Hasil untuk "Industrial hygiene. Industrial welfare"

Tasmiya Rangila, Andiswa Zondo, Andiswa Mtshali et al.

<b>Background/Objectives</b>: Although children’s playgrounds foster physical, cognitive and emotional health, sandpits can harbour antibiotic-resistant bacteria, representing a health concern for kids. Therefore, this point prevalence study investigated the presence and antimicrobial resistance of <i>Escherichia coli</i> in sandpits at four schools in Durban to ascertain the potential risk to schoolchildren and inform school authorities of the need to prevent such occurrences. <b>Methods</b>: Twenty samples were collected from schools on a single day. <i>E. coli</i> was isolated using colilert-18^® and confirmed using PCR. Antibiotic susceptibility testing was performed against 19 antibiotics using the disc diffusion method and Clinical and Laboratory Standards Institute (CLSI) guidelines. <b>Results</b>: <i>E. coli</i> was detected in 2/4 schools (50%), yielding 100 pure isolates. Of these, 71% (31 Site B and 40 Site C isolates) were resistant to at least one of the antibiotics tested, displaying 36 antibiograms. The highest resistance was to CFX (<i>n</i> = 40), and the lowest was to AMK and MEM (<i>n</i> = 1). All isolates were susceptible to CIP, CHL, GEN and TZP. At Site B, the highest resistance was against CFX (<i>n</i> = 16) and the lowest against AMK, CTX and NAL (<i>n</i> = 1). The highest resistance at Site C was against TET (n = 26), and the lowest against ATH and AUG (<i>n</i> = 1). Twenty isolates (20%) were multidrug-resistant, displaying resistance to at least one antibiotic from 3 classes. <b>Conclusions</b>: These results show that children with poor hygiene practices could get sick from playing in sandpits. Schools must change their sand regularly and ensure that sandpits are constantly exposed to the sun.

Yiran Ma, Jerome Le Ny, Zhichao Chen et al.

In modern process industries, data-driven models are important tools for real-time monitoring when key performance indicators are difficult to measure directly. While accurate predictions are essential, reliable uncertainty quantification (UQ) is equally critical for safety, reliability, and decision-making, but remains a major challenge in current data-driven approaches. In this work, we introduce a diffusion-based posterior sampling framework that inherently produces well-calibrated predictive uncertainty via faithful posterior sampling, eliminating the need for post-hoc calibration. In extensive evaluations on synthetic distributions, the Raman-based phenylacetic acid soft sensor benchmark, and a real ammonia synthesis case study, our method achieves practical improvements over existing UQ techniques in both uncertainty calibration and predictive accuracy. These results highlight diffusion samplers as a principled and scalable paradigm for advancing uncertainty-aware modeling in industrial applications.

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

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

Dan Parii, Evelyne Janssen, Guangzhi Tang et al.

Accurately predicting the lifespan of critical device components is essential for maintenance planning and production optimization, making it a topic of significant interest in both academia and industry. In this work, we investigate the use of survival analysis for predicting the lifespan of production printheads developed by Canon Production Printing. Specifically, we focus on the application of five techniques to estimate survival probabilities and failure rates: the Kaplan-Meier estimator, Cox proportional hazard model, Weibull accelerated failure time model, random survival forest, and gradient boosting. The resulting estimates are further refined using isotonic regression and subsequently aggregated to determine the expected number of failures. The predictions are then validated against real-world ground truth data across multiple time windows to assess model reliability. Our quantitative evaluation using three performance metrics demonstrates that survival analysis outperforms industry-standard baseline methods for printhead lifespan prediction.

Tong Nie, Jian Sun, Wei Ma

Networked urban systems facilitate the flow of people, resources, and services, and are essential for economic and social interactions. These systems often involve complex processes with unknown governing rules, observed by sensor-based time series. To aid decision-making in industrial and engineering contexts, data-driven predictive models are used to forecast spatiotemporal dynamics of urban systems. Current models such as graph neural networks have shown promise but face a trade-off between efficacy and efficiency due to computational demands. Hence, their applications in large-scale networks still require further efforts. This paper addresses this trade-off challenge by drawing inspiration from physical laws to inform essential model designs that align with fundamental principles and avoid architectural redundancy. By understanding both micro- and macro-processes, we present a principled interpretable neural diffusion scheme based on Transformer-like structures whose attention layers are induced by low-dimensional embeddings. The proposed scalable spatiotemporal Transformer (ScaleSTF), with linear complexity, is validated on large-scale urban systems including traffic flow, solar power, and smart meters, showing state-of-the-art performance and remarkable scalability. Our results constitute a fresh perspective on the dynamics prediction in large-scale urban networks.

