Hasil untuk "Industrial hygiene. Industrial welfare"

Emily L. Tucker, Mohammadhossein Mohammadisiahroudi

Quantum computing is rapidly emerging as a new computing paradigm with the potential to improve decision-making, optimization, and simulation across industries. For industrial engineering (IE) and operations research (OR), this shift introduces both unprecedented opportunities and substantial challenges. The learning curve is high, and to help researchers navigate the emerging field of quantum operations research, we provide a road map of the current field of quantum operations research. We introduce the foundational principles of quantum computing, outline the current hardware and software landscape, and survey major algorithmic advances relevant to IE/OR, including quantum approaches to linear algebra, optimization, machine learning, and stochastic simulation. We then highlight applied research directions, including the importance of problem domains for driving long-term value of quantum computers and how existing classical OR models can be reformulated for quantum hardware. Recognizing the steep learning curve, we propose pathways for IE/OR researchers to develop technical fluency and engage in this interdisciplinary domain. By bridging theory with application, and emphasizing the interplay between hardware and research development, we argue that industrial engineers are uniquely positioned to shape the trajectory of quantum computing for practical problem-solving. Ultimately, we aim to lower the barrier to entry into quantum computing, motivate new collaborations, and chart future directions where quantum technologies may deliver tangible impact for industry and academia.

Alice Di Bella, Toni Seibold, Tom Brown et al.

This study analyzes how Europe can decarbonize its industrial sector while remaining competitive. Using the open-source model PyPSA-Eur, it examines key energy- and emission-intensive industries, including steel, cement, methanol, ammonia, and high-value chemicals. Two development paths are explored: a continued decline in industrial activity and a reindustrialization driven by competitiveness policies. The analysis assesses cost gaps between European green products and lower-cost imports, and evaluates strategies such as intra-European relocation, selective imports of green intermediates, and targeted subsidies. Results show that deep industrial decarbonization is technically feasible, led by electrification, but competitiveness depends strongly on policy choices. Imports of green intermediates can lower costs while preserving jobs and production, whereas broad subsidies are economically unsustainable. Effective policy should focus support on sectors like ammonia and steel finishing while maintaining current production levels.

Giovanni De Gasperis, Sante Dino Facchini

Industrial monitoring systems, especially when deployed in Industry 4.0 environments, are experiencing a shift in paradigm from traditional rule-based architectures to data-driven approaches leveraging machine learning and artificial intelligence. This study presents a comparison between these two methodologies, analyzing their respective strengths, limitations, and application scenarios, and proposes a basic framework to evaluate their key properties. Rule-based systems offer high interpretability, deterministic behavior, and ease of implementation in stable environments, making them ideal for regulated industries and safety-critical applications. However, they face challenges with scalability, adaptability, and performance in complex or evolving contexts. Conversely, data-driven systems excel in detecting hidden anomalies, enabling predictive maintenance and dynamic adaptation to new conditions. Despite their high accuracy, these models face challenges related to data availability, explainability, and integration complexity. The paper suggests hybrid solutions as a possible promising direction, combining the transparency of rule-based logic with the analytical power of machine learning. Our hypothesis is that the future of industrial monitoring lies in intelligent, synergic systems that leverage both expert knowledge and data-driven insights. This dual approach enhances resilience, operational efficiency, and trust, paving the way for smarter and more flexible industrial environments.

Eneko Osaba, Iñigo Perez Delgado, Alejandro Mata Ali et al.

This article explores the current state and future prospects of quantum computing in industrial environments. Firstly, it describes three main paradigms in this field of knowledge: gate-based quantum computers, quantum annealers, and tensor networks. The article also examines specific industrial applications, such as bin packing, job shop scheduling, and route planning for robots and vehicles. These applications demonstrate the potential of quantum computing to solve complex problems in the industry. The article concludes by presenting a vision of the directions the field will take in the coming years, also discussing the current limitations of quantum technology. Despite these limitations, quantum computing is emerging as a powerful tool to address industrial challenges in the future.

