Hasil untuk "Industrial medicine. Industrial hygiene"

Chi-Hsin Sally Chen, Chang-Chuan Chan, Erik Pieter de Jong et al.

Introduction Bacterial infections and antibiotic resistance pose a growing global health threat. Current occupational risk surveillance is fragmented, focusing primarily on healthcare workers and neglecting other occupations like animal husbandry and agriculture. This study aimed to develop a novel surveillance method by applying the WHO’s AWaRe (Access, Watch, Reserve) antibiotic classification to a nationwide retrospective insurance claims cohort. Hereby comprehensively assessing and identifying at-risk occupational settings and vulnerable workers for potentially resistant bacterial infections.Method Using large claims databases in Taiwan, an occupational cohort was constructed with over 4 million workers spanning 16 years. Bacterial infections were identified, and potential antimicrobial resistance was indicated based on the AWaRe classification of prescribed antibiotics. Cox regression for recurrent events was used to assess occupational risk for infections treated with ‘Reserve’ and/or ‘Watch’ antibiotics, indicating potential resistance.Results The analysis identified almost 25 000 Reserve events and over 31 million Reserve and/or Watch events, and revealed significantly elevated risks for potentially resistant bacterial infections for certain workers. In the agricultural sector, vegetable and crop cultivators exhibited elevated HRs for possibly resistant bacterial infections treated with Reserve antibiotics, with a risk of respectively 1.49 (95% CI 1.41 to 1.58) and 1.48 (95% CI 1.44 to 1.51) times larger compared with the reference group. Within healthcare, for the same resistance scenario, workers in residential care services faced a substantially higher risk for potentially resistant infections, with an adjusted HR of 2.21 (95% CI 2.04 to 2.40).Conclusions The proposed passive surveillance method, using claims data and the AWaRe classification, offers a valuable and scalable tool for identifying and monitoring high-risk occupations, informing targeted interventions and improving worker protection globally. The findings indicate that specifically workers in vegetable and crop cultivation and residential care services in Taiwan are more prone to potentially resistant bacterial infections and in need of enhanced infection prevention.

Akhil Gupta Chigullapally, Sharvan Vittala, Razin Farhan Hussian et al.

The fast pace of modern AI is rapidly transforming traditional industrial systems into vast, intelligent and potentially unmanned autonomous operational environments driven by AI-based solutions. These solutions leverage various forms of machine learning, reinforcement learning, and generative AI. The introduction of such smart capabilities has pushed the envelope in multiple industrial domains, enabling predictive maintenance, optimized performance, and streamlined workflows. These solutions are often deployed across the Industrial Internet of Things (IIoT) and supported by the Edge-Fog-Cloud computing continuum to enable urgent (i.e., real-time or near real-time) decision-making. Despite the current trend of aggressively adopting these smart industrial solutions to increase profit, quality, and efficiency, large-scale integration and deployment also bring serious hazards that if ignored can undermine the benefits of smart industries. These hazards include unforeseen interoperability side-effects and heightened vulnerability to cyber threats, particularly in environments operating with a plethora of heterogeneous IIoT systems. The goal of this study is to shed light on the potential consequences of industrial smartness, with a particular focus on security implications, including vulnerabilities, side effects, and cyber threats. We distinguish software-level downsides stemming from both traditional AI solutions and generative AI from those originating in the infrastructure layer, namely IIoT and the Edge-Cloud continuum. At each level, we investigate potential vulnerabilities, cyber threats, and unintended side effects. As industries continue to become smarter, understanding and addressing these downsides will be crucial to ensure secure and sustainable development of smart industrial systems.

Yifan Li, Yuhang Chen, Anh Dao et al.

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.

