Driven by rapid urbanization and expanding demand for underground space, tunnel construction has become an increasingly important component of modern infrastructure. However, the confined working environment, complex geological conditions, and intensive construction processes pose substantial occupational safety challenges. This paper provides a multidisciplinary analysis of preventive safety measures in tunnel construction, with the goal of enhancing safety performance and reducing risks to workers' health and well-being. The study classifies safety strategies into three dimensions: technical, organizational, and personal, and examines the role of advanced technologies in optimizing working conditions and supporting risk prevention. It also highlights the importance of employee education and continuous safety training, and presents a statistical analysis of injury patterns during high-risk construction phases to identify major contributing factors. By integrating international safety standards, domestic engineering practices, and representative case studies, this research proposes a comprehensive framework for safety management tailored to the complex context of underground construction.
Aviation safety recommendations are the National Transportation Safety Board’s key mechanism for effecting improvements and curtailing subsequent accidents. Aviation safety recommendations and their associated correspondence have been minimally explored in the extant literature, potentially overlooking constrained versus successful risk mitigation themes. This research aimed to qualitatively explore 187 aviation safety recommendations using a framework adapted from the SHELL model. The research also examined the recommendations’ correspondence content to illuminate the characteristics typical of positive versus negative sentiments. The results included risk mitigation themes distributed across the categories of addressees, report statuses, and reiterations. Addressing company, management, manning, or regulatory issues was the most prevalent risk mitigation strategy, followed by physical environment and other human-system support mitigations. The sentiment analyses’ results included distributions across addressees, statuses, time, reiterations, and correspondences. NTSB and addressee correspondence sentiments remained mostly consistent over time and interactions, whereas differences were observed based on addressees and unacceptable report statuses. This article offers the first systematic analysis of NTSB aviation safety recommendations’ risk mitigation themes and addressee correspondences.
Industrial safety. Industrial accident prevention, Medicine (General)
Safety is the main concern in the aviation industry, where even minor operational issues can lead to serious consequences. This study addresses the need for comprehensive aviation accident analysis by leveraging natural language processing (NLP) and advanced AI models to classify the phase of flight from unstructured aviation accident analysis narratives. The research aims to determine whether the phase of flight can be inferred from narratives of post-accident events using NLP techniques. The classification performance of various deep learning models was evaluated. For single RNN-based models, LSTM achieved an accuracy of 63%, precision 60%, and recall 61%. BiLSTM recorded an accuracy of 64%, precision 63%, and a recall of 64%. GRU exhibited balanced performance with an accuracy and recall of 60% and a precision of 63%. Joint RNN-based models further enhanced predictive capabilities. GRU-LSTM, LSTM-BiLSTM, and GRU-BiLSTM demonstrated accuracy rates of 62%, 67%, and 60%, respectively, showcasing the benefits of combining these architectures. To provide a comprehensive overview of model performance, single and combined models were compared in terms of the various metrics. These results underscore the models' capacity to classify the phase of flight from raw text narratives, equipping aviation industry stakeholders with valuable insights for proactive decision-making. Therefore, this research signifies a substantial advancement in the application of NLP and deep learning models to enhance aviation safety.
Existing Embodied Question Answering (EQA) benchmarks primarily focus on household environments, often overlooking safety-critical aspects and reasoning processes pertinent to industrial settings. This drawback limits the evaluation of agent readiness for real-world industrial applications. To bridge this, we introduce IndustryEQA, the first benchmark dedicated to evaluating embodied agent capabilities within safety-critical warehouse scenarios. Built upon the NVIDIA Isaac Sim platform, IndustryEQA provides high-fidelity episodic memory videos featuring diverse industrial assets, dynamic human agents, and carefully designed hazardous situations inspired by real-world safety guidelines. The benchmark includes rich annotations covering six categories: equipment safety, human safety, object recognition, attribute recognition, temporal understanding, and spatial understanding. Besides, it also provides extra reasoning evaluation based on these categories. Specifically, it comprises 971 question-answer pairs generated from small warehouse and 373 pairs from large ones, incorporating scenarios with and without human. We further propose a comprehensive evaluation framework, including various baseline models, to assess their general perception and reasoning abilities in industrial environments. IndustryEQA aims to steer EQA research towards developing more robust, safety-aware, and practically applicable embodied agents for complex industrial environments. Benchmark and codes are available.
