Search Results (405)

The article examines the relationship between Industry 4.0 (I4.0) and the circular economy (CE), which are modern and widely used in various scientific disciplines as well as in interdisciplinary and transdisciplinary fields. It was taken into account that a modern resource for knowledge and innovation in a scientific area consists precisely in exploring conceptual interoperability, both for the purpose of clarifying aspects of the theory specific to that discipline and also in terms of offering new, less explored perspectives that are valuable for the practice of everyday economic activities. The main methodological component used is bibliometric analysis, starting from the construction of a database of existing approaches to the two key concepts considered, at the level of the Web of Science Core Collection (WoS). This research shows that there is an increase in the theoretical and practical scope of use of the two concepts, a characteristic observed through the consistency and diversification manifested within the considered frame of reference. The main conclusion of the study is that AI-driven servitization, IoT, and big data are facilitators of the implementation of the CE. The contribution lies in consolidating an updated bibliometric overview of this interdisciplinary field and in highlighting new directions. Full article

Small and medium-sized enterprises (SMEs) constitute the backbone of the EU economy, yet their uneven digital transformation raises challenges for competitiveness and territorial cohesion. This article examines the organizational and spatial aspects of SME digitalization across the European Union using Flash Eurobarometer 486 data and latent class analysis (LCA) combined with Bayesian multilevel multinomial regression. The results reveal four SME digitalization profiles—Digitally Conservative Backbone; Partially Digital and Upgrading; Digitally Advanced and Diversified; and Focused Digital Integrators—reflecting diverse adoption patterns of key technologies such as AI, big data and cloud computing. Digitalization is shaped by organizational factors (firm size, value chain integration, digital barriers) and territorial factors (urbanity, border proximity, national digital infrastructure as measured by the Digital Economy and Society Index, DESI). Contrary to linear modernization assumptions, digital adoption follows geographically embedded trajectories, with sectoral uptake occurring even in low-DESI or non-urban regions. These results challenge core–periphery models and highlight the significance of place-based innovation networks. The study contributes to modernization theory and regional innovation systems by showing that digital inequalities exist not only between countries but also within regions and among adoption profiles, emphasizing the need for nuanced, multi-level digital policy approaches across Europe. Full article

In the contemporary international context, the preventive conservation of cultural relics has become a widespread consensus. “Risk management” has emerged as a pivotal research focus at the present stage. However, the preventive protection of cultural relics is confronted with deficiencies in risk assessment and prediction. There is an urgent requirement for research to present a comprehensive and in-depth overview of the frontier technologies applicable to the preventive protection of cultural relics, with a particular emphasis on risk prevention and control. Additionally, it is essential to delineate the prospects for future investigations and developments in this domain. Consequently, this study employs bibliometric methods, applying CiteSpace (6.3.R1) and Biblioshiny (4.3.0) to perform comprehensive visual and analytical examinations of 392 publications sourced from the Web of Science (WoS) database covering the period 2010 to 2024. The results obtained from the research are summarized as follows: First, it is evident that scholars originating from China, Italy, and Spain have exhibited preponderant publication frequencies, contributing the largest quantity of articles. Second, augmented reality, digital technology, and risk-based analysis have been identified as the cardinal research frontiers. These areas have attracted significant scholarly attention and are at the forefront of innovation and exploration within the discipline. Third, the “Journal of Culture Heritage” and “Heritage Science” have been empirically determined to be the most frequently cited periodical within this particular field of study. Moreover, over the past decade, under the impetus and influence of the concept of Intangible Cultural Heritage, virtual reality, digital protection, and 3D models have progressively evolved into the central and crucial topics that have pervaded and shaped the research agenda. Finally, with respect to future research trajectories, there will be a pronounced focus on interdisciplinary design. This will be accompanied by an escalation in the requisites and standards for preventive conservation. Specifically, the spotlight will be cast upon aspects such as the air quality within the preservation environment of cultural relics held in collections, the implementation and efficacy of environmental real-time monitoring systems, the utilization and interpretation of big data analysis and early warning mechanisms, as well as the comprehensive and in-depth risk analysis of cultural relics. These multifaceted investigations will be essential for advancing understanding and safeguarding of cultural heritage. These findings deepen our grasp of how risk management in cultural heritage conservation has progressed and transformed between 2010 and 2024. Furthermore, the study provides novel insights and directions for subsequent investigations into risk assessment methodologies for heritage collections. Full article

