Hasil untuk "Industrial medicine. Industrial hygiene"

Hailiang Zhao, Ziqi Wang, Daojiang Hu et al.

The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments.

Pablo Gutierrez, Eduardo Velásquez Ayala

Objetivo: La cultura de seguridad en el trabajo es un factor clave en la prevención de riesgos laborales y la mejora del bienestar de los trabajadores. A pesar de la existencia de normativa, su implementación sigue siendo variable en diferentes sectores. Este estudio tiene como objetivo validar un instrumento para medir el nivel de cultura de seguridad en el Perú.  Método: Se llevó a cabo un proceso de validación en tres etapas: selección del instrumento mediante una revisión de la literatura científica, validación cualitativa mediante juicio de expertos, y validación cuantitativa a través de una prueba piloto con 42 trabajadores. La fiabilidad se evaluó mediante el coeficiente Alfa de Cronbach y el Omega de McDonald, mientras que la validez de constructo se analizó mediante Análisis Factorial Exploratorio con el método de Mínimos Cuadrados No Ponderados y rotación Promax. Resultados: El instrumento validado consta de 16 preguntas agrupadas en cinco dimensiones: a) participación activa en la seguridad, b) conciencia y cumplimiento de normas de seguridad, c) conocimiento de riesgos y capacitación en seguridad, d) preocupación por la seguridad y cumplimiento de procedimientos, y e) prevención y comunicación sobre seguridad. El modelo final presentó una adecuada consistencia interna (Alpha de Cronbach = 0.822, Omega total = 0.83) y una estructura factorial estable, explicando el 75.79% de la varianza. Conclusiones: El cuestionario validado es una herramienta fiable para evaluar la cultura de seguridad en las empresas del Perú, facilitando la identificación de debilidades y la implementación de estrategias de mejora.

David Scieszka, Jonathan Hulse, Haiwei Gu et al.

Abstract Background Wildland fires in the United States have increased in frequency and scale over the past 30 years exposing millions of people to hazardous air pollutants. Among others, aging individuals are particularly vulnerable to the effects of wildfire smoke. In this study, we assessed the neurobiological impacts of wood smoke (WS) on aged mice and the potential of anti-aging therapeutics to mitigate these impacts. Methods Female C57BL/6 J mice, aged 18 months, were divided into 10 groups and exposed to either filtered air (FA; 5 groups) or biomass derived WS (5 groups) for 4 h/day, every other day, for 14 days (7 exposures total) with an average particulate matter (PM) concentration of 448 µg/m3 per exposure. One FA control group and one WS exposed group were euthanized 24 h after the last exposure. The remaining 8 groups (4 FA and 4 WS exposed) were treated with either vehicle control, resveratrol and nicotinamide mononucleotide (RNMN), dasatinib and quercetin (DQ), or both RNMN and DQ (RNDQ) for 10 weeks. Results A significant reduction in NAD + within the prefrontal cortex was observed following the 14-day exposure to WS along with a reduction in serotonin. Serotonin reductions were observed up to 10 weeks post-exposure and co-occurred with neuroinflammation and behavioral alterations, including increased immobility in a forced swim test. RNMN conferred the greatest mitigating effect after WS exposure, while RNDQ treatment resulted in an upregulation of markers associated with aging in the brain. While the metabolic shift in the PFC following WS exposure was relatively modest, mice exposed to FA and vehicle control (10 weeks of natural aging) exhibited the greatest metabolic shift, including perturbed nicotinamide metabolism. Conclusion Taken together, these findings highlight that subacute (14-day) exposure to WS results in persistent neurometabolomic and behavioral alterations in an aged mouse model and that intervention with RNMN may be a useful strategy to mitigate the adverse neurological outcomes observed. Further studies are needed to assess the specific impact of either resveratrol or NMN in isolation and to fully elucidate age-specific, as well as sex- and species-determinant, WS exposure response pathways.

Dongyi Yi, Guibo Zhu, Chenglin Ding et al.

With the rapid advancement of Multimodal Large Language Models (MLLMs), numerous evaluation benchmarks have emerged. However, comprehensive assessments of their performance across diverse industrial applications remain limited. In this paper, we introduce MME-Industry, a novel benchmark designed specifically for evaluating MLLMs in industrial settings.The benchmark encompasses 21 distinct domain, comprising 1050 question-answer pairs with 50 questions per domain. To ensure data integrity and prevent potential leakage from public datasets, all question-answer pairs were manually crafted and validated by domain experts. Besides, the benchmark's complexity is effectively enhanced by incorporating non-OCR questions that can be answered directly, along with tasks requiring specialized domain knowledge. Moreover, we provide both Chinese and English versions of the benchmark, enabling comparative analysis of MLLMs' capabilities across these languages. Our findings contribute valuable insights into MLLMs' practical industrial applications and illuminate promising directions for future model optimization research.

