The Central University of Karnataka, chiefly as, CUK, is a central university situated in the Kadaganchi village in Aland taluka of Kalaburagi district in the Indian state of Karnataka. It has been established by an Act of the Parliament of India 2009.
An integrated hydrochemical, statistical, and geospatial approach was adopted in the present study to evaluate the groundwater quality of the Mangura Nala watershed in the state of Bihar, India. A total of 154 groundwater samples were collected during two seasons (77 samples from each season) and analysed for various physico-chemical parameters, major ions, and fluoride concentrations. The groundwater of the area was characterised by Ca-Mg-HCO3 and Ca-Mg-Cl water types during both seasons. The estimation of saturation states of the aqueous minerals and chloro-alkaline indices suggested dominant impacts of sediment–water interaction and ion exchange processes in the groundwater. The ANOVA test depicted seasonal changes in groundwater quality, while the multivariate analyses, including principal component analysis and cluster analysis, clearly recognised potential water quality parameters deteriorating the groundwater quality in the study area. Around 30–45
Climate variability and an increase in rainfall extremes have now become major challenges to water resource management and agricultural sustainability in semi-arid regions. Kalyana Karnataka is a drought-prone area located in the northeastern part of the state of Karnataka. The region experiences significant climatic variability, which is mainly influenced by its semi-arid nature and irregular monsoon patterns. This study focuses on spatio temporal variability of rainfall and drought conditions prevailing over Kalyana Karnataka, by analyzing long-term rainfall data over the period 1980 to 2025. Annual rainfall statistics, Percentage rainfall deviation (
The Lower Mekong Region (LMR), which includes Vietnam, Thailand, Laos, Cambodia, and Myanmar, faces growing energy demand driven by urbanization and economic development. Wind energy offers a promising alternative, yet comprehensive wind suitability assessments for this region remain limited. This study bridges this gap by using machine learning (ML) models like Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and XGBoost to assess wind power potential across the LMR. These models were trained on 11 spatial variables, including wind speed, elevation, and proximity to infrastructure, to classify land suitability. The ensemble model, combining outputs from all classifiers, demonstrated a 23.17 Wind suitability assessed via ML models; ensemble boosted mapping accuracy and coherence. Coastal Vietnam and eastern Laos show highest suitability for wind farm development. Variable importance shows wind speed and power are top predictors in all ML models. Ensemble results identify 23.17
War-traumatized refugees face significant mental health challenges, yet access to care is often limited due to financial, logistical, and awareness barriers. The present review aims to identify the existing digital self-help tools for mental health problems among war-traumatized refugees and assess their effectiveness via meta-analysis. Databases such as PubMed, Google Scholar, SAGE, PsycNET, ScienceDirect, Scopus, Web of Science, and JSTOR were searched in July 2024. A comprehensive literature search yielded 11 studies. The review identified self-help apps, including Tetris Gameplay, Step-by-Step, Happy Helping Hand Game, Sanadak, Self-Help Plus, Digital Audio Files, and a Web-Based Module (Tell Your Story), as effective in managing mental health problems. The results of the meta-analysis revealed that digital self-help tools have a mild, yet significant, overall effect (SMD = -0.34, 95
Floods are one of the major natural disasters in high rainfall regions, causing loss of life and property. The Savitri River Basin, nestled within the ecologically sensitive Western Ghats, is highly susceptible to recurrent and intense flooding due to its unique hydro-meteorological characteristics. A flood susceptibility map can help reduce disaster risks caused by human activities, especially urban development. Therefore, this study aims to identify and characterize flood hazard zones in the Savitri River basin, providing essential information for effective flood risk management and mitigation. Flooding factors such as slope, elevation, distance from the river, distance from roads, precipitation, drainage density, Normalized Difference Vegetation Index (NDVI), topographic wetness index (TWI), Land Use and Land Cover (LULC), and soil texture were analysed in a Geographic Information System (GIS). Weightages were assigned using the Analytic Hierarchy Process (AHP). With a consistency ratio (CR) of 0.05 for the correlation matrix, the flood susceptibility map indicates that 58
