Hasil untuk "Industrial medicine. Industrial hygiene"

G. Crini, E. Lichtfouse, Gilles Chanet et al.

Lequn Fu, Yijun Zhong, Xiao Li et al.

Humanoid robots deployed in industrial environments are required to perform load-carrying transportation tasks that tightly couple locomotion and manipulation. However, achieving stable and robust locomotion under varying payloads and upper-body motions is challenging due to dynamic coupling and partial observability. This paper presents a load-aware locomotion framework for industrial humanoids based on a decoupled yet coordinated loco-manipulation architecture. Lower-body locomotion is controlled via a reinforcement learning policy producing residual joint actions on kinematically derived nominal configurations. A kinematics-based locomotion reference with a height-conditioned joint-space offset guides learning, while a history-based state estimator infers base linear velocity and height and encodes residual load- and manipulation-induced disturbances in a compact latent representation. The framework is trained entirely in simulation and deployed on a full-size humanoid robot without fine-tuning. Simulation and real-world experiments demonstrate faster training, accurate height tracking, and stable loco-manipulation. Project page: https://lequn-f.github.io/LALO/

Jiayu Cao, Yuhui Yang, Xi Liu et al.

Abstract Background The advancement of nanotechnology underscores the imperative need for establishing in silico predictive models to assess safety, particularly in the context of chronic respiratory afflictions such as lung fibrosis, a pathogenic transformation that is irreversible. While the compilation of predictive descriptors is pivotal for in silico model development, key features specifically tailored for predicting lung fibrosis remain elusive. This study aimed to uncover the essential predictive descriptors governing nanoparticle-induced pulmonary fibrosis. Methods We conducted a comprehensive analysis of the trajectory of metal oxide nanoparticles (MeONPs) within pulmonary systems. Two biological media (simulated lung fluid and phagolysosomal simulated fluid) and two cell lines (macrophages and epithelial cells) were meticulously chosen to scrutinize MeONP behaviors. Their interactions with MeONPs, also referred to as nano-bio interactions, can lead to alterations in the properties of the MeONPs as well as specific cellular responses. Physicochemical properties of MeONPs were assessed in biological media. The impact of MeONPs on cell membranes, lysosomes, mitochondria, and cytoplasmic components was evaluated using fluorescent probes, colorimetric enzyme substrates, and ELISA. The fibrogenic potential of MeONPs in mouse lungs was assessed by examining collagen deposition and growth factor release. Random forest classification was employed for analyzing in chemico, in vitro and in vivo data to identify predictive descriptors. Results The nano-bio interactions induced diverse changes in the 4 characteristics of MeONPs and had variable effects on the 14 cellular functions, which were quantitatively evaluated in chemico and in vitro. Among these 18 quantitative features, seven features were found to play key roles in predicting the pro-fibrogenic potential of MeONPs. Notably, IL-1β was identified as the most important feature, contributing 27.8% to the model’s prediction. Mitochondrial activity (specifically NADH levels) in macrophages followed closely with a contribution of 17.6%. The remaining five key features include TGF-β1 release and NADH levels in epithelial cells, dissolution in lysosomal simulated fluids, zeta potential, and the hydrodynamic size of MeONPs. Conclusions The pro-fibrogenic potential of MeONPs can be predicted by combination of key features at nano-bio interfaces, simulating their behavior and interactions within the lung environment. Among the 18 quantitative features, a combination of seven in chemico and in vitro descriptors could be leveraged to predict lung fibrosis in animals. Our findings offer crucial insights for developing in silico predictive models for nano-induced pulmonary fibrosis.

