This research introduces and optimizes a novel multi-generation power system integrating a steam Rankine cycle (SRC), a gas turbine (GT), an absorption refrigeration cycle (ARC), a proton exchange membrane (PEM) electrolyzer, and a CO2 separation unit. This system is designed to improve energy efficiency while simultaneously capturing CO2 and producing hydrogen through electrolysis. Two configurations-with and without ARC-are evaluated using a genetic algorithm-based multi-objective optimization framework, which considers exergetic efficiency, CO2 emission reduction, and total cost rate. The findings demonstrate that the proposed system improves exergetic efficiency by up to 71% and reduces CO2 emissions by up to 3.9% compared to a standalone GT system. Furthermore, the system without ARC achieves higher hydrogen production, while the system with ARC provides valuable cooling. These findings demonstrate the feasibility and environmental advantages of integrated power, CO2 capture, and H 2 blending systems for sustainable energy generation.
This paper presents a robust co-design framework for differential spiral electrical impedance spectroscopy (EIS) biosensors, developed as an in silico methodological study for urine-sensing applications in bladder cancer surveillance. The objective is to improve parameter identifiability in label-free differential urine sensing when nuisance effects, fabrication tolerances, and reference mismatch reduce estimation reliability. The framework combines differential sensing to suppress shared common-mode nuisance with joint optimization of sensor geometry and frequency selection. The design is formulated as a minimax Fisher-information problem to improve worst-case identifiability.The primary co-design evaluation uses an application-motivated synthetic protocol with matched budgets and multiple baselines. The proposed method improves worst-case identifiability and Cramér–Rao lower-bound proxy metrics at the same frequency budget, with consistent gains under budget variation, uncertainty amplification, and reference mismatch. To examine transfer beyond the synthetic model family at component level, we additionally analyzed an independent measured EIS dataset using grouped hold-out validation and training-only empirical minimax frequency selection. At a four-frequency budget, the measured-data analysis achieved a balanced accuracy of 79.6%±11.6%, compared with 70.4%±14.0% for log-uniform selection and 74.1%±8.5% for the full 101-frequency spectrum. This independent analysis supports the differential sparse-frequency design principle outside the synthetic generator, but it does not validate the optimized spiral geometry, urine sensing, bladder-cancer diagnosis, or clinical readiness.
Ashwagandha (Withania somnifera), a traditional Ayurvedic adaptogen, is increasingly investigated in hormone-sensitive malignancies such as breast cancer. Its bioactive constituents, particularly withaferin A, exhibit diverse effects relevant to hormonal regulation, tumor suppression, and systemic balance. This review explores Ashwagandha’s tri-axial roles in hormonal modulation, gut microbiota interaction, and direct anticancer activity across breast cancer subtypes. Preclinical findings show that withaferin A suppresses estrogen receptor alpha (ERα), inhibits STAT3 and NF-κB signaling, induces ROS-mediated apoptosis, and alters epigenetic regulators. In HER2-positive and triple-negative models, it reduces cancer stem cell activity, epithelial-to-mesenchymal transition (EMT), and pro-inflammatory mediators. Ashwagandha also influences the hypothalamic–pituitary–gonadal axis, raising LH, FSH, estrogen, and progesterone, while lowering cortisol and normalizing thyroid function. Immunologically, it enhances CD8⁺ T cell activity, reduces myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs), and may synergize with checkpoint inhibitors. Effects on gut microbiota suggest additional roles in estrogen metabolism and inflammatory regulation. Toxicity data indicate high tolerability (LD₅₀ > 2000 mg/kg), though rare hepatic and thyroid adverse events occur. Regulatory oversight remains inconsistent, with limited phytochemical standardization. Ashwagandha shows multidimensional promise but requires rigorous, standardized clinical validation.
In 2024, a comprehensive framework for the screening, diagnosis, and management of metabolic dysfunction–associated steatotic liver disease (MASLD) was incorporated in the EASL-EASD-EASO clinical practice guidelines. However, physicians often face barriers applying these recommendations in routine clinical care, especially in the Southeastern Europe, Middle East, and Africa (SEEMEA) region. As a multidisciplinary group of physicians involved in MASLD and metabolic dysfunction-associated steatohepatitis (MASH) management, our objective is to provide a practice-oriented roadmap including practical and educational considerations beyond the hepatology field that could improve patient care and support implementation of clinical guidance within the SEEMEA region. This work is informed by a narrative review and expert input obtained through structured discussions, to examine the status quo and identify key gaps in the MASLD/MASH management, unravelling the patient journey from screening and diagnosis to treatment and follow-up. Furthermore, we advise on priorities on screening triggers and, considering the limited availability of vibration-controlled transient elastography (VCTE), discuss alternative approaches to achieve accurate and timely diagnosis. Finally, following the approval of resmetirom and semaglutide 2.4 mg for MASH treatment, we review the evolving pharmacotherapy landscape and propose a “blueprint” for a specialised MASLD clinic, suggesting mandatory and optional facilities for optimised care.
Globally, wind is one of the fastest growing renewable energy sources, requiring innovative computational methods across the spectrum of wind energy engineering tasks to boost wind energy production. Recent advancements in generative artificial intelligence (AI) models have led to the integration of the models into wind energy engineering to develop solutions. Previous surveys primarily focused on the general applications of AI in wind energy. The objectives of this survey are to: (i) review modified generative AI models in wind energy engineering tasks, (ii) develop taxonomy linking model variants to tasks, (iii) develop performance evaluation metrics taxonomy, (iv) analyze the core concepts and limitations of the modified generative AI models, (v) examine data sources, (vi) present real-world case studies, (vii) identify emerging trends and challenges. This is the first comprehensive survey exclusively for modified generative AI models across different aspects of wind energy engineering. The survey examines the modifications of generative AI models for wind energy applications, explaining the core idea behind each modification, its suitability for specific task and the limitations identified in the corresponding model. New taxonomies were introduce to support synthesis and analysis. The survey extends beyond theoretical discussion by highlighting real-world case studies where generative AI models have been deployed in real-world commercial wind farms. Additionally, emerging open challenges are identified and future research directions are proposed from new perspectives. This survey provide a fundamental reference for early career researchers, a guide to industry practitioners and a benchmark for innovations for expert researchers.
