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 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.Publication Open Access Li-ion Battery Cooling - A Computational Study of Different Phase Change Material Configurations(Faculty of Engineering, 2025-09-09) Adikaram, S; Nasser, A; Vallés, C; Abeykoon, COverheating of Li-ion batteries in Electric Vehicles (EVs) degrades performance and reduces lifespan. Hence, energyefficient and reliable Battery Thermal Management Systems (BTMS) are required. This paper investigates the use of Phase Change Materials (PCMs), a passive cooling method with high heat storage capacity, for the thermal management of prismatic Li-ion battery cells in EVs. This computational study models the influence of buoyancy-driven convective flow on the PCM cooling performance, compared against thermal conduction-only models. In addition, this study investigates how convective flow influences the cooling performance with variations in cell orientation between vertical and horizontal alignments. n- Octadecane is used as the PCM, and Computational Fluid Dynamics (CFD) simulations were conducted with the Solidification and Melting model in ANSYS Fluent. A 12 mm PCM layer placed around the cell periphery reduced the centre temperature after 1800 s by 2.7 K in the vertical orientation and 3.7 K in the horizontal orientation compared to air-cooling. The effect of natural convection was more pronounced in the horizontal orientation, providing superior cooling performance relative to the vertical case. When the same PCM volume was used to fully enclose the cell, the cooling effect was further enhanced, achieving a maximum temperature reduction of 8.3 K within the first 1800 s. The findings demonstrate that natural convection significantly enhances the PCM-based cooling effectiveness, particularly in horizontally oriented cells, while thinner PCM layers with increased heat transfer area promote faster melting and improved cooling performance.Publication 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, NThis 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.Publication Open Access Towards Safer Elderly Care: A Convolutional Neural Network Solution for Fall Detection(Faculty of Engineering, 2025-09-09) Kalupahana R.W; Maduranga M.W.PAs modern life becomes increasingly busy, computer vision-based monitoring systems have become essential, particularly in elderly care. This paper presents the development of a robust fall detection system using deep learning techniques, specifically a convolutional neural network (CNN) that processes RGB images to accurately distinguish between fall and non-fall events. The model is trained and validated on a dataset categorized into two classes: fall and non-fall. By utilizing convolutional and pooling layers, CNN effectively learns hierarchical representations of the input data, capturing both low-level and high-level features crucial for accurate fall detection. The key stages of this approach include data acquisition, pre-processing, and model training. The model's performance is evaluated using precision, recall, and F1-score metrics, demonstrating high accuracy, which is further enhanced through data augmentation, pre-processing, and crossvalidation techniques. A confusion matrix analysis confirms the model's effectiveness in correctly classifying instances across both classes. The system also extends its capabilities to video analysis by extracting frames at 30-second intervals, ensuring continuous and comprehensive monitoring. This research highlights the potential of deep learning to enhance safety and care for the elderly, offering a reliable solution for real-time fall detection. The findings underscore the importance of integrating advanced technologies into healthcare, paving the way for future innovations in monitoring and assistance systems.Publication Open Access Public Sector Role as a Key Stakeholder Towards the Circular Economy in the Built Environment in Sri Lanka(Faculty of Engineering, 2025-09-09) Gunasekara G. S. A.; Gunarathna K. A. N; Karunaratne B. C. T. M.The circular economy (CE) concept is a more appropriate approach to meet the sustainability challenges of today's construction industry, allowing construction activities to operate in a closed loop, away from the traditional linear economy model (LE). Although some CE principles are applied in Sri Lanka, they are not fully implemented. As a policy maker, the public sector should play a significant role in implementing CE in the built environment. However, limited attention has been paid to this, and no research has identified the public sector’s role in this regard. The primary aim of this research is to explore the role and potential contribution of the public sector as a key stakeholder in implementing CE in Sri Lanka’s built environment. To achieve this, global and local practices were examined to understand how CE principles are applied and to provide recommendations for improving CE adoption. The benefits and challenges to the public sector and private sector views on its role in CE implementation were also explored. A mixed approach was used for data collection. Expert interviews and questionnaires were developed after gaining a thorough understanding of CE principles through a comprehensive