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This study investigates the main factors influencing carbon emissions from Four Wheel Passenger Vehicles (FWPVs) in Sri Lanka, an emerging economy where the transport sector contributes 12 % of total emissions. Pioneering a new methodology, we integrate System Dynamics (SD) modeling and decomposition analysis to comprehensively map the factors affecting fuel efficiency for the first time in Sri Lanka. Employing the additive Logarithmic Mean Divisia Index (LMDI I), we analyze a comprehensive time series dataset covering all petrol, diesel, and electric-powered FWPVs registered between 2015 and 2019. Our study delves into significant drivers of CO2 emissions, including population dynamics, vehicle ownership trends, annual travel distances, fuel types, engine capacities, and technological advancements. Additionally, we quantify the impact of fiscal policies on FWPV emissions, revealing a surge in emissions in 2018 and 2019 driven by consumer preferences for high-capacity vehicles. Our findings offer critical insights for crafting effective policies to promote energy-efficient vehicles and mitigate carbon emissions in the transport sector, emphasizing the importance of targeted fiscal measures. By imposing taxes on high-capacity vehicles and incentivizing the adoption of low-emission options through tax variations and renewable energy incentives, policymakers can influence consumer behavior and drive significant emission reductions. Our study underscores the need for ongoing monitoring and evaluation of fiscal policies to refine existing measures and accelerate the transition towards a sustainable, low-carbon transportation sector in Sri Lanka.

The rapid expansion of urban areas and the increasing number of vehicles on the road have resulted in accidents, traffic congestion, economic repercussions, environmental deterioration, and excessive fuel consumption. A dependable traffic management system is necessary to anticipate and regulate urban traffic patterns. Traffic forecast aids in the prevention of traffic issues. Urban traffic predictions often utilise historical and current traffic flow data to forecast road conditions. This article presents a traffic flow prediction system that utilises the Internet of Things (IoT), machine learning, and feature selection. Internet of Things (IoT) devices located on highways or within cars gather sensor data in real-time. The input data set comprises both real-time Internet of Things (IoT) data and historical traffic statistics. The input data is stored in a centralized cloud. The data is subjected to preprocessing in order to eliminate any unwanted interference and identify any exceptional values. The accuracy and root mean square error are contingent upon the process of feature selection. Particle swarm optimization identifies and extracts crucial features from input data. The classification model is constructed using K Nearest Neighbor, Multi layer Perceptron, and Bayes network approaches. The UCI traffic data is used for conducting experiments. The dataset has 47 attributes and 2102 occurrences. The accuracy of traffic flow prediction using PSO KNN is 96 %. The PSO KNN algorithm achieved a Mean Square Error (MSE) of 0.00289 and a Root Mean Square Error (RMSE) of 0.0595.

Hibiscus sabdariffa L. (Rosella) has a long history of use in food preparation, herbal remedies, hot and cold beverages, food industry flavoring, and herbal drinks. More than 300 species of Hibiscus are grown all over the world. It is an annual herbaceous shrub belonging to the family Malvacea. However, limited is known about the antimicrobial properties of H. sabdariffa; the purpose of this study was to investigate the antimicrobial activity of H. sabdariffa (Rosella) extract against a range of pathogens, including Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Candida albicans. The results of the study showed that H. sabdariffa (Rosella) extract had a significant antimicrobial effect, against S. aureus and E. coli. Further the minimum inhibitory concentration (MIC) shows 128 μg/mL concentration followed by E. coli 256 μg/mL. The biocompatibility of crude metabolites shows highly compatible with Red blood cells (RBC’s). This finding suggests that the crude metabolites of H. sabdariffa (Rosella) could be the source to control the human pathogens.

Background: Osteopenia refers to bone density that is not only normal but also not as low as that noted in osteoporosis. Osteopenia leads to osteoporosis and increases the risk of fractures. Current research is focused on agents that will prevent or slow the progression of bone loss. The Objectives of the Study: To assess the pretest and posttest levels of osteopenia among postmenopausal women, and to assess the effectiveness of Cissus quadrangularis (CQ) on postmenopausal women with osteopenia. Methodology: A true experimental study design using targeted sampling techniques was used to conduct 60 patients with osteopenia. The data were collected with the help of structured questionnaires. Confidentiality was maintained throughout the process. The data collected were analyzed using descriptive and inference statistics. Result: A total of 60 participants completed this study. The percent BMD changes in the CQ-treated groups did not differ at any site after 24 weeks compared to the placebo. Reduced bone remodeling activity was detected in both CQ-treated groups. These results correlated with the within-group comparison, which showed a continuously significant increase in both BTMs in the placebo group. Conclusion: This is the first clinical report that showed a promising effect on delaying bone loss of oral administration of CQ for 24 weeks, as indicated by a slower bone remodeling process via a reduction in BTMs. However, no change in BMD was observed.

