Through the development of the metaverse concept from the Sumerian myth (5500 - 1800 BC) and mind-altering novel, “Snow Crash” in 1992, to today’s information age, human- and society-centred urban metaverse worlds, an extension of residents and urban society where the virtual and the physically real blend and are more organically integrated, are meant to mirror the fabric of urban life with no harm to their residents. The success of urban metaverse cybercommunities depends on the quality of data-driven Smart City (SC) Digital Twins (DTs), the seamless exchange of data between cyber and physical worlds (e.g. between residents and their counterpart “Avatars’’) and the processing of the data effectively and efficiently with no vicious interventions. The potential risks in this ecosystem that incorporates Web3 can be extremer than the ones in Web2 since users are immersed with multiple tightly coupled wearable sensor-rich devices perceiving the blend of the real and the virtual with possible imminent negative experiences. This study, by analysing potential cyberthreats in the urban metaverse cyberspaces, proposes a blockchain-based Decentralised Privacy-Preserving Machine Learning (DPPML) authentication and verification technique, which uses the metaverse immersive devices and can be instrumented effectively against identity impersonation and theft of credentials, identity, or avatars.

Aim: This study aims to analyze the structure of the co-authorship network in Budd-Chiari syndrome (BCS) research from 2000 to 2023 using data from the Web of Science (WoS) Core Collection. The objective is to identify key researchers, evaluate the collaborative dynamics, and understand the evolution of scientific contributions in this field. Method: This study utilized network analysis techniques to evaluate 2,877 publications related to BCS research indexed in the WoS Core Collection between 2000 and 2023. The analysis was conducted using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The co-authorship networks were assessed using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (degree of node clustering), number of components (distinct connected subgroups), and average path length (average distance between nodes). Micro-level indicators including degree centrality (importance based on the number of connections), closeness centrality (proximity to other nodes), and betweenness centrality (frequency of a node on the shortest paths between others) were also analyzed.Result: The results revealed a highly fragmented network structure in the early years (2000-2009), characterized by low network density (0.0017) and a large number of disconnected components (533). Key researchers such as Aurelie Plessier and Dominique Valla emerged as central figures in terms of degree and closeness centrality. In the 2010-2019 period, the network showed slight improvements, with density decreasing to 0.0016 but an increase in the number of components to 645, indicating persistent fragmentation. From 2020 to 2023, the network demonstrated increased cohesiveness with a density of 0.0032 and a reduction in components to 376. Influential researchers in the final period included Virginia Hernandez-Gea and Valerie Paradis, who played pivotal roles in connecting disparate research clusters.Conclusion: The co-authorship network analysis of BCS research over the past two decades highlights a gradual yet incomplete evolution towards greater collaboration and integration among researchers. While the network's cohesiveness has improved, significant fragmentation remains, suggesting that further efforts are needed to enhance global and interdisciplinary collaboration. Addressing these challenges is crucial for fostering a more connected and productive research community that can advance the understanding and management of BCS.

This study explores the integration of Artificial Intelligence (AI) and collaborative robotics in industrial surface finishing, focusing on feature detection on object reflective surfaces using neural networks in computationally constrained environments. Central to this investigation is the utilization of cameras considered as sensors that can capture detailed images of reflective surfaces. These images are then analyzed by a custom neural network, demonstrating the feasibility of feature detection with non-powerful computers. The public dataset for recognizing light reflection defects, available in the SPADD repository, supports this approach. Employing open-source tools like Darknet YOLOv3 and Yolo\_mark, this method proves particularly advantageous for small and medium-sized enterprises (SMEs) due to its low cost and maintenance requirements. This research highlights the challenges of AI and collaborative robotics in industrial applications, especially in adapting to varying conditions and the need for continuous training with new data coming from several sensors. Looking ahead, plans are in place to integrate this system with cobot-assisted polishing tasks, using cameras to enhance manufacturing efficiency and quality. This work contributes to the broader understanding of practical applications of AI in industrial automation, surface finishing processes, and the effective use of sensor technology.