Giovanna Takano Natti, Érica Regina Takano Natti, Paulo Laerte Natti
We present a review of the current and future industrial applications of neutrinos. We address the industrial applications of neutrinos in geological and geochemical studies of the Earth's interior, in monitoring earthquakes, in terrestrial communications, in applications for submarines, in monitoring nuclear power plants and fusion reactors, in the management of fissile materials used in nuclear plants, in tracking nuclear tests, among other applications. We also address future possibilities for industrial applications of neutrinos, especially concerning communications in the solar system and geotomography of solar system bodies.
Simone Peters, Matthias Marsall, Till Hasenberg
et al.
Bariatric surgery is an effective long-term treatment for severe obesity, but relapse rates remain high. Digital interventions can enhance patient care, yet research on the intention to use digital discharge management interventions is lacking. This study aims to assess the behavioral intention to use digital discharge management interventions after bariatric surgery and to identify differences in sociodemographic and medical characteristics, as well as potential key drivers and barriers. A cross-sectional study with <i>N</i> = 514 patients was conducted using the Unified Theory of Acceptance and Use of Technology (UTAUT). Mean scores for behavioral intention and predictors were calculated. Group differences were analyzed with independent <i>t</i>-tests and analyses of variance with post hoc tests. Drivers and barriers were assessed through multiple hierarchical regression analysis. The behavioral intention to use digital discharge management interventions was high. Significant predictors included age (β = −0.17, <i>p</i> < 0.001), eHealth literacy (β = 0.10, <i>p</i> = 0.037), internet anxiety (β = −0.15, <i>p</i> = 0.003), and time since bariatric operation (β = −0.13, <i>p</i> = 0.005). The predictors performance expectancy (β = 0.23, <i>p</i> < 0.001), effort expectancy (β = 0.36, <i>p</i> < 0.001), and social influence (β = 0.26, <i>p</i> < 0.001) were significantly positive key factors. These results confirm the need for implementing digital discharge interventions after bariatric surgery, with various drivers and barriers identified for application usage.
Industrial safety. Industrial accident prevention, Medicine (General)
Boyke Elyas Michael Sambeko, Nugroho Susanto, Azir Alfanan
Introduction: Manual handling activities are a main causative factor of low back pain injuries. Around 1.71 billion people worldwide live with musculoskeletal conditions, including low back pain. In the Southeast Asia region, it is estimated that around 369 million people experience low back pain. In Indonesia more than 11.9% of health workers are diagnosed with musculoskeletal disease and diagnostic specific for worker obtained 24.7%. The purpose of this study was to determine the dominant indicators of manual handling for low back pain. Method: Study design used is cross-sectional study. Sample was 62 subjects. The variables of low back pain were collected using a modified questionnaire adopted from the Oswestry Low Back Pain Disability Questionnaire. Data were analyzed with linear regression test for the main indicators contributing to low back pain. Result: Average age of workers is 26.06±7.28, education level senior high school 45%, under 4 years length of work 83.9%. Average manual handling variable is 613.45 ± 383.39, low back pain 6.48 ± 3.607. Manual handling is not significantly related to low back pain r = -0.182. Duration, frequency and load are significant in predicting low back pain. The factors of duration, frequency and lift were estimated to contribute 5.4% for low back pain. Conclusion: The main factors related to low back pain are lifting load for workers, while the factors of lifting duration and frequency are not significantly related to low back pain. The lifting load is the main factor contributing to low back pain.
Internet of Everything (IoE) is a newly emerging trend, especially in homes. Marketing forces toward smart homes are also accelerating the spread of IoE devices in households. An obvious risk of the rapid adoption of these smart devices is that many lack controls for protecting the privacy and security of end users from attacks designed to disrupt lives and incur financial losses. Today the smart home is a system for managing the basic life support processes of both small systems, e.g., commercial, office premises, apartments, cottages, and largely automated complexes, e.g., commercial and industrial complexes. One of the critical tasks to be solved by the concept of a modern smart home is the problem of preventing the usage of IoE resources. Recently, there has been a rapid increase in attacks on consumer IoE devices. Memory corruption vulnerabilities constitute a significant class of vulnerabilities in software security through which attackers can gain control of an entire system. Numerous memory corruption vulnerabilities have been found in IoE firmware already deployed in the consumer market. This paper aims to analyze and explain the resource usage attack and create a low-cost simulation environment to aid in the dynamic analysis of the attack. Further, we perform controlled resource usage attacks while measuring resource consumption on resource-constrained victims' IoE devices, such as CPU and memory utilization. We also build a lightweight algorithm to detect memory usage attacks in the IoE environment. The result shows high efficiency in detecting and mitigating memory usage attacks by detecting when the intruder starts and stops the attack.
