Search Results (153)

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

In crisis-stricken economies, leadership effectiveness increasingly hinges not on technical expertise alone but on emotional competence. While emotional intelligence (EI) has been widely acknowledged as a catalyst for effective leadership and employee outcomes, its role in volatile and resource-scarce contexts remains underexplored. This study addresses this critical gap by investigating the impact of five core EI dimensions, namely self-awareness, self-regulation, motivation, empathy, and social skills, on employee performance amid Lebanon’s ongoing multidimensional crisis. Drawing on Goleman’s EI framework and the Job Demands–Resources theory, the research employs a quantitative, cross-sectional design with data collected from 398 employees across sectors in Lebanon. Structural Equation Modeling revealed that all EI dimensions significantly and positively influenced employee performance, with self-regulation (β = 0.485) and empathy (β = 0.361) emerging as the most potent predictors. These findings underscore the value of emotionally intelligent leadership in fostering productivity, resilience, and team cohesion during organizational instability. This study contributes to the literature by contextualizing EI in an under-researched, crisis-affected setting, offering nuanced insights into which emotional competencies are most impactful during prolonged uncertainty. Practically, it positions EI as a strategic leadership asset for crisis management and sustainable human resource development in fragile economies. The results inform leadership training, policy design, and organizational strategies that aim to enhance employee performance through emotionally intelligent practices. Full article

Prescriptive Maintenance (PsM) transforms industrial asset management by enabling autonomous decisions through simultaneous failure anticipation and optimal maintenance recommendations. Yet, despite increasing research interest, the conceptual clarity, technological maturity, and practical deployment of PsM remains fragmented. Here, we conduct a comprehensive and application-oriented Systematic Literature Review of studies published between 2013–2024. We identify key enablers—artificial intelligence and machine learning, horizontal and vertical integration, and deep reinforcement learning—that map the functional space of PsM across industrial sectors. The results from our multivariate meta-synthesis uncover three main thematic research clusters, ranging from decision-automation of technical (multi)component-level systems to strategic and organisational-support strategies. Notably, while predictive models are widely adopted, the translation of these capabilities to PsM remains limited. Primary reasons include semantic interoperability, real-time optimisation, and deployment scalability. As a response, a structured research agenda is proposed to emphasise hybrid architectures, context-aware prescription mechanisms, and alignment with Industry 5.0 principles of human-centricity, resilience, and sustainability. The review establishes a critical foundation for future advances in intelligent, explainable, and action-oriented maintenance systems. Full article

As Chinese firms play pivotal roles in global supply chains, multinational corporations face increasing pressure to ensure ESG accountability across their sourcing networks. Current ESG rating systems lack transparency in incorporating China’s unique industrial, economic, and cultural factors, creating reliability concerns for stakeholders managing supply chain sustainability risks. This study develops an explainable artificial intelligence framework using SHAP and permutation feature importance (PFI) methods to predict the ESG performance of Chinese firms. We analyze comprehensive ESG data of 1608 Chinese listed companies over 13 years (2009–2021), integrating financial and non-financial determinants traditionally examined in isolation. Empirical findings demonstrate that random forest algorithms significantly outperform multivariate linear regression in capturing nonlinear ESG relationships. Key non-financial determinants include patent portfolios, CSR training initiatives, pollutant emissions, and charitable donations, while financial factors such as current assets and gearing ratios prove influential. Sectoral analysis reveals that manufacturing firms are evaluated through pollutant emissions and technical capabilities, whereas non-manufacturing firms are assessed on business taxes and intangible assets. These insights provide essential tools for multinational corporations to anticipate supply chain sustainability conditions. Full article

Asset Management (AM) is quickly transforming due to the digital revolution induced by Industry 4.0, in which Cyber–Physical Systems (CPS) and Digital Twins (DT) are taking key positions in monitoring and optimizing physical assets. With more intelligent functionalities arising in industrial contexts, Machine Learning (ML) has transitioned from playing a supporting role to becoming a core constituent of asset operation. However, while the Asset Administration Shell (AAS) has become an industry standard format for digital asset representation, incorporating ML models into this format is a significant challenge. In this research, a control valve, a common asset in industrial equipment, is used to explore the modeling of a machine learning model as an AAS submodel, including its related elements, such as parameters, hyperparameters, and metadata, in accordance with the latest guidelines issued by the Industrial Digital Twin Association (IDTA) in early 2025. The main contribution of this work is to clarify basic machine learning principles while demonstrating their alignment with the AAS framework, hence facilitating the further development of smart and interoperable DTs in modern industrial environments. Full article

This study aims to develop an information strategy plan (ISP) for the integrated management of airport facility information in South Korea by applying smart technologies such as building information modeling (BIM), digital twins, and openBIM. As the demand for intelligent lifecycle management and efficient facility operations continues to grow, airport infrastructure requires standardized and interoperable systems to manage complex assets and stakeholder collaboration. This research addresses three core challenges facing Korean airports: the lack of sustainable maintenance environments, the absence of data standards and systems, and the insufficiency of user-oriented platforms. Through system analysis, benchmarking, and SWOT assessment, the study proposes a stepwise implementation roadmap consisting of development, integration, and advancement phases and designs a “To-Be” model that incorporates 37 component technologies and a standardized information framework. The proposed ISP supports data-driven airport operations, enhances collaboration, and accelerates digital transformation, ultimately contributing to the development of smart and globally competitive airports. Full article

