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Discover research articles across all indexed journals
Emotional dimension associated with the migratory experience of adult refugees and asylum seekers
Integration of satellite data for predicting crop yields in Eastern Ethiopia using machine learning
Intensification of extreme cold events in East Asia in response to global mean sea-level rise
Estimating the peak age of chess players through statistical and machine learning techniques
N2 adsorption mechanism in shale nanopores and limitations of BET theory explored through experiment and molecular simulation
Spectral decoupling regulation through targeted ion migration in electro-optical resonators
Optimizing acoustic design for dual-function concert and speech halls
Micro-level vulnerability assessment among agricultural communities of District Kupwara, Kashmir Himalaya
Abstract Livelihood and food security are pressing issues for mountain communities, particularly in the ecologically fragile and data-deficient regions of the Kashmir Himalaya. This study evaluates the inherent vulnerability of agricultural communities at the village level in Kupwara district using an indicator-based approach. A total of 356 villages were assessed using ecological and socioeconomic indicators of sensitivity, and adaptive capacity, collected from secondary sources. These indicators were standardized, weighted using Principal Component Analysis (PCA), and aggregated into composite indices for comparative analysis. Results show that 117 villages (33.14%) fall under high and 7 villages (1.96%) under very high vulnerability, while 211 villages are moderately vulnerable and over 90% of villages face moderate to high vulnerability. A comprehensive vulnerability gap and severity analysis was conducted, where the gap indicates the shortfall in adaptive capacity and severity measures potential damage. Village Hayihama emerged as highly sensitive, with a vulnerability gap of − 0.00893 and severity of 0.00312. The Kalrooch block had the highest severity, averaging a gap of − 0.00506 and severity of 0.00327. The research aims to guide policymakers by providing a robust framework to assess vulnerability, integrate risk perceptions into governance, and enhance resilience and adaptive capacity in farming communities.
Distal mutations enhance catalysis in designed enzymes by facilitating substrate binding and product release
Evaluating real-world effects of one-off fake news exposure
Sustainable carbon quantum dots from Mahua (Madhuca longifolia) for biomedical and environmental applications
MSL10 is a high-sensitivity mechanosensor in the tactile sense of the Venus flytrap
Friction of granular systems: the role of solid–liquid interaction
Influence of calcination temperature on equine bone hydroxyapatite structure and lead adsorption efficiency
Geometrically-engineered human motor assembloids-on-a-chip for neuromuscular interaction readout and hypoxia-driven disease modeling
SARS-CoV-2 spike protein-induced inflammation underlies proarrhythmia in COVID-19
Diagnostic and prognostic value of adipose tissue content and distribution indicators for normal weight obesity in young women
Abstract The rise in global obesity and related health risks has highlighted the importance of precise body composition analysis, especially in individuals who appear healthy according to Body Mass Index (BMI). This study examines the diagnostic and prognostic significance of body composition indicators for assessing cardiometabolic risk in young women with normal weight obesity (NWO). The research included 330 women aged 18–24 years from northwestern Poland with normal BMI (18.5–24.9 kg/m2), using dual-energy X-ray absorptiometry (DXA) for detailed body composition assessment. A key achievement of the study was establishing a new cut-off point for body fat percentage (PBF) at 35.78%, which effectively identified individuals at increased cardiometabolic risk. Results showed that 27.3% of participants were classified as having NWO and exhibited higher insulin levels, increased HOMA-IR scores, and a lipid profile indicative of greater cardiometabolic risk, including elevated total cholesterol, LDL-C, non-HDL-C, and triglycerides, alongside reduced HDL-C levels. The android-to-gynoid (A/G) fat ratio emerged as a significant predictor, correlating positively with insulin resistance and negatively with HDL-C levels. These findings highlight the limitations of BMI and underscore the need for comprehensive body composition analysis. Identifying the NWO phenotype early could prompt preventive lifestyle interventions, even in those with a normal BMI.