Hasil untuk "Industrial medicine. Industrial hygiene"

Daniel Onut Badea, Doru Costin Darabont, Lucian-Ionel Cioca et al.

Objectives: This review investigated the effects of external and classroom noise on school-aged children’s cognitive performance. The analysis identified exposure patterns across studies; quantified effects on attention, memory, and reading processes; and developed a conceptual model connecting acoustic input, processing effort, and academic performance. Methods: A structured search was conducted to identify observational and experimental studies on noise exposure and cognitive outcomes in school environments. Eligible studies quantified noise using the equivalent continuous sound level, signal-to-noise ratio (SNR), and reverberation time (RT) and included outcomes in attention, memory, and reading tasks. Data were mapped across environmental noise and classroom acoustic conditions. Results: Classroom exposure levels commonly ranged between 65 and 77 dB(A), and external exposure from aircraft and road-traffic sources often exceeded 60 dB(A) during teaching periods. Lower SNRs and longer RTs reduced speech clarity and increased the processing effort needed to follow spoken information across the study design. Performance decreased in tasks involving attention, verbal memory, and reading accuracy. Children from lower socioeconomic backgrounds, bilingual learners, and pupils with weaker attention control exhibited larger performance reductions under identical acoustic conditions. Integrating these results produced a conceptual model in which external noise at school entry and classroom noise during instruction form a continuous exposure sequence that increases processing effort and reduces the learning capacity. Conclusions: Noise affects children by increasing processing load and reducing the cognitive resources available for learning. The model outlines this mechanism. The findings indicate acoustic improvements in classrooms and systematic noise monitoring in school environments.

Xiuhui Wu, Tianyu Wu, Fenglan Zhang

The sterile supply specialty, driven by the rapid advancement of medical technology and the increasing complexity of surgeries, has evolved from a logistical support department to a critical infection control department within a hospital. As a result, specialty nurses in the Central Sterile Supply Department (CSSD) are essential to the specialty development. The present paper reviews the job requirements, role functions, and development direction of specialty nurses in CSSD, suggesting that it is critical to cultivate and improve their professional technical capabilities and comprehensive abilities.

Grant R. Tomkinson, Justin J. Lang, Lukáš Rubín et al.

Background: Muscular strength is a powerful marker of current health status and robust predictor of age-related disease and disability. Handgrip strength (HGS) using isometric dynamometry is a convenient, feasible, and widely used method of assessing muscular strength among people of all ages. While adult HGS norms have been published for many countries, no study has yet synthesized available data to produce international norms. The objective of this study was to generate international sex- and age-specific norms for absolute and body size-normalized HGS across the adult lifespan. Methods: Systematic searches were conducted in 6 databases/web search engines (MEDLINE, SPORTDiscus, Embase, Web of Science, CINAHL, and Google Scholar) up to December 1, 2023. We included full-text peer-reviewed observational studies that reported normative HGS data for adults aged ≥20 years by sex and age. Pseudo data were generated using Monte Carlo simulation following harmonization for methodological variation. Population-weighted Generalized Additive Models for Location, Scale, and Shape were used to develop sex- and age-specific norms for absolute HGS (kg) and HGS normalized by height (Ht, m) squared (i.e., HGS/Ht2 in kg/m2). Norms were tabulated as percentile values (5th to 95th) and visualized as smoothed percentile curves. Results: We included data from 100 unique observational studies representing 2,405,863 adults (51.9% female) aged 20 to 100+ years from 69 countries and regions tested from the year 2000 onward. On average, absolute and normalized HGS values negligibly improved throughout early adulthood, peaked from age 30–39 years (at 49.7 kg (males) and 29.7 kg (females) for absolute HGS or 16.3 kg/m2 (males) and 11.3 kg/m2 (females) for HGS/Ht2), and declined afterwards. The age-related decline in HGS accelerated from middle to late adulthood and was slightly larger for males than for females during middle adulthood. Conclusion: This study provides the world's largest and most geographically comprehensive international norms for adult HGS by sex and age. These norms have utility for global peer-comparisons, health screening, and surveillance.

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.

Martina Bandte, Jens Ehlers, Shaheen Nourinejhad Zarghani et al.

