Hasil untuk "Industrial safety. Industrial accident prevention"

Xia Wu, Shijie Hu, Shibiao Su

This study addresses unreasonable capture velocities used in evaluating dust/toxin-removal efficiency of side-suction hoods adopted in occupational hazard control facilities. A combination of laboratory experiments and numerical simulations is employed to compare the diffusion patterns and capture velocities for side-suction hoods under different conditions. The results indicate that the initial dispersant velocities significantly impact the capture velocities of side-suction hoods. Specifically, the capture velocity for side-suction hoods was 0.12 m/s for an initial dispersant velocity of 0–0.40 m/s, 0.47 m/s for an initial velocity of 0.40–2.00 m/s, and 0.98 m/s for an initial velocity of 2.00–5.00 m/s, respectively. The dust capture efficiency of side-suction hoods was higher than that of chemical toxins, while the dust capture velocity was approximately 55.0 % that of chemical toxins. To address the challenge of undetectable capture velocities in certain scenarios, this study introduces the hood-face airflow velocity as a new indicator for evaluating the dust/toxin-removal capabilities of side-suction hoods based on an analysis of its relationship with the capture velocity. The results indicate that when the capture distance equals the equivalent hood diameter, the hood-face airflow velocity is evaluated as 0.66 m/s for an initial dispersant velocity of 0–0.40 m/s, 2.60 m/s for an initial velocity of 0.40–2.00 m/s, and 5.42 m/s for an initial velocity of 2.00–5.00 m/s, respectively. The findings of this study will provide useful theoretical guidance for the practical evaluation of the dust/toxin-removal performance of workplace side-suction hoods and for optimizing such evaluation methods.

Jiayu Fang, Zhiqi Shao, Haoning Xi et al.

The development of a cross-city accident prevention system is particularly challenging due to the heterogeneity, inconsistent reporting, and inherently clustered, sparse, cyclical, and noisy nature of urban accident data. These intrinsic data properties, combined with fragmented governance and incompatible reporting standards, have long hindered the creation of an integrated, cross-city accident prevention framework. To address this gap, we propose the Mamba Local-ttention Spatial-Temporal Network MLA-STNet, a unified system that formulates accident risk prediction as a multi-task learning problem across multiple cities. MLA-STNet integrates two complementary modules: (i)the Spatio-Temporal Geographical Mamba-Attention (STG-MA), which suppresses unstable spatio-temporal fluctuations and strengthens long-range temporal dependencies; and (ii) the Spatio-Temporal Semantic Mamba-Attention (STS-MA), which mitigates cross-city heterogeneity through a shared-parameter design that jointly trains all cities while preserving individual semantic representation spaces. We validate the proposed framework through 75 experiments under two forecasting scenarios, full-day and high-frequency accident periods, using real-world datasets from New York City and Chicago. Compared with the state-of-the-art baselines, MLA-STNet achieves up to 6% lower RMSE, 8% higher Recall, and 5% higher MAP, while maintaining less than 1% performance variation under 50% input noise. These results demonstrate that MLA-STNet effectively unifies heterogeneous urban datasets within a scalable, robust, and interpretable Cross-City Accident Prevention System, paving the way for coordinated and data-driven urban safety management.

