Hasil untuk "Industrial hygiene. Industrial welfare"

Yogha Restu Pramadi, Theodoros Spyridopoulos, Vijay Kumar

Research on Advanced Persistent Threats (APTs) in industrial environments requires experimental platforms that support realistic end-to-end attack emulation across converged enterprise IT, operational technology (OT), and Industrial Internet of Things (IIoT) networks. However, existing industrial cybersecurity testbeds typically focus on isolated IT or OT domains or single-stage attacks, limiting their suitability for studying multi-stage APT campaigns. This paper presents the design, implementation, and validation of SIMPLE-ICS, a virtualised industrial enterprise testbed that enables emulation of multi-stage APT campaigns across IT, OT, and IIoT environments. The testbed architecture is based on the Purdue Enterprise Reference Architecture, NIST SP 800-82, and IEC 62443 zoning principles and integrates enterprise services, industrial control protocols, and digital twin based process simulation. A systematic methodology inspired by the V model is used to derive architectural requirements, attack scenarios, and validation criteria. An APT campaign designed to mimic the BlackEnergy campaign is emulated using MITRE ATTACK techniques spanning initial enterprise compromise, credential abuse, lateral movement, OT network infiltration, and process manipulation. The testbed supports the synchronised collection of network traffic, host-level logs, and operational telemetry across all segments. The testbed is validated on multi-stage attack trace observability, logging completeness across IT, OT, and IIoT domains, and repeatable execution of APT campaigns. The SIMPLE-ICS testbed provides an experimental platform for studying end-to-end APT behaviours in industrial enterprise networks and for generating multi-source datasets to support future research on campaign-level detection and correlation methods.

Monica Marconi Sciarroni, Emanuele Storti

Industrial IoT ecosystems bring together sensors, machines and smart devices operating collaboratively across industrial environments. These systems generate large volumes of heterogeneous, high-velocity data streams that require interoperable, secure and contextually aware management. Most of the current stream management architectures, however, still rely on syntactic integration mechanisms, which result in limited flexibility, maintainability and interpretability in complex Industry 5.0 scenarios. This work proposes a context-aware semantic platform for data stream management that unifies heterogeneous IoT/IoE data sources through a Knowledge Graph enabling formal representation of devices, streams, agents, transformation pipelines, roles and rights. The model supports flexible data gathering, composable stream processing pipelines, and dynamic role-based data access based on agents' contexts, relying on Apache Kafka and Apache Flink for real-time processing, while SPARQL and SWRL-based reasoning provide context-dependent stream discovery. Experimental evaluations demonstrate the effectiveness of combining semantic models, context-aware reasoning and distributed stream processing to enable interoperable data workflows for Industry 5.0 environments.

Haoran Yang, Yinan Zhang, Wenjie Zhang et al.

Nowadays, industrial hybrid modeling which integrates both mechanistic modeling and machine learning-based modeling techniques has attracted increasing interest from scholars due to its high accuracy, low computational cost, and satisfactory interpretability. Nevertheless, the existing industrial hybrid modeling methods still face two main limitations. First, current research has mainly focused on applying a single machine learning method to one specific task, failing to develop a comprehensive machine learning architecture suitable for modeling tasks, which limits their ability to effectively represent complex industrial scenarios. Second, industrial datasets often contain underlying associations (e.g., monotonicity or periodicity) that are not adequately exploited by current research, which can degrade model's predictive performance. To address these limitations, this paper proposes the Recurrent Perceptron-based Channel Attention Transformer Encoder (RP-CATE), with three distinctive characteristics: 1: We developed a novel architecture by replacing the self-attention mechanism with channel attention and incorporating our proposed Recurrent Perceptron (RP) Module into Transformer, achieving enhanced effectiveness for industrial modeling tasks compared to the original Transformer. 2: We proposed a new data type called Pseudo-Image Data (PID) tailored for channel attention requirements and developed a cyclic sliding window method for generating PID. 3: We introduced the concept of Pseudo-Sequential Data (PSD) and a method for converting industrial datasets into PSD, which enables the RP Module to capture the underlying associations within industrial dataset more effectively. An experiment aimed at hybrid modeling in chemical engineering was conducted by using RP-CATE and the experimental results demonstrate that RP-CATE achieves the best performance compared to other baseline models.

