Hasil untuk "Industrial medicine. Industrial hygiene"

Lucas Shen, Subhashni Raj, Youssef Oulhote et al.

Abstract Background Policy-relevant spatial determinants of human exposure to Perfluoroalkyl Substances (PFAS), a broad class of persistent environmental contaminants affecting pregnancy and child development, remain poorly understood because of the diversity of exposure sources. This is especially true for modern, dense urban settings, which contain less well-studied built environment-related sources, including transportation-related ground and airborne contamination. Methods We link high-resolution spatiotemporal urban land use data to longitudinal residential histories to assess determinants of individual-level blood plasma PFAS exposures in two geographically- and demographically- diverse cohorts of pregnant women in urban Singapore (n = 784 in 2009–2011; n = 384 in 2015–2017). Longitudinal repeated measures allow us to rule out socio-behavioral factors (e.g., residential segregation) as alternative explanations. Actual land use occupancies were ground-truthed through automated extraction of Google Street View data. Findings Adjusting for known predictors and within-neighborhood unobserved spatial heterogeneity, a standard deviation (SD) increase ( $$\sim$$ 10,000m $$^2$$ ) in transport facility exposure was linked to 0.11 (1.78 ng/mL), 0.16, 0.11 SD increases in residents’ perfluorobutane sulfonic acid (PFBS), perfluorobutanoic acid (PFBA), and perfluorononanoic acid (PFNA) concentrations, respectively, in the 2009 cohort. Dose-response analyses suggested that associations strengthened when transport facilities exceeded 10,000 m $$^2$$ , with residents living near $$\ge$$ 12,000 m $$^2$$ exhibiting 7.3 ng/mL higher plasma PFBS (p = 0.04), consistent with footprints from large bus depots rather than smaller petrol kiosks. Associations with different PFAS congeners were replicated in the 2015 cohort. No other land use type showed similarly consistent findings. Interpretations Transport facilities are prevalent near residences in urban settings and may be potential sources of PFAS emissions from automotive-related lubricants, parts, and materials. Our findings that exposure was robustly associated with individual-level concentration, over and above behavioral and other factors, highlight the importance of monitoring these and other urban sources of exposure.

Tom Maus, Stephan Frank, Tobias Glasmachers

Reinforcement learning (RL) is still rarely applied in industrial control, partly due to the difficulty of training reliable agents for real-world conditions. This work investigates how evolution strategies can support RL in such settings by introducing a continuous-control adaptation of an industrial sorting benchmark. The CMA-ES algorithm is used to generate high-quality demonstrations that warm-start RL agents. Results show that CMA-ES-guided initialization significantly improves stability and performance. Furthermore, the demonstration trajectories generated with the CMA-ES provide a strong oracle reference performance level, which is of interest in its own right. The study delivers a focused proof of concept for hybrid evolutionary-RL approaches and a basis for future, more complex industrial applications.

Jiahui Zhou, Dan Li, Ruibing Jin et al.

Providing reliable predictive maintenance is a critical industrial AI service essential for ensuring the high availability of manufacturing devices. Existing deep-learning methods present competitive results on such tasks but lack a general service-oriented framework to capture complex dependencies in industrial IoT sensor data. While Transformer-based models show strong sequence modeling capabilities, their direct deployment as robust AI services faces significant bottlenecks. Specifically, streaming sensor data collected in real-world service environments often exhibits multi-scale temporal correlations driven by machine working principles. Besides, the datasets available for training time-to-failure predictive services are typically limited in size. These issues pose significant challenges for directly applying existing models as robust predictive services. To address these challenges, we propose MsFormer, a lightweight Multi-scale Transformer designed as a unified AI service model for reliable industrial predictive maintenance. MsFormer incorporates a Multi-scale Sampling (MS) module and a tailored position encoding mechanism to capture sequential correlations across multi-streaming service data. Additionally, to accommodate data-scarce service environments, MsFormer adopts a lightweight attention mechanism with straightforward pooling operations instead of self-attention. Extensive experiments on real-world datasets demonstrate that the proposed framework achieves significant performance improvements over state-of-the-art methods. Furthermore, MsFormer outperforms across industrial devices and operating conditions, demonstrating strong generalizability while maintaining a highly reliable Quality of Service (QoS).

Min Li, Jinghui He, Gang Li et al.

