In In the era of cyber threats evolving at lightning speed, the multinational companies (MNCs) must also incorporate an AI-driven cybersecurity framework to detect the threat, prevent intrusion, and manage the data security to continue to stay afloat. Using federated learning-based security models combined with ABSorbed ML, ABSorbed DL, and ABSorbed NLP, the AI-powered three-phase cybersecurity architecture is presented in this research for data management, intrusion detection, and real-time threat intelligence. In addition to the NSL, CICIDS, and UNSW-NB15 datasets, several AIs are used to train the AI using the AI, viz., Random Forest, XGBoost, CNN_LSTM Hybrid, Autoencoders, and Federated Learning AI in order to experiment with the effectiveness of intrusion detection. Federated Learning greatly outperformed standard security protocols: they found that Federated Learning had a collection of values of 99.0 percent accuracy and a minimum false positive rate. Few algorithms employing the use of NLP and AI for automated threat analysis had enabled proactive security intelligence, reduced detection reaction time by orders of magnitude, and enhanced IDS for intrusion detection systems. In addition, federation encryption methods also reduced the cost of computation by 2.5% and ensured high-performance data protection with homomorphic encryption and zero trust architecture (ZTA). Even in learning cybersecurity using AI-based frameworks, the adversarial attacks had suffered strong resistance, and through the usage of federated learning, the attack success rate under PGD attacks was lowest, with just a success rate of 8.5%. There are, however, several important subjects related to AI related to ethical issues, regulatory compliance, and responsibility. It leads research aimed at enhancing improved AI governance models, explainable AI (XAI), and adversarial AI defensive mechanisms for strengthening cybersecurity infrastructures in multinational corporations. After all, if used well, an AI-integrated cybersecurity framework can be utilized by MNCs to create scalable, flexible, and resilient security architecture with solid cyberthreat prevention and safe data management capabilities. Future research can also encompass a study on the federated AI cybersecurity protocols, quantum-safe cryptographic AI models, and improvements in the real-time monitoring tools in order to boost the performance of AI-driven cybersecurity defenses.

This research investigates how artificial intelligence (AI) might aid data security protocols in custodian banks. The paper evaluates custodian bankers' preparedness to adopt AI-based security solutions and the role that AI can play in securing data. To collect quantitative information from attitudes, difficulties, and readiness to integrate AI, sixty-two custodian bankers were asked to answer a structured survey. AI significantly increases the data security in risk management and fraud detection, and the majority of respondents (86.67%) agreed with this finding. It is proven that organizational readiness and financial limits have a large influence on the adoption of AI. Respondents reported being moderately to well prepared for AI, although the greatest obstacle to its deployment was budgetary restrictions. Using t-tests to test hypotheses, we were able to find that using AI actually helped data security with a mean score of 4.25 out of 5. In regression analysis, the impact of institutional readiness and budgetary limits on opinions concerning AI's ability to attract investments was identified. Cluster analysis identified three separate custodian bank groups that had different financial capabilities and preparedness. Overall, the results suggest that custodian banking needs particular tactics focused on overcoming financial obstacles and making organizations AI-ready to promote adoption of AI.

This study presents the optimization of a Heat Recovery Steam Generator (HRSG) system with integrated low-temperature heat recovery to enhance thermal efficiency and promote sustainable energy utilization in a combined cycle gas turbine (CCGT) power plant. The research addresses key industrial challenges, including high exhaust flue gas temperatures (~200°C), dependence on electric heating, and underutilization of waste heat. A secondary heat exchanger (Preheater-2) is introduced downstream of the Make-Up Water Heater (MUWH) to reduce the flue gas exit temperature to 60°C while recovering energy for potable water heating. The system was modeled and optimized using Aspen Plus. Results show that the proposed configuration can offset approximately 550 units of 3 kW electric heaters, resulting in a daily energy saving of 11.55 MWh. Thermodynamic performance improved, with HRSG heat duty increasing from 2.546 MW to 3.711 MW and overall thermal efficiency rising from 61.25% to 64.76%. Sensitivity analysis identified an optimal potable water flow rate range of 60,000–70,000 kg/h, yielding stable outlet temperatures of about 80°C. Exergy analysis confirmed reduced system irreversibility. The low sulphur content of Nigerian natural gas supports safe low-temperature heat recovery without corrosion risk. The system offers a scalable solution for industrial waste heat recovery, with applications in process heating, domestic hot water generation, and energy cost reduction.

General higher education plays a critical role in human capital formation, economic development, and social mobility. In India, state-level higher education systems display significant variation in institutional capacity, enrollment growth, and resource allocation. Rajasthan has experienced rapid extension in general higher education institutions over the past few decades; however, the relationship between educational inputs and outputs remains deficiently studied. This study models the production function of general higher education in Rajasthan using the Autoregressive Distributed Lag (ARDL) approach. The study examines the impression of key inputs such as the number of institutions, faculty strength, government expenditure, and infrastructure capacity on educational output measured through student enrollment and graduates. The ARDL bounds testing framework is in work to analyze both short-run dynamics and long-run equilibrium relationships among variables. The findings points that faculty strength and government expenditure significantly power higher education output in the long run, while infrastructure capacity subscribe to short-run adjustments. The study finds that effective resource allocation and institutional strengthening are important to improve the productivity and efficiency of general higher education in Rajasthan.