Aim: This study aims to explore the collaborative networks in Sodium/Glucose Cotransporter 2 (SGLT2) research, covering the period from 2000 to 2023. By conducting a co-authorship network analysis, I investigate the structure and dynamics of scientific collaboration, identifying key researchers, institutions, and trends in the field. This analysis provides insights into how research collaboration has evolved and highlights the influence of international cooperation on the development of SGLT2 research.Method: The data were collected from the Web of Science (WoS) Core Collection and included 6,999 publications on SGLT2 research. Using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3), I analyzed both macro and micro-level network indicators. Macro-level indicators included network density, clustering coefficient, components, and average distance, while micro-level indicators included degree centrality, closeness centrality, and betweenness centrality. These metrics were used to assess the structure of the co-authorship network and identify key contributors.Result: The network analysis revealed significant changes in collaboration patterns over the study period. From 2000 to 2023, the network became more fragmented, with an increase in the number of research clusters and components. However, influential researchers, such as Zinman B. and Heerspink Hiddo J. L., consistently emerged as central figures in terms of both degree and closeness centrality. The analysis also highlighted the rise of international collaboration, particularly involving researchers from the United States, Europe, and Asia, with Japan playing a growing role in recent years.Conclusion: This study demonstrates that SGLT2 research collaboration networks have evolved considerably over the last two decades. The identification of key researchers and institutions provides valuable insights into the field's development, emphasizing the importance of international cooperation in advancing SGLT2 research. These findings may guide future research strategies and promote further collaborative efforts.

Aim: This study aims to investigate the global collaboration network among researchers studying Sick Sinus Syndrome (SSS) from 2000 to 2023. Given the syndrome's increasing prevalence in aging populations, understanding the structure of research collaboration is crucial to advancing the field. This analysis explores co-authorship networks to identify key contributors, institutions, and trends in SSS research. Method: This study utilized network analysis techniques to evaluate 1,693 publications related to SSS research indexed in the Web of Science (WoS) Core Collection between 2000 and 2023. The analysis was conducted using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The co-authorship networks were assessed using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (degree of node clustering), number of components (distinct connected subgroups), and average path length (average distance between nodes). Micro-level indicators including degree centrality (importance based on the number of connections), closeness centrality (proximity to other nodes), and betweenness centrality (frequency of a node on the shortest paths between others) were also analyzed.Result: The analysis revealed an overall increase in collaboration over time, with rising network density and stronger local clusters, although the network remained fragmented. Key researchers, including Boriani, Giuseppe (Italy), and Glikson, Michael (Australia), emerged as central figures, demonstrating high degrees of centrality in different periods. The results also highlight persistent regional and institutional collaboration patterns, reflecting the growing importance of global partnerships in addressing SSS.Conclusion: This comprehensive network analysis underscores the importance of international collaboration in advancing SSS research. Despite growing collaborative efforts, the field remains somewhat fragmented, suggesting further opportunities for integration. This research has enabled us to identify influential researchers and research institutions, and we will be able to promote research collaboration to further understand and improve treatment methods for SSS as a response to the aging of the world's population.

Aim: This study aims to explore the collaborative networks in Marfan Syndrome (MFS) research by analyzing co-authorship patterns from 2000 to 2023. The objective is to identify key researchers, trends in collaboration, and the extent of interdisciplinary cooperation within the global MFS research community. Understanding these dynamics will inform the future direction of research in this field, emphasizing the need for a more integrated, interdisciplinary approach. Method: This study utilized network analysis techniques to evaluate 6,657 publications related to MFS research indexed in the Web of Science (WoS) Core Collection between 2000 and 2023. The analysis was conducted using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The co-authorship networks were assessed using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (degree of node clustering), number of components (distinct connected subgroups), and average path length (average distance between nodes). Micro-level indicators including degree centrality (importance based on the number of connections), closeness centrality (proximity to other nodes), and betweenness centrality (frequency of a node on the shortest paths between others) were also analyzed. Result: The network analysis revealed a persistent fragmentation within the MFS research community over the past two decades. While network density slightly increased from 0.0012 in the 2000–2009 period to 0.0016 in 2020–2023, the network remained disjointed with numerous disconnected components. Key contributors, such as Harry C. Dietz, Guillaume Jondeau, and Julie De Backer, demonstrated high centrality values, playing pivotal roles in shaping collaboration. The clustering coefficient remained high across all periods, indicating strong intra-group collaboration, yet underscoring the need for broader interdisciplinary partnerships.Conclusion: This study highlights the need for more integrated and cross-disciplinary collaboration in MFS research. Despite the significant contributions of central researchers, the overall fragmentation limits potential advancements. Fostering a more interconnected global network and encouraging interdisciplinary cooperation will be crucial to addressing the complex, multi-systemic nature of Marfan Syndrome and improving patient outcomes.

