Hasil untuk "Industrial safety. Industrial accident prevention"

Michele Banfi, Rocco Felici, Stefano Baraldo et al.

This paper presents EBuddy, a voice-guided workflow orchestrator for natural human-machine collaboration in industrial environments. EBuddy targets a recurrent bottleneck in tool-intensive workflows: expert know-how is effective but difficult to scale, and execution quality degrades when procedures are reconstructed ad hoc across operators and sessions. EBuddy operationalizes expert practice as a finite state machine (FSM) driven application that provides an interpretable decision frame at runtime (current state and admissible actions), so that spoken requests are interpreted within state-grounded constraints, while the system executes and monitors the corresponding tool interactions. Through modular workflow artifacts, EBuddy coordinates heterogeneous resources, including GUI-driven software and a collaborative robot, leveraging fully voice-based interaction through automatic speech recognition and intent understanding. An industrial pilot on impeller blade inspection and repair preparation for directed energy deposition (DED), realized by human-robot collaboration, shows substantial reductions in end-to-end process duration across onboarding, 3D scanning and processing, and repair program generation, while preserving repeatability and low operator burden.

Xiangyu Su, Juzhan Xu, Oliver van Kaick et al.

In modern industrial production, multiple robots often collaborate to complete complex manufacturing tasks. Large language models (LLMs), with their strong reasoning capabilities, have shown potential in coordinating robots for simple household and manipulation tasks. However, in industrial scenarios, stricter sequential constraints and more complex dependencies within tasks present new challenges for LLMs. To address this, we propose IMR-LLM, a novel LLM-driven Industrial Multi-Robot task planning and program generation framework. Specifically, we utilize LLMs to assist in constructing disjunctive graphs and employ deterministic solving methods to obtain a feasible and efficient high-level task plan. Based on this, we use a process tree to guide LLMs to generate executable low-level programs. Additionally, we create IMR-Bench, a challenging benchmark that encompasses multi-robot industrial tasks across three levels of complexity. Experimental results indicate that our method significantly surpasses existing methods across all evaluation metrics.

Stefan Lenz, Sotiris Michaelides, Moritz Rickert et al.

Traditionally, industrial control systems (ICS) were designed without security in mind, prioritizing availability and real-time communication. As these systems increasingly become targets of powerful adversaries, security can no longer be neglected. Driven by flexibility and automation needs, ICS are transitioning from wired to 5G communication, introducing new attack surfaces and a less reliable communication medium, thereby exacerbating existing security challenges. Given their critical role in society, a comprehensive evaluation of their security is imperative. To this end, we introduce SWICS, a fully virtual testbed simulating an ICS in a realistic 5G environment, and study how this transition affects security under varying channel conditions. Our results show three key findings: under optimal channel conditions, industrial 5G networks can achieve resilience comparable to wired systems, while degraded channel conditions can amplify traditional attacks, threaten system stability, and undermine detection mechanisms based on predictable traffic patterns. We further demonstrate the inherent limits of securing 5G channels for ICS through eavesdropping and jamming on the open-air interface. Our work highlights the interplay between security and 5G channel conditions, showing that traditional security controls may no longer be sufficient and motivating further research.

Khashayar Kazemzadeh

Vesna Batinović, Paula Brezak, Marjeta Majer

Ahmed Faleh Alanazi, Musab Rabi, Mazen J. Al-Kheetan et al.

