SLIIT International Conference On Engineering and Technology Vol. 04 [SICET] 2025

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/5064

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Open Access
A Comparative Study on TiO₂/Graphite–PEG and Graphite/Carbon Fibre- Paraffin Shape Stabilized Phase Change Materials for Thermal Energy Storage Applications
(Faculty of Engineering, 2025-09-09) Dananjaya, V; Wen, Q; Abeykoon, P
Shape-stabilized phase change materials (SSPCMs) are promising candidates for latent heat thermal energy storage systems due to their high energy density and ability to prevent leakage during phase transitions. This study presents a comparative analysis of two SSPCM systems: TiO₂/graphite–polyethylene glycol (PEG) and graphite/carbon fibre/graphene–paraffin composites. Both composites were prepared by vacuum-assisted infiltration of molten PCMs into porous expanded graphite networks, with the addition of functional fillers to enhance structural integrity and thermal stability. Scanning electron microscopy (SEM) revealed distinct microstructural features for each system; TiO₂ nanoparticles were uniformly dispersed within the PEG matrix and anchored onto graphite surfaces, while carbon fibres and graphene nanoplatelets formed a hierarchical interconnected network within the paraffin-based composites. Differential scanning calorimetry (DSC) demonstrated that both systems preserved high latent heat storage capacities with slight shifts in phase transition temperatures compared to pure PCMs. Thermogravimetric analysis (TGA) showed improved thermal stability of the SSPCMs relative to neat PCMs, with filler composition significantly affecting degradation onset temperatures. In TiO₂/graphite–PEG composites, DSC analysis showed melting temperatures of 61.4-62.7 °C and solidification temperatures of 53.1-54.0 °C, with latent heats of 185-210 J g⁻¹ depending on TiO₂ content. Graphite/carbon fibre/graphene–paraffin composites exhibited melting temperatures of 54.8-55.6 °C and solidification temperatures of 48.9-49.7 °C, with latent heats of 140-160 J g⁻¹. Thermogravimetric analysis revealed improved degradation onset temperatures: TiO₂/graphite-PEG composites showed higher thermal stability compared to pure PEG, while carbon fibre/graphene–paraffin composites exhibited enhanced thermal resistance relative to pure paraffin. The TiO₂/graphite-PEG composites exhibited higher latent heat capacities and enhanced thermal resistance, whereas the graphite/carbon fibre/graphene–paraffin composites provided superior mechanical reinforcement and phase change reliability. These findings offer insight into the design optimization of SSPCMs tailored for specific thermal management applications.
Open Access
A Cost-Effective Battery Retrofit for Non-Hybrid Grid-Tied PV Systems to Reduce Solar Energy Loss During Grid Outages
(Faculty of Engineering, 2025-09-09) Jayamanne, N
Conventional grid-tied, non-hybrid photovoltaic systems disconnect from the utility grid during outages, resulting in complete solar energy loss despite continued solar generation. This paper proposes a cost-effective retrofit solution that enables energy harvesting and storage during such outages without requiring inverter replacement or voiding manufacturer warranties. The proposed retrofit integrates three blocking diodes, a two-pole DC magnetic contactor, a DC relay, a bidirectional maximum power point tracking (MPPT) DC–DC battery charger, and a microcontroller-based supervisory controller. Under normal grid-connected conditions, the retrofit remains electrically isolated to preserve the inverter’s original operation. During grid outages, available solar energy is redirected to charge a battery through the bidirectional charger. At night, the stored energy is discharged via controlled current injection into one of the inverter’s MPPT inputs through the diode-protected pathway, enabling up to 12 hours of energy utilization depending on local night duration. This approach mitigates solar energy loss during grid failures, eliminates the need for costly hybrid inverter upgrades, and offers a scalable retrofit pathway for residential and small-commercial PV systems.
Open Access
A Data-Driven Approach to Predicting Ischemic Heart Disease Risk in Monaragala: Integrating Lifestyle and Symptom Factors with Machine Learning
(Faculty of Engineering, 2025-09-09) Meddepola, M.A.R.L.; Wickramasinghe, B.M.G.S.T.S.K.
