2024
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Publication Embargo Renewable realities: Charting a greener course for the world's high-emitting nations through information technology insights(Wiley, 2024-11-14) Ranthilake, T; Caldera, Y; Senevirathna, D; Gunawardana, H; Jayathilaka, R; Peter, SCarbon dioxide (CO₂) is the most abundant gas among all greenhouse gas emissions,severely impacting global warming. This study examines the impact of Informationand Communication Technology (ICT), population dynamics, Per Capita GrossDomestic Product (PGDP), and Renewable Energy Consumption (REC) on CO₂ on aglobal scale, representing 38 countries selected using the Pareto principle. Resultsfrom the panel regression model indicate a significantly positive relationship betweenICT, PGDP, and population on CO₂ emissions. In contrast, REC exhibits a negativerelationship. The Multiple Linear Regression model shows that an increase in PGDPleads to higher CO₂ emissions, except in Uzbekistan. ICT increases emissions in theUnited States, Argentina, Australia, Canada, and Egypt. Population growth raisesemissions, except in the United States, France, Germany, and Russia. REC reducesCO₂ emissions in most countries. Policymakers in individual countries can gain a pre-cise understanding of how these variables impact CO₂ emissions, enabling them tomitigate the risks associated with global warmingPublication Embargo Navigating economic crisis: Factors shaping resilience in Sri Lankan constructionSME supply chains(Taylor and Francis, 2024-10-05) Madhavika, N; Bandara, M; Manchanayake, M; Perera, C; Bandara, W; Jayasinghe, P; Ehalapitiya, SIn today’s construction industry, supply chains are subject to much greater disruption than they were inthe past, resulting in a greater need for resilience. However, there is a gap in the literature that examinesthe resilience of construction small and medium scale Enterprises (SMEs) specifically focusing on develop-ing countries. This article is a step towards identifying the factors influencing the resilience of construc-tion SME supply chains taking the case of Sri Lanka: a developing country which is currently amidst amajor economic crisis. This research study adopted a mixed-method approach, employing 08 structuredinterviews with employees ranging from executive level to top level management of 08 constructionSMEs followed by a questionnaire survey considering a sample of 195 construction SMEs also with execu-tive level to top level management of each construction SME. The findings indicated that Collaboration,Entrepreneurial Orientation (EO), Internal Integration, and Outsourcing have a positive significant impacton the resilience of Sri Lankan construction SMEs’ supply chains during an economic crisis, while‘collaboration’ and ‘EO’ are the most influential factors respectively. Therefore, construction SMEs mustprioritize and enhance collaboration and EO when devising supply chain strategies to strengthen resili-ence during economic crises. This paper contributes to filling the research gap by investigating factorsinfluencing construction SME supply chains in a developing country during an economic crisis. Moreover,it contributes to the knowledge by being one of the latest empirical studies focusing on the constructionSME supply chains in Sri Lanka. The findings provide a valuable reference for both policymakers and prac-titioners seeking to improve the resilience of construction SME supply chainsPublication Open Access Feasibility of Sediment Budgeting in an Urban Catchment with the Incorporation of an HEC—HMS Erosion Model: A Case Study from Sri Lanka(Springer, 2024-09-16) Abeysiriwardana, H. D.; Pattiyage, I. A. GomesThis study aimed at studying the feasibility of using a sediment model built in HEC – HMS incorporating Modified Universal Soil Loss Equation (MUSLE) in aiding the separation of sediment contribution as point and non-point, an important aspect in sediment pollution control. The model was developed and verified using a representative sub-catchment and a canal reach of a tropical climate. The field observations and model developed had a good agreement and indicated about 16% and 35% of total sediments in the canal may be from nonpoint sources for the dry and wet seasons, respectively. Results suggested that a major fraction of eroded sediment ended up in the main canal through the dense drainage network across the catchment. This meant sediment trapping should focus tributary drainage ditches or at point source inputs to canal rather than the main canal banks. The study recognized that HEC – HMS is also capable of simulating sediment generation with acceptable errors. Being a free software package, HEC – HMS would be an effective sediment modelling tool for jurisdictions where sediment analysis has been constrained by cost.Publication Open Access Waste-based composites using post-industrial textile waste and packaging waste from the textile manufacturing industry for non-structural applications(Elsevier, 2024-09-26) Sulochani, R.M.N.; Jayasinghe, R.A.; Priyadarshana, G.; Nilmini, A.H.L.R.; Ashokcline, M.; Dharmaratne, P.D.The textile industry significantly contributes to environmental pollution, generating substantial amounts of waste. The prevailing linear model exacerbates this issue, accumulating a significant portion of the waste in landfills. This research aimed to tackle these challenges by developing value-added composites from postindustrial textile waste and packaging materials, for non-structural building applications. To achieve this, shredded polyester textile waste fibers served as the reinforcement, while waste packaging was used as the matrix. Varying fiber-matrix weight percentages seven composite types were developed. The physical, mechanical, and thermal properties of the composites were evaluated. The findings indicated that these composites exhibited properties comparable to those of commercial partition boards. Notably, composites with fiber