Md. Arifur Rahman, Ardeshir Amirkhani, Durdana Chowdhury et al.

<i>Staphylococcus aureus</i> and coagulase-negative staphylococci account for about 80% of infections associated with medical devices and are associated with increased virulence due to their ability to form biofilm. In this study, we aimed to construct a comprehensive reference map followed by significant pathway analysis in the proteome of <i>S. aureus</i> biofilm grown for 3 days compared with 24 h of planktonic culture using a high-resolution Tandem Mass Tag (TMT)-based MS. We identified proteins associated with secondary metabolites, ABC transporters, biosynthesis of amino acids, and response to stress, and amino sugar and nucleotide sugar metabolism were significantly upregulated in 3-day biofilm. In contrast, proteins associated with virulence factors, microbial metabolism in diverse environments, secondary metabolites, translation, and energy metabolism were significantly downregulated. GO functional annotation indicated that more proteins are involved in metabolic processes, catalytic activity, and binding in biofilm, respectively. Among the significantly dysregulated proteins, hyaluronidase (hysA) in conjunction with chitinase may play a significant role in the elimination and/or prevention of biofilm development. This study advances the understanding of the <i>S. aureus</i> subproteome, identifying potential pathways significant to biofilm biology. The insights gained may aid in developing new therapeutic strategies, including antibiofilm agents, for treating biofilm-related infections associated with implantable medical devices.

Laura Morin, Valérie Lecureur, Alain Lescoat

Abstract Background Crystalline silica (cSiO2) is a mineral found in rocks; workers from the construction or denim industries are particularly exposed to cSiO2 through inhalation. cSiO2 inhalation increases the risk of silicosis and systemic autoimmune diseases. Inhaled cSiO2 microparticles can reach the alveoli where they induce inflammation, cell death, auto-immunity and fibrosis but the specific molecular pathways involved in these cSiO2 effects remain unclear. This systematic review aims to provide a comprehensive state of the art on omic approaches and exposure models used to study the effects of inhaled cSiO2 in mice and rats and to highlight key results from omic data in rodents also validated in human. Methods The protocol of systematic review follows PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Eligible articles were identified in PubMed, Embase and Web of Science. The search strategy included original articles published after 1990 and written in English which included mouse or rat models exposed to cSiO2 and utilized omic approaches to identify pathways modulated by cSiO2. Data were extracted and quality assessment was based on the SYRCLE’s Risk of Bias tool for animal studies. Results Rats and male rodents were the more used models while female rodents and autoimmune prone models were less studied. Exposure of animals were both acute and chronic and the timing of outcome measurement through omics approaches were homogeneously distributed. Transcriptomic techniques were more commonly performed while proteomic, metabolomic and single-cell omic methods were less utilized. Immunity and inflammation were the main domains modified by cSiO2 exposure in lungs of mice and rats. Less than 20% of the results obtained in rodents were finally verified in humans. Conclusion Omic technics offer new insights on the effects of cSiO2 exposure in mice and rats although the majority of data still need to be validated in humans. Autoimmune prone model should be better characterised and systemic effects of cSiO2 need to be further studied to better understand cSiO2-induced autoimmunity. Single-cell omics should be performed to inform on pathological processes induced by cSiO2 exposure.

Ziyan Yao

With the rapid growth and increasing complexity of industrial big data, traditional data processing methods are facing many challenges. This article takes an in-depth look at the application of cloud computing technology in industrial big data processing and explores its potential impact on improving data processing efficiency, security, and cost-effectiveness. The article first reviews the basic principles and key characteristics of cloud computing technology, and then analyzes the characteristics and processing requirements of industrial big data. In particular, this study focuses on the application of cloud computing in real-time data processing, predictive maintenance, and optimization, and demonstrates its practical effects through case studies. At the same time, this article also discusses the main challenges encountered during the implementation process, such as data security, privacy protection, performance and scalability issues, and proposes corresponding solution strategies. Finally, this article looks forward to the future trends of the integration of cloud computing and industrial big data, as well as the application prospects of emerging technologies such as artificial intelligence and machine learning in this field. The results of this study not only provide practical guidance for cloud computing applications in the industry, but also provide a basis for further research in academia.