Wenting Cai, Jiaping Wang

Objective: This paper aims to evaluate the preventive effects of light music on postpartum anxiety and depression in primiparous women. Methods: This retrospective study analyzed 120 primiparous women admitted to our hospital from June 2022 to June 2024. Participants were divided into two groups based on the postpartum nursing methods they received: those who received standard care (standard care group: n = 58) and those who received light music therapy (music therapy group: n = 62). Outcomes included Edinburgh Postnatal Depression Scale (EPDS), Perinatal Anxiety Screening Scale (PASS), Pittsburgh Sleep Quality Index (PSQI), salivary cortisol, salivary alpha-amylase (sAA), exclusive breastfeeding (EBF) rates, and World Health Organization Quality of Life Brief Version (WHOQOL-BREF) scale scores at 3-day and 6-week postpartum. Results: At 6 weeks postpartum, the music therapy group showed significantly lower scores of EPDS, PASS, and PSQI compared to the standard care group (P < 0.05). Salivary cortisol and sAA levels were also significantly reduced (P < 0.05). Additionally, the music therapy group exhibited higher EBF rates (79.03% vs. 53.45%, P < 0.05) and significantly improved scores across all domains of WHOQOL-BREF (P < 0.05). Conclusion Light music therapy significantly alleviates postpartum psychological distress, reduces physiological stress, and improves breastfeeding rate and quality of life, which supports its clinical adoption.

Djihad Bencherit, Naila Charbi, Asma Saad et al.

Background Children are generally attracted to colorful foods. However, some food dyes are suspected of exacerbating the activity of children and inducing other health problems that can reach reprotoxicity and carcinogenicity. Objective This study aims to explore the presence of dyes such as E102, E104, E110, E121, E122, E123, E124, E127, E129, E132, E133, E143 and E171 in food products widely consumed by children in Algeria notably sweets and chocolates, beverages and ice creams, yogurts and biscuits. Material and Methods This work was carried out on 228 products including 57 biscuits, 47 drinks and ice creams, 20 yogurts and 104 sweets and chocolates. Information mentioned on the composition label of this products were recorded to determine the presence of studied dyes Results Here, we report the abundance of the yellow dyes E102 (24.1%) and E110 (18%) in the tested products. Also, apart from E121, all the other assessed dyes were found. Sweets and chocolates are the products containing the most studied dyes. The analysis of the presence of combinations of these dyes shows that 7% of analyzed foods contain 2 dyes in their composition while 20% of the products contain at least 3 dyes at the same time. Additionally, 37.5% of sweets and chocolates contain a combination of at least 3 dyes in their ingredient list. Conclusions In overall, except the E121, all assessed dyes were identified on the labels of food products widely consumed by children which encourage parents to be made aware of the risks associated with the ingestion of omnipresent dyes in children’s diets.

Youli Wang, Yubin Zhang, Anrong Fu et al.

This study aims to construct and evaluate an index system for assessing property service quality following the adoption of logistics socialized outsourcing in public hospitals, providing a reference for evaluating the service quality of outsourced property companies. Based on the SERVQUAL model (also known as the gap model) and combined with literature analysis and expert interviews, a preliminary evaluation index system was established. The Delphi expert consultation method and analytic hierarchy process (AHP) were employed to screen the indices according to expert opinions and scoring results. An evaluation system including five first-level indicators of tangibility, reliability, responsiveness, assurance and empathy and 22 second-level indicators was established. The expert authority was high (Cr=0.914), and the opinions were well coordinated (W=0.47, P<0.05). The constructed evaluation index system for property service quality in public hospitals is scientific and feasible, offering a theoretical reference for accelerating the transformation and upgrading of logistics service levels in public hospitals and enhancing their performance evaluation.

Shuai Li, Chen Yizhe, Li Dong et al.

The application of artificial intelligence (AI) in industry is accelerating the shift from traditional automation to intelligent systems with perception and cognition. Vision language-action (VLA) models have been a key paradigm in AI to unify perception, reasoning, and control. Has the performance of the VLA models met the industrial requirements? In this paper, from the perspective of industrial deployment, we compare the performance of existing state-of-the-art VLA models in industrial scenarios and analyze the limitations of VLA models for real-world industrial deployment from the perspectives of data collection and model architecture. The results show that the VLA models retain their ability to perform simple grasping tasks even in industrial settings after fine-tuning. However, there is much room for performance improvement in complex industrial environments, diverse object categories, and high precision placing tasks. Our findings provide practical insight into the adaptability of VLA models for industrial use and highlight the need for task-specific enhancements to improve their robustness, generalization, and precision.

