Hasil untuk "Industrial medicine. Industrial hygiene"

Flavien Armangeon, Thibaud Ehret, Enric Meinhardt-Llopis et al.

Aligning functional schematics with 2D and 3D scene acquisitions is crucial for building digital twins, especially for old industrial facilities that lack native digital models. Current manual alignment using images and LiDAR data does not scale due to tediousness and complexity of industrial sites. Inconsistencies between schematics and reality, and the scarcity of public industrial datasets, make the problem both challenging and underexplored. This paper introduces IRIS-v2, a comprehensive dataset to support further research. It includes images, point clouds, 2D annotated boxes and segmentation masks, a CAD model, 3D pipe routing information, and the P&ID (Piping and Instrumentation Diagram). The alignment is experimented on a practical case study, aiming at reducing the time required for this task by combining segmentation and graph matching.

Chunxia Wang, Fang Luo, Mi Song et al.

Background: Acute myocardial infarction (AMI) poses significant psychosocial challenges to patients during recovery, especially for young and middle-aged patients returning to work. This study examines the effects of music therapy combined with dyadic coping (DC) on the psychosocial adaptation and marital well-being of patients with AMI and their spouses. Methods: This retrospective cohort study included 60 couples of young and middle-aged patients with AMI admitted to Wuxi Second People’s Hospital from January 2024 to June 2024 and their spouses. The subjects were divided into the following two groups: 30 couples received DC care (DC group), and 30 couples received music therapy combined with DC care (DCMT group). The treatment course was 2 weeks. Outcomes were measured using the Multidimensional Infarction Assessment Scale (MIDAS), the Psychological Adjustment to Illness Scale Self-report (PAIS-SR), the Dyadic Coping Inventory (DCI), the Locke–Wallace Marital Adjustment Test (LWMAT) and the General Well-Being Schedule (GWBS). Results: Compared with the DC group, the DCMT group demonstrated significant improvements across all measured scales, including higher scores on the physical activity and emotional response dimensions of MIDAS, 10 dimensions of DCI and lower scores of all seven dimensions of PAIS-SR (P < 0.05). The total LWMAT and GWBS scores and the nursing satisfaction level in the DCMT group were higher than those in the DC group (P < 0.05). Conclusion: Music therapy combined with DC significantly enhances psychosocial adaptation and marital well-being in patients with AMI and their spouses.

Abdelrahman Alzarooni, Ehtesham Iqbal, Samee Ullah Khan et al.

Anomaly detection from images captured using camera sensors is one of the mainstream applications at the industrial level. Particularly, it maintains the quality and optimizes the efficiency in production processes across diverse industrial tasks, including advanced manufacturing and aerospace engineering. Traditional anomaly detection workflow is based on a manual inspection by human operators, which is a tedious task. Advances in intelligent automated inspection systems have revolutionized the Industrial Anomaly Detection (IAD) process. Recent vision-based approaches can automatically extract, process, and interpret features using computer vision and align with the goals of automation in industrial operations. In light of the shift in inspection methodologies, this survey reviews studies published since 2019, with a specific focus on vision-based anomaly detection. The components of an IAD pipeline that are overlooked in existing surveys are presented, including areas related to data acquisition, preprocessing, learning mechanisms, and evaluation. In addition to the collected publications, several scientific and industry-related challenges and their perspective solutions are highlighted. Popular and relevant industrial datasets are also summarized, providing further insight into inspection applications. Finally, future directions of vision-based IAD are discussed, offering researchers insight into the state-of-the-art of industrial inspection.

Ruimin Ma, Sebastian Zudaire, Zhen Li et al.

