified species accounted for 11.67% of total parasites each. Fish infected by the parasites showed macroscopic pathological changes, which included skin erosions, soft tissue necrosis, inflammation of visceral organs (excessive bleeding), and considerable gill mucus.  At least some of the parasites have zoonotic ability, which largely increases the danger risk of consuming raw mullets and further highlights the necessity of proper fish cooking to avoid infection transfer to people. The severity of these changes depends on the type of parasite and host, their environment, and the intensity or duration of parasitic infection. How morphometric parameters relate to the quantity of pathogens present is complex. It is moderated by host mass, age, health condition, ecological setting, and type of parasite. Parasite burden is typically greater in older hosts; however, this trend can be influenced by immunological and experiential factors as well as environmental conditions. The combination of host and parasite species, environmental conditions, and infection history defines the overall severity. The potential zoonotic infections some of these parasites can give rise to emphasize the danger associated with consuming raw mullet fish and showcase the need for providing these fish to humans in a manner that avoids the risk of infection.

Key words: Macro pathological changes, Ecto parasite, Endo parasite, Jaffna lagoon, Mullet

Ground-mounted photovoltaic (PV) installations are increasingly integrated into agricultural landscapes, yet their influence on underlying soil physical properties remains underexplored, particularly in dry regions such as Vavuniya, Sri Lanka. This study investigates the spatial variability of key soil physical parameters; moisture content, bulk density, particle density and porosity, beneath and around PV panel arrays. Soil samples were collected from three distinct microenvironments: edge zones (E-series), mid-panel zones (M-series), and gap or open zones (G-series). Soil were sampled in an operational solar farms Vavuniya, Sri Lanka between January and March 2025.  At each PV arrays, four replicate plots were randomly placed on land underneath solar panels and in between the rows of solar arrays. Soils were sampled within the quadrats to 15 cm depth and 5 cm diameter with a cylindrical metal corer. Four replicate soil samples were collected from each quadrat, three samples were homogenised in the laboratory for soil analyses and one sample was kept separate for bulk density measurements.  Results revealed marked spatial heterogeneity modulated by panel shading and surface runoff dynamics. Soils beneath and adjacent to panels (E and M groups) exhibited lower moisture content (0.30 - 0.67%), higher bulk density (up to 2.39 gcm^-³), and reduced porosity (as low as 9%), indicating compaction and limited water infiltration. In contrast, G-series showed significantly higher moisture content (up to 2.58%), lower bulk density (as low as 1.43 gcm^-³), and improved porosity (up to 36%), suggesting more favorable conditions for water retention and root respiration. Statistical analysis using one-way ANOVA revealed a highly significant variation in moisture content (P = 0.0029), with the gap zone (G) exhibiting the highest values, likely due to increased exposure to rainfall and reduced shading. Bulk density (P = 0.031) and particle density (P = 0.0052) also varied significantly, suggesting compaction effects beneath the panels and structural induced changes. However, porosity did not show a statistically significant difference (P = 0.166) among the zones. Complementary non-parametric Kruskal-Wallis tests supported these findings, confirming significant differences in moisture (P = 0.009) and bulk density (P = 0.034), while particle density (P = 0.089) and porosity (P = 0.208) remained non-significant. These findings emphasize the role of PV structures in shaping soil physical conditions, with potential implications for soil health, crop productivity, and sustainable land management in agro-photovoltaic systems. Management interventions such as mulching or selective tillage may be necessary to counterbalance compaction beneath PV arrays and enhance agroecosystem resilience.

Keywords: Ground-mounted photovoltaic installations, Soil Physical Parameters, Dry Zone

Macronutrients (N, P, K) are the essential elements responsible for growth and metabolic functions of plant by serving as structural constituents of certain enzymes or stimulating their activities. Deficiency of these nutrients can hinder growth, yield and overall mechanisms of plants. 

The purpose of this study was to assess several morphological, physio-biochemical and antioxidant enzymatic (CAT, POD, APX, GST) and non-enzymatic (total phenolic and flavonoid content) activity on Nitrogen (-N), Phosphorus (-P), and Potassium (-K) deficient Aquilaria malaccensis seedlings along with control seedlings (sufficient nutrient supply) under sand medium for 60 days. 

The results revealed that shoot length, stem diameter and leaf area significantly decreased under NPK deficiency (-N, -P, -K) with -N treatment being severely affected. Total chlorophyll and carotenoid showed similar response. However, stomatal conductance was declined in K deficient plants compared to N and P deficiencies. Stomatal density increased in -P treatment, while it showed non-significant decline in -N and -K treatments. MDA, H₂O₂ increased significantly in N, P and K deficient treatments while higher proline accumulation was observed in K deficiency than P and N deficiencies. To circumvent the oxidative damage under NPK deficiencies, Aquilaria increased the synthesis of total phenolic and flavonoid contents, and antioxidant enzymatic activities. K induced higher accumulation of catalase (CAT) while, peroxidase (POD) activity was found higher in P and K deficient plants. Activity of Ascorbate peroxidase (APX) and Glutathione S-transferase (GST) was upregulated in K deficient plants then N and P deficient plants. The response of different enzymatic and non-enzymatic antioxidants demonstrates the defense mechanism of Aquilaria under NPK deficiency. Further studies should be conducted to gain molecular insights for identifying genes linked to stress tolerance.

Abstract: Scholars involved in agriculture and food sciences are excited about new waste management concepts aimed at decreasing the daily waste produced by food production. Bananas (Musa spp.) are regarded as an important fruit crop in tropical regions and are widely consumed. The flesh of raw bananas is utilized by companies that produce crispy banana chips and banana flour. Local banana varieties, such as Musa acuminata × Musa balbisiana (Sour banana / Ambul), Musa acuminata cv. Pisang awak (Honey banana / Seeni), and Musa acuminata cv. Pisang raja (Silk banana / Kolikuttu) are well-known for their high consumption as raw materials and fruits. Food manufacturing facilities generate a significant amount of waste over an extended period. Reutilizing waste products and extracting their valuable compounds can be an effective, sustainable waste management approach. This study aims to assess the bioactive compounds, including antioxidants, polyphenols, carotenoids, and antimicrobial substances, in the local banana varieties referenced earlier, and how these compounds change over the storage period under natural environmental conditions in the Anuradhapura District. Banana samples of Musa acuminata × Musa balbisiana, Musa acuminata cv. Pisang awak, and Musa acuminata cv. Pisang raja varieties were sourced from the Thambuttegama economic center located in the Anuradhapura district. Each sample was separated into three parts and kept at ambient room temperature (32◦C - 34◦C), and humidity (60% - 65%). The first part of each sample was washed, peeled, and dried using a heat pump dryer set at 50◦C for 5 hours. Dried peel fragments were ground into a fine powder and stored at freezing temperature. The other segments of each sample were processed in the same way after four days and eight days from the original stage. The fine powder was extracted using absolute ethanol via a vacuum pump extractor operating at 50 rpm and a temperature of 40◦C. All extracted ethanol samples were filtered through 0.45µm microfilters and stored in glass vials for gas chromatography mass spectrometric (GC-MS) analysis. The GC-MS analysis revealed that Musa acuminata × Musa balbisiana (Ambul banana) peel powder exhibited a significant presence of hexadecanoic acid, palmitic acid, phytol, linolenic acid, vitamin E, campesterol, and stigmasterol across all storage periods. Musa acuminata cv. Pisang awak (Seeni banana) also demonstrated hexadecanoic acid, palmitin, linolenic acid, linolein, squalene, vitamin E, campesterol, and stigmasterol at every storage phase. In the case of Musa acuminata cv. Pisang raja (Kolikuttu), the compounds found included hexadecanoic acid, palmitin, linolein, squalene, vitamin E, campesterol, stigmasterol, and c-sitosterol, along with sucrose and d-mannose noted during the second and third stages, alongside other compounds. The findings indicate that there is no notable difference in the bioactive composition of each banana variety when stored under natural environmental conditions, and banana peels are rich in bioactive compounds. Important biochemical substances can be extracted by repurposing agricultural food waste materials.

Keywords: Bioactive composition, Musa spp., Agricultural food waste management, GC-MS analysis, Natural storage conditions

Biodiversity conservation within human-modified landscapes demands tools that travel beyond the formal protected area system. The Kunming-Montreal Global Biodiversity Framework has called for effectively conserving 30% of global terrestrial and marine ecosystems by 2030 (Target 3), which cannot be met by protected areas (PAs) alone. Other Effective Area-Based Conservation Measures (OECMs) offer a complementary approach by acknowledging sites that deliver long-term in-situ conservation benefits, even where biodiversity is not the primary management objective. Sri Lanka hosts numerous biodiverse landscapes, such as tea estates and sacred groves, that exist outside the protected area network and lack a validated framework to identify them as OECMs. This study addressed the assessment gap by developing a locally adapted, scientifically validated scale for identifying OECMs in Sri Lanka. 

The primary objective involved creating a validated scale capable of assessing candidate OECMs through ecological governance and socioeconomic indicators based on the IUCN Site-Level Tool for Identifying OECMs (IUCN,2023) criteria. The initial scale was structured using 16 measurable sub-criteria for 06 main criteria (criteria 3-8, excluding the first two criteria of initial screening) given in the IUCN Tool, suitable for Sri Lanka, along with the descriptor-based scoring, enabling consistent evaluation across varied landscapes. During initial content validation by six domain experts, several sub-criteria yielded Item-level Content Validity Index (I-CVI) values between 0.66 and 0.83, falling short of the acceptable threshold of 0.83 for six validators. Scale-level indices were also suboptimal: Scale-level CVI/Universal Agreement (S-CVI/UA) was 0.65, and Scale-level CVI/Average (S-CVI/Ave) was 0.79. Guided by qualitative feedback from validators, as supported in scale development literature, the scale was refined to enhance clarity, contextual relevance, and consistency. After revisions, the sub-criteria to measure the 06 criteria were reduced to 12, and all I-CVI scores reached a unanimous 1.0, with corresponding S-CVI/UA and S-CVI/Ave values also improving to 1.00, showing high content validity. The biodiversity value, represented in Criterion 4 of the IUCN OECM Identification Tool, for instance, was validated to be assessed through six ecological sub-criteria: threatened species, endemism, ecosystem representation, ecological integrity, species aggregations, and connectivity in the revised scale. 

This tool is the first in Sri Lanka to provide a scientifically robust context-sensitive framework to support the national recognition of OECMs. The significance of this study lies in its support for evidence-based conservation planning, helping integrate policies and advancing Sri Lanka's efforts to meet global biodiversity targets in areas that are ecologically important but underrepresented in the existing protected area network.

Abstract

Biochar application can significantly impact soil greenhouse gas (GHG) emissions, particularly in loamy sand and sandy loam soils used for tree planting sites. In these coarse-textured soil, which tend to be less fertile and have lower water-holding capacity, biochar can potentially improve soil structure, enhance soil aeration, and water retention, leading to a healthier environment for root development and overall tree health. 

While numerous studies have documented reduced GHG emissions with biochar amendments in agricultural soils, field-based surveys are limited in urban settings. Urban soils can differ significantly in composition, organic matter content, and pollutant levels from agricultural soils, which can influence biochar's effectiveness. 

This study focuses on the application of biochar in urban soils, particularly its role in mitigating soil GHG emissions in tree planting pits closed to playgrounds, heavily influenced by human traffic. We hypothesised that biochar could act as a sustainable solution to reducing GHG emission by improving soil texture, water retention, and nutrient availability, while also contributing to a healthier tree that can better withstand recreational pressures. 

Using a randomized factorial experimental design, biochar was applied at two different treatments (Control and 10% v/v biochar in Savaria type 1 soil mix) in newly planted pits with Acer × freemanii in Quebec City. Emissions of soil GHGs (CO₂ and CH₄) are being measured using the Licor 7810 SC instrument. Data will be collected monthly from May to September over a three-year period. We anticipate a significant reduction in GHG emissions from soils amended with biochar compared to control soils. The long-term effects of applying biochar to urban soils are expected not only to enhance tree performance and reduce soil compaction in cities but also to mitigate GHGs emissions.

Urban areas, including soils, tend to be “hot spots” for contaminants, due to the density of pollutant emitting sources such as traffic-related activities. Soils are often especially impacted and of poor quality, lacking the lack the physical and chemical properties necessary to support healthy tree growth. One promising approach to address this, as well as the larger problem of the excessive generation of waste in cities and related contributions to climate change, is to reuse urban waste for the development of constructed engineered soils, known as technosols. While constructed soils offer a promising solution, they can be a potential source of contaminants. Additionally, attention needs to be paid to designing them in a way that helps mitigate the effects of possible urban contaminant inputs, such as non-exhaust emissions from traffic when used in roadside environments. Despite increasing interest, there is currently limited empirical evidence on the effectiveness of technosols in North American urban contexts as a tool to improve soil quality, while avoiding the potential for elevated contaminant inputs. To address current knowledge gaps, this study will conduct field experiments with four different prototypes of technosols constructed using local urban wastes from Quebec City, Canada, with a goal to assess optimal mixtures in support of healthy soils and trees growth at the local level. 

This study will be conducted on the Research Chair of Urban Trees and its Environment’s living open laboratory site at Université Laval. Four constructed technosols prototypes using brick and concrete, infested wood, and excavated soil, along with a control were developed using different ratios. A total of 32 pits (1m×1m×1.5m) were dug and the technosols treatments were randomly assigned to 4 different blocks (each block contains 2 replications of each treatment). The study site is bounded by two major roads on north and east side. To assay metal deposition in pits, 5 transects will be drawn towards roadsides. Road dust samples will be collected from the road (5m×1m) perpendicular to the transects. In order, to determine the horizontal distribution of road dust pollutants, composite soil samples (10 cm depth) will be collected along the transects at several distances (e.g. 10m, 20m, 30m, 40m) from the roadside. Furthermore, one composite soil sample (10 cm depth) will also be collected from each technosol pits. Water samples will be extracted from the bottom horizon of each technosol pit for analysis of metals leaching through in each prototype, and soil samples from the surface of the put will also be analyzed for metals and other contaminants. 

This study is anticipated to be an important scientific contribution in helping to elucidate which technosol types and constituents are best suited for increasing soil fertility and improving environmental health, while also helping to minimize contaminant inputs and metal leaching to groundwater. We also expect that repurposing city waste will be shown to be a viable and efficient long-term solution for smart urban planning.

Metabolite analysis can inform on biological status in health and disease. Approaches for high-content metabolomics are evolving for analysis of exogenous and endogenous metabolites in order to identify biomarkers of exposures or effects. In this regard, analysis of effect biomarkers can be valuable to elucidate toxicity mechanisms relevant to environmental exposure-related adverse health effects. There is paucity of research in terms of identifying endogenous metabolite profiles linked to environmental exposure-mediated adverse pregnancy outcomes. Here, we developed a method to analyse maternal plasma metabolite markers relevant to oxidative stress by a LC-Orbitrap-MS/MS detection methodology to subsequently identify associations between air pollution-related oxidative stress in pregnancy as linked to adverse birth outcomes. 

Third trimester maternal plasma samples were stabilized initially to avoid any artifacts arising during processing, followed by protein precipitation and clean-up using molecular weight cut-off filtration. Filtrates were evaporated under N₂, and reconstituted using the mobile phase A. Epinephrine, L-DOPA, Dopamine, p, m-, o-tyrosine, Cl-tyrosine, 3-nitro-tyrosine, Uric acid, GSH (Glutathione-reduced), GSSG (Glutathione oxidized), 8-OHdG (8-hydroxy-2' -deoxyguanosine) standards as well as commercially obtained plasma samples were used to develop and characterize the analytical performance of the LC-Orbitrap MS/MS methodology. Briefly, target analytes were separated on an Accucore Vanquish C18+ column (Thermo Fisher Scientific) using a solvent gradient comprising mobile phase A (water with 0.1% formic acid) and mobile phase B (90% acetonitrile in water with 0.1% formic acid). 

The optimized methodology required 500 µL of plasma, and parallel reaction monitoring (PRM) with analyte specific-collision energies to obtain intended MS2 fragments to boost the sensitivity of analysis. Run time of this method was 8 minutes. Retention times for the analytes, Epinephrine, L-DOPA, Dopamine, p,m,o-tyrosine, Cl-tyrosine, 3-nitro-tyrosine, uric acid, GSH, GSSG, 8-OHdG were 0.44, 0.52, 0.67, 0.77,0.99, 1.39, 1.84, 0.5, 0.49, 0.63,1.56 min, respectively. MS2 transitions for select metabolites analysed in this work were as follows, p-, m-, o-tyrosines 182.0812→136.0756; Cl-tyrosine: 216.0422→170.0367; 3-nitro-tyrosine 227.0662→181.0606 and 8-OHdG 284.0989→168.0516. Linear calibration curves were achieved for all analytes examined using this method (R² >0.96). 

The optimized methodology permitted simultaneous analysis of various key target metabolites in maternal plasma samples using a short run time, thus demonstrating the application of this approach towards clinical metabolomics.  

Key words: Mass spectrometry, Oxidative stress, Metabolomics, Maternal Plasma, Environmental exposure

Due to their attractive physicochemical properties, applications of manufactured nanomaterials (NMs) are evolving rapidly in fields including consumer products, environmental remediation, construction, and biomedicine. Enhanced use and production of NMs lead to increased potential for human exposure, thus necessitating toxicity testing for health risk analysis. Recent efforts to replace, reduce, refine the use of vertebrate animals for chemical toxicity testing have led to exploration of new approach methodologies to identify alternative platforms. In this work, we have examined the feasibility of using the Zebrafish (Danio rerio) embryo model as an alternative animal model for NM toxicity testing (e.g., nano SiO₂, TiO₂, ZnO) in combination with high-content proteomic analysis. 

Zebrafish embryos were exposed (dose range: 0-100 µg/mL, 24 wells) to NMs and proteomic changes at 2, 4 dpf stages were analyzed using a LC-orbitrap MS/MS methodology (run time -24h/sample). Phenotypic changes were recorded as well (e.g., morphology, hatching rate).  

Findings on proteomic changes in nanoparticle (NP) exposed fish embryos identified NP type, exposure dose- and developmental stage-related changes in up- and down-regulated proteins.   The responsive proteins implied mitochondrial protein changes (e,g., SOD2) relevant to oxidative stress and metabolism after NP exposures. Mechanistic pathways impacted by the NP exposures included cell surface interaction, cell signalling, protein processing, transcriptional regulation, apoptosis and metabolism (e.g. amino acid, lipid, glucose). 

Our findings suggest that the application of zebrafish embryo model in combination with high-content proteomic analysis for NM toxicity testing is promising and warrants further exploration.

Key words: Nanomaterials, Toxicity, Proteomics, Zebrafish embryos, Health risk analysis

Breast cancer has transcended geographical boundaries, emerging as the leading cause of mortality among women. Consequently, this study aims to develop PGMD (poly-glycerol-malic acid-dodecanedioic acid) nanoparticles for the efficient delivery of paclitaxel (PTX) in breast cancer treatment. Two polymer variants (DDA: malic acid; 7:3 and 6:4) were employed for the synthesis of PTX-encapsulated polymeric nanoparticles. The hydrodynamic sizes of the nanoparticles were determined to be 142.3 nm and 188 nm, with polydispersity indices of 0.174 and 0.367, and zeta potentials of -22 and -17.3 mV, respectively, for PTX NP 7:3 and 6:4. The drug encapsulation efficiency was found to be 64.5% for the 7:3 variant and 97% for the 6:4 variant. The percentage of cell migration was observed to be higher in MCF-7 cell lines compared to MDA-MB-231, as analysed using the scratch assay. The IC₅₀ value for MCF-7 was the lowest (9.40 ± 0.68) at 48h, while the value remained comparatively higher for MDA-MB-231 cell lines. Nuclear abnormalities and apoptotic behaviour of PTX-NPs treated cells were observed via AO/EtBr and DAPI staining assays, which were further analysed by the overexpression of caspase-9, confirmed through western blot analysis.

The Kanneliya Forest Reserve, a tropical lowland evergreen rainforest in Sri Lanka, comprises three distinct sites, each with unique microclimates, including a relatively undisturbed Natural Forest Reserve, an invasive allelopathic fern, Dicranopteris linnearis dominated forest edge, and a restoration site where Dicranopteris linnearis has been actively removed and native species reintroduced to support ecological recovery. Xylariales (phylum Ascomycota) are an ecologically significant and diverse fungal order, primarily functioning as saprophytes. This study aimed to assess the species diversity of Xylariales within the contrasting microhabitats of the Kanneliya Forest Reserve and to document previously unrecorded species through morphological and molecular characterization. Three new records of Xylariales were discovered in this study. Morphological characterization was conducted using both macroscopic and microscopic characteristics. Genomic DNA was extracted using a modified CTAB method. The ITS and β-tubulin gene regions were amplified using ITS5/4 and Bt2a/b primers, respectively. The identities of the three species were confirmed through a combined phylogenetic analysis using Maximum Likelihood (ML) analysis in the RAxML-HPC Blackbox 8.2.1.2 software via the CIPRES Science Gateway. The resulting phylogenetic tree comprised two major clades - Hypoxylaceae and Xylariaceae, and the three species clustered with well-supported bootstrap values. Daldinia eschscholtzii (Hypoxylaceae) was discovered on a fallen log within the Natural Forest Reserve. Hypoxylon hypomiltum (Hypoxylaceae) was isolated from a fallen branch within the Dicranopteris linnearis-invaded site. The presence of Hypoxylon hypomiltum in this environmentally challenging habitat suggests its resilience, likely attributed to its defensive secondary metabolites, such as Hypomiltin. Xylaria laevis (Xylariaceae) was found on a decayed branch in the drier restoration site of the forest. Its survival in these dry conditions could be attributed to its thick, carbonaceous stromata. These findings highlight site-dependent variations in Xylariales species diversity within Kanneliya’s varying microclimates and emphasize the ecological adaptations these fungi have developed for survival in distinct forest conditions.

Keywords: Ascomycota, Hypoxylaceae, Morphology, Molecular characterization, Phylogeny, Taxonomy, Xylariaceae, Xylariales

Funding: University of Kelaniya Research Grant - RP/03/02/01/01/2023

With the growing challenge of climate change, it has become imperative that more eco-friendly nature-based solutions be found. The role of wetlands and wetland complexes in this endeavour, with their host of ecosystem services, has come to the forefront in this regard with urban wetlands in particular being incorporated more and more into city planning. As this happens, the greater proximity to anthropogenic disturbances could possibly have an effect on the behaviours of the many species who inhabit them. The Little egret (Egretta garzetta) is one such species, a common wetland bird and a predator who plays a crucial ecological role in wetland ecosystems. As such, the objectives of this study were to assess how anthropogenic factors, including traffic density, proximity to road and noise levels, and the weather conditions typical of an urban wetland complex, specifically the temperature, relative humidity and cloud cover, would influence its foraging duration and foraging success. Foraging duration included both the time spent searching for prey and the time spent feeding on each prey item. Field sampling was carried out at the Kimbulawala wetland complex in Colombo, Sri Lanka, which encompasses canals, paddy fields and water ponds, bordered by a main road, from July 2024 to February 2025. The diurnal behaviours of the Little egret were recorded through focal sampling where each individual bird was observed for a period of 15 minutes. Sampling was carried out between 6.00 AM and 6.00 PM, and each sampling day was divided into slots of two hours each. Traffic and environmental factors including temperature, relative humidity and cloud cover were recorded through the use of standard equipment and methods. Generalized Linear Mixed Models (GLMMs) were used to analyse the associations between foraging duration and success with traffic density, noise and weather in R version 4.4.3. Across seventeen sampling days, 133 focal observations of Little egrets were made. 

