Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a major health challenge, particularly in low income and middle-income countries. This study focuses on the Western province of Sri Lanka, which has high population density, trade, and high human mobility, all contributing to TB transmission. This research aims to strengthen TB control strategies through several key objectives. The TB contact network model was developed and analyzed using graph theoretical measurements such as degree centrality, closeness centrality and betweenness centrality, to identify influential nodes, and structural metrics such as modularity and clustering coefficient to understand transmission patterns. The TOPSIS (technique for order preference by similarity to ideal solution) method was applied to rank nodes based on their potential to spread TB, improving targeted intervention strategies. The novelty of this research is to apply graph theoretical measurements together with the TOPSIS method to identify the highly influential nodes and control the spread of disease by isolating those nodes from the community. Epidemic data extracted from the National Programme for Tuberculosis Control and Chest Diseases (NPTCCD) annual reports (2012-2023) revealed higher projected TB incidences among males and individuals belong to the age group 45-54. The synthetic dataset constructed using percentage-based forecasting data underwent 5-fold cross-validation, and the random forest model achieved an accuracy of 81.5%, indicating strong reliability of the forecasting model. Network analysis indicated the sparse local connections (clustering coefficient: 0.002) and strong community structures (modularity: 0.856). Comparative analysis before and after applying the TOPSIS method demonstrated the improved identification of influential spreaders. Sensitivity analysis showed that the model remained stable under small variations in data and weights, confirming the robustness of the findings. The outcome of this research provides guidance to health authorities for allocating resources, prioritizing high risk groups, initiating strengthening programmes, and designing more effective community-based interventions to strengthening TB control strategies in the Western province.
A four-season consecutive field experiment was conducted from the 2023 Yala season to the 2024/25 Maha season under low country dry zone conditions in Sri Lanka. The objective was to evaluate the combined effect of rice husk biochar (RHB) and urea fertilization on the rice milling traits hardness, chalkiness, bran weight (BW), total milled rice (TMR), hull percentage, degree of milling (DM), brown rice (BR), and head grain (HG) percentages. The rice variety At 362 was used as the test cultivar and the experiment followed a split-plot design with three replicates. The main plot treatments consisted of four RHB levels: 0, 1, 2, and 3 t ha-1 (designated as BC0, BC1, BC2 and BC3) with partially burnt paddy husk (PBPH) applied at the Department of Agriculture recommended rate of 625 kg ha-1. Sub-plot treatments included four levels of urea fertiliser: 0, 30, 70, 100 % of the recommended dose (U0, U30, U70, U100, where U100 = 225 kg ha-1). Agricultural-grade urea commonly used by local farmers was applied following standard local practice. Results indicated that a one-time application of RHB combined with seasonal urea significantly enhanced total grain yield peaking at 6.50 t ha-1 in season 3 and improved rice milling traits. Higher biochar and urea application rates led to consistent improvements in grain yield and all positive milling attributes, while significantly reducing hull percentage and grain chalkiness across all seasons, demonstrating a residual biochar effect. In comparison, PBPH resulted in significantly lower grain yield and quality improvements compared to RHB treatments. Correlation analysis revealed a strong positive relationship between grain hardness, DM % and TMR %, and a strong negative relationship between hull percentage and BW %. Forecasting models suggest that rice milling traits can be further optimized with RHB application rates exceeding 3 t ha-1. Overall, the integration of RHB with urea provides greater and more sustainable benefits for rice milling traits while increasing productivity under low-country dry zone conditions in Sri Lanka.