Ischemic Heart Disease (IHD) remains a leading cause of mortality worldwide and presents a critical challenge in underserved rural areas such as Monaragala, Sri Lanka. Traditional IHD prediction methods predominantly depend on clinical diagnostics like ECGs and blood tests, which are often unavailable or inaccessible in such regions. This study aims to bridge this gap by developing a machine learning-based prediction model that utilizes only lifestyle and symptom-related data, eliminating the need for invasive clinical procedures. A dataset comprising lifestyle habits (e.g., diet, smoking, alcohol use, exercise) and symptom indicators (e.g., chest pain, fatigue, dizziness) was collected via surveys. Feature selection using Logistic Regression identified the top eight most relevant predictors. Five machine learning algorithms, Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest, were trained and evaluated. Among them, the Random Forest model achieved the highest performance with an accuracy of 83.5%, precision of 0.86, recall of 0.78, and F1- score of 0.81, demonstrating strong predictive capability based solely on non-clinical features. In addition, a web-based self-assessment tool was developed to make the model accessible to the public, particularly targeting individuals in rural areas with limited healthcare access. The tool enables users to input basic lifestyle and symptom information and receive a real-time risk assessment. The findings confirm that the model leveraging lifestyle and symptom data can effectively identify individuals at risk of IHD. This approach supports the development of scalable, low-cost, and user-friendly screening tools that can enhance early detection and preventive care, especially in rural and resource-constrained settings.
Open Access
A Numerical Investigation of the Potential of Dimpled Surface Configurations to Improve Aerodynamic and Aeroacoustic Performance of Airfoils
(Faculty of Engineering, 2025-09-09) Fernando, N.N; Nissanka, I; Samaraweera, N
This study investigated the potential of dimpled surface configurations to enhance the aerodynamic and aeroacoustic performance of airfoils. Computational Fluid Dynamics (CFD) simulations were carried out on a NACA 0012 airfoil featuring surface dimples, under flow conditions relevant to low-speed aerodynamic applications such as unmanned aerial vehicles (UAVs), light aircraft, and small-scale wind turbines. The simulations were conducted at a Reynolds number of 700,000 and a Mach number of 0.21, representing typical subsonic operating conditions. Two angle of attack, 5° and 10°, were examined to represent attached flow and near-stall behavior, respectively. Aerodynamic performance was evaluated through lift and drag coefficients, while aeroacoustic characteristics were analyzed using Overall Sound Pressure Level (OASPL) with directivity plots and frequency spectrum analysis based on the Ffowcs Williams–Hawkings (FW-H) acoustic analogy. Key findings indicate that the dimpled configuration enhances flow behavior by increasing lift and reducing drag at a 10° Angle of Attack (AoA), primarily through delayed separation and modified stall onset characteristics. Aeroacoustic analysis showed a noise reduction of 2–7 dB at various receiver positions at a 10° AoA, with reductions varying by observer angle and frequency, confirming the directional sensitivity of noise emissions. These insights contribute to the understanding of passive flow control mechanisms and their dual impact on aerodynamic performance and noise reduction in airfoil design
Open Access
A Numerical Investigation of Valve Timing and Intake Pressure Effects on Performance and Emissions in a Hydrogen Port Fuel Injection Engine
(Faculty of Engineering, 2025-09-09) Wickramaarachchi, I; Nissanka, I.D; Wijeyakulasuriya, S
Hydrogen internal combustion engines (H2ICEs) offer a viable low-emission alternative for decarbonizing transport, especially where full electrification is not practical. Among fueling strategies, port fuel injection (PFI) is particularly attractive due to its compatibility with existing engine platforms and simplicity compared to direct injection (DI). Performance and emissions in hydrogen PFI engines are strongly influenced by valve timing and intake boosting strategies. This study presents a computational framework to investigate the coupled effects of valve timing and intake pressure on the performance, thermal efficiency, and NOx emissions of a hydrogen PFI engine under fuel-lean conditions (ϕ = 0.59). A modified Sandia optical engine geometry was simulated using CONVERGE CFD v4.1, employing detailed chemistry and adaptive mesh refinement. Latin Hypercube Sampling (LHS) was employed to generate 373 design cases that span a wide parametric space. Results show that intake boosting significantly improves performance, achieving a 220% increase in indicated power (up to 43.55 kW) and an 11% improvement in thermal efficiency (up to 48.7%) over the baseline configuration. However, these gains are accompanied by elevated NOx emissions, particularly at higher valve overlaps. Conversely, the configuration that achieved the lowest NOx emissions reduced them by 76% compared to the baseline, albeit at the expense of lower power and efficiency. The three configurations representing the most favorable outcomes for power, efficiency, and emissions within the studied parameter space highlight the inherent trade-offs among these objectives. These results provide practical guidance for calibrating hydrogen PFI engines and establish a solid foundation for future studies incorporating formal optimization methods.
