The construction industry plays a vital role in economic growth, job creation, and infrastructure development worldwide. However, it remains one of the most hazardous industries, characterized by persistently high accident rates that result in injuries and fatalities. Scholars have devoted significant efforts to undertake research in exploring the causation of construction accidents. Drawing on 78 research papers published since 1998 that focus on construction causation analysis, this review paper aims to thematically synthesize what has been achieved in the last two decades to effectively help researchers identify and recognize patterns and directions to align their work with the latest developments and to contribute to better research in construction health and safety. The synthesis is organized around five themes: research focus, research methods, accident types, key factors, and modelling approaches. It clarifies and consolidates existing knowledge by demonstrating that causal factors are the main research focus, with nearly half of the examined studies addressing it. It also offers quantitative evidence confirming that the most frequent type of accident is falls in both developed and developing countries. The review also finds that the analytical methods employed generally align with the stated research focuses. Additionally, the paper finds that the most critical category in causing construction accidents is likely to be poor safety management, rather than individual human errors, and the systemic approach has been adopted in causation analysis by more and more scholars.
Industrial policy has returned to the centre of economic governance, particularly in the high-tech sectors where positive network externalities in demand make market dominance self-reinforcing. This paper studies the welfare effects of an industrial policy targeting a sector with network externalities in a two-country model with strategic trade and R&D investment. We show how the welfare consequences of this policy are determined by the interaction between the strength of the externality, the type of R&D, and the degree of product differentiation between the home and the imported goods. When externalities are weak or the goods are close substitutes, the business-stealing effect produces a race to the bottom that dissipates more surplus than it creates. Under sufficiently strong externalities and weak substitutability or complementarity of the goods, industrial policy competition can make both countries simultaneously better off compared to the laissez-faire outcome because of the mutual business-enhancement effect. The case is stronger for the product innovation than for the process innovation, as the former directly affects the demand and triggers a stronger network effects than the latter which operates indirectly through the supply. Thus, the network externalities create an opportunity for a win-win industrial policies, but its realisation depends on the market structure and the nature of innovation.
Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas
et al.
The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems.
Industrial accidents in chemical process engineering continue to pose a significant issue despite the widespread use of Industry 4.0 technology and data-driven monitoring systems. Traditional safety frameworks often depend on either purely empirical machine learning models or deterministic first-principles simulations, creating a methodological split that constrains prediction reliability in uncommon, high-impact situations. This work bridges the structural gap by incorporating physics-informed artificial intelligence into a digital twin architecture for the avoidance of industrial accidents. A methodological framework driven by simulation was established, integrating first-principles process modeling, synthetic data generation with controlled fault injection, supervised and unsupervised learning, and reinforcement learning for safety-constrained optimization. Physics-based limitations were included into the learning aim to maintain thermodynamic and transport consistency. The model's performance was assessed using safety-oriented criteria, such as detection delay, false negative rate, resilience to sensor noise, and stability amid parametric uncertainty. Results demonstrate that physics-informed models significantly reduce detection latency and false negatives in accident precursor regimes compared to purely data-driven baselines. The integration of constraint-aware learning improves extrapolation stability under class imbalance conditions typical of industrial safety datasets. Furthermore, explainable AI mechanisms enhance interpretability and regulatory transparency. These findings indicate that AI-enhanced simulation models reconfigure accident prevention strategies by shifting from reactive threshold systems to proactive, mechanism-consistent risk anticipation frameworks applicable to safety-critical chemical processes.
Fernanda Carvalho, Fernanda Carvalho, Claudine Strehl
et al.