Object 6DoF (6D) pose estimation is essential for robotic perception, especially in industrial settings. It enables robots to interact with the environment and manipulate objects. However, existing benchmarks on object 6D pose estimation primarily use everyday objects with rich textures and low-reflectivity, limiting model generalization to industrial scenarios where objects are often metallic, texture-less, and highly reflective. To address this gap, we propose a novel dataset and benchmark namely \textit{Industrial Metallic Dataset (IMD)}, tailored for industrial applications. Our dataset comprises 45 true-to-scale industrial components, captured with an RGB-D camera under natural indoor lighting and varied object arrangements to replicate real-world conditions. The benchmark supports three tasks, including video object segmentation, 6D pose tracking, and one-shot 6D pose estimation. We evaluate existing state-of-the-art models, including XMem and SAM2 for segmentation, and BundleTrack and BundleSDF for pose estimation, to assess model performance in industrial contexts. Evaluation results show that our industrial dataset is more challenging than existing household object datasets. This benchmark provides the baseline for developing and comparing segmentation and pose estimation algorithms that better generalize to industrial robotics scenarios.

Libe Vilela, Linda Schenk, Anneli Julander et al.

Abstract Background During the COVID-19 pandemic, increased hand hygiene practices using water, soap and hand disinfectants, became prevalent, particularly among frontline workers. This study investigates the impact of these practices on the skin’s ability to retain the allergenic metals nickel, cobalt, and chromium. The study constitutes three parts: (I) creating an impaired skin barrier, (II) exposing treated and untreated skin to nickel alone, and (III) in co-exposure with cobalt and chromium. Methods Using full-thickness skin from stillborn piglets, in vitro experiments were conducted to assess retention of metals in skin at conditions mimicking intense hand hygiene practices. Treatment of skin with varying concentrations of sodium lauryl sulphate (SLS), to impair its barrier integrity was assessed. This was followed by exposure of treated and untreated skin to the metals, that were dissolved in Milli-Q water, 0.5% SLS, and ethanol respectively. Results Results showed that pre-treatment with 5% SLS impaired the skin barrier with regards to the measure of trans epidermal water loss (TEWL). Metal amounts retained in the skin were generally higher in treated than untreated skin. The highest amounts of metal retained in skin were observed for exposure to nickel in ethanol. Co-exposure to nickel, cobalt, and chromium in 0.5% SLS resulted in the highest amounts of total metal retention. Conclusions The in vitro findings highlight the increased risk of metal retention in skin due to an impaired barrier. The SLS concentration used in the current study corresponds to those used in many hand hygiene products. Hence, occupational settings with frequent exposure to water, soap and disinfectants need to consider protective measures not only for the irritant exposures themselves but also simultaneous exposure to allergenic metals.

Leslie Edwards, James Milner, Paul Wilkinson et al.

Abstract Background Current evidence linking long-term exposure to fine particulate matter (PM2.5) exposure and mortality is primarily based on persons that live in the same residence, city and/or country throughout the study, with few residential moves or relocations. We propose a novel method to quantify the health impacts of PM2.5 for United States (US) diplomats who regularly relocate to international cities with different PM2.5 levels. Methods Life table methods were applied at an individual-level to US mortality statistics using the World Health Organization’s database of city-specific PM2.5 annual mean concentrations. Global Burden of Disease concentration-response (C-R) functions were used to estimate cause-specific mortality and days of life lost (DLL) for a range of illustrative 20-year diplomatic assignments for three age groups. Time lags between exposure and exposure-related mortality risks were applied. Sensitivity analysis of baseline mortality, exposure level, C-R functions and lags was conducted. The effect of mitigation measures, including the addition of air purifiers, was examined. Results DLL due to PM2.5 exposure for a standard 20-year assignment ranged from 0.3 days for diplomats’ children to 84.1 days for older diplomats. DLL decreased when assignments in high PM2.5 cities were followed by assignments in low PM2.5 cities: 162.5 DLL when spending 20 years in high PM2.5 cities compared to 62.6 DLL when spending one of every four years (5 years total) in a high PM2.5 city for older male diplomats. Use of air purifiers and improved home tightness in polluted cities may halve DLL due to PM2.5 exposure. The results were highly sensitive to lag assumptions: DLL increased by 68% without inception lags and decreased by 59% without cessation lags for older male diplomats. Conclusion We developed a model to quantify health impacts of changing PM2.5 exposure for a population with frequent relocations. Our model suggests that alternating assignments in high and low PM2.5 cities may help reduce PM2.5-related mortality burdens. Adding exposure mitigation at home may help reduce PM2.5 related mortality. Further research on outcome-specific lag structures is needed to improve the model.