Open Access
Construction Dynamics And Digitalization
(Faculty of Engineering, 2025-09-09) Premachandra, P.N
The construction industry is at the edge of a decisive transformation, moving away from fragmented, paperbased practices toward an era defined by intelligent digitalization. At the center of this shift is the Digital Twin- a living, data rich model that synchronizes the physical and virtual realms of construction. By Integrating Building Information modeling (BIM), Internet of Things (IoT) sensors, artificial intelligence, and cloud computing, Digital Twins enable Projects to move from reactive monitoring to proactive, predictive control. This paper examines their influence on Construction dynamics, demonstrating how 4D scheduling with Primavera P6 and intuitive dashboards guided by PMBOK-7 principles elevate visibility, collaboration, and decision making. A case study of the Maldives International Airport new terminal illustrates tangible outcomes: real-time clash detection, optimized sequencing, energy efficient design, and measurable carbon emission reductions. Beyond showcasing benefits, the study outlines a pragmatic roadmap for Sri Lanka, stressing the importance of regulatory reform, academia-industry partnerships, and pilot implementations. The findings suggest that Digital Twins are not distant aspirations but present-day necessities for sustainable, data driven construction.
Open Access
Design, Simulation, and Optimization of a Hybrid Hydrogen Fuel Cell–Battery Energy System for Sustainable Electric Vehicle Applications Using MATLAB Simulink
(Faculty of Engineering, 2025-09-09) Samaranayake, W.A.K.L.
The transition to sustainable transportation demands energy systems that are both efficient and environmentally friendly. This paper presents the design, simulation, and optimization of a hybrid energy system that integrates Proton Exchange Membrane (PEM) hydrogen fuel cells with lithium capacitor batteries for electric vehicle (EV) applications. The system aims to combine the high energy density of hydrogen with the fast response and recharge capabilities of advanced battery technologies to meet varying load demands efficiently. Using MATLAB Simulink, a hybrid model was developed to evaluate dynamic power sharing between the fuel cell and battery under variable driving conditions. A boost converter regulated the fuel cell output, while a bidirectional DC-DC converter managed power flow between the battery and the load. Maximum Power Point Tracking (MPPT) was implemented to optimize hydrogen fuel cell performance, enhancing energy efficiency under transient conditions (Dursun & Kilic, 2012). Simulation results demonstrated improved voltage stability, reduced stress on individual sources, and efficient energy utilization. Optimization of component sizing and control strategy further enhanced system response and fuel economy. These findings highlight the hybrid system’s potential for reducing EV range anxiety and promoting the use of renewable energy carriers such as hydrogen. This work contributes to ongoing research in sustainable mobility by offering a technically viable and scalable energy architecture that addresses the limitations of standalone battery and fuel cell systems. Future work will extend to hardware implementation and control refinement to accommodate real-world uncertainties and ensure robust performance.
Open Access
Developing A Web-Based Augmented Reality Tool For Promoting Sustainable Fashion Consumption
(Faculty of Engineering, 2025-09-09) Fernando D.T.; Methma S.L.K.K; De Silva R.K.J.
