Sir Padampat Singhania University

This study examines how hydrogeological risk assessment (HRA) could be integrated into mine planning in Ghana, marking a shift from simple contaminant inventories to comprehensive evaluations of contaminant transport and associated health risks. Using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 framework, a systematic review of previous peer-reviewed publications was conducted to assess existing research and regulatory frameworks, identify gaps, and propose strategies for sustainable mineral development that safeguard groundwater and public health. Hydrogeological controls across Birimian-Tarkwaian strata influence pollutant mobility, driving acid mine drainage, aquifer contamination, and arsenic-related carcinogenic risks. Comparative analysis of Ghana’s mining regulations against international standards reveals significant deficiencies in mandatory HRA implementation, technical oversight, and enforcement. This study proposes that effective HRA begins with a conceptual model defining contamination sources, migration pathways, and receptors within the hydrogeological setting, followed by an eight-phase process encompassing desk studies, field reconnaissance, soil and infiltration analysis, data collection, risk evaluation, and environmentally informed mine design. The current literature is more focused on contaminant presence rather than transport dynamics and exposure pathways. The study concludes that early integration of HRA into mine planning is essential for preventing groundwater contamination and mitigating health risks. Recommended measures include regulatory reforms mandating pre-operational HRA, establishment of independent technical review panels, enhanced monitoring systems, and formalization strategies for artisanal mining. This work provides a framework for developing context-specific HRA methodologies that balance mineral resource development with environmental and public health protection in Ghana and similar mining jurisdictions.

Cardiovascular disease (CVD) and diseases like diabetes present significant obstacles to public health, particularly in resource-constrained environments where timely diagnosis can profoundly influence patient outcomes. The process of selecting minimal influential features from the initial collection of features extracted from patients' diagnostics to classify healthy/diseased patients transforms the landscape. This experimental study is an attempt in the same direction of selection of the most influential minimal number of features by introducing an innovative approach for feature selection that culminates in a hybrid sampling optimization model (hLPUMA: Hybrid LASSO (Least Absolute Shrinkage and Selection Operator) and PUMA optimization algorithm). The proposed and implemented approach is validated using two distinct healthcare datasets: one focused on Indian Heart Disease (IHD) and the other on diabetes (DB). The essence of the approach lies in choosing the most insightful and diagnostic features that can greatly assist in the prompt and precise identification and categorization of illnesses in individuals. The proposed hLPUMA integrates the robust feature selection capabilities of LASSO with the efficient global search mechanism of PUMA, resulting in an improved method for identifying significant features. In the IHD dataset, hLPUMA identifies five significant clinical features, whereas in the DB dataset, it shortlists four essential features necessary for classifying individuals as healthy or infected. The enhanced feature groups are subsequently utilized to train an XGBoost classifier (employing a 70:30 train-test split and fivefold cross validation), resulting in impressive accuracy rates of 99.68

Fractal-fractional differential equations have emerged as a powerful mathematical framework for modeling complex systems exhibiting memory effects, nonlocality, and hysteresis phenomena. This study investigates a class of fractalfractional ordinary differential equations characterized by a power-law memory kernel and influenced by hysteresis behavior. The continuity of the function g(t, w(t)) over closed subsets of R, is used to establish the foundational results. A supporting lemma is introduced to facilitate the development of a uniqueness theorem. Drawing upon Borzdyko's framework, we derive existence results pertinent to the targeted family of equations.

The research selected 11 microfinance institutions (MFIs) with sufficient indicator reports in the MIX market database from 2000–2019. The data are then analyzed via a multivariate technique. The study aimed to provide insights into the elements that contribute to the financial viability of microfinance organizations and how this impacts financial depth and poverty alleviation. The study's findings suggest that the depth of outreach, operational expenditures, and loan size of microfinance organizations in Ghana are significant elements that promote financial sustainability. Research has shown that a developing financial system has the potential to enhance the financial status of microfinance firms. Nevertheless, while microfinance organizations are designed to mitigate vulnerability, it is apparent that the financial stability of these institutions does not effectively alleviate poverty in Ghana. The results indicate that the dependency ratio, depth and breadth of outreach, and cost per borrower are factors that significantly affect the financial sustainability of microfinance institutions in the country. Moreover, there was no significant nexus between financial sustainability and the capital structure of these institutions. Hence, the research proposes that microfinance institutions in Ghana should prioritize reducing their reliance on contributed money to achieve operational efficiency and ensure long-term financial viability. Additional research might explore the spatial aspects of MFIs to analyze the connections between the variables being studied.

Groundwater quality in the Voltaian aquifer of Krachi East Municipality, Oti Region, remains poorly understood, creating a gap in sustainable water resource management. Therefore, this study aimed to characterize groundwater chemistry, identify controlling processes, and assess its suitability for domestic and agricultural use. Sixteen groundwater samples were analyzed using hydrochemical parameters, spatial mapping, Gibbs diagrams, bivariate plots, and multivariate statistical techniques (factor analysis, correlation matrix, and cluster analysis). Results show slightly alkaline pH (6.85–7.87; mean = 7.32), low electrical conductivity (403–708 μS/cm, mean = 489 μS/cm), and low total dissolved solids (276–388 mg/L, mean = 363 mg/L). The dominant hydrochemical facies (81.25