The purpose of multimodal industrial anomaly detection is to detect complex geometric shape defects such as subtle surface deformations and irregular contours that are difficult to detect in 2D-based methods. However, current multimodal industrial anomaly detection lacks the effective use of crucial geometric information like surface normal vectors and 3D shape topology, resulting in low detection accuracy. In this paper, we propose a novel Geometric Prior-based Anomaly Detection network (GPAD). Firstly, we propose a point cloud expert model to perform fine-grained geometric feature extraction, employing differential normal vector computation to enhance the geometric details of the extracted features and generate geometric prior. Secondly, we propose a two-stage fusion strategy to efficiently leverage the complementarity of multimodal data as well as the geometric prior inherent in 3D points. We further propose attention fusion and anomaly regions segmentation based on geometric prior, which enhance the model's ability to perceive geometric defects. Extensive experiments show that our multimodal industrial anomaly detection model outperforms the State-of-the-art (SOTA) methods in detection accuracy on both MVTec-3D AD and Eyecandies datasets.

Shankar Ramharack, Rajiv Sahadeo

Electromagnetic transient (EMT) simulation is essential for analyzing sub-cycle switching phenomena in industrial power systems; however, commercial EMT platforms present significant cost barriers for smaller utilities, consultancies, and academic institutions, particularly in developing regions. This paper validates KESTREL EMT, a free and open-source electromagnetic transient solver with Python integration, through three progressive case studies involving industrial capacitor switching transients. This work investigates energization, switching resonance and VFD interactions with capacitor banks. The results demonstrate that KESTREL, when supported by appropriate circuit modeling techniques, produces EMT responses consistent with analytical predictions and established IEEE benchmarks. This work establishes a validated and reproducible methodology for conducting industrial EMT studies using freely available, open-source tools.

Yan Cao

Jacob M. Cowley, Cassandra E. Deering-Rice, John G. Lamb et al.

Abstract Background Climate change and human activities have caused the drying of marine environments around the world. An example is the Great Salt Lake in Utah, USA which is at a near record low water level. Adverse health effects have been associated with exposure to windblown dust originating from dried lakebed sediments, but mechanistic studies evaluating the health effects of these dusts are limited. Results Monitoring data and images highlight the impact of local crustal and Great Salt Lake sediment dusts on the Salt Lake Valley/Wasatch front airshed. Great Salt Lake sediment and derived PM< 3.1 (quasi-PM2.5 or qPM2.5) contained metals/salts, natural and anthropogenic chemicals, and bacteria. Exposure of mice via inhalation and oropharyngeal aspiration caused neutrophilia, increased expression of mRNA for Il6, Cxcl1, Cxcl2, and Muc5ac in the lungs, and increased IL6 and CXCL1 in bronchoalveolar lavage. Inhaled GSLD qPM2.5 caused a greater neutrophilic response than coal fly ash qPM2.5 and was more cytotoxic to human airway epithelial cells (HBEC3-KT) in vitro. Pro-inflammatory biomarker mRNA induction was replicated in vitro using HBEC3-KT and differentiated monocyte-derived (macrophage-like) THP-1 cells. In HBEC3-KT cells, IL6 and IL8 (the human analogue of Cxcl1 and Cxcl2) mRNA induction was attenuated by ethylene glycol-bis(β-aminoethyl ether)-N, N,N′,N’-tetraacetic acid (EGTA) and ruthenium red (RR) co-treatment, and by TRPV1 and TRPV3 antagonists, but less by the Toll-like Receptor-4 (TLR4) inhibitor TAK-242 and deferoxamine. Accordingly, GSLD qPM2.5 activated human TRPV1 as well as other human TRP channels. Dust from the Salton Sea playa (SSD qPM2.5) also stimulated IL6 and IL8 mRNA expression and activated TRPV1 in vitro, but inhibition by TRPV1 and V3 antagonists was dose dependent. Alternatively, responses of THP-1 cells to the Great Salt Lake and Salton Sea dusts were partially mediated by TLR4 as opposed to TRPV1. Finally, “humanized” Trpv1 N606D mice exhibited greater neutrophilia than C57Bl/6 mice following GSLD qPM2.5 inhalation. Conclusions Dust from the GSL playa and similar dried lakebeds may affect human respiratory health via activation of TRPV1, TRPV3, TLR4, and oxidative stress.