Padmeswari Nandiya, Ahmad Mohsin, Ahmed Ibrahim et al.

Industry 5.0's increasing integration of IT and OT systems is transforming industrial operations but also expanding the cyber-physical attack surface. Industrial Control Systems (ICS) face escalating security challenges as traditional siloed defences fail to provide coherent, cross-domain threat insights. We present BRIDG-ICS (BRIDge for Industrial Control Systems), an AI-driven Knowledge Graph (KG) framework for context-aware threat analysis and quantitative assessment of cyber resilience in smart manufacturing environments. BRIDG-ICS fuses heterogeneous industrial and cybersecurity data into an integrated Industrial Security Knowledge Graph linking assets, vulnerabilities, and adversarial behaviours with probabilistic risk metrics (e.g. exploit likelihood, attack cost). This unified graph representation enables multi-stage attack path simulation using graph-analytic techniques. To enrich the graph's semantic depth, the framework leverages Large Language Models (LLMs): domain-specific LLMs extract cybersecurity entities, predict relationships, and translate natural-language threat descriptions into structured graph triples, thereby populating the knowledge graph with missing associations and latent risk indicators. This unified AI-enriched KG supports multi-hop, causality-aware threat reasoning, improving visibility into complex attack chains and guiding data-driven mitigation. In simulated industrial scenarios, BRIDG-ICS scales well, reduces potential attack exposure, and can enhance cyber-physical system resilience in Industry 5.0 settings.

Jiin Choi, Yugyeong Jang, Kyung Hoon Hyun

AI-driven multimodal interfaces have the potential to revolutionize industrial 3D CAD modeling by improving workflow efficiency and user experience. However, the integration of these technologies remains challenging due to software constraints, user adoption barriers, and limitations in AI model adaptability. This paper explores the role of multimodal AI in CAD environments, examining its current applications, key challenges, and future research directions. We analyze Bayesian workflow inference, multimodal input strategies, and collaborative AI-driven interfaces to identify areas where AI can enhance CAD design processes while addressing usability concerns in industrial manufacturing settings.

Moritz Mock, Thomas Forrer, Barbara Russo

Deep learning solutions for vulnerability detection proposed in academic research are not always accessible to developers, and their applicability in industrial settings is rarely addressed. Transferring such technologies from academia to industry presents challenges related to trustworthiness, legacy systems, limited digital literacy, and the gap between academic and industrial expertise. For deep learning in particular, performance and integration into existing workflows are additional concerns. In this work, we first evaluate the performance of CodeBERT for detecting vulnerable functions in industrial and open-source software. We analyse its cross-domain generalisation when fine-tuned on open-source data and tested on industrial data, and vice versa, also exploring strategies for handling class imbalance. Based on these results, we develop AI-DO(Automating vulnerability detection Integration for Developers' Operations), a Continuous Integration-Continuous Deployment (CI/CD)-integrated recommender system that uses fine-tuned CodeBERT to detect and localise vulnerabilities during code review without disrupting workflows. Finally, we assess the tool's perceived usefulness through a survey with the company's IT professionals. Our results show that models trained on industrial data detect vulnerabilities accurately within the same domain but lose performance on open-source code, while a deep learner fine-tuned on open data, with appropriate undersampling techniques, improves the detection of vulnerabilities.

Hongyan Yu, Yidi Chen, Yuanyuan Wang et al.

Abstract Environmental pollutants like PM2.5 threaten hematopoietic homeostasis, yet how real-world exposure disrupts blood cell production, especially locally in the lung and systemically in the bone marrow (BM), remains poorly understood. Previous studies often used artificial particles or lacked mechanistic insights into systemic effects. Hypoxia-inducible factor-1alpha (HIF-1α) is essential for hematopoietic stem cell (HSC) maintenance. Herein, we utilized a real-ambient PM2.5 exposure system and conducted a detailed characterization of hematopoietic and downstream immune cell populations in mice with myeloid lineage-specific knockout of HIF-1α (mHIF-1α −/−) and their wild-type littermate controls. Our findings demonstrate that real-ambient PM2.5 exposure induces a HIF-1α-dependent myeloid-biased hematopoiesis within both the lung and BM. This bias results in an accumulation of mature myeloid cells, particularly neutrophils and macrophages, in peripheral organs such as the liver and spleen. Critically, this cellular redistribution precipitates inflammatory injury in a HIF-1α-dependent manner. These results provide novel insights into how environmental contaminants, exemplified by PM2.5, perturb hematopoiesis, highlighting the critical role of HIF-1α in mediating lineage-specific hematopoietic responses and subsequent inflammatory sequelae.