It was found that the Little egrets spent most of their day foraging (57.22%). Little egrets showed greater foraging durations when they were in the areas nearer to the road (<100m) than in areas farther away within the wetland complex (GLMM; p <0.05). The foraging success of the Little egrets was found to decrease significantly as temperature and relative humidity increased (GLMM; p < 0.05). As such, the results indicated that traffic-related disturbances and the weather can significantly influence the foraging behaviour and success of birds in urban wetland complexes. The findings of this study emphasise the need for careful design and planning of urban wetlands to enhance their benefits for wetland bird communities. 

Keywords: Little egret, Urban wetland complexes, Foraging behaviour, Foraging success 

Trachypenaeus granulosus is among the prevailing species found in the northern coast of Sri Lanka, with a concentration in the Jaffna area. Although of commercial importance, extensive information regarding the reproductive biology and sustainable exploitation practices of the species is limited. Examining T. granulosus reproductive characteristics using samples gathered between January and December 2024 was the aim of this investigation. As part of this, 2,500 individuals were gathered from among them, 720 were selected at random for thorough reproductive examination. The findings showed that females display isometric growth with a growth coefficient (b) of 3.1388. Observations were made, confirming that the species has its whole life cycle in the north coast's water bodies and indicates a self-sustained native population. 50% of the population reached sexual maturity at 6.3 cm carapace length (CL), which is the estimated size of sexual maturity based on the minimum size of 15.4 mm CL. The spawning process occurred year-round, but mostly in the vicinity of the mouths of the lagoon and surrounding coastal water, with the spawning being the heaviest from the months of October and November. Nevertheless, the study showed T. granulosus is absorbing considerable pressure from the portions of the coast being fished, and this could affect its population and long-run viability. The information gives vital standard data for the reproductive biology of T. granulosus and indicates the necessity for additional research to determine the implications of pressure from the fishing activity. The information will help create well-informed management strategies to ensure the sustainable use and preservation of this essential resource for shrimp off the north coast of Sri Lanka.

Keywords: Northern coastal waters, Trachypenaeus granulosus, Reproductive biology, Shrimp fishery, Spawning.

Erythrina sp. is an important plant for more than thousand years in Jaffna since it has medicinal, cultural, ornamental and agricultural values. Decline in abundance of Erythrina population is a major problem in northern region of Sri Lanka. Thus, this study was designed to assess the abundance of Erythrina population and to identify the major challenges in Erythrina variegata (Mulmurungai -T) and Erythrina variegata var. variegata (Kalyanamurungai -T) cultivation. In addition to this, to select proper planting material and to compare healthy and infected plants were the objectives of the study.

Karaveddy, Nallur, Tellippalai, Uduvil, Chankanai, Chavakachcheri, Point Pedro, Karainagar, Velanai and Kayts Divisional Secretariats (DS) divisions in Jaffna District were selected to assess the abundance of Erythrina sp. Abundance of infected and healthy plants was assessed by using scoring method of qualitative study in 100 randomly selected areas in terms of homes or waste lands. High infection rate of both Erythrina sp. was observed in Point Pedro, Karaveddy and Tellippalai DS divisions while low infection rate was observed in Nallur and Kayts DS divisions. Length and width of leaf blade, leaf area, length of petiole and stem were reduced and leaves became irregular shape in infected plants of both varieties. However, thickness of infected leaf was higher than healthy leaf due to the infection. The amount of crude protein content of infected plant was 19.69+0.34 and it was 25.44+0.79 in healthy plant. There was a significant difference (p<0.05) observed in protein content of healthy leaf compared to the leaf protein in infected plant.

Karaveddy division was selected for the quantitative study and number of healthy and infected plants were counted in randomly selected areas. The decline of Erythrina variegata population was 85% and it was 70% in Erythrina variegata var. variegata population. This decline in Erythrina population was due to the gall formation in leaves, petioles and stems of Erythrina sp. Symptomatic leaves were tested for the causative organisms of bacteria, fungi, nematodes and insects by recommended techniques. Finally, this study confirmed the causative organism of gall formation was a wasp and it was Quadrastichus erythrinae. This was the first record of Quadrastichus erythrinae in Sri Lanka and the wasp was submitted to the Department of National Museum, Sri Lanka for giving information to other scientists. 

Re-cultivation of Erythrina is more important to maintain Erythrina population in Jaffna. Percentage of decline in Erythrina population was low in Erythrina variegata var. variegata than Erythrina variegata due to the higher resistance to the wasp compared to Erythrina variegata. One year old stem cuttings with 5cm diameter were selected as planting materials based on the higher growth rate compared to other planting materials used for the growth studies. This study recommends Erythrina variegata var. variegata for initiating re-cultivation of Erythrina sp. with one year old stem cuttings with 5cm diameter.

Key words: Abundance, Percentage of decline, Quadrastichus erythrinae, Re-cultivation and. Planting material

Acknowledgements: 

Dr. John La Salle- Entomologist, CSIRO, Australia 

Prof (Mrs) R. Gnaneswaran, Entomologist, University of Jaffna, Sri Lanka 

Abstract

Breast cancer is diagnosed as a malignancy worldwide, and its incidence has increased over the past decade. Iron nanoparticles have gained significant interest in biomedical research owing to their non-toxic nature in biological systems. We developed a novel α-Fe₂O₃/CeO₂/g-C₃N₄ nanocomposite using the efficient hydrothermal method and characterized for their structural, optical, morphological, elemental composition, and anti-cancerous properties. The nanocomposite was evaluated for its in-vitro cytotoxicity properties against MDA-MB-231 cell lines, which is an aggressive Triple-negative breast cancer cell line (TNBC). The crystalline size and the band gap values of the synthesized nanocomposite were determined as 19.84 nm and 2.39 eV, respectively, using XRD and UV analysis. Furthermore, elemental composition analyses such as EDAX and XPS have depicted the presence of the elements that confirmed nanocomposite formation. The results from the MTT assay against MDA-MB-231 cancer cell lines of the synthesized nanocomposites emphasize the dose-dependent reduction in cell viability. The percentage of viability was calculated as 75.75 at the concentration of 150 μg/mL. These results indicate that the synthesized α-Fe₂O₃/CeO₂/g-C₃N₄ nanocomposite is a potential therapeutic agent for anti-cancer research.

Abroma augusta, an important medicinal plant species found in tropical Asia, traditionally used to treat various fatal diseases like diabetes, menstrual irregularities. Understanding the responses of this plant under water stress bears significant implication in its’ management. 

Therefore, this study aimed to evaluate the effects of salicylic acid (0.5mM) and plant growth promoting rhizobacteria (Bacillus altitudinis) and their combined treatment as a means of improving resilience of Abroma augusta seedlings under water stress (30% field capacity). Salicylic acid (SA) was applied through foliar spraying in the leaf parts of the seedlings, while Bacillus altitudinis was inoculated as a cell suspension in the rooting zone at 7 days interval. 

The results revealed that application of SA and inoculation of Bacillus altitudinis significantly (P<0.05) reduced the impact of water stress on plant growth parameters and physio-biochemical activities. Both SA and PGPR enhanced stomatal conductance (110-195%), pigments (29-47%) and membrane stability (31-49%) whereas the increased accumulation of oxidative stress markers (malondialdehyde, hydrogen peroxide) and secondary metabolites (total phenolics and flavonoids) was observed to decrease (45-83%) in both treatments. Non-structural carbohydrates, leaf proline content and total protein were also increased. Antioxidant enzyme activities (peroxidase, ascorbate peroxidase, catalase) were positively regulated by 21% to 67%, contributing to the reduction of oxidative stress, whereas Glutathione S-transferase activity decreased in PGPR and combined treated plants but increased under SA treatment during water stress. However, SA-treated seedlings showed the highest drought resilience, while the combined treatment of SA and Bacillus altitudinis showed no significant impact compared to the individual treatment, which may be due to antagonistic interactions. 

These findings suggest that Salicylic acid and Bacillus altitudinis independently can be a promising strategy for alleviating the drought stress in Abroma augusta. Further study should investigate the molecular mechanisms behind these effects, particularly the influence of salicylic acid and Bacillus altitudinis in regulating the expression of specific stress-responsive genes to enhance the resilience of Abroma augusta under stress conditions.

Groundwater hardness is a major concern in many rural areas, particularly where water is used for drinking and household purposes. Although Reverse Osmosis (RO) is a popular method for reducing hardness, it has some drawbacks such as high energy use, water wastage, and removal of essential minerals. This study aims to explore a more sustainable and cost-effective alternative using agricultural waste-based biosorbents, especially lemon peel, to reduce hardness in groundwater.

The groundwater sample was collected from a dug well at the University of Vavuniya, Sri Lanka, and had an initial total hardness of 650±20 mg/L, measured using EDTA titration. In the preliminary phase, five fruit peel biosorbents—beetroot, lemon, orange, banana, and pomegranate—were tested in both raw and phosphoric acid-treated forms at a fixed dose of 2 g/L, contact time of 360 minutes, and shaking speed of 200 rpm. Lemon peel showed the highest performance and was selected for further study.

Batch adsorption experiments were conducted using varying biosorbent doses (0.1–10 g/L) and contact times (15–1440 minutes) under the same experimental conditions. The results showed that phosphoric acid-treated lemon peel had an optimum dose of 5 g/L and achieved a 67% reduction in total hardness at a contact time of 300 minutes. The adsorption data best fit the Freundlich isotherm (R² = 0.78) and the pseudo-second-order kinetic model (R² = 0.88), suggesting a chemisorption mechanism.

Further column studies supported these findings, with breakthrough curves closely matching the Yoon-Nelson and Thomas models across flow rates of 0.33, 3, and 6 mL/min. The R² values were close to 1, although slightly lower at higher flow rates. Activation with phosphoric acid significantly improved the performance of all biosorbents tested.

This research highlights the potential of lemon peel as an effective and eco-friendly biosorbent for reducing groundwater hardness. It presents a simple, low-cost solution suitable for rural communities while promoting the waste-to-resource concept through the reuse of fruit peels.

Keywords: Groundwater hardness, lemon peel, biosorbent, adsorption, column, modelling

This study presents the preparation and characterization of Mg-doped biochar-bentonite nanocomposites derived from cassava peel biochar for multifunctional environmental remediation. The nanocomposites were synthesized by incorporating bentonite clay at different loadings (10%, 30%, and 50% by weight) and doping biochar with Mg to enhance adsorption and catalytic properties. FT-IR spectroscopy confirmed the successful binding of bentonite with biochar and Mg doping (peak at 519 cm⁻¹), while UV-Visible spectroscopy revealed structural modifications and electronic transitions. Adsorption studies showed that Mg-BC/C-50 exhibited the highest adsorption capacity for Cr³⁺ (85.07%) and Cu²⁺ (98.13%), with desorption efficiencies of 1.218% and 1.006%, respectively, due to enhanced surface area and cation exchange properties. For dye adsorption, Mg-BC/C-10 demonstrated superior performance, with methylene blue achieving 80.58% adsorption efficiency (1.41% desorption) and methyl orange reaching 89.09% adsorption efficiency (1.11% desorption), suggesting optimal interaction with Mg-doped nanocomposites. The adsorption behavior followed Langmuir isotherm models for Cr³⁺ and dyes, while Cu²⁺ adsorption aligned with the Freundlich isotherm for Mg-doped BC/C samples. The effects of pH, contact time, and concentration were investigated, revealing that metals and methyl orange were most effectively adsorbed at acidic pH, whereas methylene blue favoured basic conditions, and adsorption equilibrium was reached at 120 minutes, indicating site saturation, and adsorption increased with concentration but at a decreasing rate. Cationic ion exchange studies showed that Mg-BC/C-50 had the highest exchange capacity (308.13 meq/100 mg). Water retention studies indicated that Mg-BC/C-50 retained the most water across different samples (90.47%, 83.83%, and 82.33% for distilled water, saline water, and wastewater, respectively). Super-hydrophobicity studies on cotton fabric coatings using these biochars revealed that Mg-BC/C-10 achieved the highest water contact angle (165.60°) and the contact angle decreases with increasing clay content. Acid-base neutralization studies confirmed that the Mg-BC/C-50 exhibited the highest neutralization capacity. Catalytic degradation of organic pollutants over 4 hours demonstrated that the superior efficiency for Mg-BC/C-50 (52.3% for methylene blue, 45.5% for phenol). All these findings highlight that Mg-doped biochar-bentonite nanocomposites, particularly with higher bentonite content, offer cost-effective and sustainable solutions for environmental remediation.

Water pollution from heavy metals and synthetic dyes poses a significant threat to both environmental and human health. In response to this challenge, a multifunctional nanocomposite was developed using activated carbon (AC) derived from Eucalyptus camaldulensis bark and doped with Ni/Cr layered double hydroxides (LDH) via phosphoric acid activation followed by co-precipitation with Ni²⁺ and Cr³⁺ ions. The resulting AC/NiCr-LDH composite was characterized using Fourier Transform Infrared Spectroscopy (FT-IR), UV-Visible absorption spectroscopy, and point of zero charge (pHpzc) analysis to confirm successful incorporation of LDH and evaluate structural properties. The performance of the prepared composite in adsorbing Pb²⁺, Cd²⁺, methyl orange (MO), and methylene blue (MB) from aqueous solutions was assessed through batch adsorption studies, considering varying pH levels, contact times, and pollutant concentrations. Among the synthesized materials, the 15% LDH-doped variant (AC/NiCr-3) exhibited superior adsorption efficiencies: 93.53% for Pb²⁺, 92.15% for Cd²⁺, 99.25% for MO, and 98.66% for MB. The adsorption behavior followed the Langmuir isotherm model, indicating monolayer adsorption on uniform sites. This high efficiency is attributed to the enhanced surface area and abundance of functional groups, which facilitate electrostatic interactions and chemical bonding with pollutants. Beyond adsorption, AC/NiCr-3 demonstrated a high-water retention capacity of 396.2%, suggesting its potential for soil moisture conservation in agricultural applications. It also showed remarkable catalytic activity in Fenton-like reactions, achieving 93.09% phenol degradation due to the redox cycling between Ni²⁺/Ni³⁺ and Cr³⁺/Cr⁶⁺. Additionally, when applied to fabric surfaces and modified with stearic acid, the composite imparted superhydrophobicity, with AC/NiCr-3 achieving nearly complete water repellency. These findings underscore the composite’s versatility as an eco-friendly, low-cost material suitable for wastewater treatment, agricultural enhancement, and surface hydrophobic modification. Its origin from biomass and its multifunctionality make it a promising candidate for sustainable environmental remediation technologies.

Deepfake is fake visual and audio content created using Artificial Intelligence (AI). The objectives for identifying deepfake face detection are personal safety, societal trust, technological advancement, and ethical accountability. The deepfake detection has been an area of intense discussion in recent years and numerous researchers have put-up various approaches to identify real and fake content. Highlighted are recent developments in analysis approaches, especially the application of deep learning methods like convolutional neural networks (CNNs).  In this paper, Vision Transformer (ViT) technology is proposed to detect fake and real face images. Transfer learning techniques from pre-trained models such as VGG16, ResNet50, Xception, and DenseNet121 are employed to compare the proposed model. All models are evaluated on two benchmark datasets, namely the 140K Real and Fake Faces dataset and the Deepfake and real images dataset. Both datasets were divided into training, validation, and testing sets. The data normalization technique was aimed to scale the pixel values of the different images to a certain range and after the normalization process, the images were subjected to data augmentation by flipping horizontally. The proposed ViT model attained accuracies over the two datasets, namely the 140K Real and Fake Faces dataset and the Deepfake and real images dataset, which are 99.84% and 93.07% respectively. Adam optimizer was employed along with the learning rate of 0.0001 to update the model parameters iteratively during training, and the binary cross-entropy loss function was used to minimize the identification error. Based on the experimental results and the analysis process, it can be concluded that all models achieved good accuracy for testing using the 140K Real and Fake Faces dataset better than the Deepfake and real images dataset. Overall, when comparing pre-trained models, the proposed ViT model was the best performance methodology for deepfake face detection using both datasets.

Keywords- Deep learning, Deepfake, face detection, Pre-trained models, Vision Transformer

Slow-release fertilizer hydrogels (SRFH) hold promise for sustainable agriculture by minimizing nutrient leaching and improving fertilizer efficiency. Their ability to retain water and gradually release nutrients fosters healthier crop growth while reducing environmental pollution and resource waste. However their use in agriculture is still limited.

This research involved synthesizing SRFH as a polymer bead, comprising sodium alginate (SA), carboxymethylcellulose (CMC), and Urea as a nitrogen source, Tri sodium phosphate as a phosphorus source, K₂O as a potassium nutrient source, and ZnO and MgO as micronutrient components. The study also included synthesizing metal oxide nanoparticles, integrating them into the polymer matrix, and characterizing the final product bead. Micronutrient oxide nanoparticles (ZnO, MgO) were synthesized through the surfactant assisted technique and confirmed by powder X-ray diffraction (PXRD) analysis and Fourier transform infrared spectroscopy (FTIR). PXRD results reveal that the average diameters of MgO and ZnO were 68.8nm and 23.52nm respectively. In the FTIR analysis of both nanoparticle samples contain broad peaks around 3400 cm^-1 attributed to -OH stretching, and the peaks of 1554 cm^-1and 1559 cm^-1 for C=O vibrations. The metal-oxygen stretching/bending vibrations peaks for MgO and ZnO are at 423 and 429 cm^-1 respectively. The FTIR was used to characterize the nutrient-incorporated SRF beads along with the raw materials. All samples exhibit several peaks. A broad peak in the range of 3200-3600 cm⁻¹ corresponds to -OH stretching vibrations. The peak around 1600 cm⁻¹ is attributed to C=O stretching vibrations. A sharp peak near 1410 cm⁻¹ is characteristic of the asymmetric and symmetric stretching of carboxylate ions (COO⁻). The peak at approximately 1030 cm⁻¹ is associated with C-O stretching vibrations. And, the peak around 600 cm⁻¹ corresponds to the P-O bond of trisodium phosphate. There is also a peak around 1500 cm⁻¹, which indicates the bending and stretching vibrations of N-H groups found in urea. The nutrient content in SRFB was assessed through the acid digestions technique and analyzed through atomic absorption spectroscopy (AAS) and it confirmed the presence of total K, Mg, and Zn is 3.259, 0.293, and 0.121 w/w%, respectively in synthesized SRFH. Scanning electron microscopy (SEM) was used to observe the surface morphology and cross-linking nature of the synthesized SRFH beads and micronutrient oxides. The swelling ratio of SRFH beads was studied and compared to raw material.

Mung beans were used to assess slow-release fertilizer (SRFH) effects on growth and flower development through measurements of height, seed pod count, and flower number at weekly intervals. “ANOVA” analysis indicated significant differences among samples. This study details the synthesis of SRF beads composed of sodium alginate (SA), carboxymethylcellulose (CMC), and NPK, enriched with magnesium (Mg) and zinc (Zn).

Key words : Hydrogel , slow release, nanoparticles , sodium alginate , carboxymethylcellulose

Sustainability and chemical education intersect meaningfully within first-year undergraduate laboratories at the University of Toronto at Scarborough (UTSC), where students are introduced to essential principles that shape their understanding of chemistry. This presentation highlights the development and integration of three innovative, green chemistry-based experiments into the first-year chemistry curriculum at the University of Toronto Scarborough (UTSC). Designed to align with the Twelve Principles of Green Chemistry and the United Nations Sustainable Development Goals, these experiments prioritize waste reduction, chemical safety, and environmental consciousness while reinforcing critical chemical concepts. The experiments include Molar Mass Determination by Freezing Point Depression, Temperature Effects on Maillard Reaction Kinetics, and Quantitative Analysis of Food Dyes Using Absorbance Spectroscopy, all of which are developed to be both pedagogically effective and environmentally responsible. Additionally, life cycle assessment (LCA) frameworks were incorporated to promote systems thinking and long-term sustainability considerations. New lab safety training questions were also introduced to enhance safety awareness and foster a lasting culture of safe laboratory practices among first-year students. This research highlights the importance of integrating sustainable practices into chemical education, enabling students to become scientifically literate and environmentally responsible contributors to the field.

Keywords: Green Chemistry, First-Year Laboratory, Sustainable Education, Undergraduate Chemistry, Lab Safety

The use of agrochemicals in agriculture is widespread globally, as it enables increased crop yields.¹ However, they also contain heavy metals such as copper and nickel, which can leach into drinking water.^2-4 Copper and nickel, while both needed for plant and human growth & development, can be toxic in excess.^3-4 For example, excess copper and nickel have been linked to Alzheimer’s disease and cancer, respectively, in humans.^3-4 As such, it is imperative that they are removed from contaminated drinking water. One way to achieve this is through adsorption. This method is commonly researched as it is cost effective, has a high removal capacity and is easy to implement.⁵ Many adsorbents can be used, but biosorbents have been commonly utilized recently as they offer more environmentally friendly synthesis methods, higher efficiency, and can be degraded easily after use.⁵ These are important characteristics as it is crucial that we ensure high efficiency, and that we do not pollute the environment further by using toxic chemicals during synthesis, or through the adsorbent persisting in the environment long after use.⁵ One example of an adsorbent are hydrogels.⁶ When made with natural materials such as sodium alginate, these are biosorbents.⁶ Upon crosslinking, a sodium alginate hydrogel (SA-H) is made.⁶ To further enhance the function and stability of these hydrogels, pomegranate peel, which contains many bioactive compounds such as lignin, is used.⁷ This increases the stability of the hydrogel by increasing the number of crosslinks, and its function by increasing the hydrogel’s roughness and porosity.^7-9 Previously, it was demonstrated that sodium alginate-pomegranate peel hydrogels (SA-PP-H) can remove organic pollutants such as safranin-O and algal blooms from contaminated water.^8-9 However, its ability to remove heavy metals such as copper and nickel has not been tested. As such, this study aimed to synthesize SA-PP-H, characterize it via FTIR, SEM, diameter tests and water uptake capacity tests, and finally test its ability to remove copper and nickel from contaminated water. The heavy metal analyses tests included the effect contact time for both copper and nickel to test how long the adsorbent would take to reach its equilibrium adsorption with each metal. Additionally, the effect of pH and adsorbent amount was conducted for copper to test the optimal pH and amount of hydrogel. The results were promising, as the FTIR analysis confirmed that the chemical composition was correct, and SEM revealed that incorporating organic pomegranate peel enhanced the roughness and porosity of the gels. Furthermore, gels with pomegranate peel incorporation were able to absorb 1.58 times more water than sodium alginate-only gels. Moreover, the effects of contact time, pH, and adsorbent amount showed that for copper, the optimal contact time was 60 minutes, the optimal pH was ~5, and increasing the adsorbent amount decreased adsorption. In contrast, the optimal contact time for nickel was 5 minutes. Overall, these hydrogels demonstrate a cost-effective, eco-friendly way to remediate copper and nickel from contaminated drinking water, ultimately aiding in preventing their long-term health effects on humans.

