Yanbian University

BackgroundThe therapeutic role of natural medicines in pathological scars has received widespread attention in recent years. However, there is no systematic bibliometric study to analyze the research hotspots and trends in this field.PurposeThis study aimed to provide researchers with a comprehensive summary of natural medicines in pathological scars; visualize the current research status, hotspot directions, and development trends; and offer references for subsequent research and clinical translation in this field.MethodsPublications related to the topic from 2008 to 2025 were retrieved from the WOSCC and PubMed databases. Bibliometric analyses were performed using VOSviewer, CiteSpace, Scimago Graphica, Microsoft Office Excel, and GraphPad Prism.ResultsGlobal publications on natural medicines for pathological scar treatment have steadily increased. A total of 234 papers were retrieved, contributed by 1,395 authors from 396 institutions across 39 countries/regions. China far outnumbered other countries in publication output, with four of the top five authors residing in China. Shanghai Jiao Tong University ranked first among institutions by publication count. The most recent keywords—drug delivery, transdermal delivery, antioxidant, angiogenesis, and the transforming growth factor-β (TGF-β)/Smad—suggest emerging research frontiers.ConclusionResearch on natural medicines in pathological scars is still in a developmental stage. This is the first bibliometric analysis in this domain, highlighting the current hotspots and shedding light on future research directions.

The modeling and control of photovoltaic and other engineering systems highly depend on the accuracy of parameter identification. However, parameter extraction for photovoltaic equivalent models typically presents a high-dimensional, strongly nonlinear, and multimodal global optimization problem. Traditional analytical or gradient-based methods are sensitive to initial values and easily fall into local optima. To address this issue, this paper proposes a multi-strategy improvement teaching–learning-based optimization algorithm (SNTLBO). A social learning network structure with symmetric interaction topology is introduced into the classical TLBO framework to characterize the knowledge propagation relationships among individuals. Through this symmetric and balanced information exchange mechanism, learners can be guided not only by the teacher but also by multiple neighbors within the network, enabling more diverse and symmetric exploration of the search space and enhancing population diversity and global search capability. Furthermore, a teacher reputation mechanism is constructed, where historical performance is used to weight teacher influence, strengthening the guidance of high-quality solutions and accelerating convergence. Meanwhile, an adaptive teaching factor is designed to dynamically adjust the teaching intensity based on the distance between the teacher and students in the solution space, maintaining a dynamic balance (symmetry) between exploration and exploitation. To evaluate the performance of the proposed algorithm, SNTLBO is systematically compared with 11 advanced optimization algorithms on two benchmark test suites, CEC2017 (30D, 50D) and CEC2022 (10D, 20D). Non-parametric statistical tests are conducted to assess significance. The results demonstrate that SNTLBO shows competitive advantages in terms of convergence speed, solution accuracy, and stability. Finally, SNTLBO is applied to the parameter estimation of single-diode, double-diode, triple-diode, quadruple-diode, and photovoltaic module models. Experimental results show that the proposed algorithm achieves higher identification accuracy and robustness in terms of RMSE, IAE, and I–V/P–V curve fitting, verifying its effectiveness and practical value for complex global optimization and practical engineering applications.