Search Results (715)

Improving occupational health and safety (OHS) in the construction industry can contribute to the advancement of the Sustainable Development Goals (SDGs), particularly Goals 3 (Good Health and Well-being) and 8 (Decent Work and Economic Growth). Yet, workers’ risk-taking behaviors (RTBs) remain a persistent challenge. Drawing on Social Cognitive Theory and Social Information Processing Theory, this study develops and tests a social influence model to examine how foremen’s safety attitudes (SAs) shape workers’ RTBs. Drawing on survey data from 301 construction workers in China, structural equation modeling reveals that foremen’s SAs significantly and negatively predict workers’ RTBs. However, the three dimensions of SAs—cognitive, affective, and behavioral—exert their influence through different pathways. Risk perception (RP) plays a key mediating role, particularly for the cognitive and behavioral dimensions. Furthermore, interpersonal trust (IPT) functions as a significant moderator in some of these relationships. By identifying the micro-social pathways that link foremen’s attitudes to workers’ safety behaviors, this study offers a testable theoretical framework for implementing the Sustainable Development Goals (particularly Goals 3 and 8) at the frontline workplace level. The findings provide empirical support for organizations to move beyond rule-based management and instead build more resilient OHS governance systems by systematically cultivating the multidimensional attitudes of frontline leaders. Full article

In manufacturing environments, the proper use of Personal Protective Equipment (PPE) is essential to prevent workplace accidents. Despite this need, existing PPE monitoring methods remain largely manual and suffer from limited coverage, significant errors, and inefficiencies. This article focuses on addressing this deficiency by designing a computer vision desktop application for automated monitoring of PPE use. This system uses lightweight YOLOv8 models, developed to run on the local system and operate even in industrial locations with limited network connectivity. Using a Lean UX approach, the development of the system involved creating empathy maps, assumptions, product backlog, followed by high-fidelity prototype interface components. C4 and physical diagrams helped define the system architecture to facilitate modifiability, scalability, and maintainability. Usability was verified using the System Usability Scale (SUS), with a score of 87.6/100 indicating “excellent” usability. The findings demonstrate that a user-centered design approach, considering user experience and technical flexibility, can significantly advance the utility and adoption of AI-based safety tools, especially in small- and medium-sized manufacturing operations. This article delivers a validated and user-centered design solution for implementing machine vision systems into manufacturing safety processes, simplifying the complexities of utilizing advanced AI technologies and their practical application in resource-limited environments. Full article

The European Union Council’s zero vision aims to eliminate workplace fatalities, while Industry 4.0 presents new challenges for occupational safety. Despite HR professionals assessing managers’ and employees’ competencies, no system currently exists to evaluate the competencies of workers’ representatives in occupational safety and health (OSH). It is crucial to establish the necessary competencies for these representatives to avoid their selection based on personal bias, ambition, or coercion. The main objective of the study is to identify the competencies and their components required for workers’ representatives in the field of occupational safety and health by following the steps of the DACUM method with the assistance of OSH professionals. First, tasks were identified through semi-structured interviews conducted with eight occupational safety experts. In the second step, a focus group consisting of 34 OSH professionals (2 invited guests and 32 volunteers) determined the competencies and their components necessary to perform those tasks. Finally, the results were validated through an online questionnaire sent to the 32 volunteer participants of the focus group, from which 11 responses (34%) were received. The research categorized the competencies into the following three groups: core competencies (occupational safety and professional knowledge) and distinguishing competencies (personal attributes). Within occupational safety knowledge, 10 components were defined; for professional expertise, 7 components; and for personal attributes, 16 components. Based on the results, it was confirmed that all participants of the tripartite system have an important role in the training and development of workers’ representatives in the field of occupational safety and health. The results indicate that although OSH representation is not yet a priority in Hungary, there is a willingness to collaborate with competent, well-prepared representatives. The study emphasizes the importance of clearly defining and assessing the required competencies. Full article