Ray Wai Man Kong, Ding Ning, Theodore Ho Tin Kong

This article presents applied research on line balancing within the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process, by Lean Methodology for garment modernization. It explores the application of line balancing in the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process. It aligns with Lean Methodology principles for garment modernization. Without the implementation of line balancing technology, the garment manufacturing process using hanger systems cannot improve output rates. The case study demonstrates that implementing intelligent line balancing in a straightforward practical setup facilitates lean practices combined with a digitalization system and automaton. This approach illustrates how to enhance output and reduce accumulated work in progress.

Tizia Thoma, Lan Ma-Hock, Steffen Schneider et al.

Abstract Background Significant variations exist in the forms of ZnO, making it impossible to test all forms in in vivo inhalation studies. Hence, grouping and read-across is a common approach under REACH to evaluate the toxicological profile of familiar substances. The objective of this paper is to investigate the potential role of dissolution, size, or coating in grouping ZnO (nano)forms for the purpose of hazard assessment. We performed a 90-day inhalation study (OECD test guideline no. (TG) 413) in rats combined with a reproduction/developmental (neuro)toxicity screening test (TG 421/424/426) with coated and uncoated ZnO nanoforms in comparison with microscale ZnO particles and soluble zinc sulfate. In addition, genotoxicity in the nasal cavity, lungs, liver, and bone marrow was examined via comet assay (TG 489) after 14-day inhalation exposure. Results ZnO nanoparticles caused local toxicity in the respiratory tract. Systemic effects that were not related to the local irritation were not observed. There was no indication of impaired fertility, developmental toxicity, or developmental neurotoxicity. No indication for genotoxicity of any of the test substances was observed. Local effects were similar across the different ZnO test substances and were reversible after the end of the exposure. Conclusion With exception of local toxicity, this study could not confirm the occasional findings in some of the previous studies regarding the above-mentioned toxicological endpoints. The two representative ZnO nanoforms and the microscale particles showed similar local effects. The ZnO nanoforms most likely exhibit their effects by zinc ions as no particles could be detected after the end of the exposure, and exposure to rapidly soluble zinc sulfate had similar effects. Obviously, material differences between the ZnO particles do not substantially alter their toxicokinetics and toxicodynamics. The grouping of ZnO nanoforms into a set of similar nanoforms is justified by these observations.

Neža Podvratnik, Andrej Ovca, Mojca Jevšnik

Healthcare-associated infections (HAIs) are considered to be one of the biggest health problems as they continue to be an important cause of morbidity and mortality worldwide. They cannot be completely prevented, but their incidence can be significantly limited. Preventive action is the most important measure in this case. Due to the frequent interaction between healthcare professionals and patients, the crucial importance of hand hygiene is therefore emphasised. Adherence to good disinfection and hand washing practices remains around 40%, which can be improved by using a variety of nudge tools to promote desired hygienic behaviour. We conducted an open observation of employees and visitors with participation. The aim of this study was to determine the actual status of hand disinfection in a selected healthcare facility amongst doctors, registered nurses, medical technicians, cleaners, and visitors or parents of children; then, we selected and introduced three nudge tools of desired hygiene behaviour and analysed their effectiveness; finally, we provided suggestions for the use of nudge tools of desired hygiene behaviour with the aim of influencing doctors, registered nurses, medical technicians, cleaners, and visitors or parents of children so that they disinfect their hands properly. The actual state of hand disinfection was determined on the basis of observation without introducing any changes; then, we separately introduced three nudge tools, posters with an inscription and picture, the scent of citrus, and flashing lights. The obtained results were analysed with the help of the SpeedyAudit Lite application, and the effectiveness of each nudge tool and the adequacy of hand disinfection by categories of people were compared. In general, posters with a picture and an inscription contributed the most to more consistent disinfection of employees’ hands, while the scent of citrus and flashing lights contributed slightly less.

