Hasil untuk "Industrial medicine. Industrial hygiene"

Lin Liu, Rita Machacy, Simi Kuniyilh

The Industrial Internet of Things (IIoT) integrates intelligent sensing, communication, and analytics into industrial environments, including manufacturing, energy, and critical infrastructure. While IIoT enables predictive maintenance and cross-site optimization of modern industrial control systems, such as those in manufacturing and energy, it also introduces significant privacy and confidentiality risks due to the sensitivity of operational data. Contrastive learning, a self-supervised representation learning paradigm, has recently emerged as a promising approach for privacy-preserving analytics by reducing reliance on labeled data and raw data sharing. Although contrastive learning-based privacy-preserving techniques have been explored in the Internet of Things (IoT) domain, this paper offers a comprehensive review of these techniques specifically for privacy preservation in Industrial Internet of Things (IIoT) systems. It emphasizes the unique characteristics of industrial data, system architectures, and various application scenarios. Additionally, the paper discusses solutions and open challenges and outlines future research directions.

Junfeng Jiao, Saleh Afroogh, Kevin Chen et al.

The rise of Generative AI (GAI) and Large Language Models (LLMs) has transformed industrial landscapes, offering unprecedented opportunities for efficiency and innovation while raising critical ethical, regulatory, and operational challenges. This study conducts a text-based analysis of 160 guidelines and policy statements across fourteen industrial sectors, utilizing systematic methods and text-mining techniques to evaluate the governance of these technologies. By examining global directives, industry practices, and sector-specific policies, the paper highlights the complexities of balancing innovation with ethical accountability and equitable access. The findings provide actionable insights and recommendations for fostering responsible, transparent, and safe integration of GAI and LLMs in diverse industry contexts.

Despina Tomkou, George Fatouros, Andreas Andreou et al.

This paper introduces a novel integration of Retrieval-Augmented Generation (RAG) enhanced Large Language Models (LLMs) with Extended Reality (XR) technologies to address knowledge transfer challenges in industrial environments. The proposed system embeds domain-specific industrial knowledge into XR environments through a natural language interface, enabling hands-free, context-aware expert guidance for workers. We present the architecture of the proposed system consisting of an LLM Chat Engine with dynamic tool orchestration and an XR application featuring voice-driven interaction. Performance evaluation of various chunking strategies, embedding models, and vector databases reveals that semantic chunking, balanced embedding models, and efficient vector stores deliver optimal performance for industrial knowledge retrieval. The system's potential is demonstrated through early implementation in multiple industrial use cases, including robotic assembly, smart infrastructure maintenance, and aerospace component servicing. Results indicate potential for enhancing training efficiency, remote assistance capabilities, and operational guidance in alignment with Industry 5.0's human-centric and resilient approach to industrial development.

Ruiyang Ma, Tianhao Wei, Jiaxi Zhang et al.

As hardware design complexity increases, hardware fuzzing emerges as a promising tool for automating the verification process. However, a significant gap still exists before it can be applied in industry. This paper aims to summarize the current progress of hardware fuzzing from an industry-use perspective and propose solutions to bridge the gap between hardware fuzzing and industrial verification. First, we review recent hardware fuzzing methods and analyze their compatibilities with industrial verification. We establish criteria to assess whether a hardware fuzzing approach is compatible. Second, we examine whether current verification tools can efficiently support hardware fuzzing. We identify the bottlenecks in hardware fuzzing performance caused by insufficient support from the industrial environment. To overcome the bottlenecks, we propose a prototype, HwFuzzEnv, providing the necessary support for hardware fuzzing. With this prototype, the previous hardware fuzzing method can achieve a several hundred times speedup in industrial settings. Our work could serve as a reference for EDA companies, encouraging them to enhance their tools to support hardware fuzzing efficiently in industrial verification.

Eun-Jeong Kim, Su-Min Kang, Min-Jeong Ko et al.