Introduction: The sodium-to-potassium (Na:K) ratio in the diet is a critical biomarker for cardiovascular and metabolic health, yet global adherence to recommended levels remains poor. Objectives: The objective of this study was to identify dietary determinants of the dietary Na:K ratio and its associations with micronutrient intake and diet quality. Methods: An observational cross-sectional survey was conducted in a representative sample of manufacturing workers through a combined stratified proportional and two-stage probability sampling plan, with strata defined by company size and industrial sector from the state of Rio Grande do Norte, Brazil. Dietary intake was assessed using 24 h recalls via the Multiple Pass Method, with Na:K ratios calculated from quantified food composition data. Diet quality was assessed with the Diet Quality Index-International (DQI-I). Multiple linear regression was used to analyze associations of Na:K ratio with the study variables. Results: The survey was conducted in the state of Rio Grande do Norte, Brazil, in 921 randomly selected manufacturing workers. The sample mean age was 38.2 ± 10.7 years, 55.9% males, mean BMI 27.2 ± 4.80 kg/m². The mean Na:K ratio was 1.97 ± 0.86, with only 0.54% of participants meeting the WHO recommended target (<0.57). Fast food (+3.29 mg/mg per serving, p < 0.001), rice, bread, and red meat significantly increased the ratio, while fruits (−0.16 mg/mg), dairy, white meat, and coffee were protective. Higher Na:K ratios were associated with lower intake of calcium, magnesium, phosphorus, and vitamins C, D, and E, as well as poorer diet quality (DQI-I score: −0.026 per 1 mg/mg increase, p < 0.001). Conclusions: These findings highlight the critical role of processed foods in elevating Na:K ratios and the potential for dietary modifications to improve both electrolyte balance and micronutrient adequacy in industrial workers. The study underscores the need for workplace interventions that simultaneously address sodium reduction, potassium enhancement, and overall diet quality improvement tailored to socioeconomic and cultural contexts, a triple approach not previously tested in intervention studies. Future studies should further investigate nutritional consequences of imbalanced Na:K intake. Full article

Worm gears are used in various mechanical constructions, especially in heavy industrial plants, where they are exposed to high operating loads, large torques, and high temperatures, particularly in conditions where it is necessary for the input and output shafts to be at an angle of 90°. Regarding tribological optimization, the application of carbon nanotube in lubricants can lead to significant improvements in the performance characteristics of worm gears, both in terms of increasing efficiency and reducing the coefficient of friction and wear, as well as minimizing mechanical losses, noise, and vibrations. The objective of this study is for the research results, through the use of oil with varying percentages of carbon nanotube additives (CNTs), to contribute to the optimization of worm gears by improving efficiency, extending service life, and reducing vibrations—both within the gearbox itself and within the industrial facility where it is applied. The research methodology involved laboratory testing of a worm gear using lubricants with varying concentrations of carbon nanotube. During the experiment, measurements of efficiency, vibrations, and noise levels were conducted in order to determine the impact of these additives on the operational performance of the gear system. The main contribution of this research is reflected in the experimental confirmation that the use of lubricants with optimized concentrations of carbon nanotube significantly enhances the operational performance of worm gears by increasing efficiency and reducing vibrations and noise, thereby enabling tribological optimization that contributes to improved reliability, extended service life, and enhanced workplace ergonomics under demanding industrial conditions. Furthermore, experimental investigations have shown that the efficiency of the gearbox increases from an initial value of 0.42–0.65, which represents an increase of 54%, the vibrations of the worm gear decrease from an initial value of 5.83–2.56 mm/s², which represents an decrease of 56%, while the noise was reduced from 87.5 to 77.2 dB, which represents an decrease of 12% with the increasing percentage of carbon nanotube additives in the lubricant, up to a maximum value of 1%. However, beyond this experimentally determined threshold, a decrease in the efficiency of the tested worm gearbox, as well as an increase in noise and vibration levels was recorded. Full article