Motor and physical fitness play a crucial role to optimize the player performance in the soccer game. Implementing a customized workout schedule targeting Motophysic Fitness (MPF) helps to elevate their on-field performance. The present study was aimed to assess the impact of a 12-week MPF Training program targeted to have improvements in physical fitness for elite soccer players. MPF training program was implemented during the off-season for youth soccer players with average age (20 ± 1 years), height (1.75 ± 0.5 m) weight (64.3 ± 5.7 kg). Soccer-related fitness traits were assessed to evaluate performance levels, while Electroencephalography (EEG) was utilized for cognitive assessment. Statistical analysis was performed to evaluate the outcome of the fitness traits. Significant improvements were observed across diverse fitness traits, with mean values increasing by a minimum of 2 % and a maximum of 5 %. Speed emerged as a predominant contributor, showing a robust correlation (Adjusted R2 = 0.84), while agility, strength, power, endurance, balance, coordination, and reaction time also displayed substantial improvements. Despite observable gains in flexibility, its impact on overall fitness appeared comparatively modest (Adjusted R2 = 0.23). Z-test confirmed the statistical significance of all fitness assessments post-MPF, with p-values less than 0.05 for each test. Features extracted from EEG highlighted the improvement in cognitive ability before and after training.

The integration of trees into diverse land-use systems holds potential for India to meet nationally determined contribution (NDC) targets under the Paris Climate Agreement. With a target of sequestering 2.5–3 billion tons of CO2 equivalent by 2030, the study focused on the widespread and economically viable eucalyptus-based agroforestry, practiced widely in various planting geometries tailored to meet industrial end-use requirements. In this context, a detailed study was conducted to quantify the influence of five planting geometries [3 m × 3 m, 6 × 1.5 m, 17 × 1 × 1 m (paired row) and two boundary plantations (east–west and north–south directions) at 2 m away from tree to tree] of eucalyptus on intercrops [dhaincha (Sesbania aculeata)—barley (Hordeum vulgare L.) rotation] biomass, soil properties, and carbon stock of the system during 2009–2016. Results revealed that biomass accumulation of different tree components was 62.50%–74.09% in stem; 6.59%–9.14% in branch; 3.18%–5.73% in leaves; 12.20%–20.44% in stump roots; and 1.71%–3.48% in fine roots across the planting geometries. The mean carbon content of the stem, branch, leaves, and roots was 49.00, 47.00, 43.00, and 49.00%, respectively. Over the 8-year period, geometry of 3 × 3 m performed better in terms of total biomass production (344.60 Mg ha− 1 by tree biomass and 62.53 Mg ha−1 by intercrops). The independent parameter, DBH2H (DBH: diameter at breast height and H: tree height), was found to be a very good predictor of dry weight, followed by DBH alone. Among various functions (linear, allometric, logistic, Gompertz, Chapman, and exponential), the best-fit equation was allometric, i.e., B = 300.96 × DBH2H0.93 (adjusted R2 = 0.96) for eucalyptus based on universal model adequacy and validation criteria. The carbon sequestration rate was maximum (20.79 Mg C ha−1 year−1) in 3 × 3 m followed by 17 × 1 × 1 m. The total carbon stock of eucalyptus-based system (tree + crop + soil) varied significantly under different planting geometries and sole crop rotation (dhaincha–barley). The higher carbon stock (237.27 Mg ha−1) was obtained from 3 × 3 m spacing and further partitioning carbon stock in trees—166.29 Mg ha−1, crops—25.01 Mg ha−1 and soil—45.97 Mg ha−1. The paired row spacing (17 × 1 × 1 m) yielded higher crop yield and net returns (Rs. 600,475 ha−1), underscoring wide spacing’s role in system productivity and sustainability. Tree-based systems were valuable components of agriculture, advocating for their widespread adoption to reduce CO2 emissions and generate income through carbon credits. These findings will provide crucial insights into sustainable land-use practices and advance India’s commitment toward adaptation of climate change mitigation strategies.