Parental attention and care is essential for children and adolescents who are unable to take care of their own oral health. There have been studies on the characteristics of mothers and the oral conditions of children in Korea, but there are very few previous studies that report on the oral health status of children according to the employment status of mothers. The aim of this study was to investigate the relationship between maternal employment and educational status and children’s oral health. Using data from the 10th Panel Study on Korean Children (PSKC), we analyzed the association between maternal employment and education status and the occurrence of dental caries among 1175 nine-year-old Korean children. The relationship was examined through cross-tabulation and logistic regression analysis. After adjusting for the mother’s age, parental style, parental relationship, family talk time, family meal time, leisure time, area of residence, and household income, the study found that children with working and studying mothers were 1.159 times more likely to have dental caries than children with non-working and non-studying mothers. The relationship between maternal employment and educational status and children’s oral health was confirmed. Based on the results of this study, it is expected that systematic follow-up studies will be needed to better understand the association and causal relationship between dental caries and oral disease in children according to whether mothers are employed or educated.

Sotiris Michaelides, Stefan Lenz, Thomas Vogt et al.

The industrial landscape is undergoing a significant transformation, moving away from traditional wired fieldbus networks to cutting-edge 5G mobile networks. This transition, extending from local applications to company-wide use and spanning multiple factories, is driven by the promise of low-latency communication and seamless connectivity for various devices in industrial settings. However, besides these tremendous benefits, the integration of 5G as the communication infrastructure in industrial networks introduces a new set of risks and threats to the security of industrial systems. The inherent complexity of 5G systems poses unique challenges for ensuring a secure integration, surpassing those encountered with any technology previously utilized in industrial networks. Most importantly, the distinct characteristics of industrial networks, such as real-time operation, required safety guarantees, and high availability requirements, further complicate this task. As the industrial transition from wired to wireless networks is a relatively new concept, a lack of guidance and recommendations on securely integrating 5G renders many industrial systems vulnerable and exposed to threats associated with 5G. To address this situation, in this paper, we summarize the state-of-the-art and derive a set of recommendations for the secure integration of 5G into industrial networks based on a thorough analysis of the research landscape. Furthermore, we identify opportunities to utilize 5G to enhance security and indicate remaining challenges, identifying future academic directions.

Linda Kölbel, Markus Hornsteiner, Stefan Schönig

In industry, the networking and automation of machines through the Internet of Things (IoT) continues to increase, leading to greater digitalization of production processes. Traditionally, business and production processes are controlled, optimized and monitored using business process management methods that require process discovery. However, these methods cannot be fully applied to industrial production processes. Nevertheless, processes in the industry must also be monitored and discovered for this purpose. The aim of this paper is to develop an approach for process discovery methods and to adapt existing process discovery methods for application to industrial processes. The adaptations of classic discovery methods are presented as universally applicable guidelines specifically for the Industrial Internet of Things (IIoT). In order to create an optimal process model based on process evaluation, different methods are combined into a standardized discovery approach that is both efficient and cost-effective.

Xi Jiang, Jian Li, Hanqiu Deng et al.

In the field of industrial inspection, Multimodal Large Language Models (MLLMs) have a high potential to renew the paradigms in practical applications due to their robust language capabilities and generalization abilities. However, despite their impressive problem-solving skills in many domains, MLLMs' ability in industrial anomaly detection has not been systematically studied. To bridge this gap, we present MMAD, the first-ever full-spectrum MLLMs benchmark in industrial Anomaly Detection. We defined seven key subtasks of MLLMs in industrial inspection and designed a novel pipeline to generate the MMAD dataset with 39,672 questions for 8,366 industrial images. With MMAD, we have conducted a comprehensive, quantitative evaluation of various state-of-the-art MLLMs. The commercial models performed the best, with the average accuracy of GPT-4o models reaching 74.9%. However, this result falls far short of industrial requirements. Our analysis reveals that current MLLMs still have significant room for improvement in answering questions related to industrial anomalies and defects. We further explore two training-free performance enhancement strategies to help models improve in industrial scenarios, highlighting their promising potential for future research.

Shusen Wang

Recommender system (RS) is an established technology with successful applications in social media, e-commerce, entertainment, and more. RSs are indeed key to the success of many popular APPs, such as YouTube, Tik Tok, Xiaohongshu, Bilibili, and others. This paper explores the methodology for improving modern industrial RSs. It is written for experienced RS engineers who are diligently working to improve their key performance indicators, such as retention and duration. The experiences shared in this paper have been tested in some real industrial RSs and are likely to be generalized to other RSs as well. Most contents in this paper are industry experience without publicly available references.