Information availability through the web has been both a challenge and an asset for healthcare support, as evidence-based information coexists with unsupported claims. With the emergence of artificial intelligence (AI), this situation may be enhanced or improved. The aim of the present study was to compare the quality assessment of online dietary weight loss information conducted by an AI assistant (ChatGPT 4.5) to that of health professionals. Thus, 177 webpages publishing dietary advice on weight loss were retrieved from the web and assessed by ChatGPT-4.5 and by dietitians through (1) a validated instrument (DISCERN) and (2) a self-made scale based on official guidelines for weight management. Also, webpages were assessed by a ChatGPT custom scoring system. Analysis revealed no significant differences in quantitative quality scores between human raters, ChatGPT-4.5, and the AI-derived system (p = 0.528). On the contrary, statistically significant differences were found between the three content accuracy scores (p < 0.001), with scores assigned by ChatGPT-4.5 being higher than those assigned by humans (all p < 0.001). Our findings suggest that ChatGPT-4.5 could complement human experts in evaluating online weight loss information, when using a validated instrument like DISCERN. However, more relevant research is needed before forming any suggestions. Full article

Accurate extraction of table borders in scanned road engineering drawings is crucial for the digital transformation of engineering archives, which is an essential step in the development of intelligent infrastructure systems. However, challenges such as degraded borders, image blur, and character adjoining often hinder the precise delineation of table structures, making automated parsing difficult. Existing solutions, including traditional OCR tools and deep learning methods, struggle to consistently delineate table borders in the presence of these visual distortions and fail to perform well without extensive annotated datasets, which limits their effectiveness in real-world applications. We propose TableBorderNet, a semantic segmentation framework designed for precise border extraction under complex visual conditions. The framework captures structural context by guiding convolutional feature extraction along explicit row and column directions, enabling more accurate delineation of table borders. To ensure topological consistency in complex or degraded inputs, a topology-aware loss function is introduced, which explicitly penalizes structural discontinuities during training. Additionally, a generative self-supervised strategy simulates common degradation patterns, allowing the model to achieve strong performance with minimal reliance on manually annotated data. Experiments demonstrate that the method achieves an Intersection-over-Union of 94.2% and a topological error of 1.07%, outperforming existing approaches. These results underscore its practicality and scalability for accelerating the digitization of engineering drawings in support of data-driven road asset management. Full article

Water, an essential asset for life and growth, is under growing pressure due to climate change, overpopulation, pollution, and industrialization. At the same time, water distribution within cities relies on piping networks that are over 30 years old and thereby prone to leaks, cracking, and losses. Taking this into account, non-revenue water (i.e., water that is distributed to homes and facilities but not returning revenues) is estimated at almost 50%. To this end, intelligent water management via computational advanced tools is required in order to optimize water usage, to mitigate losses, and, more importantly, to ensure sustainability. To address this issue, a case study was developed in this paper, following a step-by-step methodology for the city of Heraklion, Greece, in order to introduce an intelligent water management system that integrates advanced technologies into the aging water distribution infrastructure. The first step involved the digitalization of the network’s spatial data using geographic information systems (GIS), aiming at enhancing the accuracy and accessibility of water asset mapping. This methodology allowed for the creation of a framework that formed a “digital twin”, facilitating real-time analysis and effective water management. Digital twins were developed upon real-time data, validated models, or a combination of the above in order to accurately capture, simulate, and predict the operation of the real system/process, such as water distribution networks. The next step involved the incorporation of a hydraulic simulation and modeling tool that was able to analyze and calculate accurate water flow parameters (e.g., velocity, flowrate), pressure distributions, and potential inefficiencies within the network (e.g., loss of mass balance in/out of the district metered areas). This combination provided a comprehensive overview of the water system’s functionality, fostering decision-making and operational adjustments. Lastly, automatic meter reading (AMR) devices could then provide real-time data on water consumption and pressure throughout the network. These smart water meters enabled continuous monitoring and recording of anomaly detections and allowed for enhanced control over water distribution. All of the above were implemented and depicted in a web-based environment that allows users to detect water meters, check water consumption within specific time-periods, and perform real-time simulations of the implemented water network. Full article

