Hasil untuk "Industrial medicine. Industrial hygiene"

Puyu Zeng, Zhaoxi Wang, Zhixu Duan et al.

Code generation and comprehension by Large Language Models (LLMs) have emerged as core drivers of industrial intelligence and decision optimization, finding widespread application in fields such as finance, automation, and aerospace. Although recent advancements have demonstrated the remarkable potential of LLMs in general code generation, existing benchmarks are mainly confined to single domains and languages. Consequently, they fail to effectively evaluate the generalization capabilities required for real-world industrial applications or to reflect the coding proficiency demanded by complex industrial scenarios. To bridge this gap, we introduce IndustryCode, the first comprehensive benchmark designed to span multiple industrial domains and programming languages. IndustryCode comprises 579 sub-problems derived from 125 primary industrial challenges, accompanied by rigorous problem descriptions and test cases. It covers a wide range of fields, including finance, automation, aerospace, and remote sensing-and incorporates diverse programming languages such as MATLAB, Python, C++, and Stata. In our evaluation, the top-performing model, Claude 4.5 Opus, achieved an overall accuracy of 68.1% on sub-problems and 42.5% main problems. The benchmark dataset and automated evaluation code will be made publicly available upon acceptance.

LiMan Yang, WeiWei Cai, HengCui Zhou et al.

Objective: To analyse the effects of noise exposure in emergency resuscitation rooms (ERRs) on cognitive function and hyperalgesia in patients with trauma. Methods: Clinical data from 110 patients with trauma who were treated in the ERR of Suizhou Central Hospital between June 2022 and July 2023 were retrospectively analysed. Participants were divided into the following two groups on the basis of real-time noise monitoring: the high-noise-exposure (n = 85) and low-noise-exposure (n = 25) groups. Neuron-specific enolase (NSE), brain-derived neurotrophic factor (BDNF), homocysteine (Hcy), the Mini-Mental State Examination (MMSE), and the Montreal Cognitive Assessment (MoCA) were used to measure cognitive performance. Mechanical pain threshold and serum nerve growth factor (NGF), substance P (SP), calcitonin gene-related peptide (CGRP) and 5-hydroxytryptamine (5-HT) levels were applied to assess hyperalgesia. Pearson correlation was employed to investigate the connections between noise levels and outcome factors. Results: The high-noise-exposure group demonstrated significantly lower MMSE scores, MoCA scores and serum BDNF levels but higher serum NSE and Hcy levels compared with the low-noise-exposure group (P < 0.05). Additionally, compared with the low-noise-exposure group, the high-noise-exposure group exhibited larger mechanical hyperalgesia areas around incisions and on the volar forearm, as well as elevated serum CGRP, NGF and SP levels, while showing reduced mechanical pain thresholds and lower serum 5-HT levels (P < 0.05). Pearson analysis revealed that noise exposure values had negative correlations with mechanical pain threshold, MMSE and MoCA scores and serum BDNF and 5-HT levels (r < 0, P < 0.05) but positive correlations with mechanical hyperalgesia area and serum CGRP, NSE, Hcy, NGF and SP levels (r > 0, P < 0.05).Conclusion: High noise exposure in ERRs may be associated with cognitive dysfunction and hyperalgesia in patients with trauma. Clinical management should recognise and control noise levels in these settings.

Hamidreza Pouragha, jabrail Nasirian, Mahsa Naserpour et al.