Chemistry laboratories play a crucial role in chemical education by providing students with hands-on learning opportunities. These experiences help students develop essential skills such as creativity and problem-solving. This study examines how laboratory lectures can enhance students' learning experiences in the first-year chemistry laboratory. It emphasizes the importance of fostering positive relationships between students and instructors, which have been shown to enhance student engagement, reduce anxiety, and improve overall academic satisfaction. A significant concern for students in chemistry is "chemophobia," which stems from previous negative experiences, stereotypes, and the perception that chemistry is overly abstract and complex. This anxiety can hinder performance and decrease motivation, highlighting the need for targeted interventions to support student learning. This study emphasizes the importance of self-efficacy—students' beliefs in their abilities—as a key factor influencing their attitudes toward laboratory work. Higher levels of self-efficacy are linked to lower anxiety levels and better performance outcomes. To address these issues, lab lectures were introduced into an introductory chemistry course to explain concepts and laboratory techniques. This presentation will share preliminary data collected from surveys and learning analytics involving first-year chemistry students. It will focus on their perceptions of laboratory lectures and their performance on lab assessments. The impact of lab lectures on enhancing students' self-efficacy and alleviating anxiety will also be discussed. Ultimately, these findings contribute to a better understanding of how targeted interventions can enrich the educational experience in chemistry laboratories.

Keywords: Lab Lectures, Self-Efficacy, Chemophobia, Student Engagement, Hands-On Learning 

The search for exoplanets the planets orbiting stars beyond our solar system remains a cornerstone of modern astrophysics. While millions of other celestial bodies have been cataloged, only just over 5,900 exoplanets have been confirmed to date. Over 95% of these have been discovered through geometry-dependent methods, primarily the transit technique, which detects periodic dips in stellar brightness as planets cross their host stars. This approach requires precise orbital alignment, with planetary orbits oriented nearly along the observer’s line of sight, leaving many non-transiting exoplanets undetected. Although significant progress has been made by missions such as Kepler and TESS, the relatively small number of confirmed exoplanets highlights the need for novel detection methods that do not depend on orbital geometry. A novel, geometry-independent detection technique in this study, based on tidally induced stellar pulsations, small, periodic variations in stellar brightness driven by the gravitational influence of orbiting planets. High-precision photometric time-series data from TESS were analyzed for five confirmed exoplanetary systems. HD 202772 A b, HD 2685 b, TOI-150 b, TOI-172 b, and HIP 67522 b selected for their short-period Jovian planets hosted by well-characterized variable stars. Harmonic frequency analysis using the Pyriod package identified pulsation patterns closely synchronized with planetary orbital periods, exhibiting an exponential decay in amplitude across harmonic frequencies. These recurring features across different systems suggest that tidally induced oscillations can serve as consistent indicators of planetary presence. This method significantly broadens the potential for exoplanet discovery, enabling detection in systems previously inaccessible due to geometric constraints.

Keywords: Exoplanet detection, Transit, TESS, Kepler, Pyriod, Harmonic frequency analysis.

This paper examines the harmonic-measure distribution functions of the exterior of a wedge domain, along with a deleted ray, at three different locations of the basepoint. The concept of harmonic-measure distribution functions, also known as -functions was initiated by Walden and Ward in 1995 after being motivated by Ken Stephenson’s -function. From 1995 till now, significant progress has been made on the study of  -functions for simply connected planar domains. In this paper, our focus is on the -functions of the exterior of a wedge domain along with a deleted ray on the positive real axis, with different locations of the basepoint. Also, we compare the behaviour of the -function of this domain with the exterior of the wedge domain. Consider the domain  where  is a positive real number, and the angle  To evaluate the -function, we first transform the domain  onto the halfplane. For this transformation, we start with the conformal map  to transform the double-ray domain. Then, we use the Mbius map  to transform the double-ray domain onto the complement of the ray, which entirely lies on the negative real axis. Finally, we use the square-root transformation to map this current region onto the halfplane. Here, we fix the basepoint on the real axis in the domain  We consider three cases: Fix the basepoint exactly middle of the interval  fix the basepoint closer to the origin; fix the basepoint closer to the right-hand ray. Now, to compute the -function formula, we find the subset  in the domain  and the image of in the halfplane. Finally, in the halfplane, we evaluate the angle of sight, which is the angle subtended at the image of  by the image of the basepoint in the halfplane. The -function is given by the normalised angle of sight in the halfplane.  From the -function formulas, we investigate their asymptotic behaviour as  decreases  Interestingly, for the second location of our basepoint  we obtain a similar asymptotic behaviour as obtained for the exterior of the wedge domain.

Y_3-xMg₂AlSi₂O₁₂:xSm³⁺ (x = 0, 0.5, 1, 2, 3, 4 and 5) garnet phosphors were prepared by sol-gel combustion method. The prepared garnet phosphor demonstrated with structural studies, Rietveld refinement, photoluminescence, morphological studies, elements compositional studies, elementary mapping, colour coordinates and colour purity. The XRD results were confirmed that the prepared sample was nearly single-phase garnet materials. Emission and excitation wavelengths of the materials were obtained with optimum values. The excitation was recorded 603 nm for samarium-rich garnet phosphor by exciting at 403 nm for Sm³⁺ rich phosphor. SEM was studied the topography and morphology with grain size information. The findings suggest that Sm³⁺ rich Y₃Mg₂AlSi₂O₁₂ garnet phosphors exhibit significant uses in near-UV stimulated optical devices.

The sustainable upgrading of low-solubility phosphate rock through green chemistry approaches is essential for improving fertilizer use efficiency while reducing environmental impacts. Despite the abundance of Eppawala Rock Phosphate (ERP) as a local resource, its low solubility severely restricts its agricultural use. Traditional chemical treatments for solubilizing phosphate are often unsustainable and inefficient, highlighting the need for greener alternatives. This study addresses this challenge by investigating the mechanochemical modification of ERP using oxalic acid (OA) at varying molar ratios to enhance phosphorus (P) solubility and release. Characterizations were done via XRD, FTIR, SEM, and EDX.

The total phosphorus content of ERP was 551.06 ± 5.39 mg/g, with X-ray diffraction (XRD) revealing the dominant crystalline phases as hydroxyapatite, chlorapatite, and fluorapatite, along with minor hematite and quartz impurities. Upon oxalic acid treatment, particularly under mechanochemical conditions, significant structural transformations were observed. New XRD peaks corresponding to whewellite (CaC₂O₄·H₂O) and monetite (CaHPO₄) indicated partial apatite dissolution and the formation of secondary calcium phases. Fourier-transform infrared spectroscopy (FTIR) confirmed phosphate and oxalate complex formation, evidenced by shifts in PO₄³⁻ and COO⁻ vibrational bands. Notably, a ~70 cm⁻¹ shift in PO₄³⁻ stretching and 14 cm⁻¹ in OH⁻ stretching frequencies reflected alterations in bonding environments due to oxalic acid interaction. Scanning electron microscopy (SEM) revealed distinct morphological differences between unmodified and modified ERP. The untreated ore consisted of angular, dense, irregularly shaped particles with high crystallinity and friability. However, modified samples exhibited well-defined surface crusts composed of calcium oxalate crystals, primarily rhomboid and bipyramidal in shape, indicating significant mineral reorganization. SEM images of the A_ERP-0.6:1 sample visually confirmed the hexagonal apatite particles.  Energy-dispersive X-ray spectroscopy (EDS) confirmed the presence of C, O, Al, Si, P, Cl, Ca, and Fe, aligning with the formation of calcium oxalate and phosphate complexes. Oxalic acid at lower molar ratios (OA_ERP-0.2:1) initiated apatite transformation with moderate whewellite and monetite formation, whereas higher ratios (OA_ERP-0.6:1 and above) significantly reduced apatite intensity and promoted surface encrustation. A control sample without grinding exhibited residual oxalic acid and minimal phase change, underscoring the necessity of mechanochemical energy to drive solid-state reactions. Water dissolution experiments demonstrated significantly improved phosphorus availability from oxalic acid-modified ERP. At 144 hours, OA-ERP (1.2:1) released 367.29 mg/g of phosphorus, equivalent to 99.9% of its total P, compared to triple superphosphate (TSP), which released only 82.91% of its 807.35 mg/g P content. This enhanced release is attributed to the initial dissolution of CaHPO₄, confirmed by XRD.

These findings support an ion exchange dissolution mechanism, where oxalate anions facilitate apatite breakdown and sustained phosphate release. This work clarifies the mechanistic role of organic acids in phosphate mineral transformation and highlights oxalic acid as a sustainable and effective agent for improving the agronomic efficiency of rock phosphate fertilizers.

Keywords: Rock phosphate, phosphorus fertilizer, oxalic acid, sustainability, Mechanochemical activation

With rapid global industrialization, environmental pollution has surged, adversely affecting human health. Water contamination caused by toxic organic dyes and microbial pathogens presents one of the most critical challenges, especially in industrial wastewater. Therefore, developing efficient materials for simultaneous dye degradation and antimicrobial action is vital for ensuring safe water. Zinc oxide (ZnO) nanoparticles are well-known photocatalysts, but their practical application is limited due to their wide band gap. To increase photocatalytic efficiency, this band gap must be reduced.  Additionally, rapid recombination of photogenerated electron-hole pairs reduces catalytic efficiency. Even though the electron-hole pairs generate, they have a higher tendency to recombine. This is a limitation to increase photocatalytic efficiency. There is a clear need for advanced material that overcomes these limitations while offering added antimicrobial functionality. This study aims to synthesize and evaluate Ni-doped ZnO nanoparticles incorporated onto graphene oxide (GO) nanosheets and fabricated into a cellulose acetate (CA) electrospun membrane for enhanced photocatalytic and antimicrobial performance.

Ni-doped ZnO nanoparticles were synthesized using the sol-gel method, where Ni doping effectively reduced the ZnO band gap by introducing intermediate energy states, thus enhancing visible light activity. By introducing impurities such as Ni to the ZnO crystal, the higher band gaps reduced by introducing energy levels between the conduction and valence bands of ZnO. This band gap reduction has increased the photocatalytic efficiency of ZnO significantly. When considering dye degradation, the excited free electrons on the conduction band due to the visible light irradiation, interact with oxygen to form super oxide radicals and positively charged holes form hydroxy radicals from water. These reactive oxygen species have the potential to degrade the methylene blue dye molecules into CO₂ and H₂O. GO was prepared from high-purity natural graphite using the Modified Hummer’s method. The incorporation of Ni_ZnO onto GO was carried out using microwave-assisted synthesis to improve charge separation and reduce recombination. The synergistic effect between GO and Ni_ZnO facilitates the transfer of excited electrons from Ni_ZnO to the π-conjugated system of GO, enhancing reactive oxygen species generation. The final nanocomposite was integrated into a cellulose acetate matrix via electrospinning to form a flexible, multifunctional membrane. Characterization was performed using PXRD, FTIR, Raman spectroscopy, SEM, EDX, and AAS.

 Photocatalytic activity was tested using Methylene Blue dye under sunlight and dark conditions. Under sunlight, the Ni_ZnO/GO_CA membrane achieved 98.28% dye degradation within 12 minutes, confirming its high efficiency. Antimicrobial activity was assessed using well diffusion and disk diffusion methods against fungi and both Gram-positive and Gram-negative bacteria. Inhibition zones ranged from 9.17±0.29 mm to 10.33±0.28 mm, with the Ni_ZnO_GO composite demonstrating the highest antimicrobial effectiveness. The Ni_ZnO/GO_CA membrane exhibits excellent photocatalytic and antimicrobial performance, making it a promising candidate for applications in water purification systems, including industrial wastewater treatment and protective filtration materials. These results contribute to sustainable environmental remediation strategies and support the development of multifunctional materials for public health protection.

Key words: Photocatalyst, Water purification, Antibacterial action, Electrospinning, Synergistic activity, Graphene

The growing threat of microbial diseases and antimicrobial resistance identify the urgent need for sustainable high-performance biomaterials with enhanced therapeutic activities. Addressing this requirement, the present investigation introduces a novel multifunctional electrospun polymeric membrane, developed by merging a trimetallic nanohybrid system comprising silver (Ag), copper (Cu), and nickel (Ni) with bioactive curcuminoids derived from turmeric oleoresin. The primary aim of this study was to develop and characterize a biodegradable, membrane with potent antimicrobial, antioxidant, and anti-inflammatory properties for biomedical applications.

These nanohybrids were embedded into a biodegradable polycaprolactone (PCL) electrospun matrix to create an eco-conscious, biocompatible material suitable for biomedical applications. The resulting nanohybrids and composite membranes were studied using FTIR, Raman spectroscopy, XRD, SEM and UV-Vis diffuse reflectance spectroscopy, showing that the nanomaterials were correctly inserted and dispersed. The average fiber diameters of electrospun PCL only mat and trimetallic nanohybrid + curcuminoids incorporated PCL mat are 288 ± 8 nm and 217 ± 7 nm respectively. This decrement is due to the enhancement of the conductivity of the solution when guest molecules were introduced to the electrospinning solution. As the solution contains more electric charges, it causes to overcome the surface tension of the solution and can be easily thinned and stretched the nanofibers.

 Natural bioactive compounds like turmeric oleoresin and bioactive curcuminoids have been showing high antioxidant activities, scavenging free radicals efficiently and inhibiting oxidative stress. Turmeric oil, produced in the processing of curcumin, exerts potent antioxidant and antimutagenic activity. So, the resultant trimetallic and curcuminoids incorporated PCL mat had a high antimicrobial activity against a broad spectrum of microbes including different bacterial and fungal pathogens, suggesting synergy between the trimetallic components and natural bioactive curcuminoids. The synergistic antimicrobial effect of the curcuminoid coupled trimetallic nanohybrid was demonstrated by the inhibition zones from 19.00 ± 1.00 to 24.67  ± 0.24 mm against several strains of bacteria and fungi. Metal-curcumin complexes increase solubility of curcumin and its cellular uptake, and this enhances not only the antimicrobial activity, but also the anti-inflammatory and antioxidant effects thus demonstrating enhanced bioavailability. Antimicrobial activity is primarily associated with good radical scavenging activity (RSA), suggesting high oxidative stress protection. Anti-inflammatory activity was evaluated qualitatively using egg albumin denaturation inhibition assay, demonstrating strong inhibition at higher concentrations. This multi functionality is especially important in wound care, where preventing infection and reducing inflammation are crucial for effective healing. The membranes produced by electrospinning are not harmful, break down naturally and are thought of as environmentally friendly as part of global initiatives in green nanotechnology. Polymeric metal-turmeric oleoresin and metal-curcuminoid complexes offer enhanced stability, bioavailability, and multifunctionality, making them valuable in various applications. Based on the research, trimetallic-curcuminoid-loaded PCL membranes look promising for wound healing and infection prevention, giving us a practical and environmentally friendly solution to the challenges in biomedicine today.

Key words: Antimicrobial, Anti-inflammatory, Electrospun membranes, Metal- turmeric oleoresin, Metal-curcuminoids, Synergistic activity

Bio-derived carbon quantum dots (CQDs) - To address critical challenges impacting women's health and well-being.

Dr. Shraddha Pande^1*

1 Department of Physics L.A.D. & Smt R.P. College for Women,Nagpur Maharashtra 440010,India

*Correspondence: [email protected]

Abstract: 

This research paper explores the preparation of bio-derived Carbon Quantum Dots(CQDs) by using citric acid, plant extracts, biomass, amino acids, carbohydrates and the transformative potential of sustainable, bio-derived as a novel platform to address critical and often underserved challenges. This paper aims to transform women's health and well-being through the use of sustainable, bio-derived carbon quantum dots (CQDs ) synthesized specific natural sources includes coffee grounds, tea, fruit peels ,egg shells, bamboo etc. CQDs are cutting-edge Nano materials with exceptional biocompatibility as fluorescence, and regenerative properties. Bio-derived CQDs can be used in bio medical applications, bio-imaging, drug delivery, targeted diagnostics, clean water technologies particularly for underserved communities. CQDs based therapeutic interventions designed to address the particular health issues faced by women. This project aims to develop cost-effective and environmentally friendly methods such as hydrothermal treatment, ultrasonic treatment, thermal breakdown, pyrolysis, carbonization, microwave synthesis, and solvo-thermal approaches for synthesizing CQDs with tailored properties. This research focused on a sustainable and potentially low-cost alternative to traditional materials used in healthcare and environmental applications. While focusing specifically on women's health and well-being, this project addresses a critical gap in research and development, aiming to create solutions that are directly relevant and accessible to women. This study is grounded in the principles of green technology, Nano-technology, and individual’s health. The systematic synthesis of CQDs can be done by incorporating environmentally sustainable synthesis methods such as pyrolysis, microwave assisted, hydrothermal, electrochemical method, laser ablation method etc. The scope of the study will encompass laboratory-scale synthesis and characterization, followed by proof-of-concept studies for selected applications relevant to women's health and well-being. This research is poised to make significant contributions by providing a sustainable and accessible platform for developing technologies that directly address critical needs in women's welfare. The expected outputs include novel methods for CQD synthesis, characterization data, and prototypes for diagnostic or therapeutic applications, and insights into the efficacy and safety of bio-derived CQDs for these purposes.Various analytical techniques will be used to understand the fundamental characteristics of CQDs such as XRD, XPS, SEM, TEM, PL, FTIR UV Spectroscopy, RAMAN Spectroscopy etc. This research aims to develop affordable diagnostics, targeted drug delivery systems, and clean water technologies, especially for underserved communities. Led by women, the research highlights the power of diverse perspectives in science and seeks to platforms for scalability empowering women worldwide and contributing to healthier, more equitable societies. This proposal has the potential to greatly advance scientific frontiers, encourage strategic partnerships, and provide sustainable solution in women health. 

Key words: Bio derived CQDs, Biomedical Applications, Women health

Energy storage is essential for advancing renewable energy, with supercapacitors set to play a significant role in sectors such as automotive, electronics, and industrial applications. By reducing reliance on fossil fuel-powered generators, supercapacitors support cleaner energy alternatives and lower carbon emissions. Graphene oxide (GO) and reduced graphene oxide (rGO) are valued for their exceptional properties, which are further enhanced when combined with metal oxides (MOs). Current research explores the potential of rGO and binary metal oxide/rGO (BMO/rGO) composites in supercapacitor applications. Techniques including X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Raman spectroscopy, Scanning Electron Microscopy (SEM), and Energy Dispersive X-ray Analysis (EDAX) were used to investigate the crystal structure, vibrational characteristics, morphology, and elemental composition of the synthesized material. XRD confirmed the monoclinic crystal structure of CoMoO₄ nanoparticles and their composites, while SEM revealed their unique morphologies. Electrochemical analysis indicated that incorporating rGO into BMO significantly enhanced electrochemical performance, achieving the highest specific capacitance at 10 mV/s and strong cyclic stability at 10 A/g during Galvanostatic Charge/Discharge (GCD) testing. These surfactant-assisted cobalt molybdate composites with carbon-based materials exhibit promising electrochemical properties, positioning them as strong candidates for energy storage technologies. Overall, the synthesized electrode materials provide an effective pathway to reducing fossil fuel dependence and advancing renewable energy systems, establishing them as key components in future sustainable energy strategies.

Keywords: CoMoO₄ Nps, Carbonaceous Nanocomposites, Hydrothermal method, Electrode material, Supercapacitor.

A Model-Based Approach to Explaining Quintessence in an Expanding Universe

W.G.J.N. Weerasinghe^*, K.D.W.J. Katugampola

Department of Mathematics, Faculty of Science, University of Kelaniya, Sri Lanka

[email protected]^*

Cosmology is the scientific study of the universe, combining astronomy, physics, philosophy, and mathematics. It explores the birth, evolution as well as the ultimate fate of the universe. Einstein’s introduction of the cosmological constant in his field equations, initially to maintain the concept of a static universe, was eventually rejected with the discovery of the expanding universe by Edwin Hubble in 1929. The discovery of the accelerating expansion in 1998 influenced the idea of dark energy as the power prompting this acceleration.

The quest for understanding the constituents of the universe has resulted in remarkable findings, unveiling that a considerable share of its energy density remains unidentified. Addressing this mystery, scientists presented the cosmological constant to explain the accelerating expansion of the universe. On the other hand, quintessence - a brand-new concept, was introduced by Robert R. Caldwell, Rahul Dave, and Paul J. Steinhardt in a 1998 paper proposing a dynamic behavior of the universe. Compared to the cosmological constant, quintessence model allows changes over time and space. This research intends to explore the quintessence property of the universe through a cosmological model that reflects its evolving nature.

In this study, we analyze a formerly introduced solution to Einstein’s field equations under the Robertson–Walker metric, with the intention of modeling the quintessence property of the universe. We establish a particular form for the scale factor, and determine the parameters  and  using defined boundary conditions and physical assumptions. The resulting values,  and , enable us to plot the universe's expansion across time.

This model shows an iterative cycle of deceleration, acceleration, and deceleration, reflecting the expected behavior of quintessence. This result encourages the idea that dark energy perhaps not  constant but possibly changes over time. Our findings suggest that this model offers valuable insights into how quintessence might shape the past and future of the universe.

Key words: Einstein’s field equations, Cosmological constant, Robertson–Walker metric, Quintessence.

This study explores the possibility of learning and teaching digital marketing through a participatory action research (PAR) approach in the form of a student-led digital transformation project for Super Tea, a traditional micro, small, and medium enterprise (MSME) with limited market outreach, brand exposure, and opportunities for growth as it was not digitally integrated. The project suggested to overcome the challenges through introducing low-cost digital solutions with the assistance of a collaborative iterative process through marketing specialization students of the University of Jaffna. The PAR framework enabled students to work closely with the business owner through planning cycles, action cycles, and reflection cycles. Activities included an initial needs analysis, customer surveys, and creating the most important digital assets such as Google Maps registration, and formal business profiles on Instagram, WhatsApp Business, Facebook, and LinkedIn. Students also produced promotional material such as branded posts and short videos, and taught the owner basic digital marketing and social media management skills. As a result, Super Tea gained more online visibility, customer inquiry response, and social media interaction, with the number of likes, sharing, and comments indicating their stronger brand interaction. The business owner also indicated initial unawareness of digital tools but evolved to acquire the ability to post messages and respond to customers on their own. The process also generated enhanced intellectual literacy and marketing capability for the owner and staff. This PAR shows how affordable, phased digital strategies can help resource-poor MSMEs to improve market presence without compromising their niche identity. It highlights the importance of integrating functional digital training, student participation, and infrastructure facilitation to build small businesses in rural or remote areas. Originality of the project lies in its educational worth and practical application of digital tools, which reflects a sustainable model for teaching and implementation of digital marketing in real-life MSME contexts.

Keywords: Digital marketing, Digital transformation, Participatory action research (PAR), Practical learning, Rural business development, Student-led projects

Dominating contemporary challenges make farmers stuck in the middle and compelled them to strive for capitalizing market opportunities. Thus, this study aims to reveal the problems and prospects of agricultural marketing. Qualitative research paradigm was done using in-depth thematic analysis through semi-structured interviews with farmers, intermediaries, and agriculture authorities revealed the challenges, opportunities, and current marketing strategies of agricultural sector in Sri Lanka. Findings of this study serves as a tool for farmers to identify and manipulate challenges through the available opportunities and current marketing strategies. Findings revealed that inadequate infrastructure and warehousing facilities, leading to delays, spoilage, and financial losses; limited access to reliable market information hampers decision-making, while price volatility negatively impacts profitability and stability within the agricultural supply chain; inefficiencies in the supply chain contribute to increased costs and product spoilage. However, opportunities exist in the form of digital marketing platforms, cooperative marketing structures, and government intervention and support improve bargaining power and reduce costs for farmers. This study supports farmers to stabilize their standard of living by capitalizing the opportunities with internal possibilities, also contribute to policy making, agricultural trading, and national economy.

