Hasil untuk "Industrial safety. Industrial accident prevention"

Wei Luo, Haiming Yao, Wenyong Yu

Industrial anomaly detection plays a crucial role in ensuring product quality control. Therefore, proposing an effective anomaly detection model is of great significance. While existing feature-reconstruction methods have demonstrated excellent performance, they face challenges with shortcut learning, which can lead to undesirable reconstruction of anomalous features. To address this concern, we present a novel feature-reconstruction model called the \textbf{T}emplate-based \textbf{F}eature \textbf{A}ggregation \textbf{Net}work (TFA-Net) for anomaly detection via template-based feature aggregation. Specifically, TFA-Net first extracts multiple hierarchical features from a pre-trained convolutional neural network for a fixed template image and an input image. Instead of directly reconstructing input features, TFA-Net aggregates them onto the template features, effectively filtering out anomalous features that exhibit low similarity to normal template features. Next, TFA-Net utilizes the template features that have already fused normal features in the input features to refine feature details and obtain the reconstructed feature map. Finally, the defective regions can be located by comparing the differences between the input and reconstructed features. Additionally, a random masking strategy for input features is employed to enhance the overall inspection performance of the model. Our template-based feature aggregation schema yields a nontrivial and meaningful feature reconstruction task. The simple, yet efficient, TFA-Net exhibits state-of-the-art detection performance on various real-world industrial datasets. Additionally, it fulfills the real-time demands of industrial scenarios, rendering it highly suitable for practical applications in the industry. Code is available at https://github.com/luow23/TFA-Net.

Miguel Gouveia, Micaela Pinho, Paulo Botelho Pires

Professional football players face considerable occupational hazards, with injuries posing serious challenges to player safety, club performance, and regulatory oversight. This descriptive study examines the multifaceted implications of Portugal’s Laws No. 48/2023, which formally recognises professional football as a high-risk occupation and strengthens the mandatory insurance regime through a major regulatory update. Adopting a qualitative approach, the analysis focuses on Portugal, where the professional football business model heavily relies on player commercialisation, and compares regulatory frameworks in Spain, Germany, England, Italy, France, and Brazil. Findings indicate that Portugal’s legal framework enhances player safety by ensuring comprehensive coverage and improved disability protections, yet also introduces financial pressures on clubs, particularly those with lower economic capacity. These pressures are exacerbated by limited market competition and high insurance concentration, increasing premium costs. Cross-country comparisons reveal persistent disparities in legal standards, insurance scope, and institutional coordination, which complicate risk allocation in an increasingly globalised football market. Notably, Portugal’s high-risk insurance model most closely aligns with France’s hybrid approach, in contrast to fully public schemes seen in countries like Germany and Italy. While complete harmonisation remains challenging, the study identifies key principles to guide policy reform and international cooperation. Overall, the findings advance understanding of occupational risk regulation in sport and offer practical insights for designing effective, equitable, and safety-oriented protection systems for professional athletes.

Vječislav Bohanek

HamidReza Talaie

Background and objective Recent disasters have shown that disruptions in the healthcare supply chain can lead to shortages of essential medical equipment necessary for proper patient care, creating medical and social crises, and increasing the demand for medical services. The present study aims to identify and rank the enablers of healthcare supply chain resilience in Iran, focusing on public hospitals. Method This is a descriptive mixed-method study with a cross-sectional design conducted in 2024. The participants included 20 experts from the management section of public hospitals in Isfahan, Iran, who were selected using a judgment sampling method. Based on a systematic literature review, the enablers of healthcare supply chain resilience were identified and ranked using the Analytic Hierarchy Process (AHP) by employing the paired comparison kernel in Expert Choice software. Additionally, the cross-impact matrix multiplication (MICMAC) approach was employed to analyze the interrelationships between enablers.  Results The identified enablers were classified into four main groups: elastic capabilities (Agility, flexibility), reporting capabilities (sustainability, security, supply chain network design, robustness), cognitive capabilities (supply chain risk management, awareness/sensitivity), and operational features (IT capabilities, speed, collaboration). The elastic capabilities ranked first among the enabling groups. Based on the MICMAC analysis, agility, flexibility, and speed were placed in the linkage category, indicating that these enablers had the highest impact and could be significantly influenced by other factors. Conclusion To enhance the resilience of the healthcare supply chain in public hospitals in Iran, relevant managers should pay special attention to the supply chain’s elastic capabilities (particularly agility) and reporting capabilities (mainly information technology). Also, they should incorporate long-term and sustainable strategies to increase the flexibility and speed of their plans.