IntroductionSolar ultraviolet radiation (UVR) exposure is the primary external factor associated with the development of skin cancer. Accurate, valid, and reliable objective estimates of individual UVR exposure are required to quantify the risk of skin cancer in outdoor workers. Such data can be used to develop and implement policies and practices to reduce, or at least manage, UVR exposure in outdoor workers. Currently, there is a dearth of objective exposure data for many countries. Lisbon, as a low-mid-latitude region (38°46′ N), experiences a high UV Index (UVI) for a long period of the year, increasing the potential risk of skin cancer among outdoor workers in Portugal. This is the first study to objectively measure personal solar UVR exposure among outdoor workers in Portugal.MethodsThis study used a prospective observational design during seven consecutive months (April to October 2023) studying personal UV exposure of Asphalthers, Gardeners, Gravediggers, Pavers, and Sanitation Workers. Measurements of personal exposure were conducted using the GENESIS-UV measurement system, and ambient solar UVR data was estimated Jm−2 utilizing a UV-Biometer radiometer.ResultsPersonal hourly and daily doses measured by the GENESIS-UV measurement system were lower than the solar irradiation measured on a horizontal surface by the UV-Biometer radiometer. Gravediggers and Gardeners showed in average, the highest monthly daily averages (250 Jm−2 and 266 Jm−2, respectively). The maximum monthly daily average occurred for Gravediggers in the month of April (363 Jm−2). Pavers recorded the lowest solar UVR average daily doses (62 Jm−2). Sanitation Workers recorded the highest average daily dose (837 Jm−2, July 7th). The maximum single dosimeter value was accumulated by Gravediggers (1,097 Jm−2, May 9th).DiscussionThis study measured solar UVR exposure in important occupations not so often studied. The ICNRIP occupational limit value of 133 J/m−2 was surpassed in all occupations except the Pavers. These results showcase that the design of adequate prevention campaigns for preventing occupational skin cancer in outdoor workers should include personalized exposure risk messaging in the future.
Objectives: Mental health literacy (MHL) is a crucial determinant of mental wellbeing and encompasses knowledge, understanding, and attitudes related to mental health and mental disorders. Presently, no systematic reviews or meta-analyses, or even literature reviews exist regarding MHL interventions targeting healthcare professionals in Japan. Therefore, this protocol for scoping review aims to provide strategies to report interventions intended at improving MHL among Japanese healthcare professionals in the workplace. Methods: The review will adhere to the methodological framework proposed by Arksey and O’Malley and enhanced by the Joanna Briggs Institute, which consists of five stages: identifying the research question; identifying relevant studies; selecting eligible studies; charting the data; and collating, summarizing, and reporting the results. This review will be reported in accordance with the Preferred Reporting Items for Systematic Reviews Extension for Scoping Reviews. The participants, concept, and context framework will be employed to identify the key elements of the research question, with the participants being Japanese healthcare professionals (doctors or nurses), the concept being the content and effectiveness of MHL intervention programs, and the context being the workplace setting in Japan. The search strategy will involve searching electronic databases, hand-searching the reference lists of the included studies, and consulting experts in the field. Strengths and limitations: Given the lack of research and reviews on this topic in Japan, this review will provide valuable insights into the current state of MHL interventions for Japanese healthcare professionals and inform future research and practice in this area.
Industrial safety. Industrial accident prevention, Medicine (General)
Francesca Sellaro, Roberta Pernetti, Stefano Massimo Candura
et al.
This retrospective observational study examines the impact of the COVID-19 pandemic on occupational accident rates by analyzing over 500,000 Emergency Department (ED) visits from 2014 to 2022 in a Northern Italian city. Work-related injuries comprised 3.6% of total ED visits, with significant gender disparities, particularly in commuting accidents among women. During the pandemic’s initial wave, the overall ED visits decreased by 41%, while work-related injuries saw a 60% reduction. Post-pandemic, ED visits began returning to pre-pandemic levels, reflecting the healthcare system’s adaptability. Notably, high-intensity admissions requiring intensive care remained relatively stable throughout the pandemic, suggesting that individuals with urgent medical needs continued to seek care at the ED, demonstrating the healthcare system’s resilience in handling critical cases. This study highlights the complex relationship between the pandemic and workplace safety, emphasizing the need for further research to fully understand COVID-19’s impact on healthcare services.