Paula Fraga-Lamas, Tiago M Fernandez-Carames, Oscar Blanco-Novoa et al.

Shipbuilding companies are upgrading their inner workings in order to create Shipyards 4.0, where the principles of Industry 4.0 are paving the way to further digitalized and optimized processes in an integrated network. Among the different Industry 4.0 technologies, this article focuses on Augmented Reality, whose application in the industrial field has led to the concept of Industrial Augmented Reality (IAR). This article first describes the basics of IAR and then carries out a thorough analysis of the latest IAR systems for industrial and shipbuilding applications. Then, in order to build a practical IAR system for shipyard workers, the main hardware and software solutions are compared. Finally, as a conclusion after reviewing all the aspects related to IAR for shipbuilding, it is proposed an IAR system architecture that combines Cloudlets and Fog Computing, which reduce latency response and accelerate rendering tasks while offloading compute intensive tasks from the Cloud.

Dan Lehman, Tim J. Schoonbeek, Shao-Hsuan Hung et al.

Recognizing errors in assembly and maintenance procedures is valuable for industrial applications, since it can increase worker efficiency and prevent unplanned down-time. Although assembly state recognition is gaining attention, none of the current works investigate assembly error localization. Therefore, we propose StateDiffNet, which localizes assembly errors based on detecting the differences between a (correct) intended assembly state and a test image from a similar viewpoint. StateDiffNet is trained on synthetically generated image pairs, providing full control over the type of meaningful change that should be detected. The proposed approach is the first to correctly localize assembly errors taken from real ego-centric video data for both states and error types that are never presented during training. Furthermore, the deployment of change detection to this industrial application provides valuable insights and considerations into the mechanisms of state-of-the-art change detection algorithms. The code and data generation pipeline are publicly available at: https://timschoonbeek.github.io/error_seg.

Alexander Windmann, Philipp Wittenberg, Marvin Schieseck et al.

In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges.

Tiago M. Fernández-Caramés, Paula Fraga-Lamas

The Metaverse is a concept that proposes to immerse users into real-time rendered 3D content virtual worlds delivered through Extended Reality (XR) devices like Augmented and Mixed Reality (AR/MR) smart glasses and Virtual Reality (VR) headsets. When the Metaverse concept is applied to industrial environments, it is called Industrial Metaverse, a hybrid world where industrial operators work by using some of the latest technologies. Currently, such technologies are related to the ones fostered by Industry 4.0, which is evolving towards Industry 5.0, a paradigm that enhances Industry 4.0 by creating a sustainable and resilient world of industrial human-centric applications. The Industrial Metaverse can benefit from Industry 5.0, since it implies making use of dynamic and up-to-date content, as well as fast human-to-machine interactions. To enable such enhancements, this article proposes the concept of Meta-Operator: an Industry 5.0 worker that interacts with Industrial Metaverse applications and with his/her surroundings through advanced XR devices. This article provides a description of the technologies that support Meta-Operators: the main components of the Industrial Metaverse, the latest XR technologies and the use of Opportunistic Edge Computing communications (to interact with surrounding IoT/IioT devices). Moreover, this paper analyzes how to create the next generation of Industrial Metaverse applications based on Industry 5.0, including the integration of AR/MR devices with IoT/IIoT solutions, the development of advanced communications or the creation of shared experiences. Finally, this article provides a list of potential Industry 5.0 applications for the Industrial Metaverse and analyzes the main challenges and research lines. Thus, this article provides useful guidelines for the researchers that will create the next generation of applications for the Industrial Metaverse.

