Hasil untuk "Industrial medicine. Industrial hygiene"

Thomas Claessens, Karin Sørig Hougaard, Steven Ronsmans

Abstract Background Exposure to workplace chemicals can pose serious risks to reproductive health. The European Union’s Pregnant Workers Directive requires risk assessments but lacks clear guidelines for assessing chemical reproductive hazards in workplaces. Aims This study aims to review how EU member states implement the Pregnant Workers Directive by analysing national guidance documents and relevant literature. Methods A qualitative review was conducted, combining a systematic literature search with outreach to EU national experts to gather relevant guidance documents. Thematic synthesis identified guiding principles for implementing maternity protection for chemical exposures. Results Two main themes were identified: the need for a broad perspective and for certainty in risk assessment. The broad perspective stresses the importance of considering all reproductive hazards, not limited to those listed in the EU Directive and inclusion of male workers and the preconception period, and the potential adverse socio-economic consequences of applied protective measures. The need for certainty highlights the challenges in reliable risk assessments, due to lack of knowledge about chemicals’ hazardous properties, dose-response relationships and the level of worker exposure. These themes reveal the complexity of implementing effective maternity protection and the need for improved guidelines across the EU. Conclusions This study calls for a unified approach to reproductive health protection, extending beyond pregnancy to include also preconception and paternal exposures. The findings highlight the need to support practitioners in the risk assess process at workplaces in the EU by providing a framework for the assessment of reproductive hazards and determining protective measures.

Maria Luiza dos Santos Rodrigues Vaz, Ana Beatriz da Silva Sousa, Carolina Martins Ribeiro et al.

Abstract Exposure to endocrine disruptors (EDs) is associated with increased susceptibility to obesity and metabolic dysfunction in epidemiological and preclinical studies. Accumulating evidence supports that various EDs promote energy intake and fat storage, but little is known about how they affect energy expenditure (EE). This systematic review examined the effect of EDs on EE in murine models and on mitochondrial bioenergetics in cell-based studies. We included 12 in vivo studies, which assessed the effect of phytoestrogens, DDT, tolylfluanid, benzene, bisphenol A, bisphenol S, butyl-phthalate, deltamethrin, and the mixtures of 23 chemicals and of organophosphate flame retardants. DDT, tolylfluanid, benzene, and the mixtures of 23 chemicals and of flame retardants decreased; bisphenol A, bisphenol S, and butyl-phthalate had a neutral effect; and phytoestrogens and deltamethrin increased EE. The effects of some EDs were sexually dimorphic, dose-dependent, and interacted with diet. Nine cell-based studies were included and indicated that mitochondrial bioenergetics was impaired by tolylfluanid, bisphenol A, and DDT in muscle cells; by bisphenol AF, BDE-99, DDT, DDE, and the mixture of DDE, trans-nonachlor, and oxychlordane in adipocytes; by bisphenol A in hepatocytes; and by tributyltin in pluripotent cells. Our findings indicate that EDs affect EE in mice in a sexually dimorphic pattern and impair mitochondrial bioenergetics in cellular models which are representative of key tissues involved in energy balance. While further studies are needed to fully elucidate the impact of EDs on energy balance and mitochondrial function, this review underscores the plausibility of mitochondrial dysfunction and altered EE as key pathways linking ED exposure to metabolic diseases.

Vincenzo Petrone, Enrico Ferrentino, Pasquale Chiacchio

This paper presents the full dynamic model of the UR10 industrial robot. A triple-stage identification approach is adopted to estimate the manipulator's dynamic coefficients. First, linear parameters are computed using a standard linear regression algorithm. Subsequently, nonlinear friction parameters are estimated according to a sigmoidal model. Lastly, motor drive gains are devised to map estimated joint currents to torques. The overall identified model can be used for both control and planning purposes, as the accompanied ROS2 software can be easily reconfigured to account for a generic payload. The estimated robot model is experimentally validated against a set of exciting trajectories and compared to the state-of-the-art model for the same manipulator, achieving higher current prediction accuracy (up to a factor of 4.43) and more precise motor gains. The related software is available at https://codeocean.com/capsule/8515919/tree/v2.

