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Recent Submissions

Impact of geographical variation on nutritional and antioxidant properties of Basella alba L. from Sri Lanka
(BMC Plant Biology, 2025) Dahanayaka, L. W.; Mapa, M. M. S. T.; Kadigamuwa, C. C.; Udayanga, D.
Background Basella alba L. (Malabar spinach) is a widely consumed leafy vegetable, well known for its nutritional and therapeutic properties. These properties arise from the availability of essential nutrients, phytochemicals, and antioxidant potential, which may vary depending on environmental factors induced by the geographical location. In this study our aim is to investigate the correlation between the geographical location and proximate composition, phytochemical content, and antioxidant activity of B. alba harvested from fifteen locations in Sri Lanka. Results According to the statistical analysis by ANOVA and Tukey test, the results of proximate analysis confirmed that samples from different locations showed statistically significant variance in nutritional content. Furthermore, phytochemical content and antioxidant potential varied showing a significant difference between locations in total chlorophyll (27.53 to 6.69 µg/g dry weight), carotene (4.54 to 1.15 µg/g dry weight), total flavonoid content (10.54 to 3.94 mg/g dry weight in Quercetin equivalents), total phenolic content (8.33 to 0.46 mg/g dry weight in gallic acid equivalents), 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity (38.03–11.4% inhibition), and ferric ion-reducing antioxidant power (1.23 to 3.76 mg/g dry weight in ascorbic acid equivalents) (p < 0.05). The Pearson correlation showed a strong positive correlation between total phenolic content and antioxidant activity. Principal component analysis indicates the role of antioxidant activity and chlorophyll content in location differentiation, forming distinct clusters. Cluster analysis categorized samples into four groups, linking biochemical traits to agro-climatic zones. The principal component analysis and cluster analysis showed a close relationship between some locations due to their high antioxidant and phytochemical accumulation. Conclusion This study exhibits the importance of geographical location on the phytochemical profile and antioxidant properties of B. alba. These findings can be used to refine optimal cultivation sites for B. alba to enhance the efficacy of its nutraceutical and pharmaceutical potential.
Payment and security concerns in Sri Lankan e-commerce web sites
(IEEE, 2024) Rashminda, L.; Jayatissa, Y.
The rapid growth of e-commerce in Sri Lanka has transformed how businesses operate and consumers shop. This research investigates the leading payment and security concerns within the context of Sri Lankan e-commerce websites. The study focuses on the various online transactions, encompassing different payment methods and focuses on security incidents in e-commerce platforms. Through a quantitative SmartPLS analysis, the research aims to understand the intricate dynamics among payment methods, security incidents, customer trust, and concerns. By surveying and analyzing data from consumers who engaged in online shopping and transactions, this study seeks to provide valuable insights into the factors influencing the trust and security perceptions of Sri Lankan e-commerce users. The findings will inform strategies to enhance payment security, build consumer trust, and foster sustainable growth within the evolving digital ecosystem of Sri Lankan e-commerce.
Assessing the Performance of Feedforward Neural Network Models with Random Data Split for Time Series Data: A Simulation Study
(IEEE, 2024) Basnayake, B. R. P. M.; Chandrasekara, N. V.
The majority of findings and conclusions related to the application of artificial neural networks (ANNs) for time series data have been derived through non-random data-splitting procedures. In this methodology, the initial set of observations in the dataset is employed for model training, followed by a subsequent set of observations for validation and the final set of observations for testing. However, this study presents a comprehensive simulation study on assessing the performance of Feedforward neural networks (FFNN) with the random data split procedure. For this purpose, eight nonlinear models from the literature were employed to generate multiple series where they represent different features available in the time series data. The complexity of the selected models was further improved by introducing Poisson processes with different jump sizes. From each selected time series model, 30 replications were generated using distinct initial random seeds for the error term. The data were randomly partitioned, allocating 80% for training, 10% for validation, and 10% for testing purposes. The FFNN models were fitted incorporating the inputs from past observations and moving average values. Moreover, each individual fitted model was trained 30 times to obtain a statistically robust evaluation of model performance through the averaging of predictive values. The findings reveal that the FFNN models performed well with the random data split procedures for time series data with lower minimum error values. The same idea was observed by graphs fitted between the actual test values and the average of the forecasted values. The results obtained from this simulation study are important, as they provide valuable insights into the broader utilization of FFNNs with regard to the effectiveness of random data split procedures in time series forecasting.
Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review
(Journal of Medical Systems, 2024) Jayamini, W. K. D.; Mirza, F.; Naeem, M. A.; Hai, A.
Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.
Impact of supply chain agility on customer value and customer trust: Moderating effect of price sensitivity in healthcare industry
(Journal of Infrastructure Policy and Development, 2024) Mirando, U. J.; Herath, P.
This study investigates the impact of supply chain agility on customer value and customer trust while investigating the role of price sensitivity as a mediating variable in the healthcare industry. A quantitative methodological approach was used. This was cross-sectional descriptive research based on a survey method, and data were collected using a structured questionnaire. The sample consisted of 384 respondents who had already used healthcare facilities. The sampling technique was convenience sampling and collected data were analyzed using structural equation modeling. The study indicated that supply chain agility positively impacts customer value and customer trust, while there is no moderation role of price sensitivity in the healthcare industry. Previous scholars revealed that there is a strongly available association between supply chain agility and customer value. But no attempt was undertaken to investigate the impact of supply chain agility on customer trust while moderating the role of price sensitivity.