This study explores the use of Augmented Reality (AR) as an intervention to promote sustainable fashion consumption among Sri Lankan consumers. It also involves developing a web-based AR tool designed to educate users about sustainable fashion and evaluating its acceptance within the target audience. This research identified that young consumers in Sri Lanka demonstrate a low level of awareness regarding sustainable fashion consumption. Therefore, this study addresses an important gap by analyzing how interactive digital tools can influence consumer education and promote green purchase behaviors. To gather requirements for developing the AR tool, a qualitative research method was employed through focus group discussions with 8 Gen Z participants representing diverse fashion preferences. For validation, the tool was further tested with 30 participants to evaluate usability, engagement, and effectiveness. Nine themes relevant to AR tool development were identified through thematic analysis, which highlighted the awareness about sustainable fashion, patterns of digital learning, and sensitivity to AR capabilities. This study revealed a strong consumer intention to engage with visually dense, socially sharable, and mobile-optimized AR applications. To address these points, a prototype web-based AR platform was designed using MyWebAR platform, alongside real time information on garment sustainability through the scanning of QR codes. The results demonstrate that AR can successfully engage consumers, increase awareness of the environmental footprint of fashion, and enable behavioral change when deployed on familiar social media platforms. The research concludes that interactive, culturally relevant AR tool experiences have strong potential to influence sustainable fashion practice among consumers.
Open Access
Development of an AI-Based Model with Low Computational Complexity for Accurate Load Demand Forecasting
(Faculty of Engineering, 2025-09-09) Hettiarachchi, D.R.A.; Fiernando, N
This research addresses the challenge of short-term load demand forecasting in microgrids, where renewable energy unpredictability destabilizes power systems. Current forecasting models often suffer from high computational complexity, resulting in increased power consumption and reduced real-time applicability. To overcome these limitations, this study develops and optimizes an Artificial Neural Network (ANN)-based shortterm forecasting model with significantly reduced computational demands. In this study, a model was constructed utilizing historical operational data from a microgrid system. To optimize the computational efficiency of the model, various techniques were applied to reduce its complexity. The model’s performance was systematically evaluated using appropriate performance metrics. The experimental results demonstrate that the proposed approach significantly decreases the computational complexity of the final model, while preserving an acceptable level of accuracy when compared to the original, unoptimized model. The practical implications of this research include enabling real-time demand forecasting on resource-constrained microgrid controllers and edge devices, facilitating more efficient energy management in sustainable power systems. Future work will focus on enhancing the model's generalization capabilities by incorporating additional geographical and climatic factors, enabling accurate demand forecasting across diverse microgrid environments beyond the specific conditions of the initial dataset.
Open Access
Development Of An Ai-Based Model With Low Computational Complexity For Accurate Solar Energy Forecasting
(Faculty of Engineering, 2025-09-09) Chandrasinghe, S; Fernando, N
This paper introduces a short-term solar energy forecasting model that is designed with a focus on low computational complexity and addresses the challenges posed by fluctuations in solar energy generation, which are significantly influenced by environmental factors. These fluctuations can lead to instability when solar power generation systems are integrated into national energy grids, creating difficulties in maintaining a balanced supply and demand. If solar energy generation can be accurately forecasted before fluctuations occur, potential issues can be identified in advance, allowing for better management of the energy system, including optimizing storage facilities when energy generation is high. Current solar energy forecasting systems face significant challenges due to their high computational complexity, which results in increased power consumption and lower accuracy. To address these issues, this study focuses on the development of an artificial intelligence (AI)-based forecasting model using an Artificial Neural Network (ANN). The goal is to reduce the computational complexity of the model while maintaining high accuracy. To achieve this, various data analysis and complexity reduction techniques, such as variable reduction, pruning, and quantization, were applied. The performance of the optimized AI model was evaluated by comparing the forecasted values to actual solar energy generation data. The results demonstrate that the proposed model successfully reduces computational complexity while maintaining a satisfactory level of accuracy. This optimization makes the model more suitable for real-time forecasting, particularly in resource-constrained environments, and provides a more efficient approach to solar energy management. The findings of this study suggest that AI-based forecasting models can play a critical role in enhancing the integration of solar energy into national grids, ensuring a more reliable and sustainable energy supply. Further research could explore additional optimization techniques and the introduction of generalization techniques to improve transferability of the model and applicability across diverse geographical regions. Additionally, focus on utilizing AI techniques that minimize computational complexity without compromising the accuracy of the model, aiming to maintain high forecasting precision while optimizing the efficiency of the system.