Jaxson Brown, Duc-Son Pham, Sie-Teng Soh et al.

Industrial Control Systems (ICSs) are complex interconnected systems used to manage process control within industrial environments, such as chemical processing plants and water treatment facilities. As the modern industrial environment moves towards Internet-facing services, ICSs face an increased risk of attacks that necessitates ICS-specific Intrusion Detection Systems (IDS). The development of such IDS relies significantly on a simulated testbed as it is unrealistic and sometimes hazardous to utilize an operational control system. Whilst some testbeds have been proposed, they often use a limited selection of virtual ICS simulations to test and verify cyber security solutions. There is a lack of investigation done on developing systems that can efficiently simulate multiple ICS architectures. Currently, the trend within research involves developing security solutions on just one ICS simulation, which can result in bias to its specific architecture. We present ICS-SimLab, an end-to-end software suite that utilizes Docker containerization technology to create a highly configurable ICS simulation environment. This software framework enables researchers to rapidly build and customize different ICS environments, facilitating the development of security solutions across different systems that adhere to the Purdue Enterprise Reference Architecture. To demonstrate its capability, we present three virtual ICS simulations: a solar panel smart grid, a water bottle filling facility, and a system of intelligent electronic devices. Furthermore, we run cyber-attacks on these simulations and construct a dataset of recorded malicious and benign network traffic to be used for IDS development.

Tom Maus, Asma Atamna, Tobias Glasmachers

Autonomous control of multi-stage industrial processes requires both local specialization and global coordination. Reinforcement learning (RL) offers a promising approach, but its industrial adoption remains limited due to challenges such as reward design, modularity, and action space management. Many academic benchmarks differ markedly from industrial control problems, limiting their transferability to real-world applications. This study introduces an enhanced industry-inspired benchmark environment that combines tasks from two existing benchmarks, SortingEnv and ContainerGym, into a sequential recycling scenario with sorting and pressing operations. We evaluate two control strategies: a modular architecture with specialized agents and a monolithic agent governing the full system, while also analyzing the impact of action masking. Our experiments show that without action masking, agents struggle to learn effective policies, with the modular architecture performing better. When action masking is applied, both architectures improve substantially, and the performance gap narrows considerably. These results highlight the decisive role of action space constraints and suggest that the advantages of specialization diminish as action complexity is reduced. The proposed benchmark thus provides a valuable testbed for exploring practical and robust multi-agent RL solutions in industrial automation, while contributing to the ongoing debate on centralization versus specialization.

Tom Maus, Nico Zengeler, Tobias Glasmachers

We present a novel reinforcement learning (RL) environment designed to both optimize industrial sorting systems and study agent behavior in evolving spaces. In simulating material flow within a sorting process our environment follows the idea of a digital twin, with operational parameters like belt speed and occupancy level. To reflect real-world challenges, we integrate common upgrades to industrial setups, like new sensors or advanced machinery. It thus includes two variants: a basic version focusing on discrete belt speed adjustments and an advanced version introducing multiple sorting modes and enhanced material composition observations. We detail the observation spaces, state update mechanisms, and reward functions for both environments. We further evaluate the efficiency of common RL algorithms like Proximal Policy Optimization (PPO), Deep-Q-Networks (DQN), and Advantage Actor Critic (A2C) in comparison to a classical rule-based agent (RBA). This framework not only aids in optimizing industrial processes but also provides a foundation for studying agent behavior and transferability in evolving environments, offering insights into model performance and practical implications for real-world RL applications.

Jinwei Hu, Zezhi Tang, Xin Jin et al.