Electromagnetic field (EMF) exposure mapping is increasingly important for ensuring compliance with safety regulations, supporting the deployment of next-generation wireless networks, and addressing public health concerns. While numerous surveys have addressed specific aspects of radio propagation or radio environment maps, a comprehensive and unified overview of EMF mapping methodologies has been lacking. This review bridges that gap by systematically analyzing computational, geospatial, and machine learning approaches used for EMF exposure mapping across both wireless communication engineering and public health domains. A novel taxonomy is introduced to clarify overlapping terminology—encompassing radio maps, radio environment maps, and EMF exposure maps—and to classify construction methods, including analytical models, model-based interpolation, and data-driven learning techniques. In addition, the review highlights domain-specific challenges such as indoor versus outdoor mapping, data sparsity, and model generalization, while identifying emerging opportunities in hybrid modeling, big data integration, and explainable AI. By combining perspectives from communication engineering and public health, this work provides a broader and more interdisciplinary synthesis than previous surveys, offering a structured reference and roadmap for advancing robust, scalable, and socially relevant EMF mapping frameworks. Full article

As a new type of production factor, data possesses multidimensional application value, and its pivotal role is becoming increasingly prominent in the aviation sector. Data sharing can significantly enhance the utilization efficiency of data resources and serves as one of the key tasks in building smart civil aviation. However, currently, data silos are pervasive, with vast amounts of data only being utilized and analyzed within limited scopes, leaving their full potential untapped. The challenges in data sharing primarily stem from three aspects: (1) Data owners harbor concerns regarding data security and privacy. (2) The highly dynamic and real-time nature of aviation operations imposes stringent requirements on the timeliness, stability, and reliability of data sharing, thereby constraining its scope and extent. (3) The lack of reasonable incentive mechanisms results in insufficient motivation for data owners to share. Consequently, addressing the issue of aviation big data sharing holds significant importance. Since the release of the Bitcoin whitepaper in 2008, blockchain technology has achieved continuous breakthroughs in the fields of data security and collaborative computing. Its unique characteristics—decentralization, tamper-proofing, traceability, and scalability—lay the foundation for its integration with aviation. Blockchain can deeply integrate with air traffic management (ATM) operations, effectively resolving trust, efficiency, and collaboration challenges in distributed scenarios for ATM data. To address the heterogeneous data usage requirements of different ATM stakeholders, this paper constructs a blockchain-based multi-level data security sharing architecture, enabling fine-grained management and secure collaboration. Furthermore, to meet the stringent timeliness demands of aviation operations and the storage pressure posed by massive data, this paper optimizes blockchain storage deployment and consensus mechanisms, thereby enhancing system scalability and processing efficiency. Additionally, a dual-mode data-sharing solution combining raw data sharing and model sharing is proposed, offering a novel approach to aviation big data sharing. Security and formal analyses demonstrate that the proposed solution is both secure and effective. Full article

The retrofitting of legacy systems enables upgrades that extend the lifespan of outdated equipment, improve efficiency, and reduce environmental impacts. This manuscript builds on existing approaches to retrofitting legacy systems using Industry 4.0 technologies. Therefore, it explores how the proposed modernization envisions the transition from Industry 4.0 to Industry 5.0, which emphasizes human-centric approaches, sustainability, and resilience. Tools such as RAMI 4.0 (a reference architecture model for Industry 4.0), Lean Six Sigma (a methodology for process improvement), and Big Data analytics are highlighted throughout the text as essential for optimizing processes and ensuring alignment with global challenges, including resource efficiency and environmental sustainability. This work addresses both conceptual and technical aspects of system modernization. It provides a comprehensive framework for retrofitting systems and integrating advanced technologies such as digital twins. These efforts ensure that industries are prepared for the evolving demands of Industry 4.0 and beyond. Full article

The exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual aspects of Video Big Data Analytics (VBDA), an integrated, up-to-date perspective remains limited. This paper presents a comprehensive survey of system architectures and enabling technologies in VBDA. It categorizes system architectures into four primary types as follows: centralized, cloud-based infrastructures, edge computing, and hybrid cloud–edge. It also analyzes key enabling technologies, including real-time streaming, scalable distributed processing, intelligent AI models, and advanced storage for managing large-scale multimodal video data. In addition, the study provides a functional taxonomy of core video processing tasks, including object detection, anomaly recognition, and semantic retrieval, and maps these tasks to real-world applications. Based on the survey findings, the paper proposes ViMindXAI, a hybrid AI-driven platform that combines edge and cloud orchestration, adaptive storage, and privacy-aware learning to support scalable and trustworthy video analytics. Our analysis in this survey highlights emerging trends such as the shift toward hybrid cloud–edge architectures, the growing importance of explainable AI and federated learning, and the urgent need for secure and efficient video data management. These findings highlight key directions for designing next-generation VBDA platforms that enhance real-time, data-driven decision-making in domains such as public safety, transportation, and healthcare. These platforms facilitate timely insights, rapid response, and regulatory alignment through scalable and explainable analytics. This work provides a robust conceptual foundation for future research on adaptive and efficient decision-support systems in video-intensive environments. Full article

Mining and analyzing information from multiple sources—such as geophysics and geochemistry—is a key aspect of big data-driven mineral prediction. Clustering, which groups large datasets based on distance metrics, is an essential method in multidimensional data analysis. The Two-Step Clustering (TSC) approach offers advantages by handling both categorical and continuous variables and automatically determining the optimal number of clusters. In this study, we applied the TSC method to mineral prediction in the northeastern margin of the Jiaolai Basin by: (i) converting residual gravity and magnetic anomalies into categorical variables using Ward clustering; and (ii) transforming 13 stream sediment elements into independent continuous variables through factor analysis. The results showed that clustering is sensitive to categorical variables and performs better with fewer categories. When variables share similar distribution characteristics, consistency between geophysical discretization and geochemical boundaries also influences clustering results. In this study, the (3 × 4) and (4 × 4) combinations yielded optimal clustering results. Cluster 3 was identified as a favorable zone for gold deposits due to its moderate gravity, low magnetism, and the enrichment in F1 (Ni–Cu–Zn), F2 (W–Mo–Bi), and F3 (As–Sb), indicating a multi-stage, shallow, hydrothermal mineralization process. This study demonstrates the effectiveness of combining Ward clustering for variable transformation with TSC for the integrated analysis of categorical and numerical data, confirming its value in multi-source data research and its potential for further application. Full article

At present, the construction industry has not yet fully optimized the integration of the potential of big data. Past studies signaled the potential benefits of integrating building information management (BIM) and big data in the field of sustainable building management (SBM). However, these studies have a monotonous perspective in identifying the development of BIM and big data applications in SBM. Therefore, this paper aims to explore BIM and big data from various perspectives in the field of SBM to identify the aspects where additional efforts are required and provide insights into future directions, and it adopts a mixed method of quantitative and qualitative analysis, including bibliometric analysis and knowledge mapping, providing a macro-overview of the research status and development trends of BIM and big data integration for SBM from multiple bibliometric perspectives. The results indicate the following: (1) the current studies on BIM and big data integration (BBi)-aided SBM mainly focused on data integration and interoperability for collaboration, development of information technologies and emerging technologies, data analysis and presentation, and green building and sustainability assessment; (2) the longitudinal analysis of three time-slice phases (2010–2014, 2015–2018, and 2019–2024) over the past 15 years indicates that the studies on BBi-aided SBM have been expanded from the application of BIM in construction projects to the integration and interoperability of BIM with information technology, the integration of virtual models with physical buildings, and sustainable management throughout the building life cycle stages; and (3) key research gaps and emerging directions include data integration and model interoperability across the building life cycle, model transferability in the application of technology, and a comprehensive sustainability assessment framework based on the whole building life cycle stages. Full article

