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Heat poses a major environmental risk to occupational safety, necessitating timely insights into associated risks to safeguard workers. In June 2022, the National Weather Service (NWS) initiated operational wet bulb globe temperature (WBGT) forecasts, offering valuable information for heat risk management. This study evaluates the effectiveness of NWS WBGT forecasts, aiming to identify potential areas of caution and improvements for their application for occupational safety management. To this end, the study examines 1.3 million hourly historical NWS WBGT forecast data, comparing it with observed data from 252 weather stations across the US during the summer of 2023. The results offer key insights, revealing that: (1) the accuracy of NWS WBGT forecasts is influenced more by the times of interest than by the forecast horizons; (2) NWS WBGT forecast accuracy varies across different climates in the US, with air temperature bias being the most influential factor in this inaccuracy; and (3) while NWS WBGT forecasts accurately identify the lowest heat risks (i.e. no heat risk), their performance decreases at higher risk levels, emphasizing the importance of careful interpretation in safety management. These insights offer guidance for more cautious interpretations of NWS WBGT forecasts and lay the foundation for future studies on leveraging operational weather forecasting services in effective heat mitigation strategies. [ABSTRACT FROM AUTHOR]

Background: Extracorporeal membrane oxygenation (ECMO) can be a life‐saving treatment for patients requiring advanced cardiopulmonary support. Several ECMO centres offer interhospital transport (ECMO IHT) services that involve establishing ECMO teams to initiate ECMO at referring hospitals and then transfer patients to ECMO centres. ECMO IHT is often high risk and complex. Understanding the experience of transport team members is crucial to ensure patient safety and promote quality improvement. Aim: To explore the experiences of transport teams performing ECMO IHT. Study Design: A descriptive qualitative methodology was adopted. Results: Thirteen health care professionals who have performed ECMO IHT at a general hospital in China agreed to be interviewed and enrolled in this study. Two investigators conducted face‐to‐face individual interviews in September–November 2022. All interviews were audio‐recorded, transcribed verbatim and analysed using inductive thematic analysis. Three main themes and nine sub‐themes were developed: (1) practicing with good organizational management (conducting training programs, cultivating the spirit of good teamwork and developing a standardized transport procedure), (2) dedicated to ensuring patient safety (adequate preparation and regular checking to reduce risk, accurate evaluation to avoid futility and maintaining communication to increase safety) and (3) having confidence despite being uneasy (feeling stressed is common, facing insecurity in transport settings and gaining confidence through practice). Conclusions: Health care professionals must adequately prepare and assess ECMO IHT to ensure patient safety. Supportive measures should be taken to ensure team members' health and improve patient safety. Good communication and teamwork could improve this challenging task. Further research is required for training programs and establishing standardized transport procedures. Relevance to Clinical Practice: This study presents multi‐professional perspectives on the experience of performing ECMO IHT to help management identify what needs to be further developed. With the increasing number of ECMO IHT, promoting its standardization is warranted. [ABSTRACT FROM AUTHOR]

The North China Plain and the agricultural region are crossed by the Shanxi-Beijing natural gas pipeline. Residents in the area use rototillers for planting and harvesting; however, the depth of the rototillers into the ground is greater than the depth of the pipeline, posing a significant threat to the safe operation of the pipeline. Therefore, it is of great significance to study the dynamic response of rotary tillers impacting pipelines to ensure the safe operation of pipelines. This article focuses on the Shanxi-Beijing natural gas pipeline, utilizing finite element simulation software to establish a finite element model for the interaction among the machinery, pipeline, and soil, and analyzing the dynamic response of the pipeline. At the same time, a decision tree model is introduced to classify the damage of pipelines under different working conditions, and the boundary value and importance of each influencing factor on pipeline damage are derived. Considering the actual conditions in the hemp yam planting area, targeted management measures have been proposed to ensure the operational safety of the Shanxi-Beijing natural gas pipeline in this region. [ABSTRACT FROM AUTHOR]

