Search Results (2,014)

The composition of air pollutants in industrial complexes differs from that of general urban areas, often containing more hazardous substances that pose significant health risks to both workers and residents nearby. In this study, PM_2.5 and its 29 chemical components (eight ions, two carbon species, and 19 trace elements) were measured and analyzed at five monitoring sites adjacent to the Yeosu and Gwangyang industrial complexes from August 2020 to December 2024. Chemical characterization and source identification were conducted. The average PM_2.5 concentration was 18.63 ± 9.71 μg/m³, with notably higher levels observed during winter and spring. A low correlation (R = 0.56) between elemental carbon (EC) and organic carbon (OC) suggests a dominance of secondary aerosols. The charge balance analysis of [NH₄⁺] with [SO₄²⁻], [NO₃⁻], and [Cl⁻] showed slopes below the 1:1 line, indicating that NH₄⁺ is capable of neutralizing these anions. Positive matrix factorization (PMF) identified eight contributing sources—biomass burning (10.4%), sea salt (11.8%), suspended particles (7.1%), industrial sources (4.6%), Asian dust (5.2%), steel industry (21.8%), secondary nitrate (16.4%), and secondary sulfate (22.7%). These findings provide valuable insights for the development of targeted mitigation strategies and the establishment of effective emission control policies in industrial regions. Full article

The shipbuilding industry relies heavily on welding, which accounts for approximately 70% of the overall production process. However, the recent decline in skilled workers, together with rising labor costs, has accelerated the automation of shipbuilding operations. In particular, the welding activities are concentrated in the double-bottom region of ships, where collaborative robots are increasingly introduced to alleviate workforce shortages. Because these robots must directly recognize U-shaped weld joints, this study proposes an image-based classification system capable of automatically identifying and classifying such joints. In double-bottom structures, U-shaped weld joints can be categorized into 176 types according to combinations of collar plate type, slot, watertight feature, and girder. To distinguish these types, deep learning-based image recognition is employed. To construct a large-scale training dataset, 3D Computer-Aided Design (CAD) models were automatically generated using Open Cascade and subsequently rendered to produce synthetic images. Furthermore, to improve classification performance, the input images were split into left, right, upper, and lower regions for both training and inference. The class definitions for each region were simplified based on the presence or absence of key features. Consequently, the classification accuracy was significantly improved compared with an approach using non-split images. Full article

As a pillar industry of the national economy for many countries, the construction sector has long faced challenges in workplace safety. Unsafe behaviors among construction workers are the core cause of safety incidents, and controlling these behaviors is key to enhancing safety management. Numerous studies confirm that unsafe behaviors are closely linked to cognitive biases and decision-making errors. However, existing research still has theoretical gaps in analyzing the multi-factor interaction mechanisms from a cognitive perspective. This study constructs a three-stage theoretical model to reveal the formation mechanism of unsafe behaviors, which is validated by structural equation modeling based on the data collected by a questionnaire from ongoing construction projects in Jiangxi Province, China. It is found that (1) Organizational environment (safety atmosphere, safety culture, and safety management) exerts a negative influence on unsafe behavior; (2) While safety atmosphere has no direct impact on safety motivation, the overall organizational environment positively affects individual cognition; (3) Individual cognitive factors exert a negative influence on unsafe behavior, with the following hierarchical order: safety motivation > safety competence > safety values. (4) While safety motivation does not mediate the relationship between safety atmosphere and unsafe behavior, individual cognitive factors overall mediate the relationship between organizational environment and unsafe behavior. This study theoretically enriches the knowledge system of safety behavior and provides a theoretical foundation for optimizing enterprise unsafe behavior management and formulating differentiated management policies. Full article