This review examines the application of the geophysical methods for Transportation Infrastructure Slope Monitoring (TISM). In contrast to existing works, which address geophysical methods for natural landslide monitoring, this study focuses on their application to infrastructure assets. It addresses the key aspects regarding the geophysical methods most employed, the subsurface properties revealed, and the design of monitoring systems, including sensor deployment. It evaluates the benefits and challenges associated with each geophysical approach, explores the potential for integrating geophysical techniques with other methods, and identifies the emerging technologies. Geophysical techniques such as Electrical Resistivity Tomography (ERT), Multichannel Analysis of Surface Waves (MASW), and Fiber Optic Cable (FOC) have proven effective in monitoring slope stability and detecting subsurface features, including soil moisture dynamics, slip surfaces, and material heterogeneity. Both temporary and permanent monitoring setups have been used, with increasing interest in real-time monitoring solutions. The integration of advanced technologies like Distributed Acoustic Sensing (DAS), UAV-mounted sensors, and artificial intelligence (AI) promises to enhance the resolution, accessibility, and predictive capabilities of slope monitoring systems. The review concludes with recommendations for future research, emphasizing the need for integrated monitoring frameworks that combine geophysical data with real-time analysis to improve the safety and efficiency of transportation infrastructure management. Full article

Crises and disruptive events over new technologies like artificial intelligence, have transformed supply chains management. This literature review proposes a conceptual framework to identify the relevance of digitalization and artificial intelligence for decision-making through S&OP. Selected articles use mathematical models to resolve conflicts between objectives related to value parameters (revenue, cost, cash, asset and sustainability) and optimize planning. Half of the articles integrate computational intelligence, but do not directly address the use of AI in S&OP. One promising stream is the S&OP process as study object where artificial and generative intelligence will play a key role in collective and collaborative intelligence. Full article

This research investigates the spillover effects between assets of the Fourth Industrial Revolution (4IR), focusing on the role of climate policy uncertainty in shaping these interactions. Using a time-varying parameter vector autoregressive (TVP-VAR) approach and a joint connectedness method, the analysis incorporates five global indices representing key 4IR domains: the internet, cybersecurity, artificial intelligence and robotics, fintech, and blockchain. The findings reveal significant interdependencies among 4IR assets and evaluate the effect of risk factors, including climate policy uncertainty, as a critical driver of the determinants of returns. The results indicate the growing impact of climate-related risks on the structure of connectedness between 4IR assets, highlighting their implications for portfolio diversification and risk management. These insights are vital for investors and policymakers navigating the intersection of technological innovation and environmental challenges in a rapidly changing global economy. Full article

In recent years, Facility Management has undergone significant technological and methodological advancements, primarily driven by Building Information Modelling (BIM), Computer-Aided Facility Management (CAFM), and Computerized Maintenance Management Systems (CMMS). These innovations have improved process efficiency and risk management. However, challenges remain in asset management, maintenance, traceability, and transparency. This study investigates the potential of blockchain technology and non-fungible tokens (NFTs) to address these challenges. By referencing international (ISO, BOMA) and European (EN) standards, the research develops an asset management process model incorporating blockchain and NFTs. The methodology includes evaluating the technical and practical aspects of this model and strategies for metadata utilization. The model ensures an immutable record of transactions and maintenance activities, reducing errors and fraud. Smart contracts automate sub-phases like progress validation and milestone-based payments, increasing operational efficiency. The study’s practical implications are significant, offering advanced solutions for transparent, efficient, and secure Facility Management. It lays the groundwork for future research, emphasizing practical implementations and real-world case studies. Additionally, integrating blockchain with emerging technologies like artificial intelligence and machine learning could further enhance Facility Management processes. Full article

This paper studies the performance of ETFs that invest in companies involved in artificial intelligence (AI) technologies, such as firms focused on AI research, development, and applications. Using daily data from 15 American ETFs focused on AI-related companies over the period from 1 February 2019 to 29 December 2023, this paper investigates their investment style characteristics through a returns-based style analysis (RBSA). This study offers detailed insights into the degree of active versus passive management and highlights strategic patterns that may guide investment decisions in AI-themed financial products. We highlight that asset selection drives fund performances more than active management strategies, offering practical insights for investors and policymakers. Full article

The growth in the adoption of circular economy principles in the construction industry has given rise to material passports as a critical implementation tool. Given the existing problems of high resource use and high waste generation in the construction industry, there is a pressing need to adopt novel strategies and tools to mitigate the adverse impacts of the built environment. However, research on the application of material passports in the context of construction waste management remains limited. The aim of this paper is to identify the contextual uses, stakeholders, requirements, and challenges in the application of material passports for managing waste generated from building construction and demolition processes through a systematic review approach. Comprehensive searches in Scopus and the Web of Science databases are used to identify relevant papers and reduce the risk of selection bias. Thirty-five (35) papers are identified and included in the review. The identified key contexts of use included buildings and cities as material banks, waste management and trading, and integrated digital technologies. Asset owners, waste management operators, construction and deconstruction teams, technology providers, and regulatory and sustainability teams are identified as key stakeholders. Data requirements related to material, components, building stock data, lifecycle, environmental impact data, and deconstruction and handling data are critical. Moreover, the key infrastructure requirements include modeling and analytical tools, collaborative information exchange systems, sensory tracking tools, and digital and physical storage hubs. However, challenges with data management, costs, process standardization, technology, stakeholder collaboration, market demand, and supply chain logistics still limit the implementation. Therefore, it is recommended that future research be directed towards certification and standardization protocols, automation, artificial intelligence tools, economic viability, market trading, and innovative end-use products. Full article