Open Access
Development of an AI-Based Model with Low Computational Complexity for Accurate Wind Energy Forecasting
(Faculty of Engineering, 2025-09-09) Dilshan, S; Fernando, N
Most countries primarily relay on fossil fuel for electricity generation, leading to fossil fuel depletion and environmental pollution. The countries are developed technologies for renewable energy generation. The wind energy being promoted as a superior renewable energy. However, wind energy has its challengers, particularly uncertainty that can affect overall system stability. The accurate short-term forecasting of wind energy was crucial for ensuring grid stability. Both physical and AI-based models can effectively be utilized for wind energy prediction. AI-based methodologies have shown superior effectiveness, efficiency, and accuracy when compared to traditional physical models. The lightweight AI-based forecasting model was particularly significant for processing devices, enabling faster computations and substantially more cost-effective forecasting. The research utilized simulation software to develop an Artificial Neural Network (ANN) model, initially incorporating eight meteorological parameters. Four of these parameters showed weak correlations and were subsequently removed from the model. Further optimization was achieved through pruning and quantization techniques, significantly reducing computational complexity. The optimized model demonstrates a notable reduction in both training time by 92.69% and inference time by 63.83%, while maintaining accuracy with only a marginal decrease of 3.99% compared to the initial model. These improvements were achieved with minimal loss in predictive accuracy, significantly reducing computational complexity. The study concludes that the optimized ANN model is wellsuited for real-time wind power forecasting, offering a balance between accuracy and computational efficiency. This approach not only facilitates better grid management but also extends the applicability of AI-based forecasting to devices with limited processing capabilities. Future work could explore additional complexity reduction techniques and broader deployment scenarios.
Open Access
Development of Low-cost Slipper by using NR/EVA Blend with Recycling Materials for reducing Environment Pollution in Footwear Industry
(Faculty of Engineering, 2025-09-09) Randika K.G.; Perera K.P.M.; Gunaratne R.D.
This study aims to develop a low-cost slipper compound by blending low grade Natural Rubber (NR), Ethylene Vinyl Acetate (EVA), and recycled LDPE plastics granules with crumb rubber in different phr (parts per hundred rubber) amounts. Low grade natural rubber (off grade brown scrape) and ethylene vinyl acetate (19 wt.% of vinyl acetate) were used during formulation in order to reduce cost. During this compounding process, polymeric material and other chemical ingredients were masticated by using a kneader and two roll mills then sheet was prepared by using calendaring techniques, eventually curing was performed by using a compressing molding method. Blowing agents were used to obtain the Slipper sheets’ inter cellular structure. Peroxide curing system has used due to natural rubber blend with ethylene vinyl acetate. When preparing compound batch, different phr amount of crumb rubber and recycled LDPE plastics granules blended. Firstly, crumb rubber sheets which were punctured and waste scrap sheets were obtained, then converted into 30 mesh size small particles by using grinding and crush method. Hardness and Abrasion tested of prepared slipper. After curing process higher hardness value observed when increasing crumb rubber phr. As particle size increases, there was a tendency of asymmetrical spread of compounding ingredients through the mixture and this was mitigated by additions of processing oils which increased the dispersion of the particles. In summary, through this work, ideal compounding formulation with phr values was able to determine that can be used to manufacture to low-cost slipper sheet at industrial scale.
