Search Results (2,224)

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

Background: In Bangladesh, a number of sex workers are involved in commercial sex work in different brothels in both legal and illegal settlements due to reasons such as lack of social support, depression, forced sex, abuse, violence, polyamory, being kidnapped, and unemployment. In this study, we tried to evaluate the demographic characteristics and prevalence of viral and sexually transmitted diseases (STDs) among the study population. Methods: A total of 250 female sex workers were interviewed and tested from the Daulatdia brothel of Rajbari district, Bangladesh, who had been working there for at least 1 month. Through questionnaires, demographic data were collected. Primarily, lateral flow immunoassay (LFIA) tests were used to investigate HCV (Hepatitis C Virus), HBV (Hepatitis B Virus), and Syphilis, which were reconfirmed using enzyme-linked immunosorbent assay (ELISA) in cases of positive results. Results: The mean age was 27.51 ± 6.69 years with a range of 18–50 years. Most of them (n = 243, 97.98%) had elementary knowledge of STDs. We determined that overall, 96 (38.40%) were positive for either of these diseases. Individually, 10 (4.00%), 18 (7.20%), and 68 (27.20%) were positive for HCV, HBV, and syphilis, respectively. Conclusions: Our observation indicates that females of all ages should be strictly protected from forced sex work. Current sex workers should be educated regarding the dangers and protective mechanisms of STDs. In addition, as a public health concern, regular clinical check-ups and STD associated diagnoses are necessary to ensure the safety of FSW from these highly infectious and concerning diseases. Due to their socio-economic condition, proper treatment and rehabilitation are highly recommended. 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

Millions of healthcare workers experience percutaneous exposure to bloodborne communicable infectious disease pathogens annually, with the risk of contracting occupationally acquired infections. In this study, we aimed to assess the status of occupational safety and outbreak preparedness in Congolese nurses and laboratory technicians in Kongo central and the Katanga area, amidst multiple ongoing public health emergencies in the Democratic Republic of the Congo (DRC). This was a multicenter analytical cross-sectional study conducted in five referral hospitals located in Kongo central province and the Katanga area between 2019 and 2020 amidst Ebola, Yellow fever, Cholera and Chikungunya outbreaks. Participants were adult A0 grade nurses, A1 nurses, A2 nurses and medical laboratory technicians (N = 493). They answered a structured, self-administered questionnaire related to hospital hygiene and standard precautions for occupational infection prevention. The majority of the respondents were females (53.6%), and 30.1% of them have never participated in a training session on hospital infection prevention during their career. The proportions of those who have been immunized against hepatitis B virus (HBV) was markedly low, at 16.5%. Of the respondents, 75.3% have been using safety-engineered medical devices (SEDs), whereas 93.5% consistently disinfected medical devices after use. Moreover, 78% of the respondents used gloves during medical procedures and 92.2% wore masks consistently. A large majority of the respondents, 82.9%, have been recapping the needles after use. Regarding participation in outbreak response, 24.5% and 12.2% of the respondents were Chikungunya and Cholera epidemic responders, respectively; 1.8% have served in Ebola outbreak sites. The proportion of the respondents who sustained at least one percutaneous injury by needlestick or sharp device, blood/body fluid splash or both in the previous 12-month period was high, 89.3% (41.8% for injury, 59.2% for BBF event), and most of them (73%) reported over 11 events. Compared to laboratory technicians, nurses had higher odds for sustaining percutaneous injury and BBF events [OR = 1.38 (0.16); p < 0.01], whereas respondents with longer working experience were less likely to sustain those events [OR = 0.47 (0.11); p < 0.001]. Findings from this study suggest that Congolese nurses and laboratory technicians experience a high frequency of injury and BBF events at work, and remain at high risk for occupationally acquired infection. There is a need for periodic capacity-building training for the healthcare workforce to improve infection prevention in health settings, the provision of sufficient and appropriate PPE and SEDs, post-exposure follow-up and keeping records of occupational injuries in hospitals in Congolese healthcare settings. 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

Volatile organic compounds (VOCs) are a major occupational concern in polyurethane foam production, where exposure may impact worker health. This study identified key VOCs and evaluated their concentrations across different sections of a polyurethane manufacturing facility. Area (n = 5) air samples were collected during routine full-load production using short-duration active sampling and analyzed by thermal desorption gas chromatography–mass spectrometry (TD-GC-MS). The results revealed marked spatial variability in VOC concentrations, with the curing section showing the highest totals. Dichloromethane (DCM) constituted the dominant VOC in high-emission zones. All measured concentrations of DCM and other regulated substances remained well below European and Polish short-term exposure limits. Quantitative health risk assessment demonstrated that lifetime cancer risk values for DCM and benzene were in the 10⁻⁶ range, far below the regulatory threshold of concern (10⁻⁴). Non-carcinogenic risk indices (HQ) were generally low; however, a markedly elevated HQ was identified for 1-hexanol, 2-ethyl- in the cutting area (HQ = 5.7), indicating a potential localized non-cancer health concern. Overall, existing protective measures appear effective, but additional targeted precautions are warranted in zones with elevated emissions. Enhanced ventilation, strengthened personal protective equipment, and routine air monitoring are recommended to minimize potential health risks. Regular updates of occupational safety standards should reflect evolving toxicological evidence to ensure sustainable protection of workers in polyurethane foam production. Full article