Participant safety is becoming increasingly important as outdoor sports activities gain popularity. A machine learning-based strategy for risk assessment and safety control in outdoor sports activities is presented in this paper. Our framework uses predictive modelling, sophisticated algorithms, and historical data analysis to identify potential dangers and improve safety procedures. It also considers participant profiles and environmental conditions. Comprehensive testing and validation are used to examine the model's efficacy, showing that it can offer risk evaluations in real-time and support preventive safety measures. Our approach entails placing sensor-based Internet of Things (IoT) devices at building sites to gather extremely detailed temporal and geographic weather, building, and labour data. This data is then cooperatively used on the edge nodes to train Deep Neural Network (DNN) models in a cross-silos way. The present study makes a valuable contribution to sports safety by offering a clever approach that integrates technology and outdoor leisure to ensure participants have a safe and pleasurable experience. The experiment's outcomes show how well the suggested strategy works to increase the adoption of construction safety management systems and lower the likelihood of future mishaps and fatalities. As a result, the system has improved speed and responsiveness, an important feature for time-sensitive applications like safety prediction. [ABSTRACT FROM AUTHOR]

The construction industry substantially contributes to the economic growth of a country. However, it records a large number of workplace injuries and fatalities annually due to its hesitant adoption of automated safety monitoring systems. To address this critical concern, this study presents a real-time monitoring approach that uses the Internet of Things and ensemble learning. This study leverages wearable sensor technology, such as photoplethysmography and electroencephalography sensors, to continuously track the physiological parameters of construction workers. The sensor data is processed using an ensemble learning approach called the ChronoEnsemble Fatigue Analysis System (CEFAS), comprising deep autoregressive and temporal fusion transformer models, to accurately predict potential physical and mental fatigue. Comprehensive evaluation metrics, including mean square error, mean absolute scaled error, and symmetric mean absolute percentage error, demonstrated the superior prediction accuracy and reliability of the proposed model compared to standalone models. The ensemble learning model exhibited remarkable precision in predicting physical and mental fatigue, as evidenced by the mean square errors of 0.0008 and 0.0033, respectively. The proposed model promptly recognizes potential hazards and irregularities, considerably enhancing worker safety and reducing on-site risks. [ABSTRACT FROM AUTHOR]

Featured Application: This work expresses the correlation among accident causal factors, flight phases, accident types, and consequences. It identifies the potential risks of various accident causal factors to help eliminate risk hazards and reduce the probability of aviation accidents, providing auxiliary decision making and a practical reference for preventing aviation accidents. Summarizing the causation of an aviation accident is beneficial for improving aviation safety. Currently, accident analysis mainly focuses on causal analysis, while giving less consideration to the correlation between accident causal factors and other accident factors. To clarify accident causal factors and potential patterns affecting aviation safety and to optimize data mining methods for accident causal factors, this work proposes an aviation accident causation correlation analysis model based on a knowledge graph. Firstly, the accident causal factors are identified, and a knowledge graph is constructed. Subsequently, by utilizing multi-dimensional topological analysis metrics, an aviation accident causation correlation analysis model is established, using the relationships within accident causal factors as a foundation, to determine potential patterns among accident causal factors, flight phases, accident types, and consequences and to analyze the key accident causal factors influencing accident occurrences across different flight phases. Finally, preventive measures and recommendations are provided based on the analysis conclusions. Through a case study using 437 global aviation accidents from 2018 to 2022 as samples and employing the knowledge graph-based aviation accident causation correlation analysis model, the causation relationships among accident causal factors can be expressed more clearly, the potential risks of various accident causal factors can be identified, experiences can be gained from historical accident data, and underlying patterns can be unearthed. This work can provide auxiliary decision making and be an effective reference for the prevention of aviation accidents, playing a positive role in enhancing the level of aviation safety management. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated patient safety management activities among general hospital nurses and aimed to identify the factors influencing patient safety management activities. Methods: A cross-sectional descriptive design included 159 ward nurses working at two tertiary hospitals located in Busan city. Data were collected with self-reported questionnaires from April 8 to April 20, 2024 and were analyzed using descriptive statistics, the t-test, analysis of variance, Pearson correlation coefficients, and multiple regression in SPSS 29.0. Results: Factors significantly influencing patient safety management activities in nurses were perceptions of patient safety culture (β = .47, p < .001) and perceptions of patient rounding (β = .31, p < .001). The explanatory power of these variables was 43%. Conclusion: This study found that perceptions of patient safety culture and perceptions of patient rounding had significant effects on nurses’ patient safety management activities. Thus, it is necessary to promote patient safety management activities among nurses through the development of intervention programs to improve perceptions of patient safety culture and perceptions of patient rounding. [ABSTRACT FROM AUTHOR]