Diego Vallarino

Using as a central instrument a new database, resulting from a compilation of historical administrative records, which covers the period 1974-2010, we can have new evidence on how industrial companies used tax benefits, and claim that these are decisive for the investment decision of the Uruguayan industrial companies during that period. The aforementioned findings served as a raw material to also affirm that the incentives to increase investment are factors that positively influence the level of economic activity and exports, and negatively on the unemployment rate.

Dianhui Wang, Matthew J. Felicetti

Real-time predictive modelling with desired accuracy is highly expected in industrial artificial intelligence (IAI), where neural networks play a key role. Neural networks in IAI require powerful, high-performance computing devices to operate a large number of floating point data. Based on stochastic configuration networks (SCNs), this paper proposes a new randomized learner model, termed stochastic configuration machines (SCMs), to stress effective modelling and data size saving that are useful and valuable for industrial applications. Compared to SCNs and random vector functional-link (RVFL) nets with binarized implementation, the model storage of SCMs can be significantly compressed while retaining favourable prediction performance. Besides the architecture of the SCM learner model and its learning algorithm, as an important part of this contribution, we also provide a theoretical basis on the learning capacity of SCMs by analysing the model's complexity. Experimental studies are carried out over some benchmark datasets and three industrial applications. The results demonstrate that SCM has great potential for dealing with industrial data analytics.

Tianyu Zhang, Gang Wang, Chuanyu Xue et al.

With the introduction of Cyber-Physical Systems (CPS) and Internet of Things (IoT) technologies, the automation industry is undergoing significant changes, particularly in improving production efficiency and reducing maintenance costs. Industrial automation applications often need to transmit time- and safety-critical data to closely monitor and control industrial processes. Several Ethernet-based fieldbus solutions, such as PROFINET IRT, EtherNet/IP, and EtherCAT, are widely used to ensure real-time communications in industrial automation systems. These solutions, however, commonly incorporate additional mechanisms to provide latency guarantees, making their interoperability a grand challenge. The IEEE 802.1 Time Sensitive Networking (TSN) task group was formed to enhance and optimize IEEE 802.1 network standards, particularly for Ethernet-based networks. These solutions can be evolved and adapted for cross-industry scenarios, such as large-scale distributed industrial plants requiring multiple industrial entities to work collaboratively. This paper provides a comprehensive review of current advances in TSN standards for industrial automation. It presents the state-of-the-art IEEE TSN standards and discusses the opportunities and challenges of integrating TSN into the automation industry. Some promising research directions are also highlighted for applying TSN technologies to industrial automation applications.

Aranzazu Pérez Alonso

Entrevista a Aránzazu Pérez-Alonso

Lauren A. Eaves, Alexander P. Keil, Anne Marie Jukic et al.

Abstract Background Prenatal exposure to metals in private well water may increase the risk of preterm birth (PTB) (delivery < 37 weeks’ gestation). In this study, we estimated associations between arsenic, manganese, lead, cadmium, chromium, copper, and zinc concentrations in private well water and PTB incidence in North Carolina (NC). Methods Birth certificates from 2003–2015 (n = 1,329,071) were obtained and pregnancies were assigned exposure using the mean concentration and the percentage of tests above the maximum contaminant level (MCL) for the census tract of each individuals’ residence at the time of delivery using the NCWELL database (117,960 well water tests from 1998–2019). We evaluated associations between single metals and PTB using adjusted logistic regression models. Metals mixtures were assessed using quantile-based g-computation. Results Compared with those in other census tracts, individuals residing in tracts where > 25% of tests exceeded the MCL for lead (aOR 1.10, 95%CI 1.02,1.18) or cadmium (aOR 1.11, 95% CI 1.00,1.23) had an increased odds of PTB. Conversely, those residing in areas with > 25% MCL for zinc (aOR 0.77 (95% CI: 0.56,1.02) and copper (aOR 0.53 (95% CI: 0.13,1.34)) had a reduced odds of PTB. A quartile increase in the concentrations of a mixture of lead, cadmium, and chromium was associated with a small increased odds for PTB (aOR 1.02, 95% CI 1.01, 1.03). This metal mixture effect was most pronounced among American Indian individuals (aOR per quartile increase in all metals: 1.19 (95% CI 1.06,1.34)). Conclusions In a large study population of over one million births, lead and cadmium were found to increase the risk of PTB individually and in a mixture, with additional mixtures-related impacts estimated from co-exposure with chromium. This study highlights critical racial and ethnic health disparities in relation to private well water thereby emphasizing the urgent need for improved private well water quality to protect vulnerable populations.