This study investigates the influence of adaptive leadership on crisis management effectiveness in complex construction engineering projects in Saudi Arabia. Adaptive leadership was conceptualized through six core dimensions: Flexibility in Decision-Making, Emotional Intelligence, Leader-Follower Communication, Problem-Solving Adaptability, Resilience in Leadership, and Fostering Collaboration. The study aimed to evaluate the impact of these leadership dimensions on crisis response effectiveness and safety outcomes within the high-risk, dynamic environment of the Saudi construction sector. A quantitative cross-sectional survey was conducted among managerial and supervisory personnel across major engineering and construction firms in Saudi Arabia. A total of 183 valid responses were obtained using a non-probability convenience sampling technique. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicated that five adaptive leadership dimensions—Flexibility in Decision-Making, Emotional Intelligence, Problem-Solving Adaptability, Resilience in Leadership, and Fostering Collaboration—had significant positive effects on crisis management effectiveness. However, Leader-Follower Communication did not demonstrate a statistically significant relationship with crisis outcomes. The findings contribute theoretical value by validating an adaptive leadership framework tailored to engineering project crises. Practically, the study underscores the importance of enhancing leadership flexibility, emotional intelligence, and collaborative engagement to strengthen crisis responsiveness and project continuity in Saudi construction firms. Recommendations include the development of targeted leadership training programs and the integration of digital technologies to support adaptive decision-making in real-time crisis conditions, resulting in better Safety and Crisis Management. Although, study limitations include reliance on self-reported data and the context-specific focus on the Saudi construction sector, which may affect generalizability, the findings are contextualized through comparison with international literature to support broader relevance.

Musaad M. Alruwaili, Fehmidah Munir, Patricia Carrillo et al.

<b>Background</b>: Mental health is increasingly recognized as an integral component of occupational health and safety, particularly in high-risk industries such as construction. However, in Saudi Arabia, limited attention has been given to understanding mental health knowledge, beliefs, and workplace support mechanisms, especially among a diverse workforce that includes both migrant and national employees. <b>Methods</b>: This qualitative study employed semi-structured interviews with 30 construction sector participants occupying a range of professional roles. Thematic analysis was conducted using NVivo 15 software, guided by the COM-B model and Health Belief Model, to explore perceptions related to mental health, safety practices, and organizational interventions. <b>Results</b>: The findings highlight significant disparities between migrant and national workers. Migrant workers reported greater challenges related to language barriers, cultural stigma, and a lack of access to culturally appropriate mental health support. National workers described slightly better access to safety and health initiatives but still reported inadequate mental health training. Key barriers across the workforce included limited leadership engagement, stigma, resource constraints, and insufficient organizational training. Existing health and safety programmes were largely focused on physical safety, with minimal incorporation of mental health concerns. <b>Conclusions</b>: The study reveals a pressing need to integrate mental health into occupational safety frameworks in the Saudi construction sector. Culturally sensitive, leadership-supported mental health initiatives are essential to addressing disparities and promoting holistic workers’ well-being across both migrant and national populations.

Jie Zhang, Changzai Pan, Kaiwen Wei et al.

Extensive research has been conducted to explore the capabilities of large language models (LLMs) in table reasoning. However, the essential task of transforming tables information into reports remains a significant challenge for industrial applications. This task is plagued by two critical issues: 1) the complexity and diversity of tables lead to suboptimal reasoning outcomes; and 2) existing table benchmarks lack the capacity to adequately assess the practical application of this task. To fill this gap, we propose the table-to-report task and construct a bilingual benchmark named T2R-bench, where the key information flow from the tables to the reports for this task. The benchmark comprises 457 industrial tables, all derived from real-world scenarios and encompassing 19 industry domains as well as 4 types of industrial tables. Furthermore, we propose an evaluation criteria to fairly measure the quality of report generation. The experiments on 25 widely-used LLMs reveal that even state-of-the-art models like Deepseek-R1 only achieves performance with 62.71 overall score, indicating that LLMs still have room for improvement on T2R-bench.

Henning Femmer, Frank Houdek, Max Unterbusch et al.

Requirements quality is central to successful software and systems engineering. Empirical research on quality defects in natural language requirements relies heavily on datasets, ideally as realistic and representative as possible. However, such datasets are often inaccessible, small, or lack sufficient detail. This paper introduces QuRE (Quality in Requirements), a new dataset comprising 2,111 industrial requirements that have been annotated through a real-world review process. Previously used for over five years as part of an industrial contract, this dataset is now being released to the research community. In this work, we furthermore provide descriptive statistics on the dataset, including measures such as lexical diversity and readability, and compare it to existing requirements datasets and synthetically generated requirements. In contrast to synthetic datasets, QuRE is linguistically similar to existing ones. However, this dataset comes with a detailed context description, and its labels have been created and used systematically and extensively in an industrial context over a period of close to a decade. Our goal is to foster transparency, comparability, and empirical rigor by supporting the development of a common gold standard for requirements quality datasets. This, in turn, will enable more sound and collaborative research efforts in the field.

