C. U. Shah University

This study presents detailed energy storage and biomedical application of hydrothermally synthesized MnSSe nanocomposites (NCs). Powder-XRD method was identified the coexistence of cubic MnS, MnSe2, and SeS phases with average crystallite size and dislocation density were estimated to be 16.72 nm and 1.11 & times; 10(16) m(-2) from Scherer formula and 15.78 and 0.16 & times; 10(16) from Williamson-Hall method, along with compressive microstrain of -1.1 & times; 10(-3) was observed. Furthermore, longitudinal optical phonon vibrations were analyzed by the Raman Spectroscopy. XPS and EDX analyses have been verified the materials purity as well as Mn2+, S2-, and Se2- oxidation states and near-stoichiometric composition. Additionally, FESEM and HRTEM micrographs were revealed prismatic and pyramidal morphologies, while SAED confirmed the polycrystallinity of MnSSe NCs. UV-Vis spectroscopy exhibited broad absorption, while direct bandgap was estimated to be 1.95 eV. Temperature dependent dielectric, conductivity, complex impedance/modulus analysis were investigated at temperature and frequency range of 148-423 K and 20 Hz - 1 MHz. The dielectric plots fitted with the Havriliak-Negami model exhibited a large dielectric strength (>10(3)), attributed to interfacial polarization arising from Mn-S/Se orbital hybridization. Coexistence of 3D Mott variable-range hopping and correlated barrier hopping mechanisms were confirmed from Jonscher's power-law followed conductivity. Moreover, complex impedance spectroscopy showed grain and grain-boundary contributions by modeling R-CPE equivalent circuits. Complex modulus spectroscopy was analyzed by R-Bergman and KWW model that confirmed thermally activated relaxation governed by localized charge motion and non-Debye type relaxation behavior. Antimicrobial analysis, including MIC measurements highlighted selective inhibition of Bacillus subtilis, attributed to gradual ion release and interfacial electron transfer disrupt bacterial metabolism.

We study Lipschitz algebras of holomorphic functions of the order k, 0 ≤ k ≤ ∞, and the exponent α, α ∈ (0, 1]. The Gel’fand theory and maximal ideal spaces of these algebras are discussed. Further, we solve the corona problem (1962) and Gleason’s problem (1964) for these algebras on certain bounded pseudoconvex/poly domains G in Cn (e.g., the ball and polydisc). As a welcome bonus, we affirmatively solve Fornæss and Øvrelid’s problem (1983) for holomorphic Hölder and Lipschitz spaces. In fact, we establish an equivalency between the two problems for these algebras. As an application, we establish the I.J. Schark’s theorem for Lipschitz algebras on these G’s. Indeed, we extend our recent work on Gleason’s problem, based on the functional analytic approach, as well as extend recent results of Clos for these algebras, and apply the usual Banach algebra method.

Mimosa pudica is an invasive weed widespread in India's tropical regions and is nodulated by beta-rhizobia belonging to the genera Cupriavidus and Paraburkholderia. In this study, we characterized three bacterial strains isolated from root nodules of M. pudica collected from the Eastern Himalayan (EH) and Western Ghats (WG) regions of India. Whole-genome sequencing was performed for strain SKND8 (EH), and strains WGtm5T and WGlv3T (WG). Core-gene phylogeny based on the bac120 gene set placed strain SKND8 with Paraburkholderia caribensis, and average nucleotide identity (ANI) values above 96

The rise of AI in marketing engenders crucial questions of equity in customer segmentation wherein biased data may exclude groups that are vulnerable to discrimination. Can generative AI solve this problem? We attempt to solve these issues with a Bias-Aware Generative AI (BiA-GAI) framework, using the Mall Customer Segmentation dataset. After normalizing the dataset with z-score and min-max normalization, we proceed to train a Variational Autoencoder (VAE) with an encoder (64-32-2) and decoder (2-32-64) architecture that includes dropout (0.2) alongside adversarial training to reduce bias arising from the protected attribute (income ≥ 50). Clustering of the debiased latent representations with BIRCH (K = 4) yields Davies-Bouldin Index (DBI): 0.85 (z-score) and 0.92 (min-max), lower than those of K-Means (DBI: 1.12). From the standpoint of fairness, near-perfect parity is achieved. Disparate Impact reaches 0.999 (plus 22.4

The prediction of live birth outcomes using Assisted Reproductive Technologies (ART) remains a complex task owing to the high inter-patient variability and non-linear clinical interactions. This study presents a comparative evaluation of hybrid machine-learning models to improve in vitro fertilization (IVF) success prediction using a real-world anonymized dataset of 2,000 ART cases. After pre-processing (including missing value imputation, feature selection via Recursive Feature Elimination with Cross-Validation, and class balancing using SMOTE with k=5), four hybrid models were developed: stacking with XGBoost as the meta-learner, weighted ensemble, autoencoder-based feature fusion, and cascading classifiers. Models were evaluated using accuracy, AUC, precision, recall, and F1-score metrics, and compared against a baseline Random Forest classifier. The stacking model (XGBoost with Random Forest, MLP, and SVM base learners) achieved the best performance, with an accuracy and 0.999 AUC of 0.985. The weighted hybrid ensemble followed an accuracy of 0.953 and AUC of 0.994. The statistical significance of the improvements was confirmed using Wilcoxon Signed-Rank and McNemar’s tests (p < 0.05). To enhance model transparency, SHapley Additive exPlanations (SHAP) was applied to interpret base model contributions in the stacking architecture. These results support the application of AI-driven hybrid modelling for personalized IVF treatment planning. Future work will focus on prospective validation and clinical decision support system (CDSS) integration to assess deployment feasibility.