Mechanically transmissible and stable viruses such as tobamoviruses, which include <i>Tobamovirus fructirugosum</i> (syn. tomato brown rugose fruit virus (ToBRFV), will continue to pose major challenges for farmers. Consequently, holistic hygiene concepts are being implemented to prevent the introduction and spread of these viruses. The decontamination of tires and castors was previously a weak point in many industrial hygiene concepts. For this reason, the ProfilGate clean-off zone was tested in combination with the disinfectant MENNO Florades for the decontamination of ToBRFV-contaminated tires. In total, 478 tire segments were sampled to evaluate the contamination of ToBRFV and the following decontamination of the tires. This treatment reliably removed high (4.5 µg/cm²), medium (0.45 µg/cm²), and low concentrations (0.045 µg/cm²) of ToBRFV from the tires, as shown by a bioassay. The reduction in necrotic local lesions on susceptible indicator plants <i>N. tabacum</i> cv. Xanthi NN was between 91.9 and 97.6%. The reduction in ToBRFV contamination largely depended on the length of the rollover distance, i.e., the number of tire rotations. For transport trolleys with polyamide and rubber tires, depletions of 97.4 and 97.6%, respectively, was determined after 16 rotations. For transport wagons with tires twice the size and polyurethane tread, the depletion was still at least 91% after eight wheel turns. Even in the case of gross soiling of the tires, the mean reduction from the different tread materials was 80.9 to 98.9%. Subsequent analysis of the clean-off zone revealed that ToBRFV did not accumulate, even when the contaminated tires were driven over several times, but was safely inactivated completely in the disinfectant solution. This provides growers with an effective tool for preventing the introduction and spread of ToBRFV.

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.

Alva Hansson, Gregory Rankin, Oskari Uski et al.

Abstract Background Exposure to wood smoke has been shown to contribute to adverse respiratory health effects including airway infections, but the underlying mechanisms are unclear. A preceding study failed to confirm any acute inflammation or cell influx in bronchial wash (BW) or bronchoalveolar lavage (BAL) 24 h after wood smoke exposure but showed unexpected reductions in leukocyte numbers. The present study was performed to investigate responses at an earlier phase, regarding potential development of acute inflammation, as well as indications of cytotoxicity. Methods In a double-blind, randomised crossover study, 14 healthy participants were exposed for 2 h to filtered air and diluted wood smoke from incomplete wood log combustion in a common wood stove with a mean particulate matter concentration of 409 µg/m3. Bronchoscopy with BW and BAL was performed 6 h after exposure. Differential cell counts, assessment of DNA-damage and ex vivo analysis of phagocytic function of phagocytosing BAL cells were performed. Wood smoke particles were also collected for in vitro toxicological analyses using bronchial epithelial cells (BEAS-2B) and alveolar type II-like cells (A549). Results Exposure to wood smoke increased BAL lactate dehydrogenase (LDH) (p = 0.04) and reduced the ex vivo alveolar macrophage phagocytic capacity (p = 0.03) and viability (p = 0.02) vs. filtered air. BAL eosinophil numbers were increased after wood smoke (p = 0.02), while other cell types were unaffected in BW and BAL. In vitro exposure to wood smoke particles confirmed increased DNA-damage, decreased metabolic activity and cell cycle disturbances. Conclusions Exposure to wood smoke from incomplete combustion did not induce any acute airway inflammatory cell influx at 6 h, apart from eosinophils. However, there were indications of a cytotoxic reaction with increased LDH, reduced cell viability and impaired alveolar macrophage phagocytic capacity. These findings are in accordance with earlier bronchoscopy findings at 24 h and may provide evidence for the increased susceptibility to infections by biomass smoke exposure, reported in population-based studies.

Jieyu Liu, Di Gao, Huan Wang et al.

Abstract Background Phthalate esters (PAEs) are known to have hormone-like properties, and there is a growing trend of children expressing a gender identity different from assigned sex. However, there has been limited research in the potential links between PAEs exposure and gender identity. Methods A total of 571 children (278 boys) completed the follow-up from Oct 2017 to Oct 2020 in Childhood Blood Pressure and Environmental Factors (CBPEF) cohort in Xiamen, China. Urinary PAE metabolites were measured at three time of visits using ultraperformance liquid chromatography-tandem mass spectrometry. The Children’s Sex Role Inventory scale was used to assess gender identity (masculinity, femininity, androgyny and undifferentiated), and Tanner definition was used to define puberty timing. Generalized linear models and log-binomial regression were used to assess the relationships between PAEs exposure, gender trait scores and gender identity. Results Overall, the concentration of most PAEs in more than 90% of participants was above the limit of detection values. In visit 1, there were 10.1% boys with femininity and 11.3% girls with masculinity; while these figures increased to 10.8% and 12.3% during follow-up, respectively. Early puberty onset accounted for 24.8% and 25.6% among boys and girls. Long-term exposure to mono-2-ethylhexyl phthalate (MEHP) (β = 1.20, 95%CI = 0.13, 2.28), mono-2-ethyl-5-hydroxyhexyl phthalate (MEHHP) (β = 1.25, 95%CI = 0.22, 2.28) and mono-2-ethyl-5-oxohexyl phthalate (MEOHP) (β = 1.40, 95%CI = 0.24, 2.56) was associated with the increased differences of femininity trait scores in boys who enter puberty earlier, prolonged exposure to di(2-ethylhexyl) phthalate (DEHP) might also have such a positive impact (β = 1.38, 95%CI = 0.36, 2.41). For gender identity, persistent exposure to low molecular weight phthalates (LMWP) was negatively associated with undifferentiated type among boys entering puberty earlier (RR = 0.18, 95%CI = 0.05, 0.75, P < 0.05), and most of the PAE metabolites exposures showed risk ratios > 1 for their femininity. Conclusion Long-term exposure to PAEs increase the femininity trait scores in boys with early onset of puberty. Although the mechanisms remain to be determined, environmental pollution might have subtle, yet measurable effects on childhood gender identity. Reducing these chemicals exposure has important public implications on gender development.

