This research presents a high-fidelity Digital Twin-driven framework for the sustainable operation of hybrid power systems, balancing economic efficiency with environmental mandates through the integration of real-world spatial-temporal wind and solar data from Gujarat, India. By modeling Plug-in Electric Vehicles (PEVs) as a coordinated Virtual Power Plant (VPP) with bidirectional Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) capabilities, the study addresses the inherent stochasticity of renewable-heavy grids. The resulting non-convex optimization problem is solved using the Sine Cosine algorithm (SCA), which effectively navigates complex search landscapes, such as those induced by turbine valve-point loading effects. Validated on 10-unit and 20-unit thermal systems, the proposed approach significantly reduces operational costs and achieves a net annual emission decrease of over 1.6 million tons in the larger test case. Comparative benchmarking against state-of-the-art metaheuristics confirms the SCA's superior convergence stability and technical proficiency in managing the intricacies of modern, sustainable energy infrastructures.
Folliculitis, frequently associated with Staphylococcus aureus, remains challenging to treat due to limited skin penetration of topical antibiotics and emerging resistance. The present study aimed to develop a fusidic acid (FA)-loaded microemulsion-based gel (MEG) to enhance topical delivery and antibacterial performance. Tea tree oil, Teric 862, and Transcutol P were selected based on solubility screening, and a 2³ full factorial design was employed to evaluate their effects on droplet size and drug release. The optimized microemulsion (MEFA1) produced nanosized droplets (22.2 ± 1.2 nm) with low dispersity and a zeta potential of − 16.2 mV. Microemulsion formation was confirmed by conductivity and dye solubilization tests, while TEM analysis revealed predominantly spherical droplets. Thermodynamic stability studies demonstrated no phase separation and minimal changes in droplet size and PDI under stress conditions. The optimized system was incorporated into a 1.5
The resurgence of poxviral diseases, including the global spread of monkeypox, underscores the urgent need for effective antiviral strategies. This study focuses on the structural exploration of Thymidine kinase (TK), a crucial enzyme linked to viral replication in orthopox viruses, to identify potential inhibitors. Leveraging a structure-based drug design approach, known inhibitors of TK were used to generate novel compounds via AI-driven molecular design tools. These candidate molecules underwent rigorous virtual screening, pharmacokinetic evaluations, and molecular docking to assess their efficacy compared to the standard antiviral drug AZT. The intermolecular binding energy was twice estimated, once by the heuristic method of molecular docking and then by the absolute binding free energy derivation from the specified complexes by the neural networking-based algorithm. Molecular dynamics simulations further validated the stability and binding interactions of the proposed compounds with the TK enzyme. Notably, the chosen candidates exhibited superior binding energy and favorable pharmacological profiles, marking them as promising antiviral agents against the vaccinia and monkeypox viruses. This preliminary investigation offers a foundation for subsequent in vitro and in vivo validations to advance therapeutic development. Synopsis. The computational drug discovery workflow for finding new inhibitors of the Vaccinia virus thymidine kinase is presented in this paper. A library of unique chemical entities was created using a REINVENT4 (mol2mol) deep-learning module. This library was combined with known inhibitors to create a virtual screening library. SwissADME was used to filter compounds for drug-likeness and pharmacokinetic characteristics before molecular docking against the thymidine kinase receptor was performed to benchmark both freshly created molecules and conventional medications. Based on docking performance in comparison to reference medications, top-scoring candidates were chosen as lead compounds. Their complex behavior and binding stability were then evaluated using 100 ns molecular dynamics (MD) simulations. In order to find promising antiviral leads, the pipeline combines AI-driven molecule creation, in silico screening, docking, and MD simulations.
Big data analytics has enabled highly accurate customer segmentation, but centralized machine learning approaches raise serious concerns about privacy, algorithmic bias, and regulatory compliance. This paper introduces FedXAI-Seg, a novel framework that combines Federated Learning (FL) with SHAP-based Explainable AI (XAI) to perform RFM-based customer segmentation in a private, fair, and transparent manner. In FedXAI-Seg, local models are trained on distributed client data without sharing raw records; only differentially private model updates are exchanged, providing formal (ε = 1.0, δ = 10⁻⁵) privacy guarantees. Fairness is monitored using Demographic Parity Difference (DPD), Equalized Odds Difference (EOD), and Average Odds Difference (AOD). Experiments on the UCI Online Retail and Bank Marketing datasets show that FedXAI-Seg achieves a Silhouette Score of 0.631 (within 6.1
This research furthers the design, development, and optimization of a Universal Centrifugal Casting Machine (UCCM) engineered to overcome the geometric and process-related limitations of conventional centrifugal casting systems. The UCCM offers flexibility through mold orientation adjustments ranging from 0° to 90°, thus facilitating improved control over the casting process and product quality. Al–Si alloy with composition close to the eutectic point (LM6) was selected as the casting material for its favorable mechanical and metallurgical properties. A comprehensive full factorial experimental design was implemented to systematically investigate the effects of the orientation of the mold, the speed of rotation, and the initial mold temperature on tensile strength. Regression analysis was used to develop predictive equations, and the desirability function approach was applied to determine the optimal casting conditions. Validation experiments confirmed the reliability of the developed model. The findings indicate that the UCCM provides significant enhancements in-process control and mechanical performance, making it suitable for scalable industrial applications.