Kailai Sun, Tianxiang Lan, Yang Miang Goh et al.

Dmytro Avramenko

During the full-scale armed aggression of the russian federation, which began on February 24, 2022, against Ukraine, the Armed Forces of Ukraine faced powerful challenges to protect national security and territorial integrity. Military-strategic communications played an important role in maintaining internal stability and ensuring trust in the government and the Armed Forces. Effective decision-making in such conditions requires a decision support system that ensures fast and accurate processing of a large amount of information and provides recommendations with appropriate justification based on data analysis. It is especially important to take into account the large amount of information that comes from various sources and needs to be processed and analyzed in real time. In this article, we have reviewed the functional model of the decision support system in the strategic communication system of the Armed Forces of Ukraine and analyzed the principles of its functioning and impact on the decision-making process. The purpose of this scientific article is to study the functional model of the decision support system in the strategic communications system of the Armed Forces of Ukraine. To achieve this goal, the following tasks have been identified: consider the basic principles of the functional model of a decision support system; analyze the existing theoretical and applied aspects of decision support systems; to study the peculiarities of using decision support systems in the field of strategic communications of the Armed Forces of Ukraine. When writing the article, methods of analysis, modeling and system-structural approach were used in accordance with the tasks defined by the goal. The main focus was not only on describing the structure of this system, but also on identifying specific requirements due to the peculiarities of strategic communications in the modern information environment. The identified requirements include the need to integrate existing solutions and mathematical models, as well as the ability to accumulate and store standard solutions and processes for further use. The described model of the system takes into account the aspects of information analysis and processing in the context of strategic communications, for more effective management and decision-making in the field of defense. The practical significance of this article lies in understanding approaches to optimizing management and making important decisions in the defense sector. The author emphasizes the need to integrate into the current model of the unit for preserving standard solutions for further use and experience building. This paper analyzes the functional model of a decision support system for solving tasks related to strategic communications in the Armed Forces of Ukraine and describes its structure. To build such a system, specific requirements are identified that take into account the peculiarities of strategic communications in the information environment. These requirements include the ability to integrate existing solutions and mathematical models, the ability to accumulate and store standard solutions and processes. The study has the potential for practical application in the management of strategic communications in the Armed Forces of Ukraine and similar areas. The direction of further research could be the development of additional blocks of information accumulation for decision-making in the system of strategic communications in the form of generalization of experience. The use of the "experience" block will make it possible to compare the expert assessment with the decisions already made that have received a positive or negative assessment. Thus, the decision support system is likely to reduce the time for generating a decision, and the introduction of additional relational criteria to check the likelihood of success in the case of making a decision based on the knowledge already acquired will increase efficiency.

Katrina Volgemute, Zermena Vazne, Sergio A. Useche

While the role of safe riding behavior as a safety contributor for cyclists has been increasingly studied in recent years, there have been few studies analyzing cycling behavior in relation to crash-related outcomes. Indeed, to the best of our knowledge, this is the first time this issue has been addressed in the case of Latvia. <b>Aim:</b> The objective of this study was to assess the relationships among self-reported cyclists’ behavior, traffic safety literacy, and their cycling crash involvement rates. <b>Method</b>: A total of 299 cyclists aged M = 32.8 from across Latvia participated in an online survey, which included questions regarding respondents’ demographics, frequency of riding, cycling behaviors, and the number of crashes in the previous five years. The Cycling Behavior Questionnaire (CBQ) and the Cyclist Risk Perception and Regulation Scale (RPRS) were applied to assess cyclists’ behavior patterns and traffic safety literacy. <b>Results:</b> According to the findings, it can be inferred that cyclists frequently engage in riding errors and traffic violations while cycling. Those who exhibit more anti-social behavior (such as traffic violations and riding errors) patterns are also more likely to be involved in road crashes. Conversely, cyclists with greater positive behavior rates more often also tend to possess better knowledge of traffic rules and exhibit a heightened risk perception, indicating a greater awareness of road traffic safety. <b>Conclusions:</b> This study underscores key age differences, with older individuals significantly less involved in riding crashes, exhibiting fewer driving errors and a higher level of risk perception, which serves as a relevant factor in road safety. At the practical level, these results stress the need to address both traffic safety literacy and protective cycling factors of cyclists, to improve overall road safety and promote active transport modes in Latvia.