Giovanna Takano Natti, Érica Regina Takano Natti, Paulo Laerte Natti

We present a review of the current and future industrial applications of neutrinos. We address the industrial applications of neutrinos in geological and geochemical studies of the Earth's interior, in monitoring earthquakes, in terrestrial communications, in applications for submarines, in monitoring nuclear power plants and fusion reactors, in the management of fissile materials used in nuclear plants, in tracking nuclear tests, among other applications. We also address future possibilities for industrial applications of neutrinos, especially concerning communications in the solar system and geotomography of solar system bodies.

Chaoran Zhang, Chenhao Zhang, Zhaobo Xu et al.

The combination of embodied intelligence and robots has great prospects and is becoming increasingly common. In order to work more efficiently, accurately, reliably, and safely in industrial scenarios, robots should have at least general knowledge, working-environment knowledge, and operating-object knowledge. These pose significant challenges to existing embodied intelligent robotics (EIR) techniques. Thus, this paper first briefly reviews the history of industrial robotics and analyzes the limitations of mainstream EIR frameworks. Then, a new knowledge-driven technical framework of embodied intelligent industrial robotics (EIIR) is proposed for various industrial environments. It has five modules: a world model, a high-level task planner, a low-level skill controller, a simulator, and a physical system. The development of techniques related to each module are also thoroughly reviewed, and recent progress regarding their adaption to industrial applications are discussed. A case study of real-world assembly system is given to demonstrate the newly proposed EIIR framework's applicability and potentiality. Finally, the key challenges that EIIR encounters in industrial scenarios are summarized and future research directions are suggested. The authors believe that EIIR technology is shaping the next generation of industrial robotics and EIIR-based industrial systems supply a new technological paradigm for intelligent manufacturing. It is expected that this review could serve as a valuable reference for scholars and engineers that are interested in industrial embodied intelligence. Together, scholars can use this research to drive their rapid advancement and application of EIIR techniques. The authors would continue to track and contribute new studies in the project page https://github.com/jackyzengl/EIIR

Francesco Forastiere, Hans Orru, Michal Krzyzanowski et al.

Abstract Epidemiologic research and quantitative risk assessment play a crucial role in transferring fundamental scientific knowledge to policymakers so they can take action to reduce the burden of ambient air pollution. This commentary addresses several challenges in quantitative risk assessment of air pollution that require close attention. The background to this discussion provides a summary of and conclusions from the epidemiological evidence on ambient air pollution and health outcomes accumulated since the 1990s. We focus on identifying relevant exposure-health outcome pairs, the associated concentration-response functions to be applied in a risk assessment, and several caveats in their application. We propose a structured and comprehensive framework for assessing the evidence levels associated with each exposure-health outcome pair within a health impact assessment context. Specific issues regarding the use of global or regional concentration-response functions, their shape, and the range of applicability are discussed.

Katrin Müller, Iris Poppele, Marcel Ottiger et al.

Abstract Background Rehabilitation plays a crucial role in restoring work ability and facilitating the reintegration of post-COVID patients into the workforce. The impact of rehabilitation on work ability and return to work (RTW) of post-COVID patients remains poorly understood. This study was conducted to assess the work ability and RTW of post-COVID patients before rehabilitation and 12 months after rehabilitation and to identify physical and neuropsychological health factors influencing RTW 12 months after rehabilitation. Methods This longitudinal observational study included 114 post-COVID patients with work-related SARS-CoV-2 infection who underwent inpatient post-COVID rehabilitation with indicative focus on pulmonology and/or psychotraumatology (interval between date of SARS-CoV-2 infection and start of rehabilitation: M = 412.90 days). Employment status, work ability, and the subjective prognosis of employment (SPE) scale were assessed before rehabilitation (T1) and 12 months after rehabilitation (T4). The predictors analysed at T4 were functional exercise capacity, physical activity, subjective physical and mental health status, fatigue, depression, and cognitive function. Longitudinal analyses were performed via the Wilcoxon signed-rank test. Logistic and linear regression analyses identified predictors of work ability and return to work (RTW), whereas mediation analyses examined the relationships between these predictors and work ability. Results At T4, the median of WAI total score indicated poor work ability, which significantly worsened over time (p < 0.001; r = 0.484). The SPE scale significantly increased from T1 to T4 (p = 0.022, r = -0.216). A total of 48.6% of patients had returned to work 12 months after rehabilitation. Fatigue was identified as the main predictor of reduced work ability and RTW, with each unit increase in fatigue severity decreasing the odds of RTW by 3.1%. In addition, physical capacity and subjective health status were significant predictors of perceived work ability. Conclusions The findings highlight the significant challenges that post-COVID patients face in regaining work ability and achieving successful RTW 12 months after rehabilitation. Fatigue appears to be an important predictor of work ability and RTW. To optimize recovery and enhance both biopsychosocial health and work ability, it is crucial to develop and implement personalised interventions that address fatigue, improve physical capacity, and support mental health. Trial registration This study is registered in the German Clinical Trials Register under DRKS00022928.