The field of tourism economics has consistently attracted big attention from scholars across various countries. Tourism is inherently linked to economic aspects. Concurrently, Indonesia has relocated its Ibu Kota Negara/National Capital City, now named “IKN”, from Jakarta to East Kalimantan. In addition to extensive public infrastructure development, the Indonesian government is also working to revitalize the tourism sector in IKN. To assess the economic feasibility of this sector, an in-depth study is necessary. This research aims to examine labor absorption, tourist visits, and economic growth as indicators of successful tourism economic performance. It also analyzes the variables that influence these indicators, including (1) wages, (2) occupancy rates, (3) room rates, (4) food and beverage facilities, (5) inflation, (6) hotel and lodging taxes, (7) restaurant and eating-house taxes, and (8) investment. The regression testing method employs Ordinary Least Squares (OLS). According to the data analyzed from 2013 to 2024, the authors identified three main points: First, tourist visits and inflation have positive and significant impacts on labor absorption. Second, labor absorption, wages, occupancy rates, economic growth, and investment positively and significantly influence tourist visits. Third, tourist visits, room rates, food and beverage facilities, and inflation have positive and significant effects on economic growth. The implications of this research can be enlightening for regulators and future initiatives. This is particularly important for guiding further empirical investigations and policy planning aimed at accelerating economic development in the tourism sector. Full article

The major challenge facing big cities in developing nations is allocating residential land to the urban poor, given the constraints on land availability. This article investigates how and why the management of Waqf land, a particular type of land tenure in urban informal settlements, transforms. The analysis draws on principles of responsible land management and focuses on the specific case of Waqf land owned by the Kauman Grand Mosque in Semarang City, Indonesia. With a questionnaire distributed among tenants of the Waqf land, it was possible to retrieve data on experiences with the management of the Waqf land. The questionnaire and subsequent analysis used a scoring sheet based on the 8R framework of responsible land management. The findings indicate that the management of Waqf land in the study area changed and improved over time, evolving from an informal to a more formal structure. The responses additionally demonstrate a gradual transformation whereby, gradually, nearly all aspects of the 8R framework of responsible land management are considered positively. Nevertheless, the aspects of reflexivity and retraceability still score low, as access to documentation is still limited, and evaluations of how effective the management of land is are infrequent. The study concludes that despite being an unconventional form of land management, the Waqf land tenure regime appears to secure informal types of urban tenure, especially in areas with limited land availability. Moreover, the Waqf land institution supports strong and cooperative relationships within the community. Full article

Background: Teaching methodology has recently undergone significant evolution from traditional apprenticeship models as we adapt to ever-increasing rates of technological advancement. Big data, artificial intelligence, and machine learning are on the precipice of revolutionising all aspects of surgical practice, with far-reaching implications. Robotic platforms will increase in autonomy as machine learning rapidly becomes more sophisticated, and therefore training requirements will no longer slow innovation. Materials and Methods: A search of published studies discussing surgeon training and computer-enhanced simulation robotics and emerging technologies using MEDLINE, PubMed, EMBASE, Scopus, CRANE, CINAHL, and Web of Science was performed in January 2024. Online resources associated with proprietary technologies related to the subject matter were also utilised. Results: Following a review of 3209 articles, 91 of which were published, relevant articles on aspects of robotics-based computer-enhanced simulation, technologies, and education were included. Publications ranged from RCTs, cohort studies, meta-analysis, and systematic reviews. The content of eight medical technology-based websites was analysed and included in this review to ensure the most up-to-date information was analysed. Discussion: Surgeons should aim to be at the forefront of this revolution for the ultimate benefit of patients. Surgical exposure will no longer be due to incidental experiences. Rather, surgeons and trainees will have access to a complete database of simulated minimally invasive procedures, and procedural simulation certification will likely become a requisite from graduation to live operating to maintain rigorous patient safety standards. This review provides a comprehensive outline of the current and future status of surgical training in the robotic and digital era. Full article