Lei Ren, Haiteng Wang, Jinwang Li et al.

With the remarkable success of generative models like ChatGPT, Artificial Intelligence Generated Content (AIGC) is undergoing explosive development. Not limited to text and images, generative models can generate industrial time series data, addressing challenges such as the difficulty of data collection and data annotation. Due to their outstanding generation ability, they have been widely used in Internet of Things, metaverse, and cyber-physical-social systems to enhance the efficiency of industrial production. In this paper, we present a comprehensive overview of generative models for industrial time series from deep generative models (DGMs) to large generative models (LGMs). First, a DGM-based AIGC framework is proposed for industrial time series generation. Within this framework, we survey advanced industrial DGMs and present a multi-perspective categorization. Furthermore, we systematically analyze the critical technologies required to construct industrial LGMs from four aspects: large-scale industrial dataset, LGMs architecture for complex industrial characteristics, self-supervised training for industrial time series, and fine-tuning of industrial downstream tasks. Finally, we conclude the challenges and future directions to enable the development of generative models in industry.

Dimitrios Kouzapas, Christos G. Panayiotou, Demetrios G. Eliades

Modern industrial systems require frequent updates to their cyber and physical infrastructures, often demanding considerable reconfiguration effort. This paper introduces the industrial Cyber-Physical Systems Description Language, iCPS-DL, which enables autonomic reconfigurations for industrial Cyber-Physical Systems. The iCPS-DL maps an industrial process using semantics for physical and cyber-physical components, a state estimation model, and agent interactions. A novel aspect is using communication semantics to ensure live interaction among distributed agents. Reasoning on the semantic description facilitates the configuration of the industrial process control loop. A Water Distribution Networks domain case study demonstrates iCPS-DL's application.

Wei Zhang, Xianfu Cheng, Yi Zhang et al.

Log parsing, a vital task for interpreting the vast and complex data produced within software architectures faces significant challenges in the transition from academic benchmarks to the industrial domain. Existing log parsers, while highly effective on standardized public datasets, struggle to maintain performance and efficiency when confronted with the sheer scale and diversity of real-world industrial logs. These challenges are two-fold: 1) massive log templates: The performance and efficiency of most existing parsers will be significantly reduced when logs of growing quantities and different lengths; 2) Complex and changeable semantics: Traditional template-matching algorithms cannot accurately match the log templates of complicated industrial logs because they cannot utilize cross-language logs with similar semantics. To address these issues, we propose ECLIPSE, Enhanced Cross-Lingual Industrial log Parsing with Semantic Entropy-LCS, since cross-language logs can robustly parse industrial logs. On the one hand, it integrates two efficient data-driven template-matching algorithms and Faiss indexing. On the other hand, driven by the powerful semantic understanding ability of the Large Language Model (LLM), the semantics of log keywords were accurately extracted, and the retrieval space was effectively reduced. Notably, we launch a Chinese and English cross-platform industrial log parsing benchmark ECLIPSE- BENCH to evaluate the performance of mainstream parsers in industrial scenarios. Our experimental results across public benchmarks and ECLIPSE- BENCH underscore the superior performance and robustness of our proposed ECLIPSE. Notably, ECLIPSE both delivers state-of-the-art performance when compared to strong baselines and preserves a significant edge in processing efficiency.

Jiyu Chen, Jinchuan Qian, Xinmin Zhang et al.