Sisca Mayang Phuspa, Umi Cahyantari, Hikmawani Anas

Introduction: The findings of a systematic review indicate that only a quantitative or qualitative approach was used in studies about the anxiety of health professionals during the COVID-19 pandemic. Research that aims to examine the level of anxiety experienced by Indonesian health workers during the COVID-19 pandemic, the signs and their causes will fill the scientific gap. Methods: A sequential explanatory design was used in this study. In the quantitative phase, the COVID-19 Anxiety Scale instrument was used to perform a survey on 731 healthcare workers, which was then descriptively examined. To further support its findings, 30 informants were involved to in-depth interviews, and qualitative content analysis was performed. Results: According to the poll, 15% of healthcare workers reported having high anxiety, 61% had moderate, 19% had low, and 5% had no anxiety at all. According to a qualitative content analysis, the signs of anxiety included overthinking, psychosomatic complaints, and worry about exposed to and transmit the virus at work. This is a result of managerial issues with managing pandemic, social changes, adjustments in interpersonal connection patterns, an unfriendly society, a large number of health workers who suffer with COVID-19, as well as personal variables. Conclusion: Preventive action for future health crisis situations is to improve systemic physical and non-physical preparedness in healthcare institutions. Psychosocial training programs such as cognitive coping and stress adaptation need to be carried out to improve the mental health condition of health workers so they don't ‘collapse' when dealing crisis situations.

Xianjie Li, Yinger Luo, Di Ji et al.

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

Christos Anagnostopoulos, Georgios Mylonas, Apostolos P. Fournaris et al.

Virtual and augmented reality are currently enjoying a great deal of attention from the research community and the industry towards their adoption within industrial spaces and processes. However, the current design and implementation landscape is still very fluid, while the community as a whole has not yet consolidated into concrete design directions, other than basic patterns. Other open issues include the choice over a cloud or edge-based architecture when designing such systems. Within this work, we present our approach for a monitoring intervention inside a factory space utilizing both VR and AR, based primarily on edge computing, while also utilizing the cloud. We discuss its main design directions, as well as a basic ontology to aid in simple description of factory assets. In order to highlight the design aspects of our approach, we present a prototype implementation, based on a use case scenario in a factory site, within the context of the ENERMAN H2020 project.

Sicong Xie, Binbin Hu, Fengze Li et al.

Aiming at helping users locally discovery retail services (e.g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which greatly challenge current recommender systems. With the real data in Alipay, a feeds-like scenario for O2O services, we find that recurrence based temporal patterns and position biases commonly exist in our scenarios, which seriously threaten the recommendation effectiveness. To this end, we propose COUPA, an industrial system targeting for characterizing user preference with following two considerations: (1) Time aware preference: we employ the continuous time aware point process equipped with an attention mechanism to fully capture temporal patterns for recommendation. (2) Position aware preference: a position selector component equipped with a position personalization module is elaborately designed to mitigate position bias in a personalized manner. Finally, we carefully implement and deploy COUPA on Alipay with a cooperation of edge, streaming and batch computing, as well as a two-stage online serving mode, to support several popular recommendation scenarios. We conduct extensive experiments to demonstrate that COUPA consistently achieves superior performance and has potential to provide intuitive evidences for recommendation

Fikret Basic, Christian Steger, Christian Seifert et al.

With the advent of clean energy awareness and systems that rely on extensive battery usage, the community has seen an increased interest in the development of more complex and secure Battery Management Systems (BMS). In particular, the inclusion of BMS in modern complex systems like electric vehicles and power grids has presented a new set of security-related challenges. A concern is shown when BMS are intended to extend their communication with external system networks, as their interaction can leave many backdoors open that potential attackers could exploit. Hence, it is highly desirable to find a general design that can be used for BMS and its system inclusion. In this work, a security architecture solution is proposed intended for the communication between BMS and other system devices. The aim of the proposed architecture is to be easily applicable in different industrial settings and systems, while at the same time keeping the design lightweight in nature.