Pietro Iob, Mauro Schiavo, Angelo Cenedese

This paper presents a study on transforming a traditional human-operated vehicle into a fully autonomous device. By leveraging previous research and state-of-the-art technologies, the study addresses autonomy, safety, and operational efficiency in industrial environments. Motivated by the demand for automation in hazardous and complex industries, the autonomous system integrates sensors, actuators, advanced control algorithms, and communication systems to enhance safety, streamline processes, and improve productivity. The paper covers system requirements, hardware architecture, software framework and preliminary results. This research offers insights into designing and implementing autonomous capabilities in human-operated vehicles, with implications for improving safety and efficiency in various industrial sectors.

Simon Liebl, Leah Lathrop, Ulrich Raithel et al.

The growing connectivity of industrial devices as a result of the Internet of Things is increasing the risks to Industrial Control Systems. Since attacks on such devices can also cause damage to people and machines, they must be properly secured. Therefore, a threat analysis is required in order to identify weaknesses and thus mitigate the risk. In this paper, we present a systematic and holistic procedure for analyzing the attack surface and threats of Industrial Internet of Things devices. Our approach is to consider all components including hardware, software and data, assets, threats and attacks throughout the entire product life cycle.

Alianne Hernández Chang, Arianne Medina Macías

Introducción: La comprensión del error humano en el contexto laboral ha evolucionado en las últimas décadas desde diferentes aproximaciones. Existen múltiples métodos y técnicas que han sido desarrollados en escenarios donde la problemática del error se ha investigado a profundidad. Objetivos: Realizar una revisión bibliográfica de los estudios sobre error humano en contextos laborales realizados en el período 2010-2022. Métodos: Se incluyeron 33 artículos publicados en los últimos 12 años, tanto de investigación original como de revisión bibliográfica en revistas científicas y en actas de congresos. Se realizó la búsqueda en las bases de datos SciELO, Dialnet, CISDOC y Google Académico. Resultados: Los modelos teóricos empleados en su abordaje responden a tres enfoques fundamentales: cognitivo, ergonómico y sistémico. Fue posible identificar dos filosofías o paradigmas para su análisis y tratamiento en las instituciones laborales: una visión tradicional, denominada también personal o individual, y una visión moderna que se asienta en las premisas del enfoque sistémico. Entre los principales sectores laborales de interés y aplicación se destacan el ámbito de la industria y los servicios de alto riesgo, así como el ámbito de la producción y la calidad. En Cuba, las investigaciones se han orientado principalmente a los sectores de la biotecnología, la medicina y la electricidad. Conclusiones: Se requiere incrementar la producción de investigaciones sobre el tema desde la Psicología, y se debe potenciar el trabajo multidisciplinario para el desarrollo de metodologías de análisis y prevención del error humano en las organizaciones laborales Introduction: Understanding the human error in the occupational context has evolved in recent decades and from different approaches. There are multiple methods and techniques that have been developed in scenarios where the error problem has been thoroughly investigated. Objectives: To carry out a bibliographic review of the studies on human error in occupational contexts and published in the period 2010-2022. Methods: The study included 33 articles, as either original research or bibliographic review, published in scientific journals and conference proceedings within the last 12 years. The search was carried out in the SciELO, Dialnet, CISDOC and Google Scholar databases. Results: The theoretical models used for approaching the subject respond to three fundamental perspectives: cognitive, ergonomic and systemic. It was possible to identify two philosophies or paradigms for its analysis and treatment in labor institutions: a traditional vision, also called personal or individual, and a modern vision, based on the premises of the systemic approach. Among the main employment sectors of interest and application, the field of industry and high-risk services stand out, as well as the field of production and quality. In Cuba, such research gas been oriented mainly to the sectors of biotechnology, medicine and electricity. Conclusions: It is necessary to continue increasing the production of research on the subject from psychology; also, multidisciplinary work should be promoted to develop methodologies for the analysis and prevention of human error in labor organizations