Claudio Pirrone, Stefano Fricano, Gioacchino Fazio

The foundation model industry exhibits unprecedented concentration in critical inputs: semiconductors, energy infrastructure, elite talent, capital, and training data. Despite extensive sectoral analyses, no comprehensive framework exists for assessing overall industrial vulnerability. We develop the Artificial Intelligence Industrial Vulnerability Index (AIIVI) grounded in O-Ring production theory, recognizing that foundation model production requires simultaneous availability of non-substitutable inputs. Given extreme data opacity and rapid technological evolution, we implement a validated human-in-the-loop methodology using large language models to systematically extract indicators from dispersed grey literature, with complete human verification of all outputs. Applied to six state-of-the-art foundation model developers, AIIVI equals 0.82, indicating extreme vulnerability driven by compute infrastructure (0.85) and energy systems (0.90). While industrial policy currently emphasizes semiconductor capacity, energy infrastructure represents the emerging binding constraint. This methodology proves applicable to other fast-evolving, opaque industries where traditional data sources are inadequate.

Sangbum Choi, Kyeongryeol Go, Taewoong Jang

Foundation models have revolutionized AI, yet they struggle with zero-shot deployment in real-world industrial settings due to a lack of high-quality, domain-specific datasets. To bridge this gap, Superb AI introduces ZERO, an industry-ready vision foundation model that leverages multi-modal prompting (textual and visual) for generalization without retraining. Trained on a compact yet representative 0.9 million annotated samples from a proprietary billion-scale industrial dataset, ZERO demonstrates competitive performance on academic benchmarks like LVIS-Val and significantly outperforms existing models across 37 diverse industrial datasets. Furthermore, ZERO achieved 2nd place in the CVPR 2025 Object Instance Detection Challenge and 4th place in the Foundational Few-shot Object Detection Challenge, highlighting its practical deployability and generalizability with minimal adaptation and limited data. To the best of our knowledge, ZERO is the first vision foundation model explicitly built for domain-specific, zero-shot industrial applications.

Sanjukta Ghosh

Industrial diagrams such as piping and instrumentation diagrams (P&IDs) are essential for the design, operation, and maintenance of industrial plants. Converting these diagrams into digital form is an important step toward building digital twins and enabling intelligent industrial automation. A central challenge in this digitalization process is accurate object detection. Although recent advances have significantly improved object detection algorithms, there remains a lack of methods to automatically evaluate the quality of their outputs. This paper addresses this gap by introducing a framework that employs Visual Language Models (VLMs) to assess object detection results and guide their refinement. The approach exploits the multimodal capabilities of VLMs to identify missing or inconsistent detections, thereby enabling automated quality assessment and improving overall detection performance on complex industrial diagrams.

farhad Forouharmajd, Seyed Mahdi Mousavi, mojtaba nakhaeipour et al.

Background and Objective: The present study sought to assess the effect of the Health Belief Model (HBM) on the use of Personal Protective equipment among the workers of a process industry. Materials and Methods: The study involved an experimental intervention with 100 workers from various units in a process industry. These employees were assigned to two groups of 50: intervention and control. Initially, data were gathered using a custom questionnaire based on the dimensions of the HBM. The intervention group underwent a 12-session educational program over 3 months. The post-test phase took place three months after the educational intervention. The data were analyzed using SPSS software (version 25). Results: The mean age scores of participants in intervention and control groups were 46±3 and 38± 2, respectively. The results indicated that following the educational intervention, the mean scores of awareness, perceived barriers, perceived benefits, and threats of employees were significantly higher than those of the control group (P<0.001), with respective scores of 15.45± 3.03, 22.21±2.30, 16.61±3.12. Conclusion: As evidenced by the obtained results, implementing educational interventions based on the HBM increased employees' awareness and favorable attitudes regarding using personal protective equipment.

Valentina Zaccaria, Chiara Masiero, David Dandolo et al.

While Machine Learning has become crucial for Industry 4.0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challenge exacerbated in the evolving Industry 5.0 with its human-centric focus. This paper addresses this need by testing the applicability of AcME-AD in industrial settings. This recently developed framework facilitates fast and user-friendly explanations for anomaly detection. AcME-AD is model-agnostic, offering flexibility, and prioritizes real-time efficiency. Thus, it seems suitable for seamless integration with industrial Decision Support Systems. We present the first industrial application of AcME-AD, showcasing its effectiveness through experiments. These tests demonstrate AcME-AD's potential as a valuable tool for explainable AD and feature-based root cause analysis within industrial environments, paving the way for trustworthy and actionable insights in the age of Industry 5.0.