Iskra Alexandra Nola

Mykola Konotopets , Oleksandr Smolkov , Yuliia Pshenychna

Метою статті є розроблення пропозицій з підвищення можливостей пошукових комплексів щодо виявлення роботи оптико-електронних елементів засобів негласного отримання інформації на об’єктах інформаційної діяльності в сучасних умовах. Методи дослідження Під час написання статті застосовано методи аналізу і порівняння для розгляду побудови та порівняння технічних характеристик відеокамер. Наведений в роботі математичний апарат дав змогу проаналізувати математичні залежності параметрів прихованих відеокамер на основі характеристик оптичного приймача. За допомогою методу синтезу розроблено пропозиції стосовно напрямів удосконалення технічних характеристик пошукових комплексів для виявлення оптико-електронних елементів засобів негласного отримання інформації, що можуть бути використані на об’єктах інформаційної діяльності. Отримані результати дослідження. Розроблено формалізовану модель оптико-електронного каналу витоку інформації на об’єктах інформаційної діяльності за енергетичним критерієм, що містить основні фізичні характеристики оптичного приймача. Проведено моделювання процесу перетворення прийнятого вхідного сигналу оптичним приймачем. На основі отриманих результатів запропоновано в подальшому отримати інформаційний критерій (показник), який визначає ефективність пошукових комплексів щодо виявлення роботи оптико-електронного елементу засобу негласного отримання інформації в процесі добування інформації. Входячи до складу апаратних або апаратно-програмних комплексів, призначених для вирішення пошуково-доглядових завдань, в основі свого функціонування, пошукові пристрої використовують метод неруйнівного контролю, який у поєднані з іншими методами такого контролю дає змогу отримати детальнішу інформацію про об’єкти пошуку. Основа принципу дії зазначених апаратних або апаратно-програмних комплексів ґрунтується на процесах виявлення, оцінювання та аналізу сигналів власних шумів оптико-електронних елементів засобів негласного отримання інформації, що можуть бути використані на об’єктах інформаційної діяльності. За результатами аналізу розробленої формалізованої моделі виявлення оптико-електронних засобів добування інформації на об’єктах інформаційної діяльності за енергетичним критерієм, на основі теорії виявлення та розпізнавання сигналів, запропоновано послідовність дій під час проведення пошуково-доглядових завдань для виявлення роботи оптико-електронних елементів засобів негласного отримання інформації на об’єктах інформаційної діяльності в сучасних умовах. Елементи наукової новизни означеного зводяться до конкретизації відомих даних та їх поширенні на нові об’єкти дослідження, в якості яких виступають оптико-електронні елементи засобів негласного отримання інформації, що можуть бути використані зловмисниками на об’єктах інформаційної діяльності. Теоретична й практична значущість викладеного у статті. Важливість результатів цього дослідження для військової та технічної сфер зумовлюється отриманням знань щодо напрямів удосконалення технічних характеристик пошукових комплексів пасивного контролю та визначення послідовності дій під час їх застосування з метою підвищення якості виявлення і подальшої нейтралізації роботи оптико-електронних засобів негласного отримання інформації. Ці результати можуть бути використані у процесі організування та проведення пошукових робіт на об’єктах інформаційної діяльності в сучасних умовах.

Al-Baraa Abdulrahman Al-Mekhlafi, Noreen Kanwal, Mohammed Nasser Alhajj et al.

Safety culture plays a vital role in creating safer work environments, making its understanding important. This paper comprehensively analyzes safety culture research trends through a bibliometric study using the Scopus database. This study provided a full insight by analyzing 7058 papers published between 1978 and 2023, employing the PRISMA method and VOSviewer 1.6.19 for bibliometric mapping. The USA, England, China, and Australia are the leading contributors, with Johns Hopkins University being the most active institution. Approximately 75% of publications are co-authored, indicating strong collaboration in this field. Guldenmund (2000) is the most referenced work in safety culture research. Based on the results, this work identifies significant geographical gaps, particularly in Oceania, South America, the Middle East, Southeast Asia, and Africa, as well as underexplored sectors such as transportation, logistics, energy, sports, education, and construction. The COVID-19 pandemic has profoundly impacted research in this area, particularly healthcare, while potentially diverting attention from other critical sectors. This study contributes a fresh perspective on the trends of safety culture research, offering valuable insights for scholars and practitioners. Additionally, it highlights the importance of interdisciplinary collaboration in addressing the unique challenges faced by safety culture across diverse industries and regions.

Jian Shen, Maureen Hassall

Fatal and serious injury rates remain unacceptably high in the construction industry. Leadership plays a critical role in safety management and serious and fatal injury prevention. However, limited research has examined industry practitioners’ perceptions of leadership and how it influences safety outcomes, particularly in the prevention of serious and fatal injuries in the construction industry. Therefore, a theoretical model for capturing perceptions of safety leadership was developed from a systematic literature review. To ensure that the labels and language used in the model can be understood by industry practitioners, a Delphi study was conducted involving twelve experts. Over three iterative rounds, the model was refined to include five leadership styles, seventeen elements, and eighty-five descriptive statements spanning the range from laissez-faire to transformational leadership. The refined model provides a comprehensive framework for understanding safety leadership and serves as a foundation for future empirical testing with frontline construction workers.

Teppei Imai, Noriko Nishikido, Jiro Moriguchi et al.