The bread production industry is vital in global food security, where maintaining consistent quality is crucial for customer satisfaction and brand reputation. This study presents an automated quality assessment system for post-baked white bread utilizing the Isolation Forest algorithm for anomaly detection. The system integrates hardware components, including an IR sensor, Arduino-controlled conveyor, Wi-Fi-enabled camera, and servo-controlled separator, alongside a Python-based GUI for real-time monitoring of critical parameters, including weight, texture, shape, and temperature. Implementing a machine learning model trained on less than 500 images, the system achieved 90% accuracy in defect detection with a processing capacity of 40 bread units per hour and cycle times under 90 seconds per unit. Operating at an optimal conveyor speed of 150-170 RPM, the system demonstrated effective anomaly detection and separation capabilities, though the separator mechanism's efficiency remained at 60%. The research validates the effectiveness of automated quality control in commercial bakery operations while identifying opportunities for enhancement through expanded datasets and algorithmic refinements. This approach not only streamlines quality control processes and optimizes resource utilization but also contributes to improved food safety standards and operational efficiency, offering a scalable solution for industrial bakeries with potential applications extending to broader food industry quality control processes.

Keywords— Anomaly Detection, Bread Quality Control, Isolation Forest Algorithm, Machine Learning, Real-time Monitoring

This study investigates how Industry 4.0 (I4.0) technologies act as catalysts for social innovation in Sri Lanka’s apparel industry—a sector critical to national exports yet challenged by limited technological adaptation and persistent social inequality. While I4.0 has been widely adopted for operational efficiency, its potential to enable socially innovative practices such as inclusive value creation, workforce transformation, and responsible sourcing in emerging economies remains insufficiently explored. This review addresses the core research problem: How and through what mechanisms can Industry 4.0 technologies foster social innovation in the apparel industry of a Global South context like Sri Lanka? The study aims to bridge the empirical and conceptual gap at this intersection by synthesizing existing literature into a structured model. Using the PRISMA methodology, a systematic review of 38 peer-reviewed journal articles published between 2012 and 2024 was conducted, sourced from Scopus, Web of Science, Emerald Insight, and ScienceDirect. Search terms included “Industry 4.0,” “social innovation,” “Sri Lanka,” “apparel sector,” and “knowledge transfer.” Thematic analysis was used to identify emerging patterns linking I4.0 adoption with social innovation drivers and enablers in the apparel value chain. The study proposes the DISI (Digital Innovation for Social Impact) Model, which identifies three pathways through which I4.0 technologies enable social innovation in Sri Lanka’s apparel industry: Lean–I4.0 integration for organizational learning and continuous improvement, Knowledge spillovers and ethical compliance stimulated by global buyer influence, and, Digital workforce empowerment through skill-building initiatives and inclusive digital leadership. These pathways illustrate how I4.0 is not only a technological upgrade but also a strategic enabler for building human-centered, sustainable, and collaborative innovation ecosystems. This review excludes grey literature and unpublished industrial data, which may contain complementary insights. Additionally, empirical studies specific to Sri Lanka are limited, indicating the need for future qualitative and mixed-method research to validate and refine the proposed model. The findings inform policy actors, industry leaders, and global buyers on how to align digital transformation strategies with inclusive development goals. Emphasis is placed on enhancing institutional capacity, strengthening inter-organizational knowledge flows, and designing upskilling frameworks to ensure equitable participation in the digital economy.

This research project aims to develop a reliable system for detecting and classifying plant diseases in Sri Lanka, with a focus on enhancing accuracy without relying on invasive or stressful methods. Such an approach can significantly benefit the agricultural sector by offering a robust and practical solution to a persistent problem. Plant diseases are among the major challenges affecting the sustainability and profitability of Sri Lankan agriculture, often leading to substantial reductions in crop yields and severely impacting farmers' livelihoods. For this study, we utilized the "PlantifyDr Dataset," an augmented dataset containing images of 10 plant species. However, to ensure relevance to Sri Lankan agriculture, we curated a new dataset by selecting seven commonly grown crops—bell pepper, cherry, corn, grape, potato, strawberry, and tomato. The resulting dataset includes 97,741 images across 27 classes. These were divided into training (70%), validation (10%), and testing (20%) sets. We implemented a Convolutional Neural Network (CNN) approach and employed several pre-trained models, including ResNet50, EfficientNet B0, VGG16, and DenseNet121, to analyze and compare performance. Additionally, we incorporated a Vision Transformer (ViT), achieving a notable accuracy of 99.37%. Model training was performed using the Adam optimizer with a learning rate of 0.0001, and the categorical cross-entropy loss function was used to minimize classification errors. Performance was evaluated using standard metrics: accuracy, precision, recall, and F1-score. A threshold value of 0.8 was set to identify unknown plant species. The results validate the effectiveness of the proposed methodology through rigorous experimentation and performance evaluation. Overall, this study addresses the critical challenge of plant disease detection and offers valuable tools and insights to support farmers in accurately identifying crops and associated diseases. The proposed system empowers farmers to make informed decisions, reduce crop losses, and ultimately enhance their income.

Keywords - Deep learning, Plant disease, Pre-trained models, Sri Lankan agricultural plants, Vision Transformer

Background: Hyperthermia, including heat stroke, is a critical threat for millions of athletes in summer. It is a leading cause of preventable deaths and ER visits in college and high school sports. To assess heat risk and adjust athletic activities, international, national and state athletic associations have adopted the Wet Bulb Globe Temperature (WBGT) in their safety policies. WBGT quantifies the heat stress in direct sunlight by measuring solar radiation, temperature, humidity, and wind speed. These heat safety policies, unfortunately, are not used often in schools because WBGT meters are expensive (>$500ea). Thus, several mathematical models have been developed to estimate WBGT with meteorological parameters. However, some of the parameters, such as solar radiation, are not commonly available for athletes and coaches. 

Objective: This work proposes a method to estimate a heat alert level from a set of meteorological parameters readily available for athletes and coaches. It employs machine learning (ML) in compliance with a major heat safety policy. This work also implements the proposed ML method in mobile and wearable IoT devices to continuously track the current alert level during an athletic activity and issue early warnings for precautions and activity adjustment.  

Related work: Existing ML-based work for heat risk estimation predict WBGT from solar radiation data, periodic time-series data, and personal, real-time physiological data such as heart rate. The proposed method is different from them in that it does not rely on solar radiation, time series, nor personal physiological data. 

Methodology: This work uses the decision tree (DT) and random forest (RF) algorithms to build supervised classifiers, which take a set of meteorological parameters as an input: temperature (F), relative humidity (%), cloud cover (%), precipitation (inch), wind speed (knot), and time of day. The classification output is one of the heat alert levels that are defined by Grundstein et al. and adopted by the National Federation of State High School Associations. The proposed classifiers are integrated with battery-operated RP2040 and ESP32 microcontrollers to periodically download meteorological parameters from online weather services and update the current alert level through deep sleep cycles. The parameters and alert level are shown on a batteryless e-ink display. When the alert level is in the “high risk” or “extreme” condition, a 100dB audible alarm is raised with a speaker.  

Results: The proposed classifiers are trained with 46,429 meteorological data samples in the summer of 2024, which were collected from 290 cities in the US with an online database of the National Oceanic and Atmospheric Administration (NOAA). After hyperparameter adjustment, the DT and RF classifiers yield 91% and 99% classification accuracy in five-fold stratified cross-validation. The proposed IoT devices have been empirically tested in tennis practice and matches. By saving their power consumption effectively, the devices can run over three hours, which is long enough for a practice and match. Their audible alarm is highly noticeable even in intensive practice and matches.  

Keywords: Hyperthermia, heat safety, wet bulb globe temperature (WBGT), multiclass data classification, Internet of things  

In an era where cyber threats are growing in complexity, speed, and scale, organizations face constant challenges in protecting their digital assets. Traditional cybersecurity methods—reliant on static rules and manual intervention—are increasingly inadequate against sophisticated, rapidly evolving attacks. As a result, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as game-changing technologies in the cybersecurity domain. These intelligent systems are capable of analyzing vast datasets, detecting anomalies in real time, predicting potential breaches, and automating incident responses. This research investigates the transformative role of AI and ML in enhancing organizational cybersecurity and shaping the broader cybersecurity industry. It explores how organizations are leveraging AI/ML to identify vulnerabilities, reduce response times, and improve threat detection accuracy.

However, while these technologies offer significant advantages, they are not without limitations. AI systems can be susceptible to adversarial attacks, false positives, and algorithmic bias, raising concerns about reliability and ethical use. Moreover, cybercriminals are also beginning to exploit AI to develop more evasive and adaptive threats, creating a double-edged sword scenario. Through an in-depth literature review, real-world case studies, and expert insights, this paper highlights the opportunities and risks associated with AI and ML in cybersecurity. It concludes by offering strategic recommendations for organizations seeking to implement these technologies effectively, emphasizing the need for continuous monitoring, ethical governance, and investment in skilled human oversight. Ultimately, this study underscores that while AI and ML are not silver bullets, they represent essential components in building resilient, future-proof cybersecurity frameworks.

Keywords: Artificial Intelligence, Machine Learning, cybercriminals, cybersecurity

Smart Cyber Defence for Windows Environments is an advanced malware detection and analysis system designed to identify and mitigate cyber threats in real-time. As cyber-attacks grow in complexity, traditional defenses often fail to detect sophisticated threats. This project addresses these challenges through a dual-layered approach: Machine Learning-based Detection of executable files and Static Analysis via the VirusTotal API. The system aims to provide a reliable, scalable, and user-friendly platform that swiftly analyzes threats and delivers actionable insights to enhance endpoint security in Windows environments.

The Machine Learning-based Detection module focuses on analyzing Portable Executable (PE) headers of Windows executable files (.exe and .dll). It uses feature extraction techniques to capture key attributes from PE headers, which form the basis for training a Random Forest Classifier. This classifier, known for its robustness and accuracy, is used for binary classification tasks. Through careful feature selection based on importance scores, the system identifies the most significant attributes, leading to efficient training and prediction. This results in a model that distinguishes between benign and malicious files with 99% accuracy, validated by precision, recall, and F1-score metrics during extensive testing on balanced datasets.

The Static Analysis module enables quick scanning of files, URLs, and hashes using the VirusTotal API. By submitting artifacts to the API, the system retrieves threat intelligence from a global database of malware signatures. This process allows for the rapid identification of known threats without executing files, minimizing infection risks. The module supports various file types and provides detailed reports including detection ratios, scan dates, and findings from different antivirus engines. These insights enable users to make swift cybersecurity decisions based on accurate threat data.

Built with Flask, Scikit-Learn, Pandas, NumPy, Matplotlib, and pefile, the architecture supports seamless integration and interactive user experiences. The web-based interface allows users to upload files, view real-time detection results, and access comprehensive intelligence data. Visual aids such as workflow diagrams and performance graphs help simplify the malware analysis process, making it accessible to cybersecurity professionals and general users alike.

Future enhancements include Dynamic Analysis for observing real-time malware behavior, support for more file types, improved UI for richer visualizations, and integration of sandbox environments to detect zero-day threats and polymorphic malware.

In conclusion, Smart Cyber Defence for Windows Environments offers a scalable, accurate, and user-friendly solution through a hybrid of static scanning and machine learning. Its architecture is optimized for accuracy, speed, and accessibility, serving as a powerful tool in strengthening endpoint security within Windows-based infrastructures. This report explores the system’s design, implementation, evaluation, and future improvements in the context of modern cybersecurity needs.

The boundaries of traditional architectural planning models have been brought to light by the increasing complexity of contemporary software systems and dynamic nature, which outlines the need for real -time adaptability.  This research investigates the empirical impact of real-time architectural decision-making on software project success, focusing on five critical decision domains: data acquisition and processing, event detection and immediate response, resource allocation and scheduling, feedback-driven learning, and proactive problem resolution. Despite the fact that real -time decision making is included in large -scale theoretical and technical literature, the diverse industry references still lack empirical evidence supporting its direct impact on project results.

Using a based quantitative approach, this study collected records via surveys from 122 software specialists inclusive of architects, developers, and assignment managers across domain names together with IoT, healthcare, and e-trade. The responses had been analysed the usage of Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess relationships between specific choice sorts and key venture achievement metrics: timely and value-effective shipping, superb outputs, and adaptableness of answers.

 The results reveal that three decision types (real-time data acquisition, event detection and response, and proactive problem resolution) significantly and positively influence project success. Effective data acquisition ensures that systems have timely, accurate inputs to support immediate decision-making. Event detection mechanisms enhance resilience by enabling systems to identify and respond to anomalies swiftly, thus preventing disruptions. Proactive problem resolution, supported by predictive analytics, emerged as a particularly strong contributor to project continuity and stakeholder satisfaction by reducing downtime and preventing cascading failures.

Conversely, resource allocation and scheduling and feedback-driven learning, while theoretically important, did not demonstrate statistically important effects in the current study. These findings can be attributed to the challenges of survey methods, or their real -time effects using their variable relevance in various project types. Nevertheless, the theoretical value of these regions remains stronger, which suggests the need for further refining in measurement equipment and application strategies.

The study contributes several contributions: it provides a valid empirical framework that combines specific real-time decisions to the results of the project, bridging theoretical-practical differences in architectural adaptability, and provides actionable recommendations. For physicians, the results emphasize the importance of embedding advanced mechanisms for real-time data processing, event-powered architecture and future diagnosis in software design and development workflows. Additionally, the study suggests that the way to learn loops to strengthen resource management and improve their average impact.

While the cross-sectional design of the study limits the boundaries of the causes, and its dependence on self-reported data introduces potential prejudices, the conclusions still establish a fundamental empirical basis for future research. Extending this task through longitudinal studies and mixed-method approaches can increase our understanding of how to operate the best for real-time decision making in software engineering.

This research underscores the transformative role of real-time architectural decision-making in contemporary software development. By identifying and validating the most impactful decision types, it provides both strategic guidance and a scalable framework for enhancing software project success in complex, adaptive environments.

Virgin coconut poonac (VCP) is the residue left from the extraction of a virgin coconut oil. VCP was found to contain 22.75% protein, 1800 kcal/kg of energy and 2.89% fat. The effect of replacement of expensive protein sources with phytase supplemented VCP as an alternative protein source on the performance and carcass quality of broiler birds was investigated in the study. Cobb-500 (n=50) chicks were randomly assigned to five dietary treatments (T) in a completely randomized design. Maize, coconut poonac and soybean meal based control diet (T1) and four test diets were prepared with VCP at 10% (T2), 15% (T3), 20% (T4), 25% (T5), by replacing normal coconut poonac. Feed intake was recorded daily and live weights of birds were recorded once in every five days. Percentage of carcass recovery, major meat cuts, organ to carcass ratio, feed conversion ratio (FCR), broiler performance index (BPI), and broiler efficiency index (BEI) were calculated. SAS 9.2 and SPSS 16.0 were used to analyze data statistically. This study revealed that there is no significant difference (p>0.05) between Body weights among birds of different treatments. But, all the inclusion rates of virgin coconut poonac improved the growth performance and carcass yield of broiler chicken compared to control. BPI of    T1, T2, T3, T4 and T5 are recorded as 1.44, 13.92, 21.02, 17.74, and 17.09 respectively. According to those values, T3 was the most effective in improving growth performance of broiler chicken. This study was concluded that replacing normal coconut poonac with phytase supplemented virgin coconut poonac has beneficial effects on the performance and carcass quality of broiler chicken.

WalkSafe: A Web and mobile-based system to support Visually Impaired Individuals walk safely within a premises

S. Sivaskaran
Systems Engineer, Faculty of Science, University of Jaffna, Sri Lanka
S.Thevaranchany
Senior Lecturer, Faculty of Management Studies, University of Jaffna, Sri Lanka

Abstract

Last 3 decades there are numresous affected by war and loss their vision and other injuries. There are  several numbers of affected students selected to the university of Jaffna. Visually impaired students at the University of Jaffna face significant challenges in independently navigating the campus, accessing lecture halls, exam rooms, and essential facilities like libraries and health centers. This reliance on others not only limits their independence but also affects their confidence and ability to fully participate in university life. Therefore, the WalkSafe project seeks to address these issues by developing an accessible, cost-effective system that provides real-time navigation, location-based alerts, and academic tracking for visually impaired students, empowering them to navigate the campus safely and independently.

The scope of the WalkSafe project encompasses a mobile and web-based solution designed specifically for visually impaired students at the University of Jaffna. The system includes essential features such as real-time voice-guided navigation, location detection using GPS and Bluetooth beacons, emergency alerts, and notifications for class schedules, exam venues, and important university updates. Additionally, the system offers automatic attendance tracking and academic progress monitoring, allowing students to access critical academic information independently. This comprehensive solution objectives is to foster an inclusive and supportive campus environment.

The project was developed using an agile methodology to allow for iterative testing and adaptation to user feedback. The mobile application was built on Android using Android Studio with Java, integrating Google Maps API for navigation and Android’s TalkBack for screen reader support. The web-based administration portal was developed with HTML5, CSS3, JavaScript, and Laravel (PHP) to provide an accessible, responsive interface for administrators to manage schedules, announcements, and attendance records. MongoDB and MySQL were used as the primary databases to store user profiles, schedules, and academic records. The system architecture employs a client-server model to ensure secure, centralized data management and scalability.

The core functionalities of WalkSafe have been implemented, including real-time navigation, attendance tracking, and schedule notifications. It  involves refining the user interface based on feedback from visually impaired students, improving system reliability and response times, and conducting further testing to ensure accessibility and performance.  These refinements has been completed, the WalkSafe system  be ready for deployment, offering visually impaired students a safer, more independent and self confidence for their educational activities at the university premises.

Keywords: Visually Impaired, Assistive Technology, Campus Navigation, Real-Time Navigation, Bluetooth Beacons, GPS Tracking,  Mobile Application,   Location-Based Alerts

As our world becomes more digitalized, cyber criminals have an increasing landscape to launch their attacks. Developments in Artificial Intelligence are being used both to attack and defend networks, therefore, what is the next step for cybersecurity companies when it comes to beating these criminals? A study was conducted that utilizes previous literature sources written on the topic of Artificial Intelligence (AI) in the cybersecurity industry. In addition, the insights of professionals in the industry today are included through a survey and interviews to dive into the details of this battle and what lays in its future. The purpose of this study is to educate the public on the current role of AI in the industry, as it is a relatively new advancement that is slowly becoming mainstreamed. Preliminary research has shown that Artificial Intelligence may be the key to defeating these criminals, but there is much to discuss in terms of the use of this technology by cyber attackers, how it is being implemented into defense mechanisms, and the issues in the industry that may prevent this technology from growing as fast as it could be. Results from this research show that AI has a positive impact on the cybersecurity industry, but there are challenges with its implementation that have prevented it from becoming truly mainstreamed. However, as cyberattacks continue to evolve, AI will be key to winning the battle in this technology arms race.

The study focuses on the challenge of detecting manipulation in digital images, a problem that has expanded with the increase of image-based information posted online. As manipulation techniques improve with increasing complexity, from basic to AI-supported forgery, the possibility of misinformation and deception has surged to unprecedented heights. Conventional detection methods are proving progressively inadequate, demonstrating the need to develop a robust solution. The research proposes a hybrid model that integrates selected statistical, structural image features, and deep learning methods to enhance the robustness and precision of verification of the authenticity of the digital image. A systematic literature review is conducted to identify the most discriminative features, using defined search strings and research questions evaluated through a ranking methodology to ensure optimal performance. Based on the systematic review, several methods such as metadata analysis, pixel-level statistical analysis, noise analysis, error level analysis, edge and border detection have been identified as potential components for image manipulation detection due to their computational feasibility and effectiveness. The method involves normalizing inputs and extracting features through Convolutional Neural Networks (CNNs). Then these features are used to build individual detection models for different manipulation types. To incorporate additional strength in the framework to combat the increasing amount of AI-manipulated content on the internet, an AI-generated content detection model has been added that is trained specifically to detect features of synthetic image generation methods. The utilized data sets are made up of original and manipulated images. Features extracted from various models, including compression-based, statistical, structural, and AI-generated content detectors, are fused and input to the hybrid system. The evaluation of individual models is carried out by using traditional performance metrics such as accuracy, precision, recall, and F1-score. Preliminary results indicate that individual models have accuracy rates above 91%, and precision ranges from 82% to 97%, recall from 87% to 96%, and F1 scores from 89% to 92% for authentic and manipulated image content. The AI manipulation detection module demonstrates promising 84% accuracy with a precision of 90% in the detection of AI-generated images and a recall of 76%, especially when it comes to images produced by well-known existing generative models. These results confirm the value of each model to the overall detection framework. In summary, the study demonstrates that the integration of pixel-level forensic methods, statistical analysis, and deep learning-based approaches, as well as AI manipulation detection, leads to notable gains in detecting tampered images. Future work will involve creating metadata and pixel-level analysis models and systematically integrating all individual models into a comprehensive hybrid system. Keywords: Digital image authenticity detection, Image forensics, hybrid model, CNN, AI-generated content, digital image manipulation, forensic features, Pixel-level inconsistencies, Noise analysis, Error level analysis, Edge and boundary detection, Metadata analysis, Feature fusion, Hybrid classification framework.

This study develops a music recommendation system based on user preferences, emphasizing genre classification and playlist generation. Using the Tamil Songs dataset, we extracted Mel-Frequency Cepstral Coefficients (MFCCs) from audio files, which were then reduced in dimensionality using Kernel Principal Component Analysis (KPCA) to improve computational efficiency. Random Forest model was trained on this reduced dataset, and their performance was optimized through RandomizedSearchCV. The optimized Random Forest model achieved a 58% accuracy. Our findings demonstrate that this approach not only enhances classification accuracy but also significantly reduces training time, validating the effectiveness of KPCA and hyper parameter optimization in music genre classification and recommendation systems.

Keywords: KPCA,  Random Forest, Machine Learning, RandomizedSearchCV.

In recent years, the simultaneous extraction of multiple attributes from speech signals has garnered significant interest in the speech processing community. This study introduces a unified multi-task learning (MTL) framework for the joint classification of speaker identity, gender, and emotional state using the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). The dataset comprises 24 speakers and 8 emotional categories, making it well-suited for evaluating multi-attribute learning. Mel-frequency cepstral coefficients (MFCCs), a widely adopted and effective feature representation in speech recognition, are used to generate spectrograms that serve as model inputs. The proposed architecture consists of shared convolutional layers for extracting hierarchical and task-invariant features, followed by task-specific fully connected layers tailored for speaker identification, gender classification, and emotion recognition. This multi-task structure enables the model to leverage shared representations while learning specialized features for each task, improving overall performance. Experimental evaluations reveal that the MTL framework consistently outperforms equivalent single-task models in terms of classification accuracy and training efficiency, especially in data-constrained environments. The model achieves accuracies of 99.79% for gender classification, 98.37% for speaker identification, and 87.78% for emotion recognition. These findings underscore the advantages of multi-task learning in capturing cross-task dependencies and improving generalization. Moreover, the study suggests that incorporating advanced data augmentation techniques may further enhance model robustness and adaptability. In conclusion, the proposed MTL approach offers a scalable and efficient solution for complex speech-based systems. By jointly learning multiple speech characteristics within a single model, this work contributes to the development of robust applications in speech-driven human-computer interaction, affective computing, and intelligent audio analytics.