Shadi Amer, Dina Yamin, Nurul Ainun Hamzah et al.

Introduction: In 21st century, computers are crucial devices in universities’ official operations. In academic institutions, musculoskeletal disorders (MSDs) are leading causes of decreased productivity, absenteeism, disability, and illness. Office staff who use computers extensively are vulnerable to occupational MSDs. This study aims to determine risk factors of MSDs among computer users in a public university. Methods: This cross-sectional study involved 320 respondents among computer users working in all departments in Universiti Sains Malaysia Health Campus using random sampling. Tools used were a self-administered questionnaire containing questions on socio-demographical data, Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) for assessing musculoskeletal disorder and observation and Rapid Office Strain Assessment (ROSA) to assess office equipment and quantify exposure to risk factors in office work environment. Results: Response rate was 92% and 86.2% of respondents reported work-related musculoskeletal disorders (WRMSDs). The most prevalent MSD was lower back, 62.8% of MSD cases, followed by right shoulder (53.4%), hip/buttock (46.4%), and left shoulder (45.3%).Older age was significantly associated with WRMSDs (OR=6.944, CI:1.238-39.017, p=0.0.028) and with neck MSDs (OR=3.908, CI:1.342-11.377, p=0.012), while female gender was significantly associated with neck MSDs (OR=2.042, CI:1.199-3.475, p=0.009) and with upper arm MSDs (OR=1.791, CI:1.091-2.941, p=0.021). Older age was significantly associated with upper arm MSDs (OR=3.303, CI:1.006-10.849, p=0.049), while those with healthy and overweight were significantly associated with upper arm MSDs (OR=0.092, CI:0.010-0.814, p=0.046), (OR=0.127, CI:0.014-1.123, p=0.032), respectively. Conclusion: The prevalence of reported WRMSDs and MSDs at neck and upper arm were associated with socio-demographic background and high duration of computer use; 12.2% of workstation presented musculoskeletal discomfort risk.

Md S. Zaman, S. M. Golam Alam, Mohammed S. Razzaque

The human oral microbiome plays a vital role in maintaining oral and systemic health. This diverse microbial community includes over 700 bacterial species, some of which are implicated in developing systemic diseases, particularly cardiovascular diseases (CVDs). Research highlights a strong association between periodontal disease and increased cardiovascular risk, suggesting that good oral hygiene practices may reduce the incidence of CVDs. <i>Porphyromonas gingivalis</i> and <i>Fusobacterium nucleatum</i> drive chronic inflammation in periodontal disease; these bacteria can extend beyond the mouth and contribute to systemic inflammatory responses. The inflammatory factors, including C-reactive protein (CRP), interleukins (IL-1, IL-6), and tumor necrosis factor-alpha (TNF-α), damage blood vessels, impair endothelial functions, and promote atherosclerosis, all key events in CVD progression. Additionally, oral pathogens may accelerate plaque formation in arteries, increasing the risk of ischemic heart and brain diseases. Studies show a 28% increased risk of heart disease in individuals with periodontal disease. Treating periodontal disease can improve endothelial function and reduce inflammatory markers, emphasizing oral health management as a potential preventive strategy for CVD. Public health initiatives that emphasize oral hygiene and early periodontal disease treatment are crucial for broader cardiovascular care.

Limor Hatsor, Artyom Jelnov

The premise of industrial symbiosis IS is that advancing a circular economy that reuses byproducts as inputs in production is valuable for the environment. We challenge this premise in a simple model. Ceteris paribus, IS is an environmentally friendly approach; however, implementing IS may introduce increased pollution into the market equilibrium. The reason for this is that producers' incentives for recycling can be triggered by the income gained from selling recycled waste in the secondary market, and thereby may not align with environmental protection. That is, producers may boost production and subsequent pollution to sell byproducts without internalizing the pollution emitted in the primary industry or the recycling process. We compare the market solution to the social optimum and identify a key technology parameter - the share of reused byproducts that may have mutual benefits for firms, consumers, and the environment.