The flag leaf, the most important functional leaf in the rice plant, positively influences the photosynthetic process and yield components. Meanwhile, pollen germination is a critical indicator of species compatibility for successful seed setting. This study analyzed the correlation between flag leaf traits and panicle traits and pollen germination characteristics of six wild rice (Oryza nivara) populations. Plants from six populations (N1 to N6) were collected from different geographical locations and established in a common garden. Morphological traits of the flag leaf were evaluated shortly after heading and yield traits were measured at the maturity stage. Agro-morphological, reproductive, and physiological traits related to grain yield were recorded. Data were analyzed using one-way ANOVA, followed by Duncan's multiple range test (p<0.05), least significant difference (LSD) (p<0.05), and Pearson's correlation test (p >= 0.05 to p<0.001). The Kilinochchi (N6) population recorded the highest significant values for flag leaf length (26.58 +/- 0.49), flag leaf area (23.04 +/- 1.22), number of seeds per panicle (71.40 +/- 3.52), panicle length (24.55 +/- 0.38), 100-seed weight (1.76 +/- 0.027), and pollen germination (65.74 +/- 3.51) compared to other populations. The Anuradhapura (N1) population had the highest ratio of fertile seeds per panicle (0.75 +/- 0.023). The Kurunegala (N3) population showed the lowest seed fertility (0.11 +/- 0.012) after removal of the flag leaf, showing a 71.8% reduction in seed fertility compared to the intact flag leaf. According to Pearson's correlation analysis, the highest positive correlation was observed between flag leaf length (r = 0.79, p < 0.001) and panicle length, followed by chlorophyll content (r = 0.26, p < 0.05) and pollen germination (r = 0.36, p < 0.01) with the ratio of fertile seeds per panicle. Results suggested that flag leaf length, chlorophyll content, and pollen germination could be the best yield-attributing indicators for selecting O. nivara wild rice populations to adopt in rice pre-breeding studies.
Forestfires pose a significantthreatto the conservation of forest resources in Sri Lanka. Forest fire modeling is a novel technique to study and predict the behavior of forest fires. This study utilizes simulation techniques to investigate the relationship between forest fire occurrence and climate change particularly in the dry zone of Sri Lanka. The frequency and duration of phenomena such as sea current oscillation El Ni & ntilde;o southern oscillation (ENSO) change due to climate change, can create extreme dry conditions that increase the risk of intensified fires, especially in the dry zone of Sri Lanka. To assess fire vulnerability under prevailing weather conditions and projected climate change scenarios, the FConstMTT simulation algorithm was used with 1,000 iterations per scenario. In this analysis forest types across Sri Lanka were classified into six classes based on elevation, rainfall, and fuel characteristics. Customized fuel models were created using BehavePlus and spatial data was processed through the ArcFuel extension of ArcGIS. The results indicate that under projected climate scenarios, fire hazards in the dry zone particularly in the districts of Mullaitivu, Monaragala, Anuradhapura, Polonnaruwa, and Badulla, are likely to worsen in the future. In the climate change scenarios, Monaragala district accounted for approximately 53% of total fire damage, with Badulla district showing a significant baseline vulnerability. Anuradhapura district also showed significant levels of fire vulnerability under the projected climate change conditions. Based on fire frequency and intensity, the 25 administrative districts in Sri Lanka were categorized into high, moderate, and low-risk areas. Forest types such as dry monsoon forests, shrublands, open and sparse forests, and savannahs are particularly vulnerable under the predicted climate change scenarios.
The Kelani River Basin (KRB), located in Sri Lanka’s wet zone, frequently experiences flooding and is more urbanized and densely populated than other basins. It flows through Colombo, the country’s commercial capital, making changes in the KRB highly impactful. To assess recent rainfall and water level fluctuations, daily rainfall data from 10 meteorological stations (1991–2020) were obtained from the Department of Meteorology, while water level data for Hanwella and Nagalagam Street were sourced from the Irrigation Department. Data were homogenized using a mean-based adjustment technique. The modified Mann-Kendall test, Sen’s slope, Pearson’s correlation, and lag correlation were applied. Extreme rainfall indices PRCPTOT, SDII, R99p, R95p, CWD, RX5day, RX1day were calculated. Results showed a significant increase in annual rainfall over the KRB but a contrasting trend in water levels: decreasing at Hanwella, increasing at Nagalagam Street. Around 40% of stations, especially in the upper KRB, showed decreasing rainfall trends most notably during the Southwest Monsoon. In contrast, the Northeast Monsoon showed increased rainfall in 70% of stations. While rainfall intensity has decreased, frequency and duration have risen. A moderate positive correlation between rainfall and water level suggests additional influencing factors. These findings are vital for sustainable water management in the KRB.