Aim: To explore Remote Area Nurses' experiences of the implementation of workplace health and safety policies and risk mitigation strategies in Australian very remote primary health clinics. Design: This qualitative study used online semi‐structured interviews, with participants purposively sampled to maximize variation in work location and service type. Data were analysed using a reflexive thematic analysis approach. Coding was carried out inductively, with NVivo 12 aiding data management. Setting: The interviews were conducted from 24 Februrary 2021 to 06 March 2021 with Remote Area Nurses from very remote primary health clinics in Australia. Participants: Fifteen Remote Area Nurses participated in the study. Results: Thematic analysis revealed varied approaches to workplace safety among the different health services and regions. While the spread of 'never alone' policies in many clinics addressed one of the significant risks faced by Remote Area Nurses, gaps remained even for hazards specifically highlighted in existing work health and safety legislation. Meaningful collaboration with staff and the community, local orientation, preparation for the role and providing quality care were protective factors for staff safety. Understaffing, unsafe infrastructure and inadequate equipment were common concerns among Remote Area Nurses. Conclusion: Health services need to prioritize workplace safety and take a continuous quality improvement approach to its implementation. This will include ensuring safety strategies are appropriate for the local context, improving infrastructure maintenance, and establishing sustainable second responder systems such as a pool of drivers with local knowledge. Implications for the Profession: Poor personal safety contributes to burnout and high turnover of staff. Nurses' insights into the barriers and enablers of current workplace safety strategies will aid policymakers and employers in future improvements. Reporting Method: COREQ reporting guidelines were followed. PIPE Statement: A panel of six Remote Area Nurses collaborated in the development of this project. [ABSTRACT FROM AUTHOR]

Workplace accidents can be avoided through meticulous planning, systematic organization, and through evaluating the efficacy of implemented control measures. Acquiring high standards in a safety management system can be achieved using models that facilitate the execution of proactive steps to mitigate work-related risks. This study aimed to ascertain the presence of effective safety practices throughout the implementation of safety performance measures. A systematic literature review was carried out following the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Thirty articles in English, published between 2010 and 2020, were identified by a comprehensive search in the Scopus, Science Direct, and Web of Science databases, based on certain criteria. The results demonstrated the efficacy of safety management practices and their impact on safety performance. The predominant focus of the evaluations was procedural safety, with relatively little emphasis placed on the elements of human safety that were significantly overlooked. [ABSTRACT FROM AUTHOR]

In the inherently hazardous construction industry, where injuries are frequent, the unsafe operation of heavy construction machinery significantly contributes to the injury and accident rates. To reduce these risks, this study introduces a novel framework for detecting and classifying these unsafe operations for five types of construction machinery. Utilizing a cascade learning architecture, the approach employs a Super-Resolution Generative Adversarial Network (SRGAN), Real-Time Detection Transformers (RT-DETR), self-DIstillation with NO labels (DINOv2), and Dilated Neighborhood Attention Transformer (DiNAT) models. The study focuses on enhancing the detection and classification of unsafe operations in construction machinery through upscaling low-resolution surveillance footage and creating detailed high-resolution inputs for the RT-DETR model. This enhancement, by leveraging temporal information, significantly improves object detection and classification accuracy. The performance of the cascaded pipeline yielded an average detection and first-level classification precision of 96%, a second-level classification accuracy of 98.83%, and a third-level classification accuracy of 98.25%, among other metrics. The cascaded integration of these models presents a well-rounded solution for near-real-time surveillance in dynamic construction environments, advancing surveillance technologies and significantly contributing to safety management within the industry. [ABSTRACT FROM AUTHOR]

