Browsing by Author "Rathnayake, N"

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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., 2025-03-06) Abekoon, T; Sajindra, H; Rathnayake, N; Ekanayake, I, U; Jayakody, A; Rathnayake, U
Cabbage (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.
Open Access
Beyond compensation: effect of employee benefits on job motivation, performance, and turnover intention
(Cogent OA, 2025-12-31) Peemanee, J; Weerarathna, R; Issarapaibool, A; Boonlua, S; Rathnayake, N
This study investigates the influence of employee benefits on motivation, performance, and turnover intention within contemporary workplaces that increasingly emphasize employee well-being. Addressing a key gap in the literature, it employs Structural Equation Modeling (SEM) and analyzes data from 387 Generation Y and Generation Z employees in Small and Medium Enterprises (SMEs) in Thailand. The analysis examines how diverse benefit types influence employees’ motivation, performance, and decisions to remain with their organizations. The findings reveal a direct and positive link between employee benefits, enhanced motivation, and improved performance, which together significantly reduce turnover intention. Specifically, attraction and retention strategies, organizational support mechanisms, and a growth-oriented, well-being-focused environment emerged as critical factors in motivating employees and elevating their performance. Overall, the study demonstrates that strategically designed employee benefit packages—aligned with employee needs and workplace realities—foster engagement, productivity, and loyalty. This study contributes valuable insights for organizational leaders seeking to refine benefit systems and extends the academic understanding of the strategic importance of non-monetary benefits in promoting employee satisfaction and retention.
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, Y
Planning 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.
Open Access
Carbon emissions across income groups: exploring the role of trade, energy use, and economic growth
(Springer Nature, 2025-07-10) Dharmapriya, N; Gunawardena, V; Methmini, D; Jayathilaka, R; Rathnayake, N
This study investigates the interplay of trade openness, energy consumption, and gross domestic product (GDP) on carbon emissions across different income groups, analysing data from 163 countries from 2000 to 2019. Using panel regression and multiple linear regression techniques, the findings highlight energy consumption as the principal driver of carbon emissions across all income categories, underscoring its central role in environmental sustainability challenges. High-income countries, despite technological advancements, continue to exhibit substantial emissions due to their reliance on fossil fuels. In contrast low-income nations face difficulties in balancing economic growth with environmental sustainability, often lacking the resources to adopt cleaner energy alternatives. The study emphasises the urgent need for income-specific strategies to reduce carbon emissions, advocating for the widespread adoption of renewable energy sources and tailored policy interventions. These insights align with the United Nations Sustainable Development Goals, particularly SDG 13 (Climate Action), by promoting the integration of economic development with environmental stewardship. By addressing disparities across income levels, this research offers actionable recommendations for policymakers to support equitable and sustainable practices globally.
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, S
This 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.
Open Access
Cascaded Adaptive Network-Based Fuzzy Inference System for Hydropower Forecasting
(MDPI, 2022-04-10) Rathnayake, N; Rathnayake, U; Dang, T. L; Hoshino, Y
Hydropower stands as a crucial source of power in the current world, and there is a vast range of benefits of forecasting power generation for the future. This paper focuses on the significance of climate change on the future representation of the Samanalawewa Reservoir Hydropower Project using an architecture of the Cascaded ANFIS algorithm. Moreover, we assess the capacity of the novel Cascaded ANFIS algorithm for handling regression problems and compare the results with the state-of-art regression models. The inputs to this system were the rainfall data of selected weather stations inside the catchment. The future rainfalls were generated using Global Climate Models at RCP4.5 and RCP8.5 and corrected for their biases. The Cascaded ANFIS algorithm was selected to handle this regression problem by comparing the best algorithm among the state-of-the-art regression models, such as RNN, LSTM, and GRU. The Cascaded ANFIS could forecast the power generation with a minimum error of 1.01, whereas the second-best algorithm, GRU, scored a 6.5 error rate. The predictions were carried out for the near-future and mid-future and compared against the previous work. The results clearly show the algorithm can predict power generation's variation with rainfall with a slight error rate. This research can be utilized in numerous areas for hydropower development.
