Cardiovascular disease remains the leading cause of mortality worldwide, driving substantial research investment and necessitating robust evaluation of institutional research performance. This study employed the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to evaluate and rank the research performance of 30 leading universities based on their contributions to the top 100 highly cited cardiovascular medicine articles from 2010–2026, extracted from Scopus. Three evaluation criteria were selected: Unique Authors (minimized as a resource input measure), Total Citations (maximized as an impact indicator), and Number of Papers (maximized as a productivity measure), with equal weights (33.3% each) assigned to all criteria. TOPSIS analysis revealed substantial variation in institutional performance, with Harvard University emerging as the top-performing institution (TOPSIS score: 0.6050), demonstrating dominance across all criteria with 228 unique authors, 9,430 citations, and 83 papers, followed by Cleveland Clinic (0.4882), University of Toronto (0.4537), and Mayo Clinic (0.4158). US institutions dominated the ranking, accounting for 27 of 30 universities with an average TOPSIS score of 0.4112, while Canadian institutions achieved an average of 0.4078. The analysis identified an “efficiency gap” between high-citation, low-paper institutions (e.g., UCSF with 87.4 citations per paper and 1.3 authors per paper) and those demonstrating the inverse pattern. The findings highlight the dominance of US institutions, varying institutional strategies (high-volume vs. high-impact approaches), and the concentration of research activity among leading academic medical centers, with the TOPSIS methodology offering a robust framework for multidimensional research evaluation that can inform institutional benchmarking, strategic planning, and resource allocation in cardiovascular medicine.

The COVID-19 pandemic has created an exponential trend in global research mobilization, with vaccines as a primary objective. This present survey gives a scientometric study on the landscape of research concerning the COVID-19 pandemic and vaccines, using a dataset of 19,749 records from the Scopus database where only 200 records are chosen for the review. The analysis maps the conceptual structure and dynamics of this area by looking at the publication trends, key contributors, core research themes, and impact of the published papers. The results have disclosed an exponential trend in publications, peaking in 2021-2022, driven by the quick requirement for development, evaluating, and deploying vaccines. The study was investigated by large-scale international collaborations, with prolific contributions from universities as well as vaccine makers in the United States, China, and Europe. High-impact journals like The Lancet and The New England Journal of Medicine considered critical dissemination channels. Thematic clusters are dominated by vaccine development and immunology, real-world effectiveness, SARS-CoV-2 variants and immune evasion, vaccine safety, and public acceptance. The evolution of study is concentrated on from initial clinical trials to real-world evidence, variant-specific challenges, and eventually, long-term impact and systemic lessons. The survey gives a comprehensive review of a defining scientific effort, showing the collaborative and rapid-response nature of research during a global health crisis.

At the edge of the network, the exponential increase of data produced along with the growing concerns over data privacy coming from regulations and society have all together triggered the rise of Federated Learning (FL) as the main approach in distributed machine learning (DML). Fed learning allows the model training to be performed on decentralized devices or data silos even without the raw data being transferred. Hence, FL is completely in line with the objectives of the privacy-preserving techniques. In this paper, we carry out a scientometric analysis on the 200 most cited papers, which are the first 200 papers at the intersection of "Distributed Machine Learning," "Federated Learning," and "Privacy-Preserving" published between 2020 and 2024, and the Scopus database is where they are indexed. The literature of publication trends, prominent authors and works, the thematic clusters, and research fronts that are changing are all systematically examined in this study; hence, the intellectual landscape of this fast developing field is mapped out. Our findings point to the existence of certain streams of research such as the algorithms with differential privacy being the mainstay, secure aggregation methods through the use of homomorphic encryption and multi-party computation, blockchain-based FL systems which ensure security and trust, and resource-efficient FL that supports IoT and edge computing. The results also show an area that is nearly enjoying a complete transformation as a result of the overpowering need to address the triad of model quality, data protection, and system efficiency. The review not only encourages researchers, and practitioners but also helps the policymakers by providing the current trend to which the key challenges can be identified and the future directions in privacy-preserving distributed intelligence anticipated.

This article provides an extensive scientometric assessment of the research on deteriorating inventory models, relying on a dataset of 2227 publications obtained from the Scopus database. The goal of the analysis is to draw a map of the intellectual terrain of this small but important area within supply chain management and operational research. By means of bibliometric indicators, the most influential publications, leading authors, core research institutions, and contributing countries are revealed in the analysis. The methodology is based on the statistical analysis of publication metrics, including citation counts, to determine impact and influence. Among the main findings are the identification of the seminal works that have built up the discipline, the networks of collaboration among researchers, and the shift of research topics towards sustainability, preservation technology, and supply chain integration.