Background/Objectives: While nurses showed a willingness to work during the pandemic and wartime, little is understood about how they managed the conflict between their roles as caregivers and personal or family obligations. They are deemed “essential workers,” risking their safety to fulfill their duties. Objectives: This study aims to explore the lived experience of nurses during COVID-19 and wartime, delving deeper into their emotional and moral experiences, providing insights for nurses and nursing management about how nurses negotiate dilemmas. Methods: A focused interpretive, hermeneutic, phenomenological approach was employed. From December 2022 to January 2023, ten hospital-based nurses from two hospitals were purposively sampled for in-depth, semi-structured interviews, which were transcribed and analyzed. The study was approved by the University Ethics Committee (31102022). Results: The essence of “ Moral Conflicts of Dual Identity and Dual Allegiance” revealed profound moral and emotional struggles among nurses. Four key themes emerged: (1) Moral Stressors and Identity Negotiation, (2) Competing Responsibilities and Ethical Double-binds, (3) Virtual and Practical Wisdom in Crises, (4) Responses of Stress and Erosion of Support Conclusions: Understanding nurses’ ethical dilemmas is essential for healthcare leadership. Leaders must make it a priority for workplace safety for their nurses. In wartime, it is not obvious that the workplace is unsafe; leaders must foster open dialog and support systems in response to these crises. This study highlights the significance of peer support, emphasizing the need for policies that address the complex moral challenges nurses face daily. 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

The construction industry faces severe safety challenges with over 80% of accidents stemming from unsafe behaviors, yet traditional management overlooks the role of individual differences, and existing research fails to address the specific psychological mechanisms operative in this high-risk, dynamic environment. To effectively curtail unsafe behaviors in such high-risk environments, this study aims to reveal the underlying mechanisms through which personality traits influence unsafe behaviors. Grounded in causal chain theory, the theory of planned behavior, and trait activation theory, this study constructs a hypothetical model of personality traits and unsafe behaviors, with fluke mentality serving as a mediating variable and safety climate as a moderating variable. A comprehensive approach combining questionnaire surveys, confirmatory factor analysis, correlation tests, and linear regression was employed to test the hypotheses. The results indicate that neuroticism, openness, and extraversion have significant positive effects on unsafe behaviors, while conscientiousness has a significant negative effect; agreeableness shows no significant influence. Fluke mentality plays a partial mediating role between personality traits and unsafe behaviors, while safety climate plays a negative moderating role. By clarifying the cognitive pathways of individual differences, this study enriches the theoretical framework of unsafe behavior research. The findings provide a theoretical basis for construction enterprises to optimize safety management from the perspective of individual differences, offering practical pathways to promote high-quality development in the construction industry. Full article

Construction projects on tropical islands face a high incidence of safety accidents due to complex environmental conditions, construction technologies, and varying levels of worker safety awareness. Traditional risk analysis frameworks, constrained by narrow analytical perspectives, struggle to account for the escalating uncertainties and safety perturbations inherent in tropical island construction processes. To address this gap, and to improve upon both Health Safety and Environment Management System (HSE) and Bayesian Networks (BN) methods, an IHIB model for construction safety risk analysis of tropical island buildings was established. The Improve Health Safety and Environment Management System (IHSE) method constructs an indicator system from six dimensions: institutional, health, organizational, safety, environmental, and emergency response factors. The Improved Bayesian network (IBN)method, by introducing fuzzy set theory and an improved similarity aggregation method, more accurately infers the influencing factors and the most probable causal chains for construction safety on tropical islands. Taking the Sanya Haitang Bay construction project as a case study, the IHIB analysis model reveals that high temperatures and strong winds are the decisive factors influencing construction safety risks on tropical islands. The findings contribute to proactive risk prevention and mitigation, offering practical guidance for enhancing construction safety management on tropical islands. Full article

Ensuring workers’ safety is a critical component of social sustainability in the construction industry. Accidents that occur while workers are walking on construction sites constitute a significant portion of overall accidents, yet they are often overlooked in conventional task-oriented safety risk assessments. This study proposes novel Accident-During-Walk (ADW) risk indices, hierarchical and data-driven metrics designed to quantify workers’ accident risk during walks. The indices are built on Association Rule Mining and utilize structured accident data, accounting for both environmental and work-related attributes. By integrating these indices with project-specific work schedules and worker allocation plans, this study establishes an automated method for daily and weekly look-ahead ADW risk monitoring aligned with construction progress. Case studies on two construction projects validate the discriminative power of the proposed method. The results demonstrate that the indices effectively capture risk fluctuations driven by concurrent multi-trade operations and environmental severity. Notably, the analysis reveals counterintuitive patterns where adverse weather conditions paradoxically reduce risk values by constraining worker mobility, a nuance often missed by static assessments. Ultimately, this framework serves as a data-driven decision-support tool, enabling safety managers to transition from uniform inspections to targeted interventions during high-risk periods, thereby fostering a safer and more socially sustainable construction environment. Full article

The construction industry remains high-risk, and in Saudi Arabia, these risks are amplified by a multinational workforce. This study examines the relationship between safety training (ST) and two facets of safety behavior: safety compliance (SC) and safety participation (SP). It investigates whether this effect operates through individual responsibility (IR) and varies by nationality. A questionnaire was administered to 252 construction workers across large projects. Data were analyzed in SPSS using descriptive statistics, reliability tests, correlations, multiple regression, and PROCESS with 5000 bootstraps. ST was positively associated with SC but not with SP. IR was positively related to SC. Mediation analysis revealed partial mediation of the ST to SC link via IR, suggesting that training enhances compliance both directly and by strengthening a personal sense of responsibility. Nationality did not significantly moderate the ST to IR path or the direct effects of ST on behavior, suggesting broadly similar training mechanisms across national groups. These findings support the integration of responsibility-building elements into safety training to enhance compliance, while separate organizational strategies (e.g., participatory programs, leadership engagement) may be necessary to foster discretionary participation. Limitations include reliance on self-report measures, a cross-sectional design, and limited subgroup sizes in moderation analyses. Future research should employ longitudinal designs, refine the measurement of responsibility, and test additional moderators (e.g., language proficiency, education, tenure). Full article