Embargo
Classification of Human Emotions using Ensemble Classifier by Analysing EEG Signals
(IEEE, 2021-04-13) Mampitiya, L. I; Nalmi, R; Rathnayake, N
This study is based on EEG brain wave classification of a well-known dataset called the EEG Brainwave Dataset. The dataset combines three classes such as positive, negative, and neutral. The classification is performed using an ensemble classifier that combines RF, KNN, DT, SVM, NB, and LR. The meta classifier is LR, while the other five algorithms work as the base classifiers. Furthermore, PCA is used as the dimension reduction method to increase the accuracy of the final output. The results are evaluated under 11 different parameters. Moreover, the accuracy of this study is compared with the seven other EEG emotion classification methods. The proposing method attained 99.25% of accuracy, outperforming the other state-of-the-art algorithms.
Open Access
An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm
(MDPI, 2022-06-10) Rathnayake, N; Rathnayake, U; Dang, T. L; Hoshino, Y
Automated fruit identification is always challenging due to its complex nature. Usually, the fruit types and sub-types are location-dependent; thus, manual fruit categorization is also still a challenging problem. Literature showcases several recent studies incorporating the Convolutional Neural Network-based algorithms (VGG16, Inception V3, MobileNet, and ResNet18) to classify the Fruit-360 dataset. However, none of them are comprehensive and have not been utilized for the total 131 fruit classes. In addition, the computational efficiency was not the best in these models. A novel, robust but comprehensive study is presented here in identifying and predicting the whole Fruit-360 dataset, including 131 fruit classes with 90,483 sample images. An algorithm based on the Cascaded Adaptive Network-based Fuzzy Inference System (Cascaded-ANFIS) was effectively utilized to achieve the research gap. Color Structure, Region Shape, Edge Histogram, Column Layout, Gray-Level Co-Occurrence Matrix, Scale-Invariant Feature Transform, Speeded Up Robust Features, Histogram of Oriented Gradients, and Oriented FAST and rotated BRIEF features are used in this study as the features descriptors in identifying fruit images. The algorithm was validated using two methods: iterations and confusion matrix. The results showcase that the proposed method gives a relative accuracy of 98.36%. The Fruit-360 dataset is unbalanced; therefore, the weighted precision, recall, and FScore were calculated as 0.9843, 0.9841, and 0.9840, respectively. In addition, the developed system was tested and compared against the literature-found state-of-the-art algorithms for the purpose. Comparison studies present the acceptability of the newly developed algorithm handling the whole Fruit-360 dataset and achieving high computational efficiency.
Open Access
Efficient Hotspot Detection in Solar Panels via Computer Vision and Machine Learning
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-07-15) Fernando, N; Seneviratne, L; Weerasinghe, N; Rathnayake, N; Hoshino, Y
Solar power generation is rapidly emerging within renewable energy due to its cost-effectiveness and ease of deployment. However, improper inspection and maintenance lead to significant damage from unnoticed solar hotspots. Even with inspections, factors like shadows, dust, and shading cause localized heat, mimicking hotspot behavior. This study emphasizes interpretability and efficiency, identifying key predictive features through feature-level and What-if Analysis. It evaluates model training and inference times to assess effectiveness in resource-limited environments, aiming to balance accuracy, generalization, and efficiency. Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. Explainable AI (XAI) techniques guide the analysis, with a particular focus on MPEG (Moving Picture Experts Group)-7 features for hotspot discrimination, supported by statistical validation. Medium Gaussian SVM achieved the best trade-off, with 99.3% accuracy and 18 s inference time. Feature analysis revealed blue chrominance as a strong early indicator of hotspot detection. Statistical validation across datasets confirmed the discriminative strength of MPEG-7 features. This study revisits the assumption that DL models are inherently superior, presenting an interpretable alternative for hotspot detection; highlighting the potential impact of domain mismatch. Model-level insight shows that both absolute and relative temperature variations are important in solar panel inspections. The relative decrease in “blueness” provides a crucial early indication of faults, especially in low-contrast thermal images where distinguishing normal warm areas from actual hotspot is difficult. Feature-level insight highlights how subtle changes in color composition, particularly reductions in blue components, serve as early indicators of developing anomalies.