Open Access
Diabetic Retinopathy Screening Using Image Processing
(Faculty of Engineering, 2025-09-09) Wijesekara, W.M.M.P.; Karunarathna, A.E.S.H.; Haluwana, H.M.R.M.K.; Jayawardhana, S.M.M.S
Diabetic retinopathy, a grave consequence of diabetes mellitus, has emerged as the leading cause of visual impairment worldwide. This ocular condition arises from the deterioration of blood vessels situated behind the retina and progresses insidiously, ultimately leading to blindness. Early detection is paramount in mitigating vision loss among afflicted individuals. In this study, we propose three distinct approaches Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Residual Networks (ResNets) for the accurate detection of diabetic retinopathy. Our aim is to determine the most effective model for this purpose, thereby improving screening efficiency. Utilizing a pre-processed dataset sourced from Kaggle, we conducted comprehensive experiments to evaluate the performance of each model. This curated dataset was instrumental in optimizing the classification algorithms. Our findings reveal notable disparities in the performance of these models. Through meticulous testing and validation, we sought to identify the model exhibiting the highest accuracy in diabetic retinopathy detection. Leveraging a dataset comprising 2750 retinal images, our experiments yielded accuracy values of 68% for SVM, 74% for CNN, and 63% for ResNet.
Open Access
Enhancement of Quality Management Through Lean in Sri Lankan Construction Industry
(Faculty of Engineering, 2025-09-09) Jayanetti J.K.D.D.T.; Perera B.A.K.S.; Ranadewa K.A.T.O
Quality management remains a critical concern in the construction industry of developing countries, where inefficiencies, rework, and inconsistencies in quality practices negatively impact project outcomes. Although lean construction features prominently in the global literature, the rigorous integration of lean principles into quality management frameworks remains underexplored. While lean construction is widely recognised for enhancing process efficiency and value delivery, its integration with quality management, particularly through structured frameworks, has received limited attention in the Sri Lankan context. This study addresses this gap by investigating how lean can be applied to enhance quality management in Sri Lankan construction organisations. Guided by a pragmatic research philosophy, this study employed the Delphi technique involving experts with expertise in lean construction and quality management. Data was analysed using NVivo through directed content analysis. The study identified lean-related quality process areas and performance indicators based on established literature and expert judgement. The validated indicators were organised into four core process areas: continuous improvement, benchmarking, standardisation, and error detection and prevention. A total of 22 performance indicators corresponding to these areas were confirmed through expert consensus. The findings show strong alignment with established lean concepts such as Kaizen, the Plan Do Check Act cycle, standard work, and quality at source. The results also reflect local priorities such as proactive error management and regulatory alignment, emphasising the need for contextual adaptation. The study extends lean quality theory to a new geographic setting, offers a practical framework for Sri Lankan construction organisations, demonstrates the methodological value of the Delphi approach in data-limited contexts, and supports societal goals by promoting more reliable and accountable construction practices. These contributions advance understanding and implementation of lean-based quality management in emerging construction sectors.
Open Access
Enhancing Healthcare Predictive Models Through Privacy- Preserving Synthetic Data Generation
(Faculty of Engineering, 2025-09-09) Edirisinghe M.M; Gunarathne J H M S M; Wanniarachchi W A A M
The advancement of healthcare predictive modeling is closely tied to the availability and quality of patient data. However, privacy regulations and ethical concerns often hinder data sharing, making it a persistent challenge. As a solution, privacy-preserving synthetic data generation has emerged, enabling the creation of artificial datasets that retain the statistical properties of real data while protecting individual privacy. This paper explores the use of such synthetic data throughout the clinical risk prediction pipeline by leveraging state-of-the-art generative models. We evaluate their utility in exploration data analysis, feature selection, model training, and deployment. Our study focuses on synthetic data generated using advanced models such as Differentially Private GANs (DPGAN), Private Aggregation of Teacher Ensembles GANs (PATEGAN), and Anonymization through Data Synthesis GANs (ADSGAN). Using these techniques, we created synthetic versions of the UK Biobank ever- smoker cohort. These synthetic datasets were shown to reproduce key statistical patterns, support effective feature selection, and enable accurate lung cancer risk prediction modeling all without using real patient data. We compare synthetic data with other privacy-enhancing technologies like federated learning and highlight a key advantage: synthetic data allows the direct use of existing analytical and machine learning tools without modification. Additionally, we examine deployment models such as "no- release" and "delayed-release," emphasizing how synthetic data can speed up research and enable broader data sharing while maintaining GDPR compliance. Overall, this study demonstrates the potential of synthetic data to transform healthcare research, software testing, education, and collaboration while carefully navigating the trade-off between privacy and utility.
