Hasil untuk "Industrial medicine. Industrial hygiene"

Daniel Ruan, Salma Mozaffari, Sigrid Adriaenssens et al.

Industrial robots are increasingly deployed in contact-rich construction and manufacturing tasks that involve uncertainty and long-horizon execution. While learning-based visuomotor policies offer a promising alternative to open-loop control, their deployment on industrial platforms is challenged by a large observation-execution gap caused by sensing, inference, and control latency. This gap is significantly greater than on low-latency research robots due to high-level interfaces and slower closed-loop dynamics, making execution timing a critical system-level issue. This paper presents a latency-aware framework for deploying and evaluating visuomotor policies on industrial robotic arms under realistic timing constraints. The framework integrates calibrated multimodal sensing, temporally consistent synchronization, a unified communication pipeline, and a teleoperation interface for demonstration collection. Within this framework, we introduce a latency-aware execution strategy that schedules finite-horizon, policy-predicted action sequences based on temporal feasibility, enabling asynchronous inference and execution without modifying policy architectures or training. We evaluate the framework on a contact-rich industrial assembly task while systematically varying inference latency. Using identical policies and sensing pipelines, we compare latency-aware execution with blocking and naive asynchronous baselines. Results show that latency-aware execution maintains smooth motion, compliant contact behavior, and consistent task progression across a wide range of latencies while reducing idle time and avoiding instability observed in baseline methods. These findings highlight the importance of explicitly handling latency for reliable closed-loop deployment of visuomotor policies on industrial robots.

Mengxin Lyu, Zhiying Teng, Min Shi et al.

ObjectiveThe present study aims to investigate the application efficacy of the Supply-Process-Output (SPO) and Healthcare Failure Mode and Effects Analysis (HFMEA) methodologies in infection prevention and quality management practices within a central sterile supply department (CSSD).MethodsBased on literature reviews and the specific circumstances in an institution, an integrated systemic prevention framework was constructed by combining HFMEA-based failure mode analysis with the SPO model. A risk assessment scale for infection control in CSSD was developed to identify and analyze potential failure modes and their consequences, thereby formulating targeted quality control measures and mitigating hospital-acquired infection (HAI) risks.ResultsComparative data before (March to June 2024) and after (October to December 2024) implementation demonstrated significant improvements in key metrics, including instrument cleaning compliance rate, equipment failure frequency, emergency item processing efficiency, and clinical department satisfaction scores. The Risk Priority Numbers (RPN) were markedly reduced (P<0.01) with statistically significant differences.ConclusionA scientific and systematic assessment of HAI risks in CSSD based on the HFMEA and SPO methodologies and in consideration of actual operational conditions effectively mitigates infection risks and enhances medical safety and quality assurance.

Tuğçe Bilen, Mehmet Ozdem

The convergence of Information Technology (IT) and Operational Technology (OT) is a critical enabler for achieving autonomous and intelligent industrial systems. However, the increasing complexity, heterogeneity, and real-time demands of industrial environments render traditional rule-based or static management approaches insufficient. In this paper, we present a modular framework based on the Knowledge-Defined Networking (KDN) paradigm, enabling adaptive and autonomous control across IT-OT infrastructures. The proposed architecture is composed of four core modules: Telemetry Collector, Knowledge Builder, Decision Engine, and Control Enforcer. These modules operate in a closed control loop to continuously observe system behavior, extract contextual knowledge, evaluate control actions, and apply policy decisions across programmable industrial endpoints. A graph-based abstraction is used to represent system state, and a utility-optimization mechanism guides control decisions under dynamic conditions. The framework's performance is evaluated using three key metrics: decision latency, control effectiveness, and system stability, demonstrating its capability to enhance resilience, responsiveness, and operational efficiency in smart industrial networks.

SoYoung Park, Hyewon Lee, Mingyu Choi et al.