In an age of unprecedented global complexity, interconnectedness, and urgency, the inadequacy of traditional, reductionist models of knowledge production has become glaringly apparent. This pioneering, landmark article offers a sweeping, paradigm-shifting exploration of the revolutionary emergence of "Mode 4" knowledge production - a fundamental reconceptualization of the epistemological, organizational, and methodological foundations of the research enterprise. Drawing on cutting-edge theoretical frameworks and a vast corpus of rigorous empirical evidence, this work argues that Mode 4 represents a transformative leap towards a more collaborative, transdisciplinary, and adaptive approach to knowledge creation - one that holds the potential to catalyze a profound and lasting transformation in the way we conceive of, organize, and mobilize research to address the complex, interconnected challenges facing our world. At the heart of this paradigm shift lies the groundbreaking "decuple helix" framework, which expands the scope of stakeholder engagement and knowledge co-creation to incorporate a comprehensive range of actors, from academia and industry to marginalized communities, the natural environment, and international organizations. This article delves deeply into the multifaceted roles and invaluable contributions of this diverse array of stakeholders, demonstrating how their active integration can unlock the transformative power of collaborative, values-oriented research and innovation. Furthermore, the paper provides a comprehensive example of how Mode 4 knowledge production concepts could be implemented using cutting edge innovationology research. By drawing on a rich tapestry of theoretical foundations, including complexity theory, quantum physics, humanities, social sciences, spirituality, and the arts, innovationology exemplifies the transdisciplinary ethos at the core of this paradigm shift. The article delves deeply into the collaborative co-creation, iterative and adaptive methodologies, and holistic, values-driven vision that define this groundbreaking transdisciplinary science. However, this work also candidly explores the significant institutional, methodological, equity-related, and scalability challenges that continue to hinder the widespread adoption and implementation of the Mode 4 and decuple helix frameworks. In doing so, it charts a course forward, outlining a comprehensive set of practical implications and recommendations to address these barriers and unlock the transformative potential of these emerging paradigms. Ultimately, this article offers a sweeping, cohesive, and visionary analysis of the revolutionary emergence of Mode 4 knowledge production and the decuple helix framework - positioning itself as a landmark contribution that has the potential to catalyze a profound transformation in the way we conceive of, organize, and mobilize research for a sustainable and equitable future. With its groundbreaking insights, bold vision, and rigorous interdisciplinary foundation, this work stands as a clarion call for a new era of collaborative, transdisciplinary knowledge production that can truly address the complex, interconnected crises facing our world.

Aim: This study aims to analyze the collaborative structure of Intestinal Behçet’s Disease (IBD) research by examining co-authorship networks in publications indexed in the Web of Science (WoS) Core Collection from 2000 to 2023. The goal is to identify key researchers, institutions, and collaboration patterns that have shaped the field over the past two decades. Method: This study utilized network analysis techniques to evaluate 484 publications related to IBD research indexed in the WoS Core Collection between 2000 and 2023. The analysis was conducted using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The co-authorship networks were assessed using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (degree of node clustering), number of components (distinct connected subgroups), and average path length (average distance between nodes). Micro-level indicators including degree centrality (importance based on the number of connections), closeness centrality (proximity to other nodes), and betweenness centrality (frequency of a node on the shortest paths between others) were also analyzed.Result: From 2000 to 2009, the co-authorship network was highly fragmented with a low network density of 0.0114 and 84 distinct components, indicating limited collaboration. Key contributors during this period included Kim, Won Ho, and Cheon, Jae Hee (South Korea), who played central roles in connecting disparate research clusters. In the following decade (2010-2019), network density decreased slightly to 0.0086, and the number of components increased to 113, reflecting continued fragmentation despite growing research output. However, from 2020 to 2023, network density increased to 0.0171, and the number of components decreased to 73, indicating a trend towards greater integration and collaboration. Notable researchers in this period included Emmi, Giacomo (Italy), and Hatemi, Gulen (Turkey), who emerged as central figures, highlighting a shift in research leadership from Asia to Europe..Conclusion: The co-authorship network analysis of IBD research reveals an evolving collaborative landscape with increasing network density and decreasing fragmentation over time, particularly in the most recent period. From 2000 to 2019, Japanese and Korean researchers were prominent contributors, highlighting the significance of Asia in the global research arena. However, since 2020, researchers from Italy and Turkey have gained significant international prominence, suggesting a shift in the research focus from Asia to Europe. This transition may be attributed to changes in the incidence rates of IBD, where rapid increases were observed in Asian countries like Japan, Korea, and China until 2020, followed by stabilization or decline, while incidence rates have been increasing in European countries such as Italy and Turkey. These findings underscore the importance of expanding international collaborations to enhance the understanding of IBD. Fostering global partnerships could further advance the field, promoting more integrated and innovative research efforts, and addressing the shifting dynamics of IBD prevalence across regions.