With the development of intelligent manufacturing and the increasing complexity of industrial production, root cause diagnosis has gradually become an important research direction in the field of industrial fault diagnosis. However, existing research methods struggle to effectively combine domain knowledge and industrial data, failing to provide accurate, online, and reliable root cause diagnosis results for industrial processes. To address these issues, a novel fault root cause diagnosis framework based on knowledge graph and industrial data, called Root-KGD, is proposed. Root-KGD uses the knowledge graph to represent domain knowledge and employs data-driven modeling to extract fault features from industrial data. It then combines the knowledge graph and data features to perform knowledge graph reasoning for root cause identification. The performance of the proposed method is validated using two industrial process cases, Tennessee Eastman Process (TEP) and Multiphase Flow Facility (MFF). Compared to existing methods, Root-KGD not only gives more accurate root cause variable diagnosis results but also provides interpretable fault-related information by locating faults to corresponding physical entities in knowledge graph (such as devices and streams). In addition, combined with its lightweight nature, Root-KGD is more effective in online industrial applications.

William Farlessyost, Shweta Singh

Industrial ecosystems are coupled with natural systems through utilization of feedstocks and waste disposal. To ensure resilience in production of industrial systems under the threat of climate change scenarios, it is necessary to evaluate the impact of this coupling on productivity and waste generation. In this work, we present a novel methodology for modeling and assessing the resilience of coupled natural-industrial ecosystems under climate change scenarios. We develop a computationally efficient framework that integrates liquid time-constant (LTC) neural networks as surrogate models to capture complex, nonlinear dynamics of coupled agricultural and industrial systems. The approach is demonstrated through a case study of a soybean-based biodiesel production network in Champaign County, Illinois. LTC models are trained to capture dynamics of nodes and are then coupled and driven by statistically downscaled climate projections for RCP 4.5 and 8.5 scenarios from 2006-2096. The framework enables rapid simulation of system-wide material flow dynamics and exploration of cascading effects from climate-induced disruptions. Results reveal non-linear behaviors and potential tipping points in system resilience under different climate scenarios and farm sizes. The RCP 8.5 scenario led to earlier and more frequent production failures, increased reliance on imports for smaller farms, and complex patterns of waste accumulation and stock levels. The methodology provides valuable insights into system vulnerabilities and adaptive capacities, offering decision support for enhancing the resilience and sustainability of coupled natural-industrial ecosystems in the face of climate change. The framework's adaptability suggests potential applications across various industrial ecosystems and climate-sensitive sectors

Tomislav Duricic, Peter Müllner, Nicole Weidinger et al.

Many industrial sectors rely on well-trained employees that are able to operate complex machinery. In this work, we demonstrate an AI-powered immersive assistance system that supports users in performing complex tasks in industrial environments. Specifically, our system leverages a VR environment that resembles a juice mixer setup. This digital twin of a physical setup simulates complex industrial machinery used to mix preparations or liquids (e.g., similar to the pharmaceutical industry) and includes various containers, sensors, pumps, and flow controllers. This setup demonstrates our system's capabilities in a controlled environment while acting as a proof-of-concept for broader industrial applications. The core components of our multimodal AI assistant are a large language model and a speech-to-text model that process a video and audio recording of an expert performing the task in a VR environment. The video and speech input extracted from the expert's video enables it to provide step-by-step guidance to support users in executing complex tasks. This demonstration showcases the potential of our AI-powered assistant to reduce cognitive load, increase productivity, and enhance safety in industrial environments.

Aihua Liu

ObjectiveTo investigate the sterilization of OLYMPUS flexible endoscopes with a low temperature steam formaldehyde sterilizer of a national brand model FS-130 and to verify its compatibility.MethodsEight flexible endoscopes sterilized in our hospital central sterile supply department from June 2020 to December 2022, with a total of 1836 batches sterilization data, were selected as a study group test and evaluated for sterilization effectiveness, compatibility and appearance.ResultsThe eight flexible endoscopes used in the test, after the low temperature steam formaldehyde sterilization test, material compatibility is good, there is no aging of the rubber, adhesive peeling and other problems, all physical, chemical and biological monitoring during the test were qualified, there is no blurred imaging, insensitive operation and other abnormal performance conditions.ConclusionOLYMPUS flexible fiber optic endoscopes and electronic endoscopes can be sterilized by a national brand model FS-130 low temperature steam formaldehyde sterilizer, and the sterilization effectiveness as well as the compatibility and functionality of the flexible endoscopes themselves can be guaranteed.