Rongfei Li, Francis Assadian

Image-based visual servoing (IBVS) methods have been well developed and used in many applications, especially in pose (position and orientation) alignment. However, most research papers focused on developing control solutions when 3D point features can be detected inside the field of view. This work proposes an innovative feedforward-feedback adaptive control algorithm structure with the Youla Parameterization method. A designed feature estimation loop ensures stable and fast motion control when point features are outside the field of view. As 3D point features move inside the field of view, the IBVS feedback loop preserves the precision of the pose at the end of the control period. Also, an adaptive controller is developed in the feedback loop to stabilize the system in the entire range of operations. The nonlinear camera and robot manipulator model is linearized and decoupled online by an adaptive algorithm. The adaptive controller is then computed based on the linearized model evaluated at current linearized point. The proposed solution is robust and easy to implement in different industrial robotic systems. Various scenarios are used in simulations to validate the effectiveness and robust performance of the proposed controller.

Matthieu Mastio, Paul Saves, Benoit Gaudou et al.

Industrial symbiosis fosters circularity by enabling firms to repurpose residual resources, yet its emergence is constrained by socio-spatial frictions that shape costs, matching opportunities, and market efficiency. Existing models often overlook the interaction between spatial structure, market design, and adaptive firm behavior, limiting our understanding of where and how symbiosis arises. We develop an agent-based model where heterogeneous firms trade byproducts through a spatially embedded double-auction market, with prices and quantities emerging endogenously from local interactions. Leveraging reinforcement learning, firms adapt their bidding strategies to maximize profit while accounting for transport costs, disposal penalties, and resource scarcity. Simulation experiments reveal the economic and spatial conditions under which decentralized exchanges converge toward stable and efficient outcomes. Counterfactual regret analysis shows that sellers' strategies approach a near Nash equilibrium, while sensitivity analysis highlights how spatial structures and market parameters jointly govern circularity. Our model provides a basis for exploring policy interventions that seek to align firm incentives with sustainability goals, and more broadly demonstrates how decentralized coordination can emerge from adaptive agents in spatially constrained markets.

Rafael Castro, Alexandre dos Santos Roque

Nowadays, more effective control of hand hygiene (HH) by healthcare teams has become essential. HH control is crucial to prevent cross-contamination and healthcare-associated infections (HAI), according to Brazilian regulatory standards and WHO guidelines. The lack of widespread technology to measure acceptable hygiene rates within hospital environments leads to the practice of a manual sample audit reading, requiring more time for decision making. Thus, the present study addresses the lack of automation technologies for HH, aiming to record, measure, and provide data for internal audits in hospitals. This article introduces an embedded system for HH control and recording, comprising low-cost hardware architecture with IoT connectivity and online monitoring. Results with practical evaluation in a real hospital setting for 3 hours demonstrated the system's effectiveness in recording HH indices.

Meng Li, Yu Zhang, Leshan Li

The Industry 4.0 refers to a industrial ecology which will merge the information system, physical system and service system into an integrate platform. Since now the industrial designers either conceive the physical part of products, or design the User Interfaces of computer systems, the new industrial ecology will give them a chance to redefine their roles in R&D work-flow. In this paper we discussed the required qualities of industrial designer in the new era, according to an investigation among Chinese enterprises. Additionally, how to promote these qualities though educational program.