Keywords: Multi-Task Learning (MTL), Speech Signal Processing, Speaker Identification, Emotion Recognition, Gender Classification, RAVDESS, MFCC Spectrogram

The world is gaining understanding regarding the importance of mental health, with increasing awareness and acceptance of mental disorders. Although records indicate that one out of eight people suffer from mental illness in Sri Lanka, only 40% are noted to be receiving treatment. This gap is due to cultural and behavioural patterns, alongside a lack of resources such as psychiatrists, therapists, and facilities. The primary aim of this, titled Enhancing Mental Health Care with Sinhala Voice-Based Chatbot, is to develop an intelligent chatbot capable of providing mental health support to Sinhala-speaking individuals seeking mental health support. This explores the development and implementation of a Sinhala language-based chatbot designed to provide mental health support through an intelligent, interactive platform. Titled “Enhancing Mental Health Care with Sinhala Voice-Based Chatbot” this combines natural language processing (NLP) and machine learning to create a chatbot that comprehends and responds accurately to Sinhala-language queries, making mental health resources more accessible to Sinhala-speaking individuals. The Bi-LSTM model, trained on preprocessed mental health queries and enhanced with GloVe embeddings, achieved 95.54% accuracy in intent recognition, while the Retrieval-Augmented Generation (RAG) system ensured reliable responses for complex queries by retrieving information from trusted sources. The chatbot supports voice and text inputs, utilizing the Web Speech API for speech-to-text conversion and Google Text-to-Speech (gTTS) for Sinhala audio outputs, improving accessibility for users with varying literacy levels. A confidence-based response system ensures reliability, with the RAG model activating when confidence falls below 90%. Another core feature of the chatbot is its ability to interact through both text and speech, enhancing accessibility for users with varying preferences or literacy levels. Conclusively, this provides a comprehensive view of the chatbot’s development, addressing its background, technical architecture, and real-world applicability in delivering mental health support.

Recent advancements in technologies such as vehicle edge computing (VEC) and 5G have significantly contributed to the expansion of vehicle-to-everything (V2X) systems in recent years.  The rapidly developing domain of VEC offloads computationally intensive activities to an on-premises VEC server, thereby enabling real-time applications for vehicles. In this paper, we introduce a combined model of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to implement malicious activity detection in the Internet of Vehicles (IoV). We transformed spatial feature maps into sequential data, enabling the LSTM to capture long-range dependencies and contextual relationships across spatial regions. The LSTM layer, consisting of 64 hidden units and a dropout rate of 0.5 to prevent overfitting, processed this sequential data to enhance classification performance. On observing our testing accuracy, we found that the combined CNN-LSTM model demonstrates improved accuracy compared to the conventional CNN and ResNet architectures. This highlights the effectiveness of incorporating temporal feature dependencies for malicious activity detection in the IoV. 

Sri Lanka's agricultural sector faces significant challenges in maintaining sustainable and productive practices, largely due to the complexity of soil management. This research investigates the potential of machine learning techniques to optimize soil management in Sri Lankan agriculture, particularly focusing on the Mannar and Batticaloa districts. Specifically, it develops predictive models using Support Vector Machines (SVM) for fertilizer requirements, crop suitability, weather forecasting, and soil property analysis. For data preprocessing, both primary and secondary data were used, with primary data collected from field visits in each district. The dataset was divided into 80% for training and 20% for testing. The fertilizer prediction model achieved 100% accuracy by analyzing key nutrients such as Nitrogen (N), Potassium (K), and Phosphorus (P), optimizing fertilizer use while reducing environmental impact. The crop prediction model, trained on environmental data, reached a 97% accuracy in identifying suitable crops. Weather forecasting models, using historical data from Sri Lankan regions, demonstrated significant promise in predicting rainfall, with an accuracy of 97.66%. Additionally, Support Vector Regression (SVR) was used to predict soil properties, focusing on pH, electronic conductivity (EC), and organic matter. The study also proposes a novel hybrid algorithm to further enhance soil property prediction by combining classification and regression tasks. These results highlight the efficacy of machine learning in providing data-driven insights, which can improve agricultural practices, increase crop yields, and promote sustainable farming in Sri Lanka.

    Abstract - Traditional network architectures face challenges in regard to scalability, flexibility, and dynamic control due to a tight coupling between the data and control plane. Software Defined Networking (SDN) eases these issues due to separate data and control planes, allowing a centralized, programmable control mechanism. However, dependent on the default reactive strategy used to install flow rules, delaying flow rules in SDN can add excessive latency and is inefficient in terms of network resource utilization.

This paper proposes an adaptive flow rule placement mechanism that combines proactive and reactive approaches in order to improve a performance. The system proactively installs the necessary forwarding rules in the scheduled Access Point (AP). We classify flows into delay-sensitive, loss-sensitive, and best-effort flows, using DSCP values of incoming packets. Each set of flow types is mapped to a queue with guaranteed priority and handling. The proactive approach thoughtfully places rules ahead of time, when possible, to APs expected to be part of that communication path, which reduces latency for delay-sensitive flows and upholds expected drop performance. Reactive rule installations are a backed-up mechanism that account for any unclassified flows which can be dealt with dynamically.

The solution proposed is entirely standalone at the network layer. Therefore, we automated the capabilities of the OpenFlow protocol via the Ryu controller and used Mininet to simulate the topology.

Index Terms –Software Defined Networking, Flow Rule Placement, Proactive Flow Installation, Quality of Service (QoS), Delay-Sensitive Traffic, Loss-Sensitive Traffic, Ryu Controller, Mininet, OpenFlow, Network Layer, DSCP.

Background: Cultural events are significant to a community, as they provide an opportunity to promote heritage, and diversity, and strengthen community cohesion. While they offer attendees a memorable experience, they tend to generate serious environmental impacts, such as large amounts of waste, high energy consumption, and transportation-related CO2 emissions. Unlike large-scale festivals, small and midsize non-profit festivals often don't practice sustainability initiatives, due to limited budgets, staff, and awareness. The Japan Festival Boston was also one of them since its start in 2012, where no sustainability efforts such as waste separation have been conducted.

Objective: To address one of these challenges, we developed “RecycleMeter,” an innovative IoT device that visualizes visitors’ waste sorting contributions in real time. RecycleMeter promotes recycling through proper waste separation, incentivizes environmentally-conscious disposal behavior, and arms organizers with actionable data to refine waste management strategies. By combining technology with education, the project raises sustainability awareness, reduces landfill waste, and embodies the Japanese cultural philosophy of “mottainai,” emphasizing resource mindfulness.

Methodology: To develop this smart waste tracking system, we assembled a load cell kit and added a Python code to allow users to select the waste category and automatically calculate the estimated number of collected chopsticks and the reduction in CO2 emissions equivalent from the weight to an open-source driver on GitHub to read the weight. Festival visitors can instantly view their recycling contributions on the website, fostering a sense of involvement and promoting behavioral change. Additionally, organizers gain actionable waste management data to drive future improvements.

Conclusion: During the 2025 Japan Festival Boston, RecycleMeter-enabled waste management yielded remarkable outcomes: 150.01 kg of waste was properly sorted over two days, significantly reducing landfill loads. Notably, 56.44 kg (estimated 19,936 pieces) of disposable chopsticks were collected for upcycling, storing 29.24 kg of CO₂ equivalent. Another 93.57 kg of recyclables were responsibly separated. By showcasing numerical “visibility” of environmental contributions, the project merged technology, sustainability, and the Japanese concept of “mottainai,” turning cultural events into platforms for environmental education, proactive behavior, and actionable insights for organizers. The success of RecycleMeter underscores its transformative potential in fostering sustainability in public festivals.

Keywords: Carbon emissions, waste separation, recycling, data analytics, internet of things (IoT)

Phishing attacks have emerged as a significant cybersecurity threat, targeting both individuals and organizations with increasing frequency and sophistication. These attacks often exploit email communication to deceive users into revealing sensitive data, including credentials, financial information, and personal identifiers. Traditional rule-based filtering methods have proven inadequate in addressing the evolving complexity of phishing techniques, necessitating the development of intelligent and adaptive detection solutions. This study introduces a machine learning-based phishing email detection system with a primary emphasis on the Extreme Gradient Boosting (XGBoost) algorithm. Employing a structured methodological approach, the system was developed using a balanced and labelled dataset of 10,000 emails, equally split between phishing and legitimate samples. Pre-processing steps, including noise reduction, feature extraction, and label encoding, were implemented to transform raw email data into machine-readable input formats. Key features such as keyword occurrences, URL frequency, HTML tag usage, and text structure were extracted to enhance model effectiveness in detecting both conventional and advanced phishing strategies. A comparative analysis was conducted by training and evaluating five classification models—Naive Bayes, K-Nearest Neighbours (KNN), XGBoost, Light Gradient Boosting Machine (LightGBM), and CatBoost—using performance metrics such as accuracy, precision, recall, F1-score, and confusion matrix. All models achieved 100% accuracy on the curated dataset; however, XGBoost demonstrated superior capabilities due to its high computational efficiency, robust regularization, support for large-scale and high-dimensional data, and enhanced interpretability through feature importance scores. The optimized model was further validated on unseen data, maintaining perfect performance across all metrics and demonstrating its potential for real-world deployment in applications such as enterprise email filtering and browser-based security extensions. Future research will focus on expanding the dataset with real-world samples, integrating advanced natural language processing (NLP) models such as Word2Vec and BERT for deeper contextual understanding, and incorporating continuous learning mechanisms to adapt to novel phishing strategies. Additionally, the integration of explainable AI (XAI) techniques like SHAP and LIME will enhance transparency and user trust in model predictions. This work underscores the efficacy of advanced machine learning techniques in countering phishing threats and contributes to the broader field of intelligent cybersecurity systems.

Keywords: Phishing email detection, XGBoost, cybersecurity, machine learning, Natural Language Processing, explainable AI

Background: Activity tracking is effective to assess the physical and mental health of domestic animals such as cats and dogs. It is expected to aid exercise planning, calorie expenditure estimation, obesity prevention, stress reduction, and early detection of health issues. However, the availability of activity tracking tools is limited for domestic animals. Commercial fitness trackers are often expensive for pet owners and not configurable for them to calibrate tracking algorithms for their pets. They lack enough clinical validation yet because they are not customizable for veterinary researchers to experiment on tracking algorithms. 

Objective: This work proposes an affordable and open platform to carry out wearable activity tracking for domestic animals. It consists of open-source software components as well as low-cost, low-footprint hardware equipment. The proposed platform allows pet owners to continuously monitor the activity levels and patterns of their pets and estimate their calorie expenditure. Its baseline configuration can be customized for individual animals. Veterinary researchers can specialize the baseline for their research and clinical studies. 

Methodology: The proposed platform uses an ESP32 microcontroller and a 3-axis accelerometer for a wearable device to track an animal’s posture (e.g., on all fours, sitting up, belly up, and sideways) and activity intensity level (e.g., stationary, lightly to moderately active, and highly active). Posture detection is performed through tilt-sensing. Activity intensity is measured by collecting the magnitude, direction, and frequency of acceleration signals periodically. Given the duration and intensity of activities, the proposed platform estimates the animal’s daily calorie expenditure with Metabolic Equivalent (MET) values. It uploads posture, activity intensity, and calorie expenditure data to a cloud database. For activity intensity classification, the baseline threshold-based algorithm can be replaced with other statistical and machine learning-based algorithms as needed. All major algorithm parameters are exposed to be configurable; for example, age, body weight, sensing interval, thresholds, and MET values and coefficients. 

Results: The proposed platform has been implemented with CircuitPython and tested with a domestic cat in a free-living environment. For the cat’s safety and comfort, various mounting options were examined to attach a wearable device to it without interfering with its activities. The device is as lightweight as about 100 grams, including a battery. Based on feline-specific MET values, the proposed platform successfully provides real-time and quantifiable insights on the cat’s activity over the extended time. Its cloud integration allows the user to view and analyze activity data through web or mobile interfaces. To the best of the author’s knowledge, this work is the first attempt to study an open-source platform for pet owners and veterinary researchers to perform activity tracking for domestic animals with a wearable device.    

Keywords: Animals, activity tracking, calorie expenditure estimation, Internet of Things

Market dynamics are inherently complex, shaped by continuous interactions between buyers and sellers as well as external factors such as weather and time. These dynamics are particularly visible in energy markets where demand fluctuates and pricing varies based on real-time conditions. Traditional models often fail to capture localized market behaviors and the nuances of time-sensitive bidding activities. To address this challenge, simulations that mimic real-world trading environments are vital to improve strategic decision-making. However, there remains a gap in adaptable and context-specific simulation tools for such market environments.

In this research an agent-based simulation model that reflects realistic buyer and seller interactions under diverse conditions has been developed using the Mesa framework in Python. The model introduces dynamic agents representing buyers and sellers, each equipped with adjustable characteristics such as price thresholds, quantity requirements, and profit margins. The simulation environment incorporates varying weather conditions, times of day, and changes in the number of market participants. Data is continuously collected using Mesa’s built-in Data Collector for further analysis.

Significant progress has been made in constructing a working prototype of the market simulator. Agents respond dynamically to simulated environmental changes, and the generated datasets include detailed metrics such as pricing behavior, trading volumes, and profit margins. The inclusion of variables like weather and regional impact has enhanced the model’s realism, producing datasets that reflect plausible market fluctuations and allowing visual exploration through mini-tables and graphs. This framework offers a foundation for studying market responses and optimizing bidding strategies.

Future development includes implementing negotiation mechanisms between agents, enabling more realistic interactions and dynamic decision-making. Additionally, the model will be expanded to incorporate more complex influences such as economic indicators. This research contributes to the advancement of agent-based economic simulations and aims to support improved decision-making in competitive market environments.

 Aligned with the global objective to eliminate malaria, continuous efforts are made in the field of malaria biology. However, the parasite is similarly advancing itself to be in competition. Molecular mechanisms of host-parasite interaction during malaria remain elusive, especially in the case of Plasmodium vivax. Many alternative pathways of invasion are now coming up. P. vivax has a specific gene family named Pv-fam-a that encodes a large number of tryptophan-rich antigens (PvTRAgs). We describe here the identification of erythrocyte receptors for one of the significant P. vivax tryptophan-rich antigens, PvTRAg36.6 which is known to be expressed in merozoites, has its sequences conserved in the parasite population, and produces cellular and humoral immune responses during P. vivax infection. PvTRAg36.6 binds to the human reticulocytes, with the reticulocyte specific receptor, CD71, based on LC-MS analysis of proteins obtained during pull down assay. This receptor-ligand interaction was specific as confirmed by direct binding between PvTRAg36.6 and CD71 during solid-phase binding, and SPR and many more methods. In this study we found out that the parasite ligand PvTRAg36.6 interacts with its reticulocyte receptor CD71 through its two peptide regions, and is involved in red cell invasion. These results may help in developing the immuno- therapeutics against this parasitic infection.

The surge in antibiotic resistance in bacteria has left the medical community in an enigma. According to The Lancet, in 2021, an estimated 4.71 million deaths were associated with bacterial antimicrobial resistance (AMR), including 1.14 million deaths attributable to bacterial AMR. Recent data also reveals that countries with more youth and children would be considered safe as the AMR in the young population has decreased by 50% from 1990 to 2021. On the other hand, countries with a constrictive population pyramid would face faster and more significant deaths due to AMR. Thus, the data also suggests that the deaths have increased by over 80% for adults 70 years and older. The forecasts predict that an estimated 1.91 million deaths attributable to AMR and about 8.22 million deaths associated with AMR could occur globally in 2050. Improved health facilities, novel drug and vaccine development, proper hygiene, access to prescribed antibiotics, and promotion of antibiotic stewardship could help combat antibiotic resistance. Since Gram-negative bacteria are the most notorious among the bacterial community, more focus should be placed on developing strategies that can efficiently combat antimicrobial resistance for Gram-negative and Gram-positive bacteria. 

Thus, in this study, the most economical, effective, and easy-to-handle nanoparticles have been prepared using a tropical plant's bark and leaf extract. Interestingly, the extract of this genus has already been reported to cure many communicable and non-communicable diseases, including some digestive system-related diseases and dengue, and has also been reported as an effective antimicrobial agent. The study combines the goodness of the phytochemicals to stabilise the cationic silver into silver nanoparticles (AgNPs). It evaluates their antimicrobial, antibiofilm, and antioxidant properties on E. coli, P. aeruginosa, S. aureus, and B. subtilis through in silico and in vitro studies, which include biosynthesis, optimisation and characterisation of AgNPs, LC/MS of plant extracts, antibacterial assays, and DPPH assays. The cytotoxicity of the biofabricated silver nanoparticles was also studied on HEK 293 cell lines. 

The result suggested that two types of nanoparticles could be synthesised from a single plant with two different extracts from leaf and bark. The spherical-shaped AgNPs, having an average size of approximately 17-20 nm, are more effective than that of 45-50 nm synthesised through leaf and bark extracts, respectively, in inhibiting bacterial growth and biofilm formation. The MIC values for both AgNPs were less than one µg/ml for most organisms. The antioxidant study revealed that the plant extract had more antioxidant properties than AgNPs due to their large size. The AgNPs were also non-toxic up to 1µg/ml concentration. In the study, we also found that the AgNPs severely corrode the cell walls of bacteria, which results in the leakage of genetic material and protein content out of bacterial cells. The corrosion of bacterial cells was visualised through FESEM. Thus, it may be concluded that the AgNPs can treat bacterial infection without causing primary cytotoxicity.

Biodegradable polymers have gained significant attention for their potential applications in modern biomedical and environmental fields specially orthopedic fixation devices, tissue engineering and bioresorbable medical implants. Among these Poly (L-lactic acid) (PLLA) has gained significant attention due to its tunable mechanical properties, controlled degradation rates, and superior biocompatibility, making it an ideal candidate for load-bearing medical applications. In this study, molecular dynamics (MD) simulations were conducted using LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) to investigate the interaction of PLLA with a water reservoir. The primary objective was to analyze the structural stability and potential energy variations of the PLLA system over time. The simulation was conducted by placing a PLLA polymer in a water environment and allowing interactions to occur under controlled conditions such as constant temperature 310.15 K and constant pressure, 1.123 atm., respectively. Using water as the medium ensures that the simulation closely mimics actual usage conditions in biological and environmental applications, making the results more applicable to practical scenarios. Various energy components, including bond stretching, bond angles, bond-dihedral, and non-bond interactions, were computed to evaluate the system's behavior. The results show a gradual increase in bond-stretch energy over time, indicating conformational adjustments within the polymer. Furthermore, bond-angle energy remains relatively stable with fluctuations, while bond-dihedral energy exhibits minimal variations. The non-bonded interactions play a crucial role in determining the overall potential energy, which remains negative, indicating a stable molecular system. Additionally, variations in temperature, pressure and enthalpy were analyzed to assess thermodynamic behavior. The findings highlight the molecular interactions governing PLLA behavior in aqueous environments, providing insights into its stability and degradation mechanisms. This study contributes to understanding the structural dynamics of biodegradable polymers, aiding in the design of sustainable materials for biomedical and environmental applications.

A comprehensive investigation of various biological communities is important for evaluating diversity within mangrove ecosystems; however, such studies are rare. Environmental DNA (eDNA) metabarcoding allows simultaneous examination of multiple organisms within a single ecosystem. In this study, 16S rRNA, cytochrome C oxidase I (COI), and a novel primer derived from 'Folmer primers' were used. These primers were used to analyze the microbiome, eukaryotic plankton, and their interactions across 24 samples from the three mangrove reservoirs. The generated dataset included 2027 taxonomic groups (genus level), ranging from the microbiome to vertebrates, through high-throughput sequencing, also known as next-generation sequencing (NGS). Seven bacterial taxa were identified as differentially abundant: Altererythrobacter, Erythrobacter, Halomonas, Lewinella, Pleurocapsa, Rubrivirga, and Tunicatimonas. In contrast, phyla such as Arthropoda, Mollusca, Annelida, and Cnidaria were abundant as zooplankton at all three sites. Diversity analysis revealed that the microbiome community exhibited greater stability than plankton, highlighting the higher specificity of plankton to the sites. Correlation analysis identified temperature and turbidity as the primary factors influencing biodiversity. Notably, turbidity had a more significant impact on the microbiome than plankton, highlighting their different sensitivities to site-specific conditions. Network analysis led to the construction of ten biological interaction subnetworks, representing various community connections. The most interconnected genera in each subnetwork, which were highly responsive to different environmental factors, may serve as indicators of distinct ecosystem states. This study provides a comparative analysis of the response sensitivities of different communities and the construction of interaction networks in mangrove ecosystems. These findings offer valuable insights into marine ecosystem dynamics and the effects of environmental factors on biodiversity. Improperly implemented sea cucumber farms and solid waste dumping act as barriers and impede nutrient accumulation in the lagoon.

The Siddha system of medicine plays a vital role in geriatric (Moopu Iyal) care, emphasizing health maintenance and well-being through a lifestyle-oriented approach. Kaya Karpam, a central concept in Siddha, incorporates traditional remedies like ginger (Zingiber officinale), known for its neuroprotective and anti-inflammatory properties. Anxiety, a prevalent disorder among older adults, impairs quality of life and increases healthcare costs. With the potential side effects and hazards of pharmacological treatments, natural alternatives like ginger are gaining attention. This review assesses the efficacy of ginger in alleviating anxiety in older adults by conducting a comprehensive analysis of published clinical, preclinical, and biochemical studies. Databases including PubMed, Scopus, and Web of Science were searched for studies published between 2005 and 2024, using keywords such as “ginger,” “Zingiber,” “anxiety,” “geriatric,” and “mental health.” After reviewing 38 articles and removing duplicates, and with poor methodological quality, 12 were retained, including 7 clinical studies, 3 animal studies, and 2 focusing on mechanical insights. Clinical studies revealed significant reductions in anxiety symptoms (p < 0.05) with daily ginger doses ranging from 500 mg to 1500 mg. However, geriatric-specific cohorts were limited. Preclinical studies demonstrated that ginger extract reduces corticosterone levels, increases serotonin and dopamine, and alleviates anxiety-like behaviours in animal models. Key compounds, gingerol and shogaol, were found to modulate GABAergic pathways, decrease neuroinflammation, and act as antioxidants, critical mechanisms for anxiety management. Despite its promising anxiolytic effects, further research is required to establish ginger's safety, efficacy, and optimal dosage for geriatric populations. Larger, age-specific cohorts and standardised methodologies are essential for future clinical studies to validate its potential as an alternative treatment for anxiety in older adults.

Key words: Anxiolytic, Ginger, Kaya karpam, Oxidative stress, Siddha medicine

IQ MOTIFS AND ITS INTERACTIONS WITH TARGETS

Faseeha Nasar & Sevvel Pathmanathan (Department of Botany, University of Jaffna) 

IQ motifs, named after the conserved Isoleucine (I) and Glutamine (G) residues, are short calmodulin- binding sequences that play crucial roles in various signaling pathways. This study focuses on a comprehensive analysis of IQ motifs, particularly from human and yeast proteins, to elucidate their structural characteristics, evolutionary relationships, and functional interactions.

Human IQGAP1, IQGAP2 and IQGAP3 sequences taken from NCBI were used to identify the IQ motifs. Yeast Iqg1p sequences taken from Saccharomyces Genome Database were used to identify the IQ motifs of Saccharomyces. Structures of IQ motifs were predicted using Phyre2 online server. The accuracy of these predicted structures was assessed using Ramachandran plot. MAFFT online server was used to align multiple sequence alignment of IQ motifs. Phylogenetic analyses were performed using MEGA 11 to infer evolutionary relationships. Finally, Peppi-protein interacting bioinformatics tool was used to model interactions between IQ motifs and their known targets, including calmodulin and related EF- hand proteins.