Vasiliki Demertzi, Stavros Demertzis, Konstantinos Demertzis

Thanks to rapid technological developments, new innovative solutions and practical applications of the Industrial Internet of Things (IIoT) are being created, upgrading the structures of many industrial enterprises. IIoT brings the physical and digital environment together with minimal human intervention and profoundly transforms the economy and modern business. Data flowing through IIoT feed artificial intelligence tools, which perform intelligent functions such as performance tuning of interconnected machines, error correction, and preventive maintenance. However, IIoT deployments are vulnerable to sophisticated security threats at various levels of the connectivity and communications infrastructure they incorporate. The complex and often heterogeneous nature of chaotic IIoT infrastructures means that availability, confidentiality and integrity are difficult to guarantee. This can lead to potential mistrust of network operations, concerns about privacy breaches or loss of vital personal data and sensitive information of network end-users. This paper examines the privacy requirements of an IIoT ecosystem in industry standards. Specifically, it describes the industry privacy dimensions of the protection of natural persons through the processing of personal data by competent authorities for the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties. In addition, it presents an overview of the state-of-the-art methodologies and solutions for industrial privacy threats. Finally, it analyses the privacy requirements and suggestions for an ideal secure and private IIoT environment.

Anamai Thetkathuek, Chan Pattama Polyong, Wanlop Jaidee et al.

Background. The Thai government has been developing its Eastern Economic Corridor (EEC), which spans three provinces, with the aim of improving connections with other Asian nations. Since this strategic development, the number of trucks, private car, and passenger car registrations have continued to grow, with a corresponding increase in related to benzene, toluene, ethylbenzene, and xylene (BTEX). Objectives. This study aims to compare the levels of trans, trans-muconic acid (t, t MA); toluene (TU); mandelic acid (MA); and methyl hippuric acid (MHA) in the urine of gas station employees, considering demographic and occupational factors. Material and methods. These employees worked either near or away from the fuel dispenser, and there 100 people in each group. Data were collected using interviews and testing environmental air and urine samples for benzene, toluene, ethyl benzene, and xylene (BTEX). Results. The results showed that BTEX concentrations were just detectable in all 200 cases (100%). The mean (±SD) urine level of t, t MA was 449.28 (±213.32) μg/g creatinine, while the median (min-max) was 428.23 (95.58-1202.56) μg/g creatinine. The mean TU was 0.011 (0.001) mg/L, while the median (min-max) was 0.011 (0.010-0.013) mg/L. MA levels were higher inside the pollution control zone than they were outside the zone (p=.009). Employees who practiced poor personal hygiene had relatively high urinary toluene and MHA levels (p=.009) and those who did not wear personal protective equipment (PPE) had relatively high MA levels (p=.040). Conclusion. The results of this study revealed statistically significant biomarkers influencing the levels of t, t MA; TU; MA; and MHA in urine. The recommendations of this study are that employers should provide their employees with suitable PPE, check regularly to ensure that it is worn, and strongly encourage employees to take care of their sanitation. Employees should take appropriate breaks and days off to minimize their exposure to BTEX.

Ayan Chatterjee, Bestoun S. Ahmed, Erik Hallin et al.

