Artículos

Purpose: In recent years, the rapid advancement of artificial intelligence technology has brought opportunities for the acceleration and improvement of the drug discovery process by aiding in all stages of drug discovery like drug target identification and validation, virtual screening, de novo drug design, and ADMET property prediction. The present study aims to provide an overview of the developing tendency, cooperation, and influence of academic groups and individuals, hotspots, and crucial problems in the field of AI-aided drug discovery using bibliometric methods. Methods: Publications on AI-aided drug discovery published from January 1, 2009, to December 31, 2023, were retrieved from the Web of Science core collection. The document type was limited to articles or reviews, and the language was set to English. Citespace was used to conduct the bibliometric analysis. Results: A total of 9700 publications were included, and the number of them generally increased over time, with a rapid increase tendency since 2018. The US and China were the leading countries in this field. The Chinese Academy of Sciences was the most influential institution. Ekins, Sean was the most productive author and Hou, Tingjun formed the largest cooperation network. Networks and clusters of keywords highlighted terms like “virtual screening”, “expression” and “drug delivery” as focused topics, and burst analysis showed that “support vector machines”, and “classification” received the longest attention. Meanwhile the keywords “sars cov 2”, “molecular design” and “clinical trials” were hotspots in recent years. The content analysis of the co-cited literature identified the significant questions to be tackled in future research. Conclusions: This study offers a comprehensive landscape of the global contributions given to this increasingly important and prolific field of research and points out several areas that might be addressed by future research to better develop the field of AI-aided drug discovery.

In recent years, the intensification of the urban heat island (UHI) effect has become a significant concern as urbanization accelerates. This survey comprehensively explores the current status of surface UHI research, emphasizing the role of land use and land cover changes (LULC) in urban environments. We conducted a systematic review of 8260 journal articles from the Web of Science database, employing bibliometric analysis and keyword co-occurrence analysis using CiteSpace to identify research hotspots and trends. Our investigation reveals that vegetation cover and land use types are the two most critical factors influencing UHI intensity. We analyze various computational intelligence techniques, including machine learning algorithms, cellular automata, and artificial neural networks, used for simulating urban expansion and predicting UHI effects. The study also examines numerical modeling methods, including the Weather Research and Forecasting (WRF) model, while examining the application of Computational Fluid Dynamics (CFD) in urban microclimate research. Furthermore, we evaluate potential mitigation strategies, considering urban planning approaches, green infrastructure solutions, and the use of high-albedo materials. This comprehensive survey not only highlights the critical relationship between land use dynamics and UHIs but also provides a direction for future research in computational intelligence-driven urban climate studies.

The 2023 Global Sustainable Development Report identified sustainable cities and communities as a critical area for in-depth review, emphasizing the need for systematically examining theoretical knowledge and guidance on the direction of development This article reviews the relevant literature from the Web of Science core database over the past decade and introduces Professor Verganti’s theoretical perspective of “design-driven innovation” to summarize technological research, user needs/demand, and design, providing a new theoretical dimension for the research on sustainable communities. To this end, this study employs three econometric tools—VOSviewer, RStudio Bibliometric, and CiteSpace—to analyze status and trends visually. The findings reveal that the design level has garnered the most research results, with the USA as the primary contributor and China as the country with the most development potential. Moreover, the most prominent research topics within the three perspectives are microbial communities, sustainable development goals, and ecosystem services, with recent research highlights focusing on artificial intelligence, social innovation, and tourism. In conclusion, this article proposes a strategic framework for the future development of sustainable communities, encompassing consolidation of technical foundations, clarification of demand orientation, and updating design specifications and theories to provide diverse solutions.

With the construction of smart cities advancing, research on big data and smart cities has become crucial for sustainable development. This study seeks to fill gaps in the literature and elucidate the significance of big data and smart city research, offering a comprehensive analysis that aims to foster academic understanding, promote urban development, and drive technological innovation. Using bibliometric methods and Citespace software (6.2.R3), this study comprehensively examines the research landscape from 2015 to 2023, aiming to understand its dynamics. Under the guidance of the United Nations, global research on big data and smart cities is progressing. Using the Web of Science (WOS) Core Collection as the data source, an exhaustive visual analysis was conducted, revealing various aspects, including the literature output, journal distribution, geographic study trends, research themes, and collaborative networks of scholars and institutions. This study reveals a downward trend despite research growth from 2015 to 2020, focusing on digital technology, smart city innovations, energy management and environmental applications, data security, and sustainable development. However, biases persist towards technology, information silos, homogenised research, and short-sighted strategies. Research should prioritise effectiveness, applications, diverse fields, and interdisciplinary collaboration to advance smart cities comprehensively. In the post-COVID-19 era, using big data to optimise city management is key to fostering intelligent, green, and humane cities and to exploring efficient mechanisms to address urban development challenges in the new era.