Background and Objective: Occupational exposure to Respirable Crystalline Silica (RCS) and respirable dust in the cement industry increases the risk of respiratory diseases, including Chronic Obstructive Pulmonary Disease (COPD). This study aimed to evaluate the effects of these pollutants on spirometric indices and respiratory health among cement factory workers in Urmia, Iran. Materials and Methods: This study was conducted on 375 workers over a four-year period. Spirometric data, including Forced Expiratory Volume in one second (FEV1), Forced Vital Capacity (FVC), and FEV1/FVC ratio, were collected in two two-year periods and analyzed using statistical tests. Results: The results indicated that prolonged exposure to RCS and inhalable dust led to significant reductions in FEV1 and FVC, as well as the emergence of restrictive lung disorders, particularly among workers who are working in production and maintenance occupational groups. Additionally, individual factors, such as BMI and smoking were significantly associated with decreased pulmonary function. Smoking was identified as the most critical factor influencing the development of COPD patterns. Conclusion: In this study, smoking was identified as the most significant factor influencing COPD patterns among cement industry workers. Additionally, the findings indicate that prolonged exposure to respirable dust and crystalline silica in the workplace, particularly in the cement industry, leads to a significant decline in pulmonary function indices, such as FEV1 and FVC, contributing to a restrictive impairment pattern.

Hasna Kachache, Sara Ait Lachguer, Ilham Rhzali et al.

Background Overweight and obesity are major public health challenges, yet access to appropriate healthcare and effective management remains limited. This study aimed to assess healthcare access, consultation behaviors, and barriers among overweight and obese adults. Material and Methods A cross-sectional study was conducted among 134 adults in Kénitra, Morocco. Sociodemographic information, body mass index (BMI), and healthcare access variables were collected using structured questionnaires and clinical assessments. Descriptive statistics were used to summarize consultation behaviors, barriers, types of healthcare providers consulted, and follow-up practices. Results Among participants, 47.0% were classified as obese, 25.4% as overweight, and 27.6% had normal BMI. Only 19.6% reported consulting specifically for weight-related issues, while 78.4% did not seek care. The main barriers were perception of no need (34.6%), financial constraints (25.6%), and lack of physicians (21.8%). Consultations primarily took place in the private sector (84.2%). Dietitians (40.4%) and specialist physicians (38.6%) were the most frequently consulted professionals, whereas general practitioners accounted for only 10.9%. Follow-up and referral rates were low, with just 3.1% of participants referred to specialists or dietitians and 91.8% receiving no regular monitoring. Conclusions Access to healthcare for overweight and obese adults is constrained by economic, social, and systemic factors. The low rates of consultation, referral, and follow-up underscore the need for structured care pathways, enhanced provider awareness, and multidisciplinary management strategies in Morocco.

Małgorzata Wrotek, Iulia Marginean, Zofia Boni et al.

Abstract Background Increasing temperatures across the globe, including in Europe, pose one of the biggest threats to human health and wellbeing. Different kinds of inequalities, determined by age, sex/gender, isolation, socio-economic status, occupation, living in the city, and health situation, create vulnerability factors influencing people’s heat-related mortality risk and their daily experiences during summer. Methods Our study uses an interdisciplinary approach to research how intersecting inequalities generate vulnerabilities to heat stress among older adults (65+) in two European cities: Warsaw and Madrid. We combine three methodological approaches juxtaposing quantitative and qualitative data: (1) epidemiological analysis that uses daily mortality data in Warsaw and Madrid coupled with meteorological station temperature data from HadISD; (2) the OLS regression based on the survey conducted in Warsaw and Madrid in 2022; and (3) the focus group interviews conducted in Warsaw in 2021. Results Our data confirms that good health and financial situation protect people both from mortality risk and negative heat experiences. Interestingly, both air conditioning (A/C) usage and being physically active increase the negative heat experiences people reported. Finally, we identified two vulnerability paradoxes understood as situations when a person or a group might be more at risk but not experience or perceive negative impacts of heat. These paradoxes affect the oldest adults (80+) and older people living alone in both cities. Conclusions Studies on vulnerability and adaptation need to incorporate both large scale top-down data sets and bottom-up, localized data based on individual experience. Combining various methods and disciplinary approaches enables identification of inequality factors and vulnerability paradoxes that remain unnoticed or underestimated while increasing people’s vulnerability to heat stress.