The construction industry relies on the Internet of Things (IoT) and wearable technologies to enhance workplace safety. This research investigates the use of IoT and wearable technology among Saudi Arabian construction sector employees, analyzing their implementation difficulties and the factors contributing to successful implementation. A structured questionnaire was distributed to 567 construction professionals across different roles and projects. Frequency analysis was used to study adoption patterns, chi-square tests to study demographic factors, and principal component analysis for exploratory factor analysis to discover hidden adoption factors. The findings show that smart safety vests and helmets receive the highest level of recognition. On the other hand, advanced monitoring systems, including fatigue and environmental sensors, are not used enough. Group differences in device adoption were investigated in terms of years of experience, academic qualification, job role, and project budget. The findings from factor analysis show that three main factors determine adoption rates, which include (1) safety and operational effectiveness, (2) worker acceptance and support structures, and (3) technical and adoption barriers. A data-driven system is created to help policymakers and industry leaders accelerate construction safety digitalization efforts. Full article

Workers’ exposure to silica dust is a global occupational and public health concern and is particularly prevalent in Southern Africa, mainly because of inadequate dust control measures. It is worsened by the high prevalence of HIV/AIDS, which exacerbates tuberculosis and other occupational lung diseases. The prevalence of silicosis in the region ranges from 9 to 51%; however, silica dust exposure levels and controls, especially in the informal mining sector, particularly in artisanal small-scale mines (ASMs), leave much to be desired. This is important because silicosis is incurable and can only be eliminated by preventing worker exposure. Additionally, several studies have indicated inadequate occupational health and safety policies, weak inspection systems, inadequate monitoring and control technologies, and inadequate occupational health and hygiene skills. Furthermore, there is a near-absence of silica dust analysis laboratories in southern Africa, except in South Africa. This protocol aims to systematically evaluate the effectiveness of respirable dust and respirable crystalline silica dust exposure evaluation and control methodology for the mining industry. The study will entail testing the effectiveness of current dust control measures for controlling microscale particles using various exposure dose metrics, such as mass, number, and lung surface area concentrations. This will be achieved using a portable Fourier transform infrared spectroscope (FTIR) (Nanozen Industries Inc., Burnaby, BC, Canada), the Nanozen DustCount, which measures both the mass and particle size distribution. The surface area concentration will be analysed by inputting the particle size distribution (PSD) results into the Multiple-Path Particle Dosimetry Model (MPPD) to estimate the retained and cleared doses. The MPPD will help us understand the sub-micron dust deposition and the reduction rate using the controls. To the best of our knowledge, the proposed approach has never been used elsewhere or in our settings. The proposed approach will reduce dependence on highly skilled individuals, reduce the turnaround sampling and analysis time, and provide a reference for regional harmonised occupational exposure limit (OEL) guidelines as a guiding document on how to meet occupational health, safety and environment (OHSE) requirements in ASM settings. Therefore, the outcome of this study will influence policy reforms and protect hundreds of thousands of employees currently working without any form of exposure prevention or protection. Full article

This comprehensive review synthesizes current advances and persistent challenges in integrating worker safety and food safety through human-robot collaboration (HRC) in poultry processing. Rapid industry expansion and rising consumer demand for ready-to-eat poultry products have heightened occupational risks and foodborne contamination concerns, necessitating holistic safety strategies. The review examines ergonomic, microbiological, and regulatory risks specific to poultry lines, and maps how state-of-the-art collaborative robots (“cobots”)—including power and force-limiting arms, adaptive soft grippers, machine vision, and biosensor integration—can support safer, more hygienic, and more productive operations. The authors analyze technical scientific literature (2018–2025) and real-world case studies, highlighting how automation (e.g., vision-guided deboning and intelligent sanitation) can reduce repetitive strain injuries, lower contamination rates, and improve production consistency. The review also addresses the psychological and sociocultural dimensions that affect workforce acceptance, as well as economic and regulatory barriers to adoption, particularly in small- and mid-sized plants. Key research gaps include gripper adaptability, validation of food safety outcomes in mixed human-cobot workflows, and the need for deeper workforce retraining and feedback mechanisms. The authors propose a multidisciplinary roadmap: harmonizing ergonomic, safety, and hygiene standards; developing adaptive food-grade robotic end-effectors; fostering explainable AI for process transparency; and advancing workforce education programs. Ultimately, successful HRC deployment in poultry processing will depend on continuous collaboration among industry, researchers, and regulatory authorities to ensure both safety and competitiveness in a rapidly evolving global food system. Full article