Purpose: The research aimed to verify the usefulness of selected subjective methods of task load assessment for evaluating task load in general aviation organizations and forecasting preflight load. The study also highlighted the importance of the pilot's task load in the context of flight safety. Design/methodology/approach: The study included a comprehensive literature review to present the specifics of general aviation organization and the role of the human factor in aviation accidents. The research discussed the division of task load methods and characterized four selected subjective task load methods (NASA-TLX, SWAT, ISA, Bedford Scale). Expert assessments were conducted to evaluate these methods' effectiveness from the perspective of post-flight load assessment and pre-flight load prediction, considering the feasibility of implementation in general aviation organizations. Findings: The findings emphasized the need to develop a dedicated method for predicting task load before a flight. The study concluded that the methods used should be simple and require minimal financial outlay, considering the specificity of general aviation organizations. Research limitations/implications: Future research should focus on developing a dedicated pre-flight task load prediction method tailored to the unique requirements of general aviation. Identified limitations include the need for further validation of the subjective methods in different organizational contexts. Practical implications: The research suggests that implementing simple and cost-effective task load assessment methods can enhance flight safety in general aviation. The development of a specialized pre-flight load prediction tool could significantly improve operational planning and pilot performance. Social implications: By improving task load assessment and prediction in general aviation, the research can contribute to enhanced flight safety, thereby potentially reducing accident rates and improving public trust in aviation safety measures. Originality/value: This paper contributes to the field by addressing the gap in pre-flight task load prediction in general aviation. It provides valuable insights for aviation safety researchers, practitioners, and policymakers interested in optimizing pilot performance and ensuring flight safety. [ABSTRACT FROM AUTHOR]

Any organization linked with construction needs to apply suitable standards, procedures, and structures to support the right acts by all the involved construction structures that are related to health and safety. This paper evaluates the safety management procedure of building construction projects by rating the importance of safety management factors and calculating the average practice. This has been done by conducting a case study of (94) projects constructed during 2013-2023 by designing a questionnaire that represents a successful plan of 49 factors of seven phases for a safety management system. And delivering it to stakeholders who worked on those projects. The results of the study show that the average practice of safety management is 61.27%, which is unacceptable. The typical safety management process falls between 37.54% for initial planning and 79.93% for precautions before scaffold implementation. The results revealed that the shortage in practicing safety management stages is noticeable in all building construction projects, especially for initial planning with a rate of practice of 37.54%, and the use of personal protection equipment with a rate of practice of 47.41%. There is a lack of practicing safety procedures. A team for emergency issues is rarely available in addition to the shortage of first aid centers. There are not enough medications in case of accidents. Also, the workers are not using suitable protection equipment often. There must be an emphasis on the issue of safety management in future construction projects. [ABSTRACT FROM AUTHOR]

• Traffic climate affect safety driving. • Psychological factors play mediating roles. In the research field of safe driving behavior, the emergence of prosocial driving research complements the more traditional dangerous driving research. In recent years, studies on the relationship between the urban traffic climate and prosocial driving have even been taken as a new direction for further improving urban traffic safety based on the construction of traffic safety facilities and the development of traffic safety regulations. The mechanism by which urban traffic climate affects prosocial driving is explored in this study. From a dual research perspective, i.e., a variable-centered and person-centered perspective, a theoretical model of prosocial driving is constructed based on the following three levels: environmental, psychological and behavioral. According to the results, First, rational, affective and moral factors at the individual psychological level play mediating roles between urban traffic climate at the environmental level and prosocial driving behavior at the behavioral level. As indicated by the results of a mediating pathway analysis, rational, affective and moral factors are intricately linked by five complex mediating paths at the psychological level. Second, there is a differentiated environmental-psychological activation mechanism between individuals' prosocial driving and aggressive driving. Third, urban traffic climate consists of a variety of typical safety climate types, and individuals have diverse psychological tendencies regarding safe driving in traffic climates of varying safety levels. The study concludes with a systematic discussion of its theoretical contributions and practical value. [ABSTRACT FROM AUTHOR]