Roosa Risto, Mohit Sethi, Mika Katara

The Cyber Resilience Act (CRA) is a new European Union (EU) regulation aimed at enhancing the security of digital products and services by ensuring they meet stringent cybersecurity requirements. This paper investigates the challenges that industrial equipment manufacturing companies anticipate while preparing for compliance with CRA through a comprehensive survey. Key findings highlight significant hurdles such as implementing secure development lifecycle practices, managing vulnerability notifications within strict timelines, and addressing gaps in cybersecurity expertise. This study provides insights into these specific challenges and offers targeted recommendations on key focus areas, such as tooling improvements, to aid industrial equipment manufacturers in their preparation for CRA compliance.

Lily Stelling, Mick Yang, Rokas Gipiškis et al.

This report provides a detailed comparison between the Safety and Security measures proposed in the EU AI Act's General-Purpose AI (GPAI) Code of Practice (Third Draft) and the current commitments and practices voluntarily adopted by leading AI companies. As the EU moves toward enforcing binding obligations for GPAI model providers, the Code of Practice will be key for bridging legal requirements with concrete technical commitments. Our analysis focuses on the draft's Safety and Security section (Commitments II.1-II.16), documenting excerpts from current public-facing documents that are relevant to each individual measure. We systematically reviewed different document types, such as companies' frontier safety frameworks and model cards, from over a dozen companies, including OpenAI, Anthropic, Google DeepMind, Microsoft, Meta, Amazon, and others. This report is not meant to be an indication of legal compliance, nor does it take any prescriptive viewpoint about the Code of Practice or companies' policies. Instead, it aims to inform the ongoing dialogue between regulators and General-Purpose AI model providers by surfacing evidence of industry precedent for various measures. Nonetheless, we were able to find relevant quotes from at least 5 companies' documents for the majority of the measures in Commitments II.1-II.16.

Anwar Ahmed Khan, Indrakshi Dey

Efficient medium access control (MAC) is critical for enabling low-latency and reliable communication in industrial Machine-to-Machine (M2M) net-works, where timely data delivery is essential for seamless operation. The presence of multi-priority data in high-risk industrial environments further adds to the challenges. The development of tens of MAC schemes over the past decade often makes it a tough choice to deploy the most efficient solu-tion. Therefore, a comprehensive cross-comparison of major MAC protocols across a range of performance parameters appears necessary to gain deeper insights into their relative strengths and limitations. This paper presents a comparison of Contention window-based MAC scheme BoP-MAC with a fragmentation based, FROG-MAC; both protocols focus on reducing the delay for higher priority traffic, while taking a diverse approach. BoP-MAC assigns a differentiated back-off value to the multi-priority traffic, whereas FROG-MAC enables early transmission of higher-priority packets by fragmenting lower-priority traffic. Simulations were performed on Contiki by varying the number of nodes for two traffic priorities. It has been shown that when work-ing with multi-priority heterogenous data in the industrial environment, FROG-MAC results better both in terms of delay and throughput.

Peijian Zeng, Feiyan Pang, Zhanbo Wang et al.