Manolis Kogevinas

Night-shift work has become a structural component of modern economies, supporting essential services from healthcare and transportation to logistics and manufacturing. But with this systemic need comes a significant burden on health. Mounting evidence links night-shift work to a wide range of adverse outcomes, from acute fatigue to chronic diseases including cancer. While scientific understanding of its mechanisms has grown rapidly, preventive policies — both at institutional and individual levels — have lagged behind. In this article, I argue that the health consequences of night-shift work are no longer an occupational hazard to be suffered, but a modifiable public health issue requiring targeted prevention strategies…

Zainab Alwaisi, Simone Soderi, Rocco De Nicola

Internet of Everything (IoE) is a newly emerging trend, especially in homes. Marketing forces toward smart homes are also accelerating the spread of IoE devices in households. An obvious risk of the rapid adoption of these smart devices is that many lack controls for protecting the privacy and security of end users from attacks designed to disrupt lives and incur financial losses. Today the smart home is a system for managing the basic life support processes of both small systems, e.g., commercial, office premises, apartments, cottages, and largely automated complexes, e.g., commercial and industrial complexes. One of the critical tasks to be solved by the concept of a modern smart home is the problem of preventing the usage of IoE resources. Recently, there has been a rapid increase in attacks on consumer IoE devices. Memory corruption vulnerabilities constitute a significant class of vulnerabilities in software security through which attackers can gain control of an entire system. Numerous memory corruption vulnerabilities have been found in IoE firmware already deployed in the consumer market. This paper aims to analyze and explain the resource usage attack and create a low-cost simulation environment to aid in the dynamic analysis of the attack. Further, we perform controlled resource usage attacks while measuring resource consumption on resource-constrained victims' IoE devices, such as CPU and memory utilization. We also build a lightweight algorithm to detect memory usage attacks in the IoE environment. The result shows high efficiency in detecting and mitigating memory usage attacks by detecting when the intruder starts and stops the attack.

Shiying Zhang, Jun Li, Long Shi et al.

As an emerging technology that enables seamless integration between the physical and virtual worlds, the Metaverse has great potential to be deployed in the industrial production field with the development of extended reality (XR) and next-generation communication networks. This deployment, called the Industrial Metaverse, is used for product design, production operations, industrial quality inspection, and product testing. However, there lacks of in-depth understanding of the enabling technologies associated with the Industrial Metaverse. This encompasses both the precise industrial scenarios targeted by each technology and the potential migration of technologies developed in other domains to the industrial sector. Driven by this issue, in this article, we conduct a comprehensive survey of the state-of-the-art literature on the Industrial Metaverse. Specifically, we first analyze the advantages of the Metaverse for industrial production. Then, we review a collection of key enabling technologies of the Industrial Metaverse, including blockchain (BC), digital twin (DT), 6G, XR, and artificial intelligence (AI), and analyze how these technologies can support different aspects of industrial production. Subsequently, we present numerous formidable challenges encountered within the Industrial Metaverse, including confidentiality and security concerns, resource limitations, and interoperability constraints. Furthermore, we investigate the extant solutions devised to address them. Finally, we briefly outline several open issues and future research directions of the Industrial Metaverse.

Md Bokhtiar Al Zami, Shaba Shaon, Vu Khanh Quy et al.

Industrial networks are undergoing rapid transformation driven by the convergence of emerging technologies that are revolutionizing conventional workflows, enhancing operational efficiency, and fundamentally redefining the industrial landscape across diverse sectors. Amidst this revolution, Digital Twin (DT) emerges as a transformative innovation that seamlessly integrates real-world systems with their virtual counterparts, bridging the physical and digital realms. In this article, we present a comprehensive survey of the emerging DT-enabled services and applications across industries, beginning with an overview of DT fundamentals and its components to a discussion of key enabling technologies for DT. Different from literature works, we investigate and analyze the capabilities of DT across a wide range of industrial services, including data sharing, data offloading, integrated sensing and communication, content caching, resource allocation, wireless networking, and metaverse. In particular, we present an in-depth technical discussion of the roles of DT in industrial applications across various domains, including manufacturing, healthcare, transportation, energy, agriculture, space, oil and gas, as well as robotics. Throughout the technical analysis, we delve into real-time data communications between physical and virtual platforms to enable industrial DT networking. Subsequently, we extensively explore and analyze a wide range of major privacy and security issues in DT-based industry. Taxonomy tables and the key research findings from the survey are also given, emphasizing important insights into the significance of DT in industries. Finally, we point out future research directions to spur further research in this promising area.