Earthmoving operations in the construction process are complex environments that involve interactions between equipment, the workforce, and materials within an overarching construction plan. Over the past two decades, researchers in construction have focused on improving the safety of construction earthmoving equipment due to their omnipresence in the construction environment. Although previous studies have explored safety risks and the causes of accidents involving construction earthmoving equipment, their approaches were common and lacked a comprehensive perspective. Hence, this systematic literature review applies Rasmussen's (1997) risk management framework using a systems thinking approach to identify and classify the risk factors influencing earthmoving equipment operation safety in construction sites. Utilizing a multistep methodology, this research first identifies 38 risk factors pertinent to earthmoving equipment operations and then classifies them based on systems thinking. Social network analysis (SNA) is employed to analyze the data. The results show that most research on earthmoving equipment safety focuses on monitoring construction sites, but very little on government and regulatory roles. When considering the interdependencies of risk factors, safety training is the most important factor, followed by the largely overlooked earthmoving machinery characteristics and manufacturer's performance. The results of this review inform both the research community and industry practitioners regarding the less-understood aspects of earthmoving equipment operation safety and future research directions. [ABSTRACT FROM AUTHOR]

Repeated pesticide residue detection in chili peppers in the Republic of Korea has become a serious health concern. Thus, monitoring domestically grown and imported chili peppers for pesticide residues is of great significance. Here, we investigated pesticide residues detected in imported and domestically grown chili peppers using global pesticide residue monitoring data. Our analysis involved organizing inspection and detection data from different sources. Global pesticide residue monitoring data for chili peppers revealed 139 pesticide types, 43,532 inspections, and 3966 detections (detection rate, 9.11%). Peppers from Mexico were sampled the most (39,927 inspections) and showed the highest number of detected cases (2998 cases). Globally, the top 10 most frequently detected pesticides were clothianidin, imidacloprid, thiamethoxam, chlorpyrifos, thiacloprid, metalaxyl, myclobutanil, azoxystrobin, carbendazim, and cyhalothrin, with detection rates in the range of 10.52–28.66%. Furthermore, domestic chili pepper pesticide residue monitoring revealed 73 pesticide types, 3535 inspections, and 332 detected cases (detection rate, 9.39%), and the top 10 most frequently detected pesticides were chlorfenapyr, tebuconazole, flonicamid, dinotefuran, boscalid, pyraclostrobin, fluxametamide, thiamethoxam, pyridaben, and azoxystrobin, with detection rates in the range of 13.89–32.58%. These findings may serve as fundamental data for safety management related to chili pepper pesticide residues in the Republic of Korea. [ABSTRACT FROM AUTHOR]

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Introduction: Following the Republic of Korea electric power industry site-specific safety management system, this paper proposes a novel safety autonomous platform (SAP) architecture that can automatically and precisely manage on-site safety through ensemble artificial intelligence (AI) models. The ensemble AI model was generated from video information and worker's biometric information as learning data and the estimation results of this model are based on standard operating procedures of the workplace and safety rules. Methods: The ensemble AI model is designed and implemented by the Hadoop ecosystem with Kafka/NiFi, Spark/Hive, HUE, and ELK (Elasticsearch, Logstash, Kibana). Results: The functional evaluation shows that the main function of this SAP architecture was operated successfully. Discussion: The proposed model is confirmed to work well with safety mobility gateways to provide some safety applications.

In order to prevent the occurrence of coal mine roof accidents, it is of great significance to realize the knowledge discovery of accident causes and the network analysis of disaster chain. Taking the accident causes in the coal mine roof accident report as the data set, the knowledge map of accident causes was constructed. Further, based on the complex network theory, the causal disaster chain network is constructed and analyzed. The research results show that 22 factors, such as roof collapse, empty roof and support deformation, are the main disaster causing factors of coal mine roof accidents; the side with the greatest risk is from illegal command to illegal operation of operators in disaster chain network analysis. The control of key nodes and edges can effectively control the occurrence or derivative development of coal mine roof accidents.