Background: Type 2 diabetes (T2D) is a major global health concern influenced by sociodemographic and lifestyle factors. This study compared T2D risk scores between commerce and industry sectors and assessed the associations of age, sex, education, physical activity, diet, and smoking with elevated risk. Methods: This cross-sectional study included 56,856 men and 12,872 women employed in the commerce (n = 27,448) and industry (n = 42,280) sectors across Spain. Anthropometric, clinical, and biochemical data were collected. Four validated T2D risk scores (QDscore, Finrisk, Canrisk, and TRAQ-D) were calculated. Multinomial logistic regression models estimated adjusted odds ratios (ORs) for high-risk categories by sociodemographic and lifestyle characteristics. Results: Women in the industrial sector had significantly higher age, BMI, waist circumference, and lipid levels than those in commerce; differences among men were less marked. Across all participants, higher T2D risk scores were independently associated with physical inactivity (OR up to 12.49), poor Mediterranean diet adherence (OR up to 6.62), industrial employment (OR up to 1.98), and older age. Male sex was strongly associated with high Canrisk scores (OR = 6.31; 95% CI: 5.12–7.51). Conclusions: Employment in the industrial sector, combined with sedentary behavior and poor dietary habits, is independently associated with higher predicted T2D risk. Workplace prevention strategies should prioritize multicomponent interventions targeting modifiable risk factors, especially in high-risk subgroups such as older, less-educated, and inactive workers. Full article

In the context of the construction industry in Chile, the presence of women has historically been limited—i.e., approximately 12% of participation. Despite efforts to increase female participation in recent years, various barriers persist that hinder their development in construction. To leverage the existing challenges faced by women in the Chilean construction industry, interviews were conducted with industry professionals, and a qualitative analysis was performed to identify existing challenges in Chile and recommendations to deal with such challenges. We found that existing challenges can be classified into three professional development stages: entering the sector, retention, and advancement in women’s professional careers. The results reveal that women working in the construction industry face biases and stereotypes that negatively impact their recognition and professional advancement. Furthermore, women were much more aware of challenges compared to men, for instance, entrenched machismo in the workplace, the social burden associated with motherhood, and their effects on women within this industry. Ultimately, this is a construction sector problem and not a problem with the women in the industry; thus, we all need to participate in the solution to this problem, men and women alike. Full article

Ergonomic risk classification during manual lifting tasks is crucial for the prevention of workplace injuries. This study addresses the challenge of classifying lifting task risk levels (low, medium, and high risk, labeled as 0, 1, and 2) using multi-modal time-series data comprising raw facial landmarks and bio-signals (electrocardiography [ECG] and electrodermal activity [EDA]). Classifying such data presents inherent challenges due to multi-source information, temporal dynamics, and class imbalance. To overcome these challenges, this paper proposes a Multi-Adaptive Functional Neural Network (Multi-AdaFNN), a novel method that integrates functional data analysis with deep learning techniques. The proposed model introduces a novel adaptive basis layer composed of micro-networks tailored to each individual time-series feature, enabling end-to-end learning of discriminative temporal patterns directly from raw data. The Multi-AdaFNN approach was evaluated across five distinct dataset configurations: (1) facial landmarks only, (2) bio-signals only, (3) full fusion of all available features, (4) a reduced-dimensionality set of 12 selected facial landmark trajectories, and (5) the same reduced set combined with bio-signals. Performance was rigorously assessed using 100 independent stratified splits (70% training and 30% testing) and optimized via a weighted cross-entropy loss function to manage class imbalance effectively. The results demonstrated that the integrated approach, fusing facial landmarks and bio-signals, achieved the highest classification accuracy and robustness. Furthermore, the adaptive basis functions revealed specific phases within lifting tasks critical for risk prediction. These findings underscore the efficacy and transparency of the Multi-AdaFNN framework for multi-modal ergonomic risk assessment, highlighting its potential for real-time monitoring and proactive injury prevention in industrial environments. Full article