Winnie Koskei, Peterson Warutere, Bernard Awuonda

Hairdressers are exposed to awkward posture, prolonged standing, long working hours and chemical hazards capable of causing adverse health effects. The present study aimed to evaluate hairdressers' safety and health risks. The study adopted a descriptive cross-sectional and analytical design. Systematic random sampling was used to select salons and hairdressers. Closed and open-ended questionnaires were distributed to 286 hairdressers who consented to participate in the study. An observation checklist, WISHA caution checklist, thermometer, light meter and noise level meter were used to collect data in the sampled salon. Data were analyzed descriptively and with regression analysis. It was found that the average space for salons was 8.79 m2, and 68.5% of hairdressers work for long hours (11-12 hours). It was established that 5.48% of salons have an adequate amount of light and that 8.22% of salons have high temperatures. Aprons were the most used personal protective equipment by hairdressers. Manual handling of salon equipment and awkward posture cause musculoskeletal disorders among hairdressers. Their odd ratios impacting the health and safety of hairdressers were 2.706 and 2.728, respectively. The study reveals that hairdressing salon designs, space, lighting, and temperatures affect the health and safety of hairdressers. The hours off work and minimal or no breaks also have negative impacts on the health and safety of hairdressers

He Wen

In contemporary safety management, despite the abundance of safety data gathered from routine operation tasks and safety management activities, actions cannot prevent all accidents effectively due to a lack of effective utilization of these data as safety knowledge. To bridge this gap, this paper proposes a hybrid proactive safety model integrating data-driven and knowledge-driven approaches. The model comprises three main steps: proactive safety actions to generate safety data, data-driven approaches to mine safety data, and knowledge-driven approaches to depicting risk knowledge graphs. Application of this model to a continuous stirred tank reactor (CSTR) scenario demonstrates its efficacy in identifying and addressing safety issues proactively. The results demonstrate the effectiveness and practicality of the proposed proactive safety model, suggesting its endorsement within both academic and industrial applications.

Vishal Verma, Vinod Reddy, Jaiprakash Ravi

The increasing need for causal analysis in large-scale industrial datasets necessitates the development of efficient and scalable causal algorithms for real-world applications. This paper addresses the challenge of scaling causal algorithms in the context of conducting causal analysis on extensive datasets commonly encountered in industrial settings. Our proposed solution involves enhancing the scalability of causal algorithm libraries, such as EconML, by leveraging the parallelism capabilities offered by the distributed computing framework Ray. We explore the potential of parallelizing key iterative steps within causal algorithms to significantly reduce overall runtime, supported by a case study that examines the impact on estimation times and costs. Through this approach, we aim to provide a more effective solution for implementing causal analysis in large-scale industrial applications.

Shengye Wan, Joshua Saxe, Craig Gomes et al.

Recent research advances in Artificial Intelligence (AI) have yielded promising results for automated software vulnerability management. AI-based models are reported to greatly outperform traditional static analysis tools, indicating a substantial workload relief for security engineers. However, the industry remains very cautious and selective about integrating AI-based techniques into their security vulnerability management workflow. To understand the reasons, we conducted a discussion-based study, anchored in the authors' extensive industrial experience and keen observations, to uncover the gap between research and practice in this field. We empirically identified three main barriers preventing the industry from adopting academic models, namely, complicated requirements of scalability and prioritization, limited customization flexibility, and unclear financial implications. Meanwhile, research works are significantly impacted by the lack of extensive real-world security data and expertise. We proposed a set of future directions to help better understand industry expectations, improve the practical usability of AI-based security vulnerability research, and drive a synergistic relationship between industry and academia.