Jinfan Liu, Yichao Yan, Junjie Li et al.

Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames, and existing large-scale VAD researches primarily focus on road traffic and human activity scenes. In industrial scenes, there are often a variety of unpredictable anomalies, and the VAD method can play a significant role in these scenarios. However, there is a lack of applicable datasets and methods specifically tailored for industrial production scenarios due to concerns regarding privacy and security. To bridge this gap, we propose a new dataset, IPAD, specifically designed for VAD in industrial scenarios. The industrial processes in our dataset are chosen through on-site factory research and discussions with engineers. This dataset covers 16 different industrial devices and contains over 6 hours of both synthetic and real-world video footage. Moreover, we annotate the key feature of the industrial process, ie, periodicity. Based on the proposed dataset, we introduce a period memory module and a sliding window inspection mechanism to effectively investigate the periodic information in a basic reconstruction model. Our framework leverages LoRA adapter to explore the effective migration of pretrained models, which are initially trained using synthetic data, into real-world scenarios. Our proposed dataset and method will fill the gap in the field of industrial video anomaly detection and drive the process of video understanding tasks as well as smart factory deployment.

Xiao Wang, Yutong Wang, Jing Yang et al.

As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer ``Human-Centric" Safe, Secure, Sustainable, Sensitive, Service, and Smartness ``6S" manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental environment for optimizing and remodeling the process. Besides, the customized user needs that are hidden in social media data can be discovered by social computing technologies, which introduces an input channel for building the whole social manufacturing process including industrial metaverses. This makes the fusion of multi-source data cross Cyber-Physical-Social Systems (CPSS) the foundational and key challenge. This work firstly proposes a multi-source-data-fusion-driven operational architecture for industrial metaverses on the basis of conducting a comprehensive literature review on the state-of-the-art multi-source data fusion methods. The advantages and disadvantages of each type of method are analyzed by considering the fusion mechanisms and application scenarios. Especially, we combine the strengths of deep learning and knowledge graphs in scalability and parallel computation to enable our proposed framework the ability of prescriptive optimization and evolution. This integration can address the shortcomings of deep learning in terms of explainability and fact fabrication, as well as overcoming the incompleteness and the challenges of construction and maintenance inherent in knowledge graphs. The effectiveness of the proposed architecture is validated through a parallel weaving case study. In the end, we discuss the challenges and future directions of multi-source data fusion cross CPSS for industrial metaverses and social manufacturing in Industries 5.0.

Ahmed Gailani, Peter Taylor

Decarbonising the industrial sector is vital to reach net zero targets. The deployment of industrial decarbonisation technologies is expected to increase industrial electricity demand in many countries and this may require upgrades to the existing electricity network or new network investment. While the infrastructure requirements to support the introduction of new fuels and technologies in industry, such as hydrogen and carbon capture, utilisation and storage are often discussed, the need for investment to increase the capacity of the electricity network to meet increasing industrial electricity demands is often overlooked in the literature. This paper addresses this gap by quantifying the requirements for additional electricity network capacity to support the decarbonisation of industrial sectors across Great Britain (GB). The Net Zero Industrial Pathways model is used to predict the future electricity demand from industrial sites to 2050 which is then compared spatially to the available headroom across the distribution network in GB. The results show that network headroom is sufficient to meet extra capacity demands from industrial sites over the period to 2030 in nearly all GB regions and network scenarios. However, as electricity demand rises due to increased electrification across all sectors and industrial decarbonisation accelerates towards 2050, the network will need significant new capacity (71 GW + by 2050) particularly in the central, south, and north-west regions of England, and Wales. Without solving these network constraints, around 65% of industrial sites that are large point sources of emissions would be constrained in terms of electric capacity by 2040. These sites are responsible for 69% of industrial point source emissions.

