Sir Padampat Singhania University (SPSU) is a private, residential university located in Udaipur, India. The university was established by the J K Cement group of companies. The university is named after the founder of the J K Organisation.The University came into existence through an Ordinance passed by the Government of Rajasthan in 2007 and which was subsequently made into the Sir Padampat Singhania University Act 2008. It was conceived as a boutique university that will not have more than a total of 1500 students but currently has around a total of 400 students enrolled.SPSU, Udaipur has two constituent schools, namely the School of Engineering and the School of Management with both the schools offering Bachelors's, Masters and Doctoral programs..
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.
We present a unified fractional framework for quantum transport in dissipative chemical systems, incorporating temporal and spatial fractional operators to model memory-driven dynamics, anomalous diffusion, and long-range coherence. Starting from a generalized open-system Hamiltonian, we derive time-and space-fractional Schr & ouml;dinger equations with dissipative potentials and nonlinear reactive couplings. Rigorous analysis establishes well-posedness, spectral properties, and algebraic energy decay, while linear stability and dispersion studies reveal memory-induced spectral shifts and fractional exceptional points. Numerical simulations using spectral and exponential time-differencing schemes illustrate subdiffusive spreading, anomalous decoherence, and quasi-stationary wave localization in reactive media. The framework provides a versatile mathematical and computational platform for predicting and interpreting fractional quantum transport phenomena in complex molecular and condensed-phase environments.
Nowadays, Gastric cancer is the utmost commonly diagnosed cancer and the fifth leading cause of death throughout the world. Identification of gastric cancer at the initial stage ensures fast treatment and minimizes mortality, but manual identification consumes more time and heavily depends on the experience of diagnosticians. So, a Deep Learning (DL) model named Dense Pyramid CAViaR Network (DPC-Net) is proposed for detecting gastric cancer. The histopathological images are initially taken and filtered using anisotropic diffusion, and then, lymph node segmentation is performed with the Directional Connectivity Network (DConnNet) with a novel loss function, such as Tversky, Jaccard loss, and Binary Cross Entropy (TJB-DConnNet). Afterwards, segmented images are further processed to extract the features, like Pyramid Histogram of Oriented Gradients (PHOG), Speeded-Up Robust Features (SURF), and Gray-Level Co-occurrence Matrix (GLCM), in feature extraction. Lastly, Gastric cancer is detected using an established DPC-Net approach that is formed by fusing a Dense Network (DenseNet), Conditional Autoregressive Value at Risk (CAViaR), and Pyramid Network (PyramidNet). Thus, DPC-Net attained the maximum accuracy of 93.899%, True Positive Rate (TPR) of 95.876%, and True Negative Rate (TNR) of 91.899% at K-value 8.
