2025

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Author: search.filters.author.Rathnayake, NHas files: Yes

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Now showing 1 - 3 of 3
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    PublicationOpen 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.
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    PublicationOpen 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.
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    PublicationOpen 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, 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.