Jiao Chen, Jiayi He, Fangfang Chen et al.

Industrial AI is transitioning from traditional deep learning models to large-scale transformer-based architectures, with the Industrial Internet of Things (IIoT) playing a pivotal role. IIoT evolves from a simple data pipeline to an intelligent infrastructure, enabling and enhancing these advanced AI systems. This survey explores the integration of IIoT with large models (LMs) and their potential applications in industrial environments. We focus on four primary types of industrial LMs: language-based, vision-based, time-series, and multimodal models. The lifecycle of LMs is segmented into four critical phases: data foundation, model training, model connectivity, and continuous evolution. First, we analyze how IIoT provides abundant and diverse data resources, supporting the training and fine-tuning of LMs. Second, we discuss how IIoT offers an efficient training infrastructure in low-latency and bandwidth-optimized environments. Third, we highlight the deployment advantages of LMs within IIoT, emphasizing IIoT's role as a connectivity nexus fostering emergent intelligence through modular design, dynamic routing, and model merging to enhance system scalability and adaptability. Finally, we demonstrate how IIoT supports continual learning mechanisms, enabling LMs to adapt to dynamic industrial conditions and ensure long-term effectiveness. This paper underscores IIoT's critical role in the evolution of industrial intelligence with large models, offering a theoretical framework and actionable insights for future research.

Matthias Christenson, Cove Geary, Brian Locke et al.

The success of precision medicine requires computational models that can effectively process and interpret diverse physiological signals across heterogeneous patient populations. While foundation models have demonstrated remarkable transfer capabilities across various domains, their effectiveness in handling individual-specific physiological signals - crucial for precision medicine - remains largely unexplored. This work introduces a systematic pipeline for rapidly and efficiently evaluating foundation models' transfer capabilities in medical contexts. Our pipeline employs a three-stage approach. First, it leverages physiological simulation software to generate diverse, clinically relevant scenarios, particularly focusing on data-scarce medical conditions. This simulation-based approach enables both targeted capability assessment and subsequent model fine-tuning. Second, the pipeline projects these simulated signals through the foundation model to obtain embeddings, which are then evaluated using linear methods. This evaluation quantifies the model's ability to capture three critical aspects: physiological feature independence, temporal dynamics preservation, and medical scenario differentiation. Finally, the pipeline validates these representations through specific downstream medical tasks. Initial testing of our pipeline on the Moirai time series foundation model revealed significant limitations in physiological signal processing, including feature entanglement, temporal dynamics distortion, and reduced scenario discrimination. These findings suggest that current foundation models may require substantial architectural modifications or targeted fine-tuning before deployment in clinical settings.

Hamed Yarmohammadi, Mahdi Jahangiri, Moslem Alimohammadlou et al.

Background and Objective: Occupational safety is an important and significant issue that helps maintain the health and safety of employees and prevent occupational accidents and injuries. Therefore, the present study was conducted with the aim of reviewing and analyzing books related to occupational safety. Materials and Methods: The current research employed a descriptive and bibliometric methodology. The research population included all the books registered on the National Library website from the beginning to the end of March 2023 using keywords such as safety, occupational accidents, and fire safety. The data were analyzed using Excel (version 2016) and Minitab (version 19.2020.1). Descriptive statistics and frequency distribution tables were used for data analysis. Results: The results indicated that a total of 2,617 book titles were published by the end of March 2023. Furthermore, the trend of book publishing has been generally increasing over the last five decades. The majority of the published books were in the subject categories of general safety (17.7%), fire (11.9%), as well as crisis management and emergency response (7.94%). Conclusion: Overall, it can be concluded that safety is a crucial concern in the industry and workplace, and Iranian authors have devoted significant attention to this field by writing various books. To enhance the quality of published books, authors are advised to conduct regular research, consult with experts in the field of occupational safety, assess industry needs through communication, engage with book audiences and gather feedback, and plan for updates to their published works.