Nathan Lane

I study the impact of industrial policies on industrial development by considering an important episode during the East Asian miracle: South Korea's heavy and chemical industry (HCI) drive, 1973--1979. Based on newly assembled data, I use the introduction and termination of industrial policies to study their impacts during and after the intervention period. (1) I reveal that heavy-chemical industrial policies promoted the expansion and dynamic comparative advantage of directly targeted industries. (2) Using variation in exposure to policies through the input-output network, I demonstrate that the policy indirectly benefited downstream users of targeted intermediates. (3) The benefits of HCI persisted even after the policy ended, as some results were slower to appear. The findings suggest that the temporary drive shifted Korean manufacturing into more advanced markets and supported durable change. This study helps clarify the lessons drawn from the East Asian growth miracle. JEL Codes: L5, O14, O25, N6. Keywords: industrial policy, East Asian miracle, economic history, industrial development, Heavy-Chemical Industry Drive, Heavy and Chemical Industry Drive.

Periklis Petridis, Georgios Margaritis, Vasiliki Stoumpou et al.

With the increasing interest in deploying Artificial Intelligence in medicine, we previously introduced HAIM (Holistic AI in Medicine), a framework that fuses multimodal data to solve downstream clinical tasks. However, HAIM uses data in a task-agnostic manner and lacks explainability. To address these limitations, we introduce xHAIM (Explainable HAIM), a novel framework leveraging Generative AI to enhance both prediction and explainability through four structured steps: (1) automatically identifying task-relevant patient data across modalities, (2) generating comprehensive patient summaries, (3) using these summaries for improved predictive modeling, and (4) providing clinical explanations by linking predictions to patient-specific medical knowledge. Evaluated on the HAIM-MIMIC-MM dataset, xHAIM improves average AUC from 79.9% to 90.3% across chest pathology and operative tasks. Importantly, xHAIM transforms AI from a black-box predictor into an explainable decision support system, enabling clinicians to interactively trace predictions back to relevant patient data, bridging AI advancements with clinical utility.

Wenbo Sui, Daniel Lichau, Josselin Lefèvre et al.

Recent studies of multimodal industrial anomaly detection (IAD) based on 3D point clouds and RGB images have highlighted the importance of exploiting the redundancy and complementarity among modalities for accurate classification and segmentation. However, achieving multimodal IAD in practical production lines remains a work in progress. It is essential to consider the trade-offs between the costs and benefits associated with the introduction of new modalities while ensuring compatibility with current processes. Existing quality control processes combine rapid in-line inspections, such as optical and infrared imaging with high-resolution but time-consuming near-line characterization techniques, including industrial CT and electron microscopy to manually or semi-automatically locate and analyze defects in the production of Li-ion batteries and composite materials. Given the cost and time limitations, only a subset of the samples can be inspected by all in-line and near-line methods, and the remaining samples are only evaluated through one or two forms of in-line inspection. To fully exploit data for deep learning-driven automatic defect detection, the models must have the ability to leverage multimodal training and handle incomplete modalities during inference. In this paper, we propose CMDIAD, a Cross-Modal Distillation framework for IAD to demonstrate the feasibility of a Multi-modal Training, Few-modal Inference (MTFI) pipeline. Our findings show that the MTFI pipeline can more effectively utilize incomplete multimodal information compared to applying only a single modality for training and inference. Moreover, we investigate the reasons behind the asymmetric performance improvement using point clouds or RGB images as the main modality of inference. This provides a foundation for our future multimodal dataset construction with additional modalities from manufacturing scenarios.

Zhipeng Ma, Bo Nørregaard Jørgensen, Zheng Grace Ma

Advanced machine learning algorithms are increasingly utilized to provide data-based prediction and decision-making support in Industry 4.0. However, the prediction accuracy achieved by the existing models is insufficient to warrant practical implementation in real-world applications. This is because not all features present in real-world datasets possess a direct relevance to the predictive analysis being conducted. Consequently, the careful incorporation of select features has the potential to yield a substantial positive impact on the outcome. To address the research gap, this paper proposes a novel hybrid framework that combines the feature importance detector - local interpretable model-agnostic explanations (LIME) and the feature interaction detector - neural interaction detection (NID), to improve prediction accuracy. By applying the proposed framework, unnecessary features can be eliminated, and interactions are encoded to generate a more conducive dataset for predictive purposes. Subsequently, the proposed model is deployed to refine the prediction of electricity consumption in foundry processing. The experimental outcomes reveal an augmentation of up to 9.56% in the R2 score, and a diminution of up to 24.05% in the root mean square error.

