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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Publication Open Access Predicting Cognitive Test Performance from New Onset Behavioral and Personality Changes in Adults over 50 using Post-Selection Boosted Random Forest Classifier(Faculty of Engineering, 2025-09-09) Mervyn M.; Welhenge A.; Creese B.Mild Behavioral Impairment (MBI) refers to neuropsychiatric symptoms of various severity levels that might not be discovered by conventional psychiatric nosology. These symptoms should persist for more than six (06) months. MBI is typically observed in adults of age 50 and above. This study investigates the prediction of cognitive test performance of cognitive and behavioral changes in adults over 50 years of age using a post-selection boosted Random Forest (RF) Classifier. The baseline cognitive aging data of the Simple Reaction Time (SRT) metric and Mild Behavioral Impairment Checklist (MBI-C) from the ongoing PROTECT study in the United Kingdom was used to classify the participants’ cognitive ability into five classes. Using the post-selected boosted RF classifier, the study obtained an accuracy of 96.26% which was an improvement compared to the 95.52% accuracy obtained by the RF classifier. These findings suggest that machine learning-based prediction models can provide valuable insights into analyzing the cognitive decline of adults of a late age.Publication 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.Publication Open Access Piezoelectric Energy Harvesting in Interactive Environments: A Case Study Using a Playpen-Based Prototype(Faculty of Engineering, 2025-09-09) Bandara, H. M. U. J.; Fernando, M. N. V.; Samaranayake, H. D. K.As modern cities evolve towards sustainable and efficient infrastructure, implementation of micro-energy harnessing technologies caught the attention of researchers. This is mostly because non-renewable energy sources are being depleted, and the consequences of them are also marginal to the world. Piezoelectric materials are known for their conversion ability of mechanical stress, such as motion, vibrations, pressure, to electrical energy. This study provides a piezoelectric mechanism-based prototype that demonstrates the suitability of converting mechanical energy to electricity, especially footsteps from people, while being low maintenance and cleaner. In this design, 70 PZT piezoelectric disks are implemented beneath a playpen floor, suspended by a spring mechanism and further suspension components. Output voltages of 9.2V to 11.4V could be observed from this demonstration, with the help of 2-5kg loads applied. 76 LEDs were used to visualize this power in real-time, which also offers interactive feedback for children. The output analysis shows a low-current and somewhat non-linear power output. These results validate the possible outcome of deploying a piezoelectric system in high footfall infrastructure and recreational spaces. Even though it is low powered, such systems can enhance built environment functionality, and further improvements in the design can increase the output of such designs. This project represents a small-scale demonstration of this unpopular yet effective technology.Publication Open Access Construction Dynamics And Digitalization(Faculty of Engineering, 2025-09-09) Premachandra, P.NThe 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.Publication Open Access Keynote 01:Optimization Methods in Computational Intelligence for Joint Wireless Channel Parameter Estimation over Advanced Radio Interface Technologies(Faculty of Engineering, 2025-09-09) Affes,SofieneAdvanced Radio Interface Technologies (RITs) combine broadband signalling—hence multicarrier operation and richly multipath propagation—with multi-antenna transceivers. In these regimes, joint estimation of channel parameters (angles of arrival/departure, delays, Doppler/frequency offsets, gains/phases, etc.) becomes a central yet challenging inference problem. Objective or cost functions are often nonconvex, multimodal, and simulator-defined, with scarce gradients, tight pilot budgets, and low signal-to-noise ratios (SNRs). Therefore, among computational intelligence (CI) categories that encompass 1) neural networks and 2) fuzzy systems, the third or 3) population-based and bioinspired optimization (PBO/BO) methods – such as particle swarm optimization (PSO), differential evolution (DE), genetic algorithms (GA), grey wolf optimizer (GWO), and related swarms – have gained traction as global search engines that either directly minimize maximum-likelihood (ML) or mean-square error (MSE) criteria or act as robust initializers for hybrid pipelines. In this talk, first we integrate a disciplinary taxonomy relating artificial intelligence (AI), optimization, and Monte Carlo inference to place CI and PBO/BO within a broader computational context worth contemplating. Then we survey the current state of the art on CI optimization for wireless channel parameter estimation and analyze the strengths and weaknesses of each CI subcategory versus the others and against conventional estimation methods. We synthesize algorithmic patterns, objectives, accuracy, convergence/complexity trends, and empirical findings, etc., over advanced RITs, and we discuss most recent progress and open challenges.Publication Open Access Keynote 02: Triple Play: AI, MV, and HTC(Faculty of Engineering, 2025-09-09) Koucheryavy, A.At the beginning of the 21st century, with the convergence of communication networks, the term Triple Play, which included voice, video, and data services, became a popular term for the provision of services by next-generation networks. At that time, this approach meant the unity of the convergent network and the universality of the services provided to the user.Now, about a quarter of a century after that time, the development of communication networks is taking place in the direction of creating various kinds of universes based on artificial intelligence technologies. Even urban universes have appeared as a development of smart cities. At the same time, the design Artificial Intelligence (AI) plus Metaverse (MV) was considered quite popular for some time. At the global standardization symposium in New Delhi in October 2024 during WTSA-24, it was proposed to supplement this dual with a holographic type of communications (HTC). This was necessary because there is a fairly widespread opinion that society will become holographic by 2030. This proposal was made by the ITUT Focus Group Leader Richard Lee in 2018 at the first meeting of the focus group. Considering the great potential of holographic universes and the development of technologies in the field of providing holographic telepresence services, it seems useful to use the triune design of AI + MV + HTC at present. The report further discusses the development of artificial