The construction industry is shifting towards more sustainable practices to address environmental concerns. This review examines the impact of LEED certification on design-build projects, highlighting how the two work together to promote sustainability. By analyzing the article under review, we explore how LEED standards—such as energy efficiency, water conservation, and the use of sustainable materials—are incorporated into design-build projects to create environmentally responsible buildings. The article also discusses the benefits of the design- build approach, which encourages collaboration and early integration of sustainability principles. The review critically assesses the strengths and weaknesses of the article, emphasizing the importance of cost considerations and scalability. Ultimately, it underscores the value of combining LEED certification with design-build to promote green building practices.

BackgroundThe present review summarizes the state-of-the-art of in silico methods and techniques that are the most useful in drug discovery, their relationship with data science, as well as the successful application of data science, machine learning (ML) and artificial intelligence (AI) applications. A meta-analysis of the various technologies available is furthermore proposed as a guideline for the non-expert, reader relative to the several subject areas is also discussed in this article. The scope of this meta-analysis is to rank the enlisted technologies by their field of applications and to depict the latter according to knowledge accessibility, from students to experts.Method The search strategy utilized for this review first produced a general collection of 900 papers without duplications, which were subsequently streamlined and divided into two independent collections: the top 300 most-cited papers of all time (since 2000) and the papers with the highest interest for a systematic review analysis (high-impact exciting papers). Results In Part 1, we discuss the most cited and quality 97 articles in these top 300 papers most relevant to the field of in silico drug discovery. The different disciplines are listed according to their industrial and economic incurred to society, independently from the “metric” results of how many new drug approvals (NDAs) each discipline has generated to date.ConclusionBig data, the ensemble of known items stored in publicly available databases, has improved our understanding of the many fates of a potential drug candidate during its development and even after its commercialization. Moreover, the combination of new screening techniques and “omics” with old drugs has led to a new paradigm in which the unknown knowledge of any biological molecule and its cellular structure, now plays an important role as a target for a series of yet-to-be-developed drugs: the chemical space. Furthermore, leveraging big data, data science, ML, and AI can revolutionize drug discovery by swiftly analyzing massive datasets, predicting efficacy and safety profiles, streamlining development, cutting costs, and boosting success rates for new drugs. AI also speeds up the search for promising drug candidates, advancing innovative therapies.

This paper explores the pivotal role of personalized product recommendations in enhancing the e-commerce experience. As online shopping becomes increasingly prevalent, the demand for tailored user experiences has surged, prompting the development of sophisticated recommendation systems. This study presents a comprehensive analysis of various methodologies employed to deliver personalized suggestions, including collaborative filtering, content-based filtering, and hybrid approaches. The implementation of a user-centric recommendation engine demonstrates significant improvements in user engagement, satisfaction, and conversion rates. Furthermore, the paper discusses the importance of real-time adaptation mechanisms and user feedback loops in optimizing recommendations. By providing insights into the challenges and solutions associated with recommendation systems, this research aims to equip e-commerce businesses with the tools necessary to leverage personalization effectively, ultimately leading to enhanced customer experiences and increased sales

This paper presents MADS (Multi-Agents for Data Science), an innovative and simple open-source multi-agent framework designed for systemic pipeline execution in applied supervised machine learning. By leveraging the capabilities of multi-agent systems (MAS), we introduce a universal approach to optimize and streamline machine learning pipelines. Our framework highlights the differences between various types of agents, such as reinforcement learning (RL) agents and large language model (LLM) agents, and their distinct contributions to the process. While we currently employ LLM agents to automate and enhance machine learning tasks, we acknowledge the potential of incorporating RL agents in future iterations to further improve performance and adaptability. The primary objective is to enhance the efficiency, scalability, and adaptability of supervised learning applications across various domains. This integration addresses the complexity and manual effort typically associated with machine learning workflows, paving the way for more automated, robust, and scalable solutions. Our approach demonstrates significant improvements in task automation, reduced human intervention, and enhanced model performance. The MADS framework, which will soon be available as an open-source implementation, represents a pivotal contribution to the field of machine learning, promising to facilitate broader adoption and collaborative advancement.