Industrial safety. Industrial accident prevention, Medicine (General)
The recent development of Agentic AI systems, empowered by autonomous large language models (LLMs) agents with planning and tool-usage capabilities, enables new possibilities for the evolution of industrial automation and reduces the complexity introduced by Industry 4.0. This work proposes a conceptual framework that integrates Agentic AI with the intent-based paradigm, originally developed in network research, to simplify human-machine interaction (HMI) and better align automation systems with the human-centric, sustainable, and resilient principles of Industry 5.0. Based on the intent-based processing, the framework allows human operators to express high-level business or operational goals in natural language, which are decomposed into actionable components. These intents are broken into expectations, conditions, targets, context, and information that guide sub-agents equipped with specialized tools to execute domain-specific tasks. A proof of concept was implemented using the CMAPSS dataset and Google Agent Developer Kit (ADK), demonstrating the feasibility of intent decomposition, agent orchestration, and autonomous decision-making in predictive maintenance scenarios. The results confirm the potential of this approach to reduce technical barriers and enable scalable, intent-driven automation, despite data quality and explainability concerns.
Fatemeh Mohammadi Amin, Darwin G. Caldwell, Hans Wernher van de Venn
The robust interpretation of 3D environments is crucial for human-robot collaboration (HRC) applications, where safety and operational efficiency are paramount. Semantic segmentation plays a key role in this context by enabling a precise and detailed understanding of the environment. Considering the intense data hunger for real-world industrial annotated data essential for effective semantic segmentation, this paper introduces a pioneering approach in the Sim2Real domain adaptation for semantic segmentation of 3D point cloud data, specifically tailored for HRC. Our focus is on developing a network that robustly transitions from simulated environments to real-world applications, thereby enhancing its practical utility and impact on a safe HRC. In this work, we propose a dual-stream network architecture (FUSION) combining Dynamic Graph Convolutional Neural Networks (DGCNN) and Convolutional Neural Networks (CNN) augmented with residual layers as a Sim2Real domain adaptation algorithm for an industrial environment. The proposed model was evaluated on real-world HRC setups and simulation industrial point clouds, it showed increased state-of-the-art performance, achieving a segmentation accuracy of 97.76%, and superior robustness compared to existing methods.
Rapid industrial digitalization has created intricate cybersecurity demands that necessitate effective validation methods. While cyber ranges and simulation platforms are widely deployed, they frequently face limitations in scenario diversity and creation efficiency. In this paper, we present SpiderSim, a theoretical cybersecurity simulation platform enabling rapid and lightweight scenario generation for industrial digitalization security research. At its core, our platform introduces three key innovations: a structured framework for unified scenario modeling, a multi-agent collaboration mechanism for automated generation, and modular atomic security capabilities for flexible scenario composition. Extensive implementation trials across multiple industrial digitalization contexts, including marine ranch monitoring systems, validate our platform's capacity for broad scenario coverage with efficient generation processes. Built on solid theoretical foundations and released as open-source software, SpiderSim facilitates broader research and development in automated security testing for industrial digitalization.
Juha-Matti Runtti, Usman Virk, Pekka Kyosti
et al.
6G radio access architecture is envisioned to contain a network of short-range in-X subnetworks with enhanced capabilities to provide efficient and reliable wireless connectivity. Short-range communications in industrial environments are actively researched at the so-called mid-bands or FR3, e.g., in the EU SNS JU 6G-SHINE project. In this paper, we analyze omni-directional radio channel measurements at 10--12 GHz frequency band to estimate large-scale channel characteristics including power-delay profile, delay spread, K-factor, and pathloss for 254 radio links measured in the Industrial Production Lab at Aalborg University, Denmark. Moreover, we perform a comparison of estimated parameters with those of the 3GPP Indoor Factory channel model.