weight percentages of 7.5 % and 10 % demonstrated the most favorable performance among the tested variations. Emphasizing the application of sustainable chemistry, this study highlights the potential of these composites to develop substitute materials for non-structural building applications. Moreover, it presents a promising solution to address the textile waste management challenge and value-added materials for the construction industry in a developing context.Publication Open Access Dense Convolutional Neural Network-Based Deep Learning Pipeline for Pre-Identification of Circular Leaf Spot Disease of Diospyros kaki Leaves Using Optical Coherence Tomography(Multidisciplinary Digital Publishing Institute (MDPI), 2024-08) Kalupahana, D; Kahatapitiya, N.S; Silva, B.N; Kim, J; Jeon, M; Wijenayake, U; Wijesinghe, R. ECircular leaf spot (CLS) disease poses a significant threat to persimmon cultivation, leading to substantial harvest reductions. Existing visual and destructive inspection methods suffer from subjectivity, limited accuracy, and considerable time consumption. This study presents an automated pre-identification method of the disease through a deep learning (DL) based pipeline integrated with optical coherence tomography (OCT), thereby addressing the highlighted issues with the existing methods. The investigation yielded promising outcomes by employing transfer learning with pre-trained DL models, specifically DenseNet-121 and VGG-16. The DenseNet-121 model excels in differentiating among three stages of CLS disease (healthy (H), apparently healthy (or healthy-infected (HI)), and infected (I)). The model achieved precision values of 0.7823 for class-H, 0.9005 for class-HI, and 0.7027 for class-I, supported by recall values of 0.8953 for class-HI and 0.8387 for class-I. Moreover, the performance of CLS detection was enhanced by a supplemental quality inspection model utilizing VGG-16, which attained an accuracy of 98.99% in discriminating between low-detail and high-detail images. Moreover, this study employed a combination of LAMP and A-scan for the dataset labeling process, significantly enhancing the accuracy of the models. Overall, this study underscores the potential of DL techniques integrated with OCT to enhance disease identification processes in agricultural settings, particularly in persimmon cultivation, by offering efficient and objective pre-identification of CLS and enabling early intervention and management strategies. © 2024 by the authors.Publication Open Access Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning(Elsevier, 2024-09) Ranasinghe, R.S.S.; Kulasooriya, W.K.V.J.B; Perera, U.S; Ekanayake, I.U.; Meddage, D.P.P.; Mohotti, D; Rathanayake, UGeopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC (Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With various supplementary cementitious materials, the compressive strength of geopolymer concrete should be accurately predicted. Recent studies have applied deep learning techniques to predict the compressive strength of geopolymer concrete yet its hidden decision-making criteria diminish the end-users’ trust in predictions. To bridge this gap, the authors first developed three deep learning models: an artificial neural network (ANN), a deep neural network (DNN), and a 1D convolution neural network (CNN) to predict the compressive strength of slag ash-based geopolymer concrete. The performance indices for accuracy revealed that the DNN model outperforms the other two models. Subsequently, Shapley additive explanations (SHAP) were used to explain the best-performed deep learning model, DNN, and its compressive strength predictions. SHAP exhibited how the importance of each feature and its relationship contributes to the compressive strength prediction of the DNN model. Finally, the authors developed a novel DNN-based open-source software interface to predict the mix design proportions for a given target compressive strength (using inverse modeling technique) for slag ash-based geopolymer concrete. Additionally, the software calculates the Global Warming Potential (kg CO2 equivalent) for each mix design to select the mix designs with low greenhouse emissions.Publication Embargo Kinetic study of in vitro release of curcumin from chitosan biopolymer and the evaluation of biological efficacy(Elsevier B.V., 2024-09) Wijayawardana, S; Thambiliyagodage, C; Jayanetti, MSustained release of curcumin from the polymeric carrier system chitosan, a natural biopolymer material derived from chitin originated from natural shrimp shell waste, was studied. Six kinetic models, zero order, first order, Korsmeyer–Peppas (KP), Peppas – Sahlin (PS), Higuchi, and Hixson–Crowell, were applied to study the drug release kinetics. The release mechanism of the drug from the curcumin-chitosan composite was evaluated by changing the pH, ionic strength of the release media, and drug concentration. KP and PS models were selected among the studied models to investigate the drug release mechanism from the chitosan biopolymer based on the R2 values (R2 > 0.99). The model constants m in the PS model and n in the KP model stand for the case II relaxation and Fickian diffusion contribution, respectively. The n being < 0.43 in the KP model suggests that the Fickian diffusion governs the drug release. Furthermore, there is a noticeable difference between the values obtained for model parameters m and n in the PS and KP models, indicating that Case II relaxation and Fickian diffusion play crucial roles in the curcumin release mechanism from chitosan. Polymer relaxation has been proven to play a predominant role in releasing curcumin from the composite at lower ionic strengths and higher pH values. Anti-inflammatory activity was tested using the egg-albumin denaturation assay, and the diphenyl-2-picrylhydrazyl assay was carried out to determine the antioxidant activity of the composite. The composite material showed IC50 values of 0.29 mg/ mL and 1.08 mg/ mL