Open Access Journals, Books and Research Articles - Gatha Cognition

This study analyzes the spatial and temporal dynamics of occupational specialization across districts of Eastern Uttar Pradesh, an area characterized by persistent socio-economic disparities and a predominantly rural workforce. Using district-level data from the 1991, 2001, and 2011 Censuses of India, the research applies the Location Quotient (LQ) method to four major occupational groups—cultivators, agricultural laborers, household industry workers, and other workers—to trace long-term structural shifts. A standardized three-tier classification identifies districts with high, moderate, and low specialization, and GIS-based mapping (ArcGIS 10.2) visualizes spatial patterns and inter-decadal changes. The findings reveal distinct and evolving spatial clusters: an agrarian core with high cultivator concentration, labor-intensive belts dominated by agricultural laborers, traditional artisanal districts marked by strong household industry specialization, and emerging urban-industrial nodes showing diversification into non-farm activities. Several transitional districts display balanced occupational structures, indicating gradual rather than abrupt shifts. An evident east-west gradient emerges, with service-oriented growth near urban centers and persistent agrarian dependence in northern border districts. Overall, the study highlights uneven structural transformation across the region and underscores the need for context-specific policies to enhance agricultural productivity, strengthen rural industries, build skills, and expand urban-linked employment. Multi-period LQ mapping is shown to be a robust tool for assessing regional development trajectories in Eastern Uttar Pradesh.

A flood is an excess of water that swamps normally dry land due to heavy rainfall, storm surges, or dam failures, often leading to significant damage and displacement. This study evaluates flood susceptibility in Krishna River Basin (KRB) (India) using geospatial techniques and the Analytical Hierarchy Process (AHP). Thirteen physiographic parameters elevation, slope, proximity to rivers, geomorphology, drainage density, flow accumulation, rainfall, land use/land cover (LULC), geology, soil type, Stream Power Index (SPI), Topographic Wetness Index (TWI), and curvature were integrated into a multi-criteria decision framework and processed in ArcGIS 10.8. Pairwise comparison matrices assigned weights to parameters, validated by a consistency ratio (CR = 0.04), ensuring robust model reliability.

The results classify the KRB into five susceptibility zones: very low (21%), low (21%), moderate (20%), high (19%), and very high (19%). High-risk areas correlate with low-lying floodplains, clay-loam soils, dense drainage networks, and built-up-agricultural zones. The validation of the Analytical Hierarchy Process (AHP) using the Area Under the Curve (AUC) indicated robust performance, achieving 79% accuracy. Approximately 30 cities, including Pune, Vijayawada, and Solapur, face significant flood threats. The present study offers actionable intelligence for providing a spatial decision-support framework for prioritizing flood mitigation investments, enforcing land-use zoning in high-risk zones, and optimizing reservoir operations to manage downstream flood peaks. This research underscores the value of geospatial-AHP integration, offering actionable insights for urban planners and disaster management authorities to enhance community resilience. Future research should integrate real-time data and machine learning for dynamic predictions while considering local human impacts.

Throughout the world, countries have gone mandatorily into lockdown to stop the spread of Coronavirus. The institutions’ efforts to save lives have put one vulnerable group of people more at risk. Moroccan women who live with domestic violence have no escape from their abusers during the pandemic, and Moroccan organizations and activists reveal that they are witnessing a huge number of reports of domestic violence. This study shows how the spread of Coronavirus, an alarming rise in partner abusers, has increased against women. Despite the efforts made by the national media channels, the Moroccan government tried to raise people’s awareness about this issue, but few measures have been taken by the government to reach out to women who were undergoing violence. For this reason, this study unravels the voices of women who suffered domestic violence during the lockdown. It also aims to examine the reasons that triggered an increase in the issue within Moroccan families. Mixed methods were used to collect our data, precisely through Google form questionnaires and focus group discussions. The aftermaths of this study demonstrate that the lockdown has led to the rise of domestic violence against Fassi women. We discussed domestic violence against women in the context of social isolation, highlighting significant factors, especially financial stability, an increase in drug consumption, and women’s unemployment. In addition to studies that demonstrate the significant increase of domestic violence in Morocco. The fact that the limited social and economic conditions and lack of services could be a limitation in this case.

Groundwater quality assessment is crucial for providing safe drinking water and supportive sustainable irrigation, especially in areas facing water scarcity and contamination risks. This review analyses 45 studies that used different techniques to evaluate groundwater quality in varied hydrogeological settings. The main objectives are to: 1) identify common assessment methods 2) examine their applications for drinking and irrigation purposes, and 3) highlight gaps and future directions.
 

The findings show that most studies focus on physiochemical parameters such as pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Total Hardness (TH), and major ions like Ca²⁺, Mg²⁺, Na⁺, K⁺, Cl⁻, HCO₃⁻, SO₄²⁻, NO₃⁻, and F⁻, as they strongly influence the water usability. The water quality index (WQI) is the most widely applied method, used in about 93% of studies, because it combines multiple parameters into a single value for drinking suitability. Hydrochemical classification methods like Piper Diagram (84%) and Gibbs Diagram (62%) are also common for identifying water types and geochemical process.  For irrigation, indices such as Sodium Adsorption Ratio (SAR) (60 %), Residual Sodium Carbonate (RSC), Magnesium Hazard (MH) (27%), Permeability Index (PI) (31%), Kelly’s Ratio (KR) (24%), and graphical tools like the Wilcox Diagram (40%) are frequently used. Advance statistical methods, including Principal Components Analysis (PCA) and cluster analysis, along with GIS and Remote Sensing are increasingly applied for spatial mapping and source identification.

The synthesis indicates that while traditional hydrochemical and index-based methods dominate groundwater quality evaluation, significant research gaps persist in the integration of emerging contaminants, real-time monitoring, and machine learning-based prediction models. Future studies should adopt hybrid frameworks combining chemical, biological, and spatial datasets to improve groundwater quality forecasting and resource management. The findings of this review provide a consolidated reference for developing standardized, data-driven approaches for groundwater quality assessment and sustainable utilization.