A low birth weight (LBW) newborn is one who weighs less than 2,500 grams at birth. Birth weight is described as the main determinant of survival chances in newborns. Low birth weight is associated with infant mortality and postpartum health complications. The aim of our study was to evaluate the factors contributing to low birth weight in newborns in the Markala Health District. Patients and Methods: We conducted a descriptive, quantitative cross-sectional study in the Markala Health District. This study included newborns weighing less than 2,500 g at birth who were born and/or cared for in a health facility in the Markala District during the data collection period. Newborns weighing less than 2,500 g at birth and coming from another health district were not included. Sampling was non-probabilistic and exhaustive: all low birth weight newborns treated in health facilities in the Markala district during the collection period were included, as far as possible. The main data collection tool in this study was a structured questionnaire, developed on the basis of the specific objectives of the research. Data were collected over a three-month period after birth, from May to August 2025. Results: The study identified several factors associated with low birth weight, including twin births (25.4%), young maternal age (22.8% among 15–19-year-olds) and medical conditions such as high blood pressure (17.5%) and malaria (10.5%). The average weight of low birth weight newborns was 1964.58 grams, with a mode of 2000. The standard deviation was 402.972. The sex ratio favoured females, at 51.8%. Mothers aged 30 to 34 were the most represented, at 25.4%, followed by the two extreme age groups, 15-19 and 35-39, at 22.8% each. Conclusion: This study identified factors associated with low birth weight, the main determinants being twin births, teenage mothers, high blood pressure, infections, malaria and low attendance at prenatal consultations.

Magnesium (Mg) is the second most abundant intracellular cation and the fourth most abundant mineral in the human body. Mg is involved in multiple biochemical reactions, and its numerous activities are beneficial to our bodies. This review outlines the health significance of Mg in its physiologically beneficial role in function, the sources of dietary Mg along with symptoms of Mg deficiency and the health problems that come from it. Mg is a cofactor in various (more than 300) enzymes and essential for the synthesis of certain neurotransmitters, muscle cells’ capacity to contract and relax, and brain functionality. The proper levels of Mg in cells are achieved through membrane channels and transporters (e.g., TRPM7, MagT1, SLC41A1). These include green leafy vegetables, nuts, seeds, legumes, and whole grains as good sources for Mg. Low levels of such an essential substance in the body can heighten susceptibility to chronic diseases such as metabolic syndrome, Type 2 diabetes, obesity, and cardiovascular morbidity. And inadequate Mg can manifest in symptoms like muscle weakness, fatigue, and cardiac arrhythmias. Not only that, but adequate Mg is needed to maintain bone density and reduce susceptibility to osteoporosis. A sufficient intake of Mg will help to mitigate health problems caused by a deficit of Mg and reduce the incidence of chronic diseases. Healthcare providers need to educate patients on consuming Mg-rich foods and, when indicated, when Mg supplementation is indicated, especially with high-risk individuals and/or those with chronic conditions.

Background: Abnormal uterine bleeding due to ovulatory dysfunction (AUB-O) is a common gynaecological problem in women of reproductive age. Thyroid hormones influence the hypothalamic-pituitary-ovarian axis, ovarian steroidogenesis, sex hormone-binding globulin, and endometrial response. Therefore, both overt and subclinical thyroid dysfunction may present with menstrual disturbances. Objective: To determine the association between thyroid disorders and AUB-O, compare serum thyroid-stimulating hormone (TSH) and free thyroxine (FT4) levels between cases and controls, and describe the pattern of menstrual abnormality in relation to thyroid status. Methods: This case-control study was conducted in the Department of Obstetrics and Gynaecology, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh, from July 2015 to November 2016. Sixty (60) women aged 18-45 years with diagnosed AUB-O were compared with 60 matched controls with normal menstrual patterns. Serum TSH and FT4 were measured by chemiluminescent immunoassay. Data were analyzed and compared by statistical tests. Results: Thyroid dysfunction was significantly more frequent among cases than controls (50.0% versus 21.7%; OR 3.62, 95% CI 1.52-8.69; p= 0.001). Mean serum TSH was higher in cases than controls (12.6±10.6 versus 6.4±21.2 mIU/L; p= 0.045), and mean serum FT4 was also significantly higher (2.4±3.6 versus 1.3±0.7 ng/dl; p= 0.021). Menorrhagia was the commonest complaint (46.7%). Hypothyroidism and subclinical hypothyroidism were mainly associated with menorrhagia, whereas hyperthyroidism and subclinical hyperthyroidism were mainly associated with oligomenorrhoea. Anaemia was significantly more common in cases than controls (60.0% versus 15.0%; p= 0.001). Conclusion: Thyroid dysfunction was significantly associated with AUB-O in reproductive-age women. Routine thyroid function assessment, particularly serum TSH and FT4, should be included in the evaluation of women with AUB-O to support targeted medical treatment and reduce unnecessary hormonal or surgical intervention.