This paper presents HERO (Hierarchical Testing with Rabbit Optimization), a novel black-box adversarial testing framework for evaluating the robustness of deep learning-based Prognostics and Health Management systems in Industrial Cyber-Physical Systems. Leveraging Artificial Rabbit Optimization, HERO generates physically constrained adversarial examples that align with real-world data distributions via global and local perspective. Its generalizability ensures applicability across diverse ICPS scenarios. This study specifically focuses on the Proton Exchange Membrane Fuel Cell system, chosen for its highly dynamic operational conditions, complex degradation mechanisms, and increasing integration into ICPS as a sustainable and efficient energy solution. Experimental results highlight HERO's ability to uncover vulnerabilities in even state-of-the-art PHM models, underscoring the critical need for enhanced robustness in real-world applications. By addressing these challenges, HERO demonstrates its potential to advance more resilient PHM systems across a wide range of ICPS domains.

Keno Moenck, Duc Trung Thieu, Julian Koch et al.

In recent years, the upstream of Large Language Models (LLM) has also encouraged the computer vision community to work on substantial multimodal datasets and train models on a scale in a self-/semi-supervised manner, resulting in Vision Foundation Models (VFM), as, e.g., Contrastive Language-Image Pre-training (CLIP). The models generalize well and perform outstandingly on everyday objects or scenes, even on downstream tasks, tasks the model has not been trained on, while the application in specialized domains, as in an industrial context, is still an open research question. Here, fine-tuning the models or transfer learning on domain-specific data is unavoidable when objecting to adequate performance. In this work, we, on the one hand, introduce a pipeline to generate the Industrial Language-Image Dataset (ILID) based on web-crawled data; on the other hand, we demonstrate effective self-supervised transfer learning and discussing downstream tasks after training on the cheaply acquired ILID, which does not necessitate human labeling or intervention. With the proposed approach, we contribute by transferring approaches from state-of-the-art research around foundation models, transfer learning strategies, and applications to the industrial domain.

Imran Nasim, Joaõ Lucas de Sousa Almeida

Scientific Machine Learning is transforming traditional engineering industries by enhancing the efficiency of existing technologies and accelerating innovation, particularly in modeling chemical reactions. Despite recent advancements, the issue of solving stiff chemically reacting problems within computational fluid dynamics remains a significant issue. In this study we propose a novel approach utilizing a multi-layer-perceptron mixer architecture (MLP-Mixer) to model the time-series of stiff chemical kinetics. We evaluate this method using the ROBER system, a benchmark model in chemical kinetics, to compare its performance with traditional numerical techniques. This study provides insight into the industrial utility of the recently developed MLP-Mixer architecture to model chemical kinetics and provides motivation for such neural architecture to be used as a base for time-series foundation models.

Ana Cabanillas-Franco, Alba Hernández-Blázquez, Raquel Mendoza-Aragón et al.

Resumen Objetivo: Estimar el grado de alfabetización para la salud (AS) de las técnicas en cuidados auxiliares de enfermería (TCAE) y del personal no sanitario perteneciente a la plantilla laboral de 4 hospitales españoles. Método: Estudio descriptivo-correlacional, siendo la población diana las TCAE a quienes se preguntó, mediante formulario online HLS-EU-Q16 (Health Literacy Survey - European Union), con escala Likert de 4 valores (de 1 muy fácil a 4 muy difícil). Resultados: Respondieron 477 profesionales. Los resultados para la AS global fueron: media 1,95 ± 0,03 y mediana 1,94. El nivel de alfabetización en salud (NAS) demostró AS suficiente en 293 (61,43%) individuos. La mediana sobre la AS de los 3 componentes del cuestionario fue: cuidado sanitario = 2, prevención de enfermedades = 2 y promoción de la salud = 1,75. El ítem sobre sobre la manera de abordar problemas de salud mental es el que presentó mayor dificultad con media de 2,45 ± 0,04 y mediana igual a 2. No hubo diferencias significativas entre TCAE y el personal no sanitario. Conclusiones: El grado de AS de las TCAE resultó ser bueno, tanto a nivel global como en las dimensiones de atención y cuidado sanitario, prevención de enfermedades y promoción de la salud. Asimismo, el nivel de alfabetización en salud que se obtuvo resultó ser suficiente en la mayoría de ellas. No se encontró diferencias con el personal no sanitario perteneciente a la plantilla laboral de 4 hospitales estudiados.