Daniel Agbaji, Brady Lund, Nishith Reddy Mannuru

The Fourth Industrial Revolution, particularly Artificial Intelligence (AI), has had a profound impact on society, raising concerns about its implications and ethical considerations. The emergence of text generative AI tools like ChatGPT has further intensified concerns regarding ethics, security, privacy, and copyright. This study aims to examine the perceptions of individuals in different information flow categorizations toward AI. The results reveal key themes in participant-supplied definitions of AI and the fourth industrial revolution, emphasizing the replication of human intelligence, machine learning, automation, and the integration of digital technologies. Participants expressed concerns about job replacement, privacy invasion, and inaccurate information provided by AI. However, they also recognized the benefits of AI, such as solving complex problems and increasing convenience. Views on government involvement in shaping the fourth industrial revolution varied, with some advocating for strict regulations and others favoring support and development. The anticipated changes brought by the fourth industrial revolution include automation, potential job impacts, increased social disconnect, and reliance on technology. Understanding these perceptions is crucial for effectively managing the challenges and opportunities associated with AI in the evolving digital landscape.

Rizaldy Fathur Rachman, Iin Zulaiha Tuasikal, Abdul Rohim Tualeka et al.

Introduction: Benzene is one of the pollutants in the shoe home industry that can cause cancer among the workers. The present research aimed to analyze the relationship between exposure to benzene and spmA (s-phenylmercapturic Acid) in the urine of shoe-making home industry workers in Surabaya. Methods: This was an observational study using an analytical research method where the total number of respondents in the sample was 10. The concentration of benzene was measured using Gas Chromatography-FID (Flame Ionization Detector). The data collection technique was descriptive analysis for each variable from among the worker’s characteristics. The analysis of the relationship between the level of spmA in their urine and the worker’s characteristics was performed using regression tests while the analysis of the relationship between the level of benzene in the air and the levels of workers’ spmA was performed using the Spearman correlation test. Results: The benzene I levels in the work environment were found to be between 0.06 ppm - 53.8 ppm. The average spmA was 6.68 μg/g creatinine. The p value of the relationship between the variable levels of benzene and the levels of spmA was 0.879 with a Spearman correlation coefficient of 0.056. Conclusion: The mean concentration of benzene in the air at the 6 point uptake was over the threshold. Based on the results of the spmA examination, the mean value of spmA was below the threshold value. The test results on the level of benzene in the air and the spmA indicate a very weak relationship.

Sanjay Adhikesaven

Workplace accidents are a critical problem that causes many deaths, injuries, and financial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively find unsafe environments where injuries could occur by detecting the use of personal protective equipment (PPE) and identifying unsafe activities. Thus, we propose an industrial workplace alerting and monitoring platform to detect PPE use and classify unsafe activity in group settings involving multiple humans and objects over a long period of time. Our proposed method is the first to analyze prolonged actions involving multiple people or objects. It benefits from combining pose estimation with PPE detection in one platform. Additionally, we propose the first open source annotated data set with video data from industrial workplaces annotated with the action classifications and detected PPE. The proposed system can be implemented within the surveillance cameras already present in industrial settings, making it a practical and effective solution.

