Hasil untuk "Industrial hygiene. Industrial welfare"

Mirella Di Dio, Marco Santulli, Mariangela Pagano et al.

<i>Legionella</i> bacterium has the aquatic environment as its natural reservoir. In humans, it can cause a form of interstitial pneumonia called legionellosis which can be transmitted by inhalation of contaminated water aerosols. <i>Legionella</i> infection occurs more frequently in certain more susceptible population groups, including smokers, alcoholics, men, the elderly, as well as people with acquired immunodeficiency syndrome, hematological cancers, and diabetes mellitus. This study aimed to evaluate the effectiveness of the new Italian National Guidelines for the prevention of <i>Legionella</i> colonization in water systems application by analyzing the environmental monitoring data of <i>Legionella</i> carried out in healthcare facilities in the Campania region from 2019 to 2022. The secondary objectives were to estimate the most observed serogroups of <i>L. pneumophila</i> and to analyze the possible link between water temperature and the presence of <i>Legionella</i>, respectively. From our data, it emerged that in 2019, 41.1% of the examined facilities were contaminated by the <i>Legionella</i> genus; in 2020, the contamination percentage was 42.9%; in 2021, it was 54.5%; in 2022, it was 45.5%. Instead, the <i>Legionella</i> positivity rate decreased from 2019 (54.3%) to 2022 (52.4%), suggesting a possible positive influence of more restrictive prevention and control measures. The prevalent species was <i>Legionella pneumophila</i>, particularly serogroup 1; water temperature was the risk factor implicated in <i>Legionella</i> contamination.

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

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.

Palpouguini Lompo, Esenam Agbobli, Anne-Sophie Heroes et al.

We conducted a systematic review of healthcare-associated outbreaks and cross-sectional surveys related to the contamination of antiseptics, disinfectants, and hand hygiene products in healthcare settings in low- and middle-income countries (PROSPERO CRD42021266271). Risk of bias was assessed by selected items of the ORION and MICRO checklists. From 1977 onwards, 13 outbreaks and 25 cross-sectional surveys were found: 20 from Asia and 13 from Africa. Products most associated with outbreaks were water-based chlorhexidine, chlorhexidine-quaternary ammonium compound combinations (7/13), and liquid soap products (4/13). Enterobacterales (including multidrug-resistant <i>Enterobacter cloacae, Klebsiella pneumoniae</i>, and <i>Serratia marcescens</i>) and non-fermentative Gram-negative rods were found in 5 and 7 outbreaks and in 34.1% and 42.6% of 164 isolates, respectively, from cross-sectional surveys. Risk factors included preparation (place, utensils, or tap water high and incorrect dilutions), containers (reused, recycled, or inadequate reprocessing), and practices (topping-up or too long use). Potential biases were microbiological methods (neutralizers) and incomplete description of products’ identity, selection, and denominators. External validity was compromised by low representativeness for remote rural settings and low-income countries in sub-Saharan Africa. Outstanding issues were water quality, biofilm control, field-adapted containers and reprocessing, in-country production, healthcare providers’ practices, and the role of bar soap. A list of “best practices” to mitigate product contamination was compiled.

Nabylla Sharfina Sekar Nurriwanti, Y. Denny A. Wahyudiono, Indriati Paskarini et al.

Introduction: The steel box girder of Suramadu Bridge is a confined work area with sensor maintenance activities and potential hazards. The purpose of this study was to determine the potential hazards and risk levels in the Suramadu Bridge steel box girder work area. Methods: This descriptive study involved cross-sectional data collection. This study used a qualitative risk assessment method. The primary data used in this research included interviews with informants, which consisted of five key informants from experts and five main informants from technicians. The secondary data of the study include a job safety analysis document issued by the Suramadu Bridge Structural Health Monitoring System (SHMS). Risk assessment was performed by determining the level of likelihood and consequences using a risk analysis matrix. Data processing techniques and analysis are based on job safety analysis documents and interviews, whereas the risk analysis table is based on AS/NZS 4360 (2004). Results: The study results show that sensor maintenance work in the steel box girder area involves eight activities, 15 potential hazards, and 19 risks. Conclusion: The study concludes that, Out of the 19 identified risks, three risks (16%) were in the low-risk category, 15 risks (79%) were in the medium-risk category, and one risk (5 %) was in the high-risk category with the potential for fire.

John E. Moore, Beverley C. Millar

Disinfectants purchased from retail outlets form the cornerstone of infection control and prevention within the domestic household. The growing utilisation of the concept of “hospital-at-home” places greater emphasis on domestic disinfection by the householder in helping to prevent the acquisition of infections within the home. No reports or data exist that indicate how readable the information provided on disinfectants is, which would help householders use disinfectants optimally. The aim of this study was therefore to quantitatively examine the readability (Flesch Reading Ease; Flesch–Kinkaid Grade Level; text metrics) of consumer (public)-facing information (n = 108) of domestic household disinfectants sourced from (i) UK high street supermarket chains (n = 4) and (ii) disinfectant manufacturers (n = 6). The readability of all supermarket and manufacturer information (n = 108) gave a mean Flesch Reading Ease score of 51.7 (target ≥ 60) and a Flesch–Kinkaid Grade Level score of 8.1 (target ≤ 8), thereby failing to achieve readability reference target values. Authors preparing information on the domestic use of disinfectants should be aware of the value of quantitative readability metrics and online tools to help support their writing of such information in order to produce materials which are within target readability values, thereby further supporting health literacy in this population and disinfectant efficacy.