Open Access
Enhancing Modern Education Through an AI-Integrated Learning Management and Support System (LMSS)
(Faculty of Engineering, 2025-09-09) Rashminda, J
The rapid advancement of educational technologies has underscored the limitations of conventional Learning Management Systems (LMS) in effectively supporting the evolving demands of learners and educators. While traditional LMS platforms primarily focus on content delivery and administrative tasks, they often lack the capacity to foster active engagement, facilitate meaningful collaboration, and promote participation in broader learning experiences. This paper presents the design and functional implementation of a prototype for a Learning Management and Support System (LMSS), an AI-enhanced platform built to address these limitations by offering a more holistic and student-centred approach to digital education. LMSS integrates course management with interactive features that encourage student collaboration, peer-to-peer communication, and involvement in academic and extracurricular events. These capabilities are designed to support a more engaging and socially connected learning experience while also simplifying instructional workflows for educators. The system incorporates adaptive learning tools and real-time insights to better align learning processes with individual needs and institutional goals. This paper reviews the existing literature, highlights gaps in current LMS implementations, and details the development methodology, architecture, and feature set of LMSS. The system’s anticipated impact is grounded in established research findings demonstrating that adaptive learning approaches can significantly enhance student engagement, AI-driven early intervention can improve retention rates among at-risk learners, and realtime analytics can reduce instructor workload related to feedback provision. By integrating these evidence-based practices into a unified platform, LMSS is designed to foster learner motivation, deepen engagement, and support teaching effectiveness. Ethical considerations such as user privacy and data governance are also addressed to ensure responsible and transparent use.
Open Access
Evaluation of the Knowledge Base in Agriculture and Food to Reduce and Prevent Chronic Kidney Disease of Unknown Etiology (CKDu)
(Faculty of Engineering, 2025-09-09) Ariyawansha, R.T.K.; Basnayake, B.F.A.; Dharmasena, D.A.N.; Gamage, A
Kidney disease is a growing global problem, more so in tropical regions. The cause of CKDu is multifactorial and influenced by heavy metal (HM) contamination, inhibiting essential enzymatic reactions. Fertilizers and water contamination are believed to cause the disease. This study aimed to review the existing knowledge base, focusing on a transitional approach to advanced technologies with the least HMs and to use justifiable scientific reasoning supported by published data, to used to demonstrate the movement of Cadmium (Cd) at both low and high concentrations from applied fertilizer through the soil to grain and rice. The quantity of fertilizer applied per ha with the given Cd levels was equated to Cd concentrations in the harvested grain and rice per ha, considering positive or negative contributions from the soil. Weekly consumption levels of rice at the threshold limits by an average Sri Lankan were determined for low and high Cd levels in rice using the tolerance limits of two international standards. It is best to characterize watersheds and determine the movement of nutrients and HM in ferruginous soils. Hinderance to phosphate immobility in these soils can be overcome by applying biochar biofertilizer with possible enrichment of biofilm biofertilizers to replace totally inorganic fertilizers contaminated with HMs. Cd levels of 836.25 and 393.75 of the two publications equate to the assumed harvest: lowest 21.22, average 385.13, and the highest 1246.10 mg Cd ha-1. Allowable standards indicate that the weekly limit of a Sri Lankan to consume rice is 300 g, containing a high concentration of 0.2618 mg Cd kg-1 and 1kg or 604 g, having 0.1339 mg Cd kg-1 for an average harvest of 4350 kg.ha-1. Water contains HM, particularly arsenic from fertilizer and pesticides. Recommended researching while implementing phytoremediation, mechanized farming, preventing UVB, Integrated Pest Management (IPM), and organic agriculture with supporting technologies of watershed resource management.