Human Factors and Ergonomics (HF/E) play an essential role in the development of sustainable manufacturing systems. By prioritizing worker wellbeing through the mitigation of occupational hazards and the enhancement of workplace health, HF/E contributes significantly to improved system performance. In accordance with the principles of Industry 5.0 and Society 5.0, which emphasize human-centered design and wellbeing, organizations that effectively integrate HF/E principles can achieve a competitive advantage on the market. Based on a globally recognized ranking system utilized by investors in making informed decisions, the study focuses on manufacturing companies ranked by their occupational health and safety (OHS) scores, a key criterion for assessing the social dimension of company performance. This research aims to identify and analyze top-ranked companies that explicitly highlight HF/E-related benefits within their public documents and sustainability reports. The paper investigates aspects related to the integration of AI and digital technologies to enhance safety and health in manufacturing systems, with a specific focus on human presence detection in hazardous zones, improvements in machines and equipment design, occupational risk assessments, and initiatives for enhancing worker wellbeing. The findings are expected to provide compelling evidence for companies to prioritize HF/E consideration during the design and redesign phases of sustainable manufacturing systems. The paper provides significant value to non-indexed companies by offering a dual approach for improving OHS performance, based on an empirical evaluation assessment method and practical strategies for effective OHS implementation in different manufacturing industries and countries. Full article

The modern workplace grapples with a human energy crisis, characterized by chronic exhaustion, disengagement, and emotional depletion among employees. Traditional well-being initiatives often fail to address this systemic challenge, particularly in industrial contexts. This study introduces the Human Energy Management System (HEMS), a strategic framework to develop, implement, and refine strategies for optimizing workforce energy. Grounded in Industry 5.0’s human-centric, resilient, and sustainable principles, HEMS integrates enterprise risk management (ERM), design thinking, and the Plan-Do-Check-Act (PDCA) cycle. Employing a qualitative Design Science Research (DSR) methodology, the study reframes human energy depletion as an organizational risk, providing a proactive, empathetic, and iterative approach to mitigate workplace stressors. The HEMS framework is developed and evaluated through theoretical modeling, literature benchmarking, and secondary case studies, rather than empirical testing, aligning with DSR’s focus on conceptual validation. Findings suggest HEMS offers a robust tool to operationalize human energy reinforcement strategies in industrial settings. Consistent with the European Union’s vision for human-centric industrial transformation, HEMS enables organizations to foster a resilient, engaged, and thriving workforce in both stable and challenging times. Full article

Digital transformation has reshaped knowledge creation patterns, business models, and practices within the legal industry. However, many organizations have struggled to realize the anticipated benefits of digital transformation due to individual adaptation barriers. Drawing on the Job Demands–Resources model, this study employs both regression analysis and fuzzy-set qualitative comparative analysis (fsQCA) to investigate the mechanisms and the boundary conditions through which digital transformation job autonomy affects lawyers’ supportive behaviors toward digital change in law firms. The regression analysis of multi-wave survey data from 423 lawyers demonstrates that digital transformation job autonomy not only has a direct positive effect on lawyers’ digital transformation-supportive behaviors, but also indirectly promotes such behaviors through lawyers’ cognitive adjustment in the workplace. Furthermore, leader empathy enhances the relationship between digital transformation job autonomy and supportive behaviors. The fsQCA results identify multiple pathways leading to high and low levels of digital transformation-supportive behaviors among lawyers. These findings contribute to a deeper understanding of how organizations foster individual support for digital transformation. Full article

Workplace bullying is a widespread yet rarely recognized stressor that impairs employee productivity and organizational harmony. It requires attention in the hospitality industry, where a high volume of interpersonal interactions occurs. It is essential to address employees’ overall outlook and attitudes toward hardships resulting from stressful work environments. This study examined workplace bullying by highlighting the role of psychological capital in employees’ responses to hostile work environments. The relationships among employee voice, perceived organizational support, organizational commitment, and intention to stay were further elaborated based on a conceptual model. An online survey was distributed to hotel employees, and the results were analyzed using structural equation modeling. The indirect effects of psychological capital on perceived organizational support and organizational commitment were stronger than those of workplace bullying. The results demonstrate that employees with higher psychological capital have more proactive response tendencies to workplace bullying. Full article