Aida Navas de Serrato

Esta editorial recupera algunos datos y reflexiones de lo sucedido en la 35ª Reunión de delegados de la Federación Mundial de Terapeutas Ocupacionales -WFOT y el 18º Congreso Mundial de la WFOT, eventos celebrados en agosto de 2022 en la ciudad de París, Francia. Resalta la fortaleza del reencuentro y señala la importante agenda que tuvo el Congreso, en una amplia y poderosa divulgación del saber, la investigación y la experiencia de las y los terapeutas ocupacionales del mundo, en todas las áreas y dominios de su ejercicio profesional.

Andika Savira Putri, Endang Dwiyanti, Ahmad Rido'i Yuda Prayogi et al.

Introduction: Work fatigue can be caused of excessive workload and work capacity such as age and tenure. This study aimed to analyze the strength of the correlation between individual factors and mental workload with work fatigue on the Surabaya Patchouli Terminal crane operator. Methods: The study design is a cross sectional. The sampling technique taken was total sampling so that all populations were a sample of 30 people, consisting of CC and RTG operators in Nilam Terminal Surabaya. The independent variables are individual factors including age and years of service obtained from the questionnaire, mental workload which is assessed based on the NASA-TLX questionnaire, while the dependent variable is work fatigue measured using a reaction timer. The collected data were then analyzed using the Spearman correlation. Results: The results show 23 operators (76.7%) experienced heavy work fatigue and 7 operators (23.3%) experienced moderate work fatigue. Conclusion: The strongest correlation is mental workload with work fatigue and the weakest correlation is age with work fatigue. Meanwhile, correlation tenure with work fatigue is in between. It is recommended to provide psychological consultation once a week for operators.

Irlangga Wisnu Wardana, Ahmad Rido'i Yuda Prayogi, Dani Nasirul Haqi et al.

Introduction: Workers of swallow nest industry are at risk of experiencing musculoskeletal disorders due to monotonous swallow nest cleaning activity which puts heavy load on their extremity area of the upper body. This research seeks to analyze the relationships of age, gender, nutritional status, exercise habits, working hours, and working position with complaints of musculoskeletal disorders in swallow nest cleaning workers at PT. Lentera Alam Nusantara. Method: This type of research uses observational with a cross-sectional approach. The population in this study were all workers in the cleaning and washing section of swallow nests at PT. Lentera Alam Nusantara Surabaya, totaling 50 people. Determination of the number of samples using simple random sampling lemmeshow formula, obtained a sample of 36 workers. Results: The majority of workers are 35 years old, female, obese nutritional status, rarely exercise, have 8 hours of work, moderate work position, and the majority have moderate muculoskeletal complaints. Based on the analysis test results using the spearman test, it shows that the factors that have a significant p-value <0.05 include ages, gender, nutrition status, exercise habits, and working hours. Conclusion: Thus, it can be concluded that there is a relationship between age, gender, nutrition status, exercise habits, and working hours with complaints of musculoskeletal disorders.

Colombian College of Occupational Therapy

Estos nuevos Estatutos del Colegio Colombiano de Terapia Ocupacional reemplazan a aquellos que regían desde abril de 2011. El proceso de reforma, que tomó cerca de cuatro años, fue liderado por una comisión integrada por las terapeutas ocupacionales Marian Amaya Quintero, Camila Rodríguez Guevara, Alejandra Jiménez Moreno y Aida Navas de Serrato.

Gurjot Singh Bhatia, Yoann Corre, M. Di Renzo

Industrial environments are considered to be severe from the point of view of electromagnetic (EM) wave propagation. When dealing with a wide range of industrial environments and deployment setups, ray-tracing channel emulation can capture many distinctive characteristics of a propagation scenario. Ray-tracing tools often require a detailed and accurate description of the propagation scenario. Consequently, industrial environments composed of complex objects can limit the effectiveness of a ray-tracing tool and lead to computationally intensive simulations. This study analyzes the impact of using different propagation models by evaluating the number of allowed ray path interactions and digital scenario representation for an industrial environment. This study is realized using the Volcano ray-tracing tool at frequencies relevant to 5G industrial networks: 2 GHz (mid-band) and 28 GHz (high-band). This analysis can help in enhancing a ray-tracing tool that relies on a digital representation of the propagation environment to produce deterministic channel models for Indoor Factory (InF) scenarios, which can subsequently be used for industrial network design.

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.

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.

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.

Santhosh E Kumar, Antonisamy B, Henry Kirupakaran et al.

Introduction: Shift workers and metabolic syndrome are on the rise in developing nations. The link between Metabolic syndrome and shiftwork is not clear. This study aims to measure the prevalence of metabolic syndrome among shift workers and daytime workers and to assess the association between metabolic syndrome and shift work. Methods: Cross- sectional study was done in a South Indian hospital. Participants were selected via systematic random sampling between the age group of 25 -50 years. There were two study groups – day and shift workers. Sample size calculation was done with an alpha error of 0.05 and power of 80% to detect a 12.5% difference for metabolic syndrome prevalence between the two groups. Outcomes studied include the prevalence of metabolic syndrome and odds of developing metabolic syndrome among shift workers. The Chi-square test and independent t-test were the tests of significance used. The impact of relevant parameters on metabolic syndrome was assessed using univariate and multivariable analysis. Results: Eighty employees were studied in each group. At baseline, differences include; daytime workers were older in age, had a better quality of sleep, were less active physically, and consisted of more vegetarians. The prevalence of metabolic syndrome prevalence was thirty-three percent among the participants. The odds ratio (adjusted for relevant confounders) for shift workers to develop metabolic syndrome was 0.55 (95% CI 0.24 -1.29). Conclusion: Metabolic syndrome was diagnosed in a third of the hospital employees studied. There was no statistical difference between shift and daytime workers for the prevalence of metabolic syndrome. Increased awareness, screening, and preventive measures of the disease are recommended. Keywords: daytime, hospital, metabolic syndrome, shift work, sleep