Sepideh Bahadoripour, Ethan MacDonald, Hadis Karimipour

The growing number of cyber-attacks against Industrial Control Systems (ICS) in recent years has elevated security concerns due to the potential catastrophic impact. Considering the complex nature of ICS, detecting a cyber-attack in them is extremely challenging and requires advanced methods that can harness multiple data modalities. This research utilizes network and sensor modality data from ICS processed with a deep multi-modal cyber-attack detection model for ICS. Results using the Secure Water Treatment (SWaT) system show that the proposed model can outperform existing single modality models and recent works in the literature by achieving 0.99 precision, 0.98 recall, and 0.98 f-measure, which shows the effectiveness of using both modalities in a combined model for detecting cyber-attacks.

Tal Michael, Elkana Kohn, Sharon Daniel et al.

Abstract Background Numerous studies have suggested significant associations between prenatal exposure to heavy metals and newborn anthropometric measures. However, little is known about the effect of various heavy metal mixtures at relatively low concentrations. Hence, this study aimed to investigate associations between prenatal exposures to a wide range of individual heavy metals and heavy metal mixtures with anthropometric measures of newborns. Methods We recruited 975 mother–term infant pairs from two major hospitals in Israel. Associations between eight heavy metals (arsenic, cadmium, chromium, mercury, nickel, lead, selenium, and thallium) detected in maternal urine samples on the day of delivery with weight, length, and head circumference at birth were estimated using linear and Bayesian kernel machine regression (BKMR) models. Results Most heavy metals examined in our study were observed in lower concentrations than in other studies, except for selenium. In the linear as well as the BKMR models, birth weight and length were negatively associated with levels of chromium. Birth weight was found to be negatively associated with thallium and positively associated with nickel. Conclusion By using a large sample size and advanced statistical models, we could examine the association between prenatal exposure to metals in relatively low concentrations and anthropometric measures of newborns. Chromium was suggested to be the most influential metal in the mixture, and its associations with birth weight and length were found negative. Head circumference was neither associated with any of the metals, yet the levels of metals detected in our sample were relatively low. The suggested associations should be further investigated and could shed light on complex biochemical processes involved in intrauterine fetal development.

Kapeena Sivakumaran, Jennifer A Ritonja, Haya Waseem et al.

Background: Exposure to acute noise can cause an increase in biological stress reactions, which provides biological plausibility for a potential association between sustained noise exposure and stress-related health effects. However, the certainty in the evidence for an association between exposures to noise on short- and long-term biomarkers of stress has not been widely explored. The objective of this review was to evaluate the strength of evidence between noise exposure and changes in the biological parameters known to contribute to the development of stress-related adverse cardiovascular responses. Materials and Methods: This systematic review comprises English language comparative studies available in PubMed, Cochrane Central, EMBASE, and CINAHL databases from January 1, 1980 to December 29, 2021. Where possible, random-effects meta-analyses were used to examine the effect of noise exposure from various sources on stress-related cardiovascular biomarkers. The risk of bias of individual studies was assessed using the risk of bias of nonrandomized studies of exposures instrument. The certainty of the body of evidence for each outcome was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach. Results: The search identified 133 primary studies reporting on blood pressure, hypertension, heart rate, cardiac arrhythmia, vascular resistance, and cardiac output. Meta-analyses of blood pressure, hypertension, and heart rate suggested there may be signals of increased risk in response to a higher noise threshold or incrementally higher levels of noise. Across all outcomes, the certainty of the evidence was very low due to concerns with the risk of bias, inconsistency across exposure sources, populations, and studies and imprecision in the estimates of effects. Conclusions: This review identifies that exposure to higher levels of noise may increase the risk of some short- and long-term cardiovascular events; however, the certainty of the evidence was very low. This likely represents the inability to compare across the totality of the evidence for each outcome, underscoring the value of continued research in this area. Findings from this review may be used to inform policies of noise reduction or mitigation interventions.

{Nurul Huda Mahmood, Gilberto Berardinelli, Emil J. Khatib et al.