Today, machine learning (ML) is widely used in industry to provide the core functionality of production systems. However, it is practically always used in production systems as part of a larger end-to-end software system that is made up of several other components in addition to the ML model. Due to production demand and time constraints, automated software engineering practices are highly applicable. The increased use of automated ML software engineering practices in industries such as manufacturing and utilities requires an automated Quality Assurance (QA) approach as an integral part of ML software. Here, QA helps reduce risk by offering an objective perspective on the software task. Although conventional software engineering has automated tools for QA data analysis for data-driven ML, the use of QA practices for ML in operation (MLOps) is lacking. This paper examines the QA challenges that arise in industrial MLOps and conceptualizes modular strategies to deal with data integrity and Data Quality (DQ). The paper is accompanied by real industrial use-cases from industrial partners. The paper also presents several challenges that may serve as a basis for future studies.

Qifan Jin, Li Chen

The surface defect detection method based on visual perception has been widely used in industrial quality inspection. Because defect data are not easy to obtain and the annotation of a large number of defect data will waste a lot of manpower and material resources. Therefore, this paper reviews the methods of surface defect detection of industrial products based on a small number of labeled data, and this method is divided into traditional image processing-based industrial product surface defect detection methods and deep learning-based industrial product surface defect detection methods suitable for a small number of labeled data. The traditional image processing-based industrial product surface defect detection methods are divided into statistical methods, spectral methods and model methods. Deep learning-based industrial product surface defect detection methods suitable for a small number of labeled data are divided into based on data augmentation, based on transfer learning, model-based fine-tuning, semi-supervised, weak supervised and unsupervised.

Johan Bergelin, Per Erik Strandberg

There is an increasing interest in research on the combination of AI techniques and methods with MDE. However, there is a gap between AI and MDE practices, as well as between researchers and practitioners. This paper tackles this gap by reporting on industrial requirements in this field. In the AIDOaRt research project, practitioners and researchers collaborate on AI-augmented automation supporting modeling, coding, testing, monitoring, and continuous development in cyber-physical systems. The project specifically lies at the intersection of industry and academia collaboration with several industrial use cases. Through a process of elicitation and refinement, 78 high-level requirements were defined, and generalized into 30 generic requirements by the AIDOaRt partners. The main contribution of this paper is the set of generic requirements from the project for enhancing the development of cyber-physical systems with artificial intelligence, DevOps, and model-driven engineering, identifying the hot spots of industry needs in the interactions of MDE and AI. Future work will refine, implement and evaluate solutions toward these requirements in industry contexts.

Bin Jiang, Jianqiang Li, Guanghui Yue et al.

The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT. We firstly review related papers on IIoT and privacy protection, respectively. Then we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up researches on industrial differential privacy.

Annie Doubleday, Jill Schulte, Lianne Sheppard et al.

Abstract Background Wildfire events are increasing in prevalence in the western United States. Research has found mixed results on the degree to which exposure to wildfire smoke is associated with an increased risk of mortality. Methods We tested for an association between exposure to wildfire smoke and non-traumatic mortality in Washington State, USA. We characterized wildfire smoke days as binary for grid cells based on daily average PM2.5 concentrations, from June 1 through September 30, 2006–2017. Wildfire smoke days were defined as all days with assigned monitor concentration above a PM2.5 value of 20.4 μg/m3, with an additional set of criteria applied to days between 9 and 20.4 μg/m3. We employed a case-crossover study design using conditional logistic regression and time-stratified referent sampling, controlling for humidex. Results The odds of all-ages non-traumatic mortality with same-day exposure was 1.0% (95% CI: − 1.0 - 4.0%) greater on wildfire smoke days compared to non-wildfire smoke days, and the previous day’s exposure was associated with a 2.0% (95% CI: 0.0–5.0%) increase. When stratified by cause of mortality, odds of same-day respiratory mortality increased by 9.0% (95% CI: 0.0–18.0%), while the odds of same-day COPD mortality increased by 14.0% (95% CI: 2.0–26.0%). In subgroup analyses, we observed a 35.0% (95% CI: 9.0–67.0%) increase in the odds of same-day respiratory mortality for adults ages 45–64. Conclusions This study suggests increased odds of mortality in the first few days following wildfire smoke exposure. It is the first to examine this relationship in Washington State and will help inform local and state risk communication efforts and decision-making during future wildfire smoke events.