Adrian Zlocki, Lutz Eckstein

Raiyaan Abdullah, Yogesh Singh Rawat, Shruti Vyas

Recent advances in vision-language models (VLMs) have enabled impressive generalization across diverse video understanding tasks under zero-shot settings. However, their capabilities in high-stakes industrial domains-where recognizing both routine operations and safety-critical anomalies is essential-remain largely underexplored. To address this gap, we introduce iSafetyBench, a new video-language benchmark specifically designed to evaluate model performance in industrial environments across both normal and hazardous scenarios. iSafetyBench comprises 1,100 video clips sourced from real-world industrial settings, annotated with open-vocabulary, multi-label action tags spanning 98 routine and 67 hazardous action categories. Each clip is paired with multiple-choice questions for both single-label and multi-label evaluation, enabling fine-grained assessment of VLMs in both standard and safety-critical contexts. We evaluate eight state-of-the-art video-language models under zero-shot conditions. Despite their strong performance on existing video benchmarks, these models struggle with iSafetyBench-particularly in recognizing hazardous activities and in multi-label scenarios. Our results reveal significant performance gaps, underscoring the need for more robust, safety-aware multimodal models for industrial applications. iSafetyBench provides a first-of-its-kind testbed to drive progress in this direction. The dataset is available at: https://github.com/iSafetyBench/data.

Vincenzo Petrone, Enrico Ferrentino, Pasquale Chiacchio

This paper presents the full dynamic model of the UR10 industrial robot. A triple-stage identification approach is adopted to estimate the manipulator's dynamic coefficients. First, linear parameters are computed using a standard linear regression algorithm. Subsequently, nonlinear friction parameters are estimated according to a sigmoidal model. Lastly, motor drive gains are devised to map estimated joint currents to torques. The overall identified model can be used for both control and planning purposes, as the accompanied ROS2 software can be easily reconfigured to account for a generic payload. The estimated robot model is experimentally validated against a set of exciting trajectories and compared to the state-of-the-art model for the same manipulator, achieving higher current prediction accuracy (up to a factor of 4.43) and more precise motor gains. The related software is available at https://codeocean.com/capsule/8515919/tree/v2.

Rongfei Li, Francis Assadian

Image-based visual servoing (IBVS) methods have been well developed and used in many applications, especially in pose (position and orientation) alignment. However, most research papers focused on developing control solutions when 3D point features can be detected inside the field of view. This work proposes an innovative feedforward-feedback adaptive control algorithm structure with the Youla Parameterization method. A designed feature estimation loop ensures stable and fast motion control when point features are outside the field of view. As 3D point features move inside the field of view, the IBVS feedback loop preserves the precision of the pose at the end of the control period. Also, an adaptive controller is developed in the feedback loop to stabilize the system in the entire range of operations. The nonlinear camera and robot manipulator model is linearized and decoupled online by an adaptive algorithm. The adaptive controller is then computed based on the linearized model evaluated at current linearized point. The proposed solution is robust and easy to implement in different industrial robotic systems. Various scenarios are used in simulations to validate the effectiveness and robust performance of the proposed controller.

Matthieu Mastio, Paul Saves, Benoit Gaudou et al.

Industrial symbiosis fosters circularity by enabling firms to repurpose residual resources, yet its emergence is constrained by socio-spatial frictions that shape costs, matching opportunities, and market efficiency. Existing models often overlook the interaction between spatial structure, market design, and adaptive firm behavior, limiting our understanding of where and how symbiosis arises. We develop an agent-based model where heterogeneous firms trade byproducts through a spatially embedded double-auction market, with prices and quantities emerging endogenously from local interactions. Leveraging reinforcement learning, firms adapt their bidding strategies to maximize profit while accounting for transport costs, disposal penalties, and resource scarcity. Simulation experiments reveal the economic and spatial conditions under which decentralized exchanges converge toward stable and efficient outcomes. Counterfactual regret analysis shows that sellers' strategies approach a near Nash equilibrium, while sensitivity analysis highlights how spatial structures and market parameters jointly govern circularity. Our model provides a basis for exploring policy interventions that seek to align firm incentives with sustainability goals, and more broadly demonstrates how decentralized coordination can emerge from adaptive agents in spatially constrained markets.

