Hasil untuk "Industrial hygiene. Industrial welfare"

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.

Lanazha Belfield-Simpson, Jessica R. Martin, Matthew K. McPeek et al.

Abstract Background Burn pits, a method for disposal of military waste outside the United States, produce toxic substances, to which 3.5 million military personnel have been and continue to be exposed. Mild asthma (persistent or intermittent symptoms of asthma but no change in pulmonary function tests) is found among military personnel. We investigated whether burn pit combustion products (CPs) are more detrimental to the airways of asthmatic than non-asthmatic mice. Methods Mice were exposed to house dust mite antigen (HDM) or phosphate-buffered saline (PBS) 5 times over 2 weeks to initiate asthma-like airway injury. Condensates of CPs or saline were generated by flaming combustion of military cardboard, plastic and military plywood. CPs were aspirated oropharyngeally at 24 h after the final HDM or PBS instillation. The lungs were studied 24 h later. Results HDM increased recruitment of eosinophils and mucus projection, both Muc5ac and Muc5b mRNAs and protein. Following exposure to CPs, mice exposed to HDM had a greater inflammatory response and injury, as measured by increased neutrophil recruitment and the concentration of protein in the bronchoalveolar lavage (BAL), than control mice exposed to PBS. Expression of neutrophil chemokines was enhanced. CPs had no effect on HDM-induced eosinophil recruitment or expression of Th2 cytokines. CPs had no effect on mucus production in PBS or HDM mice. However, CPs increased intraluminal mucus, as revealed by AB-PAS staining, only in HDM mice, suggesting that CPs impaired mucociliary clearance (MCC), the lung's primary defense system, only in asthmatic airways. Lung RNA sequencing revealed that CPs increased genes and gene pathways describing inflammatory processes and impaired structure and function of cilia to a greater degree in HDM mice. Conclusions These data indicate that asthmatic mice are more susceptible to CP-induced lung remodeling and dysfunction than non-asthmatic mice. Enhanced chemokine expression suggests that the CXCL1,2,5/CXCR2 axis may be the mechanism of the increased neutrophil recruitment. A potential mechanism of mucus accumulation is that inhalation of CPs amplifies the changes in cilia and MCC caused by asthma and triggers a positive feedback loop of enhanced inflammation induced by this accumulating mucus.

Shima Shakiba, Ahmad Sharifi, Farnaz Hashemi et al.

Introduction: Burnout is a psychological syndrome that develops due to chronic stressors in a person's professional life, resulting in emotional exhaustion and detachment. The objective of this study was to determine the prevalence of burnout among medical residents, considering socio-demographic variables and job characteristics, and to predict burnout in this group. Medical residents often face specific pressures such as long working hours, sleep deprivation, high patient loads, and emotional demands from patient care, which contribute to their overall stress levels. Methods: A cross-sectional study was conducted in the academic year 2019-2020, involving 164 residents from two general hospitals who completed the Persian versions of the Job Content Questionnaire (JCQ) and Maslach Burnout Inventory (MBI-HSS). Results: A significant proportion of residents reported burnout symptoms, with 73.7% experiencing moderate to high levels of emotional exhaustion and 64.4% indicating moderate to high levels of depersonalization. Additionally, 90.1% of residents reported low perceived professional efficacy. Among the subscales of MBI-HSS, reduced professional efficacy was found to be the highest. Psychological demands and limited decision latitude were significant predictors of burnout, particularly in relation to emotional exhaustion and depersonalization. Conversely, support from family and co-workers, as well as higher levels of experience, were associated with lower depersonalization and improved professional efficacy. Conclusion: Overall, medical residents in Iran face significantly high levels of burnout, which are influenced by specific personal and job characteristics. Consequently, preventive and therapeutic interventions are necessary to address this pressing issues.

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.

Bárbara Lázara Hernández González, Violeta González González , Kely Rivero Domínguez et al.