Antonio José Obregón-La Rosa, Eliana Contreras-López, Eduardo Flores Juárez et al.

Background. Physalis peruviana L. fruit contains nutritional and bioactive compounds of immense importance to public health and represents a potential ingredient for the development of functional foods and beverages. Objective. This study aimed to determine the chemical and nutritional composition as well as the antioxidant capacity of the P. peruviana L. fruit grown in Peru in three areas of the Central Andean region. Material and methods. Proximal and physicochemical analyses and estimation of mineral content, vitamin C, total carotenoids, total polyphenols, and antioxidant capacity (2, 2-diphenyl-1-picrylhydrazyl [DPPH] and 2, 2′-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) [ABTS] assays) were performed using standardized methods. Results. The fruits were collected from three regions of the Peruvian Andes (Ancash, Cajamarca, and Cusco). The results showed that the content of potassium (306.54–327.60 mg/100 g) and iron (12.93–14.47 mg/kg) was prominent. The Physalis fruit had high levels of vitamin C (47.20–52.20 mg/100 g), total polyphenols (68.17–83.40 mg equivalents of gallic acid/100 g), and carotenoids (1.12–1.73 mg β-carotene/100 g). Higher values for antioxidant capacity were obtained with the ABTS method (896–1003.33 μmol Trolox/100 g) than with the DPPH method (290–309 μmol Trolox/100 g). Conclusions. This study confirms that the P. peruviana fruit has properties that could provide important health benefits and that it could be used for the development of functional foods and food supplement

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.

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.

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.

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.

N. Hopf

“Excuse me. I did not capture the research area you mentioned. Could you please repeat?” said the woman in charge of registering new faculty members at the University of Lausanne, Switzerland, whom I had on the phone. This was about a decade ago. “Occupational hygiene specializing in biomonitoring” I repeated. Well, she informed me that this field was not listed as an option for research areas at the School of Biology and Medicine. “What about occupational hygiene?” I asked (I had been informed that in Europe, we call industrial hygienists, occupational hygienists). Again, a negative answer. “What about occupational health?” I asked. Surely, this long-standing discipline exists, I thought. I had moved from Cincinnati, Ohio, 2 years prior with a PhD from the Medical College and the Department of Occupational and Environmental Health at the University of Cincinnati (UC), and several years working at the National Institute of Occupational Safety and Health (NIOSH). In our Occupational Health class at UC, we had studied the groundbreaking work the legendary occupational medical physician, Alice Hamilton, had carried out in the early 1900s. Then about the rights of working people to have a safe and healthy workplace, which gave way for the New Deal in the 1930s [1] that profoundly increased the role of the US federal government in Occupational Safety and Health. In the 1960s, Unions pushed for federal legislation that ultimately resulted in the passage of the Mine Safety and Health Act of 1969 and the Occupational Safety and Health Act of 1970 [1]. Occupational health experts began monitoring workers’ chemical exposures by measuring the internal dose of a chemical of interest. Human biomonitoring became a new tool in this era for assessing population-wide exposures to hazardous chemicals [2]. It gave a more reliable estimate of total exposures such as lead concentrations in urine [3]. Later, human biomonitoring methods sought to measure a chemical’s biotransformations in the body, its metabolites. Biomonitoring research is exploding now, several centuries after the occupational physician Bernardino Ramazzini formed the field of occupational medicine during the 17th century in Italy. The birthplace of the father of Occupational Health was in Europe, where I now work. “No,” said my university of Lausanne colleague, “these research fields are not listed either. Could occupational hygiene be listed as physics, chemistry, biology, medicine, or mathematics?” the university colleague asked. I opted for chemistry because, as a nonclinician, medicine was out, but where does biomonitoring belong?