Embargo
An Efficient Ocular Disease Recognition System Implementation using GLCM and LBP based Multilayer Perception Algorithm
(IEEE, 2022-08-03) Rathnayake, N; Mampitiya, L. I
This research study is focused on the classification of ocular diseases by referring to a well-known dataset. The data is divided into seven classes: diabetes, glaucoma, cataract, normal, hypertension, age-related macular degeneration, pathological myopia, and other diseases/abnormalities. A Neural Network is used for the classification of diseases. In addition, the GLCM and LBP feature extracting methods have been used to carry out the feature extraction for the fundus images. This study compares five different ocular disease recognizing techniques. Moreover, the proposed model was evaluated regarding precision, recall, and accuracy. The proposed solution outperformed existing state-of-the-art algorithms, achieving 99.58% accuracy.
Open Access
Enhancing the Understanding of climate dynamics: analysis of global warming’s influence on Climatic changes across continents
(Springer, 2025-07-14) Dharmapriya, N; Edirisinghe, S; Gunawardena, V; Methmini, D; Rathnayake, N; Jayathilaka, R
Global warming, primarily due to increased atmospheric carbon dioxide, poses a significant threat to climate stability, yet research on its combined effects across different geographical areas is limited. In order to fill that gap, this study examines how carbon emissions (CE) are impacted by greenhouse gas emissions (GHG), agricultural nitrogen oxide (ANO), urban population (UP), and fossil fuel consumption (FFC) in 185 different nations between 2000 and 2019. With the exception of urban population, which was expressed as a percentage, all variables were standardised to metric tonnes per capita using panel regression analysis. The results draw attention to geographic disparities. Africa has the lowest carbon and greenhouse gas emissions due to its extensive forest cover and minimal industrial production. Although Oceania’s greenhouse gas emissions have decreased, the region continues to emit high amounts of agricultural nitrous oxide. Rapid industrialisation is the primary cause of Asia’s growing consumption of fossil fuels. Agricultural nitrous oxide and carbon emissions have a negative correlation in Asia, Oceania, and the globe, but a positive correlation in Africa, America, and Europe. Carbon emissions and the use of fossil fuels are strongly positively correlated in every region but Asia. These results highlight the complex, location-specific factors affecting carbon emissions. For policymakers to effectively cut emissions, they must develop customised, geographically specific initiatives. In order to accomplish Sustainable Development Goal 13: Climate Action by 2030, emission controls should be strengthened, and sustainable practices should be encouraged, particularly in the use of fossil fuels and farming.
Open Access
Evolving Expectations of HR Professionals Amid the Covid-19 Pandemic in Sri Lanka
(researchgate.net, 2022-07) Weerarathna, R; Rathnayake, N; Perera, H; Wickramasena, D; Arambawatta, V; Kaluarachchi, R
This study explores the expectations of HR professionals in Sri Lanka in terms of their workplaces during the COVID19 pandemic. A qualitative research methodology was employed in this study with 16 semi-structured interviews of HR professionals in Sri Lanka. Results reveal that on-premise and hybrid work cultures are much preferred by HR professionals in Sri Lanka. Further, if the work culture transformation remains, their expectations are high regarding concerns in new work practices at the workplace triggered by the pandemic including worklife balance practices, crisis management practices, financial incentives, career progress and Work from Home (WFH) resources.