Introducción: Potenciar el desempeño en salud ocupacional presupone una superación profesional que imbrique necesidades individuales de aprendizaje con requerimientos sociales de salud en el contexto de la atención al trabajador. Para incrementar la racionalidad y eficiencia económica, se indica a los claustros revitalizar la oferta de actividades de superación autofinanciadas teniendo en cuenta sus fortalezas académicas, a lo cual el Instituto Nacional de Salud de los Trabajadores responde con el dossier que se presenta. Objetivo: Describir el dossier de actividades formativas del instituto acreditadas a nivel internacional por la Universidad de Ciencias Médicas de La Habana en 2018-2022. Método: Estudio descriptivo al proceso de diseño, revisión y acreditación de los expedientes de las actividades propuestas, partiendo de los planes anuales de superación profesional, los programas de las actividades y sus dictámenes. Resultados: Se acreditaron 14 actividades a nivel internacional, 11 cursos, 2 diplomados, un entrenamiento; 2 en 2018, 10 en 2019, una en 2020, otra en 2021. Según tópicos centrales, 3 dedicadas a aspectos generales de salud ocupacional y seguridad laboral, 3 sobre riesgos ocupacionales y accidentes de trabajo, 3 de epidemiología ocupacional, 2 con perfil psicológico, una con proyección comunitaria, una con enfoque de género y otra sobre la medicina natural en esta esfera. Conclusiones: Predominaron los cursos en las actividades autofinanciadas acreditadas, la mayoría correspondientes al 2019, que abordan el estudio de lo clásico y lo actual así como lo general y lo particular en materia de salud ocupacional para garantizar un entorno laboral seguro y saludable Introduction: To enhance the performance in occupational health presupposes professional overcoming that mixes individual learning needs with social health requirements in the context of worker care. In order to increase rationality and economic efficiency, the professors are instructed to revitalize the offer of self-financed professional overcoming activities taking into account their academic strengths, to which the National Institute of Workers' Health of Cuba responds with the dossier that is presented. Objective: To describe the dossier of professional overcoming activities of the institute, internationally accredited by the University of Medical Sciences of Havana in 2018-2022. Method: Descriptive study of the process of design, review and accreditation of the files of the proposed activities, based on the annual plans for professional overcoming, the activities programs and their approbation documents. Results: 14 activities were accredited internationally, 11 courses, 2 diploma projects, one training; 2 in 2018, 10 in 2019, one in 2020, another in 2021. According to central themes, 3 dedicated to general aspects of occupational health and working safety, 3 on occupational risks and accidents at work, 3 on occupational epidemiology, 2 with a psychological profile, one with a community projection, one with a gender focus and another on natural medicine in this area. Conclusions: The courses predominated in the accredited self-financed activities, the majority corresponding to 2019, which addressed the study of the classic and the current as well as the general and the particular in occupational health to guarantee a safe and healthy work environment

Suguru Otani

I investigate how explicit cartels, known as ``shipping conferences", in a global container shipping market facilitated the formation of one of the largest globally integrated markets through entry, exit, and shipbuilding investment of shipping firms. Using a novel data, I develop and construct a structural model and find that the cartels shifted shipping prices by 20-50\% and encouraged firms' entry and investment. In the counterfactual, I find that cartels would increase producer surplus while slightly decreasing consumer surplus, then may increase social welfare by encouraging firms' entry and shipbuilding investment. This would validate industry policies controlling prices and quantities in the early stage of the new industry, which may not be always harmful. Investigating hypothetical allocation rules supporting large or small firms, I find that the actual rule based on tonnage shares is the best to maximize social welfare.

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.

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.

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

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.

Rabia Azzemou, Myriam Noureddine

Faced with health pandemic, companies producing goods and / or services must continue their activities to ensure their sustainability. They must organize accordingly by putting in place an organization and working means and hygiene in the company remains the most effective means of preventing risks. In this context, this article proposes implementation of a behavior hygiene and prevention model during the COVID-19 and post COVID-19 periods. Indeed, preserving human potential in the workplace is a fundamental performance lever to boost economy and company must take appropriate preventive measures. The adopted approach is based on the 5S method, which the main objective is continuous improvement of company's activities, thus offering an adequate solution to reduce risks in terms of health and safety. Besides the economic aspect, the proposed sustainable model is also part of pedagogical perspective for educating the population over time.