Javal Vyas, Mehmet Mercangöz

As chemical plants evolve towards full autonomy, the need for effective fault handling and control in dynamic, unpredictable environments becomes increasingly critical. This paper proposes an innovative approach to industrial automation, introducing validation and reprompting architectures utilizing large language model (LLM)-based autonomous control agents. The proposed agentic system, comprising of operator, validator, and reprompter agents, enables autonomous management of control tasks, adapting to unforeseen disturbances without human intervention. By utilizing validation and reprompting architectures, the framework allows agents to recover from errors and continuously improve decision-making in real-time industrial scenarios. We hypothesize that this mechanism will enhance performance and reliability across a variety of LLMs, offering a path toward fully autonomous systems capable of handling unexpected challenges, paving the way for robust, adaptive control in complex industrial environments. To demonstrate the concept's effectiveness, we created a simple case study involving a temperature control experiment embedded on a microcontroller device, validating the proposed approach.

Carmine Cesarano, Roberto Natella

Application Layer Gateways (ALGs) play a crucial role in securing critical systems, including railways, industrial automation, and defense applications, by segmenting networks at different levels of criticality. However, they require rigorous security testing to prevent software vulnerabilities, not only at the network level but also at the application layer (e.g., deep traffic inspection components). This paper presents a vulnerability-driven methodology for the comprehensive security testing of ALGs. We present the methodology in the context of an industrial case study in the railways domain, and a simulation-based testing environment to support the methodology.

Muhammad Iman Tsalatsa Raihan Tjahjono, Adhitya Ryan Ramadhani

Introduction: An oil and gas refinery operates various equipment with specific functions for different processes. Each piece of equipment has potential hazards that can damage the equipment and injure or kill workers. This study focuses on the hydrotreated vegetable oil (HVO) export facility from the jetty loading area at an oil and gas company that processes flammable liquid using various equipment. Methods: The HAZOP method determined the hazardous spots, and the probability of each equipment failure corresponding to the system was also determined using fault tree analysis (FTA). Furthermore, every event tree analysis (ETA) output probability was also determined. The probability and radius of pool fire varied for different leak hole scenarios. The final steps are individual risk per annum and potential loss of life to measure the risk level of the system. Results: Based on HAZOP deviation scenarios, every operating equipment can potentially cause a pool fire. In FTA, scenarios were developed based on different leakage hole sizes, ranging from 1-3 mm, 3-10 mm, 10-50 mm, 150 mm, and >150 mm. The results indicated that leakage could occur across all operating equipment. Similarly, the ETA applied the same bore size scenarios. The consequence analysis yielded a worst-case outcome of pool fire and a best-case outcome of un-ignited fluid release. Subsequently, the pool fire output was modeled using ALOHA, which resulted in three heat flux zones: the red zone (10 kW/m²), the orange zone (5 kW/m²), and the yellow zone (2 kW/m²). Smaller leak holes had a higher probability but smaller pool fire radius. The initial risk of the export facility was unacceptable. Furthermore, insufficient safeguards contribute significantly to the resulting high-risk level. Two mitigations were implemented: adding safeguards and reducing worker hours. Conclusion: The final results showed that for every piece of equipment, the overall risk of the export facility became acceptable after mitigation..

Eugen Šlapak, Enric Pardo, Matúš Dopiriak et al.

The proliferation of technologies, such as extended reality (XR), has increased the demand for high-quality three-dimensional (3D) graphical representations. Industrial 3D applications encompass computer-aided design (CAD), finite element analysis (FEA), scanning, and robotics. However, current methods employed for industrial 3D representations suffer from high implementation costs and reliance on manual human input for accurate 3D modeling. To address these challenges, neural radiance fields (NeRFs) have emerged as a promising approach for learning 3D scene representations based on provided training 2D images. Despite a growing interest in NeRFs, their potential applications in various industrial subdomains are still unexplored. In this paper, we deliver a comprehensive examination of NeRF industrial applications while also providing direction for future research endeavors. We also present a series of proof-of-concept experiments that demonstrate the potential of NeRFs in the industrial domain. These experiments include NeRF-based video compression techniques and using NeRFs for 3D motion estimation in the context of collision avoidance. In the video compression experiment, our results show compression savings up to 48\% and 74\% for resolutions of 1920x1080 and 300x168, respectively. The motion estimation experiment used a 3D animation of a robotic arm to train Dynamic-NeRF (D-NeRF) and achieved an average peak signal-to-noise ratio (PSNR) of disparity map with the value of 23 dB and an structural similarity index measure (SSIM) 0.97.