Xianjie Li, Yinger Luo, Di Ji et al.

Abstract Background Extensive production and usage of commercially available products containing TiO2 NPs have led to accumulation in the human body. The deposition of TiO2 NPs has even been detected in the human placenta, which raises concerns regarding fetal health. Previous studies regarding developmental toxicity have frequently focused on TiO2 NPs < 50 nm, whereas the potential adverse effects of large-sized TiO2 NPs received less attention. Placental vasculature is essential for maternal–fetal circulatory exchange and ensuring fetal growth. This study explores the impacts of TiO2 NPs (100 nm in size) on the placenta and fetal development and elucidates the underlying mechanism from the perspective of placental vasculature. Pregnant C57BL/6 mice were exposed to TiO2 NPs by gavage at daily dosages of 10, 50, and 250 mg/kg from gestational day 0.5–16.5. Results TiO2 NPs penetrated the placenta and accumulated in the fetal mice. The fetuses in the TiO2 NP-exposed groups exhibited a dose-dependent decrease in body weight and length, as well as in placental weight and diameter. In vivo imaging showed an impaired placental barrier, and pathological examinations revealed a disrupted vascular network of the labyrinth upon TiO2 NP exposure. We also found an increase in gene expression related to the transforming growth factor-β (TGF-β) -SNAIL pathway and the upregulation of mesenchymal markers, accompanied by a reduction in endothelial markers. In addition, TiO2 NPs enhanced the gene expression responsible for the endothelial-to-mesenchymal transition (EndMT) in cultured human umbilical vein endothelial cells, whereas SNAIL knockdown attenuated the induction of EndMT phenotypes. Conclusion Our study revealed that maternal exposure to 100 nm TiO2 NPs disrupts placental vascular development and fetal mice growth through aberrant activation of EndMT in the placental labyrinth. These data provide novel insight into the mechanisms of developmental toxicity posed by NPs.

Man Zhang, Andrea Arcuri, Yonggang Li et al.

With several microservice architectures comprising of thousands of web services, used to serve 630 million customers, companies like Meituan face several challenges in the verification and validation of their software. This paper reports on our experience of integrating EvoMaster (a search-based white-box fuzzer) in the testing processes at Meituan over almost 2 years. Two user studies were carried out in 2021 and in 2023 to evaluate two versions of EvoMaster, respectively, in tackling the test generation for industrial web services which are parts of a large e-commerce microservice system. The two user studies involve in total 321,131 lines of code from five APIs and 27 industrial participants at Meituan. Questionnaires and interviews were carried out in both user studies with employees at Meituan. The two user studies demonstrate clear advantages of EvoMaster (i.e., code coverage and fault detection) and the urgent need to have such a fuzzer in industrial microservices testing. To study how these results could generalize, a follow up user study was done in 2024 with five engineers in the five different companies. Our results show that, besides their clear usefulness, there are still many critical challenges that the research community needs to investigate to improve performance further.

Marcela G. dos Santos, Fabio Petrillo

Industrial robots are important machines applied in numerous modern industries that execute repetitive tasks with high accuracy, replacing or supporting dangerous jobs. In this kind of system, with increased complexity in which cost is related to the time the system keeps working, the system must operate with a minimum number of failures. In other words, a quality aspect important in industry is reliability. We hypothesize that Automated Acceptance Testing improves reliability for industrial robot program. We present the research question, the motivation for this study, our hypothesis and future research efforts.

Nikita Thomas, Stefanie T. Ebelt, Andrew J. Newman et al.