I. Moreira-Silva, R. Queiros, N. Ventura et al.

According to previous studies, the prevalence of musculoskeletal symptoms among call center operators is high. However, although this is a developing occupation in Portugal, there is a lack of studies assessing this issue as well as its associations with different risk factors. Therefore, the aim of this study is to investigate the 7-day and 12-month prevalence of musculoskeletal symptoms among Portuguese call center operators and their associations with gender, body mass index, hours of work and sitting time. The study was conducted in a call center company in Portugal. One-hundred and forty-eight workers agreed to participate, and filled out questionnaires to evaluate sociodemographic, anthropometric, and occupational variables, as well as the Nordic Musculoskeletal Questionnaire (NMQ) to assess musculoskeletal symptoms in the last 7 days and 12 months of 9 body regions. NMQ revealed the 12-month prevalence of musculoskeletal symptoms, the 3 most affected body regions were the neck (56.1%), the low back (54.7%) and the shoulders (43.9%). And the 7-day prevalence, the three most affected body region were the same, but in different percentages: low back (31.8%), neck (23.6%) and shoulders (21.6%). Significant associations were found between gender and reporting symptoms in the wrist/hands (p=0.033) and the knees (p=0.031), with females reporting significantly more symptoms than males; and between body mass index and reporting symptoms in the thighs/hips, with overweight operators reporting more symptoms (p=0.010). No significant associations were found for the hours of work, neither in sitting time. Conclusion: Workplace interventions are needed to decrease the prevalence of musculoskeletal complaints among call center workers.

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.

Teguh Satrio, M. Robiul Fuadi

Introduction: Laboratory is a place for the analysis of a material that aims for research, education, quality testing and diagnose diseases. Working in the laboratory will always be faced with a variety of risk and sources of danger that can cause workplace accidents. The purpose of this study was to determine the hazards, hazard identification, risk identification, risk assessments, risk control, and residual risk in working at the clinical pathology laboratory. Method: Data collection method used was observational and descriptive research. When viewed from the time of the study, this study was cross sectional. The data used were primary and secondary data, in which the primary data were obtained by direct observation on site, while secondary data were obtained from SOPs available in the laboratory. Results: The results of the study can identify as many as 9 hazards. In the assessment of risk, it obtained the highest level of danger which was high risk. The level of risk was as high as 3 hazards including needling, splattered patient samples, and reagents or hazardous substances. Conclusion: The conclusion from the study is that this laboratory is classified as dangerous because it finds some of the highest risk levels, which is high risk. The existing risk control in this laboratory is quite complete and good, but it needs to be an improvement in terms of compliance in wearing Personal Protective Equipment (PPE) to minimize the risk. Keywords: clinical pathology laboratory, hazard identification, risk assessment

Florian Hofer, Martin Sehr, Alberto Sangiovanni-Vincentelli et al.

Industry 4.0 is changing fundamentally data collection, its storage and analysis in industrial processes, enabling novel application such as flexible manufacturing of highly customized products. Real-time control of these processes, however, has not yet realized its full potential in using the collected data to drive further development. Indeed, typical industrial control systems are tailored to the plant they need to control, making reuse and adaptation a challenge. In the past, the need to solve plant specific problems overshadowed the benefits of physically isolating a control system from its plant. We believe that modern virtualization techniques, specifically application containers, present a unique opportunity to decouple control from plants. This separation permits us to fully realize the potential for highly distributed, and transferable industrial processes even with real-time constraints arising from time-critical sub-processes. In this paper, we explore the challenges and opportunities of shifting industrial control software from dedicated hardware to bare-metal servers or (edge) cloud computing platforms using off-the-shelf technology. We present a migration architecture and show, using a specifically developed orchestration tool, that containerized applications can run on shared resources without compromising scheduled execution within given time constraints. Through latency and computational performance experiments we explore limits of three system setups and summarize lessons learned.