Ho Young Lee, Dorothy J. You, Alexia Taylor-Just et al.

Abstract Background Pulmonary exposure to multi-walled carbon nanotubes (MWCNTs) has been reported to exert strong pro-inflammatory and pro-fibrotic adjuvant effects in mouse models of allergic lung disease. However, the molecular mechanisms through which MWCNTs exacerbate allergen-induced lung disease remain to be elucidated. We hypothesized that protease-activated receptor 2 (PAR2), a G-protein coupled receptor previously implicated in the pathogenesis of various diseases including pulmonary fibrosis and asthma, may play an important role in the exacerbation of house dust mite (HDM) allergen-induced lung disease by MWCNTs. Methods Wildtype (WT) male C57BL6 mice and Par2 KO mice were exposed to vehicle, MWCNTs, HDM extract, or both via oropharyngeal aspiration 6 times over a period of 3 weeks and were sacrificed 3-days after the final exposure (day 22). Bronchoalveolar lavage fluid (BALF) was harvested to measure changes in inflammatory cells, total protein, and lactate dehydrogenase (LDH). Lung protein and RNA were assayed for pro-inflammatory or profibrotic mediators, and formalin-fixed lung sections were evaluated for histopathology. Results In both WT and Par2 KO mice, co-exposure to MWCNTs synergistically increased lung inflammation assessed by histopathology, and increased BALF cellularity, primarily eosinophils, as well as BALF total protein and LDH in the presence of relatively low doses of HDM extract that alone produced little, if any, lung inflammation. In addition, both WT and par2 KO mice displayed a similar increase in lung Cc1-11 mRNA, which encodes the eosinophil chemokine CCL-11, after co-exposure to MWCNTs and HDM extract. However, Par2 KO mice displayed significantly less airway fibrosis as determined by quantitative morphometry compared to WT mice after co-exposure to MWCNTs and HDM extract. Accordingly, at both protein and mRNA levels, the pro-fibrotic mediator arginase 1 (ARG-1), was downregulated in Par2 KO mice exposed to MWCNTs and HDM. In contrast, phosphorylation of the pro-inflammatory transcription factor NF-κB and the pro-inflammatory cytokine CXCL-1 was increased in Par2 KO mice exposed to MWCNTs and HDM. Conclusions Our study indicates that PAR2 mediates airway fibrosis but not eosinophilic lung inflammation induced by co-exposure to MWCNTs and HDM allergens.

O. Korotenko, E. Filimonov, N. I. Panev

Introduction. The leading role in the industrial structure of Kuzbass belongs to the coal and metallurgical industry associated with exposure to harmful production factors and high risks to the health of workers. The main pathogenetic factors triggering occupational and production-related diseases are hypoxic conditions potent of promoting changes in the heart structure and function. Materials and methods. The study included one hundred sixteen miners and 84 workers in the main occupations of aluminum production. The average age of miners was 47.63±0.33 years, of workers in the aluminum production - 48.41±0.53 years, p=0.191. Work experience in harmful working conditions in the study groups exceeded 20 years and did not differ significantly (p=0.281). All subjects underwent echocardiography according to the standard technique with automatic calculation of the global longitudinal deformity of the left ventricle. Results. The left ventricular ejection fraction and its longitudinal deformation were lower in aluminum industry workers with arterial hypertension not only in comparison with metallurgists with normal blood pressure (p<0.0001), but also with miners with arterial hypertension (p=0.015). Longitudinal deformation of the left ventricle in aluminum workers without arterial hypertension was significantly lower than in miners with normal blood pressure (p=0.0062). The parameters of the diastolic function of the left ventricle changed in the study groups under the influence of arterial hypertension. Limitations. This investigation is limited to a selection of workers in the main professions of the aluminum and coal industries undergoing periodic medical examinations at the Research Institute for Complex Problems of Hygiene and Occupational Diseases. Conclusion. A decrease in global longitudinal left ventricular myocardial deformation in miners and aluminum industry workers was associated with the presence of arterial hypertension and with the specifics of the main adverse production factors. The indices of left ventricular contractile function in aluminum industry workers were significantly lower compared to those in miners, regardless of the presence of arterial hypertension.