5G deployment in Malaysia is increasing the need for safe and efficient radio-frequency (RF) scanning at telecommunication towers, but service providers lack a clear, structured way to choose among available methods. This study develops a decision framework using a hybrid Delphi–Analytic Hierarchy Process (AHP) approach. A literature review identified criteria, sub-criteria, and six RF scanning alternatives. Ten experts then participated in three Delphi rounds: Rounds 1 and 2 confirmed five criteria and twenty-five sub-criteria, while Round 3 produced an expert ranking of the six alternatives, with drone-based and human-based scanning as the top priorities. Thirty practitioners subsequently completed AHP pairwise comparisons based on the Delphi-validated hierarchy. The AHP results show that Safety and Environment are the most important criteria, with ‘Fall’ and ‘Thunderstorm’ having the highest global weights. Drone-based scanning ranks highest, followed by human-based and ground-based methods, and the AHP ranking closely matches the expert ranking. The study provides a clear decision method for industry and policymakers to improve worker safety, guide inspection decisions, and strengthen telecommunication infrastructure in line with SDG 8 (Decent Work), SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities), and SDG 13 (Climate Action). Full article

Exposure to chemical irritants in laboratory and medical environments poses significant health risks to workers, particularly in relation to asthma-like symptoms. Routine cleaning practices, which often involve the use of strong chemical agents to maintain hygienic settings, have been shown to contribute to respiratory issues. Laboratories, where chemicals such as hydrochloric acid and ammonia are frequently used, represent an underexplored context in the study of occupational asthma. While much of the research on chemical exposure has focused on industrial and high-risk occupations or large cohort populations, less attention has been given to the risks in laboratory and medical environments, particularly for professional cleaning staff. Given the growing reliance on cleaning agents to maintain sterile and safe workspaces in scientific research and healthcare facilities, this gap is concerning. This study developed an exploratory simulation tool, using a simulated cohort based on key demographic and exposure patterns from foundational research, to assess the impact of chemical exposure from cleaning products in laboratory environments. Four supervised machine learning models were applied to evaluate the relationship between chemical exposures and asthma-like symptoms: (1) Decision Trees, (2) Random Forest, (3) Gradient Boosting, and (4) XGBoost. High exposures to hydrochloric acid and ammonia were found to be significantly associated with asthma-like symptoms, and workplace type also played a critical role in determining asthma risk. This research provides a data-driven framework for assessing and predicting asthma-like symptoms in professional cleaning workers exposed to cleaning agents and highlights the potential for integrating predictive modeling into occupational health and safety monitoring. Future work should explore dose–response relationships and the temporal dynamics of chemical exposure to further refine these models and improve understanding of long-term health risks. Full article

To address the high incidence of work-related musculoskeletal disorders (WMSDs) at manual edge-banding workstations in furniture factories, and in an effort to tackle the existing research challenges of poor cumulative risk quantification and inconsistent evaluations, this paper proposes a three-stage system for continuous, automated, non-invasive WMSD risk monitoring. First, MediaPipe 0.10.11 is used to extract 33 key joint coordinates, compute seven types of joint angles, and resolve missing joint data, ensuring biomechanical data integrity for subsequent analysis. Second, joint angles are converted into graded parameters via RULA, REBA, and OWAS criteria, enabling automatic calculation of posture risk scores and grades. Third, an Adaptive Pooling Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) dual-branch hybrid model based on the Efficient Channel Attention (ECA) mechanism is built, which takes nine-dimensional features as the input to predict expert-rated fatigue states. For validation, 32 experienced female workers performed manual edge-banding tasks, with smartphones capturing videos of the eight work steps to ensure authentic and representative data. The results show the following findings: (1) system ratings strongly correlate with expert evaluations, verifying its validity for posture risk assessment; (2) the hybrid model successfully captures the complex mapping of expert-derived fatigue patterns, outperforming standalone CNN and LSTM models in fatigue prediction—by integrating CNN-based spatial feature extraction and LSTM-based temporal analysis—and accurately maps fatigue indexes while generating intervention recommendations. This study addresses the limitations of traditional manual evaluations (e.g., subjectivity, poor temporal resolution, and inability to capture cumulative risk), providing an engineered solution for WMSD prevention at these workstations and serving as a technical reference for occupational health management in labor-intensive industries. Full article