Industrial Anomaly Detection (IAD) is critical for ensuring product quality by identifying defects. Traditional methods such as feature embedding and reconstruction-based approaches require large datasets and struggle with scalability. Existing vision-language models (VLMs) and Multimodal Large Language Models (MLLMs) address some limitations but rely on mask annotations, leading to high implementation costs and false positives. Additionally, industrial datasets like MVTec-AD and VisA suffer from severe class imbalance, with defect samples constituting only 23.8% and 11.1% of total data respectively. To address these challenges, we propose a reward function that dynamically prioritizes rare defect patterns during training to handle class imbalance. We also introduce a mask-free reasoning framework using Chain of Thought (CoT) and Group Relative Policy Optimization (GRPO) mechanisms, enabling anomaly detection directly from raw images without annotated masks. This approach generates interpretable step-by-step explanations for defect localization. Our method achieves state-of-the-art performance, outperforming prior approaches by 36% in accuracy on MVTec-AD and 16% on VisA. By eliminating mask dependency and reducing costs while providing explainable outputs, this work advances industrial anomaly detection and supports scalable quality control in manufacturing. Code to reproduce the experiment is available at https://github.com/LilaKen/LR-IAD.

Iva Kertusha, Gebremariem Assress, Onur Duman et al.

Web application testing is an essential practice to ensure the reliability, security, and performance of web systems in an increasingly digital world. This paper presents a systematic literature survey focusing on web testing methodologies, tools, and trends from 2014 to 2025. By analyzing 259 research papers, the survey identifies key trends, demographics, contributions, tools, challenges, and innovations in this domain. In addition, the survey analyzes the experimental setups adopted by the studies, including the number of participants involved and the outcomes of the experiments. Our results show that web testing research has been highly active, with ICST as the leading venue. Most studies focus on novel techniques, emphasizing automation in black-box testing. Selenium is the most widely used tool, while industrial adoption and human studies remain comparatively limited. The findings provide a detailed overview of trends, advancements, and challenges in web testing research, the evolution of automated testing methods, the role of artificial intelligence in test case generation, and gaps in current research. Special attention was given to the level of collaboration and engagement with the industry. A positive trend in using industrial systems is observed, though many tools lack open-source availability

Tomi Zlatar, Denisse Bustos, José Torres Costa et al.

Various jobs, indoors and outdoors, are subjected to severe cold temperatures during daily activities. Extremely low-temperature exposure and work intensity affect health, safety, and occupational performance. This work aimed to assess the physiological and thermal sensation responses before, during, and following a 60 min exposure to cold (−20 °C), during which occupational activities were developed. Using ingestible telemetric temperature pills, eight skin temperature sensors, blood pressure equipment, and the Thermal Sensation Questionnaire, experiments were conducted with 11 healthy male volunteers wearing highly insulating cold protective clothing. The most notorious alterations were reported in mean skin temperatures and thermal sensation responses during the first 20 min of cold exposure. Among the eight skin temperature points, the forehead and left hand showed a higher sensitivity to cold. The mean core temperature reported significant variations throughout the protocol, with decreases during the initial 10 min of cold exposure and posterior increases despite the cold environment. Blood pressure showed slight increases from the initial to the recovery period. Overall, outcomes contribute to current scientific knowledge on physiological and perception responses in extremely cold environments while describing the influence of protective clothing and occupational activities on these responses. Future research should be developed with additional skin temperature measurements in the extremities (fingers, face, and toes) and the analysis of thermal sensation potential associations with performance changes, which can also be of great significance for future thermal comfort models.