Stefano Mangini, Alessia Marruzzo, Marco Piantanida et al.

Quantum computing technologies are in the process of moving from academic research to real industrial applications, with the first hints of quantum advantage demonstrated in recent months. In these early practical uses of quantum computers it is relevant to develop algorithms that are useful for actual industrial processes. In this work we propose a quantum pipeline, comprising a quantum autoencoder followed by a quantum classifier, which are used to first compress and then label classical data coming from a separator, i.e., a machine used in one of Eni's Oil Treatment Plants. This work represents one of the first attempts to integrate quantum computing procedures in a real-case scenario of an industrial pipeline, in particular using actual data coming from physical machines, rather than pedagogical data from benchmark datasets.

Zonghang Li, Yihong He, Hongfang Yu et al.

Nowadays, the industrial Internet of Things (IIoT) has played an integral role in Industry 4.0 and produced massive amounts of data for industrial intelligence. These data locate on decentralized devices in modern factories. To protect the confidentiality of industrial data, federated learning (FL) was introduced to collaboratively train shared machine learning models. However, the local data collected by different devices skew in class distribution and degrade industrial FL performance. This challenge has been widely studied at the mobile edge, but they ignored the rapidly changing streaming data and clustering nature of factory devices, and more seriously, they may threaten data security. In this paper, we propose FedGS, which is a hierarchical cloud-edge-end FL framework for 5G empowered industries, to improve industrial FL performance on non-i.i.d. data. Taking advantage of naturally clustered factory devices, FedGS uses a gradient-based binary permutation algorithm (GBP-CS) to select a subset of devices within each factory and build homogeneous super nodes participating in FL training. Then, we propose a compound-step synchronization protocol to coordinate the training process within and among these super nodes, which shows great robustness against data heterogeneity. The proposed methods are time-efficient and can adapt to dynamic environments, without exposing confidential industrial data in risky manipulation. We prove that FedGS has better convergence performance than FedAvg and give a relaxed condition under which FedGS is more communication-efficient. Extensive experiments show that FedGS improves accuracy by 3.5% and reduces training rounds by 59% on average, confirming its superior effectiveness and efficiency on non-i.i.d. data.

Hejia Zhou, Shantanu Pal, Zahra Jadidi et al.

The Industrial Internet of Things (IIoT) is a developing research area with potential global Internet connectivity, turning everyday objects into intelligent devices with more autonomous activities. IIoT services and applications are not only being used in smart homes and smart cities, but they have also become an essential element of the Industry 4.0 concept. The emergence of the IIoT helps traditional industries simplify production processes, reduce production costs, and improve industrial efficiency. However, the involvement of many heterogeneous devices, the use of third-party software, and the resource-constrained nature of the IoT devices bring new security risks to the production chain and expose vulnerabilities to the systems. The Distributed Denial of Service (DDoS) attacks are significant, among others. This article analyzes the threats and attacks in the IIoT and discusses how DDoS attacks impact the production process and communication dysfunctions with IIoT services and applications. This article also proposes a reference security framework that enhances the advantages of fog computing to demonstrate countermeasures against DDoS attacks and possible strategies to mitigate such attacks at scale.

Jiayu Cao, Qing Yang, Jie Jiang et al.

Abstract As an emerging pollutant in the life cycle of plastic products, micro/nanoplastics (M/NPs) are increasingly being released into the natural environment. Substantial concerns have been raised regarding the environmental and health impacts of M/NPs. Although diverse M/NPs have been detected in natural environment, most of them display two similar features, i.e.,high surface area and strong binding affinity, which enable extensive interactions between M/NPs and surrounding substances. This results in the formation of coronas, including eco-coronas and bio-coronas, on the plastic surface in different media. In real exposure scenarios, corona formation on M/NPs is inevitable and often displays variable and complex structures. The surface coronas have been found to impact the transportation, uptake, distribution, biotransformation and toxicity of particulates. Different from conventional toxins, packages on M/NPs rather than bare particles are more dangerous. We, therefore, recommend seriously consideration of the role of surface coronas in safety assessments. This review summarizes recent progress on the eco–coronas and bio-coronas of M/NPs, and further discusses the analytical methods to interpret corona structures, highlights the impacts of the corona on toxicity and provides future perspectives.