Nesreen Mufid

Nowadays, electric robots play big role in many fields as they can replace humans and/or decrease the amount of load on humans. There are several types of robots that are present in the daily life, some of them are fully controlled by humans while others are programmed to be self-controlled. In addition there are self-control robots with partial human control. Robots can be classified into three major kinds: industry robots, autonomous robots and mobile robots. Industry robots are used in industries and factories to perform mankind tasks in the easier and faster way which will help in developing products. Typically industrial robots perform difficult and dangerous tasks, as they lift heavy objects, handle chemicals, paint and assembly work and so on. They are working all the time hour after hour, day by day with the same precision and they do not get tired which means that they do not make errors due to fatigue. Indeed, they are ideally suited to complete repetitive tasks.

Ekarat Sombatsawat, Titaporn Luangwilai, Chuthamat Kaewchandee et al.

Background. Thailand is a tropical developing country which has a serious increase in health risk due to hot weather exposure among outdoor workers. Objectives. The aims of this study were to compare the factors related to environmental heat exposure in three different seasons, and to assess the relationship between environmental heat and dehydration status in each season among farmworkers in Nakhon Ratchasima, Thailand. Methods. A semi-longitudinal study was carried out in 22 male farmworkers throughout a year of farming. The primary data were collected in farmworkers for socio-demographic information, clinical assessments, and heat-related illnesses. Results. Average of environmental heat index (Median, SD) were severe in summer (WBGT=38.1, 2.8°C), rainy season (WBGT=36.1, 2.1°C), and winter (WBGT=31.5, 2.7°C). Average urine Sp. Gr. in summer, rainy season, and winter were 1.022, 1.020, and 1.018 respectively. The third sentence should be corrected as follows: The Friedman analysis revealed a statistically significant difference between the three different seasons in WBGT (wet bulb globe temperature), body temperature, heart rate (P<0.01), and respiratory rate (P<0.05). There was a statistically significant difference between the three different seasons for skin rash/itching, dizziness, muscle cramp dyspnea (P<0.05), and weakness (P<0.01). Wilcoxon signed-ranks analysis found a significant difference in the medians of the paired sets of urine Sp. Gr. values between baseline and summer (P<0.05). Spearman's rank correlation coefficient did not find a relationship between WBGT and urine Sp. Gr. in the three different seasons. Conclusions. This study demonstrated that farmworkers had exposure to environmental heat stress which was expressed through physical changes. Therefore, there is a need for either interventions or guidelines to prevent dehydration for outdoor workers in this region.

Samidh Pal

This paper investigates the inter-regional intra-industry disparity within selected Indian manufacturing industries and industrial states. The study uses three measures - the Output-Capital Ratio, the Capital-Labor Ratio, and the Output-Labor Ratio - to critically evaluate the level of disparity in average efficiency of labor and capital, as well as capital intensity. Additionally, the paper compares the rate of disparity of per capita income between six major industrial states. The study finds that underutilization of capacity is driven by an unequal distribution of high-skilled labor supply and upgraded technologies. To address these disparities, the paper suggests that policymakers campaign for labor training and technology promotion schemes throughout all regions of India. By doing so, the study argues, the country can reduce regional inequality and improve economic outcomes for all.

Mahbubul Islam, Hossain Md. Mubashshir Jamil, Samiul Ahsan Pranto et al.

The Internet of Things (IoT) will bring about the next industrial revolution in Industry 4.0. The communication aspect of IoT devices is one of the most critical factors in choosing the suitable device for the suitable usage. So far, the IoT physical layer communication challenges have been met with various communications protocols that provide varying strengths and weaknesses. Moreover, most of them are wireless protocols due to the sheer number of device requirements for IoT. This paper summarizes the network architectures of some of the most popular IoT wireless communications protocols. It also presents a comparative analysis of critical features, including power consumption, coverage, data rate, security, cost, and Quality of Service (QoS). This comparative study shows that Low Power Wide Area Network (LPWAN) based IoT protocols (LoRa, Sigfox, NB-IoT, LTE-M ) are more suitable for future industrial applications because of their energy efficiency, high coverage, and cost efficiency. In addition, the study also presents an industrial Internet of Things (IIoT) application perspective on the suitability of LPWAN protocols in a particular scenario and addresses some open issues that need to be researched. Thus, this study can assist in deciding the most suitable protocol for an industrial and production field.

