DRC 2024

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/29875

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Subject: search.filters.subject.Sri Lanka

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    OVERVIEW OF THE TECHNICAL TERMS IN THE INDUSTRY OF PALM-LEAF MANUSCRIPTS: SPECIAL REFERENCE TO SRI LANKA
    (The Library, University of Kelaniya, Sri Lanka., 2024) Alahakoon, C. N. K.
    The writing on palm-leaves in Sri Lanka began in the 1st century BC. Nevertheless, these materials couldn’t be used in their ordinary form without having processing of the leaves. According to the historical evidences, the production of palm-leaf is a massive process which the ancient society involved for this special task. In fact, there are special customs engross from the begging of the cutting of Tala tree to the final process of preparation of written palm-leaf manuscript. However, there is a dearth of writing documentation and discussion regarding the various terminology used for the palm-leaf manuscript creation process. The objective of the study involved into explores and clarify the technical terms related to palm-leave preparation in Sri Lanka. The methodology adheres for this research is the desk research method which tempts to use the existing documents related to the subject, previous research carried out over a particular topic, and the document distributed at the workshops in practical aspect also extensively used to identify the glossary of the technical terms used in the industry. According to the published literature, and documents, it was apparent that the very specific and special terms are being used in the process of palm-leaves. Further to that, the terminologies have some relationship with the Buddhism as these are highly used by the priest in the temples and monasteries for their education and dissemination of knowledge in the ancient society. More specifically, the research provides clear explanations of these terms, offering readers a better understanding of their meanings of the manuscript production process. Additionally, it highlights the grey areas that lacked sufficient in the so far discussions. On the whole, the study contributes to the comprehensive understanding of the rich technical vocabulary associated with palm-leaf manuscript production in Sri Lanka.
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    IMPROVING CARDIOVASCULAR RISK PREDICTION OF SRI LANKANS USING ARTIFICIAL INTELLIGENCE
    (The Library, University of Kelaniya, Sri Lanka., 2024) Mettananda, C.; Solangaarachchige, M. B.; Haddela, P. S.; Dassanayake, A. S.; Kasturiratne, A.; Wickramasinghe, A. R.; Kato, N.; De Silva, H. J.
    There are no CV risk prediction models derived from Sri Lankan cohorts. Therefore, the World Health Organization(WHO) risk charts developed for the Southeast Asia Region are being used to risk stratify Sri Lankans. However, Sri Lankans are quite different to some Southeast Asian countries and may not agree with Sri Lankans. Therefore, we aimed to develop a CV risk prediction model specific to a cohort of Sri Lankans. Using supervised machine learning of 10-year follow-up data of a randomly selected, population-based cohort of Sri Lankans, we developed a model to predict the 10-year risk of developing a cardiovascular event. We compared predictions of the new model at baseline in 2007 with the observed events in 2017 following a 10-year follow-up using receiver operating characteristic curves(ROC) to find the predictive performance. We compared the predictions of the new model and the currently used WHO risk charts. We selected 2596 Sri Lankans between 40 and 65 years old with no history of previous CV diseases (CVD) at recruitment and who had completed 10-year follow-ups. There were 179 hard CVDs recorded over the ten years. CVD included all cardiovascular deaths confirmed or presumed cases as mentioned in death certificates, non-fatal strokes, and physician-diagnosed non-fatal acute coronary syndromes, including elective percutaneous coronary interventions and coronary artery bypass grafts done on patients with symptomatic unstable angina. Any cardiac presentation except those mentioned here was excluded. Of 179 events, the ML-based model predicted 124; only 33 were predicted by the new model, while only 33 were predicted by 2019 WHO risk charts. The new ML-based model had 0.93 accuracy with an AUC-ROC of 0.74 ± 0.06. Machine learning of individual data of a Sri Lankan cohort improved CV risk prediction of Sri Lankans than using risk charts developed for an epidemiological region using a modelling approach.