Advances in high-throughput technologies, digital phenotyping, and increased accessibility of publicly available datasets offer opportunities for big data to be applied in infectious disease surveillance, diagnosis, treatment, and outcome prediction. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to analyze complex clinical and molecular data. However, it remains unclear which AI or ML models are most suitable for infectious disease management, as most existing studies use non-scoping literature reviews to recommend AI and ML models for data analysis. This scoping literature review thus examines the ML models and applications that are most relevant for infectious disease management, with a proposed actionable workflow for implementing ML models in clinical practice. We conducted a literature search on PubMed, Google Scholar, and ScienceDirect, including papers published in English between January 2020 and April 2024. Search keywords included AI, ML, public health, surveillance, diagnosis, prognosis, and infectious disease, to identify published studies using AI and ML in infectious disease management. Studies without public datasets or lacking descriptions of the ML models were excluded. This review included a total of 77 studies applied in surveillance, prognosis, and diagnosis. Different types of input data from infectious disease surveillance, clinical diagnosis, and prognosis required different ML and AI models to achieve the maximum performance in infectious disease management. Our findings highlight the potential of Explainable AI and ensemble learning models to be more broadly applicable in different aspects of infectious disease management, which can be integrated in clinical workflows to improve infectious disease surveillance, diagnosis, and prognosis. Explainable AI and ensemble learning models can be suitably used to achieve high accuracy in prediction. However, as most of the studies have not been validated in different cohorts, it remains unclear whether these ML models can be broadly applicable to different populations. Nonetheless, the findings encourage deploying ML and AI to complement clinicians and augment clinical decision-making. Full article

Analyses of thermokarst hazard risk are becoming increasingly crucial in the context of global warming. A significant aspect of thermokarst research is the mapping of landscapes based on their vulnerability to thermokarst processes. The exponential growth of remote sensing data and the advent of novel techniques have paved the way for the creation of sophisticated techniques for the study of natural disasters, including thermokarst phenomena. This study applies machine learning techniques to assess the vulnerability of tundra landscapes to thermokarst by integrating supervised classification using random forest with morphometric analysis based on the Topography Position Index. We recognized that the thermokarst landscape with the greatest potential for future permafrost thawing occupies 20% of the study region. The thermokarst-affected terrains and water bodies located in the undegraded uplands account for 13% of the total area, while those in depressions and valleys account for 44%. A small part (6%) of the study region represents areas with stable terrains within depressions and valleys that underwent topographic alterations and are likely to maintain stability in the future. This approach enables big geodata-driven predictive modeling of permafrost hazards, improving thermokarst risk assessment. It highlights machine learning and Google Earth Engine’s potential for forecasting landscape transformations in vulnerable Arctic regions. Full article

This study examines whether the gaps in four financial well-being (FWB) indicators—emergency fund availability, spending less than income, perceived financial comfort, and no credit card debt—between groups with varying levels of financial literacy changed during the economic disruptions of 2020–2022 compared to the more stable period of 2017–2019. Using data from the 2017–2022 waves of the Survey of Household Economics and Decisionmaking conducted by the Federal Reserve Board, this study applies difference-in-differences and event study methods to explore these trends. Descriptive findings, consistent with prior research, show that respondents with higher financial literacy reported greater FWB across all years. Regression estimates based on respondents who provided definitive answers (correct or incorrect) to the Big Three financial literacy questions suggest that the pre-existing gaps in emergency fund availability and perceived financial comfort between respondents with higher and lower financial literacy widened in 2020–2022, whereas the gap in spending less than income remained unchanged. There is some evidence of a widening gap in the likelihood of having no credit card debt, but the estimates are less conclusive. In general, these results indicate that higher financial literacy might have served as a protective factor for some aspects of FWB amid the challenging economic conditions of 2020–2022. However, results based on respondents who provided either correct or “don’t know” answers to the same questions differ in direction from the results of the earlier analysis. The findings of this study have implications for measuring financial literacy and investigating its role in shaping FWB. Full article