Abstract The construction industry substantially contributes to the economic growth of a country. However, it records a large number of workplace injuries and fatalities annually due to its hesitant adoption of automated safety monitoring systems. To address this critical concern, this study presents a real-time monitoring approach that uses the Internet of Things and ensemble learning. This study leverages wearable sensor technology, such as photoplethysmography and electroencephalography sensors, to continuously track the physiological parameters of construction workers. The sensor data is processed using an ensemble learning approach called the ChronoEnsemble Fatigue Analysis System (CEFAS), comprising deep autoregressive and temporal fusion transformer models, to accurately predict potential physical and mental fatigue. Comprehensive evaluation metrics, including mean square error, mean absolute scaled error, and symmetric mean absolute percentage error, demonstrated the superior prediction accuracy and reliability of the proposed model compared to standalone models. The ensemble learning model exhibited remarkable precision in predicting physical and mental fatigue, as evidenced by the mean square errors of 0.0008 and 0.0033, respectively. The proposed model promptly recognizes potential hazards and irregularities, considerably enhancing worker safety and reducing on-site risks.

To clarify the influencing factors and mechanisms of safety cognition among coal mine workers, reduce the occurrence of unsafe behaviors and accidents, and take safety attitude as a mediator, a hypothesis of the relationship between safety cognition, safety attitude, and safety cognition influencing factors is proposed. A structural process model is constructed using AMOS software. The research results indicate that all four factors have a significant positive impact on the safety awareness of miners, and the enterprise safety management system has the greatest impact; enterprise safety education and training, as well as the enterprise safety atmosphere, indirectly affect safety awareness through safety attitudes; on the one hand, the safety management system of enterprises and the safety support of managers directly affect safety awareness, and on the other hand, indirectly affect safety awareness by influencing individual safety attitudes.

Construction projects are prone to accidents and injuries, necessitating a focus on implementing safety programs. However, the implementation of such programs is influenced by various factors. Developing countries often have poor safety performance in their building sectors, with limited research in this area. This study aimed to identify essential safety program activities (SPAs) specific to the building sector. Through a literature review and survey, 25 SPAs were identified and validated via a pilot survey involving building sector experts. A questionnaire survey was conducted with 105 participants from the construction industry and academia. They were then categorized into four interconnected measurements using Exploratory Factor Analysis (EFA): Safety Program Management and Development (SPMD), Safety Culture Development (SCD), Safety Risk and Hazard Management (SRHM), and Safety Leadership, Responsibility, and Commitment (SLRC). The impact of safety implementation (SI) on overall project success (OPS) was analyzed using Partial Least Square- Structural Equation Modelling (PLS-SEM). Subsequently, Synthetic Fuzzy Evaluation (SFE) was employed to determine the criticality and importance of each SPA grouping for construction projects. The PLS-SEM analysis indicates that SI has a moderate impact on OPS, with an R2 value of 45.4%. Moreover, the findings of the SFE highlight that the SLRC group is the most significant in enhancing the safety implementation of the construction industry.

In order to establish principles for implementing requirements contained in legal acts regarding methods of preventing serious industrial accidents, a study of the procedures in place at NITROERG SA, a company manufacturing explosives, was conducted. The analysis also included regulations concerning actions to be taken in the event of an accident, aimed at minimizing negative impacts on people and the environment. Legislative solutions imply the necessity for business entities to submit reports to emergency services, while also carrying out a range of activities to counteract threats associated with serious accidents. This article presents how these recommendations are implemented by the company and the methods of obtaining data to improve the undertaken projects. However, incidents in recent years indicate the need for further verification of processes to eliminate risks. [ABSTRACT FROM AUTHOR]