Artificial intelligence (AI) technology, serving as an indispensable component within intelligent construction systems, has become a cornerstone for driving the digital and intelligent transformation of the construction industry. Although AI demonstrates autonomous decision-making capabilities in specific operational contexts, because of the dynamic and often unforeseeable nature of construction workflows, human–AI collaboration (HAIC) still dominates the operational paradigm. This study undertakes a systematic review of the prior research on human–AI collaboration in intelligent construction. Through a bibliometric search, scientometric analysis, and in-depth literature classification, 191 highly cited articles in the past five years, which are in the top 10% by citation count within the dataset (as of May 2025, based on Scopus, Google Scholar, and WOS), were screened, and four research streams were formed based on a co-citation analysis and clustering, namely, construction robotics, productivity and safety, intelligent algorithms and modelling, and factors related to construction workers. Finally, a three-dimensional knowledge framework covering the technical layer, application layer, and management layer was constructed. Through this comprehensive synthesis, the study developed a human–AI collaboration knowledge framework in the field of construction science that integrates technology, scenarios, and management dimensions, revealing the co-evolutionary path of artificial intelligence technology and industry digital transformation. Full article

Under the dual driving forces of negative population growth and the cross-regional agglomeration of factors, the trend of urban shrinkage in China continues to intensify. This study examines 461 small towns in 26 mountainous counties of Zhejiang Province, constructing a multi-dimensional shrinkage identification model based on “population–economy–land use.” The spatiotemporal patterns of shrinkage were visualized using ArcGIS 10.8, while the driving factors were analyzed using the MGWR method. ① From 2010 to 2020, the shrinkage phenomenon in small towns across the 26 mountainous counties rapidly spread, with medium- and severe-shrinking towns increasing markedly, showing an irreversible trend. ② The spatial evolution pattern shows a phased characteristic, transitioning from “disordered scattered points” to “striped aggregation.” A “V”-shaped shrinkage belt formed along the “Kaihua–Jingning–Yongjia” axis, demonstrating strong spatial aggregation. ③ The shrinkage of small towns is driven by multiple factors. Rugged mountainous terrain constrains development, while urbanization and industrial restructuring, coupled with outmigration of young and middle-aged workers, accelerate aging and limit local specialty industries. Transportation, social services, and policy frameworks further influence shrinkage patterns. In response to the continuous shrinkage trend of small towns in mountainous areas, future efforts should adopt coordinated strategies such as smart shrinkage, industrial restructuring, and institutional innovation to achieve structural and systemic reshaping. Full article