Jay Lee, Hanqi Su

The recent emergence of large language models (LLMs) demonstrates the potential for artificial general intelligence, revealing new opportunities in Industry 4.0 and smart manufacturing. However, a notable gap exists in applying these LLMs in industry, primarily due to their training on general knowledge rather than domain-specific knowledge. Such specialized domain knowledge is vital for effectively addressing the complex needs of industrial applications. To bridge this gap, this paper proposes a unified industrial large knowledge model (ILKM) framework, emphasizing its potential to revolutionize future industries. In addition, ILKMs and LLMs are compared from eight perspectives. Finally, the "6S Principle" is proposed as the guideline for ILKM development, and several potential opportunities are highlighted for ILKM deployment in Industry 4.0 and smart manufacturing.

Lucía Rodriguez-Loureiro, Sylvie Gadeyne, Mariska Bauwelinck et al.

Abstract Background Living in greener areas is associated with slower cognitive decline and reduced dementia risk among older adults, but the evidence with neurodegenerative disease mortality is scarce. We studied the association between residential surrounding greenness and neurodegenerative disease mortality in older adults. Methods We used data from the 2001 Belgian census linked to mortality register data during 2001–2014. We included individuals aged 60 years or older and residing in the five largest Belgian urban areas at baseline (2001). Exposure to residential surrounding greenness was assessed using the 2006 Normalized Difference Vegetation Index (NDVI) within 500-m from residence. We considered all neurodegenerative diseases and four specific outcomes: Alzheimer’s disease, vascular dementia, unspecified dementia, and Parkinson’s disease. We fitted Cox proportional hazard models to obtain hazard ratios (HR) and 95% confidence intervals (CI) of the associations between one interquartile range (IQR) increment in surrounding greenness and neurodegenerative disease mortality outcomes, adjusted for census-based covariates. Furthermore, we evaluated the potential role of 2010 air pollution (PM2.5 and NO2) concentrations, and we explored effect modification by sociodemographic characteristics. Results From 1,134,502 individuals included at baseline, 6.1% died from neurodegenerative diseases during follow-up. After full adjustment, one IQR (0.22) increment of surrounding greenness was associated with a 4–5% reduction in premature mortality from all neurodegenerative diseases, Alzheimer’s disease, vascular and unspecified dementia [e.g., for Alzheimer’s disease mortality: HR 0.95 (95%CI: 0.93, 0.98)]. No association was found with Parkinson’s disease mortality. Main associations remained for all neurodegenerative disease mortality when accounting for air pollution, but not for the majority of specific mortality outcomes. Associations were strongest in the lower educated and residents from most deprived neighbourhoods. Conclusions Living near greener spaces may reduce the risk of neurodegenerative disease mortality among older adults, potentially independent from air pollution. Socioeconomically disadvantaged groups may experience the greatest beneficial effect.

Roberto Figliè, Riccardo Amadio, Marios Tyrovolas et al.

Today, one of the biggest challenges for digital transformation in the Industry 4.0 paradigm is the lack of mutual understanding between the academic and the industrial world. On the one hand, the industry fails to apply new technologies and innovations from scientific research. At the same time, academics struggle to find and focus on real-world applications for their developing technological solutions. Moreover, the increasing complexity of industrial challenges and technologies is widening this hiatus. To reduce this knowledge and communication gap, this article proposes a mixed approach of humanistic and engineering techniques applied to the technological and enterprise fields. The study's results are represented by a taxonomy in which industrial challenges and I4.0-focused technologies are categorized and connected through academic and grey literature analysis. This taxonomy also formed the basis for creating a public web platform where industrial practitioners can identify candidate solutions for an industrial challenge. At the same time, from the educational perspective, the learning procedure can be supported since, through this tool, academics can identify real-world scenarios to integrate digital technologies' teaching process.

Luis Manuel Blanco-Donoso

Entrevista a Luis Manuel Blanco-Donoso

Barry Dowdeswell, Roopak Sinha, Dennis Jarvis et al.

We have come to rely on industrial-scale cyber-physical systems more and more to manage tasks and machinery in safety-critical situations. Efficient, reliable fault identification and management has become a critical factor in the design of these increasingly sophisticated and complex devices. Teams of co-operating software agents are one way to coordinate the flow of diagnostic information gathered during fault-finding. By wielding domain knowledge of the software architecture used to construct the system, agents build and refine their beliefs about the location and root cause of faults. This paper examines how agents constructed within the GORITE Multi-Agent Framework create and refine their beliefs. We demonstrate three different belief structures implemented within our Fault Diagnostic Engine, showing how each supports a distinct aspect of the agent's reasoning. Using domain knowledge of the IEC 61499 Function Block architecture, agents are able to examine and rigorously evaluate both individual components and entire subsystems.

