Abstract: This study aims to examine total quality management, regulation, and blood production relationships of blood banks in Uganda. A structured questionnaire was used to collect data from 146 randomly selected respondents. The model was validated using Smart PLS-SEM analysis. The findings indicate that both total quality management and regulation have a significant and positive influence on blood production, accounting for a 17.8% variation at a 95% confidence interval. Regulation exhibited no mediation effect in the relationship between total quality management and blood production. Total quality management and regulation are essential factors enhancing blood production. Prioritizing total quality management practices in areas such as determining and meeting customer needs; customer and employee satisfaction surveys; and market research can optimize blood production. This study contributes to the blood bank management knowledge body and identifies areas to support blood production. Blood bank managers can apply these insights to improve operational performance.

Abstract: Banana diseases remarkably influence the worldwide production of bananas. Innumerable studies have focused on timely recognition, prediction, and management of banana plant diseases using various chemical, biological, socio-economic, and AI-based methods. The survey scrutinizes 184 articles accumulated from Scopus, Web of Science, and Google Scholar using defined keywords. These findings reveal the global distribution of the previous studies on plant disease detection, the evolution of ML techniques, and the most frequently studied diseases. The literature shows a swift progress towards machine learning, deep learning, remote sensing, and IoT systems for banana plant disease detection. However, numerous AI models lack real-world validation, datasets are fragmented, and severity quantification mechanisms are understudied. The synthesis analyzes the strong dominance of CNN-based models, which account for the highest proportion of published works and remain the foundational architecture for banana disease detection. Countries such as India, China, the Philippines, Ecuador, and Indonesia have contributed significantly to disease detection. Despite notable progress, many existing systems still rely on single-source and limited datasets, which leads to a lack of cross-source robustness. Evolution of a robust framework integrating multiple datasets, explainable AI, decision support systems and socio-economic insights can lead to more enhanced farmer-friendly banana plant disease management in future This survey provides a detailed overview of the global research studies, highlighting key research gaps that need to be addressed and outlines future directions for building more reliable, interpretable, and comprehensive decision-support pipelines, which will guide the future research work.

Abstract: This scientometric study gives a comprehensive survey of the highly influential scientific literature at the intersection of cardiovascular disease and scaffold technology. By testing a curated dataset of 200 highly cited articles, this review maps the intellectual landscape, determines key research fronts, and keeps tracking the evolution of this dynamic field. The analysis discloses a dominant concentration on tissue engineering applications, specifically for myocardial infarction repair, vascular graft development, and heart valve replacement. Key themes incorporate the exploration of novel biomaterials such as biodegradable polymers, decellularized matrices, hydrogels, and electrospun nanofibers, and the integration of advanced fabrication methods such as 3D bioprinting. The survey also determines seminal contributions from leading research groups and highlights the synergistic relationship between material science, cell biology, and clinical cardiology which drives innovation. In addition, the survey tracks the rising prominence of enabling technologies which include conductive scaffolds for cardiac patches and the application of stem cells. The study not only synthesizes the current state of knowledge but also determines emergent trends and potential future trajectories, underscoring the critical role of scaffold-based strategies in advancing cardiovascular regenerative medicine. The results consolidate a vast body of literature to inform researchers and funding agencies about the field's structure and its most essential avenues of investigation.

Abstract: We present a scientometric analysis of the research landscape about the application of Total Quality Management (TQM) rules within surgical and broader healthcare contexts. The study utilizes a dataset of 200 highly cited articles extracted from Scopus and maps the intellectual structure, key themes, and evolving priorities in this critical field. The study discloses a mature yet dynamically evolving survey domain characterized by a distinct shift from theoretical process frameworks to patient-centric and data-driven methodologies. Key study clusters determined include Patient Safety Culture and Adverse Event Reduction, Specific Surgical Procedure Optimization, Methodological Frameworks for Quality Improvement (QI), and Ethical & Inclusive Care Considerations. Highly cited articles and authors as well as influential institutions are determined, representing a global collaboration network with strong representation from the United States and Northern Europe. The most effective publications, as stated by citation frequency, are studied in detail, briefing their contributions to building safety protocols, validating QI methodologies like DMAIC, and expanding the discourse on patient engagement and health equity. The present review summarizes that the field is advancing towards more predictive, equitable, and technologically integrated models of care, with future research poised to leverage artificial intelligence and federated learning to personalize and enhance surgical quality improvement on a global scale.

Abstract: The combination of machine learning (ML) and neural networks (NN), specifically deep learning (DL), is making a big breakthrough to breast cancer studies. This scientometrics survey studies 200 highly cited publications to map the intellectual landscape and studies trends in this dynamic field. The survey discloses a dominant concentration on computer-aided diagnosis (CAD) systems using convolutional neural networks (CNNs) for the classification of breast cancer from different imaging modalities, including mammography, histopathology, ultrasound, and magnetic resonance imaging (MRI). Key survey directions identified include: (1) the development of comprehensive deep learning techniques for image-based detection and classification; (2) the application of transfer learning to resolve data scarcity; (3) the combination of multi-omics and clinical data for personalized prognosis and treatment prediction; and (4) the exploration of explainability and robustness in ML-driven clinical tools. This study synthesizes the methodological advancements, sheds light on the evolution from traditional machine learning to deep learning, and surveys the challenges associated with data heterogeneity, model interpretability, and clinical integration. By giving a structured overview of the seminal work and emerging paradigms, the study serves as a reference for graduate students and other interested parties to have a better understanding about the current state and future trajectories of AI in breast oncology.