Simon Liebl, Leah Lathrop, Ulrich Raithel et al.

As part of the Internet of Things, industrial devices are now also connected to cloud services. However, the connection to the Internet increases the risks for Industrial Control Systems. Therefore, a threat analysis is essential for these devices. In this paper, we examine Industrial Internet of Things devices, identify and rank different sources of threats and describe common threats and vulnerabilities. Finally, we recommend a procedure to carry out a threat analysis on these devices.

Seán Lyons, Anne Nolan, Philip Carthy et al.

Abstract Background Mental illness is the leading cause of years lived with disability, and the global disease burden of mental ill-health has increased substantially in the last number of decades. There is now increasing evidence that environmental conditions, and in particular poor air quality, may be associated with mental health and wellbeing. Methods This cross-sectional analysis uses data on mental health and wellbeing from The Irish Longitudinal Study on Ageing (TILDA), a nationally representative survey of the population aged 50+ in Ireland. Annual average PM2.5 concentrations at respondents’ residential addresses over the period 1998–2014 are used to measure long-term exposure to ambient PM2.5. Results We find evidence of associations between long-term exposure to ambient PM2.5 and depression and anxiety. The measured associations are strong, and are comparable with effect sizes for variables such as sex. Effects are also evident at relatively low concentrations by international standards. However, we find no evidence of associations between long-term ambient particulate pollution and other indicators of mental health and well-being such as stress, worry and quality of life. Conclusions The measured associations are strong, particularly considering the relatively low PM2.5 concentrations prevailing in Ireland compared to many other countries. While it is estimated that over 90 per cent of the world’s population lives in areas with annual mean PM2.5 concentrations greater than 10 μg/m3, these results contribute to the increasing evidence that suggests that harmful effects can be detected at even low levels of air pollution.

Redouane Haddad, Hicham Madadi

Support for subcontractors is not limited to industrial safety but also to employee hygiene and health. Indeed, occupational medicine and hygiene at work are essential components in the support process. Physical fitness and continuous medical monitoring are the main components of worker health monitoring. Medical surveillance is adapted to each workstation and its environment. Hygiene is also monitored through measurement campaigns and wellbeing at work. Subcontractors’ employees have evolved in their understanding of risk in the same way as own employees and actively participate in initiatives and the promotion of health and safety at work.

Mona Mannevuo

Summary This article focuses on the advancement of Finnish occupational medicine in the immediate post-war period, situating its development within a transnational context. Its objective is to offer insight into Finnish post-war industrial medicine and particularly developments in mental health care. The empirical methodology addresses a previously unexplored case study: the connections between the Finnish Institute of Occupational Health (FIOH) and Roffey Park Rehabilitation Centre, established in 1943 to address various cases of industrial neurosis. The case study sheds light on the ways in which FIOH adopted reformist ideas from transnational medical communities by aligning them with the needs of Finland’s war reparations industry. The article argues that FIOH’s experts advanced new theories of mental disorder for Finland’s newly modern industrial society, and that these initiatives should be situated within broader transnational endeavours in the mental hygiene movement.