Personal protective equipment (PPE) plays a crucial role in safeguarding the well-being of workers on construction sites by mitigating the risk of accidents and incidents. Nevertheless, incidents stemming from a lack of awareness regarding PPE usage persist. To enhance the oversight of construction laborers for accident prevention, most current approaches involve overseeing the availability and correct utilization of PPE using intricate data processing techniques. However, these methods encounter challenges related to their applicability and capacity for universal implementation. This study presents the hybridization of seahorse optimization (SHO) algorithm with the YOLOv5 model to ultimate detection accuracy. First, an improved YOLO network is constructed through scale and loss function enhancement. Second, the SHO is utilized to tune the parameters of the improved YOLOv5. A data set with 4535 construction work images was collected for training, validation and testing the hybrid SHO-YOLOv5 model. Experimental results supported by statistical tests show that the SHO-YOLOv5 is a capable method for detecting automatically the PPE with a precision value of 0.74, recall value of 0.65 and F1 score of 0.68. The SHO-YOLOv5 surpasses the benchmarked models like YOLOv8, YOLOv5, Faster-R-CNN MobilenetV3, and Faster-R-CNN Resnet50. Therefore, the proposed hybrid approach is a promising tool to support the project managers in the task of safety inspection. [ABSTRACT FROM AUTHOR]

The task of recognizing personal protective elements in workplace environments in real time is fundamental to protecting the employees in case of any accidents. This can be achieved by deploying a convolutional neural network (CNN) algorithm that can efficiently detect protective elements through surveillance devices. Therefore, this work proposes the construction of a model, implementing the you only look once (YOLO) detector, whose architecture has been one of the most tested according to literature review. YOLOv5 and YOLOv7 versions were used and a dataset of 2,000 images for four classes considered. This dataset was collection from various sources and labelled by the authors, of which 80% was used for training, 15% for testing and 5% for model validation. The most important metrics are presented, making a comparison between the models, and finally it was identified that YOLOv7 achieved a higher success rate, which could be considered a more complete solution for occupational health and safety management in companies. [ABSTRACT FROM AUTHOR]

Introduction: For years, chemical process industries have reported the unexpected release of highly hazardous liquids and gases. A disaster could not be avoided when these materials were not adequately controlled. The issue of Process Safety Management (PSM) is still being discussed in developing and developed countries. PSM was established to manage the risks of a company’s personnel, properties, products, environment, and credit risks. This study aimed to review the challenges and achievements of PSM implementation and provide recommendations for improvement. Material and Methods: A collection of scholarly articles published from 2000 to 2023, such as Science Direct, WOS, PubMed, and Scopus, was chosen through a systematic, meticulous review. After thoroughly examining these abstracts, titles, and complete contexts, 49 articles were finally selected for inclusion in the study and classified based on different criteria, such as publication year, authors, achievements, and challenges. Results: PSM in the United States has been chiefly studied due to the high number of chemical process industries and its presence in the form of OSHA legislation in this country. The areas of operations, audits, and resources have the most challenges in implementing PSM. On the other hand, reducing the severity of incidents in chemical processes and increasing productivity are considered the most significant achievements of PSM implementation. Conclusion: PSM as an interdisciplinary field has improved and become more effective over the years.. However, incidents still exist, and the number can increase, considering the growth of industries’ chemical processes. Therefore, a deep look at the issues of risk-based regulations, competency, operational excellence, and learning from incidents is necessary to achieve excellence in PSM.