Jiaqi Liu, Guoyang Xie, Jinbao Wang et al.

The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.

Yue Wang, Jinlong Peng, Jiangning Zhang et al.

2D-based Industrial Anomaly Detection has been widely discussed, however, multimodal industrial anomaly detection based on 3D point clouds and RGB images still has many untouched fields. Existing multimodal industrial anomaly detection methods directly concatenate the multimodal features, which leads to a strong disturbance between features and harms the detection performance. In this paper, we propose Multi-3D-Memory (M3DM), a novel multimodal anomaly detection method with hybrid fusion scheme: firstly, we design an unsupervised feature fusion with patch-wise contrastive learning to encourage the interaction of different modal features; secondly, we use a decision layer fusion with multiple memory banks to avoid loss of information and additional novelty classifiers to make the final decision. We further propose a point feature alignment operation to better align the point cloud and RGB features. Extensive experiments show that our multimodal industrial anomaly detection model outperforms the state-of-the-art (SOTA) methods on both detection and segmentation precision on MVTec-3D AD dataset. Code is available at https://github.com/nomewang/M3DM.

Rajabali Hokmabadi, Farzaneh Mehri, Ali Karimi

Background and Objective: Excessive working hours, insufficient rest, and irregular driver schedules are factors that could cause drowsiness in professional drivers; consequently, reducing the driver's ability and increasing the chance of road accidents. Therefore, the present study aimed to Investigate the relation between drivers working and resting hours with the occurrence of road accidents among heavy vehicle drivers. Materials and Methods: This analytical and cross-sectional research was performed in 2018 and 200 heavy vehicle drivers in Tehran province, Iran, were evaluated. A standard driver safety questionnaire was used to collect data. Data analysis was performed using SPSS software (version 21). Results: The mean age of drivers, driving hours per day, continuous driving hours, and rest hours per day were calculated as 47.5 ± 9.05 years, 10.66 ± 2.52, 5.82 ± 1.87, and 8.7 ± 1.13, respectively. The average number of road quasi-accidents and accidents for drivers in the last five years was 4.26 ± 2.29 and 1.1 ± 1.2, respectively. The number of accidents was significantly associated with age, drowsiness, safety and health education, driving hours, the number of continuous driving hours, and the number of rest hours. No significant association was found between the number of accidents and smoking. Conclusion: Behaviors such as long driving hours, long continuous driving hours, short rest hours, and drowsiness are among the causes of heavy vehicle drivers’ accidents in the country. Planning with the approach of informing, and raising awareness and skills of drivers to change their harmful habits and high-risk driving behaviors could significantly reduce road accidents.

Man Zhang, Andrea Arcuri, Yonggang Li et al.

Remote Procedure Call (RPC) is a communication protocol to support client-server interactions among services over a network. RPC is widely applied in industry for building large-scale distributed systems, such as Microservices. Modern RPC frameworks include for example Thrift, gRPC, SOFARPC and Dubbo. Testing such systems is very challenging, due to the complexity of distributed systems and various RPC frameworks the system could employ. To the best of our knowledge, there does not exist any tool or solution that could enable automated testing of modern RPC-based services. To fill this gap, in this paper we propose the first approach in the literature, together with an open-source tool, for white-box fuzzing modern RPC-based APIs with search. To assess our novel approach, we conducted an empirical study with two artificial and four industrial APIs selected by our industrial partner. The tool has been integrated into a real industrial pipeline, and could be applied to real industrial development process for fuzzing RPC-based APIs. To further demonstrate its effectiveness and application in industrial settings, we also report results of employing our tool for fuzzing another 50 industrial APIs autonomously conducted by our industrial partner in their testing processes. Results show that our novel approach is capable of enabling automated test case generation for industrial RPC-based APIs (i.e., two artificial and 54 industrial). We also compared with a simple grey-box technique and existing manually written tests. Our white-box solution achieves significant improvements on code coverage. Regarding fault detection, by conducting a careful review with our industrial partner of the tests generated by our novel approach in the selected four industrial APIs, a total of 41 real faults were identified, which have now been fixed. Another 8,377 detected faults are currently under investigation.

