Search Results (4)

Digital twin technology has become increasingly popular and has revolutionized data integration and system modeling across various industries, such as manufacturing, energy, and healthcare. This study aims to explore the evolving research landscape of digital twins using Keyword Co-occurrence Network (KCN) analysis. We analyze metadata from 9639 peer-reviewed articles published between 2000 and 2023. The results unfold in two parts. The first part examines trends and keyword interconnection over time, and the second part maps sensing technology keywords to six application areas. This study reveals that research on digital twins is rapidly diversifying, with focused themes such as predictive and decision-making functions. Additionally, there is an emphasis on real-time data and point cloud technologies. The advent of federated learning and edge computing also highlights a shift toward distributed computation, prioritizing data privacy. This study confirms that digital twins have evolved into complex systems that can conduct predictive operations through advanced sensing technologies. The discussion also identifies challenges in sensor selection and empirical knowledge integration. Full article

With the potential of Industry 4.0 technologies to enable sustainable manufacturing, asset life cycle management (ALCM) has been gaining increasing attention in recent years. This study explores the evolution of Industry 4.0 technology applications to sustainable ALCM from 2002 to 2021. This study is based on keywords collected from 3896 ALCM-related scientific articles published in the Web of Science, IEEE Xplore and Engineering Village between 2002 and 2021. We conducted a review analysis of these keywords using a network science-based methodology, which unlike the tedious traditional literature review methods, gives the capability to analyze a huge number of scientific articles efficiently. We built keyword co-occurrence networks (KCNs) from the keywords and explored the network characteristics to uncover meaningful knowledge patterns, knowledge components, knowledge structure, and research trends in the body of literature at the intersection of ALCM and Industry 4.0. The network modeling and data analysis results identify the emerging Industry 4.0-related keywords in ALCM literature and indicate the recent explosion of connectivity among keywords. We found IoT, predictive maintenance and big data to be the top three most popular Industry 4.0-related keywords in ALCM literature. Furthermore, this study maps relevant ALCM keywords in contemporary literature to the nine pillars of Industry 4.0 to help the responsible manufacturing community identify research trends and emerging technologies for sustainability. Full article

In an effort to reconnect urban populations to the biosphere, which is an urgent task to ensure human sustainability, the concept of urban ecosystem services (UES) has recently garnered scholarly and political attention. With an aim to examine the emerging research trends and gaps in UES, we present an up-to-date, computer-based meta-analysis of UES from 2010 to 2019 by implementing a keyword co-occurrence network (KCN) approach. A total of 10,247 author keywords were selected and used to analyze undirected and weighted networks of these keywords. Specifically, power-law distribution fitting was performed to identify overall UES keyword trends, and clusters of keywords were examined to understand micro-level knowledge trends. The knowledge components and structures of UES literature exhibited scale-free network characteristics, which implies that the KCN of the UES throughout the 2010s was dominated by a small number of keywords such as “urbanization”, “land use and land cover”, “urban green space” and “green infrastructure”. Finally, our findings indicate that knowledge of stakeholder involvement and qualitative aspects of UES are not as refined as spatial UES approaches. The implications of these knowledge components and trends are discussed in the context of urban sustainability and policy planning. Full article

Resilience is being widely adopted as a comprehensive analytical framework for understanding sustainability dynamics, despite the conceptual challenges in developing proxies and indicators for researchers and policy makers. In our study, we observed how the concept of resilience undergoes continued extension within the rural resilience literature. We comprehensively reviewed rural resilience literature using keyword co-occurrence network (KCN) analysis and a systematic review of shortlisted papers. We conducted the KCN analysis for 1186 papers to characterize the state of the rural resilience literature, and systematically reviewed 36 shortlisted papers to further examine how rural resilience analysis and its assessment tools are helping understand the complexity and interdependence of rural social-ecological systems, over three three-year periods from 2010 to 2018. The results show that the knowledge structure built by the high frequency of co-occurrence keywords remains similar over the three-year periods, including climate change, resilience, vulnerability, adaptation, and management, whereas the components of knowledge have greatly expanded, indicating an increased understanding of rural system dynamics. Through the systematic review, we found that developing resilience assessment tools is often designed as a process to strengthen adaptive capacity at the household or community level in response to global processes of climate change and economic globalization. Furthermore, community resilience is found to be an interesting knowledge component that has characterized rural resilience literature in the 2010s. Based on our study, we summarized conceptual characteristics of rural resilience and discussed the challenges and implications for researchers and policy makers. Full article