Marc Schmitt

The last decades have been characterized by unprecedented technological advances, many of them powered by modern technologies such as Artificial Intelligence (AI) and Machine Learning (ML). The world has become more digitally connected than ever, but we face major challenges. One of the most significant is cybercrime, which has emerged as a global threat to governments, businesses, and civil societies. The pervasiveness of digital technologies combined with a constantly shifting technological foundation has created a complex and powerful playground for cybercriminals, which triggered a surge in demand for intelligent threat detection systems based on machine and deep learning. This paper investigates AI-based cyber threat detection to protect our modern digital ecosystems. The primary focus is on evaluating ML-based classifiers and ensembles for anomaly-based malware detection and network intrusion detection and how to integrate those models in the context of network security, mobile security, and IoT security. The discussion highlights the challenges when deploying and integrating AI-enabled cybersecurity solutions into existing enterprise systems and IT infrastructures, including options to overcome those challenges. Finally, the paper provides future research directions to further increase the security and resilience of our modern digital industries, infrastructures, and ecosystems.

Sara Tabanfar, Seyvan Sobhani

Background: Due to the Covid-19 pandemic and the increase in anxiety in the community, this study aims to investigate the relationship between anxiety symptoms and demographic characteristics of administrative staff during the Covid-19 pandemic. Methods: We selected 193 Administrative staff in Qazvin to participate in a cross-sectional descriptive study, using a multi-stage sampling method. Data collection tools included a demographic information questionnaire and the Corona Disease Anxiety Scale (CDAS). Data were analyzed using SPSS software, independent t-test, ANOVA, and Pearson correlation coefficient. The significance level was considered to be 0.05. Results: The mean age of participants was 33.61±8.3. 62.6% were female and 75.2% were married. Anxiety score with a mean and standard deviation of 24.88±7.52 was evaluated to be moderate. There was a significant positive correlation between anxiety score and age (P= 0.007 and r= 0.267), and work experience (P= 0.003 and r= 0.313). Participants with a family member over the age of 65, or a member with a chronic illness, had significantly higher mean anxiety scores than other participants. Conclusion: Employees' anxiety in this study was assessed as moderate. To bring anxiety to a low level and increase the general health of individuals, it is suggested that managers and heads of departments consider programs to reduce the anxiety of employees. By reducing anxiety scores, they can increase the productivity of these people.

Shihui Han, Roopak Sinha, Andrew Lowe

Industrial standards for developing medical device software provide requirements that conforming devices must meet. A number of reference software architectures have been proposed to develop such software. The ISO/IEC 25010:2011 family of standards provides a comprehensive software product quality model, including characteristics that are highly desirable in medical devices. Furthermore, frameworks like 4+1 Views provide a robust framework to develop the software architecture or high level design for any software, including for medical devices. However, the alignment between industrial standards and reference architectures for medical device software, on one hand, and ISO/IEC 25010:2011 and 4+1 Views, on the other, is not well understood. This paper aims to explore how ISO/IEC 25010:2011 and 4+1 Views are supported by current standards, namely ISO 13485:2016, ISO 14971:2012, IEC 62304:2006 and IEC 62366:2015, and current reference architectures for medical device software. We classified requirements from medical devices standards into qualities from ISO/IEC 25010:2011 and architectural views from 4+1 Views. A systematic literature review (SLR) method was followed to review current references software architectures and a mapping of their support for the identified ISO/IEC 25010:2011 qualities in the previous step was carried out. Our results show that ISO/IEC 25010:2011 qualities like functional suitability, portability, maintainability, usability, security, reliability and compatibility are highly emphasised in medical device standards. Furthermore, we show that current reference architectures only partially support these qualities. This paper can help medical device developers identify focus areas for developing standards-compliant software. A wider study involving under-development medical devices can help improve the accuracy of our findings in the future.