Returning to work after extended holidays poses significant challenges to employees’ psychological adjustment, yet this phenomenon remains underexplored in organizational research. Drawing on the Conservation of Resources (COR) theory, this study develops and tests a moderated mediation model to examine how pre-holiday work-related irritation influences post-holiday workplace vigor through heightened need for recovery, and how perceived organizational support buffers this process. Data were collected through a four-wave time-lagged design surrounding the Chinese Spring Festival, with a final sample of 349 employees across diverse industries. Results show that pre-holiday emotional strain increases employees’ recovery needs, which in turn undermines their workplace vigor. Moreover, boundary strength at home and perceived organizational support buffer the indirect negative pathway, highlighting the critical roles of both personal and organizational resources in the recovery process. By shifting attention from burnout to positive energy states such as vigor, this study advances theoretical understanding of post-holiday adjustment dynamics and offers practical insights for organizations seeking to foster employee resilience and sustained engagement after structured breaks. Full article

With the advent of Industry 4.0, sensor-based human activity recognition has become increasingly vital for improving worker safety, enhancing operational efficiency, and optimizing workflows in Internet-of-Things (IoT) environments. This study introduces a novel deep learning-based framework for construction worker activity recognition, employing a deep residual convolutional neural network (ResNet) architecture integrated with multi-sensor fusion techniques. The proposed system processes data from multiple inertial measurement unit sensors strategically positioned on workers’ bodies to identify and classify construction-related activities accurately. A comprehensive pre-processing pipeline is implemented, incorporating Butterworth filtering for noise suppression, data normalization, and an adaptive sliding window mechanism for temporal segmentation. Experimental validation is conducted using the publicly available VTT-ConIoT dataset, which includes recordings of 16 construction activities performed by 13 participants in a controlled laboratory setting. The results demonstrate that the ResNet-based sensor fusion approach outperforms traditional single-sensor models and other deep learning methods. The system achieves classification accuracies of 97.32% for binary discrimination between recommended and non-recommended activities, 97.14% for categorizing six core task types, and 98.68% for detailed classification across sixteen individual activities. Optimal performance is consistently obtained with a 4-second window size, balancing recognition accuracy with computational efficiency. Although the hand-mounted sensor proved to be the most effective as a standalone unit, multi-sensor configurations delivered significantly higher accuracy, particularly in complex classification tasks. The proposed approach demonstrates strong potential for real-world applications, offering robust performance across diverse working conditions while maintaining computational feasibility for IoT deployment. This work advances the field of innovative construction by presenting a practical solution for real-time worker activity monitoring, which can be seamlessly integrated into existing IoT infrastructures to promote workplace safety, streamline construction processes, and support data-driven management decisions. Full article

Ensuring employee safety is a top priority for every enterprise, and it is especially critical in high-risk industries like coal mining. To achieve this goal, it is essential to focus efforts on identifying existing hazards and thoroughly assessing the associated risks. Accurate identification and detailed characterization of occupational hazards play a pivotal role in the occupational risk assessment process, providing the foundation for effective safety strategies. This article presents an analysis of the process of identifying occupational hazards in hard coal mining, based on applicable legal regulations and a review of the relevant literature. The analysis reveals, on the one hand, a diversity of approaches to hazard classification, and on the other, a limited use of the characteristic features of hazards in classification processes. The findings of this review form the basis for proposing a systematic classification of occupational hazards in hard coal mining, taking into account the specific features of hazards in relation to their sources and potential consequences. The proposed classification not only categorizes hazards but also describes the specifics of hazard sources, such as environmental conditions, machinery, chemicals, and human factors, as well as the possible outcomes of these hazards, including physical injury, health impacts, and even fatalities. The aim of this article is to present a proposed classification of occupational hazards in hard coal mining and to provide a detailed characterization of these hazards based on the description of their sources and potential consequences. The proposed approach, grounded in the identification of characteristic features of hazards, facilitates the effective selection of preventive measures that can be implemented to reduce risk and improve workplace safety. Due to the presence of the full spectrum of natural hazards in Polish hard coal mining, the analysis draws on available statistical data, focusing on those hazards that contribute most significantly to fatal accidents and serious injuries. In conclusion, the article emphasizes the importance of a structured and systematic approach to identifying and assessing occupational hazards in the coal mining industry. By drawing on legal and literature-based insights, it aims to contribute to the development of more effective safety practices that protect workers and minimize the occurrence of workplace accidents and illnesses. Full article