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Introduction: This study presents a global research scenario in the broad domain of osteoarthritis (OA) research, using quantitative and qualitative publication and citation indicators. Methods: The study is based on 45,368 global publications, sourced from the Scopus bibliographical database, covering three decades (1994–2023). We studied the performance of the top 12 developed and top 12 developing countries. The key countries, organizations and authors at national and international levels were identified. The broad subject areas and key journals contributing to global OA research were delineated, besides identifying the broad characteristics of highly cited papers in the field. Results: The United States and China were the most productive countries, while the Netherlands and Canada made the largest citation impact. Harvard Medical School and the University of Sydney made the most contribution, while Boston University and Pfizer Inc., USA registered the highest citation impact. Hunter DJ and Guermazi A were the most productive authors, while Lohmander LS, and Hochberg MC registered the highest citation impact. Osteoarthritis and Cartilage (n = 4879) and Annals of the Rheumatic Diseases (n = 786) published the maximum papers, while Arthritis and Rheumatism and Nature Reviews Rheumatology registered the largest citation impact. The highly cited papers with 100 or more citations constituted 6.25% of the total publications. Conclusions: There has been a systematic growth of publications on OA. The research on OA was mainly done in developed countries, with the maximum publications coming from the United States of America, China and Canada. The most impactful publications on OA were from the Netherlands, Canada and the United States of America. [ABSTRACT FROM AUTHOR]

Prediction of renewable energy consumption structure (RECS) can provide important guidance for energy development planning and energy structure transformation. The RECS refer to the proportion of various renewable energy consumptions and belong to compositional data, which could reflect the structural shapes of a complete system better. The multivariate compositional data's vector autoregressive model (CDVAR) on the basis of the Simplex space and its algebraic system is proposed in this study aiming at the multi-dimensional small sample size. Firstly, the algebraic system of the Simplex space is introduced and the statistics of the compositional data are defined. Secondly, the novel model with the form of the compositional data is obtained and the least square parameter estimation of the model is derived according to Aitchison geometry. Third, the validation of the novel model is verified by the data on RECS in countries (China, USA, and Canada). The validation presents that the proposed model performs better in fitting, prediction, stability, and applicability compared with other five models under transformation. Last, the proposed model is applied to analyze and forecast the RECS of the above countries in 2021–2025 to provide an important basis for the optimization of the RECS. [ABSTRACT FROM AUTHOR]