Unsafe behaviors among construction workers remain a leading cause of accidents in the construction industry. Previous studies have primarily relied on structural equation modeling and causal inference approaches to investigate the determinants of workers’ safety behavior. However, these methods are often limited in their ability to address confounding bias inherent in observational data and tend to focus on isolated effects of individual variables, thereby overlooking the complex interactions between organizational and individual factors. To overcome these limitations, this study applies the Categorical Boosting (CatBoost) algorithm to examine the joint organizational and individual mechanisms underlying construction workers’ safety behavior. CatBoost is particularly suitable for small- to medium-sized datasets and is capable of automatically capturing complex, nonlinear relationships among variables. Leveraging the SHAP interpretability framework, both main-effect and interaction analyses are conducted to systematically identify the most influential determinants. The results demonstrate that CatBoost outperforms eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) models in predicting safety-related outcomes. Prosociality (PSO) is identified as the most influential predictor, followed by personal proactivity (PAC). Interaction analyses further reveal that organizational attributes—such as prosociality, loyalty, and mutual assistance—play a critical role in cultivating a safety-oriented organizational climate, while an optimistic personal attitude further enhances safety performance on construction sites. Overall, these findings provide meaningful theoretical insights and practical implications for improving safety management in the construction sector. Full article

Recognizing human actions in low-light industrial environments remains a significant challenge for safety-critical applications in power systems. In this paper, we propose a Low-Light Pose-Action Collaborative Network (LPAC-Net), an integrated framework specifically designed for monitoring scenarios in underground electrical vaults and smart power stations. The pipeline begins with a modified Zero-DCE++ module for reference-free illumination correction, followed by pose extraction using YOLO-Pose and a novel rotation-invariant encoding of keypoints optimized for confined industrial spaces. Temporal dependencies are captured through a bidirectional LSTM network with attention mechanisms to model complex operational behaviors. We evaluate LPAC-Net on the newly curated ARID-Fall dataset, enhanced with industrial monitoring scenarios representative of electrical infrastructure environments. Experimental results demonstrate that our method outperforms state-of-the-art models, including DarkLight-R101, DTCM, FRAGNet, and URetinex-Net++, achieving 95.53% accuracy in recognizing worker activities and safety-critical events. Additional studies confirm LPAC-Net’s robustness under keypoint noise and motion blur, highlighting its practical value for intelligent monitoring in challenging industrial lighting conditions typical of underground electrical facilities and automated power stations. Full article

In the context of advancing sustainable urban development, the spatial organization of industries plays a critical role in shaping environmental quality, economic vitality, and public health. This study examines the health effects of furniture enterprises agglomeration in Chinese cities, using a unique dataset combining point-of-interest (POI) big data and micro-level survey responses from 13,217 individuals. The results show that a one-unit increase in furniture enterprises agglomeration intensity is associated with a 0.656-unit improvement in physical health and a 0.060-unit improvement in mental health. These benefits are driven by three synergistic mechanisms: environmental improvement, income growth, and enhanced public health services. However, the health gains are unevenly distributed, with greater benefits observed in less-developed cities and among vulnerable groups such as low-skilled and middle-aged workers. We further reveal divergent effects between specialized and diversified agglomeration patterns, moderated by environmental regulation. Our findings underscore the need for health-oriented industrial policies that align with sustainable urban planning, emphasizing spatial adaptation, targeted support for vulnerable populations, and innovative regulatory approaches to foster both industrial growth and resident well-being. Full article

Understanding the causal relationships between safety factors is essential for successful intervention in industries with intrinsically high-risk environments such as the construction industry. Therefore, the aim of this study is to employ the Interpretive Structural Modeling (ISM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques to analyze and map the interdependencies among various safety-related elements affecting construction safety. According to the results, resource allocation was shown to be the highest-level, most independent element in the analysis, highlighting its function as the primary facilitator of safety initiatives. This strategic commitment directly drives Management Commitment and Competence, which form the core organizational support structure. Mid-level elements that translate management intent into site-level practice include workers’ training, safety motivation, and communication structure. The frequency of safety observations, workers’ involvement in safety decisions, and subcontractor and procurement management—the immediate procedural controls—are then used to assess operational efficacy. Crucially, the most dependent factor was found to be Workers’ Compliance, indicating that frontline safety behavior is the result of efficient management at all higher levels. Therefore, in order to improve overall safety performance in construction, this research emphasizes the importance of improving resource provision and leadership commitment. The outputs of the current study provide an organized, evidence-based roadmap for selecting interventions. Full article