Scopus Index Publications
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/2162
This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.
Browse
8 results
Search Results
Publication Open Access Identifying the causes of adolescent malnutrition in Nuwara-Eliya District, Sri Lanka(Nature Research, 2025-05-06) Nandajeewa, S; Aluthwatta, S; Weerarathna, R; Rathnayake, N; Rajapakse, V; Wijesinghe, N; Liyanaarachchi, TMalnutrition, a persistent illness, significantly reduces fat, muscle and bone levels, harming internal organs. The economic crisis in Sri Lanka has led to widespread malnutrition among children, including adolescents experiencing growth spurts. This study identifies factors influencing malnutrition in grade 10 pupils in the Nuwara-Eliya District, with the highest rates of malnutrition and also a multicultural area with many estate sector residents. Using a cross-sectional, quantitative approach, the data was collected from 379 respondents via a Likert scale questionnaire. Structural Equation Model (SEM) analysis was conducted using Smart PLS 4.0. Key findings indicate that environmental factors, such as access to clean water and sanitation, significantly influence adolescent malnutrition. A comprehensive strategy incorporating education, healthcare, and environmental improvements is essential for this. Ongoing observation, community engagement, and cooperative tactics are crucial for sustainable solutions. Addressing environmental issues and promoting a holistic approach to health education and infrastructure improvements are vital to combat adolescent malnutrition in vulnerable populations.Publication Open Access Carbon emissions and global R&D patterns: a wavelet coherence perspective(Springer, 2025-03-23) Senevirathna, D; Gunawardana, H; Ranthilake, T; Caldera, Y; Jayathilaka, R; Rathnayake, N; Peter, SThis study examines the causality between Research and Development (R&D) and Carbon dioxide (CO2) emissions at the global level, utilising data gathered from 2000 to 2020 across various countries categorised as developed, developing, economies in transition, and least-developed. The data collected for the study are analysed using the Wavelet coherence methodology. The findings reveal both bidirectional and unidirectional causality between the variables, which have evolved over time. Globally, a bidirectional relationship is present in the short-term, no causality in the medium-term and unidirectional causality in the long-term. Developed countries exhibit a two-way causality in the short-term, while no causality exists in the medium-term and long-term. Developing countries show a bidirectional relationship across all time frequencies. In economies in transition, a bidirectional relationship appears towards the end of the period over the short, medium, and long-term. The least developed countries show no causality in the short and long-term, but a one-way causality in the medium-term. Governments and the policymakers can implement environmental policies to mitigate carbon emissions through R&D. The findings suggest targeted and strategic strategies to enhance the impact of R&D on emissions reduction. Policymakers can use this analysis to prioritize funding for clean energy innovations, establish incentives for low-tech technologies, and promote international cooperation in green technology research. Additionally, focusing on these carbon mechanisms and aligning R&D efforts to support development goals can increase the effectiveness of climate policies, ensuring a balance between economic growth and environmental sustainability.Publication Open Access A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation(Elsevier B.V., 2025-08) Abekoon, T; Sajindra, H; Rathnayake, N; Ekanayake, I.U.; Jayakody, A; Rathnayake, UCabbage (Brassica oleracea var. capitata) is commonly cultivated in high altitudes and features dense, tightly packed leaves. The Green Coronet variety is well-known for its robust growth and culinary versatility. Maximizing yield is crucial for food sustainability. It is essential to predict the soil's major nutrients (nitrogen, phosphorus, and potassium) to maximize the yield. Artificial intelligence is widely used for non-linear predictions with explainability. This research assessed the predictive capabilities of soil nitrogen, phosphorus, and potassium levels with explainable machine learning methods over an 85-day cabbage growth period. Experiments were conducted on cabbage plants grown in central hills of Sri Lanka. