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Byline: Vijay. Pandey, Aaditya. Prabhudesai, Subhash. Gupta Sir, Acute respiratory distress after liver transplantation (LT) in infants has varied aetiology. Clinicians, at times, are so much intrigued by common complications, [...]

This research introduces and validates a finite‐element (FE) model for drying tomato flakes in a passive greenhouse dryer, supported by experimental data. The simulations accurately monitor crop and ground temperatures, reaching 49.1 and 61.1 °C, respectively. The moisture reduction is 96–4.25% (Wb), with a minimum relative humidity of 28.40% and a drying efficiency of 50.30%. Energy and exergy efficiencies are evaluated at 80% and 52.82%, respectively. The embodied energy is 1209.56 kWh, and the total cost of the proposed system is 15433.50 INR. The FE model effectively monitors temperatures and achieves a remarkable reduction in moisture, demonstrating its potential to optimize greenhouse dryer and promote sustainable, cost‐effective drying practices in the food industry. The rigorous comparison between experimental and simulated results confirms the precision of the FE model, highlighting its potential in optimizing greenhouse dryers and promoting sustainable, cost‐effective drying practices in the food industry. [ABSTRACT FROM AUTHOR]

It is now apparent that growth hormone (GH), an anterior pituitary hormone predominantly regulating postnatal somatic growth and metabolism, is also expressed in extrapituitary tissues. An extrapituitary synthetic site of GH that has garnered interest is the de novo or enhanced expression of GH in carcinoma or other cancers. In a number of cancers, including carcinoma of the mammary gland, endometrium, liver, prostate, and colon, the expression of GH is independently associated with more advanced clinicopathologic parameters of the cancer. In some of these cancers, tumor human growth hormone (hGH) expression portends worse survival outcomes for patients. Functionally, tumor-derived hGH exerts both autocrine and paracrine functions on carcinoma cells and cancer-associated stroma. Expression of autocrine/paracrine hGH in cancer drives tumor growth, angiogenesis, metastasis, and resistance to therapy by promotion of cancer cell proliferation, survival, epithelial-to-mesenchymal transition, motility, invasion, cancer stem cell-like behavior, and metastasis. Autocrine/paracrine hGH activates oncogenic signaling pathways and specific transcriptome signatures and enhances the expression of an oncogenic secretome to promote these functions. Hence, extrapituitary expression of GH in cancer promotes cancer progression independent of endocrine hGH, and may be considered as a validated target in oncology. [ABSTRACT FROM AUTHOR]

Purpose of Study: The study aims to develop an AI-driven decision support system tailored to the specific needs of higher education institutions. This system is designed to assist administrators, educators, and stakeholders in making informed decisions related to student enrollment, course planning, resource allocation, academic performance, and institutional effectiveness. Methodology: The study employs a comprehensive framework that integrates machine learning algorithms, data analytics, and predictive modeling to process vast amounts of data from diverse sources within higher education. The system incorporates adaptive learning algorithms to personalize student experiences, optimize curricula, and identify at-risk students for intervention. Ethical considerations are embedded in the system design, ensuring fairness, accountability, and transparency. Results: The proposed decision support system demonstrated its potential to enhance decision-making processes by providing actionable insights into student performance, faculty effectiveness, and resource utilization. The system proved effective in supporting both administrative and academic decisions, while prioritizing ethical standards such as fairness and transparency. Implication of Study: The implementation of this AI-based decision support system has significant implications for higher education institutions. It offers the potential to transform decision-making processes, improve institutional effectiveness, and personalize student experiences. Furthermore, it highlights the importance of collaboration between data scientists, educators, and administrators to maintain ethical standards and ensure data integrity and security. Novelty: The novelty of this study lies in its tailored approach to higher education, incorporating adaptive learning and ethical decision-making principles within an AIbased system. This comprehensive model not only supports traditional decisionmaking processes but also addresses emerging challenges in higher education by leveraging data-driven insights and advanced machine learning techniques. [ABSTRACT FROM AUTHOR]