The alignment revealed significant conservation in the core IQ motif region, particularly around the hydrophobic and basic residues critical for calmodulin interaction. Structural predictions showed that human IQ motifs tend to form more stable alpha helical conformations compared to yeast counterparts. Phylogenetic analysis indicated that IQ motifs have diverged in a species- specific manner. Interactions of proteins confirmed the preferential binding of IQ motifs to calmodulin in a calcium independent manner, consistent with existing experimental data.

This study presents a detailed comparative evaluation of IQ motifs from structural, evolutionary, and functional perspectives. The insights gained from this work enhance the understanding of molecular determinants underlying IQ motif – mediated interactions and offer a basis for further experimental validation and therapeutic exploration. The specificity and affinity of interactions are modulated by the surrounding amino acid sequence, the structural context of the IQ motif within the protein, and the presence of additional binding partners. IQ3 and IQ11 of yeast IQ motifs can only bind with apo-calmodulin while IQ1, IQ2, IQ4, IQ5, IQ6, IQ7, IQ8, IQ9 and IQ10 will not bind with apo-calmodulin. IQ1 of IQGAP2 does not bind with apo-calmodulin while IQ2, IQ3 and IQ4 of IQGAP2 can bind with apo-calmodulin. At the same time IQ1 and IQ4 of IQGAP3 do not bind with apo-calmodulin while IQ2 and IQ3 of IQGAP3 can bind to apo-calmodulin.

Key words:

IQ motifs, Calmodulin, Structural modelling, Phylogenetic, Protein interaction

Cannabis is the most abused drug among pregnant women, and its use is expected to rise as 39 states have medical marijuana laws and 19 have recreational marijuana laws. Alarmingly, the concentration of tetrahydrocannabinol in cannabis has doubled globally over the past 40 years, potentially exacerbating associated problems. Clinical data indicate that cannabis use during pregnancy leads to learning and memory deficits in adolescent offspring. Given the increasing prevalence and potency of cannabis, understanding its neurodevelopmental impact is more critical than ever. Therefore, we hypothesize that prenatal exposure to cannabinoids increases gamma-aminobutyric acid signaling, resulting in synaptic plasticity and memory deficits. In this study, pregnant rats were exposed to a vaporized solution of tetrahydrocannabinol (100 milligrams per milliliter in polyethylene glycol 400 via passive inhalation) from gestational day 5 to 21/22. Pharmacokinetic studies showed that tetrahydrocannabinol levels in the dams were consistent with moderate human doses, and tetrahydrocannabinol was present in the brains and plasma of pups immediately after birth. Additionally, tetrahydrocannabinol-exposed pregnant rats gained more weight and consumed more food than those exposed to the vehicle. Offspring exposed to tetrahydrocannabinol weighed significantly less from postnatal day 1 to 40. During adolescence, exposed offspring exhibited deficits in the novel object recognition test, indicating long-term memory impairments. They also showed increased anxiety in the elevated plus maze task. These behavioral deficits were associated with changes in gamma-aminobutyric acid-related proteins in the hippocampus. Our findings shed light on how gamma-aminobutyric acid dysfunction may contribute to behavioral changes in offspring exposed to cannabinoids prenatally.

Escherichia coli, a commensal bacterium found in the intestine of warm-blooded animals, is responsible for causing urinary tract infections, blood infections and gastrointestinal infections. Antibiotic resistance has become a major global health concern due to the increase in morbidity and mortality rates. To combat antibiotic resistance, bacteriophages are utilised in treatments. In this study, a lytic phage was isolated from sewage and showed lysis against 21 E. coli strains. The efficiency of plating was performed to analyse the phage efficiency in different host spectra. The phage was tested for stability under different environmental parameters like pH, temperature and bile salts. The phage was stable across the pH range of 3 to 11, the temperature range of -80°C to 75°C, and the bile salt range of 0.3% to 2%. The lytic activity of phage was observed at different MOIs from 0.0001 to 100. The life cycle revealed that around 80% of the phages were adsorbed within 4 minutes, followed by an 8-minute latency period and a burst size of 40 phage particles per infected cell. Antibiofilm activity was evaluated using 2 strong biofilm-producing hosts. Finally, the metal ion interaction was determined using Ca²⁺ and Mg²⁺ ions. 

Keywords: E. coli, drug resistance, metal ion interaction, bacteriophages, antibiofilm activity 

Prenatal cannabinoid exposure (PCE) has been linked to long-term cognitive and behavioral impairments in offspring, potentially driven by disruptions in glutamatergic signaling. Given the critical role of glutamate in hippocampal development and function, understanding how PCE affects glutamate dynamics is essential for identifying the mechanisms underlying these neurodevelopmental changes. In this study, Pregnant dams were exposed daily to ∆9-THC vapor from gestational day 5 to birth using a custom E-Vape system with timed puffs and controlled chamber airflow, delivering physiologically relevant THC levels. Control animals received vaporized PEG 400, and all offspring were culled and weaned following standard protocols before adolescent testing. We examined the effects of PCE on hippocampal glutamate neurotransmission using high-resolution in vivo neurochemical technique. We employed an advanced microelectrode array (MEA) system featuring ceramic-based multisite electrodes and integrated micropipettes for local drug delivery. This technique enabled subsecond-resolution measurements of tonic glutamate levels, KCl-evoked release, spontaneous glutamate transients, and glutamate clearance following exogenous application in the CA1, CA3, and dentate gyrus (DG) regions of adolescent rat hippocampus (postnatal days 52–65). To investigate the molecular basis of altered glutamate dynamics, we performed immunoblotting and immunohistochemistry to quantify expression and colocalization of key glutamate transporters (GLT-1 and xCT), the astrocytic marker GFAP, and presynaptic proteins involved in glutamate release (vGLUT1 and synaptophysin). Our findings indicate that PCE impairs glutamate clearance, likely due to transporter downregulation or dysfunction, contributing to a pro-excitotoxic environment. Disruptions in spontaneous and evoked glutamate signaling, as well as altered responses to exogenous glutamate, point to widespread dysregulation of extracellular glutamate homeostasis in the hippocampus. These results provide new insight into how prenatal cannabinoid exposure destabilizes hippocampal glutamate regulation during adolescence. By identifying specific impairments in glutamate transport, release, and uptake, this work advances our understanding of the neurochemical consequences of PCE and highlights the need for increased public awareness as cannabis use during pregnancy continues to rise.

Neuroinflammation is a critical factor in the progression of neurodegenerative diseases, often leading to glutamate dysregulation and excitotoxicity. The astrocytic system x_c^_ antiporter, with xCT as its catalytic subunit, plays a crucial role in extracellular glutamate homeostasis. Dysregulation of this transporter has been implicated in conditions such as Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis. In this study, we investigate whether pharmacological inhibition of xCT can mitigate inflammation-induced glutamate dysregulation, a mechanism that may contribute to neurodegenerative disease pathology. To examine this, we utilized polyinosinic-polycytidylic acid (PIC), a viral mimetic known to induce an acute phase response and elevate extracellular glutamate levels in the hippocampus. Enzyme-based microelectrode arrays (MEAs) were used in vivo to measure real-time changes in tonic glutamate levels in anesthetized mice 24 hours post-PIC injection. Pharmacological inhibition of xCT was achieved using sulfasalazine (SAS), an FDA-approved anti-inflammatory drug, which was administered intraperitoneally during recordings. Additionally, xCT knockout (KO) mice were used to determine whether genetic global deletion of xCT prevents glutamate dysregulation. Sickness behavior was assessed to confirm the efficacy of PIC administration, including reduced rearing behavior, along with changes in body weight and temperature. Western blot analysis was performed to evaluate changes in the relative expression of key regulators of astrocytic glutamate transport, xCT, and glutamate transporter 1 (GLT-1)/human excitatory amino acid transporter 2 (EAAT2). Our findings demonstrate that PIC significantly increases hippocampal extracellular glutamate, an effect that was effectively reversed by SAS treatment, supporting the role of xCT in inflammation-induced excitotoxicity. Moreover, xCT KO mice did not exhibit PIC-induced glutamate elevations, further confirming xCT’s involvement. Given that chronic neuroinflammation is a hallmark of many neurodegenerative disorders, these results suggest that targeting astrocytic glutamate transport could be a viable strategy for reducing excitotoxic damage and preserving synaptic integrity. Understanding the mechanistic role of xCT in inflammation-induced excitotoxicity may therefore inform the development of pharmacological interventions-such as xCT inhibitors like sulfasalazine-that restore glutamate balance and offer neuroprotection against cognitive decline and neuronal loss in diseases like Alzheimer’s and multiple sclerosis.

The rise of antifungal resistance and limitations of conventional treatments have driven the exploration of heterometallic nanoparticle-based hydrogel systems as innovative platforms for biomedical antifungal applications. Immunocompromised individuals are particularly vulnerable to invasive fungal infections, which often lead to severe complications due to their weakened defense mechanisms and limited treatment options. To address this challenge, metallic nanoparticles present a promising solution, particularly when embedded in polymeric hydrogel systems for targeted and sustained antifungal therapy. 

In this study, a heterometallic nanohybrid system was developed by individually doping silver (Ag), copper (Cu), and cobalt (Co) into silica nanoparticles via the sol-gel method. The metal-doped particles were then combined in equal ratios through mechanochemical grinding. These nanohybrids were subsequently embedded into a hydrogel matrix composed of 2% (w/v) sodium alginate and carboxymethyl cellulose. Final gel formulations were prepared at concentrations of 5, 10, and 15 mg/mL by dispersing the nanohybrids into the polymer blend, followed by ionic cross-linking using 2M calcium chloride solution (20% v/v). 

Characterization of the gel formulations included Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Raman spectroscopy, and scanning electron microscopy (SEM). The formation of the bonds and the relevant peak positions were analyzed by FTIR and Raman spectroscopic analysis.  The crystalline structures of the metal nanoparticles were confirmed by the X-ray diffractograms. Scanning electron microscopy imaging characterized the morphology of the hydrogel formulations and confirmed that their dimensions fall within the nanoscale.  Atomic absorption spectroscopy (AAS) was employed to quantify the metal loading efficiency within each hydrogel formulation. UV diffuse reflectance spectroscopy revealed a band gap energy of approximately 2.94 eV, suggesting potential for visible light-induced radical scavenging. The radical scavenging activity was assessed via the DPPH assay, and kinetic profiles were plotted to evaluate radical scavenging performance. Swelling behavior and porosity analyses indicated that the 15 mg/mL formulation exhibited the highest performance, with a swelling index of 717.10 ± 19.35% and porosity of 129.46 ± 2.23%. Extensibility studies were also performed to assess the spreadability and mechanical behavior of the gel formulations, further supporting their suitability for topical application. Finally, the antifungal efficacy was determined using the agar overlay method, followed by colony count assays against Candida albicans, Trichophyton rubrum, Microsporum gypseum, and Aspergillus fumigatus. Notably, the 15 mg/mL gel formulation demonstrated complete fungal inhibition, showing zero colony formation across all tested strains. 

These results highlight the synergistic antifungal efficacy of heterometallic nanoparticle-loaded hydrogels, particularly at higher concentrations, demonstrating their capability for effective fungal inhibition and favorable swelling and porosity characteristics. Overall, this study reinforces the potential of hydrogel-based delivery systems incorporating metallic nanohybrids as a novel and efficient therapeutic platform for the treatment of dermatophytic and opportunistic fungal skin infections. 

Mosquitoes are among the most disturbing blood-sucking insects afflicting human beings. Major vector mosquito species belonging to genera Anopheles, Culex, and Aedes are responsible for transmitting diseases like Malaria, Japanese Encephalitis, Filariasis, Dengue fever, Chikungunya fever, Yellow fever, and Zika. Mosquitoes alone transmit diseases to more than 700 million people and over one million deaths are reported annually across the globe. The objective of this study was to design and develop a Herbal Mosquito repellent cream for improved healthcare using herbal essential oils.   The carrier cream base was developed using an emulsion technique to deliver repellent properties by holding the natural herbal essential oils. The combination of essential oils of Neem, Eucalyptus, Citronella, chamomile, Orange, Turmeric, Basil, Clove, and Peppermint was used for the study. The Repellent potentials were studied using Aedes aegyptiMosquito species.  The emulsion cream was designed and optimized to deliver mosquito-repellent properties by the combination of herbal Essential oils.  This unique composition helps to provide the mosquito-repellent effect through Repellent activity.  The optimized emulsion cream formula was evaluated as per ICH stability guidelines (real-time, 30°C and 65% Relative Humidity (RH) and accelerated, 45°C and 75% RH) to ensure emulsion cream stability and shelf life of the product.  The Cytotoxicity activity of the final optimized formula was analysed by MTT assay. The results showed that an increase in final optimized formula cream concentration up to 1000 μg/mL did not reduce the cell viabilities in all tested concentrations in both B16F10 and NIH 3T3 cells. For the Skin Irritation study to final mosquito repellent formulation for Wistar albino rats caused no dermal irritation or reactions such as erythema or Edema. The primary or cumulative skin irritation index of the final mosquito repellent formulation was recorded to be 0.0. The performance of the final product was measured by mosquito Repellent property by using the test cage as described in the American Society for Testing and Materials (ASTM) standard E951-83 Laboratory testing and the results observed indicated better mosquito inhibiting and 86±4.18% protection compared with control. Hence the Developed Herbal essential oils Repellent cream proved the Repellent property as a Preventive aspect of Vector Borne Diseases. 

Background and Objectives: Mosquitoes are among the most vexing bloodsucking insects that torment the public. They alone transmit diseases to about 700 million people worldwide, with one million deaths reported each year. Controlling Mosquitoes is a big challenge at present environmental conditions. In ancient times and traditional Indian system of medicines like ayurvedic and siddha suggested natural medicinal plants based bioinsecticides to manage the insects and mosquitoes. The present study aimed to control Mosquito species using herbal essential oil as a potential larvicidal using Osmium sanctum (Basil). 

Materials and Methods: Essential oils of Osmium sanctum (Basil) were used for the larvicidal study and its chemical composition studies like Fourier Transform Infra-Red Spectroscopy (FTIR), and Gas Chromatographic techniques 3rd or early 4th instar larva of Aedes aegypti of 30 larvae with serial dilution of essential oils, like 100 µL, 500 µL, 1000 µL and 2000 µL used. The mortality rate was observed for each replicate after 0, 1 hour, 4 hours, 20 hours, 24 hours, and 48 hours of treatment. The control was set up with the same conditions with 500 ml of Dechlorinated water.

Results: The FT-IR spectrum analysis revealed the presence of various functional groups presented in the Essential oils of Osmium sanctum and confirmed the presence of   GC-MS studies revealed the major Phytoconstituents. The results of mosquitocidal activity of EOs oils exhibited LC 50 values @126 µL and LC 90@ 762 µL after 24 hours time limit.  

Interpretations and Conclusions: The study concluded the FTIR and GC fingerprints revealed the major chemical components inside the EOs of Osmium sanctum and highlighted the potential natural bio-pesticide properties against Aedes aegypti Mosquito species and help society control aspects of various vector-borne diseases as the larva stage itself.

Keywords: Osmium sanctum, Aedes aegypti Mosquito, Toxicological, Basil oil

This project is centred around the software and hardware development of a prototype for an innovative, microcontroller-driven standalone infusion pump. The function of the pump is to induce microbubble cavitation for blood-brain barrier opening, enabling targeted drug delivery for patients with brain tumors, brain disorders, Alzheimer’s, etc. The lab’s existing device delivered microbubbles into a patient at a controlled rate set by the user, but suffered from inefficiency, outdated hardware and software, and a limited fluid capacity (20 mL), necessitating a complete reengineering.

The objectives of this project were to design a system that increases the pumping force or pressure on the syringe to pump larger volumes precisely (i.e., 50 mL), and to avoid obstructions. The envisaged system was planned to be a completely standalone system without the need for a separate computer, by utilizing a microcontroller to run and control the infusion pump.

A new portable, robust pump driven by a programmable Arduino microcontroller was created.  The microcontroller of the pump collects input from the user and the pump’s linear encoders to move the syringe and mix the microbubbles. The Arduino is capable of driving both the pump’s previous linear HR4 Nanomotion Motor and its new rotary USR60-NM Ultrasonic Motor, which provides additional force. A custom library was also written for the Arduino, enabling it to interface with the pump’s encoders and a unique PID control system was developed for precise operation and to regulate the voltage. Additionally, a level shifter and an adjustable regulator were included in the pump to stimulate the voltage and control the pump’s speed. The novel pump was designed on CAD (Autodesk Fusion 360) and allows the syringe to travel 106 mm linearly, which is much more space-efficient. It was then 3D printed using a Bambu Lab printer.

When tested, the prototype exceeded expectations and delivered microbubbles 10x more effectively than the previous model. Next steps include assembling the rotational portion of the infusion pump, switching from the breadboard to a custom PCB in the hardware configuration, and implementing GUI interfacing to control the novel infusion pump. 

Key Words: Biomedical device, Infusion pump, Neurosurgery, Arduino

Antioxidant potential of medicinal plant are important in reducing risk of diseases in human beings. This study investigates the antioxidant activity of six antidiabetic medicinal plants—Syzygium cumini, Avicennia marina, Lumnitzera racemosa, Excoecaria agallocha, Gymnema sylvestre, and Acanthus ilicifolius collected from maritime ecosystems in Jaffna. Anti- diabetic plants used in this study were collected from Mandaitivu, Sarasaalai Chavakachcheri and Nagarkovil area. Syzygium cumini and Gymnema sylvestrae were collected from home garden in Chavakachcheri. Excoecaria agallocha, Acanthus illicifolius, Avicennia marina and Lumnitzera racemosa were collected from mangrove vegetation. Avicennia marina and Lumnitzera racemosa from Nagar kovil and Mandaitivu respectively while Excoecaria agallocha, and Acanthus illicifolius from Sarasaalai. Two spectrophotometric assays were used to measure antioxidant activity such as 2,2-diphenyl-1-picrylhydrazyl (DPPH) and Resazurin methods. Plant extracts were prepared by dissolving 0.04 g of powdered plant material in 10 ml of methanol, followed by shaking at 400 rpm for 1 hour and centrifugation at 2500 rpm for 30 minutes. Various concentrations (0.5, 1.0, 1.5, and 2.0 µg/ml) of each extract were prepared and absorbance was measured at 517 nm for DPPH assay and 570 nm for Resazurin method. Antioxidant activity was assessed by determining the percentage inhibition of absorbance, calculated as the relative decrease in absorbance of the sample compared to the control. Results from both assays demonstrated a concentration-dependent increase in antioxidant activity. In the DPPH assay, Acanthus ilicifolius showed the highest antioxidant activity with an IC₅₀ of 1.26 µg/ml, followed by Syzygium cumini (1.32 µg/ml), Excoecaria agallocha (1.43 µg/ml), Lumnitzera racemosa (1.46 µg/ml), Gymnema sylvestre (1.46 µg/ml), and Avicennia marina (1.48 µg/ml). Similar trends were observed in the Resazurin assay, where Excoecaria agallocha (1.33 µg/ml), Syzygium cumini (1.34 µg/ml), and Acanthus ilicifolius (1.40 µg/ml) exhibited strong antioxidant potential. No significant difference (p > 0.05) was observed between the IC₅₀ values obtained from DPPH and Resazurin assays. Lower IC₅₀ values indicate higher antioxidant activity, because a smaller amount of the substance was enough to achieve the effect. Additionally, the Resazurin assay proved to be more cost-effective than the DPPH method, making it a practical alternative for large-scale antioxidant screening. These findings support the potential therapeutic use of these plants as natural sources of antioxidants for managing oxidative stress-related conditions.

[Introduction] Understanding the mechanisms of natural cryopreservation provides insight into human health applications such as medical hibernation, resuscitation science, and organ transplantation. However, these applications also extend into space exploration and extraterrestrial environments. This study simulates a cold environment to evaluate the sugar-mediated tolerance of brine shrimp through experimentation. Brine shrimps are crustaceans that tolerate extreme environments like intense heat, salinity, dehydration, and freezing temperatures. Their dormant cysts–multicellular embryos in a diapause state–can survive through freezing temperatures and hatch with no internal injury or developmental damage. This is mainly due to the presence of natural sugars like glucose and trehalose, which stabilize cellular structures and prevent internal ice formation. Due to their exceptional cryotolerance, brine shrimp are an ideal model organism for analyzing natural sugar-based cryopreservation. This project gains deeper insight into the efficiency of naturally derived sugar cryoprotectants compared to the conventional synthetic cryoprotectants, which are often toxic to cells. This study has the potential to expand human capability in space, with technology for space bio-sustainability, improved life support, long-lasting food supply, and medical preservation advances. 

[Method] Nauplii (Larvae of brine shrimp) were hatched and treated with different sugars in their solutions. Frozen with trehalose, glucose, glycerol, saccharin, and sucrose, Nauplii were kept at -20ºC for 24 hours. Viability was measured by counting the survival of individuals after 1 hour of thawing. The results that followed presented a 0% success rate of survival in any solution of sugar. Therefore, it suggests that the passive absorption of sugars from the solutions was not sufficient for protection from extreme measures such as freezing.

[Result] To further explore the effects of these same sugars, however, in less extreme conditions, a second experiment was conducted. For 36 hours, Nauplii were stored at 4ºC. The results of this experiment represented the success of trehalose, with a survival rate of 95%, compared to glucose, which had a survival rate of 72%. Although not extreme, the experimentation of a stress-induced environment (low temperatures) results in observable data on the effects the sugars have on the viability of brine shrimp. 

[Conclusion] As humans embark on longer space missions, food availability becomes limited, and can degrade from prolonged storage. Space missions utilize freeze-dried foods to reduce degradation, but lack their original nutritional content. Cryopreserving methods to temporarily preserve biologically active food, such as algae and brine shrimp, can significantly extend the storage life and maintain their nutritional value. Using traditional synthetic cryoprotectants like Dimethyl Sulfoxide is highly toxic to cells. However, naturally derived sugars in bio-preservation can be more effective than chemically derived cryoprotectants due to their non-toxicity. Additionally, long-term space flight limits professional medical care and treatment, making in-space emergency treatments critical. Increasing medical accessibility includes cryopreserving therapeutic cells, such as stem cells, used for regenerating tissue, recovering from radiation exposure, or blood cells for emergency transfusions.

By studying cryotolerance with natural sugars in brine shrimp, this project aims to contribute to the advancement of non-toxic preservation techniques for medical applications, improving interstellar travel.

Cerebral Palsy (CP) is a group of neurological conditions affecting gross- and fine-motor movement. It is usually caused during the formation of a lesion in the developing nervous system, affecting motor function. CP can be identified as either spastic (increased muscle tone), dyskinetic (difficulty in controlling muscle movement), ataxic (difficulty in balance and control), or mixed/other. CP may also be further categorized into the affected areas of the body including hemiplegia (on one side of the body), diplegia (on either both upper limbs or both lower limbs), and quadriplegia (on all limbs). Although CP is not a progressive disease, individuals with CP may also experience other comorbid conditions that are associated with sensation, communication, cognition, behaviour, and secondary musculoskeletal issues; this may include hip pain, balancing problems, and hand dysfunction.

While interventions such as constraint-induced movement therapy (CIMT) have shown promising results in the improvement of motor functions, they lack socially engaging components and can also be intensive and repetitive. Emerging literature suggests that gamified therapy may enhance motivation and outcome. Nonetheless, these interventions are limited in clinical settings, especially for children with hemiparetic and hemiplegia CP.