Anomaly segmentation is essential for industrial quality, maintenance, and stability. Existing text-guided zero-shot anomaly segmentation models are effective but rely on fixed prompts, limiting adaptability in diverse industrial scenarios. This highlights the need for flexible, context-aware prompting strategies. We propose Image-Aware Prompt Anomaly Segmentation (IAP-AS), which enhances anomaly segmentation by generating dynamic, context-aware prompts using an image tagging model and a large language model (LLM). IAP-AS extracts object attributes from images to generate context-aware prompts, improving adaptability and generalization in dynamic and unstructured industrial environments. In our experiments, IAP-AS improves the F1-max metric by up to 10%, demonstrating superior adaptability and generalization. It provides a scalable solution for anomaly segmentation across industries

Hai Dinh-Tuan

The convergence of Information Technology (IT) and Operational Technology (OT) in Industry 4.0 exposes the limitations of traditional, hierarchical architectures like ISA-95 and RAMI 4.0. Their inherent rigidity, data silos, and lack of support for cloud-native technologies impair the development of scalable and interoperable industrial systems. This paper addresses this issue by introducing CRACI, a Cloud-native Reference Architecture for the Industrial Compute Continuum. Among other features, CRACI promotes a decoupled and event-driven model to enable flexible, non-hierarchical data flows across the continuum. It embeds cross-cutting concerns as foundational pillars: Trust, Governance & Policy, Observability, and Lifecycle Management, ensuring quality attributes are core to the design. The proposed architecture is validated through a two-fold approach: (1) a comparative theoretical analysis against established standards, operational models, and academic proposals; and (2) a quantitative evaluation based on performance data from previously published real-world smart manufacturing implementations. The results demonstrate that CRACI provides a viable, state-of-the-art architecture that utilizes the compute continuum to overcome the structural limitations of legacy models and enable scalable, modern industrial systems.

Alpana Dubey, Suma Mani Kuriakose, Nitish Bhardwaj

We propose an approach to generate synthetic data to train computer vision (CV) models for industrial wear and tear detection. Wear and tear detection is an important CV problem for predictive maintenance tasks in any industry. However, data curation for training such models is expensive and time-consuming due to the unavailability of datasets for different wear and tear scenarios. Our approach employs a vision language model along with a 3D simulation and rendering engine to generate synthetic data for varying rust conditions. We evaluate our approach by training a CV model for rust detection using the generated dataset and tested the trained model on real images of rusted industrial objects. The model trained with the synthetic data generated by our approach, outperforms the other approaches with a mAP50 score of 0.87. The approach is customizable and can be easily extended to other industrial wear and tear detection scenarios

Ariel Monzón Velasco, Alexis Lorenzo Ruiz, Yudanis González González et al.

Introducción: El estudio de la personalidad de trabajadores y aspirantes es primordial en los procesos de gestión del capital humano en la Unión Eléctrica. Para su evaluación es necesario el empleo de herramientas válidas y confiables que permitan una interpretación precisa, estable y ética de los resultados. Objetivos: Examinar la forma 5 del cuestionario de 16 factores de personalidad de Cattell en trabajadores de la Unión Eléctrica; obtener evidencias de la validez, la confiabilidad de esta prueba y establecer los valores normativos para su calificación en la población de referencia. Métodos: Se desarrolló una investigación cuantitativa, de tipo descriptiva y corte transversal, entre los meses de mayo de 2022 y junio de 2023. Se aplicó un muestreo no probabilístico intencional a 445 trabajadores. Se estudió la consistencia interna del instrumento, su estabilidad, estructura factorial y las puntuaciones descriptivas de los factores de primer orden. Resultados: Los resultados muestran que el instrumento cuenta con una consistencia interna adecuada y su estructura factorial se ajusta a las planteadas por los manuales de la prueba para las versiones en inglés y español. Se establecieron los valores normativos para la calificación del instrumento en la población de referencia. Conclusiones: La investigación examinó las propiedades psicométricas y las puntuaciones fundamentales del instrumento; permitió la elaboración de sus normas de calificación para la población de referencia y deja una serie de recomendaciones para hacer más válidas y confiables la utilización de los cuestionarios de personalidad de Cattell en la Unión Eléctrica Introduction: The study of the personality of workers and applicants is essential in the processes of human capital management in the Electric Union. For its evaluation, it is necessary to use valid and reliable tools that allow an accurate, stable and ethical interpretation of the results. Objectives: To examine Form 5 of the Cattell´s questionnaire of 16 personality factors in workers of the Electric Union; to collect evidence of the validity and reliability of this test and to establish the normative values for its qualification in the reference population. Methods: A quantitative, descriptive and cross-sectional research was developed between May 2022 and June 2023. Intentional non-probability sampling was applied to 445 workers. The internal consistency of the instrument, its stability, factorial structure and the descriptive scores of the first-order factors were studied. Results: The results show that the instrument has an adequate internal consistency and its factorial structure conforms to those proposed by the test manuals for the English and Spanish versions. The normative values for the qualification of the instrument in the reference population were established. Conclusions: The research examined the psychometric properties and fundamental scores of the instrument; allowed the elaboration of its qualification standards for the reference population and leaves a series of recommendations to make the use of Cattell's personality questionnaires in the Electric Union more valid and reliable