Abstract Background Ambient temperature observations from single monitoring stations (usually located at the major international airport serving a city) are routinely used to estimate heat exposures in epidemiologic studies. This method of exposure assessment does not account for potential spatial variability in ambient temperature. In environmental health research, there is increasing interest in utilizing spatially-resolved exposure estimates to minimize exposure measurement error. Methods We conducted time-series analyses to investigate short-term associations between daily temperature metrics and emergency department (ED) visits for well-established heat-related morbidities in five US cities that represent different climatic regions: Atlanta, Los Angeles, Phoenix, Salt Lake City, and San Francisco. In addition to airport monitoring stations, we derived several exposure estimates for each city using a national meteorology data product (Daymet) available at 1 km spatial resolution. Results Across cities, we found positive associations between same-day temperature (maximum or minimum) and ED visits for heat-sensitive outcomes, including acute renal injury and fluid and electrolyte imbalance. We also found that exposure assessment methods accounting for spatial variability in temperature and at-risk population size often resulted in stronger relative risk estimates compared to the use of observations at airports. This pattern was most apparent when examining daily minimum temperature and in cities where the major airport is located further away from the urban center. Conclusion Epidemiologic studies based on single monitoring stations may underestimate the effect of temperature on morbidity when the station is less representative of the exposure of the at-risk population.

Antonio León-Jiménez, José M. Mánuel, Marcial García-Rojo et al.

Abstract Background Engineered stone silicosis is an emerging disease in many countries worldwide produced by the inhalation of respirable dust of engineered stone. This silicosis has a high incidence among young workers, with a short latency period and greater aggressiveness than silicosis caused by natural materials. Although the silica content is very high and this is the key factor, it has been postulated that other constituents in engineered stones can influence the aggressiveness of the disease. Different samples of engineered stone countertops (fabricated by workers during the years prior to their diagnoses), as well as seven lung samples from exposed patients, were analyzed by multiple techniques. Results The different countertops were composed of SiO2 in percentages between 87.9 and 99.6%, with variable relationships of quartz and cristobalite depending on the sample. The most abundant metals were Al, Na, Fe, Ca and Ti. The most frequent volatile organic compounds were styrene, toluene and m-xylene, and among the polycyclic aromatic hydrocarbons, phenanthrene and naphthalene were detected in all samples. Patients were all males, between 26 and 46 years-old (average age: 36) at the moment of the diagnosis. They were exposed to the engineered stone an average time of 14 years. At diagnosis, only one patient had progressive massive fibrosis. After a follow-up period of 8 ± 3 years, four patients presented progressive massive fibrosis. Samples obtained from lung biopsies most frequently showed well or ill-defined nodules, composed of histiocytic cells and fibroblasts without central hyalinization. All tissue samples showed high proportion of Si and Al at the center of the nodules, becoming sparser at the periphery. Al to Si content ratios turned out to be higher than 1 in two of the studied cases. Correlation between Si and Al was very high (r = 0.93). Conclusion Some of the volatile organic compounds, polycyclic aromatic hydrocarbons and metals detected in the studied countertop samples have been described as causative of lung inflammation and respiratory disease. Among inorganic constituents, aluminum has been a relevant component within the silicotic nodule, reaching atomic concentrations even higher than silicon in some cases. Such concentrations, both for silicon and aluminum showed a decreasing tendency from the center of the nodule towards its frontier.

Deval Bhamare, Maede Zolanvari, Aiman Erbad et al.

Industrial Control System (ICS) is a general term that includes supervisory control & data acquisition (SCADA) systems, distributed control systems (DCS), and other control system configurations such as programmable logic controllers (PLC). ICSs are often found in the industrial sectors and critical infrastructures, such as nuclear and thermal plants, water treatment facilities, power generation, heavy industries, and distribution systems. Though ICSs were kept isolated from the Internet for so long, significant achievable business benefits are driving a convergence between ICSs and the Internet as well as information technology (IT) environments, such as cloud computing. As a result, ICSs have been exposed to the attack vectors used in the majority of cyber-attacks. However, ICS devices are inherently much less secure against such advanced attack scenarios. A compromise to ICS can lead to enormous physical damage and danger to human lives. In this work, we have a close look at the shift of the ICS from stand-alone systems to cloud-based environments. Then we discuss the major works, from industry and academia towards the development of the secure ICSs, especially applicability of the machine learning techniques for the ICS cyber-security. The work may help to address the challenges of securing industrial processes, particularly while migrating them to the cloud environments.