Barry Dowdeswell, Roopak Sinha, Stephen G. MacDonell

Integrating the design and creation of fault identification and diagnostic capabilities into Model-Driven Development methodologies is one approach to enhancing the resilience of Industrial Cyber-Physical Systems. We present a Fault Diagnostic Engine designed to recognise and diagnose faults in IEC 61499 Function Block Applications. Using diagnostic agents that interact directly with the target application, we demonstrate fault monitoring and analysis techniques and as well as failure scenario intervention. By designing and building fault diagnostic resources during early phases of Model-Driven Development, both iterative testing and long-term fault management capabilities can be created. While applying and refining appropriate model artifacts, we demonstrate that the concurrent development of function blocks alongside fault management capabilities is both feasible and worthwhile.

Awais Tanveer, Roopak Sinha, Stephen G. MacDonell et al.

Programmable Logic Controllers (PLCs) execute critical control software that drives Industrial Automation and Control Systems (IACS). PLCs can become easy targets for cyber-adversaries as they are resource-constrained and are usually built using legacy, less-capable security measures. Security attacks can significantly affect system availability, which is an essential requirement for IACS. We propose a method to make PLC applications more security-aware. Based on the well-known IEC 61499 function blocks standard for developing IACS software, our method allows designers to annotate critical parts of an application during design time. On deployment, these parts of the application are automatically secured using appropriate security mechanisms to detect and prevent attacks. We present a summary of availability attacks on distributed IACS applications that can be mitigated by our proposed method. Security mechanisms are achieved using IEC 61499 Service-Interface Function Blocks (SIFBs) embedding Intrusion Detection and Prevention System (IDPS), added to the application at compile time. This method is more amenable to providing active security protection from attacks on previously unknown (zero-day) vulnerabilities. We test our solution on an IEC 61499 application executing on Wago PFC200 PLCs. Experiments show that we can successfully log and prevent attacks at the application level as well as help the application to gracefully degrade into safe mode, subsequently improving availability.

Javane Momeni, Elham Yahyaei, Razieh Janizadeh et al.

Background: Today human resources are strategic parts of the organizations and are considered as intelligent and valuable assets. Human resources are the most valuable asset of organizations that face the major problem. Research has shown that working life and personal life have an interactive and exacerbate effect on each other. Based on the vast variety of harmful effects that endanger welders health it was tried to investigate the relationship between life quality and working life quality of contract welders in a shipbuilding factory in Bandar Anzali city. Methods: In this analytical descriptive cross–a sectional study carried in 2014, 100 contract welders in the shipbuilding industry in Bandar Anzali was selected with the systematic method, and participated as contract welders. Data were collected with reliable and validated questionnaires include demographic information, life quality questionnaire (SF-36), and Walton life quality questionnaire (1975). Data were analyzed using SPSS18 software by using the Pearson correlation test. Result: %84 of contract welders were very happy with their life quality, and %60 were satisfied with working life quality. There was a significant meaningful relationship between life quality and working life quality (P<= 0.00, r= 0.4). The overall living space variable from life quality had a significant meaningful relationship with life quality score changes (P <= 0.042, r= 0.342). There wasn’t any relationship between age, degree, and job experience with life quality and working life quality. But a single job or not having a second job had a significant meaningful relationship with life quality satisfaction and working life quality (P<= 0.023, r=0.262). Conclusion: This study showed that there was a relationship between life quality and working life quality. Therefore improvement in either of these two can improve the other one and also help the health promotion of welders.

Harald Lux, Xaver Baur, Lygia Therese Budnik et al.