Lin Zhang, Zhen Shao, Jose Benitez

The embeddedness of digital platforms in firms to improve industrial accident prevention performance remains a challenge for safety management. A pressing question exists regarding appropriate pathways to improve firm accident prevention performance by leveraging digital platforms. Prior research focuses on individual and organizational factors to prevent industrial accidents while ignoring information technology (IT) from a holistic perspective. Following a socio-technical lens, this study fills the research gap by proposing a new conceptualization of technical and institutional components and examining their combined effects in shaping individual and organizational accident prevention outcomes (i.e., employee accident prevention competence and firm accident prevention performance). Specifically, we introduce and conceptualize the concept of digital platform affordance to represent the technical component and theorize the external institutional force (government pressure) and the internal human agency (top management support) as the institutional components of digital platforms. By integrating the socio-technical lens, IT affordance theory, and institutional theory, we develop a theoretical model to examine the interaction effects between digital platform affordance, government pressure, and top management support on accident prevention performance. We conduct a multilevel modeling approach to test the proposed research model using a fine-grained matched-pair survey from multiple respondents (i.e., employees and safety executives) in 161 industrial firms in China. We find that digital platform affordance enhances firm accident prevention performance by improving employee accident prevention competence. The results also indicate that the magnitude of the moderating effect of government pressure is contingent upon top management support. This study conceptualizes and assesses the role of digital platforms in safety management and indicates that the amalgamation of their technical/institutional components is a promising means to prevent industrial accidents. We also provide a new perspective for Operations Management scholars to reinterpret the socio-technical lens in the emerging digitally enabled safety management subdiscipline.

Rong Yang, Mingguang Zhang, Y. Zhang

Risk perception is one of the factors that guide human behavior in the workplace. In accident prevention and emergency response, the risk perception of employees affects the safety behaviour and efficiency of emergency disposal. Besides, risk perception is a complicated process restricted by many influencing factors. The study aims to identify the most important factors affecting risk perception. This study had three phases. In the first, factors affecting the risk perception were extracted based on the questionnaire survey and the expert scoring. Then, the Interpretive Structure Model (ISM) was used to stratify the 14 influencing factors of risk perception and obtain a hierarchical structure chart. Finally, by analyzing the influence paths in the ISM, the system dynamics feedback loop diagram was constructed. The model took the state variable “risk perception→ risk response→ risk identification→ risk communication→ risk perception” as the main circular loop, and was supplemented and perfected by multiple feeder loop circuits. Research indicates risk experience is the most fundamental factor affecting risk perception. In the aspect of sensitivity analysis, the study shows that the risk perception of employees is related to the distance of the risk source. Its effectiveness in quantifying qualitative data, identifying influential factors, and establishing cause-effect relationships make it a valuable tool for enhancing risk perception in chemical industry park.

I. Petrenko, O. Chencheva, S. Zozulia et al.

The article aims to analyse the causes of occupational injuries, identify risk factors for employees, and improve workplace safety and accident prevention standards. To achieve the stated aim, the authors applied a comprehensive approach, which included an in-depth study of recent injury trends, identification of the main causal factors, and a thorough examination of the risks affecting working conditions and leading to injuries. The research methodology included a variety of tools, such as a deep analysis of statistical data on injuries in the context of an industrial enterprise, a review of internal documentation, and an evaluation of risk factors. The study results provided a detailed picture of the changing trajectory of injuries among industrial workers, which is closely related to their length of service and experience at the enterprise. Based on this understanding, we derived a specific risk assessment formula that synthesised the relationship between injuries and length of service. In addition, a thorough analysis of the structure of injuries during different shifts revealed a pronounced tendency for accidents to occur in the evening and at night, partly due to the psychological and physiological stress experienced by employees during these periods and the impact of microclimatic working conditions. To enhance the effectiveness of the risk assessment methodology, we proposed to integrate a comfort factor coefficient that shows the microclimatic factors’ impact on occupational safety and injury rates. These efforts have resulted in an improved risk assessment formula that can provide a more accurate prognosis of injury incidents and help optimise occupational health and safety protocols for employees at industrial enterprises. In addition, using statistical data, the study determined the cause-effect relation between risks to industrial workers, shown through the visual representation of an Ishikawa diagram, thus providing a further perspective for risk assessment in the enterprise. Keywords: injuries, risks, injury analysis, mining and processing plant, industry.

Michael D. Keall, Nevil Pierse, Chris W. Cunningham et al.