Open Access
Factors Affecting Hybrid Learning Methods (Case study in One of the Leading Private Higher Education Institutes in Sri Lanka)
(Emerald Publishing, 2022-12-01) Rathnayake, N; Jayasinghe, P; Dias, C; Jayamalki, A; Wickramasinghe, P; Sivaguru, R
Hybrid Learning (HL) become a major part of the learning style for the higher education sector in the Sri Lankan context. Hybrid learning allows for a part of the students to go to the course physically and simultaneously permitted the rest to connect the sessions utilizing video conferencing from different locations. The objective of this research study is to discover the factors affecting hybrid learning to enhance student outcome in the business faculty of one of the leading private higher education institutes in Sri Lanka. The researchers extracted the variables that affected the hybrid learners from the previous studies that investigated hybrid learning concepts. The purpose of the study was to assess the factors affecting for hybrid learning experience. The data for the study was gathered through 12 semi-structured interviews and the data were analysed by using thematic analysis. The results show that the factors affecting hybrid learning are somewhat higher than traditional techniques from the perspective of the students. In addition, based on the thematic analysis researchers have identified themes such as: learner attitudes, interactions, obstacles, and benefits of HL. Researchers determined the factors affecting hybrid learning in students' perceptions. The output of this study was helpful to recognize how students perceive the factors affecting Hybrid Learning with these significant themes in one of the leading private higher education institutes.
Open Access
Factors influencing migration intention of undergraduates in Sri Lanka: ‘About more than employment
(Elsevier Ltd, 2026-01-26) Marawila, R; Weerarathna, R; Rathnayake, N; Guruge, R; Wehella, B; Udugahapattuwa, T; Weligodapola, M
The objective of this study is to examine the factors influencing Sri Lankan undergraduates' intention to migrate. Persistent economic, social, and political challenges have driven many youngsters and professionals to leave their Country of Origin (COO). The economic collapse triggered by COVID-19 further intensified this trend, leading to a sharp increase in outward migration. Recently, a growing number of Sri Lankan undergraduates and skilled professionals have expressed a strong desire to relocate abroad, often immediately after completing secondary education. For this study, a sample of 385 undergraduates from state and non-state universities across Sri Lanka was analysed. Given the national concerns of brain drain and shortages of trained and skilled workers, the study specifically focused on understanding undergraduates' aspirations to migrate. Structural Equation Modeling (SEM) was applied to identify and test the variables influencing migration intentions within the Sri Lankan context. The findings provide a holistic picture of the drivers of undergraduate migration. These carry important implications not only for students but also for policymakers and Higher Education Institutions (HEIs), by informing policies and strategies that could encourage young people to realise their potential within Sri Lanka rather than abroad.
Open Access
How Do Different Types of University Academics Perceive Work from Home Amidst COVID-19 and Beyond?
(MDPI, 2022-04-19) Rathnayake, N; Kumarasinghe, P; Kumara, A. S
The COVID-19 pandemic resulted in a massive and unintentional shift to work from home (WFH) or working remotely, as well as broad adoption of web-based platforms. The goal of this research is to uncover the attitudes to WFH among different types of academics in the Sri Lankan higher education sector. An online questionnaire survey was conducted amidst a severe COVID-19 wave during June–September 2021, and 337 valid responses were received. The questionnaire contained 49 questions under four sections excluding demographic questions. The gathered data were analysed using multiple regression models. Results of the study ascertained a significant (p < 0.01) positive attitude among academics towards online teaching (academic orientation), other than academics who from disciplines with more practical components, and there was a significant (p < 0.01) positive attitude among academics to conducting research (research orientation) while WFH. Further, the findings indicate a significant (p < 0.01) negative attitude to WFH when disseminating knowledge and engaging in community services with various stakeholders. When considering the criticality of demographics variables in the new normal, a hybrid working model can be introduced by reaping the benefits of WFH based on different types of academic orientations and their favourability towards the WFH model, rather than reverting to a full physical academic working environment. As a developing country, Sri Lanka can formulate policies on effective hybrid working models for academics to realise the potential from the lessons learned. This experience will enable the country to accomplish or move towards accomplishing the fourth goal of SDGs, quality education by 2030.
Open Access
How Do Different Types of University Academics Perceive Work from Home Amidst COVID-19 and Beyond?