Federico Cunico, Marco Cristani

In recent years, the development of deep learning approaches for the task of person re-identification led to impressive results. However, this comes with a limitation for industrial and practical real-world applications. Firstly, most of the existing works operate on closed-world scenarios, in which the people to re-identify (probes) are compared to a closed-set (gallery). Real-world scenarios often are open-set problems in which the gallery is not known a priori, but the number of open-set approaches in the literature is significantly lower. Secondly, challenges such as multi-camera setups, occlusions, real-time requirements, etc., further constrain the applicability of off-the-shelf methods. This work presents MICRO-TRACK, a Modular Industrial multi-Camera Re_identification and Open-set Tracking system that is real-time, scalable, and easy to integrate into existing industrial surveillance scenarios. Furthermore, we release a novel Re-ID and tracking dataset acquired in an industrial manufacturing facility, dubbed Facility-ReID, consisting of 18-minute videos captured by 8 surveillance cameras.

Ashok Urlana, Charaka Vinayak Kumar, Ajeet Kumar Singh et al.

Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks. From natural language processing and sentiment analysis to content generation and personalized recommendations, their unparalleled adaptability has facilitated widespread adoption across industries. This transformative shift driven by LLMs underscores the need to explore the underlying associated challenges and avenues for enhancement in their utilization. In this paper, our objective is to unravel and evaluate the obstacles and opportunities inherent in leveraging LLMs within an industrial context. To this end, we conduct a survey involving a group of industry practitioners, develop four research questions derived from the insights gathered, and examine 68 industry papers to address these questions and derive meaningful conclusions. We maintain the Github repository with the most recent papers in the field.

Xuejiao Li, Cheng Yang, Charles Møller et al.

In the era of Industry 4.0, artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as perceived. A significant factor contributing to this lag is the data issues in AI implementation. How to address these data issues stands as a significant concern confronting both industry and academia. To address data issues, the first step involves mapping out these issues. Therefore, this study conducts a meta-review to explore data issues and methods within the implementation of industrial AI. Seventy-two data issues are identified and categorized into various stages of the data lifecycle, including data source and collection, data access and storage, data integration and interoperation, data pre-processing, data processing, data security and privacy, and AI technology adoption. Subsequently, the study analyzes the data requirements of various AI algorithms. Building on the aforementioned analyses, it proposes a data management framework, addressing how data issues can be systematically resolved at every stage of the data lifecycle. Finally, the study highlights future research directions. In doing so, this study enriches the existing body of knowledge and provides guidelines for professionals navigating the complex landscape of achieving data usability and usefulness in industrial AI.

Maryam Ahang, Todd Charter, Mostafa Abbasi et al.

Condition monitoring is essential for ensuring the safety, reliability, and efficiency of modern industrial systems. With the increasing complexity of industrial processes, artificial intelligence (AI) has emerged as a powerful tool for fault detection and diagnosis, attracting growing interest from both academia and industry. This paper provides a comprehensive overview of intelligent condition monitoring methods, with a particular emphasis on chemical plants and the widely used Tennessee Eastman Process (TEP) benchmark. State-of-the-art machine learning (ML) and deep learning (DL) algorithms are reviewed, highlighting their strengths, limitations, and applicability to industrial fault detection and diagnosis. Special attention is given to key challenges, including imbalanced and unlabeled data, and to strategies by which models can address these issues. Furthermore, comparative analyses of algorithm performance are presented to guide method selection in practical scenarios. This survey is intended to benefit both newcomers and experienced researchers by consolidating fundamental concepts, summarizing recent advances, and outlining open challenges and promising directions for intelligent condition monitoring in industrial plants.