Rustam Murasov , Yaroslav Melnyk

Сьогодні досить часто використовуються методи експертного оцінювання, що базується на залученні експертів із різних областей знань, які надають свої фахові оцінки стосовно ймовірності виникнення загроз та характеру їх впливу на об’єкти критичної інфраструктури. Метою статті є оцінювання загроз критичної інфраструктури методом експертного оцінювання для запобігання виникнення надзвичайних ситуацій, а в разі неможливості їхнього запобігання – мінімізації їх наслідків та оперативного ліквідування. Під час проведення дослідження застосовано наступні методи: методи аналізу під час аналізування існуючих джерел за напрямом досліджень, існуючих підходів оцінювання загроз, методи аналізу ризиків, методи математичного моделювання для оцінювання загроз та аналізу ризиків, методи машинного навчання такі як штучний інтелект, глибоке навчання та метод експертного оцінювання. Зазначений методологічний підхід дає змогу отримати результати оцінювання загроз критичної інфраструктури для раціонального розподілу засобів захисту, що використовуються складовими сил безпеки і оборони держави. У статті наведено існуючі підходи оцінювання загроз і розглянуто можливість використання методу експертного оцінювання для визначення загроз критичній інфраструктурі в сучасних умовах російсько-української війни. Також створено методологію оцінювання загроз критичній інфраструктурі, засновану на методі експертного оцінювання. Наведено результати оцінювання загроз для об'єктів критичної інфраструктури. Запропоновано порядок оцінювання загроз критичній інфраструктурі з пріоритизацією загроз. Пояснена суть методу експертного оцінювання та удосконалення цього методу завдяки введенню ідентифікаторів загроз критичної інфраструктури та укладенню таких визначень: усереднена експертна імовірність, окремі та сукупні деструктивні наслідки, імовірності загроз захисту критичної інфраструктури. Це дасть змогу зосередити зусилля на найбільш небезпечних загрозах і запобігти значним втратам критичної інфраструктури. Елементами наукової новизни статті є механізм пріоритизації ризиків (наслідків, сукупних деструктивних ефектів) з визначенням найнебезпечніших і відокремленні таких, що мають незначний ефект. Зроблено висновки стосовно можливостей та доцільності застосування методу експертного оцінювання з метою ідентифікації потенційних ризиків для об’єктів критичної інфраструктури та розроблення стратегій їх захисту. Проведено практичні розрахунки з висновками стосовно загроз і їх пріоритезації щодо критичної інфраструктури. До теоретичної значущості статті слід віднести вклад у розвиток методології оцінки загроз критичній інфраструктурі. Запропонований метод експертного оцінювання дозволяє отримати кількісні оцінки загроз, що є важливим для прийняття ефективних управлінських рішень та дозволяє враховувати різноманітні фактори, що впливають на рівень загрози в цілому. Практична значущість статті полягає в тому, що отримані результати дослідження можуть бути використані для: забезпечення безпеки критичної інфраструктури, формування пріоритетів захисту об'єктів критичної (військової) інфраструктури та розробки заходів щодо підвищення рівня безпеки критичної (військової) інфраструктури. Результати статті дають змогу здійснювати аналіз загроз критичної інфраструктури в умовах війни та ракетно-дронових ударів, формувати пріоритезований список загроз, відокремити незначні загрози з метою оптимального застосування наявних сил і засобів та мінімізації надзвичайних наслідків.

Benjamin Alt, Florian Stöckl, Silvan Müller et al.

Surface treatment tasks such as grinding, sanding or polishing are a vital step of the value chain in many industries, but are notoriously challenging to automate. We present RoboGrind, an integrated system for the intuitive, interactive automation of surface treatment tasks with industrial robots. It combines a sophisticated 3D perception pipeline for surface scanning and automatic defect identification, an interactive voice-controlled wizard system for the AI-assisted bootstrapping and parameterization of robot programs, and an automatic planning and execution pipeline for force-controlled robotic surface treatment. RoboGrind is evaluated both under laboratory and real-world conditions in the context of refabricating fiberglass wind turbine blades.

Ronny Müller, Claudia De Lazzari, Fernando Chirici et al.

Information Reconciliation is a critical component of Quantum Key Distribution, ensuring that mismatches between Alice's and Bob's keys are corrected. In this study, we analyze, simulate, optimize, and compare the performance of two prevalent algorithms used for Information Reconciliation: Cascade and LDPC codes in combination with the Blind protocol. We focus on their applicability in practical and industrial settings, operating in realistic and application-close conditions. The results are further validated through evaluation on a live industrial QKD system.