Industrial AI is transitioning from traditional deep learning models to large-scale transformer-based architectures, with the Industrial Internet of Things (IIoT) playing a pivotal role. IIoT evolves from a simple data pipeline to an intelligent infrastructure, enabling and enhancing these advanced AI systems. This survey explores the integration of IIoT with large models (LMs) and their potential applications in industrial environments. We focus on four primary types of industrial LMs: language-based, vision-based, time-series, and multimodal models. The lifecycle of LMs is segmented into four critical phases: data foundation, model training, model connectivity, and continuous evolution. First, we analyze how IIoT provides abundant and diverse data resources, supporting the training and fine-tuning of LMs. Second, we discuss how IIoT offers an efficient training infrastructure in low-latency and bandwidth-optimized environments. Third, we highlight the deployment advantages of LMs within IIoT, emphasizing IIoT's role as a connectivity nexus fostering emergent intelligence through modular design, dynamic routing, and model merging to enhance system scalability and adaptability. Finally, we demonstrate how IIoT supports continual learning mechanisms, enabling LMs to adapt to dynamic industrial conditions and ensure long-term effectiveness. This paper underscores IIoT's critical role in the evolution of industrial intelligence with large models, offering a theoretical framework and actionable insights for future research.
This paper presents a study on transforming a traditional human-operated vehicle into a fully autonomous device. By leveraging previous research and state-of-the-art technologies, the study addresses autonomy, safety, and operational efficiency in industrial environments. Motivated by the demand for automation in hazardous and complex industries, the autonomous system integrates sensors, actuators, advanced control algorithms, and communication systems to enhance safety, streamline processes, and improve productivity. The paper covers system requirements, hardware architecture, software framework and preliminary results. This research offers insights into designing and implementing autonomous capabilities in human-operated vehicles, with implications for improving safety and efficiency in various industrial sectors.
To analyze the predominant frequencies of hearing threshold shift and the prevalence of hearing loss related to the co-exposure to noise and solvents. A systematic review and meta-analysis were performed by retrieving published articles from Web of Science, PubMed, Scopus, Embase, and ProQuest until July 2023. Data were extracted in line with the Cochrane Collaboration Handbook, and the Newcastle-Ottawa Scale and Agency for Healthcare Research and Quality were used to assess the studies’ quality. The meta-analysis was used to estimate the odds ratios (ORs) with 95% confidence interval (CI). I<sup>2</sup> and Q statistics were used to prove the heterogeneity. A total of 22 selected studies (9948 workers), six cohort studies and 16 cross-sectional studies were included. The results revealed that 43.7%, 41.3%, and 53.6% of the participants presented with hearing loss due to noise exposure, solvent exposure, and combined exposure to noise and solvent, respectively. The workers exposed to both noise and solvents had a higher risk of hearing loss than those exposed to noise (overall weighted odds ratio [OR]: 1.76) or solvents (overall-weighted OR: 2.02) alone. The poorer hearing threshold in the combined noise and solvents exposure group was mainly at high frequencies (3, 4, 6, and 8 kHz), with a peak of 29.47 dB HL at 6 kHz. The noise-exposed group’s peak hearing threshold was 28.87 dB HL at 4 kHz. The peak hearing threshold of the solvent-exposed group was 28.65 dB HL at 6 kHz. The workers exposed to noise and solvent simultaneously had a higher prevalence of hearing loss than those exposed to solvents. Co-exposure to noise and solvents increases the odds of hearing loss. The dominant hearing threshold changes occurred at 3, 4, 6, and 8 kHz, and the peak value appeared at 6 kHz in workers co-exposed to noise and solvents.
Industrial safety. Industrial accident prevention, Medicine (General)
Paediatric homecare is an advancing field of healthcare, bringing care direct to patients in their own homes. Risk management is an integral component of homecare services, including incident and risk assessment management. The objective of the study was to investigate risk management in homecare focusing on two aspects: incident reporting and risk assessments. A Grounded Theory approach was used to gather key functions of these aspects; these were then mapped using the Functional Resonance Analysis method (FRAM). Nineteen nurses working in paediatric homecare services were interviewed for the study. The interviews were semi-structured and focused on risk, quality, complaints, audit, care, and management. The interview data were transcribed and coded using Nvivo; the data were then converted into functions for utilization in the FRAM tool. The FRAM detailed the process of incident reporting and risk assessment management of the actual work carried out as viewed by the participants of the study. The information was then analysed and contrasted with the organizational policy to gain an understanding of the systems of incident reporting and risk assessments, which then led to the development of a refined process that could have less variability in function. Consequently, changes to policy and training in risk management were recommended to enhance the systems.