T. Taleb, I. Afolabi, M. Bagaa

Industry 4.0 aims at shaking the current manufacturing landscape by leveraging the adoption of smart industrial equipment with increased connectivity, sensing, and actuation capabilities. By exploring access to real-time production information and advanced remote control features, servitization of manufacturing firms promises novel added value services for industrial operators and customers. On the other hand, industrial networks would face a transformation process in order to support the flexibility expected by the next-generation manufacturing processes and enable inter-factory cooperation. In this scenario, the 5G systems can play a key role in enabling Industry 4.0 by extending the network slicing paradigm to specifically support the requirements of industrial use cases over heterogeneous domains. We present a novel 5G-based network slicing framework which aims at accommodating the requirements of Industry 4.0. To interconnect different industrial sites up to the extreme edge, different slices of logical resources can be instantiated on-demand to provide the required end-to-end connectivity and processing features. We validate our proposed framework in three realistic use cases which enabled us highlight the envisioned benefits for industrial stakeholders.

Petr Kostka, Bruno Rossi, Mouzhi Ge

The fourth industrial revolution and the digital transformation, commonly known as Industry 4.0, is exponentially progressing in recent years. Connected computers, devices, and intelligent machines communicate with each other and interact with the environment to require only a minimum of human intervention. An important issue in Industry 4.0 is the evaluation of the quality of the process in terms of KPIs. Monte Carlo simulations can play an important role to improve the estimations. However, there is still a lack of clear workflow to conduct the Monte Carlo simulations for selecting different Monte Carlo methods. This paper, therefore, proposes a simulation flow for conducting Monte Carlo methods comparison in Industry 4.0 applications. Based on the simulation flow, we compare Cumulative Monte Carlo and Markov Chain Monte Carlo methods. The experimental results show the way to use the Monte Carlo methods in Industry 4.0 and possible limitations of the two simulation methods.

Markus Dahlmanns, Johannes Lohmöller, Jan Pennekamp et al.

The ongoing trend to move industrial appliances from previously isolated networks to the Internet requires fundamental changes in security to uphold secure and safe operation. Consequently, to ensure end-to-end secure communication and authentication, (i) traditional industrial protocols, e.g., Modbus, are retrofitted with TLS support, and (ii) modern protocols, e.g., MQTT, are directly designed to use TLS. To understand whether these changes indeed lead to secure Industrial Internet of Things deployments, i.e., using TLS-based protocols, which are configured according to security best practices, we perform an Internet-wide security assessment of ten industrial protocols covering the complete IPv4 address space. Our results show that both, retrofitted existing protocols and newly developed secure alternatives, are barely noticeable in the wild. While we find that new protocols have a higher TLS adoption rate than traditional protocols (7.2% vs. 0.4%), the overall adoption of TLS is comparably low (6.5% of hosts). Thus, most industrial deployments (934,736 hosts) are insecurely connected to the Internet. Furthermore, we identify that 42% of hosts with TLS support (26,665 hosts) show security deficits, e.g., missing access control. Finally, we show that support in configuring systems securely, e.g., via configuration templates, is promising to strengthen security.

Tamara L. Young, Ekaterina Mostovenko, Jesse L. Denson et al.

Abstract Background Multiwalled carbon nanotubes (MWCNT) are an increasingly utilized engineered nanomaterial that pose the potential for significant risk of exposure-related health outcomes. The mechanism(s) underlying MWCNT-induced toxicity to extrapulmonary sites are still being defined. MWCNT-induced serum-borne bioactivity appears to dysregulate systemic endothelial cell function. The serum compositional changes after MWCNT exposure have been identified as a surge of fragmented endogenous peptides, likely derived from matrix metalloproteinase (MMP) activity. In the present study, we utilize a broad-spectrum MMP inhibitor, Marimastat, along with a previously described oropharyngeal aspiration model of MWCNT administration to investigate the role of MMPs in MWCNT-derived serum peptide generation and endothelial bioactivity. Results C57BL/6 mice were treated with Marimastat or vehicle by oropharyngeal aspiration 1 h prior to MWCNT treatment. Pulmonary neutrophil infiltration and total bronchoalveolar lavage fluid protein increased independent of MMP blockade. The lung cytokine profile similarly increased following MWCNT exposure for major inflammatory markers (IL-1β, IL-6, and TNF-α), with minimal impact from MMP inhibition. However, serum peptidomic analysis revealed differential peptide compositional profiles, with MMP blockade abrogating MWCNT-derived serum peptide fragments. The serum, in turn, exhibited differential potency in terms of inflammatory bioactivity when incubated with primary murine cerebrovascular endothelial cells. Serum from MWCNT-treated mice led to inflammatory responses in endothelial cells that were significantly blunted with serum from Marimastat-treated mice. Conclusions Thus, MWCNT exposure induced pulmonary inflammation that was largely independent of MMP activity but generated circulating bioactive peptides through predominantly MMP-dependent pathways. This MWCNT-induced lung-derived bioactivity caused pathological consequences of endothelial inflammation and barrier disruption.