Míra Mohr, Mária Törőcsik

Background Food content on social media platforms has emerged as a powerful influence on consumer perceptions, preferences, and purchasing decisions, with growing implications for public health. Given that food preferences and eating habits are rooted in cultural background, understanding how these cultural dimensions shape digital food communication patterns represents a critical research gap. Objective This study aims to explore whether cultural value orientations are mirrored in the food content consumption patterns observed across European social media communities, and how these patterns reflect broader public health-related perceptions of food, health, and authenticity. Material and Methods A comparative quantitative and netnographic analysis was conducted on the social media profiles of food influencers from 14 European countries. The structure and thematic focus of food-related content were examined across cultural clusters. The segmentation of these cultural groups was based on the Inglehart-Welzel Cultural Map, an internationally recognized framework for analyzing cross-cultural value differentiation. Results The findings indicate culturally distinct patterns in how health is communicated through food-related content. Among food influencers from Mediterranean and Central European countries, health is predominantly communicated implicitly through homemade meals, traditional dishes, and mindful ingredient selection, rather than explicit nutritional or dietary claims. In contrast, influencers from Northern and Western European countries more frequently embed health communication within personal narratives and lifestyle-oriented content, where everyday experiences and emotional self-disclosure play a central role. Discussion and Conclusions The study demonstrates that health narratives in food communication are culturally constructed. Understanding such culturally embedded consumption behaviors contributes to more effective food communication and may support preventive health communication in online environments.

Nicolas van Larebeke, Bianca Cox, Sylvie Remy et al.

Abstract Background Near Antwerp a 3M factory has been active since 1971 emitting PFAS, mainly PFOS, in the local environment. Production of C8 compounds was stopped in 2002, production of other PFAS continued until 2024. This study aimed to examine the association between internal PFAS concentrations and thyroid hormones, sexual hormones, and pubertal development in adolescents living in the neighborhood of the factory. Methods We measured PFAS in serum of 146 female and 139 male adolescents. For males sex hormones (LH, testosterone, estradiol, progesterone, inhibin B, FSH) and SHBG were measured in serum. For males and females we assessed serum thyroid hormone levels (TSH, T3, T4 and T3/T4) and pubertal development parameters self-assessed through a standardized questionnaire. Associations between PFAS concentrations and effect biomarkers/health effects were assessed through Generalized Estimating Equations (GEE), using linear models for continuous outcomes, logistic models for binary outcomes, and proportional odds models for ordinal outcomes. Results For males LH, total and bioavailable testosterone showed significant negative associations with PFHxS and PFOA. LH and bioavailable testosterone also showed significant negative associations with other PFAS compounds. SHBG showed significant positive associations with PFDA, PFNA, PFHxS, PFOS and the sum of the linear forms of PFOS, PFOA, PFNA and PFHxS. Males’ length and growth spurt showed significant negative associations with PFOS, PFOA and PFAS sum parameters and length and growth spurt separately also with other PFAS compounds. For females growth spurt showed significant negative association with PFOA and a significant positive association with PFOS(branched). For both males and females body hair development showed significant negative associations with PFHxS, and, for males and females separately also with other PFAS compounds. For females, breast development showed significant negative associations with PFOA, pubertal development scale showed significant negative associations with PFOA, PFHxS, PFOS(linear) and the sum of 4 PFAS. For males, TSH showed a significant negative association with PFDA and FT3 showed significant positive associations with PFOA, PFOA and PFNA. For females, FT3 showed a significant negative association with PFOS(branched). Conclusion We observed significant, consistent and biologically relevant associations of PFAS serum concentrations with sex hormone and SHBG levels in male adolescents. Moreover, a significant delay in physiological processes occurring in puberty was observed in females and males. Associations with thyroid hormones differed significantly by sex

Alexander Windmann, Benedikt Stratmann, Mariya Lyashenko et al.

Industrial AI practitioners face vague robustness requirements in emerging regulations and standards but lack concrete, implementation ready protocols. This paper introduces the Industrial AI Robustness Card (IARC), a lightweight, task agnostic protocol for documenting and evaluating the robustness of AI models on industrial time series. The IARC specifies required fields and an empirical measurement and reporting protocol that combines drift monitoring, uncertainty quantification, and stress tests, and it maps these to relevant EU AI Act obligations. A soft sensor case study on a biopharmaceutical fermentation process illustrates how the IARC supports reproducible robustness evidence and continuous monitoring.