Industrial safety. Industrial accident prevention, Medicine (General)
Autonomous electric vehicles (AEVs) hold great promise for the future of automotive engineering, but safety remains a significant challenge in their development and commercialization. Therefore, conducting a comprehensive analysis of AEV development and reported accidents is crucial. This paper reviews the levels of automation in AEVs, their disengagement frequencies, and on-road accident reports. According to the report, numerous manufacturers thoroughly tested AEVs across a distance of more than 3.9 million miles between 2014 and 2022. Disengagement frequencies vary among manufacturers, and approximately 65% of accidents during this period occurred while AEVs were operating in autonomous mode. Notably, the majority of accidents (90%) were caused by other road users, with only a small fraction (approximately 8%) directly attributed to AEVs. Enhancing AEVs' ability to detect and mitigate safety risks from external sources has the potential to significantly improve their safety. This paper provides valuable insights into AEV safety by emphasizing the importance of comprehensively understanding AEV development and reported accidents. Through the analysis of disengagement and accident reports, the study highlights the prevalence of passive accidents caused by other road users. Future research should concentrate on enabling AEVs to effectively detect and respond to safety risks originating from external sources to enhance AEV safety. Overall, this analysis contributes to the ongoing efforts in AEV development and provides guidance for strategies aimed at improving their safety features.
Workers play an important role in recognizing and preventing risks at work sites. However, the current Occupational Safety and Health Act does not provide rights and obligations commensurate with the worker's role as an industrial accident prevention agent. The study analyzed the regulations on workers' industrial accident prevention roles and obligations in major countries and presented the following implications to improve workers' duties and roles as subjects of industrial accident prevention. First, the obligations of workers are specified: ① the duty to pay attention to the safety and health of others affected by their work performance (duty of care), ② the duty to work in accordance with training and instructions on safe work provided by the employer (obligation to comply), ③ reflects the obligation to report immediately upon discovery of an imminent risk of industrial accident (reporting obligation), ④ obligation to cooperate to identify the risks of the workplace and to select appropriate measures(duty of cooperation). Second, by limiting the scope of workers' obligations, it prevents comprehensive obligations from being imposed or the employer's obligations from being passed on to the employees. Third, there should be punishment provisions for workers' violation of their obligations, but punishment should be given for intentional and obvious violations, and the worker's violation of the law should not be interpreted as a reason to defend the employer's violation of the law. Fourth, judicial liability, such as disciplinary action and compensation for damages resulting from a violation of an employee's obligations, is limited to an appropriate scope considering the characteristics of the employment contract. Fifth, give workers the right to improve safety and health (right to participate, right to suggest, right to request improvement, right to report, etc.) and promote active activities. I‘m expecting that by using the research results, future workers' participation in workplace safety and health improvement and industrial accident prevention activities will be promoted, and self-discipline based on the responsibility of industrial accident prevention subjects will be quickly established.
Due to the rapid expansion of the leisure industry, there were about 32,000 golf caddies in South Korea in 2020, an increase of 18.5% compared to 2016. Consequently, they face an increasing industrial accident rate, which is presumed to be the result of exposure to various harmful factors. Through a survey and oral interview of 221 caddies across more than 20 golf courses, health protective measures, protective measures for caddies, preparation for golf cart operation, physical burden, compliance with golf cart safety during games, and golf course responses to emotional labor were investigated in this study to identify safety and health problems of caddies and suggest prevention measures. Preliminary interviews were conducted to confirm golf courses’ safety and health status and participants’ characteristics. Golf caddies’ health and safety were confirmed using frequency analysis, independent sample t-test, one-way analysis of variance (ANOVA), Pearson correlation analysis, and multiple regression analysis. The results showed that caddies’ workplaces were relatively vulnerable to safety and health issues and caddies were exposed to various harmful risk factors. In addition, it was confirmed that golf caddy protective measures, golf cart safety compliance, physical burden, and health protection affect golf courses’ response level to caddies’ emotional labor.
