Hypoglycemia poses a critical challenge in managing diabetes. Existing literature, while extensive, lacks a holistic perspective. This study aims to bridge this gap by combining bibliometric analysis and a comprehensive review of Bayesian analysis-related hypoglycemic issues. This study employed data from the SCI-EXPANDED database for bibliometric analysis. The keywords "symptom" or "symptoms," "hypoglycemic" or "hypoglycemia," or "hypoglycaemia" or "hypoglycaemic," and "Diabetes" or "Diabetic" or "Diabetics" were used to locate 1,596 documents from 2000 to 2022. Document types, authorship patterns, and citation metrics were examined. Bayesian methodologies were systematically reviewed across various diabetes types and evaluated using specific assessment tools. Most of the articles published in "Endocrinology & Metabolism" contributed 37.2% of total articles, with a notable CPP2022 (Citations Per Publication (CPP)) of 35, and the main publication type were articles with an average of about six authors and over 32,000 citations in 2022. The United States (US) consistently leads in the number of published articles, followed by China, Japan, and India. Novo Nordisk led institutions with 36 publications and a substantial CPP2022 of 60.9. The comprehensive review emphasized that Bayesian statistical modeling is widely used for adult Type 1 and Type 2 diabetes but is limited in child Type 1 and absent in Gestational Diabetes (GAD) research. In contrast, Bayesian Networks (BNs) are mainly applied to adult Type 2 diabetes, with gaps in other types. Furthermore, Bayesian Neural Networks (BNNs) are prevalent in adult and child Type 1 studies but not applied to Type 2 or GAD. Since 2010, Total Publications (TP) have increased rapidly, indicating increased interest in researching hypoglycemia. Outlining potential research directions and emphasizing the transformative impact of Bayesian methodologies provides valuable insights for clinicians, researchers, and healthcare stakeholders.

Synthetic dyes used for the coloration of textile goods are not readily biodegradable and are a major concern for water pollution. Nature has abundant sources of potential colorants for textile applications. There are many challenges in textile coloration using natural dyes and pigments, and significant research efforts are currently put into replacing synthetic textile dyes successfully. In order to gain insight into the future trajectory of dyeing research utilizing natural colorants, a bibliometric analysis from 1990 to 2021 using the Science Citation Index Expanded database was conducted. The analysis focused on how well the publication performed in terms of outputs and citations annually, mainstream journals, Web of Science categories, top universities, top nations, research trends, and hotspots. An overview of the most frequently used keywords derived from terms in the article title analysis, authors’ keyword analysis, and KeyWords Plus analysis served as the foundation for determining current research goals and future trends. The findings indicated that no noteworthy research on this topic was conducted in the final decade of the previous century but that it did begin to get the attention of scholars in the first decade of this century. The use of natural dyes in industry has significantly expanded during the past ten years. “Fastness” is the interest point that has received the most attention. Mordants, or environmentally friendly extraction techniques such as ultrasonic, gamma irradiation, etc., represent the future of this research area. On the other hand, ultraviolet protection and antibacterial or antimicrobial properties are becoming more and more popular in the field of textile dyeing research using natural colorants.

As the level of automation is increasing among industries, the increasing amount of waste has become one of the main barriers to having a sustainable environment. That’s why the sustainable manufacturing concept came to bring economic benefits while focusing on minimizing negative environmental impacts. The purpose of this study is to show the importance of waste reduction in manufacturing processes to achieve a higher level of sustainability. This work uses the production processes of a textile garment factory located in Bangladesh as a case study to examine how waste reduction can be achieved. First, the major steps of the production process are mapped and visualized to identify improvement opportunities by using the Business Process Modeling Methodology and discrete event simulation. Second, after the production process is improved, an experimental test was conducted to see how much waste reduction can be achieved by the proposed solution. The research found that the in-sewing wastage in the factory was too much and used compressed air as a suggested solution to minimize this problem. The final result of the test showed that the in-sewing fabric wastage percentage had been reduced almost by 75 %, indicating the possible benefits of this solution regarding sustainability, if applied on a larger scale within the factory.

Higher education institutions face accelerating skills change, expanding digital delivery, and rising expectations for workforce relevance. This study examines how machine intelligence can support predictive–adaptive, data-driven curriculum design by integrating learning analytics, adaptive learning platforms, and institutional decision systems. Evidence from the analysis indicates that predictive models strengthen alignment between program outcomes and labor-market needs, while adaptive learning features improve student engagement and learning support. At the institutional level, analytics-enabled planning enhances resource allocation, training design, and operational efficiency. Qualitative insights further highlight perceived benefits (personalization, timely feedback, and transparency) alongside persistent barriers, including data quality constraints, implementation capacity, governance and ethics, and uneven access that may reinforce inequities if not addressed. The findings imply that effective adoption requires clear data governance, stakeholder readiness, faculty development, and equity-by-design safeguards. Overall, machine intelligence is positioned as an enabling infrastructure for continuous curriculum improvement when paired with responsible oversight and inclusive implementation.