Diego Vallarino

This paper examines the impact of fiscal incentives on industrial investment in Uruguay from 1974 to 2010. Using a mixed-method approach that combines econometric models with machine learning techniques, the study investigates both the short-term and long-term effects of fiscal benefits on industrial investment. The results confirm the significant role of fiscal incentives in driving long-term industrial growth, while also highlighting the importance of a stable macroeconomic environment, public investment, and access to credit. Machine learning models provide additional insights into nonlinear interactions between fiscal benefits and other macroeconomic factors, such as exchange rates, emphasizing the need for tailored fiscal policies. The findings have important policy implications, suggesting that fiscal incentives, when combined with broader economic reforms, can effectively promote industrial development in emerging economies.

Christoforos Vasilatos, Dunia J. Mahboobeh, Hithem Lamri et al.

Industrial Control Systems (ICS) are extensively used in critical infrastructures ensuring efficient, reliable, and continuous operations. However, their increasing connectivity and addition of advanced features make them vulnerable to cyber threats, potentially leading to severe disruptions in essential services. In this context, honeypots play a vital role by acting as decoy targets within ICS networks, or on the Internet, helping to detect, log, analyze, and develop mitigations for ICS-specific cyber threats. Deploying ICS honeypots, however, is challenging due to the necessity of accurately replicating industrial protocols and device characteristics, a crucial requirement for effectively mimicking the unique operational behavior of different industrial systems. Moreover, this challenge is compounded by the significant manual effort required in also mimicking the control logic the PLC would execute, in order to capture attacker traffic aiming to disrupt critical infrastructure operations. In this paper, we propose LLMPot, a novel approach for designing honeypots in ICS networks harnessing the potency of Large Language Models (LLMs). LLMPot aims to automate and optimize the creation of realistic honeypots with vendor-agnostic configurations, and for any control logic, aiming to eliminate the manual effort and specialized knowledge traditionally required in this domain. We conducted extensive experiments focusing on a wide array of parameters, demonstrating that our LLM-based approach can effectively create honeypot devices implementing different industrial protocols and diverse control logic.

Dongmug Kang

The purpose of this paper was to review the problems relating to Korea’s occupational health services and suggest ways to improve them. Korea can be classified as a welfare state type of conservative corporatism partially interwoven with liberalism. While experiencing compressed economic growth, the economic sectors of developed (excess areas) and developing (deficient areas) countries are interwoven. Therefore, it is necessary to perfect conservative corporatism along with a complementary reinforcement of liberal contents and to apply a multilayered approach focusing on complementing the deficient areas. It is essential to form a national representative indicator related to occupational health, and a strategy for selection and concentration is needed. The proposed central indicator is the occupational health coverage rate (OHCR), which is the number of workers who have applied for mandatory occupational health services under the Occupational Safety and Health Act in the numerator with the total working population in the denominator. This paper proposes ways to raise the OHCR, which is currently at the level of 25%–40%, to 70%–80%, which is the level of Japan, Germany, and France. To achieve this target, it is necessary to focus on small businesses and vulnerable workers. This is an area of market failure and requires the active input of community-oriented public resources. For access to larger workplaces, the marketability of services should be strengthened and personal intervention using digital health resources should be actively attempted. Taking a national perspective, work environment improvement committees with tripartite (labor, management, and government) participation for improvement of the working environment need to be established at the center and in the regions. Through this, prevention funds linked to industrial accident compensation and prevention could be used efficiently. A national chemical substance management system must be established to monitor the health of workers and the general public.