Miaohao Wang, Haiqing Bi, Qichao Ma

Objective: Noise pollution has been listed as one of the three major types of pollution, along with air and water pollution. Hospitals should pay attention to noise control, which is of great importance for the treatment and rehabilitation of patients. This study focuses on the application value of noise control and relaxation training. Methods: This study retrospectively collected and analyzed the clinical data of 184 patients who underwent skin laser cosmetology in Ningbo Yinzhou No. 2 Hospital from January 2021 to December 2022. Twelve patients who did not meet the criteria were excluded, and the remaining 172 patients were divided based on the type of intervention into the control group (CG, n = 82) and the study group (SG, n = 90). The CG received relaxation training and routine noise management, while the SG received noise control combined with relaxation training. The intervention effect was discussed from the aspects of noise, psychology, and satisfaction. Results: After the intervention, the SG had overtly lower noise levels and lower scores of anxiety and depression compared to the CG (all P < 0.001). Correlation analysis showed that noise levels were positively correlated with scores of anxiety and depression (r = 0.553, r = 0.592, P < 0.001). The two groups had no significant difference in total satisfaction (P > 0.05). Conclusion: Noise poses a significant threat to the human body. Strengthening noise control in hospitals is beneficial for patients’ recovery. Combining noise control with relaxation training is an intervention method worthy of clinical application. It can improve the hospitalization environment and reduce the noise levels to a great extent, thereby improving the psychological state of patients and enhancing the medical satisfaction.

B. Prabadevi, N. Deepa, Nancy Victor et al.

With the advent of new technologies and endeavors for automation in almost all day-to-day activities, the recent discussions on the metaverse life have a greater expectation. Furthermore, we are in the era of the fifth industrial revolution, where machines and humans collaborate to maximize productivity with the effective utilization of human intelligence and other resources. Hence, Industry 5.0 in the metaverse may have tremendous technological integration for a more immersive experience and enhanced communication.These technological amalgamations are suitable for the present environment and entirely different from the previous perception of virtual technologies. This work presents a comprehensive review of the applications of the metaverse in Industry 5.0 (so-called industrial metaverse). In particular, we first provide a preliminary to the metaverse and industry 5.0 and discuss key enabling technologies of the industrial metaverse, including virtual and augmented reality, 3D modeling, artificial intelligence, edge computing, digital twin, blockchain, and 6G communication networks. This work then explores diverse metaverse applications in Industry 5.0 vertical domains like Society 5.0, agriculture, supply chain management, healthcare, education, and transportation. A number of research projects are presented to showcase the conceptualization and implementation of the industrial metaverse. Furthermore, various challenges in realizing the industrial metaverse, feasible solutions, and future directions for further research have been presented.

Ben-Hur Francisco Cardoso, Eva Yamila da Silva Catela, Guilherme Viegas et al.

Research on productive structures has shown that economic complexity conditions economic growth. However, little is known about which type of complexity, e.g., export or industrial complexity, matters more for regional economic growth in a large emerging country like Brazil. Brazil exports natural resources and agricultural goods, but a large share of the employment derives from services, non-tradables, and within-country manufacturing trade. Here, we use a large dataset on Brazil's formal labor market, including approximately 100 million workers and 581 industries, to reveal the patterns of export complexity, industrial complexity, and economic growth of 558 micro-regions between 2003 and 2019. Our results show that export complexity is more evenly spread than industrial complexity. Only a few -- mainly developed urban places -- have comparative advantages in sophisticated services. Regressions show that a region's industrial complexity is a significant predictor for 3-year growth prospects, but export complexity is not. Moreover, economic complexity in neighboring regions is significantly associated with economic growth. The results show export complexity does not appropriately depict Brazil's knowledge base and growth opportunities. Instead, promoting the sophistication of the heterogeneous regional industrial structures and development spillovers is a key to growth.