Eduard Hirsch, Simon Hoher, Stefan Huber

Industry 4.0 factories are complex and data-driven. Data is yielded from many sources, including sensors, PLCs, and other devices, but also from IT, like ERP or CRM systems. We ask how to collect and process this data in a way, such that it includes metadata and can be used for industrial analytics or to derive intelligent support systems. This paper describes a new, query model based approach, which uses a big data architecture to capture data from various sources using OPC UA as a foundation. It buffers and preprocesses the information for the purpose of harmonizing and providing a holistic state space of a factory, as well as mappings to the current state of a production site. That information can be made available to multiple processing sinks, decoupled from the data sources, which enables them to work with the information without interfering with devices of the production, disturbing the network devices they are working in, or influencing the production process negatively. Metadata and connected semantic information is kept throughout the process, allowing to feed algorithms with meaningful data, so that it can be accessed in its entirety to perform time series analysis, machine learning or similar evaluations as well as replaying the data from the buffer for repeatable simulations.

Estela María Hernández Runque, Nelsy Mirabal Rodríguez, Mercedes Berenice Blanco et al.

Introducción: La resiliencia puede definirse como la capacidad que tiene un individuo de crecer, ser fuerte y hasta triunfar a pesar de las adversidades. De ahí que las empresas cuenten con un equipo de trabajo para apoyar y alentar acciones que contribuyan a la proliferación de conductas resilientes individuales y organizacionales. Objetivo: Analizar el nivel de resiliencia en trabajadores de una empresa venezolana productora de concreto. Métodos: Investigación de campo, no experimental, descriptiva. Las propiedades psicométricas de la resiliencia se presentaron a través de la Resilience Scale de 14 ítems aplicada a 73 trabajadores con edades entre 18 y 61 años, de los cuales 37 eran operarios y 36 administrativos. Resultados: El nivel de resiliencia de los trabajadores fue de 44.84, lo cual clasifica como bajo. En el caso de las mujeres se detectó que eran más resilientes que los hombres y el nivel del personal administrativo fue superior al de los operarios. La trabajadora con un nivel alto de resiliencia mostró mayor capacidad de disciplina y los trabajadores con niveles normales y bajos presentaron como factor protector la autoestima. Conclusiones: Los trabajadores de la empresa estudiada poseen un nivel general de baja resiliencia, coincidente con signos de apatía laboral, desinterés por las actividades asignadas, ausencias injustificadas al trabajo, renuncias sin motivos aparentes. Los resultados proyectan que los factores resilientes subyacentes en esta conducta están relacionados con las variables edad, sexo, estado civil, nivel educativo y condición de empleo Introduction: Resilience may be defined as the capacity of an individual to grow, be strong and even succeed despite adversity. Hence the presence in enterprises of a work team whose aim is to support and foster actions contributing to the spread of individual and organizational resilient behavior. Objective: Analyze the level of resilience in workers from a Venezuelan concrete producing enterprise. Methods: A field non-experimental descriptive study was conducted. The psychometric properties of resilience were presented through the 14-Item Resilience Scale as applied to 73 workers aged 18-61 years, of whom 37 were operators and 36 were administrative employees. Results: The resilience level of workers was 44.84, which classifies as low. It was found that women were more resilient than men and administrative workers were more resilient than operators. Female workers with a high resilience level showed greater discipline capacity, and workers with normal and low levels presented self-esteem as a protective factor. Conclusions: Workers from the study enterprise have a low overall resilience level, coinciding with signs of work apathy, disinterest in the tasks assigned, unjustified absences to work and resignations for no apparent reason. Results suggest that the resilience factors underlying this behavior are related to the variables age, sex, marital status, educational level and employment conditions

Geetanjali Rathee, Farhan Ahmad, Naveen Jaglan et al.

Industrial Internet-of-Things (IIoT) is a powerful IoT application which remodels the growth of industries by ensuring transparent communication among various entities such as hubs, manufacturing places and packaging units. Introducing data science techniques within the IIoT improves the ability to analyze the collected data in a more efficient manner, which current IIoT architectures lack due to their distributed nature. From a security perspective, network anomalies/attackers pose high security risk in IIoT. In this paper, we have addressed this problem, where a coordinator IoT device is elected to compute the trust of IoT devices to prevent the malicious devices to be part of network. Further, the transparency of the data is ensured by integrating a blockchain-based data model. The performance of the proposed framework is validated extensively and rigorously via MATLAB against various security metrics such as attack strength, message alteration, and probability of false authentication. The simulation results suggest that the proposed solution increases IIoT network security by efficiently detecting malicious attacks in the network.