Emanuele Caruso, Alessandro Simoni, Francesco Pelosin

Synthetic dataset generation in Computer Vision, particularly for industrial applications, is still underexplored. Industrial defect segmentation, for instance, requires highly accurate labels, yet acquiring such data is costly and time-consuming. To address this challenge, we propose a novel diffusion-based pipeline for generating high-fidelity industrial datasets with minimal supervision. Our approach conditions the diffusion model on enriched bounding box representations to produce precise segmentation masks, ensuring realistic and accurately localized defect synthesis. Compared to existing layout-conditioned generative methods, our approach improves defect consistency and spatial accuracy. We introduce two quantitative metrics to evaluate the effectiveness of our method and assess its impact on a downstream segmentation task trained on real and synthetic data. Our results demonstrate that diffusion-based synthesis can bridge the gap between artificial and real-world industrial data, fostering more reliable and cost-efficient segmentation models. The code is publicly available at https://github.com/covisionlab/diffusion_labeling.

Junping Wang, Bicheng Wang, Yibo Xuea et al.

Resilience non-equilibrium measurement, the ability to maintain fundamental functionality amidst failures and errors, is crucial for scientific management and engineering applications of industrial chain. The problem is particularly challenging when the number or types of multiple co-evolution of resilience (for example, randomly placed) are extremely chaos. Existing end-to-end deep learning ordinarily do not generalize well to unseen full-feld reconstruction of spatiotemporal co-evolution structure, and predict resilience of network topology, especially in multiple chaos data regimes typically seen in real-world applications. To address this challenge, here we propose industrial brain, a human-like autonomous cognitive decision-making and planning framework integrating higher-order activity-driven neuro network and CT-OODA symbolic reasoning to autonomous plan resilience directly from observational data of global variable. The industrial brain not only understands and model structure of node activity dynamics and network co-evolution topology without simplifying assumptions, and reveal the underlying laws hidden behind complex networks, but also enabling accurate resilience prediction, inference, and planning. Experimental results show that industrial brain significantly outperforms resilience prediction and planning methods, with an accurate improvement of up to 10.8\% over GoT and OlaGPT framework and 11.03\% over spectral dimension reduction. It also generalizes to unseen topologies and dynamics and maintains robust performance despite observational disturbances. Our findings suggest that industrial brain addresses an important gap in resilience prediction and planning for industrial chain.

Zixuan Xue, Xinran Ling, Xinru Zhao et al.

Due to the abnormal structure and function of brain neural networks in special populations, such as children, elderly individuals, and individuals with mental disorders, noise exposure is more likely to have negative psychological and cognitive nonauditory effects on these individuals. There are unique and complex neural mechanisms underlying this phenomenon. For individuals with mental disorders, there are anomalies such as structural atrophy and decreased functional activation in brain regions involved in emotion and cognitive processing, such as the prefrontal cortex (PFC). Noise exposure can worsen these abnormalities in relevant brain regions, further damaging neural plasticity and disrupting normal connections and the transmission of information between the PFC and other brain areas by causing neurotransmitter imbalances. In the case of children, in a noisy environment, brain regions such as the left inferior frontal gyrus and PFC, which are involved in growth and development, are more susceptible to structural and functional changes, leading to neurodegenerative alterations. Furthermore, noise exposure can interrupt auditory processing neural pathways or impair inhibitory functions, thus hindering children’s ability to map sound to meaning in neural processes. For elderly people, age-related shrinkage of brain regions such as the PFC, as well as deficiencies in hormone, neurotransmitter, and nutrient levels, weakens their ability to cope with noise. Currently, it is feasible to propose and apply coping strategies to improve the nonauditory effects of noise exposure on special populations based on the plasticity of the human brain.

Thomas Rosenstatter, Christian Schäfer, Olaf Saßnick et al.

As Industry 4.0 and the Industrial Internet of Things continue to advance, industrial control systems are increasingly adopting IT solutions, including communication standards and protocols. As these systems become more decentralized and interconnected, a critical need for enhanced security measures arises. Threat modeling is traditionally performed in structured brainstorming sessions involving domain and security experts. Such sessions, however, often fail to provide an exhaustive identification of assets and interfaces due to the lack of a systematic approach. This is a major issue, as it leads to poor threat modeling, resulting in insufficient mitigation strategies and, lastly, a flawed security architecture. We propose a method for the analysis of assets in industrial systems, with special focus on physical threats. Inspired by the ISO/OSI reference model, a systematic approach is introduced to help identify and classify asset interfaces. This results in an enriched system model of the asset, offering a comprehensive overview visually represented as an interface tree, thereby laying the foundation for subsequent threat modeling steps. To demonstrate the proposed method, the results of its application to a programmable logic controller (PLC) are presented. In support of this, a study involving a group of 12 security experts was conducted. Additionally, the study offers valuable insights into the experts' general perspectives and workflows on threat modeling.