literature review. Ten expert interviews and thirty-eight questionnaire responses were analyzed. Although the public sector has initiated efforts such as green building standards and waste management, the study found a need for a stronger regulatory framework, institutional support, and collaboration with the private sector. Challenges such as limited awareness, high start-up costs, and regulatory gaps were identified. The research emphasized the importance of CE policies, financial incentives, and public-private partnerships. It also stressed the need for training and awareness programs to equip stakeholders with the knowledge required to implement CE.Publication Open Access Preserving Built Heritage in Sri Lanka through Digital Twin Technology: Opportunities and Implications(Faculty of Engineering, 2025-09-09) Kudasinghe, K S K N J; Jeewananda, W D B J; Padmaja, MThe "digital twin" concept has gained prominence in architecture and construction for maintaining accurate digital representations of physical structures. There is no generalized digital twin system which can be applicable for all purposes; thus, the digital twin development is context and site specific. Building Information Modeling proves invaluable for performance modeling, behavior analysis, and preventive maintenance of historic sites, yet its implementation complexities demand customized approaches. In Sri Lanka, digital twin adoption lags due to uncertainties in construction, operation, utility, and limited research on their cultural heritage impacts. Modernization threatens colonial street architecture, intensifying conservation urgencies. Digital twins offer detailed virtual models capturing architectural nuances and historical contexts, crucial for UNESCO World Heritage sites facing modernization and climate change threats. The UN's 2030 Sustainable Development Goals prioritize cultural and environmental sustainability, underscoring the need for effective conservation strategies. This study integrates a literature review, two case studies with a detailed algorithm for the creation of a digital twin, and professional interviews for identifying challenges and strategies for digital twin implementation. Case studies of the De Soysa building and Rangiri Dambulla Caves illustrate their potential respectively: the former preserving legacy amid urban development, the latter optimizing preservation through microclimatic analysis. Project-specific digital twins are pivotal for safeguarding cultural identity and managing heritage properties. Challenges in digital twin use for heritage preservation include data capture, costs, integration, and ethical considerations. Solutions entail advanced technologies, funding strategies, standard data formats, cloud storage, and ethical data handling to enhance management and preservation.Publication 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, SHydrogen 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.Publication 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, NThis 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.Publication Open Access The Influence Of Project Managers' Decision-Making Styles On Schedule Variance In Building Construction Projects(Faculty of Engineering, 2025-09-09) Pulasinghe, K; De Silva, PThis research focus to assess the relationship between project managers decision making styles and schedule variance in building construction projects in Sri Lanka. Timely completion of construction projects is one of the major performance indicators, yet delays are a long-standing issue in Sri Lankan construction projects. Though there are many causal factors, decision-making styles of project managers have not been studied yet, creating a significant knowledge gap with regard to their influence on project schedule variance. This study attempts to analyze the relationship between project managers' decision-making styles: directive, analytical, conceptual, and behavioural and schedule variance in Sri Lankan building construction projects. A mixed-methods research approach was adopted. Primary data were gathered by holding semi-structured interviews with nine industry practitioners and a questionnaire survey of 50 respondents covering key project roles. To analyze the data code based content analysis and descriptive statistical tools (percentage count, mean, weighted average etc.) were used and to examine the relationship between decision-making styles and schedule variance Pearson correlation was conducted. Results revealed that decision making styles play a significant role in influencing project schedules. However, it was found that directive and behavioural styles are most prevalent and successful styles in the Sri Lankan context. The data revealed both positive and negative influences of managerial decisionmaking styles on schedule performance. These results contribute to the link between leadership and project performance and make a theoretical and practical contribution by revealing decision-making as a key influence to minimize schedule variance.Publication 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 MThe 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.Publication 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.CThis 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.