M. Riva, M. Paladino, M. Belingheri et al.

Luigi Carozzi (1880–1963) is known for his role as Head of the Industrial Hygiene Service at the International Labour Office (ILO) from 1920 to 1940 and as the secretary of the ICOH from 1906 to 1954. However, Carozzi’s interest in the history of occupational medicine remained in the background. This study is based on the examination of largely unpublished documents from the ILO Historical Archives (Geneva), and his personal papers. During his career, Carozzi showed a profound interest in the history of occupational medicine. This interest was likely inherited from his mentor Luigi Devoto (1864–1936), who rediscovered the contributions of Bernardino Ramazzini (1633–1714). Carozzi’s dedication to the history of occupational diseases is evident in writings published in French and Italian in 1930. Notably, his major work, the 1930 Encyclopaedia of Safety, begins with a motto from Ramazzini, and references to the founding figure of the discipline are frequent throughout the text. Carozzi was keen on presenting a historical image on the first pages of the Encyclopaedia, such as the “Landscape with a Foundry” (c. 1525) by the Flemish painter Herri met de Bles (c. 1490–1566). In 1941, Carozzi published his main work dedicated to the history of silicosis. Finally, according to the testimony of his son, Albert (1925–2014), Carozzi worked in his last few years on an unfinished treatise on the history of occupational medicine. Luigi Carozzi’s originality, depth of analysis, and methodology established him as one of the early modern historians of occupational medicine.

Ankush Meshram, Markus Karch, Christian Haas et al.

Since 2010, multiple cyber incidents on industrial infrastructure, such as Stuxnet and CrashOverride, have exposed the vulnerability of Industrial Control Systems (ICS) to cyber threats. The industrial systems are commissioned for longer duration amounting to decades, often resulting in non-compliance to technological advancements in industrial cybersecurity mechanisms. The unavailability of network infrastructure information makes designing the security policies or configuring the cybersecurity countermeasures such as Network Intrusion Detection Systems (NIDS) challenging. An empirical solution is to self-learn the network infrastructure information of an industrial system from its monitored network traffic to make the network transparent for downstream analyses tasks such as anomaly detection. In this work, a Python-based industrial communication paradigm-aware framework, named PROFINET Operations Enumeration and Tracking (POET), that enumerates different industrial operations executed in a deterministic order of a PROFINET-based industrial system is reported. The operation-driving industrial network protocol frames are dissected for enumeration of the operations. For the requirements of capturing the transitions between industrial operations triggered by the communication events, the Finite State Machines (FSM) are modelled to enumerate the PROFINET operations of the device, connection and system. POET extracts the network information from network traffic to instantiate appropriate FSM models (Device, Connection or System) and track the industrial operations. It successfully detects and reports the anomalies triggered by a network attack in a miniaturized PROFINET-based industrial system, executed through valid network protocol exchanges and resulting in invalid PROFINET operation transition for the device.

Yuan Luo, Song Han, Jingjing Liu

Machine learning is expected to enable the next Industrial Revolution. However, lacking standardized and automated assembly networks, ML faces significant challenges to meet ever-growing enterprise demands and empower broad industries. In the Perspective, we argue that ML needs to first complete its own Industrial Revolution, elaborate on how to best achieve its goals, and discuss new opportunities to enable rapid translation from ML's innovation frontier to mass production and utilization.

Fouad Oubari, Raphael Meunier, Rodrigue Décatoire et al.

Generative design is an increasingly important tool in the industrial world. It allows the designers and engineers to easily explore vast ranges of design options, providing a cheaper and faster alternative to the trial and failure approaches. Thanks to the flexibility they offer, Deep Generative Models are gaining popularity amongst Generative Design technologies. However, developing and evaluating these models can be challenging. The field lacks accessible benchmarks, in order to evaluate and compare objectively different Deep Generative Models architectures. Moreover, vanilla Deep Generative Models appear to be unable to accurately generate multi-components industrial systems that are controlled by latent design constraints. To address these challenges, we propose an industry-inspired use case that incorporates actual industrial system characteristics. This use case can be quickly generated and used as a benchmark. We propose a Meta-VAE capable of producing multi-component industrial systems and showcase its application on the proposed use case.