(MDPI, 2022-04-19) Rathnayake, N; Kumarasinghe, P. J
The COVID-19 pandemic resulted in a massive and unintentional shift to work from home (WFH) or working remotely, as well as broad adoption of web-based platforms. The goal of this research is to uncover the attitudes to WFH among different types of academics in the Sri Lankan higher education sector. An online questionnaire survey was conducted amidst a severe COVID19 wave during June–September 2021, and 337 valid responses were received. The questionnaire contained 49 questions under four sections excluding demographic questions. The gathered data were analysed using multiple regression models. Results of the study ascertained a significant (p < 0.01) positive attitude among academics towards online teaching (academic orientation), other than academics who from disciplines with more practical components, and there was a significant (p < 0.01) positive attitude among academics to conducting research (research orientation) while WFH. Further, the findings indicate a significant (p < 0.01) negative attitude to WFH when disseminating knowledge and engaging in community services with various stakeholders. When considering the criticality of demographics variables in the new normal, a hybrid working model can be introduced by reaping the benefits of WFH based on different types of academic orientations and their favourability towards the WFH model, rather than reverting to a full physical academic working environment. As a developing country, Sri Lanka can formulate policies on effective hybrid working models for academics to realise the potential from the lessons learned. This experience will enable the country to accomplish or move towards accomplishing the fourth goal of SDGs, quality education by 2030.
Open Access
How successful the online assessment techniques in distance learning have been, in contributing to academic achievements of management undergraduates?
(Springer, 2023-03-06) Thathsarani, H; Ariyananda, D.K; Jayakody, C; Manoharan, K; Munasinghe, A.A.S.N; Rathnayake, N
The implementation of online teaching and assessments was prompted by the current COVID-19 pandemic. Therefore, all universities had to adopt the distance-learning method as the only choice to continue education delivery. This study’s main objective is to understand the effectiveness of assessment techniques followed through distance learning in Sri Lankan management undergraduates during COVID-19. Furthermore, utilizing a qualitative approach and thematic analysis for data analysis, semi-structured interviews with 13 management faculty lecturers selected through the purposive sample technique were used for data collection. The survey was conducted via an online questionnaire that was distributed to Sri Lankan undergraduates, and a total of 387 samples from management undergraduates were drawn for the quantitative data analysis using a simple random sampling technique. The study's main findings revealed that five online assessments are currently being utilized to evaluate management undergraduates' academic performance under distance learning, including online examinations, online presentations, online quizzes, case studies, and report submissions. In addition, this study statistically and with some qualitative empirical evidences in the existing literature proved that online examinations, online quizzes, and report submissions have a significant impact on undergraduates’ academic performance. Further, this study also recommended that universities should implement procedures for online assessment techniques in order to assess the quality assurance of assessment techniques.
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, T
Malnutrition, 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
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, T
Malnutrition, 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.
Open Access
Institutional Best Practices Amidst and Beyond the COVID-19: The Case of Higher Educational Institutes in Sri Lanka
(SLIIT Business School, 2023-12-24) Rathnayake, N; Weerasinghe, A; Weerasinghe, N; Kumarasinghe, J
COVID-19 is a blessing for the higher education industry in developing nations since it has accelerated the digitization of higher education. Education is essential to transforming people into human capital. The COVID-19 restrictions on physically entering educational institutions gave boost to the biggest educational disaster in the world. The objective of this study is to investigate the best practices employed by the Higher Education Institutions (HEIs) in Sri Lanka to enhance university academic role both amid and beyond the pandemic. The technique of nonprobability purposive sampling was employed, and the results were then analyzed thematically. Best practices in academic research and knowledge dissemination fields, and teaching have been recognized by the study from the viewpoint of the HEIs. Beyond the pandemic, virtual laboratories, concurrent delivery, and hybrid deliveries are still in use, while academic research and knowledge dissemination are being digitalized and exposed to a global audience. The shift from traditional classrooms to the distance learning environment in developing nations has accelerated the process of meeting the sustainable development objective of high-quality education by 2030. As a result, policymakers in these nations can emphasize digitally enabling the higher education sector.