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Background: Cardiovascular Diseases (CVD) account for the highest number of deaths and disability globally and within Sri Lanka. A CVD risk prediction tool is a simple means of early identification of high-risk groups which is a cost-effective preventive strategy, especially for resource-poor countries. Distribution of risk factor levels varies in different regions even within the same country, thus a common risk estimation tool for the country may give false local predictions. Since there are few published data related to Sri Lanka the aim of this study was to recalibrate the Framingham equation according to the local risk factor profile of a population in the Kurunegala region in Sri Lanka. Method: A cross-sectional study was conducted with the participation of 1 102 persons from the Kurunegala Regional Director of Health Services area and the data was collected using an interviewer-administered questionnaire, anthropometric, blood pressure, and biochemical measurements. CVD risk was estimated using Framingham original and recalibrated CVD risk assessment methods. Current CVD mortality and morbidity data and the recalibration method conducted by the method described by Wilson and colleagues were used for calculations. Results: Original and recalibrated Framingham CVD risk scores predicted 55.5% (N = 612) and 62.3% (N = 687) to be having less than 10% CVD risk respectively. Further, the original and recalibrated CVD Risk Scores predicted 2.2% (N = 24) and 1.8% (N = 20) to be having CVD risk more than 40% respectively. Conclusion: These findings show an over prediction of the CVD risk with the original Framingham risk calculations which signifies the importance of development of a region-specific risk prediction tool using local risk factor data in Sri Lanka which will prevent unnecessary expenditure to manage people without risk of CVD. [ABSTRACT FROM AUTHOR]

The CYN degradation kinetics of each bacterial species were studied using High Performance Liquid Chromatography. The greatest CYN degradation (28.22 ± 0.24%) was shown by the bacterium B. cereus-S in 5.0 mg/L CYN within 14 days. The CYN degradation by the other strains was lower than 10% under the same conditions. Further studies employing different initial concentrations of CYN revealed that B. cereus-S could degrade lower CYN concentrations at a higher percentage (1.0 mg/L, 2.5 mg/L, and 5.0 mg/L of CYN removal percentages were 36.83 ± 2.43%, 32.25 ± 1.25%, and 24.72 ± 0.40%, respectively, after 14 days of incubation at 28℃ and pH 7). The maximum average degradation rates were recorded for 1.0 mg/L, 2.5 mg/L, and 5.0 mg/L CYN on the 6th (0.05 ± 0.00 mg/L/day), 8th (0.04 ± 0.01 mg/L/day), and 12th (0.02 ± 0.01 mg/L/day) days of incubation, respectively. The study showed the potentiality of the bacterium B. cereus-S on the application for degrading CYN among the tested bacteria species. [ABSTRACT FROM AUTHOR]

Lung cancer, a leading cause of cancer-related deaths worldwide, necessitates early detection and effective management tools. This research introduces an assistive system leveraging machine learning to identify risk factors and prioritize treatments based on severity. Critical sociodemographic data, like age, gender, and smoking history, enable accurate risk assessment and personalized profiles. The system adapts to lifestyle changes post-diagnosis, offering tailored healthcare solutions. Integrating Mask RCNN, a deep learning algorithm for medical imaging, enhances lung cancer diagnosis precision and treatment strategies. The user-friendly interface allows healthcare providers, caregivers, and patients easy access via mobile or computer applications. The implementation includes natural language processing for questionnaires and data preprocessing. The system's design consists of three components: front-end interface, back-end server, and Mask R-CNN for tumor identification. Correlation analysis for patient prioritization to generate a list. The proposed system aims to revolutionize lung cancer care, delivering accurate risk assessments, personalized treatment plans, and continuous monitoring, ultimately improving patient outcomes and saving lives. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa L.) is the staple food in Sri Lanka and phosphorus (P) and potassium (K) are major nutrients for the rice plant. However, the variation of P and K contents (mg kg-1) in rice soils and rice grains as affected by agro-climatic zones (ACZs), water source used (i.e., major irrigation, minor irrigation and rain-fed) and cropping systems adopted (i.e., fallow, vegetable, perennials, other field crops) by Sri Lankan farmers are not well elucidated, and are thus investigated in the present study. A total of 200 rice soil and 230 rice grain samples across the country were collected from farmer fields, representing different ACZs, water sources used, and cropping systems adopted using a stratified random sampling approach. The total and available P and K contents in rice soil, and the P and K contents in rice grains were determined. The plant-available P contents in soil were similar among ACZs, water sources, and rice-based cropping systems. Exchangeable K content was higher in rice fields where vegetables were cultivated in the previous season. Grain P and K contents were similar among the water sources used and rice-based cropping systems. Grain P contents was the lowest in the Low country Wet zone. Soil available-P and total-P contents (r = 0.29, p < 0.0001), and grain P and K contents were positively correlated (r = 0.51, p < 0.0001). The knowledge generated in the present study is important in P and K nutrient management in rice cultivation in the country. [ABSTRACT FROM AUTHOR]

The transportation problem (TP) is a significant factor in operational research. Numerous researchers have put forth various solutions to these problems. The goal is to reduce the overall cost of distributing resources from multiple sources to numerous destinations. If there are road risks (snow, flood, etc.), traffic limitations, etc., it might not be feasible to transport products from one place to another. In these circumstances, the appropriate route(s) can be given an extremely high unit cost, such as M (or ∞). Following that, a specific case of the prohibited transportation problem is introduced. Therefore, the focus of this study is to provide a novel algorithm that will reduce the cost of restricted transportation problems. With a few modifications, the traditional Vogel approach has been enhanced. The proposed method would perform better than the other approaches now in use. The numerical problem is resolved to demonstrate the effectiveness of the proposed approach and make comparisons with different approaches already in use. [ABSTRACT FROM AUTHOR]

Inspired by multiple benefits, including competitive advantages from developing resilient supply chains, this study was designed for the development of effective assessment models to evaluate Supply Chain Capabilities (SCC), improving resilience in Industrialised Construction (IC) in one of the high-density cities: Hong Kong (HK). First identifying appropriate SCC, this study aimed to develop multi-stage-mathematical models to evaluate the adoption of SCC of IC in HK. Experts' judgements were solicited and analysed using fuzzy synthetic evaluation. Forty-one measurement items were grouped under nine critical SCC components, and their 'importance' and 'current practice' indices were determined. The importance index of SCC is high, spotlighting the significance of the contribution of SCC to resilience whereas the current practice index is comparatively low, highlighting the dire need to bridge this gap with capability improvements. To the authors' knowledge, these evaluation models are the first set of structured models designed to assess SCC of IC, providing valuable insights to practitioners for well-informed decision-making in formulating strategies to initiate and nurture resilient supply chains in IC in HK. [ABSTRACT FROM AUTHOR]