Yidan Feng, Biqi Yang, Xianzhi Li et al.

Industrial bin picking is a challenging task that requires accurate and robust segmentation of individual object instances. Particularly, industrial objects can have irregular shapes, that is, thin and concave, whereas in bin-picking scenarios, objects are often closely packed with strong occlusion. To address these challenges, we formulate a novel part-aware instance segmentation pipeline. The key idea is to decompose industrial objects into correlated approximate convex parts and enhance the object-level segmentation with part-level segmentation. We design a part-aware network to predict part masks and part-to-part offsets, followed by a part aggregation module to assemble the recognized parts into instances. To guide the network learning, we also propose an automatic label decoupling scheme to generate ground-truth part-level labels from instance-level labels. Finally, we contribute the first instance segmentation dataset, which contains a variety of industrial objects that are thin and have non-trivial shapes. Extensive experimental results on various industrial objects demonstrate that our method can achieve the best segmentation results compared with the state-of-the-art approaches.

Davide Fucci, Emil Alégroth, Thomas Axelsson

The concept of traceability between artifacts is considered an enabler for software project success. This concept has received plenty of attention from the research community and is by many perceived to always be available in an industrial setting. In this industry-academia collaborative project, a team of researchers, supported by testing practitioners from a large telecommunication company, sought to investigate the partner company's issues related to software quality. However, it was soon identified that the fundamental traceability links between requirements and test cases were missing. This lack of traceability impeded the implementation of a solution to help the company deal with its quality issues. In this experience report, we discuss lessons learned about the practical value of creating and maintaining traceability links in complex industrial settings and provide a cautionary tale for researchers.

L. Pasteur

Anastase Tchicaya, Nathalie Lorentz, Hichem Omrani et al.

Abstract Background The outbreak of coronavirus disease (COVID-19) began in Wuhan, China in December 2019 and was declared a global pandemic on 11 March 2020. This study aimed to assess the effects of temperature and long-term exposure to air pollution on the COVID-19 mortality rate at the sub-national level in France. Methods This cross-sectional study considered different periods of the COVID-19 pandemic from May to December 2020. It included 96 departments (or NUTS 3) in mainland France. Data on long-term exposure to particulate matter (PM2.5), annual mean temperature, health services, health risk, and socio-spatial factors were used as covariates in negative binomial regression analysis to assess their influence on the COVID-19 mortality rate. All data were obtained from open-access sources. Results The cumulative COVID-19 mortality rate by department increased during the study period in metropolitan France—from 19.8/100,000 inhabitants (standard deviation (SD): 20.1) on 1 May 2020, to 65.4/100,000 inhabitants (SD: 39.4) on 31 December 2020. The rate was the highest in the departments where the annual average of long-term exposure to PM2.5 was high. The negative binomial regression models showed that a 1 μg/m3 increase in the annual average PM2.5 concentration was associated with a statistically significant increase in the COVID-19 mortality rate, corresponding to 24.4%, 25.8%, 26.4%, 26.7%, 27.1%, 25.8%, and 15.1% in May, June, July, August, September, October, and November, respectively. This association was no longer significant on 1 and 31 December 2020. The association between temperature and the COVID-19 mortality rate was only significant on 1 November, 1 December, and 31 December 2020. An increase of 1 °C in the average temperature was associated with a decrease in the COVID-19-mortality rate, corresponding to 9.7%, 13.3%, and 14.5% on 1 November, 1 December, and 31 December 2020, respectively. Conclusion This study found significant associations between the COVID-19 mortality rate and long-term exposure to air pollution and temperature. However, these associations tended to decrease with the persistence of the pandemic and massive spread of the disease across the entire country.