Sheng Tian, Xintan Zeng, Yifei Hu et al.

Graph-based patterns are extensively employed and favored by practitioners within industrial companies due to their capacity to represent the behavioral attributes and topological relationships among users, thereby offering enhanced interpretability in comparison to black-box models commonly utilized for classification and recognition tasks. For instance, within the scenario of transaction risk management, a graph pattern that is characteristic of a particular risk category can be readily employed to discern transactions fraught with risk, delineate networks of criminal activity, or investigate the methodologies employed by fraudsters. Nonetheless, graph data in industrial settings is often characterized by its massive scale, encompassing data sets with millions or even billions of nodes, making the manual extraction of graph patterns not only labor-intensive but also necessitating specialized knowledge in particular domains of risk. Moreover, existing methodologies for mining graph patterns encounter significant obstacles when tasked with analyzing large-scale attributed graphs. In this work, we introduce GraphRPM, an industry-purpose parallel and distributed risk pattern mining framework on large attributed graphs. The framework incorporates a novel edge-involved graph isomorphism network alongside optimized operations for parallel graph computation, which collectively contribute to a considerable reduction in computational complexity and resource expenditure. Moreover, the intelligent filtration of efficacious risky graph patterns is facilitated by the proposed evaluation metrics. Comprehensive experimental evaluations conducted on real-world datasets of varying sizes substantiate the capability of GraphRPM to adeptly address the challenges inherent in mining patterns from large-scale industrial attributed graphs, thereby underscoring its substantial value for industrial deployment.

Yuxuan Lin, Yang Chang, Xuan Tong et al.

In the advancement of industrial informatization, unsupervised anomaly detection technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliability of smart manufacturing. As an important branch, industrial image anomaly detection focuses on automatically identifying visual anomalies in industrial scenarios (such as product surface defects, assembly errors, and equipment appearance anomalies) through computer vision techniques. With the rapid development of Unsupervised industrial Image Anomaly Detection (UIAD), excellent detection performance has been achieved not only in RGB setting but also in 3D and multimodal (RGB and 3D) settings. However, existing surveys primarily focus on UIAD tasks in RGB setting, with little discussion in 3D and multimodal settings. To address this gap, this artical provides a comprehensive review of UIAD tasks in the three modal settings. Specifically, we first introduce the task concept and process of UIAD. We then overview the research on UIAD in three modal settings (RGB, 3D, and multimodal), including datasets and methods, and review multimodal feature fusion strategies in multimodal setting. Finally, we summarize the main challenges faced by UIAD tasks in the three modal settings, and offer insights into future development directions, aiming to provide researchers with a comprehensive reference and offer new perspectives for the advancement of industrial informatization. Corresponding resources are available at https://github.com/Sunny5250/Awesome-Multi-Setting-UIAD.

Tanureza Jaya, Benjamin Michalak, Marcel Radke et al.

Cabling tasks (pulling, clipping, and plug insertion) are today mostly manual work, limiting the cost-effectiveness of electrification. Feasibility for the robotic grasping and insertion of plugs, as well as the manipulation of cables, have been shown in research settings. However, in many industrial tasks the complete process from picking, insertion, routing, and validation must be solved with one system. This often means the cable must be directly manipulated for routing, and the plug must be manipulated for insertion, often in cluttered environments with tight space constraints. Here we introduce an analysis of the complete industrial cabling tasks and demonstrate a solution from grasp, plug insertion, clipping, and final plug insertion. Industrial requirements are summarized, considering the space limitations, tolerances, and possible ways that the cabling process can be integrated into the production process. This paper proposes gripper designs and general robotic assembly methods for the widely used FASTON and a cubical industrial connector. The proposed methods cover the cable gripping, handling, routing, and inserting processes of the connector. Customized grippers are designed to ensure the reliable gripping of the plugs and the pulling and manipulation of the cable segments. A passive component to correct the cable orientation is proposed, allowing the robot to re-grip the plug before insertion. In general, the proposed method can perform cable assembly with mere position control, foregoing complex control approaches. This solution is demonstrated with an industrial product with realistic space requirements and tolerances, identifying difficult aspects of current cabling scenarios and potential to improve the automation-friendliness in the product design.