for anti-inflammatory and anti-oxidant activities, respectively. The drug composite has shown antibacterial activity against Pseudomonas aeruginosa, Klebsiella pneumoniae, and Staphylococcus aureus, which are highly effective against S.aureus. The resulting inhibition zones for S.aureus were 13.34 ± 0.34 mm, 16.34 ± 0.60 mm, and 13.34 ± 0.73 mm for 5, 10, and 20 mg/ml concentrations, respectively. The drug composite’s minimum inhibitory concentration/ minimum bactericidal concentration ratio for S.aureus, K. pneumoniae, and P.aeruginosa was greater than 4, suggesting that they cause bacteriostatic effects.Publication Embargo Applicability of machine learning techniques to analyze Microplastic transportation in open channels with different hydro-environmental factors(Elsevier Ltd, 2024-09-15) Fazil, A. Z; Gomes, P. I.A.; Sandamal, R.M. KThis research utilized machine learning to analyze experiments conducted in an open channel laboratory setting to predict microplastic transport with varying discharge, velocity, water depth, vegetation pattern, and microplastic density. Four machine learning (ML) models, incorporating Random Forest (RF), Decision Tree (DT), Extreme Gradient Boost (XGB) and K-Nearest Neighbor (KNN) algorithms, were developed and compared with the Linear Regression (LR) statistical model, using 75% of the data for training and 25% for validation. The predictions of ML algorithms were more accurate than the LR, while XGB and RF provided the best predictions. To explain the ML results, Explainable artificial intelligence (XAI) was employed by using Shapley Additive Explanations (SHAP) to predict the global behavior of variables. RF was the most reliable model, with a coefficient of correlation of 0.97 and a mean absolute percentage error of 1.8% after hyperparameter tuning. Results indicated that discharge, velocity, water depth, and vegetation all influenced microplastic transport. Discharge and vegetation enhanced and reduced microplastic transport, respectively, and showed a response to different vegetation patterns. A strong linear positive correlation (R2 = 0.8) was noted between microplastic density and retention. In the absence of dedicated microplastic transport analytical models and infeasibility of using classical sediment transport models in predicting microplastic transport, ML proved to be helpful. Moreover, the use of XAI will reduce the black-box nature of ML models with effective interpretation enhancing the trust of domain experts in ML predictions. The developed model offers a promising tool for real-world open channel predictions, informing effective management strategies to mitigate microplastic pollution.Publication Open Access The interconnectedness of energy consumption with economic growth: A granger causality analysis(Elsevier Ltd, 2024-09-15) Perera, N; Dissanayake, H; Samson, D; Abeykoon, S; Jayathilaka, R; Jayasinghe, M; Yapa, SIn considering today's energy challenges, the link between the usage of renewable and non-renewable energy sources and economic growth has gained substantial policy attention. This research examines the complex relationship between these three variables to understand how non-renewable energy consumption and renewable energy consumption interact and what that means for economic growth. This study uses the Granger causality approach to explore the relationships between non-renewable energy consumption, renewable energy consumption, and economic development. It draws on a comprehensive dataset from the Word Bank database, including 152 nations from 1990 to 2019. The analysis is further disaggregated by four subgroups of countries; least developed, developed, transitional economies and developing countries. The result of this study provides valuable empirical evidence of uni-directional causality running from renewable energy consumption to economic growth and non-renewable energy consumption to economic growth in transitional economies. Furthermore, policymakers should focus on both variables when making decisions because the results show that energy consumption and economic growth are interconnected. Implementing global energy efficiency standards, reducing fossil fuel usage, and adopting regulatory measures are all viable policies for limiting adverse effects on the environment while encouraging economic development.Publication Open Access Web Block Craft: web development for children using Google Blockly(Institute of Advanced Engineering and Science, 2024-10) Gunaratne, M; Weerasekara, S; Weerakkody, D; Sashmitha, N; De Zoysa, R; Kodagoda, NWeb Block Craft is an innovative educational application that uses the Google Blockly framework to teach web development to children aged eleven and above. The application serves as a comprehensive learning tool, allowing users to explore both frontend project and backend project development. The frontend project includes HTML, CSS, JavaScript, and DOM manipulation, while the backend project covers server building, web app security, application programming interfaces(APIs), and database management. Web Block Craft's unique block-based interface allows users to easily drag anddrop components into a dynamic working environment, resulting in an engaging experience with live output display and simultaneous code presentation. A unique feature of Web Block Craft is the integration of a platform within the application, which allows teachers to create lessons with step-by-step instructions for students. This new feature allows for a more structured learning experience, which improves understanding of web development concepts. To enhance the learning experience, the application provides extensive documentation, serving as a valuable resource for users to grasp the intricacies of web programming. By combining the power of Google Blockly with a creative user interfaceand educational resources, Web Block Craft provides a comprehensive learning environment that empowers creative web programming with confidence.