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used to clarify the model's predictions. SHAP analysis showed that high feature values of the number of days and plant average leaf area negatively impacted for nutrient predictions, while high feature values of leaf count and plant height had a positive effect on the nutrient predictions. To validate the results, 15 greenhouse-grown cabbage plants at various growth stages were selected. The nitrogen, phosphorus, and potassium levels were measured and compared with the predicted values. These insights help refine predictive models and optimize agricultural practices. A user-friendly application was developed to improve the accessibility and interpretation of predictions. This tool is a user-friendly platform for end-users, enabling effective use of the model's predictive capabilities.Publication Open Access Towards a greener future: examining carbon emission dynamics in Asia amid gross domestic product, energy consumption, and trade openness(Springer Nature, 2024-02-10) Dharmapriya, N; Edirisinghe, S; Gunawardena, V; Methmini, D; Jayathilaka, R; Dharmasena, T; Wickramaarachchi, C; Rathnayake, NThe purpose of this study is to examine the impact of gross domestic product, energy consumption, and trade openness on carbon emission in Asia. Among the 48 countries in Asia, 42 were included in the analysis, spanning a period of 20 years. Given that Asia is the predominant contributor, accounting for 53% of global emissions as of 2019, a comprehensive examination at both continental and individual country levels becomes imperative. Such an approach aligns with local, regional, and global development agendas, contributing directly and indirectly to climate change mitigation. The analytical techniques employed in this study encompassed panel regression and multiple linear regression, illuminating the specifc contributions of each country to the study variables and their impact on carbon emissions. The fndings suggest that gross domestic product (13 out of 42 countries), energy consumption (21 out of 42 countries), and trade openness (eight out of 42 countries) have a highly signifcant impact (p<0.01) on carbon emissions in Asia. Energy consumption plays a vital role in increasing carbon emissions in Asia, driven by rising populations, urbanisation, and oil and gas production. Policymakers can take several actions such as adopting a carbon pricing system, using sustainable transportation, renewable energy development,and international cooperation within Asia to reach the goal of being carbon neutral by 2050.Publication Open Access Sustainability practices and organizational performance during the COVID-19 pandemic and economic crisis: A case of apparel and textile industry in Sri Lanka(NLM (Medline), 2023-07-04) Weerasinghe, N; Weerasinghe, A; Perera, Y; Tennakoon, S; Rathnayake, N; Jayasinghe, PThe apparel and textile industry is the backbone of the Sri Lankan economy, contributing significantly to the country's gross domestic product (GDP). The coronavirus (COVID-19) pandemic, which also triggered the ongoing economic crisis in Sri Lanka, has a profound effect on the organizational performance of apparel sector firms in Sri Lanka. In this context, the study examines the impact of multi-dimensional corporate sustainability practices on organizational performance in the said sector. The study employed the partial least squares structural equation modelling (PLS-SEM) technique for analysing and testing the hypothesis of the study while using Smart PLS 4.0 software as the analysis tool. Relevant data were collected through a questionnaire from 300 apparel firms registered with the Board of Investment of Sri Lanka (BOI). The study results indicated that "economic vigour," "ethical practices," and "social equity" have a significant impact on organizational performance, while "corporate governance" and "environmental performance" have an insignificant impact. Unique discoveries from this study would be useful to prosper organizational performance and formulate novel sustainable future strategies not limited to the garment industry even during harsh economic conditions. Copyright: © 2023 Weerasinghe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Publication Open Access Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka(IEEE, 2023-01) Rathnayake, N; Rathnayake, U; Chathuranika, I; Dang, T. L; Hoshino, YThe Rainfall-Runoff (R-R) relationship is essential to the hydrological cycle. Sophisticated hydrological models can accurately investigate R-R relationships; however, they require many data. Therefore, machine learning and soft computing techniques have taken the attention in the environment of limited hydrological, meteorological, and geological data. The accuracy of such models depends on the various parameters, including the quality of inputs and outputs and the used algorithms. However, identifying a perfect algorithm is still challenging. This study develops a fuzzy logic-based algorithm called Cascaded-ANFIS to accurately predict runoff based on rainfall. The model was compared against three regression algorithms: Long Short-Term Memory, Grated Recurrent Unit, and Recurrent Neural Networks. These algorithms have been selected due to their outstanding performances in similar studies. The models were tested on the Mahaweli River, the longest in Sri Lanka. The results showcase that the Cascaded-ANFIS-based model outperforms the other algorithms. The correlation coefficient of each algorithm’s predictions was 0.9330, 0.9120, 0.9133, 0.8915, 0.6811, 0.6811, and 0.6734 for the Cascaded-ANFIS, LSTM, GRU, RNN, Linear, Ridge, and Lasso regression models respectively. Hence, this study concludes that the proposed algorithm is 21% more accurate than the second-best LSTM algorithm. In addition, Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5 scenarios) were used to generate