This project proposes a wearable and sensor-integrated gamified therapy for children with CP. It aims to improve motor limb function and increase engagement through feedback and goal-oriented play at the leisure of one’s home. This proposed system will consist of a lightweight wristband embedded with inertial measurement units (IMUs) and electromyography (EMG) sensors to track movement and muscle activation. Additionally, a camera module will be attached to track motion and gestures. The wristband will be connected to a desktop or laptop with a custom game interface. Children will be involved in motion-based challenges similar to Just Dance or Dance Dance Revolution. This game will encourage children to use their affected hand and reward them for their personalized achievements. Social therapy principles will be applied to encourage active participation, self-esteem growth, and friendship. Nonetheless, this activity may be played at home in lieu of some in-person therapy sessions, where information may be sent to their child’s therapist for feedback and assessment. 

This phase will primarily delve deeper into literature review, and system prototyping – including the hardware and game interface. Verification testing will also be conducted to evaluate engagement, comfort, and accuracy. 

By creating an accessible and motivating therapeutic intervention, this platform has the potential to improve the quality of life for children with CP.

This study addresses a critical gap in research on the flipped classroom model in e-learning, focusing on first-year Management students at the Open University of Sri Lanka in the Jaffna Region. While much existing literature examines this model among broader student populations, there is limited research specifically targeting first-year students who are transitioning from traditional classrooms to e-learning. This study aims to explore how these students engage with and perceive the flipped classroom model, providing valuable insights into their academic outcomes and identifying potential barriers to effective learning in this new educational environment. A qualitative case study approach was adopted, utilizing semi-structured interviews with 20 first-year Management students enrolled in e-learning programs at OUSL Jaffna. Purposive sampling was used to select participants with direct experience in flipped classrooms. Thematic analysis was employed to identify recurring themes from the interviews. The study found that most first-year e-learning students at the Open University of Sri Lanka considered the flipped classroom model an engaging approach, though they faced some challenges. While they appreciated the flexibility of pre-class videos and reading materials, many felt disconnected from the content due to the lack of immediate interaction to clarify doubts, especially with complex subjects. In contrast, in-class activities were more positively received, as they allowed for discussions, questions, and immediate feedback, which helped reinforce understanding. However, students struggled with time management, balancing online learning with other commitments, and faced technical issues with digital tools and internet connectivity. The study's findings are based on a small sample of students from a single regional center, limiting generalizability to other regions or educational contexts. The study highlights the need for more interactive and accessible pre-class materials, such as quizzes or discussion forums, to help students feel more connected and clarify doubts. Time management strategies and better technological infrastructure, especially in rural areas, are essential to ensure consistent access to learning. In-class activities should continue to provide immediate feedback, with the teacher’s role focusing on facilitation and peer learning. These changes can improve the effectiveness of the flipped classroom model for first-year students in Sri Lanka.

Keywords:

 Flipped classroom, e-learning, student perceptions, learning outcomes, Sri Lanka

The rapid evolution of financial technologies (FinTech) has outpaced the availability of skilled talent globally, creating a pressing demand for targeted educational and training initiatives. This paper explores a collaborative approach to address the FinTech talent gap through curated training programs that leverage academic and industry partnerships across India, North America, and Sri Lanka. Anchored by the GIFT International Financial Institute (GIFT IFI) and the Indian Institute of Technology Gandhinagar (IITGN), the initiative emphasizes modular, interdisciplinary curricula designed to equip learners with competencies in digital finance, data analytics, and regulatory technologies. Drawing on case studies and programmatic outcomes, we highlight how tailored training—co-developed with stakeholders from both developed and emerging markets—can bridge regional disparities in FinTech proficiency. The model presents a scalable framework adaptable to diverse educational ecosystems, with implications for global FinTech workforce development.

This study was conducted to determine the impact of several factors on the use of Pirated software among Jaffna University students. Six independent factors were examined in this study. Computer Experience, Attitudes, Perceived Behavioral Control, Perceived Moral Obligation, Subjective Norms, and Student Perception are the variables. The dependent variable is Jaffna university students' intention to use Pirated software. This study's sample size is 500 students from Jaffna University's six faculties. Which ones were chosen at random? The researcher conducted this investigation in five categories: Management studies & Commerce, Medicine, Technology, Engineering, Art and Science. It has a significant association with the use of Pirated software, and Gender has a significant relationship with the dependent variable. Internet Access has a considerable association with Pirated Software Intentions. It has a significant association with the use of Pirated software, and Gender has a significant relationship with the dependent variable. Internet Access has a considerable association with Pirated Software Intentions. Demographic considerations have no substantial influence on overall results. There is a substantial link between Computer Experience and Intention to Pirated Software. Attitude has a big impact on software piracy. It owns.000 of major significance in Perceived Behavioral Control. Perceived Moral Obligation has no substantial influence on software Pirated intentions. Subjective norms have a strong association with the propensity to use Pirated software.

Keywords: Computer Experience, Attitudes, Perceived Behavioral Control, Perceived Moral Obligation, Subjective Norms, Student Perception, Pirated software

This study aims to explore the influence of generative artificial intelligence (AI) tool, namely ChatGPT, on the development of undergraduate students' critical thinking skills. Although existing literature has a tendency to discuss either the ethical concerns or performance-based implications of AI integration in learning to a large extent, a significant knowledge gap in the literature pertains to how generative AI affects students' cognitive engagement and critical thinking processes. This research bridges that gap by analysing whether AI is a facilitating learning tool or promotes overreliance and superficial thinking. This offers new insights by positioning AI not merely as a technological tool but as a cognitive partner that is capable of reshaping traditional learning tendencies. This study applied a qualitative design and semi-structured interviews to gain an in-depth understanding of how Faculty of Management Studies, University of Jaffna undergraduate students of 2nd to 4th year make use of generative AI tool of ChatGPT, in academic work. Purposive sampling was utilized to choose 25 participants representing 2nd to 4th year. The interviews were conducted to discover more about the opinions, experiences, and attitudes of students on the use of AI tools in learning tasks, with an emphasis on the effects on critical thinking. In an attempt to identify and investigate data patterns, thematic analysis was utilized, which enabled a thorough comprehension of the effects of AI use on learning habits, critical thinking, and cognitive engagement. The study identified three main themes. First, all year groups demonstrated AI as a supportive learning tool. 4th year students utilized AI for more complicated activities like case study analysis, creative Innovative idea generation, while other students used it to develop ideas, do the assignments, and clarify concepts. Second, Emerging Dependency revealed that a large number of students, especially those in their second and third years, substantially depended on AI to complete assignments and prepare for tests, raising concerns about the potential decline of autonomous thought. Lastly, Context Dependent Value showed that the influence of AI differed depending on the task and the year of study, with second year students mostly using it for Basic tasks and more experienced students using it for higher order tasks. Students' critical engagement with AI-generated information was also influenced by their level of digital literacy. The study is limited to a single institutional setting and depends on self-reported perceptions, which could be biased. For a more comprehensive understanding, future studies should examine longitudinal effects and incorporate instructors’ viewpoints. For educators, curriculum developers, and legislators hoping to responsibly incorporate AI tools into higher education, this study provides pertinent insights. It highlights the necessity of ethical standards and training in digital literacy to make sure AI strengthens rather than weakens students' capacity for critical thought.

Keywords:

 Generative Artificial Intelligence, Critical Thinking, ChatGPT, Undergraduate Learning, AI Literacy, Cognitive Engagement, Higher Education

Artificial Intelligence (AI) is increasingly influencing the landscape of accounting education, prompting a shift in the way knowledge is delivered, skills are developed, and professional readiness is assessed. With the increasing global integration of AI into financial systems and professional accounting practices, the academic sector faces both the challenge and the opportunity to prepare students for this technological shift. This study critically examines the perceptions of undergraduate accounting students in Sri Lanka regarding the emerging opportunities and challenges associated with AI integration in accounting education. This study used a qualitative methodology, and information was gathered from undergraduate accounting students at Sri Lankan state universities through interviews and focus group discussions. According to a thematic analysis, accounting students in Sri Lanka see AI as a game-changing tool that may improve learning effectiveness and customize educational experiences. Through interactive tools and adaptive technology, students think AI has the ability to make complicated accounting ideas easier to understand, especially in areas like financial reporting and auditing. Students also recognized the significance of AI literacy for future professional success in the digital age, identifying it as a critical competency that would improve their employability and competitiveness in a global labor market that is changing quickly. Notwithstanding these encouraging prospects, the study also raises several important issues. Students expressed apprehension about the lack of structured AI-related content in the existing accounting curriculum, limited access to AI tools and infrastructure, and insufficient training for academic staff.  Furthermore, there were underlying concerns that AI would someday replace humans in accounting positions, which could have an effect on future graduates' job security. The findings underscore the importance of proactive policy measures and institutional reforms to support the meaningful integration of AI in accounting education. Universities must prioritize curriculum updates, invest in digital infrastructure, and provide capacity-building programs for educators. Policymakers and academic leaders are urged to create a supportive framework that promotes innovation while ensuring equity and ethical considerations in AI adoption.

Artificial Intelligence (AI) has become more dominant in the education sector with the availability of AI tools like ChatGPT revolutionizing the way undergraduates interact with academic activities. This research focused on investigating The Impact of ChatGPT usage on the Academic Achievement of University Undergraduates in the Matale District, Sri Lanka. The study is followed by the Technology Acceptance Model (TAM), incorporating its core variables such as Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Behavioral Intention (BI) and additional factors such as Student Engagement (SE) and Critical Thinking Ability (CT), which influence academic performance.

A quantitative research approach was adopted to collect data using a structured online questionnaire based on a 5-point Likert scale. 250 undergraduates from the Matale District, studying at various universities in Sri Lanka, were selected using a random sampling method. 212 responses were collected and analyzed using SPSS, applying descriptive statistics, correlation analysis, and multiple regression techniques.

The findings demonstrate a statistically significant positive relationship between ChatGPT usage and academic achievement. Perceived Usefulness emerged as a key predictor, indicating that undergraduates who find ChatGPT helpful in understanding and completing academic tasks tend to perform better. Perceived Ease of Use also positively influenced academic performance by encouraging frequent and confident interaction with the tool. Additionally, Student Engagement increased with ChatGPT usage as undergraduates reported greater participation in class discussions and motivation in learning activities. The tool also supported the development of Critical Thinking Ability with many undergraduates using it to compare viewpoints evaluate the accuracy of content and explore deeper academic insights. The frequency and purpose of ChatGPT use further moderated these relationships with academic-focused users reporting better academic outcomes.

In conclusion the study if ChatGPT is used effectively it can significantly increase the academic achievement of undergraduates by supporting their engagement, critical thinking, and learning effectiveness. The study can be used to recommend promoting responsible AI integration in higher education and embedding digital literacy training within university curricula to encourage the appropriate use of tools like ChatGPT.

Keywords: ChatGPT, Artificial Intelligence, Student Engagement, Critical Thinking Ability, Academic Achievement

In the contemporary era of artificial intelligence, AI assistants have become prevalent across various sectors. However, developing a reliable, trustworthy, and highly efficient AI assistant specifically tailored for educational purposes remains a significant challenge for educators. This study presents ProBA, an advanced AI-powered laboratory assistant designed to provide essential support and guide students in problem-solving within relevant subject areas, without offering direct answers during online-based practical sessions. The primary objectives of this research are: (1) to facilitate interactive methods that actively engage students in online-based laboratory practicals, (2) to encourage students to utilize AI-driven tools ethically to enhance their subject knowledge, and (3) to strengthen student-centred learning systems within educational institutions.

The methodology involves integrating a Large Language Model (LLM) into an offline bot, thereby creating an AI assistant capable of delivering instructional support during laboratory practicals and fostering a more interactive learning environment. The AI assistant is designed to address critical theoretical questions and practical procedures that may arise during practical sessions, thereby supporting students in online sessions. The responsiveness of voice commands and the voice + structured text responses highlighted as the special features of this system to reduce the gap between the student-teacher interaction. To enhance the usability and effectiveness of this system, a feedback mechanism and few-shot learning algorithms have been incorporated. 

The study evaluates the effectiveness of the AI assistant by comparing it with existing systems and assessing student satisfaction and performance during laboratory practicals. The findings of this study demonstrate that the implementation of ProBA significantly improves student engagement, the ethical use of AI technologies, and overall learning outcomes in practical settings. This research contributes valuable insights into the development and deployment of AI-driven educational tools, offering a promising approach to enhancing practical instruction and supporting the evolving needs of modern education.

Keywords:  Large Language Models, AI-Assistant, Student-centred Learning Systems, Interactive online Classroom

The integration of Artificial Intelligence (AI) into e-learning platforms has opened new avenues for enhancing the educational experience of children in their formative years. With the rapid digital transformation accelerated by global events and technological advancements, there is an urgent need to ensure that educational tools are not only accessible and effective but also developmentally appropriate for young learners. This paper explores how AI-powered technologies are being tailored to meet the unique cognitive, emotional, and behavioural needs of children aged 4 to 12, particularly in the context of early childhood and primary education.

AI in children’s e-learning environments facilitates personalized learning by adapting content, pace, and delivery methods based on a child’s learning style and performance. Intelligent tutoring systems, voice recognition, and emotion-aware interfaces are enabling more interactive and engaging learning experiences. Through gamification and storytelling powered by machine learning algorithms, children are now able to receive real-time feedback, personalized challenges, and encouragement, which boost motivation and retention.

The paper presents real-world examples and case studies, including implementations in South Asian and under-resourced settings, to demonstrate how AI is helping bridge learning gaps, especially in literacy and numeracy. Additionally, the use of AI in diagnosing early learning difficulties, such as dyslexia or attention deficits, is discussed as a tool for timely intervention.

However, the use of AI in children's education also brings forward ethical concerns. This study critically examines issues related to data privacy, consent, algorithmic bias, and screen time. It emphasizes the importance of designing AI systems that are transparent, secure, and aligned with child development principles. Furthermore, the need for culturally relevant and linguistically appropriate content is highlighted, ensuring inclusivity and equity in digital learning environments.

By synthesizing current research, technologies, and pedagogical practices, this presentation aims to provide a comprehensive view of the benefits, limitations, and future directions of AI in children’s e-learning. The discussion will be valuable for educators, policymakers, researchers, and developers seeking to create meaningful, responsible, and scalable educational solutions for young learners.

The exponential growth of academic literature has rendered it increasingly challenging and time-consuming for researchers, students, and professionals to thoroughly read and comprehend entire research papers. The manual review of extensive documents to extract key ideas, understand methodologies, and identify research gaps is cognitively demanding and inefficient, particularly in fast-paced research environments. To address these challenges, this study presents Cognify Companion, an intelligent web-based application powered by advanced artificial intelligence techniques. The system offers automated summarization, methodology extraction, and research idea generation from academic texts, thereby addressing a critical need in the domains of computer science, research methodology, and academic publishing, where rapid comprehension and innovation are essential. Although digital libraries have enhanced access to scholarly resources, there remains a significant gap in intelligent systems capable of deeply analyzing and interpreting academic content in a structured and meaningful manner. The proposed system enables users to upload research articles in Portable Document Format (PDF) and receive concise summaries, extracted methodological details, and automatically generated suggestions for future research directions. The backend architecture leverages transformer-based natural language processing (NLP) models and machine learning techniques for core text analysis, while the frontend developed using the Next.js framework provides a responsive and user-friendly interface. Pre-trained models such as Bidirectional and Auto-Regressive Transformers and Text-to-Text Transfer Transformers are utilized for abstractive summarization and keyphrase extraction. Furthermore, large-scale language generation models, accessed through prompt-based programming via external application programming interfaces (APIs), facilitate contextual understanding for methodology extraction and ideation. Empirical evaluations conducted on a diverse set of academic papers demonstrate the system’s efficacy in generating coherent summaries, accurately identifying research methodologies, and producing actionable research suggestions. By substantially reducing reading time and cognitive load, Cognify Companion enhances academic productivity and assists researchers in navigating complex scholarly literature more efficiently. This work represents a significant advancement in intelligent academic support systems and contributes to the development of next-generation digital research tools.

Abstract

This article presents an experimental curriculum designed to incorporate Artificial Intelligence (AI) tools, especially generative AI, into a lower-division Information Technology (IT) literacy course at a U.S. university. The course aims to foster digital literacy and critical thinking while equipping students with practical skills in AI-enhanced productivity, such as spreadsheet analysis and database modeling, all at the introductory level. It also briefly touches on the use of AI for brainstorming, report writing, preparing study guides, and generating practice questions for examinations.

The primary focus of this article is the use of AI for technical applications such as spreadsheet analysis, database modeling, and code generation. The key takeaway from this experiment was that it is now possible to introduce database concepts and modeling at an early stage, which are often reserved for upper-division courses. A chatbot such as ChatGPT can now generate database models and SQL code for implementing database applications. There is now a new approach to teaching database modelling or advanced spreadsheet analysis. Both can be taught before teaching higher-level spreadsheet formulas and SQL database code.

This article presents a few case examples of how AI is used in spreadsheet analysis and the design and implementation of database models. It also emphasizes the need to provide a sound theoretical foundation for students in topics such as database design and prompt engineering. The foundation is needed for them to chat intelligently with a chatbot and to design and implement a database. The article highlights the potential to introduce advanced IT topics in an introductory information systems course. It also brings to light the challenges of adopting a new paradigm in teaching where model-building can be introduced early, before teaching formulas and computer coding.

The reversed approach to teaching computer application development brings the potential to attract students from all fields to learn valuable IT skills in areas such as database modelling and web design.  For example, websites can now be designed without writing a single line of code, as they can be created using visual programming and AI-generated code. Teaching computer programming at the beginning of an undergraduate program, such as information systems, is often considered a deterrent to attracting students to major in information systems. With the reversed approach to teaching information system development, there might be a better opportunity to entice and recruit students to major in information systems.

Keywords: Artificial Intelligence, ChatGPT, Information Literacy, Database Modeling, Microsoft Access, Copilot, Educational Technology

Abstract

California State University is the largest state university system in the United States. Recently, it announced a landmark initiative to become the nation's first and largest AI-empowered university system. The first of its kind public-private initiative involved Adobe, Alphabet, AWS, IBM, Instructure, Intel, LinkedIn, Microsoft, NVIDIA, and OpenAI. AI tools and training are now available to nearly 460,000 students and 63,000 faculty members.

Although the initiative started in February 2025, the adoption of AI progressed rapidly in many of the California State University campuses. This paper describes the use of AI tools in business core courses offered by the Information Systems Department at the College of Business and Economics at California State University, Los Angeles. It also discusses students' adoption of AI tools based on informal observations and a survey conducted at the end of the Spring 2025 semester.

The survey questions focused on the following: chatbots used frequently, activities for which students use chatbots, different AI tools used by students, challenges faced when using a chatbot, rating of chatbots, concerns about AI tools, use of chatbots for technical applications, use of AI tools in other classes, and effectiveness of AI tools.

The survey and informal observation indicated that the students initially used the chatbots for advanced AI-aided Internet search, brainstorming, report writing, and project assignments. As they became more familiar with the AI tools, it became evident that they could generate study guides and multiple-choice practice questions to prepare for quizzes and examinations with AI. The students started summarizing PowerPoint slides and lecture notes to help them learn. AI tools thus enabled them to devise self-study pathways to enhance their learning experience.

Although the students readily adopted the AI tools for many applications, they were slow to use the tools for more computationally intensive tasks, such as manipulating data in spreadsheets. The slow adoption may be attributed to chatbots such as Copilot being available in the student version of chatbots as a stand-alone AI tool. Only the pro version of Copilot offered the option to integrate Copilot as an add-on within Excel.  

Another technical application of ChatGPT where students were slow to adopt was computer modeling and code generation.  The adoption of chatbots for computer modelling and code generation was slow because both required a good understanding of the models and excellent prompt engineering skills.

Overall, the students at the College of Business and Economics embraced the AI tools as quickly as possible. At the same time, they felt that AI tools were ineffective in helping them with critical thinking. AI tools are generative and not creative. Critical thinking is necessary to augment generative AI tools. Therefore, instructors designing curriculum in the future must give primacy to critical thinking and prompt engineering.

Finally, contrary to popular sentiments, as the initiator and curator of learning, the instructor will continue to be at the center of instructional pedagogy, including curriculum design and instruction delivery.

Keywords: E-learning, AI in Learning, AI Tools in Education, ChatGPT, Copilot, Business Curriculum

The introduction of chatbots in the e-commerce world is transforming the user shopping experience and propelling a shift toward digital bargaining. A growing number of consumers are adopting and continuously using negotiation bots for online shopping. Research found that developing better relationships with consumers—such as building trust and increasing customer satisfaction—played an important role in the tool’s re-usage.1 These bots aim to increase sales for e-commerce stores while offering customers the prices they want to pay. However, the conversion rate for turning website visits into sales currently sits at less than three percent, which falls short compared to the projected growth in global retail e-commerce sales from 6 trillion USD in 2024 to 8 trillion USD in 2028.2,3 Tech start-ups seek to increase the conversion rate dramatically by enabling digital bargaining on e-commerce platforms.

A Toronto-based start-up offers a plug-in bargaining bot for online businesses. It guides customers through a five-step process that begins with the bot making a lower-price offer and concludes with a better deal negotiated by both parties. An analysis of the industry partner revealed common issues faced by customers and online stores when interacting with bargaining bots. Namely, the large range of the Zone of Possible Agreement (ZOPA) enables the bot to immediately accept a customer’s offer if it falls within the fixed range. The re-negotiation feature allows customers to “re-try” and estimate a resistance point, which may hinder the store’s ability to profit sufficiently. As price is the sole issue available for discussion, neither party has the flexibility to go beyond distributive negotiation techniques to create a bargaining mix. These shortcomings pose a potential issue for e-commerce stores in maintaining a balance between capturing revenue and providing customer satisfaction.

Using theories learned in an undergraduate negotiation course and recent research in the behavioural sciences, the authors present three strategies for bargaining bots to overcome these issues. Integrating distributive bargaining strategies will promote value creation—as opposed to value claiming—and allow the influence of resistance points. Diversifying responses by applying a scenario-based communication approach will expose customers to a wider range of responses from the bot. Finally, a verbal option will offer customers a more humanistic experience by mimicking the qualities of a face-to-face negotiation. Research provides evidence for and against these strategies, which the authors assess in terms of situational factors and sustainability. These strategies are designed to aid bargaining bots in driving up sales, reducing shopping cart abandonment, and fostering a positive relationship between customers and e-commerce stores.

The authors conducted this research as part of a course project collaboration with industry partners on how AI can be used in negotiation. It is related to the project described by the course instructional team led by Radhakrishnan.

Key Words: chatbots, negotiation, conversion rates, e-commerce

Smartphones are a part of students' daily lives today, with many functions either inhibiting or improving their performance. While mobile phones offer many learning platforms, excessive and unguided usage has raised concerns about their negative impact on students' academic engagement. This study investigates the influence of smartphone addiction on the academic engagement of students at the Vavuniya branch of the Sri Lanka Institute of Advanced Technological Education (SLIATE).

The primary objective of this research is to investigate whether there is any relationship between smartphone addiction and academic engagement among students. Further, with a quantitative research paradigm, information was collected from a convenience sample of 75 students using a structured self-administered questionnaire. Standardized scales for measuring smartphone addiction and academic engagement were added to the questionnaire to obtain valid and reliable data.

Descriptive statistics indicated that the participants' mean age was 23.11 years, and they were predominantly female and primarily in their first few years of study. The participants' mean smartphone addiction score was moderate at 2.89 out of 5, while the average academic engagement score was relatively higher at 3.83. Pearson correlation indicated that there was a significant and strong negative correlation between smartphone addiction and academic engagement (r = -0.634, p < 0.01), substantiating that higher smartphone addiction is associated with lower academic participation.