Routine inspections and emergency response are unavoidable needs for power plants, oil refineries, iron works, and industrial units, as they directly influence output and safety. By utilizing autonomous robots, they can be improved. With the exception of facilities located in hazardous areas, such as off-shore factories, where dispatching people might be impossible, accidents caused by human mistakes can be prevented by autonomous inspections and diagnosis of facilities (pumps, tanks, boilers, and so on). Furthermore, if any disaster or accident happens in the plant victims should get immediate assistance. Autonomous robots can enable quick emergency assistance for victims once they are detected. The primary obstacles in robot-assisted inspection operations and victim detection are identifying various types of gauges and reading them, detecting the actual victims in any lighting condition, and taking appropriate actions. This study describes a unique robot vision system for plant inspection and victim detection system that may be used to enhance the frequency of routine checks, hence minimizing equipment faults and accidents (explosions or fires caused by gas leaks) caused by human mistakes or degradation and detecting victims to provide an immediate response. This suggested system can conduct facility inspections by detecting and reading a variety of gauges and finding victims, and it issues reports if any anomalies are discovered. Furthermore, this system can respond to unforeseen anomalous events that are potentially harmful to people and execute specific activities such as valve control if necessary.
A serious accident refers to an industrial accident in which the severity of the accident, such as death, is severe, or in which a large number of injured persons occur. According to the Occupational Safety and Health Act, a serious accident is defined as an accident in which one or more fatalities, two or more injured persons requiring treatment for 3 months or longer, or ten or more injured or occupational diseases occur simultaneously. The reason that the law distinguishes major disasters from general disasters and stipulates separate treatment procedures is to prevent recurrence of such disasters in the future in consideration of the severity of the damage. It should be understood that the Major Accident Punishment Act, which recently strengthened the responsibility of business owners and business managers for the occurrence of serious accidents, is for the prevention and recurrence of serious accidents rather than punishment for those responsible. As the Serious Accident Punishment Act came into effect on January 27, 2022, business owners and managers of businesses or workplaces that are actually controlled, operated, and managed have the duty to secure safety and health for workers. Here, the obligation to secure safety and health means to prevent serious industrial accidents by strengthening safety and health measures through the establishment of a safety and health management system. However, in the Serious Disaster Punishment Act, the specific scope and action plan for each project for the establishment of a safety and health management system are not presented, so preparations for and response to the enforcement of the law are insufficient. Therefore, the establishment of a safety and health management system (hereinafter referred to as the “safety and health management system”) at the workplace where a serious accident occurs is the key, and how to effectively and specifically establish and operate it is an important factor. However, in order to establish an effective and specific safety and health management system through the Serious Disaster Punishment Act and related instructions, it is difficult to respond to the law because the scope and direction of the safety and health management system are insufficient. In this study, for one month from November 18th to December 17th, 2021, 7 construction sites were inspected through three institutions: a health and safety diagnosis institution (Article 48 of the Industrial Safety and Health Act) and a construction accident prevention expert guidance institution. For a total of 12 sites including 5 civil engineering sites, 13 items were checked on the fulfillment of obligations in accordance with safety and health laws Therefore, this study draws on the scope of the safety and health management system at construction sites required by the Serious Disaster Punishment Act and the direction of effective system construction.
While many companies worldwide are still striving to adjust to Industry 4.0 principles, the transition to Industry 5.0 is already underway. Under such a paradigm, Cyber-Physical Human-centered Systems (CPHSs) have emerged to leverage operator capabilities in order to meet the goals of complex manufacturing systems towards human-centricity, resilience and sustainability. This article first describes the essential concepts for the development of Industry 5.0 CPHSs and then analyzes the latest CPHSs, identifying their main design requirements and key implementation components. Moreover, the major challenges for the development of such CPHSs are outlined. Next, to illustrate the previously described concepts, a real-world Industry 5.0 CPHS is presented. Such a CPHS enables increased operator safety and operation tracking in manufacturing processes that rely on collaborative robots and heavy machinery. Specifically, the proposed use case consists of a workshop where a smarter use of resources is required, and human proximity detection determines when machinery should be working or not in order to avoid incidents or accidents involving such machinery. The proposed CPHS makes use of a hybrid edge computing architecture with smart mist computing nodes that processes thermal images and reacts to prevent industrial safety issues. The performed experiments show that, in the selected real-world scenario, the developed CPHS algorithms are able to detect human presence with low-power devices (with a Raspberry Pi 3B) in a fast and accurate way (in less than 10 ms with a 97.04% accuracy), thus being an effective solution that can be integrated into many Industry 5.0 applications. Finally, this article provides specific guidelines that will help future developers and managers to overcome the challenges that will arise when deploying the next generation of CPHSs for smart and sustainable manufacturing.