Abstract Background Until today, industrial sources contribute to the multifaceted contamination of environmental air. Exposure to air pollutants has the potential to initiate and promote asthma and chronic obstructive pulmonary disease (COPD). At global scale, both entities cause the majority of about 4 million annual deaths by respiratory disease. However, we identified industrial contamination as a subgroup of air pollution that may be associated with this burden and is underinvestigated in research. Therefore, the aim of this study is to investigate associations between substances industrially released into environmental air and the occurrence of asthma and COPD in the human population. Here we present the protocol for our systematic review of the current evidence. Methods The following determinations will be applied during the systematic review process and are specified in the protocol that complies with the PRISMA-P statement. Populations of children and adults, as well as outdoor workers, exposed to industrially released air pollutants are of interest. Eligible studies may include subjects as controls who are non- or less exposed to the investigated air pollutants. The outcomes new-onset asthma and/or COPD investigated with risk ratio, odds ratio, hazard ratio, incidence rate ratio, cumulative incidence, and incidence rate are eligible. We will search the electronic literature databases EMBASE, MEDLINE, and Web of Science for peer-reviewed reports of incidence studies and incidence case-control studies. After systematic sorting of initial records, included studies will be subjected to quality assessment. Data will be synthesized qualitatively and, if appropriate, quantitatively for risk ratio and odds ratio. We will maintain and provide a PRISMA report. Discussion Results of this systematic review may indicate alterations of incidence and risk of asthma and/or COPD in populations within industrial exposure radiuses including outdoor workplaces. Specific causal substances and compositions will be identified, but results will depend on the exposure assessment of the eligible studies. Our approach covers effects of industrial contributions to overall air pollution if studies reportedly attribute investigated emissions to industry. Results of this study may raise the question wether the available higher-level evidence sufficiently covers the current scale of industrial exposure scenarios and their potential harm to respiratory health. Trial registration This protocol was registered in PROSPERO, registration number CRD42020151573 .

Filipe M. de Brito, Gélson da Cruz Júnior, Enzo M. Frazzon et al.

In the current industrial revolution, additive manufacturing (AM) embodies a promising technology that can enhance the effectiveness, adaptability, and competitiveness of supply chains (SCs). Moreover, it facilitates the development of distributed SCs, thereby enhancing product availability, inventory levels, and lead time. However, the wide adoption of AM in industrial SCs creates various challenges, leading to new difficulties for SC design. In this context, this paper proposes a new design approach to AM SCs using optimization methods. More specifically, the proposed approach, comprising the p-median and mixed-integer linear programming models, considers the decision of deploying productive resources (3D printers) in specific locations of generic spare part SCs. The approach was evaluated in a real-world use case of an elevator maintenance service provider. The obtained results demonstrated the promising capabilities of the proposed design approach in managing the challenges arising from the forthcoming widespread use of 3D printers in manufacturing SCs.

Alessandro Erba, Nils Ole Tippenhauer

In recent years, a number of process-based anomaly detection schemes for Industrial Control Systems were proposed. In this work, we provide the first systematic analysis of such schemes, and introduce a taxonomy of properties that are verified by those detection systems. We then present a novel general framework to generate adversarial spoofing signals that violate physical properties of the system, and use the framework to analyze four anomaly detectors published at top security conferences. We find that three of those detectors are susceptible to a number of adversarial manipulations (e.g., spoofing with precomputed patterns), which we call Synthetic Sensor Spoofing and one is resilient against our attacks. We investigate the root of its resilience and demonstrate that it comes from the properties that we introduced. Our attacks reduce the Recall (True Positive Rate) of the attacked schemes making them not able to correctly detect anomalies. Thus, the vulnerabilities we discovered in the anomaly detectors show that (despite an original good detection performance), those detectors are not able to reliably learn physical properties of the system. Even attacks that prior work was expected to be resilient against (based on verified properties) were found to be successful. We argue that our findings demonstrate the need for both more complete attacks in datasets, and more critical analysis of process-based anomaly detectors. We plan to release our implementation as open-source, together with an extension of two public datasets with a set of Synthetic Sensor Spoofing attacks as generated by our framework.