Furthermore, multiple regression analysis with predictors such as smartphone addiction, age, and gender found that the model was a significant predictor of 40.2% of the variance in academic engagement (F (4, 68) = 86.588, p < 0.001). Of the predictors, smartphone addiction made a statistically significant negative contribution to academic engagement (β = -0.071, p < 0.001), lending support for the hypothesis that smartphone overuse detracts from students' active participation in academic affairs.

These findings emphasize the detrimental impact of smartphone addiction on students' academic engagement and the need for educational establishments and policymakers to develop effective interventions. Increasing awareness and encouraging balanced smartphone use may foster healthier digital behavior and, as a consequence, enhance academic participation and attainment among Sri Lankan students.

Limitations of the study are its limited sample size of one branch, which reduces the external validity of the findings to other students. A cross-sectional study design also does not allow for causal inferences. Future research can employ larger and more heterogeneous samples and longitudinal designs to find out the long-term effects of smartphone addiction on learning.

Key words: smartphone addiction, academic engagement, higher education, academic affairs, Vavuniya

We examined how AI technologies shape how students learn negotiation and conflict resolution. This multi-year, multi-project collaboration uniquely integrates AI simulation, reflective writing, and real-world industry engagement to advance both negotiation competence and critical awareness of bias in business education. Business undergraduates in a North American university enrolled in an advanced negotiation course practiced the skill of generating and interpreting multiple and simultaneous offers (MESOs) with a customized negotiation simulator bot. Then they formed teams to conduct academic research and generate recommendations for application cases of AI in negotiation in industry projects.

To do these projects, students first reflected how they reacted to the simulator bot and then conducted research on how to implement a negotiation strategy with AI technologies.   Fifty students completed 700+ negotiation attempts using a car-purchase simulator with k-modes clustering revealing cooperative strategies outperformed competitive approaches. Large language model analyses of student reflections on learning from the bot revealed that those who used more emotionally regulated and strategic language performed better. Students then integrated their learning by conducting research for industry partners on projects using AI in business negotiations. In one project, students acted as consultants for a Toronto-based e-commerce startup, developing negotiation strategies for an AI-driven bargaining platform intended for Shopify integration. In another, students addressed workplace conflict by designing and presenting challenging scenarios involving manipulation and bias, which were then used to create AI chatbot training modules for women and ethnic minority professionals. Student projects describing their classroom research will be presented alongside ours. These include studies on AI-driven bargaining bots for e-commerce, AI chatbot training modules for workplace conflict and equity, and gamified negotiation scenarios, each submitted as separate abstracts by student teams as part of this initiative.

Analysis of engagement metrics revealed that the use of the professor’s academic research on negotiation peaked before team presentations. Explicit grading of attendance and participation in the second iteration of the course improved accountability and allowed clearer differentiation between highly engaged and less involved students. While attendance remained high, term test scores were more widely distributed when attendance and participation were explicitly graded, suggesting that active participation were more closely linked to learning negotiation than physical presence in the class alone.

Our results reinforce the value of pairing AI simulations structured onboarding, reflective writing, and real-world industry engagement in teaching negotiation and conflict resolution. Students initially struggled with the mathematical complexities of creating and interpreting 3 MESOs of 8 issues with multiple positions in each and interpreting similar responses from the negotiation bot, highlighting the need for pre-simulation workshops and clearer instructional framing. The rigidity of the bot’s numerical responses simulator sparked reflections about emotional control, predictability, and information exchange in human vs AI-mediated negotiations.

Our initiative demonstrates that integrating validated AI tools, scenario-based experiential learning, and industry partnerships can foster both negotiation competence and a critical awareness of bias in future business leaders. We offer recommendations for educators and developers, emphasizing transparent AI logic, culturally nuanced training, and ongoing academia-industry collaboration

Bananas are one of the most widely consumed fruits worldwide, and they come in various shapes, sizes, and colors. Bananas are one of the few tropical crops that have not been bred successfully, and all presently cultivated varieties are natural selections. Traditionally, human experts perform the classification based on visual inspection, which can be subjective and time-consuming. Therefore, developing an automated system that can accurately classify bananas based on visual features would be highly beneficial. Feature engineering became easier after the Convolutional Neural Network (CNN) was developed. Five distinct varieties of bananas were categorized in this proposed work using the CNN model. To improve classification performance, many training images are needed when using the CNN model for classification. Both the original and enhanced images were used to train and evaluate the suggested CNN model. During training, the CNN model achieved an overall validation accuracy of 87%.

Sri Lanka has an increasing number of patients affected by various skin diseases. Accurate identification of these diseases remains a critical yet challenging task due to visual similarities in texture, color, and patterns across different conditions. Misdiagnosis based on visible characteristics can delay treatment and worsen patient outcomes. Early and precise identification is essential to ensure effective treatment, minimize diagnostic errors, and avoid unnecessary or incorrect medication. The purpose of this study is to develop an automated model capable of accurately classifying skin disease images. Conditions such as Erythema (a bacterial infection) and Tinea Ringworm Candidiasis (a fungal infection) often exhibit visually similar features, complicating manual diagnosis. However, these diseases differ significantly in terms of etiology, symptoms, and treatment protocols. Accurate early detection empowers patients with limited medical knowledge to seek appropriate care in a timely manner. Convolutional Neural Networks (CNNs) have been widely used for skin disease detection and classification, with several studies in Sri Lanka applying CNNs to identify local skin conditions. This study adopts a CNN-based approach using transfer learning, applying pretrained models to a specialized dataset containing two classes: Erythema and Tinea Ringworm Candidiasis. A total of 2100 images were used, with appropriate image enhancement techniques applied. In the initial phase, the VGG-16 CNN model was trained on the dataset, achieving a final training accuracy of 94.26 % and a validation accuracy of 95.02%. To further improve performance, the VGG-19 CNN model, comprising 19 layers, was implemented using the Adam optimizer. The VGG-19 model achieved a training accuracy of 97.71%% and a validation accuracy of 98.41%. Despite VGG-19 achieving higher accuracy in this case, both models demonstrated strong potential for automated skin disease classification. The findings indicate that CNNs, particularly when fine-tuned using transfer learning, can be highly effective in aiding medical professionals and enhancing early diagnosis. 

Keywords: Skin Disease Identification, VGG-16, VGG-19, Erythema, Tinea Ringworm Candidiasis.

Abstract 

It is now possible to generate abstracts and even complete articles with the help of Artificial Intelligence (AI) tools. As a conference organizer and reviewer of numerous abstracts and articles, the author has recently witnessed submissions being either created or curated by AI. While AI is now an integral part of academic writing, including writing abstracts or articles, using AI primarily to generate an article or abstract cannot be accepted by any standard of academic integrity.

For example, AI can be allowed to generate ideas based on a writer's original thoughts, perform a literature survey, and polish the language of the abstract before submitting it to a conference. However, it is unacceptable to generate an entire abstract based on a generic chat, such as “write an article on the use of AI in cybersecurity”.

When an abstract is generated by AI, it is even more challenging to review the abstract, given its brevity. It then becomes difficult to authenticate an abstract to ensure that submissions to academic conferences are original, not generated predominantly by AI, and that the content does not closely replicate an abstract or study published elsewhere.

The process of authenticating an abstract will depend on the expected rigor of the review process. A four-step approach to authenticating an abstract that combines human effort and AI tools is proposed in this paper to strike a balance between the rigor of review and the burden placed upon the reviewers' time and effort.

The initial step of the review process should start with a human review. The review should proceed to the next step only if substantial AI involvement is detected in generating or curating the abstract. In this case, the next step would be originality detection. Publication verification would be the third step. The fourth and final step would be the identification of AI-generated content.

A combined approach to originality detection, publication verification, and AI-generated content identification was tested on abstracts submitted to a conference using ChatGPT. Some of the findings from that test are presented in the paper. The paper also lists individual tools that can be used in steps two to four to authenticate the abstract. 

The conclusion reached was that for any abstract or paper to add value to the academic discourse, it must have original ideas as the inception of the content, details of experimentations conducted, and the author's experience listed to substantiate the theory or recommendations presented. The content also should not duplicate or enhance previously published content.

Finally, the ideas and thoughts expressed in the paper should be considered as a starting point for developing an authentication process for conference submissions. The articles created with the help of AI have started appearing only recently. As more experience is acquired collectively by authors and reviewers, a robust approach to reviewing submissions to conferences and journals is likely to emerge, which should help maintain the integrity of conferences and journals.

Keywords: AI Tools, Academic Writing, Abstract Authentication, Academic Integrity, Conference Reviews

In the evolving landscape of digital education, fostering a collaborative and online learning environment has become increasingly vital. This study investigates the impact of ethical leadership on knowledge sharing within e-learning platforms, specifically analyzing the mediating role of learner engagement. Ethical leadership, typically characterized by traits such as integrity, transparency, and fairness, plays a pivotal role in shaping learner behavior and fostering trust among participants in virtual education settings. Drawing from social learning theory and the job demands-resources model, this study adapts these organizational behavior concepts to the educational domain, proposing that ethical instructional practices positively influence learners’ willingness to share knowledge and collaborate.

The research employs a quantitative approach, collecting data from students engaged in online learning environments across various institutions. Validated instruments were used to measure perceptions of ethical leadership by instructors or facilitators, levels of engagement, and the frequency of knowledge sharing behaviors. Statistical analyses, including multiple regression and mediation/moderation testing via the PROCESS macro, were conducted to explore the relationships among these variables.

The findings indicate a strong and significant positive relationship between ethical leadership and knowledge sharing. Ethical leadership was also found to significantly enhance learner engagement. Further, learner engagement mediate the relationship between ethical leadership and knowledge sharing to a statistically significant extent.

These results highlight the direct importance of ethical leadership in promoting collaborative knowledge practices in e-learning environments. While engagement remain valuable factors in educational success, their roles as mediating influences in this specific model are limited. This research contributes to the growing body of knowledge on online pedagogy and offers practical insights for educators, administrators, and instructional designers aiming to cultivate ethical, engaging, and knowledge-rich virtual learning spaces.

Keywords : Ethical Leadership, Knowledge Sharing, E-Learning, Learner Engagement

It is obvious that mastering present perfect tense can significantly improve a student’s ability to analyze narrative frameworks, understand character development, and appreciate thematic significance in English poetry. Further, it empowers students to articulate complex ideas, refine their writing skills, and elevate their overall language proficiency. In today’s digitally driven world, the integration of computer technology in language and literature education is not just beneficial, but almost indispensable. When coupled with the power of computer technology, mastering the present perfect tense can become a catalyst for students to unlocking deeper comprehension and appreciation of poetry. Having realized this fact, this study focused on developing a computational tool, called “Present Perfect Tense: Learner and Tester”, to serve as a valuable educational resource, facilitating the mastery of the present perfect tense by immersing students in its usage within authentic poetic contexts. The study employed a qualitative and descriptive approach, involving twenty second-year language students from Eastern University, Sri Lanka (EUSL). The data for the study were collected from a variety of English poems. To gauge the effectiveness of the developed tool, two tests were administered. First, a pretest, applying the traditional methods assessed the students’ initial understanding of the present perfect tense, followed by a post-test, utilizing the developed computational tool as a learning and testing platform. The results indicated a significant improvement in the participants’ performance on the post-test compared to the pretest. Thus, the findings reveal that a significant improvement in students’ comprehension and engagement with English poetry can be gained when they utilize the computer-technology-incorporated tools in demonstrating the proficiency in the present perfect tense. Further, the implications of this research suggest that the mastery in present perfect tense can serve as an innovative foundational tool for not only understanding English poetry, but also advancing overall English literature education.

Keywords: present perfect tense, English poetry, language proficiency, EUSL, computational tool 

The rapid integration of Artificial Intelligence (AI) technologies into organizational processes has revolutionized the global IT industry, and Sri Lanka is no exception. While AI adoption promises enhanced efficiency and competitive advantage, its implementation often induces uncertainty, job insecurity, and employee resistance. This study explores the relationship between AI adoption and psychological safety in the workplace, specifically focusing on the moderating role of ethical leadership within the Sri Lankan IT sector. Drawing upon the theoretical underpinnings of the Technology Acceptance Model (TAM) and the concept of psychological safety, this research investigates how employees’ perceptions of AI adoption affect their sense of safety to express themselves without fear of negative consequences. Psychological safety is crucial in determining the success of AI implementation, as it influences innovation, collaboration, and openness to technological change. Using a quantitative methodology, data were collected from 312 IT professionals across a diverse range of Sri Lankan IT firms through a structured questionnaire. The study employed hierarchical regression analysis to examine the direct effects of AI adoption on psychological safety and the moderating effect of ethical leadership. Findings reveal a significant negative relationship between AI adoption and psychological safety when ethical leadership is perceived as low. However, in environments where ethical leadership is strong, the negative effects of AI adoption on psychological safety are significantly mitigated. Ethical leaders, through transparent communication, fairness, and support, cultivate a culture of trust and inclusiveness, thereby buffering the anxieties related to AI-driven organizational changes. This study contributes to both academic literature and managerial practice by emphasizing the critical role of ethical leadership in ensuring employee well-being during technological transitions. It highlights that while AI adoption is inevitable, its human implications cannot be overlooked. Leaders in the Sri Lankan IT sector must not only champion technological change but also serve as moral agents who prioritize psychological safety and ethical standards. The findings have broader implications for emerging economies where digital transformation is accelerating but socio-cultural sensitivities and job security concerns are pronounced. Future research could explore longitudinal impacts of AI on employee attitudes and examine sectoral differences beyond IT. Overall, this study underscores that ethical leadership is not just desirable, but essential in navigating the complexities of AI integration in a psychologically safe and sustainable manner.

Keywords: AI Adoption; Ethical Leadership; Psychological Safety; Sri Lankan IT sector

The rapid evolution of work arrangements accelerated by technological advancements has positioned the hybrid work environment as a predominant model, especially within the Information Technology industry. This study explores the impact of the hybrid work environment on the quality of work life of IT professionals in Sri Lanka, with a special focus on the moderating role of Artificial Intelligence technologies. To recognise the pivotal role of the IT sector in Sri Lankan economic growth and the unique challenges posed by the transition to hybrid work, this research addresses a critical gap in understanding how flexible work structures influence employee well-being and organisational outcomes in a developing country context.

Adopting a positivist philosophy and a quantitative research design, the study collected data through a structured online questionnaire distributed to 384 employees from ten leading IT companies. The questionnaire measured participants’ experiences of a hybrid work environment, quality of work life, and the extent of AI integration in their work processes. 

Furthermore, the study identified AI as a significant moderator that strengthens the positive effects of the hybrid work environment on the quality of work life. This suggests that AI tools facilitate smoother transitions and adaptations to hybrid work, enabling employees to manage workloads more efficiently and reduce stress associated with remote or flexible work conditions.

These findings carry substantial implications for IT organisations seeking to optimise hybrid work strategies. It is recommended that companies invest in sophisticated AI technologies tailored to support hybrid workflows and provide adequate training to employees to leverage these tools effectively. The results offer practical insights for policymakers, HR professionals, and IT leaders aiming to navigate the evolving landscape of the workplace and ensure that hybrid work arrangements are leveraged to maximise both employee satisfaction and organisational performance. 

Keywords: Artificial Intelligence, Hybrid Work Environment, Quality of Work Life, IT Industry

This abstract describes my journey of the adaptation of meditation and mindfulness sessions from in-person to online during the COVID-19 pandemic, and explores participant engagement and lessons learned from using digital platforms. The pandemic restrictions changed the way in which we work and interact in the world. Most of us could not go to offices and had to find ways to work from home using the internet. Before pandemic my meditation and yoga sessions used to be mostly in person. But because of the lockdowns and pandemic restrictions, I had to switch my meditation classes from face-to-face sessions to online sessions. Another motivating factor for me to offer the classes was that a lot of people were experiencing symptoms of anxiety and depression during the pandemic period. It is well known that meditation helps to reduce the symptoms of anxiety and depression, as has been discussed in my book “Roots of Health and Happiness” ⁽¹⁾.

The meditation sessions were initially offered on a voice-only app called Clubhouse. In the beginning, 4 sessions were offered every day with an average turnout of about 20 people in each session. The demographic was mostly from US, and Europe, but also other countries from Asia and North America. The voice-only app was quite popular in 2021 and 2022, and therefore the turnout remained consistent for that time. After that, as people started going back to their offices, they had lesser time on their phones. So, I had to offer Zoom sessions additionally in order to provide more flexibility to the participants.

Feedbacks from the participants were regularly taken. The benefits reported by the participants include improvement in sleep quality, reduced anxiety, depression and stress, mental clarity and many others. This study highlights the effective use of technology to offer meditation classes online and help people overcome mental health related issues.

Background

Career-relevant problem-based learning (CBPL) and structured reflection have been shown to enhance management students’ analytical and metacognitive skills. In our course, students participated in iterative CBPL cycles—practice, feedback, and revision—where each role-play exercise was preceded by a plan and was followed by a brief reflection that were graded on a credit/no credit to encourage writing and sustain engagement, while longer assignments received targeted, rubric-based feedback. This approach aligns with Kellogg’s model, guiding students from knowledge-telling (recounting experiences), to knowledge-transforming (analyzing experiences with feedback), and ultimately to knowledge-crafting (tailoring writing for an audience). 

Building on this foundation, we examined how students reflected on the use of AI tools in a real‐time, high‐pressure “electronic in‐box” simulation. Previous research suggests that students recognize both the benefits (efficiency, usefulness) and risks (metacognitive blunting, overreliance) of AI. Situating the inbox simulation within the CBPL framework was aimed to ensure that students’ reflections on AI were grounded in strong analytical and metacognitive habits.

Methodology

We conducted a thematic analysis of 34 student reflections on the inbox simulation in which students role-played a district manager of a donut company tasked with responding to 10 emails in 30 minutes. The simulation occurred midway through the semester as part of a curriculum emphasizing practice reflection, and feedback. After each role-play, students wrote short reflections for credit. On the final exam, they submitted longer reflections on all course exercises, including the simulation. Each reflection was graded for analytical, and argumentative writing. We used NVivo to identify themes and calculated point-biserial correlations to examine relationships between reflection themes and their quality.

Results

Of the 34 participants, 41% reported using AI for the inbox simulation while the remaining 59% did not. The three most commonly cited benefits of AI were efficiency (100%), usefulness (88%), and enhanced communication (65%). One participant noted, “It enabled me to respond more effectively by making the response time quicker.” The most frequently cited risks were over-reliance (29%), metacognitive blunting (29%), and lack of authenticity (24%). One participant cautioned, "There is a huge risk of over-dependence on AI.” Lack of confidentiality (17%) was the least frequently noted risk, suggesting privacy concerns were relatively minor.

Students who used AI during the inbox simulation received higher grades on their reflections (r = .71, p < .001). Those who recognized the risk of overreliance on AI earned higher grades for their insights (r = .60, p < .001), as did those who noted AI’s potential to improve communication (r = .58, p < .001).

Conclusion

Embedding real-time managerial simulations within a CBPL framework—anchored by structured reflection prompts and targeted feedback—fosters deeper analytical and metacognitive engagement. Reflections demonstrating deeper analytical reasoning about AI in managerial tasks earned higher grades, supporting our thematic analysis. Student concerns about metacognitive blunting and over-reliance highlight the importance of discussing AI’s role in management education. Educators should leverage CBPL’s cycle of guided reflection, feedback, and revision to balance AI integration with opportunities that strengthen students’ independent analysis and reflective skills.

Gamification as a Training Tool in Negotiations: Women vs. Gaslighters

Caitlin Dymond, Nana Takai, Samantha Wang, Monique Canalee

Management Department, University of Toronto at Scarborough, Canada

This abstract outlines the design and implementation of a gamified negotiation training tool for women, grounded in the R.E.N.T. framework, and evaluates its potential to address workplace gaslighting.

The manipulative tactic of gaslighting is a form of psychological abuse that will make the victims seem or feel “crazy.” Individuals in power use gaslighting to control others by reinforcing harmful stereotypes and taking advantage of existing inequalities.

We created a case as a learning tool relevant to all workplaces. The case includes various forms of gender inequality in the workplace, such as the wage gap, slower career progression, and fewer leadership opportunities. The new principal at the school consistently gaslights female teachers and treats them unfairly. He dismisses their concerns about major changes by calling them overreactions. When two experienced female teachers were overlooked for a promotion in favour of a less qualified male, he gave vague reasons and denied making experience a factor, causing them to doubt themselves. He dismissed a female teacher’s complaint about unequal pay, saying she misinterpreted the situation. 

The negotiation strategy is built around the R.E.N.T. framework:

• Recognize & Document – Identify and record instances of gaslighting with specifics.

• Escalate & Communicate – Use calm, fact-based language to confront behaviour.

• Network with Allies – Build support from peers to negotiate collectively.

• Take Action – Involve external bodies like HR or unions if necessary.

The strategy acknowledges gender differences in negotiation styles—men often favour assertiveness and data, while women lean towards collaboration and empathy. However, women negotiating in groups feel more empowered and face less backlash. 

We want a bot to teach our scenario through an online game. The player will be shown a video reenactment of our scenario, followed by a video about gaslighting tactics. Afterward, they will play an online game (as the teachers) where they negotiate with a bot (the principal). The user can practice the skills they were taught as the game progresses. The bot will chat with the user and try to gaslight them via dismissing concerns, undermining experience, shifting blame, manipulating perceptions and creating doubt. Tips and facts about the female negotiation style will pop up on the screen to help guide the player. The game will be a quiz where options are presented and the user is to pick which ones will yield the best outcome. Afterward, their results will be analyzed, and they will be given feedback on ways to improve. Participants will practice recognizing and responding to gaslighting, building confidence and collaborative negotiation skills in a simulated environment. This tool is in pilot development and has not yet been formally evaluated.

Keywords: Negotiation Training, Gamification, Gender Equity

This study is part of a collaborative teaching initiative with Radhakrishnan et al, examining technology-driven negotiation training tools, and is intended to be presented alongside related work on AI and equity in management education.

We report on learning outcomes from using an AI-enabled experiential learning platform that facilitates diverse student experiences and tracks negotiation skill development over time. The course combined structured negotiation pedagogy with data-driven practice, beginning with foundational role-plays involving integrative and distributive elements and progressing toward multi-issue, multi-party scenarios with added contingency components. Students were randomly paired with different classmates for each of the four negotiation exercises, exposing them to a range of styles, strategies, and contexts—while eliminating self-selection bias. 

The AI-enabled platform recorded joint outcomes, issue-level concessions, and agreement rates, enabling richer debriefs and individual feedback. Performance improved significantly across sessions: mutually beneficial joint outcome scores rose from M=4737.5 in a foundational salary negotiation to M=736,416.67 in a complex contract dispute—an increase that reached statistical significance (F(3,86) = 3.87, p = .02). 

Our findings underscore the pedagogical value of AI-facilitated experiential learning in fostering collaborative bargaining, strategic decision-making, and reflective practice. By tracking performance longitudinally and offering real-time analytics, the system supported iterative skill refinement in a scalable, asynchronous-friendly format. The platform’s design is especially well-suited to learning environments where repeated, structured peer interaction is critical but instructor oversight is limited—such as hybrid, international, or large-enrollment classrooms. 

We conclude with implications for instructors implementing AI-enhanced learning: scaffold complexity, diversify student pairings, and use outcome metrics as the basis for formative feedback. These strategies not only enhance negotiation outcomes but also support transferable skills such as collaboration, ethical reasoning, and perspective-taking across disciplines.