Large truck rollover crashes present significant financial, industrial, and social impacts. This paper presents an effort to investigate the contributing factors to large truck rollover crashes. Specific focus was placed on exploring the role of heterogeneity and the potential sources of heterogeneity regarding their impacts on injury-severity outcomes. The data used in this study contained large truck rollover crashes that occurred between 2007 and 2016 in the state of Florida. A random parameter ordered logit (RPOL) model was applied. Various driver, vehicle, roadway, and crash attributes were explored as potential predictors in the model. Their impacts were examined for the presence of heterogeneity. Interaction effects were then added to the random variables in order to detect potential sources of heterogeneity. Model results showed that the impacts of lighting conditions and driving speed had significant variation across observations, and this variation could be attributed to driver actions and driver conditions at the time of the crash, as well as driver vision obstruction. Findings from this study shed light on the direction, magnitude, and randomness of the factors that contribute to large truck rollover crashes. Findings associated with heterogeneity could help develop more effective and targeted countermeasures to improve freight safety. Driver education programs could be planned more efficiently, and advisory and warning signs could be designed in a more insightful manner by taking into account specific roadway attributes, such as sandy surfaces, downhill, curved alignment, unpaved shoulders, and lighting conditions.
Rustam Murasov , Anatolii Nikitin , Ivan Meshcheriakov
et al.
В умовах широкомасштабного вторгнення російської федерації в Україну на рівні відсічі збройної агресії ворога виникла нагальна потреба захисту не тільки сил оборони держави, а й цивільних об’єктів, які не мають жодного відношення до військового сектора. Для країни-агресора цілями для ураження стали як місця проживання населення, так і об’єкти критичної інфраструктури, що забезпечують життєдіяльність на всій території України. Внаслідок одностороннього виходу російської федерації із зернової угоди, під ураження потрапили зерносховища та навколишня інфраструктура. Таким чином росія тероризує не лише Україну, але й призводить до недостачі харчових продуктів у багатьох країнах світу. Метою статті є удосконалення методики оцінювання загроз і ризиків для об’єктів критичної інфраструктури за сценаріями розвитку надзвичайних ситуацій для запобігання втрат населення та особового складу сил оборони. Під час проведення дослідження застосовано аналітичний метод для аналізу останніх досліджень і публікацій, метод оптимізації (за мінімальним і максимальним критеріями) та метод мінімаксу для вибору кращого варіанту дій та синтезу для досягнення мети дослідження. Зазначений методичний підхід дає змогу провести аналіз і декомпозицію існуючих методик оцінювання загроз для об’єктів критичної інфраструктури в зоні ведення бойових дій та оцінювання ризиків для об’єктів критичної інфраструктури внаслідок бойових дій. Удосконалено методику оцінювання загроз і ризиків для об’єктів критичної інфраструктури за сценаріями розвитку надзвичайних ситуацій. Зазначена методика включає п’ять послідовних блоків, що дають змогу приймати раціональні управлінські рішення для впровадження відповідних заходів безпеки і оборони, здійснення оптимального розподілу сил і засобів та мінімізації наслідків надзвичайних ситуацій, застосовуючи інформаційні технології. Теоретичною значущістю методики є те, що дає змогу оцінювати можливі ризики та визначати стратегії захисту об’єктів критичної інфраструктури. Удосконалена методика дає змогу практично визначати варіанти сценаріїв надзвичайних подій, оцінювати збитки та вибирати найгірші сценарії з метою їх запобігання та локалізації. Це мінімізує наслідки виникнення надзвичайних ситуацій в умовах обмеженості сил і засобів оборони об’єктів критичної інфраструктури.
Photo-realistic avatar is a modern term referring to the digital asset that represents a human in computer graphic advanced systems such as video games and simulation tools. These avatars utilize the advances in graphic technologies in both software and hardware aspects. While photo-realistic avatars are increasingly used in industrial simulations, representing human factors such as human workers psychophysiological states, remains a challenge. This article contributes to resolving this issue by introducing the novel concept of MetaStates which are the digitization and representation of the psychophysiological states of a human worker in the digital world. The MetaStates influence the physical representation and performance of a digital human worker while performing a task. To demonstrate this concept, this study presents the development of a photo-realistic avatar enhanced with multi-level graphical representations of psychophysiological states relevant to Industry 5.0. This approach represents a major step forward in the use of digital humans for industrial simulations, allowing companies to better leverage the benefits of the Industrial Metaverse in their daily operations and simulations while keeping human workers at the center of the system.