Open Access
Feature Analysis of Blood Spatter Patterns with Image Processing
(Faculty of Engineering, 2025-09-09) Khemaratne, T; Malasinghe, L
Bloodstain Pattern Analysis (BPA) is a vital component in forensic investigations that aids in reconstructing the sequence of events at a crime scene. It is centralized in and revolves around the categorization of the patterns based on their features, as this is the most significant and critical stage of BPA. Therefore, a preliminary measure of BPA is via the thorough evaluation of images photographed of the crime scene to collect evidence as much as possible to arrive at the correct conclusion and to deduce the relevant details accurately. However, currently existing BPA methods are vulnerable to subjectivity, hence which can lead to pre-assumptions, without thoroughly and completely observing the crime scene, and consequently cause the arrival of incorrect conclusions and discrepancies in BP feature classification. Additionally, other flaws such as unintentional crime scene contamination and evidence tampering exist in these current methods as well. Henceforth, it is imperative that a novel method is constructed to eliminate these issues and arrive at the correct conclusions. This study introduces a robust image-processing-based methodology for extracting and quantifying bloodstain pattern features, thereby enhancing objectivity and reducing human error. The proposed technique encompasses critical stages: image acquisition, preprocessing, segmentation, feature extraction, and analysis. Through the use of image enhancement and segmentation algorithms, essential attributes such as impact angles, tail-to-body ratios, shape irregularities, and distribution densities are computed. The results were validated against original findings and show close agreement in feature values such as convergence area and circularity. The approach demonstrates the potential to integrate with existing BPA tools, facilitating automated, accurate, and reproducible forensic analysis.
Open Access
FreshSight: An Accessibility-Focused Approach to Produce Freshness and Shelf- Life Detection for Food Safety and Waste Reduction
(Faculty of Engineering, 2025-09-09) Fernando, W.P. R.; Kirupananda, A.
Colour-blind individuals encounter daily challenges, particularly in distinguishing colour-based indicators of food spoilage. This limitation significantly impacts their ability to assess the freshness and safety of fruits and vegetables accurately. Concurrently, global concerns regarding food spoilage have intensified, with millions worldwide affected by foodborne illnesses each year. The modern lifestyle, characterized by its rapid pace and time constraints, exacerbates this issue, often leading to unnoticed spoilage and substantial waste. The resulting annual waste, estimated at one-third of all edible food, imposes significant societal and environmental burdens, underscoring the urgency for effective solutions. FreshSight enables users, including those with colour vision deficiencies, to assess the condition of fruits and vegetables through a Convolutional Neural Network (CNN)- based real-time image analysis engine and an intuitive interface. This system provides immediate visual feedback to help users make informed decisions and avoid the consumption of spoiled produce. It also offers inclusive design features that support individuals with visual impairments. Beyond individual benefits, FreshSight promotes responsible food handling and contributes to the broader goal of sustainable food systems. By combining advanced technology with user-centered design, the solution enhances both safety and accessibility in everyday food-related decisions. In addressing the critical challenges of food safety, inclusivity, and waste reduction, FreshSight aims to support healthier lifestyles and contribute positively to environmental and societal well-being in the modern world.
Open Access
High-Speed Path Tracking of a Small Mobile Robot Using PD and ADRC Controllers with Experimental Validation
(Faculty of Engineering, 2025-09-09) Senarath, P; Kumara, K.J.C
This paper presents the development, modeling, and validation of a compact ground-based mobile robot designed for high-speed trajectory tracking with low-cost hardware and advanced control strategies. Originally designed for micromouse competitions, the platform was upgraded with a vacuum-assisted friction enhancement mechanism and a lightweight sensor fusion system comprising Time-of-Flight (ToF) sensors, infrared (IR) proximity sensors, a 6-axis IMU, and wheel encoders. Due to the lack of manufacturer-provided system parameters, a data-driven system identification approach was employed to derive a MIMO state-space model representing the robot's dynamics. Two control strategies, Proportional-Derivative (PD) and Active Disturbance Rejection Control (ADRC) were implemented and evaluated through both numerical simulations and real-world experiments across three benchmark trajectories: straight-line, 90° smooth turn, and figure-eight. The results show that both controllers achieved trajectory tracking within 5% RMSE, with ADRC offering improved heading accuracy and energy efficiency. Experimental observations indicate that ADRC reduces battery current fluctuations, although current modeling was not included in the control design. The proposed platform and methodology offer a cost-effective and robust solution for mobile robot control in constrained environments, with future work focusing on energy-aware control integration.