Future industrial applications will encompass compelling new use cases requiring stringent performance guarantees over multiple key performance indicators (KPI) such as reliability, dependability, latency, time synchronization, security, etc. Achieving such stringent and diverse service requirements necessitates the design of a special-purpose Industrial Internet of Things (IIoT) network comprising a multitude of specialized functionalities and technological enablers. This article proposes an innovative architecture for such a special-purpose 6G IIoT network incorporating seven functional building blocks categorized into: special-purpose functionalities and enabling technologies. The former consists of Wireless Environment Control, Traffic/Channel Prediction, Proactive Resource Management and End-to-End Optimization functions; whereas the latter includes Synchronization and Coordination, Machine Learning and Artificial Intelligence Algorithms, and Auxiliary Functions. The proposed architecture aims at providing a resource-efficient and holistic solution for the complex and dynamically challenging requirements imposed by future 6G industrial use cases. Selected test scenarios are provided and assessed to illustrate cross-functional collaboration and demonstrate the applicability of the proposed architecture in a wireless IIoT network.

Rodrigo Hernangómez, Alexandros Palaios, Cara Watermann et al.

This paper presents two wireless measurement campaigns in industrial testbeds: industrial Vehicle-to-vehicle (iV2V) and industrial Vehicle-to-infrastructure plus Sensor (iV2I+), together with detailed information about the two captured datasets. iV2V covers sidelink communication scenarios between Automated Guided Vehicles (AGVs), while iV2I+ is conducted at an industrial setting where an autonomous cleaning robot is connected to a private cellular network. The combination of different communication technologies within a common measurement methodology provides insights that can be exploited by Machine Learning (ML) for tasks such as fingerprinting, line-of-sight detection, prediction of quality of service or link selection. Moreover, the datasets are publicly available, labelled and prefiltered for fast on-boarding and applicability.

Stephen Stansfeld, Charlotte Clark, Melanie Smuk et al.

Abstract Background Both physical and psychological health outcomes have been associated with exposure to environmental noise. Noise sensitivity could have the same moderating effect on physical and psychological health outcomes related to environmental noise exposure as on annoyance but this has been little tested. Methods A cohort of 2398 men between 45 and 59 years, the longitudinal Caerphilly Collaborative Heart Disease study, was established in 1984/88 and followed into the mid-1990s. Road traffic noise maps were assessed at baseline. Psychological ill-health was measured in phase 2 in 1984/88, phase 3 (1989/93) and phase 4 (1993/7). Ischaemic heart disease was measured in clinic at baseline and through hospital records and records of deaths during follow up. We examined the longitudinal associations between road traffic noise and ischaemic heart disease morbidity and mortality using Cox Proportional Hazard Models and psychological ill-health using Logistic Regression; we also examined whether noise sensitivity and noise annoyance might moderate these associations. We also tested if noise sensitivity and noise annoyance were longitudinal predictors of ischaemic heart disease morbidity and mortality and psychological ill-health. Results Road traffic noise was not associated with ischaemic heart disease morbidity or mortality. Neither noise sensitivity nor noise annoyance moderated the effects of road traffic noise on ischaemic heart disease morbidity or mortality. High noise sensitivity was associated with lower ischaemic heart disease mortality risk (HR = 0.74, 95%CI 0.57, 0.97). Road traffic noise was associated with Phase 4 psychological ill-health but only among those exposed to 56-60dBA (fully adjusted OR = 1.82 95%CI 1.07, 3.07). Noise sensitivity moderated the association of road traffic noise exposure with psychological ill-health. High noise sensitivity was associated longitudinally with psychological ill-health at phase 3 (OR = 1.85 95%CI 1.23, 2.78) and phase 4 (OR = 1.65 95%CI 1.09, 2.50). Noise annoyance predicted psychological ill-health at phase 4 (OR = 2.47 95%CI 1.00, 6.13). Conclusions Noise sensitivity is a specific predictor of psychological ill-health and may be part of a wider construct of environmental susceptibility. Noise sensitivity may increase the risk of psychological ill-health when exposed to road traffic noise. Noise annoyance may be a mediator of the effects of road traffic noise on psychological ill-health.