The aim of this study is to investigate an automated industrial manipulation pipeline, where assembly tasks can be flexibly adapted to production without the need for a robotic expert, both for the vision system and the robot program. The objective of this study is first, to develop a synthetic-dataset-generation pipeline with a special focus on industrial parts, and second, to use Learning-from-Demonstration (LfD) methods to replace manual robot programming, so that a non-robotic expert/process engineer can introduce a new manipulation task by teaching it to the robot.
Jakub Jakubowski, Natalia Wojak-Strzelecka, Rita P. Ribeiro
et al.
Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0, and became crucial for enhancing operational efficiency, allowing to minimize downtime, extend lifespan of equipment, and prevent failures. A wide range of PdM tasks can be performed using Artificial Intelligence (AI) methods, which often use data generated from industrial sensors. The steel industry, which is an important branch of the global economy, is one of the potential beneficiaries of this trend, given its large environmental footprint, the globalized nature of the market, and the demanding working conditions. This survey synthesizes the current state of knowledge in the field of AI-based PdM within the steel industry and is addressed to researchers and practitioners. We identified 219 articles related to this topic and formulated five research questions, allowing us to gain a global perspective on current trends and the main research gaps. We examined equipment and facilities subjected to PdM, determined common PdM approaches, and identified trends in the AI methods used to develop these solutions. We explored the characteristics of the data used in the surveyed articles and assessed the practical implications of the research presented there. Most of the research focuses on the blast furnace or hot rolling, using data from industrial sensors. Current trends show increasing interest in the domain, especially in the use of deep learning. The main challenges include implementing the proposed methods in a production environment, incorporating them into maintenance plans, and enhancing the accessibility and reproducibility of the research.
Purpose: Industrial robots allow manufacturing companies to increase productivity and remain competitive. For robots to be used, they must be accepted by operators on the one hand and bought by decision-makers on the other. The roles involved in such organizational processes have very different perspectives. It is therefore essential for suppliers and robot customers to understand these motives so that robots can successfully be integrated on manufacturing shopfloors. Methodology: We present findings of a qualitative study with operators and decision-makers from two Swiss manufacturing SMEs. Using laddering interviews and means-end analysis, we compare operators' and deciders' relevant elements and how these elements are linked to each other on different abstraction levels. These findings represent drivers and barriers to the acquisition, integration and acceptance of robots in the industry. Findings: We present the differing foci of operators and deciders, and how they can be used by demanders as well as suppliers of robots to achieve robot acceptance and deployment. First, we present a list of relevant attributes, consequences and values that constitute robot acceptance and/or rejection. Second, we provide quantified relevancies for these elements, and how they differ between operators and deciders. And third, we demonstrate how the elements are linked with each other on different abstraction levels, and how these links differ between the two groups.
This manuscript presents a study on the spatial relationships between bike accidents, the built environment, land use, and transportation network characteristics in Budapest, Hungary using geographic weighted regression (GWR). The sample period includes bike crash data between 2017 and 2022. The findings provide insights into the spatial distribution of bike crashes and their severity, which can be useful for designing targeted interventions to improve bike safety in Budapest and be useful for policymakers and city planners in developing effective strategies to reduce the severity of bike crashes in urban areas. The study reveals that built environment features, such as traffic signals, road crossings, and bus stops, are positively correlated with the bike crash index, particularly in the inner areas of the city. However, traffic signals have a negative correlation with the bike crash index in the suburbs, where they may contribute to making roads safer for cyclists. The study also shows that commercial activity and PT stops have a higher impact on bike crashes in the northern and western districts. GWR analysis further suggests that one-way roads and higher speed limits are associated with more severe bike crashes, while green and recreational areas are generally safer for cyclists. Future research should be focused on the traffic volume and bike trips’ effects on the severity index.
Industrial safety. Industrial accident prevention, Medicine (General)