Ali Beikmohammadi, Mohammad Hosein Hamian, Neda Khoeyniha et al.

The rapid influx of data-driven models into the industrial sector has been facilitated by the proliferation of sensor technology, enabling the collection of vast quantities of data. However, leveraging these models for failure detection and prognosis poses significant challenges, including issues like missing values and class imbalances. Moreover, the cost sensitivity associated with industrial operations further complicates the application of conventional models in this context. This paper introduces a novel cost-sensitive transformer model developed as part of a systematic workflow, which also integrates a hybrid resampler and a regression-based imputer. After subjecting our approach to rigorous testing using the APS failure dataset from Scania trucks and the SECOM dataset, we observed a substantial enhancement in performance compared to state-of-the-art methods. Moreover, we conduct an ablation study to analyze the contributions of different components in our proposed method. Our findings highlight the potential of our method in addressing the unique challenges of failure prediction in industrial settings, thereby contributing to enhanced reliability and efficiency in industrial operations.

Akos Nagy, Yannis Spyridis, Gregory J Mills et al.

The paper introduces an innovative approach to industrial maintenance leveraging augmented reality (AR) technology, focusing on enhancing the user experience and efficiency. The shift from traditional to proactive maintenance strategies underscores the significance of maintenance in industrial systems. The proposed solution integrates AR interfaces, particularly through Head-Mounted Display (HMD) devices, to provide expert personnel-aided decision support for maintenance technicians, with the association of Artificial Intelligence (AI) solutions. The study explores the user experience aspect of AR interfaces in a simulated industrial environment, aiming to improve the maintenance processes' intuitiveness and effectiveness. Evaluation metrics such as the NASA Task Load Index (NASA-TLX) and the System Usability Scale (SUS) are employed to assess the usability, performance, and workload implications of the AR maintenance system. Additionally, the paper discusses the technical implementation, methodology, and results of experiments conducted to evaluate the effectiveness of the proposed solution.

Philku Lee, Jin Kwon Kim, Mi Seong Jo et al.

Abstract Background Toxicokinetics of nanomaterials, including studies on the absorption, distribution, metabolism, and elimination of nanomaterials, are essential in assessing their potential health effects. The fate of nanomaterials after inhalation exposure to multiple nanomaterials is not clearly understood. Methods Male Sprague–Dawley rats were exposed to similar sizes of silver nanoparticles (AgNPs, 10.86 nm) and gold nanoparticles (AuNPs, 10.82 nm) for 28 days (6-h/day, 5-days/week for four weeks) either with separate NP inhalation exposures or with combined co-exposure in a nose-only inhalation system. Mass concentrations sampled from the breathing zone were AuNP 19.34 ± 2.55 μg/m3 and AgNP 17.38 ± 1.88 μg/m3 for separate exposure and AuNP 8.20 μg/m3 and AgNP 8.99 μg/m3 for co-exposure. Lung retention and clearance were previously determined on day 1 (6-h) of exposure (E-1) and on post-exposure days 1, 7, and 28 (PEO-1, PEO-7, and PEO-28, respectively). In addition, the fate of nanoparticles, including translocation and elimination from the lung to the major organs, were determined during the post-exposure observation period. Results AuNP was translocated to the extrapulmonary organs, including the liver, kidney, spleen, testis, epididymis, olfactory bulb, hilar and brachial lymph nodes, and brain after subacute inhalation and showed biopersistence regardless of AuNP single exposure or AuNP + AgNP co-exposure, showing similar elimination half-time. In contrast, Ag was translocated to the tissues and rapidly eliminated from the tissues regardless of AuNP co-exposure. Ag was continually accumulated in the olfactory bulb and brain and persistent until PEO-28. Conclusion Our co-exposure study of AuNP and AgNP indicated that soluble AgNP and insoluble AuNP translocated differently, showing soluble AgNP could be dissolved into Ag ion to translocate to the extrapulmonary organs and rapidly removed from most organs except the brain and olfactory bulb. Insoluble AuNPs were continually translocated to the extrapulmonary organs, and they were not eliminated rapidly.