Maroš Bihari, Marta Habánová, Kristína Jančichová et al.

Background. Body mass index (BMI) is the most commonly used parameter for identifying obesity. However, it is a tool that can distort the diagnosis as misdiagnose. Objective. The aim of the study was to evaluate the BMI and visceral fat area (VFA) and to determine the presence of obesity in a group of young people and to assess their suitability for use together with other parameters indicating excessive body fat and increased risk of non-communicable disease and premature death. Material and Methods. The study group consisted of 339 university students. We used InBody 720 for diagnosis body composition. The following body composition parameters were measured – BMI, waist circumference (WC), fat-free mass (FFM), VFA, percentage of body fat (PBF). Results. The BMI values by gender indicate overweight in the male group compared to females (25.2 ± 3.1 and 22.2 ± 3.4 kg.m-2, respectively; p<0.001). Women had higher values of VFA than men (70.1 ± 26.4 and 56.2 ± 28.3 cm2, respectively; p<0.001). Although the group of men had an increased average BMI, which allows us to talk about overweight, the risk of premature death was low. In the case of the male group, a high proportion of fat-free mass had a major impact on BMI. Lower values of fat parameters also contributed to the low risk of premature death. We found a nonlinear relationship in the BMI assessment in terms of premature risk of death. Higher values of the premature death risk were found in the subgroups of underweight and obesity. In the case of the VFA and ABSI relationship a linear increase in the curve and the risk of premature death was observed. Conclusions. In order to evaluate the presence of overweight or obesity it is necessary to use not only BMI but other diagnostic elements for this purpose. The components of the body composition need to be evaluated comprehensively. Evidence of this is the risk of premature death, where optimal BMI values may pose an increased risk and vice versa.

Seba Anna Varghese, Alireza Dehlaghi Ghadim, Ali Balador et al.

Digital twins have recently gained significant interest in simulation, optimization, and predictive maintenance of Industrial Control Systems (ICS). Recent studies discuss the possibility of using digital twins for intrusion detection in industrial systems. Accordingly, this study contributes to a digital twin-based security framework for industrial control systems, extending its capabilities for simulation of attacks and defense mechanisms. Four types of process-aware attack scenarios are implemented on a standalone open-source digital twin of an industrial filling plant: command injection, network Denial of Service (DoS), calculated measurement modification, and naive measurement modification. A stacked ensemble classifier is proposed as the real-time intrusion detection, based on the offline evaluation of eight supervised machine learning algorithms. The designed stacked model outperforms previous methods in terms of F1-Score and accuracy, by combining the predictions of various algorithms, while it can detect and classify intrusions in near real-time (0.1 seconds). This study also discusses the practicality and benefits of the proposed digital twin-based security framework.

Philipp Staudacher, Curdin Brugger, Mirko S. Winkler et al.

Abstract Background Pesticides can have negative effects on human and environmental health, especially when not handled as intended. In many countries, agro-input dealers sell pesticides to smallholder farmers and are supposed to provide recommendations on application and handling. This study investigates the role of agro-input dealers in transmitting safety information from chemical manufacturers to smallholder farmers, assesses the safety of their shops, what products they sell, and how agro-input dealers abide by laws and recommendations on best practices for preventing pesticide risk situations. Methods Applying a mixed-methods approach, we studied agro-input dealers in Central and Western Uganda. Structured questionnaires were applied to understand agro-input dealers’ knowledge, attitude and practices on pesticides (n = 402). Shop layout (n = 392) and sales interaction (n = 236) were assessed through observations. Actual behavior of agro-input dealers when selling pesticides was revealed through mystery shopping with local farmers buying pesticides (n = 94). Results While 97.0% of agro-input dealers considered advising customers their responsibility, only 26.6% of mystery shoppers received any advice from agro-input dealers when buying pesticides. 53.2% of products purchased were officially recommended. Sales interactions focused mainly on product choice and price. Agro-input dealers showed limited understanding of labels and active ingredients. Moreover, 25.0% of shops were selling repackaged products, while 10.5% sold unmarked or unlabeled products. 90.1% of shops were lacking safety equipment. Pesticides of World Health Organization toxicity class I and II were sold most frequently. Awareness of health effects seemed to be high, although agro-input dealers showed incomplete hygiene practices and were lacking infrastructure. One reason for these findings might be that only 55.7% of agro-input dealers held a certificate of competency on safe handling of pesticides and even fewer (5.7%) were able to provide a government-approved up-to-date license. Conclusion The combination of interviews, mystery shopping and observations proved to be useful, allowing the comparison of stated and actual behavior. While agro-input dealers want to sell pesticides and provide the corresponding risk advice, their customers might receive neither the appropriate product nor sufficient advice on proper handling. In light of the expected increase in pesticide use, affordable, accessible and repeated pesticide training and shop inspections are indispensable.