The Japan Society for Occupational Health May 25, 2022

Sergey Zabolotny, Vitaliy Katsalap

У статті розглянуто питання обґрунтування інформаційної технології забезпечення функціональної стійкості систем моніторингу інформаційного простору в інтересах військ (сил). Основна увага досліджень зосереджена на побудові варіантів системи моніторингу інформаційного простору. Формалізовано інформаційну технологію забезпечення функціональної стійкості та визначено підходи щодо методів її оцінювання. Наведено актуальність та перспективність процесу представлення формалізованої інформаційної технології забезпечення функціональної стійкості для подальшої автоматизації процесу класифікації систем моніторингу інформаційного простору в інтересах військ (сил). Метою статті є обґрунтування інформаційної технології забезпечення функціональної стійкості систем моніторингу інформаційного простору в інтересах військ (сил). Запропоновано інформаційну технологію забезпечення функціональної стійкості систем моніторингу інформаційного простору в інтересах військ (сил), що може вважатися методичним підходом для оцінювання доцільних варіантів системи моніторингу інформаційного простору. Визначається, що поєднання методів оптимізації аналізу варіантів побудови систем моніторингу інформаційного простору дозволяє визначити та оцінити порядок роботи особи, яка приймає рішення на побудову відповідної системи моніторингу інформаційного простору. Це у подальшому дає можливість розробити архітектуру спеціалізованого програмного забезпечення для підвищення оперативності оцінки інформаційних ресурсів, що циркулюють у системах моніторингу інформаційного простору. Використання інформаційної технології забезпечення функціональної стійкості систем моніторингу інформаційного простору в інтересах військ (сил) дозволяє орієнтовно визначити складові моніторингу інформаційного простору та провести деталізацію систем, які до нього входять. Наведені результати наукового дослідження підтверджують адекватність застосування інформаційної технології для забезпечення функціональної стійкості систем моніторингу інформаційного простору в інтересах військ (сил).

Emma D’Antoine, Janis Jansz, Ahmed Barifcani et al.

The offshore oil and gas working environment is an inherently dangerous one, with risks posed to physical safety on a daily basis. One neglected field of research is the added psychosocial stressors present in this environment. This research examined the experiences of offshore oil and gas workers through one-on-one online interviews which were recorded and transcribed. Transcripts were analyzed through the qualitative software NVivo, which generated themes and patterns for the responses given to questions that were developed through a focus group. The results of the analysis showed that multiple psychosocial stressors are present in this population, such as fear of speaking up, unsatisfactory company-provided facilities, work–life interference, work status, micromanaging, gender harassment and bullying. In addition, interviews identified that production and time pressures, along with fatigue, can influence accidents and mistakes. Climate factors also cause discomfort. However, these are managed according to best practices by organizations. Due to the timing of the study, COVID-19 was a significant stressor for some, but not all, employees. In conclusion, offshore oil and gas workers face multiple stressors in a dangerous environment that may lead to devastating consequences.

Gurjot Singh Bhatia, Yoann Corre, Marco Di Renzo

This paper presents an innovative method that can be used to produce deterministic channel models for 5G industrial internet-of-things (IIoT) scenarios. Ray-tracing (RT) channel emulation can capture many of the specific properties of a propagation scenario, which is incredibly beneficial when facing various industrial environments and deployment setups. But the environment's complexity, composed of many metallic objects of different sizes and shapes, pushes the RT tool to its limits. In particular, the scattering or diffusion phenomena can bring significant components. Thus, in this article, the Volcano RT channel simulation is tuned and benchmarked against field measurements found in the literature at two frequencies relevant to 5G industrial networks: 3.7 GHz (mid-band) and 28 GHz (millimeter-wave (mmWave) band), to produce calibrated ray-based channel model. Both specular and diffuse scattering contributions are calculated. Finally, the tuned RT data is compared to measured large-scale parameters, such as the power delay profile (PDP), the cumulative distribution function (CDF) of delay spreads (DSs), both in line-of-sight (LoS) and non-LoS (NLoS) situations and relevant IIoT channel properties are further explored.