Xuanchao Ma, Yanlin Jiang, Hongyan Liu et al.

Recent years have witnessed a broader range of applications of image processing technologies in multiple industrial processes, such as smoke detection, security monitoring, and workpiece inspection. Different kinds of distortion types and levels must be introduced into an image during the processes of acquisition, compression, transmission, storage, and display, which might heavily degrade the image quality and thus strongly reduce the final display effect and clarity. To verify the reliability of existing image quality assessment methods, we establish a new industrial process image database (IPID), which contains 3000 distorted images generated by applying different levels of distortion types to each of the 50 source images. We conduct the subjective test on the aforementioned 3000 images to collect their subjective quality ratings in a well-suited laboratory environment. Finally, we perform comparison experiments on IPID database to investigate the performance of some objective image quality assessment algorithms. The experimental results show that the state-of-the-art image quality assessment methods have difficulty in predicting the quality of images that contain multiple distortion types.

Mehran Eskandari Torbaghan, Manu Sasidharan, Louise Reardon et al.

Saliha Çetinyokuş

Abstract H 2 S gas emissions occur in various processes, especially in the purification of polluted waters, mining, and petroleum refining processes and this extremely dangerous chemical has serious toxic emission, explosion, and fire effects. These physical effects vary considerably depending on the source strength. Therefore, consequence analyzes based on source strength models are critical components in the process of assessing hazards. In this study, a consequence analysis was performed with accident scenarios related to different sources of H 2 S gas. ALOHA 5.4.7 and EFFECTS 8.0.1 Software were used for physical effects modeling. Modeling studies were conducted on direct source (continuous release:100 m 3 /s, 5 m 3 /s), puddle source (100 m 3 , 5 m 3 ), tank source (vertical‐horizontal cylindrical, spherical, 1.57 m 3 ), and gas pipeline source with ALOHA Software. Possible Gas LOC (Levels of Concern) scenario Leak (G3) and Gas LOC scenario release in 10 min (G2) scenarios were studied with EFFECTS Software. When the results of both software were compared with each other, it was seen that the effects of thermal radiation and explosion were of similar order and value, and the toxic effect values were very different from each other. Both software allow for a release above ground level for gases that behave as neutrally buoyant. The main difference between the dispersion models used in the software is that the gas is assumed to behave as non‐interacting ideal gas in ALOHA, but in EFFECTS deviations from ideal gas are considered. And, ALOHA dispersion models use surface roughness lengths in a limited fashion and only include 60‐min AEGLs. ALOHA does not account for topographic steering or winds that vary with time in EFFECTS. It can be said that the results of the ALOHA Software are more conservative than the EFFECTS Software.

Amir hamzeh SABZI, Amir Hoshang NAZARPOURI, Sharareh SABZI

Introduction: One of the most important key factors in achieving development is the optimal use of human resources. Therefore, it is of especial importance to pay attention to the talent and role of women in managerial positions. The purpose of this study is to investigate the relationship between glass rock, and job motivation and organizational maturity in female employees of Lorestan University of Medical Sciences. Methods: This was a descriptive-correlational study. 367 female employees of Lorestan University of Medical Sciences were selected based on Morgan table and available sampling. To collect data, questionnaires regarding glass rock, organizational maturity and job motivation were used. Data were analyzed using Pearson correlation and regression. Results: The results showed that there was a significant negative relationship between glass rock, and job motivation and organizational maturity of female employees of Lorestan University of Medical Sciences. The results of regression analysis also showed that the glass rock was able to predict the job motivation and organizational maturity of female employees. Conclusion: According to the results, the glass rock has a negative relationship with job motivation and organizational maturity of female employees. The presence of glass rock in the organization reduces the organizational maturity and job motivation of female employees.