(1) Background: Fall injuries in the home present a major health burden internationally for all age groups. One effective intervention to prevent falls is home modification, but safety is only increased if opportunities to install safety modifications are taken up. This study sought to identify factors that may lead to a higher uptake of no-cost home modifications when these are offered to people living in the community. (2) Methods: We studied 1283 houses in the New Zealand cities of New Plymouth and Wellington. Using logistic regression, we modelled the odds of uptake according to the number of access steps, the provider who was undertaking the modifications, occupant ethnicity, prior fall injury history, and age group. (3) Results: Homes with older residents (age 65+) had higher uptake rates (35% vs. 27% for other homes). Larger numbers of access steps were associated with higher uptake rates. There was indicative evidence that homes with Indigenous Māori occupants had a higher uptake of the modifications for one of the two providers, but not the other. (4) Conclusions: No-cost home safety modifications offered via cold calling are likely to have relatively low uptake rates but the households that do consent to the modifications may be those who are more likely to benefit from the concomitant increased safety.

V. Bene, Barbara Elek, Norbert Daruka

Chemical plants play a key role in modern industrial society, producing and processing essential chemicals for many industries. However, these plants also face potential threats to the environment and human health, which they must address through appropriate safety measures. Chemical safety is a priority in terms of accident prevention, environmental protection, and public information. This is the only way to ensure the sustainable operation of chemical plants and the effective reduction of environmental risks, which are essential to protect the public and promote sustainable development. Developing and enforcing appropriate chemical safety measures and regulations is key to protecting not only the plants but also communities and the environment. This will ensure harmonious coexistence and a sustainable future for the chemical industry and the surrounding social environment.

Y. Jeon, Yuha Yang

This study investigates the role of labor union leadership and participatory leadership in the process of workers' expressions being acknowledged and subsequently implemented by management in the context of the increasingly emphasized industrial accident prevention. Specifically focusing on issues related to industrial safety closely associated with the quality of workers' lives, we explore how labor unions exercise leadership in fostering employee participation and innovation within a collaborative labor-management environment. Through case analyses, this study reveals that when labor unions actively collaborate with or exhibit proactive leadership towards management, they play a crucial role as channels that actively listen to workers' expressions, convert them into collective voices, and convey them to the management. Furthermore, in such cases, labor unions also function as coordinating bodies, actively seeking additional opinions and performing supplementary adjustments to decisions established in collective agreements. These improvements subsequently lead to a virtuous cycle, creating an environment conducive to the realization of genuine participatory organizations through sustained and more proactive expressions and improvement activities. The findings of this study suggest that for individual and collective expressions of workers to influence managerial decision-making, a foundation of trust built through prolonged labor-management collaboration activities and systems is essential. Additionally, continuous educational efforts to develop workers' participation capabilities are necessary. Importantly, the leadership of labor unions, representing workers, and the leadership of management, accepting and incorporating workers' opinions into actual managerial activities, both play crucial roles in enhancing worker participation. Thus, for effective communication of on-site workers' proposals and demands to the management, a paradigm shift in the perceptions of both management and labor unions, coupled with the development of leadership competencies aligned with the times, is deemed necessary.

J. V. D. Santos, Guilherme Silva, E. Borges et al.

This paper presents a comprehensive study on the application of computer vision technologies for scenario recognition and tracking in industrial cargo handling operations, particularly within non-sparse and autonomous outdoor environments. We introduce a novel dataset and methodology aimed at real-time identification of various elements including personnel, containers, cages, equipment, boxes, and piping. Using the YOLOv8 neural network, our experiments achieved high accuracy, with precision reaching up to 87.3%. The model demonstrated effective detection and segmentation capabilities even in complex, non-sparse environments. These results suggest a significant enhancement in the decision-making processes and accident prevention strategies within industrial operations, underscoring the potential of advanced computer vision systems in improving safety and operational efficiency.

Hafiz Mughees Ahmad, Afshin Rahimi

Workplace accidents continue to pose significant risks for human safety, particularly in industries such as construction and manufacturing, and the necessity for effective Personal Protective Equipment (PPE) compliance has become increasingly paramount. Our research focuses on the development of non-invasive techniques based on the Object Detection (OD) and Convolutional Neural Network (CNN) to detect and verify the proper use of various types of PPE such as helmets, safety glasses, masks, and protective clothing. This study proposes the SH17 Dataset, consisting of 8,099 annotated images containing 75,994 instances of 17 classes collected from diverse industrial environments, to train and validate the OD models. We have trained state-of-the-art OD models for benchmarking, and initial results demonstrate promising accuracy levels with You Only Look Once (YOLO)v9-e model variant exceeding 70.9% in PPE detection. The performance of the model validation on cross-domain datasets suggests that integrating these technologies can significantly improve safety management systems, providing a scalable and efficient solution for industries striving to meet human safety regulations and protect their workforce. The dataset is available at https://github.com/ahmadmughees/sh17dataset.