This study explores the evolution and impact of research on the challenges and opportunities in the implementation of artificial intelligence (AI) in manufacturing between 2019 and August 2024. By addressing the growing integration of AI technologies in the manufacturing sector, the research seeks to provide a comprehensive view of how AI applications are transforming production processes, improving efficiency, and opening new business opportunities. A bibliometric analysis was conducted, examining global scientific production, influential authors, key sources, and thematic trends. Data were collected from Scopus, and a detailed review of key publications was carried out to identify knowledge gaps and unresolved research questions. The results reveal a steady increase in research related to AI in manufacturing, with a strong focus on automation, predictive maintenance, and supply chain optimization. The study also highlights the dominance of certain institutions and key authors driving this field of research. Despite the progress, significant challenges remain, particularly regarding the scalability of AI solutions and ethical considerations. The findings suggest that while AI holds considerable potential for the manufacturing industry, more interdisciplinary research is needed to address existing gaps and maximize its benefits.

Sliding mode control (SMC) has been widely used to control linear and nonlinear dynamics systems because of its robustness against parametric uncertainties and matched disturbances. Although SMC design has traditionally addressed process model-based approaches, the rapid advancements in instrumentation and control systems driven by Industry 4.0, coupled with the increased complexity of the controlled processes, have led to the growing acceptance of controllers based on data-driven techniques. This review article aims to explore the landscape of SMC, focusing specifically on data-driven techniques through a comprehensive systematic literature review that includes a bibliometric analysis of relevant documents and a cumulative production model to estimate the deceleration point of the scientific production of this topic. The most used SMC schemes and their integration with data-driven techniques and intelligent algorithms, including identifying the leading applications, are presented.

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users’ privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007–2022 were collected from the Scopus Database and then preprocessed. Following this, the VOSviewer was used to visualize the network of authors’ keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.

Biocementation is an innovative and sustainable technique with wide-ranging applications in slope stabilization, watershed management, and erosion control. Despite its potential, comprehensive evaluations of its use in hydrology and geotechnical engineering are limited. This study addresses this gap through a bibliometric analysis of 685 articles (2013–2023) from the Scopus database, employing VOSviewer and RStudio to explore global research trends, key contributors, and emerging themes. The analysis reveals that China, the United States, and Japan are leading contributors to this field, with significant advancements in microbial-induced (MICP) and enzyme-induced calcium carbonate precipitation (EICP) techniques. These methods have demonstrated effectiveness in improving soil strength, reducing erosion, and enhancing hydrological properties such as infiltration, runoff control, and water retention. Co-occurrence analysis identifies interdisciplinary connections between geotechnics and hydrology, highlighting research clusters focused on biomineralization, erosion resistance, and durability. The findings underscore biocementation’s pivotal role in addressing sustainability challenges by providing environmentally friendly alternatives to traditional soil stabilization techniques. This study not only maps the current research landscape but also offers valuable insights into the practical implications of biocementation for slope stability and hydrological management, laying the foundation for future advancements in sustainable engineering practices.

This study presents a thorough bibliometric analysis of Neuroinformatics over the past 20 years, offering insights into the journal’s evolution at the intersection of neuroscience and computational science. Using advanced tools such as VOS viewer and methodologies like co-citation analysis, bibliographic coupling, and keyword co-occurrence, we examine trends in publication, citation patterns, and the journal’s influence. Our analysis reveals enduring research themes like neuroimaging, data sharing, machine learning, and functional connectivity, which form the core of Neuroinformatics. These themes highlight the journal’s role in addressing key challenges in neuroscience through computational methods. Emerging topics like deep learning, neuron reconstruction, and reproducibility further showcase the journal’s responsiveness to technological advances. We also track the journal’s rising impact, marked by a substantial growth in publications and citations, especially over the last decade. This growth underscores the relevance of computational approaches in neuroscience and the high-quality research the journal attracts. Key bibliometric indicators, such as publication counts, citation analysis, and the h-index, spotlight contributions from leading authors, papers, and institutions worldwide, particularly from the USA, China, and Europe. These metrics provide a clear view of the scientific landscape and collaboration patterns driving progress. This analysis not only celebrates Neuroinformatics’s rich history but also offers strategic insights for future research, ensuring the journal remains a leader in innovation and advances both neuroscience and computational science.