future rainfalls, forecast the near-future and mid-future water levels, and identify potential flood events. The future forecasting results indicate a decrease in flood events and magnitudes in both SSP2-4.5 and SSP5-8.5 scenarios. Furthermore, the SSP5-8.5 scenario shows drought weather from May to August yearly. The results of this study can effectively be used to manage and control water resources and mitigate flood damages.Publication Open Access Towards work-life balance or away? The impact of work from home factors on worklife balance among software engineers during Covid-19 pandemic(Public Library of Science, 2022-12-14) Weerarathna, R; Rathnayake, N; Yasara, I; Jayasekara, P; Ruwanpura, D; Nambugoda, SThe paradigm shifts of conventional office spaces for virtual workspaces which practiced Work from Home (WFH) due to Covid-19, created a serious change in the lifestyles of employees, due to the overlap of ‘work’ and ‘life’ domains in one’s life. Since software engineers have a possibility of permanently adapting into WFH, the objective of this study is to unveil factors which would have a significant impact on the work-life balance of software engineers in Sri Lanka, while WFH. Only a very limited researches have shed light on this context, thereby this study would contribute to fill the empirical gap. The study undertook a quantitative approach by collecting primary data through a questionnaire from 384 participants, based on simple random sampling, and analyzing collected data based on Partial Least Squares Structural Equation Modelling (PLS-SEM), using Smart PLS 3.3.9 software. Study results revealed that ‘supervisor’s trust and support’ and the ‘individual workspace,’ have a significant impact on work-life balance, while ‘working conditions,’ ‘possibility to access the organization’s networks’ and ‘number of children’ have no such significant impact. Thereby the study infers that, sound support and trust extended by supervisors and a designated distraction-free workspace; as measures to demarcate the boundary of work and life. Distinctive findings of this study would primarily be fruitful for software engineers to dive into a balanced state of work and life not only during Covid-19 but in future too. Study findings will also contribute to software industry personnel and policymakers in Sri Lanka as well as other developing countries, to establish effective strategies in favor of software engineers who WFH. Further, considering IT industry’s significant contribution towards Sri Lanka’s economic growth amidst Covid-19, results of this study would be high-yielding to indirectly succor IT-services-supported economic growth amidst the pandemic-driven hardships in Sri Lanka.Publication Open Access Brain Activity Associated with the Planning Process during the Long-Time Learning of the Tower of Hanoi (ToH) Task: A Pilot Study(MDPI, 2022-10-28) Mitani, K; Rathnayake, N; Rathnayake, U; Linh Dang, T; Hoshino, YPlanning and decision-making are critical managerial functions involving the brain’s executive functions. However, little is known about the effect of cerebral activity during long-time learning while planning and decision-making. This study investigated the impact of planning and decision-making processes in long-time learning, focusing on a cerebral activity before and after learning. The methodology of this study involves the Tower of Hanoi (ToH) to investigate executive functions related to the learning process. Generally, ToH is used to measure baseline performance, learning rate, offline learning (following overnight retention), and transfer. However, this study performs experiments on long-time learning effects for ToH solving. The participants were involved in learning the task over seven weeks. Learning progress was evaluated based on improvement in performance and correlations with the learning curve. All participants showed a significant improvement in planning and decision-making over seven weeks of time duration. Brain activation results from fMRI showed a statistically significant decrease in the activation degree in the dorsolateral prefrontal cortex, parietal lobe, inferior frontal gyrus, and premotor cortex between before and after learning. Our pilot study showed that updating information and shifting issue rules were found in the frontal lobe. Through monitoring performance, we can describe the effect of long-time learning initiated at the frontal lobe and then convert it to a task execution function by analyzing the frontal lobe maps. This process can be observed by comparing the learning curve and the fMRI maps. It was also clear that the degree of activation tends to decrease with the number of tasks, such as through the mid-phase and the end-phase of training. The elucidation of this structure is closely related to decision-making in human behavior, where brain dynamics differ between “thinking and behavior” during complex thinking in the early stages of training and instantaneous “thinking and behavior” after sufficient training. Since this is related to human learning, elucidating these mechanisms will allow the construction of a brain function map model that can be used universally for all training tasks.