Giuliano Franco

Introduction: Bernardino Ramazzini, an academic at the School of Medicine of Modena, lived in the second half of the 17th century. Although his work is remembered mainly for being the first systematic contribution to the knowledge of occupational diseases, it deserves more detailed and complete consideration. This essay aims to illustrate his visionary commitment to visiting workplaces, identifying health threats, suggesting measures to prevent risks, and protecting workers' health. Development: Many aspects of his thinking can be recognized: understanding the association between environment and health; suspect the environmental origin of each disease; propose interventions aimed at risk protection; provide appropriate recommendations for a healthy lifestyle; and suggest a relevant strategy to combat a devastating bovine plague epidemic that was taking place at the time. Conclusion: Ramazzini had a broad vision covering multiple aspects from: observations of health disorders to studies on air and climate impact; from workplace inspection to recommendations for effective health protection; from proposals for personal protective devices to advices on lifestyle behavior. His scientific stature is evident in the modernity of his thinking in light of the current trend of occupational and public health that requires a strong alliance and a better integration with other medical and non-medical fields.

Amin Nowfal, Beste Cubukcuoglu

Lebanon and Northern Cyprus are two developing regions where both have been witnessing an increase in population size and medium-rise buildings. Therefore, workers and construction sites increase, which makes workers more vulnerable to various fatal/non-fatal accidents. The effective and efficient management of health and safety is crucial for all projects undertaken under significant risk levels. This study investigates the Occupational Health and Safety regulations and how both countries deal with them to achieve maximum knowledge regarding construction health and safety. The data collected based on personal observations by site visits and conducting brief face-to-face informal interviews.  Both oral interviews and observations are informal data collection methods but are suitable for certain kinds of data collection methods or techniques. The most common type of accident is falling from heights, electrical shocks that occur in construction sites of both countries. The findings of this research work proved that accidents could be prevented and even eliminated if all the required safety precautions are implemented. The root causes of the accidents need to be identified, and effective prevention measures should be taken to minimize the frequency and intensity of the accidents. This will surely improve the safety performance of the personnel on construction sites.

Jiehan Zhou, Shouhua Zhang, Qinghua Lu et al.

Federated learning (FL) brings collaborative intelligence into industries without centralized training data to accelerate the process of Industry 4.0 on the edge computing level. FL solves the dilemma in which enterprises wish to make the use of data intelligence with security concerns. To accelerate industrial Internet of things with the further leverage of FL, existing achievements on FL are developed from three aspects: 1) define terminologies and elaborate a general framework of FL for accommodating various scenarios; 2) discuss the state-of-the-art of FL on fundamental researches including data partitioning, privacy preservation, model optimization, local model transportation, personalization, motivation mechanism, platform & tools, and benchmark; 3) discuss the impacts of FL from the economic perspective. To attract more attention from industrial academia and practice, a FL-transformed manufacturing paradigm is presented, and future research directions of FL are given and possible immediate applications in Industry 4.0 domain are also proposed.