intelligence in the field of communication networks, the transformation of metauniverses into holographic network universes (HolNetVerse), work in the field of creating various terminals for holographic interactions and implementing telepresence services, the creation of holographic universities, the creation of holographic cities using the example of St. Petersburg, the development of a telepresence suit with parametric feedback, the capabilities of such telepresence suits together with a network universe for remote rehabilitation of patients using the example of recovery for children with upper limb injuries. All these applications show how effective the use of the new Triple Play AI + MV + HTC can bePublication 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, NThis 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 designPublication 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.Publication Open Access Enhancing Modern Education Through an AI-Integrated Learning Management and Support System (LMSS)(Faculty of Engineering, 2025-09-09) Rashminda, JThe 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.Publication 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, AKidney 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.Publication Open Access Predictive Modeling for Personalized Cancer Therapy Using Reinforcement Learning(Faculty of Engineering, 2025-09-09) Edirisinghe M.M; Gunarathne, J H M S MAdaptive therapy is transforming cancer treatment by enabling dynamic, patient-specific interventions that adapt to tumor progression and individual variability. Unlike traditional fixed-dose regimens, adaptive therapy leverages the evolutionary dynamics of tumors to extend treatment effectiveness and delay resistance. Reinforcement Learning (RL), an area of artificial intelligence focused on sequential decision-making, offers a robust framework for optimizing these adaptive strategies. RL can learn optimal treatment policies by interacting with computational models of tumor growth and drug response, continuously adjusting regimens based on observed tumor states, resistant cell populations, and biomarkers. This approach allows for the creation of personalized therapies that maintain long-term tumor control while minimizing toxicity and the emergence of resistance. The integration of RL into predictive modeling for cancer therapy represents a paradigm shift, enabling smarter, safer, and more effective treatments that are dynamically tailored to each patient’s evolving disease. This paper reviews the foundational concepts of adaptive therapy and RL discusses tumor modeling approaches, examines RL algorithms, and addresses current challenges and future directions in the field.Publication 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.Publication Open Access Penetratingz the Defenses: An Investigation into the Achilles' Heel of HTTP and SSH(Faculty of Engineering, 2025-09-09) Joshi, A.P; Bandrevu, S; Kaur, N; Sharma, I; Maduranga, M.W.P.; Wanniarachchi, W A A MAs the yber threat increases, it becomes necessary for organizations to start securing their digital valuables and infrastructures. This research is mainly about analysing the weaknesses lying in the vulnerabilities of the HTTP and SSH protocols. Investigation here is into how the intruder can escalate his privileges and illegally access computers. Under open-source tools like Netcat and Gobuster, the article examines the vulnerability-assessment methodologies culminating in root access to the target machine. This paper emphasizes the need for proactive security measures and gives recommendations on improving defences against future attacks. The study, as bright as the findings may be, awaits empirical dimensions to affirm the proposed measures.Publication 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, PShape-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.Publication Open Access Feature Analysis of Blood Spatter Patterns with Image Processing(Faculty of Engineering, 2025-09-09) Khemaratne, T; Malasinghe, LBloodstain 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.Publication 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.Publication Open Access Multifactorial Drivers of Chronic Kidney Disease of Unknown Etiology (CKDu): A Review of Cadmium Exposure, Ultraviolet B B Radiation, and the Potential Role of Vitamin D Toxicity(Faculty of Engineering, 2025-09-09) Ariyawansha, R.T.K.; Basnayake, B.F.A.; Dharmasena, D.A.N.; Siribaddana, SThere are many research studies conducted to determine the cause/origin of CKDu for preventing this increasingly occurring disease, particularly among poor farmers. The disease is known to be multifactorial with heat stress enduring period > 3 months, but there is difficulty in distinguishing between harsher environments than endemic locations. This study identified that the likely cause is UVB actuating Vitamin D Toxicity (VDT), thus affecting the kidneys. Hence, a focused literature review was undertaken to find the links between cadmium (Cd), calcium, fluoride, enzymes, inhibitions, and the like. Moreover, 2nd law of thermodynamics was applied to determine the entropy differences between cold and hot source. The mean values of climate models were obtained from one publication on radiative forcing (RF) in the tropopause of 1.28 Wm- 2 and climate feedback (CF) 0.25 Wm-2K-1. The energy of RF was used to determine entropy value 𝑆𝑅𝐹(𝑈𝑉𝐵)at 𝑇2 = 230𝐾 as mean atmospheric temperature and the maximum temperature, 𝑇1 at locations. It was then equated to the energy value of UVB, 𝑄𝐶𝐹(𝑈𝑉𝐵) to be found between 1/273 and 1/𝑇1. It was also validated using CF. The endemic location resulted 3.697 Wm-2 at 303K, and low RH compared to 3.239 Wm-2 at 311 K high RH. Although there is much comfort in endemic location, the chances of VDT or heat stress are higher more so with Cd inhibition of enzyme 7-dehydrocholesterol reductase (DHCR7), which is crucial for cholesterol synthesis. Instead, 7-dehydrocholesterol in excess switches more to form VDT, causing symptomatic hypercalcemia. Cadmium can disrupt vitamin D metabolism, contributing to osteomalacia and osteoporosis, actuating hypercalciuria, an indirect marker of low-level cadmium exposure. The kidneys, already compromised due to cadmium (Cd) accumulation and reabsorption during systemic distribution, ultimately eliminate Cd via the urine. Notably, no significant Cd accumulation is observed in end-stage renal tissues. Further basic research is required to elucidate the VDT in response to UVB exposure.Publication 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.OQuality 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.Publication 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.Publication 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, NMost 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.