Hossein Amouei1, Mahnaz Mirza Ebrahim Tehrani, Seyed Ali Jozi et al.

Background and Objective: The analysis of system resilience is one of the ways to increase the factor of safety. The present study aimed to develop a model for the assessment of safety resilience in process industries with a passive defense approach based on the Delphi method. Materials and Methods: This cross-sectional study was conducted in Phase 19 of the South Pars Gas Field Development Project in 2018-2020. This three-round Delphi study was performed in three rounds stages with the participation of 18 experts in the fields of chemical and process engineering, safety, occupational health, and environment. Results: After three rounds of the Delphi study, the safety resilience assessment model was developed based on the three components of preparedness, the likelihood of occurrence, and consequence. Based on the results, the preparedness component included the variables of hardware, software, and defensive preparedness, as well as access to external resources. The findings showed that experimental data, technical inspection, and the professional competence of individuals were among effective parameters in the likelihood component. Moreover, the parameters of human damage, property damage, process damage, environmental damage, and strategic and defense damage were among the effective parameters in the consequence component. In this Delphi study, all members of the expert panel confirmed the items in the algorithm, including resilience components and the variables of each component with a 75% acceptance level. Conclusion: Based on the opinions of the expert panel, the results of this Delphi study indicated that this semi-quantitative model has good reliability for the assessment of safety resilience in process industries. Therefore, the use of this model can be of great in the provision of an acceptable estimate of safety resilience in the process industry.

Nahid Tavassoli, Hamid Noghanibehambari, Farzaneh Noghani et al.

This paper aims to assess the effects of industrial pollution on infant mortality between the years 1850-1940 using full count decennial censuses. In this period, US economy experienced a tremendous rise in industrial activity with significant variation among different counties in absorbing manufacturing industries. Since manufacturing industries are shown to be the main source of pollution, we use the share of employment at the county level in this industry to proxy for space-time variation in industrial pollution. Since male embryos are more vulnerable to external stressors like pollution during prenatal development, they will face higher likelihood of fetal death. Therefore, we proxy infant mortality with different measures of gender ratio. We show that the upswing in industrial pollution during late nineteenth century and early twentieth century has led to an increase in infant mortality. The results are consistent and robust across different scenarios, measures for our proxies, and aggregation levels. We find that infants and more specifically male infants had paid the price of pollution during upswing in industrial growth at the dawn of the 20th century. Contemporary datasets are used to verify the validity of the proxies. Some policy implications are discussed.

Felicien Ihirwe, Davide Di Ruscio, Silvia Mazzini et al.

The development of Industrial Internet of Things systems (IIoT) requires tools robust enough to cope with the complexity and heterogeneity of such systems, which are supposed to work in safety-critical conditions. The availability of methodologies to support early analysis, verification, and validation is still an open issue in the research community. The early real-time schedulability analysis can help quantify to what extent the desired system's timing performance can eventually be achieved. In this paper, we present CHESSIoT, a model-driven environment to support the design and analysis of industrial IoT systems. CHESSIoT follows a multi-view, component-based modelling approach with a comprehensive way to perform event-based modelling on system components for code generation purposes employing an intermediate ThingML model. To showcase the capability of the extension, we have designed and analysed an Industrial real-time safety use case.