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana et al.

The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries. Recently, artificial intelligence (AI) has been widely utilized for realizing intelligent IIoT applications where AI techniques require centralized data collection and processing. However, this is not always feasible in realistic scenarios due to the high scalability of modern IIoT networks and growing industrial data confidentiality. Federated Learning (FL), as an emerging collaborative AI approach, is particularly attractive for intelligent IIoT networks by coordinating multiple IIoT devices and machines to perform AI training at the network edge while helping protect user privacy. In this article, we provide a detailed overview and discussions of the emerging applications of FL in key IIoT services and applications. A case study is also provided to demonstrate the feasibility of FL in IIoT. Finally, we highlight a range of interesting open research topics that need to be addressed for the full realization of FL-IIoT in industries.

Yangyi Lu, Ziping Xu, Ambuj Tewari

The Oxford English Dictionary defines precision medicine as "medical care designed to optimize efficiency or therapeutic benefit for particular groups of patients, especially by using genetic or molecular profiling." It is not an entirely new idea: physicians from ancient times have recognized that medical treatment needs to consider individual variations in patient characteristics. However, the modern precision medicine movement has been enabled by a confluence of events: scientific advances in fields such as genetics and pharmacology, technological advances in mobile devices and wearable sensors, and methodological advances in computing and data sciences. This chapter is about bandit algorithms: an area of data science of special relevance to precision medicine. With their roots in the seminal work of Bellman, Robbins, Lai and others, bandit algorithms have come to occupy a central place in modern data science ( Lattimore and Szepesvari, 2020). Bandit algorithms can be used in any situation where treatment decisions need to be made to optimize some health outcome. Since precision medicine focuses on the use of patient characteristics to guide treatment, contextual bandit algorithms are especially useful since they are designed to take such information into account. The role of bandit algorithms in areas of precision medicine such as mobile health and digital phenotyping has been reviewed before (Tewari and Murphy, 2017; Rabbi et al., 2019). Since these reviews were published, bandit algorithms have continued to find uses in mobile health and several new topics have emerged in the research on bandit algorithms. This chapter is written for quantitative researchers in fields such as statistics, machine learning, and operations research who might be interested in knowing more about the algorithmic and mathematical details of bandit algorithms that have been used in mobile health.

Bin Han, Hans D. Schotten

The next revolution of industry will turn the industries as well as the entire society into a human-centric shape. The human presence in industrial environment and the human participation in industrial processes will be magnified more than ever before. To cope with the emerging challenges raised by this revolution, 6G ambitions to bridge the three domains of digital information, physical assets and humans into one merged cyber-physical-human world. This proposes not only an unprecedented demand for digital twin solutions, but also new technical requirements. Especially, aiming at a human-centric industrial DT system, novel multi-sensory human-machine interfaces will play a key role in this paradigm shift.

Daochuan Li, Shen Chen, Qiong Li et al.

Abstract Background Caloric restriction (CR) is known to improve health and extend lifespan in human beings. The effects of CR on adverse health outcomes in response to particulate matter (PM) exposure and the underlying mechanisms have yet to be defined. Results Male C57BL/6 J mice were fed with a CR diet or ad libitum (AL) and exposed to PM for 4 weeks in a real-ambient PM exposure system located at Shijiazhuang, China, with a daily mean concentration (95.77 μg/m3) of PM2.5. Compared to AL-fed mice, CR-fed mice showed attenuated PM-induced pulmonary injury and extra-pulmonary toxicity characterized by reduction in oxidative stress, DNA damage and inflammation. RNA sequence analysis revealed that several pulmonary pathways that were involved in production of reactive oxygen species (ROS), cytokine production, and inflammatory cell activation were inactivated, while those mediating antioxidant generation and DNA repair were activated in CR-fed mice upon PM exposure. In addition, transcriptome analysis of murine livers revealed that CR led to induction of xenobiotic metabolism and detoxification pathways, corroborated by increased levels of urinary metabolites of polycyclic aromatic hydrocarbons (PAHs) and decreased cytotoxicity measured in an ex vivo assay. Conclusion These novel results demonstrate, for the first time, that CR in mice confers resistance against pulmonary injuries and extra-pulmonary toxicity induced by PM exposure. CR led to activation of xenobiotic metabolism and enhanced detoxification of PM-bound chemicals. These findings provide evidence that dietary intervention may afford therapeutic means to reduce the health risk associated with PM exposure.