Background and purpose: An organization's safety culture is the product of its values, attitudes, beliefs, and behavioral patterns that define the organization's commitment and framework for managing safety and health. The purpose of evaluating the safety culture is to identify the current state of the safety culture raise the awareness of the employees in this field evaluate the interventions related to safety and track the changes that have been made. The present study was conducted to analyze the status of patient safety culture among the employees of hospitals covered by Mazandaran University of Medical Sciences in 2022. Materials and methods: The current descriptive, analytical, and cross-sectional study was conducted in 1401 on 440 employees of medical and educational hospitals covered by Mazandaran University of Medical Sciences. The sample size was calculated using Cochran's formula and the sampling method was simply random. The tool used in the Standard Patient Safety Culture Questionnaire (HSOPSC) The questionnaire had 42 questions, and 12 dimensions of the patient safety culture including dimensions of general understanding of the patient, organizational learning and continuous improvement, discipline within hospital departments, non-punitive policy in cases of errors, issues Work related to staff, manager/supervisor expectations and actions to promote patient safety, open communication channels, feedback and exchange of information about errors, hospital management support, teamwork among hospital departments, hospital handovers and transfers, and frequency of incident reporting. Unwantedness was measured based on a five-point Likert scale. The average score of less than 2.5 indicates a low patient safety culture, between 2.5 and 3.75 an acceptable state of safety culture, and 3.75 and above indicates the patient safety culture is a strength of the hospital. The study was conducted with IR.TUMS.SPH.REC.1400.289 code of ethics. Analysis was done using Spss 24 software and independent t-tests, one-way analysis of variance, and Friedman. Results: Most of the participants (47%) were 30 to 40 years old and had work experience of 1 to 10 years (44.6%). Also, most of the studied people (59%) were on rotating shifts. The overall status of patient safety culture in hospitals covered by Mazandaran University of Medical Sciences was 64.4% with an average of 3.22±0.43, so 86.1% of the participating employees considered the patient safety status as an average. Among the dimensions of patient safety culture, the highest and lowest scores are respectively related to the dimensions of "teamwork within hospital departments" with a mean and standard deviation of 3.90±0.79 (78 percent) and "hospital delivery and transfers" with a mean, and the standard deviation was 2.75±0.92 (55%). According to the single-sample independent parametric T-test, the score obtained from the status of patient safety culture in covered hospitals was higher than the average standard score of 3 (P

Abstract Objective To explore the application effect of the direct reporting system of adverse nursing events and special continuous nursing quality improvement measures in the management of these adverse events. Methods The implementation time of continuous nursing improvement based on the direct reporting system was the demarcation point. We retrospectively collected and analyzed nursing adverse event reports and hospitalization data from Xiangtan Central Hospital before implementation (2015–2018) and after implementation (2019–2022). The active reporting rate of adverse events, the composition of these events and the processing time were compared between the two groups. Results The rate of active reporting of adverse events before the implementation was lower than that after the implementation (6.7% vs. 8.1%, X 2 = 25.561, P

Abstract Background Work-related musculoskeletal disorders are common among healthcare workers (HCWs) but might be prevented by risk assessment and further promotion of occupational safety and health. The aim of this study was to investigate if the risk assessment instrument TilThermometer can be used to identify risk profiles of physical exposure in HCWs working with patient handling and movement (PHM). Further aims were to describe HCWs’ perceptions and experiences of using the TilThermometer. Methods This feasibility study has a mixed design methodology. In total, 54 HCWs from 17 Swedish care units participated and performed risk assessments with the TilThermometer. Data collected from the risk assessments were used to identify risk profiles of physical exposure. HCWs’ experiences of using the TilThermometer were collected from activity logs and analysed qualitatively. Three questionnaires were used to assess perceived acceptability, appropriateness, and feasibility of the risk assessment, and eight study specific questions were used for perceived usefulness. Results The TilThermometer was used at the care units by assessing each care recipient, and when compiling the data at a group level, a summarized risk profile for the care unit could be provided. Risk for physical exposure was reported as high in two work tasks; no care unit used the high-low adjustable seat when showering care recipients sitting down, and 13% used the recommended assistive devices when putting compression stockings on. However, 99% used high-low adjustable assistive devices when caring and bathing care recipients lying down. TilThermometer was described as easy to use, enabling team reflections and providing an overview of the care units’ recipients and workload, but difficulties in categorizing for mobility groups were also reported. The TilThermometer was, on a five-point scale, perceived as acceptable (mean 3.93), appropriate (mean 3.9), and feasible (mean 3.97). These scores are in line with questions evaluating usefulness. Conclusion The risk assessment provided risk profiles with potential to contribute to care units’ development of a safe patient handling and movement practice. The findings suggest that the TilThermometer can be used to assess risks for physical exposure in relation to patient handling and movement in care units at hospital and nursing homes.