Risk Assessment Method Research Articles

Nationwide meta-analysis of microplastic distribution and risk assessment in China's aquatic ecosystems, soils, and sediments

Microplastic (MP) accumulation has recently become a pressing global environmental challenge. As a major producer and consumer of plastic products, China’s MP pollution has garnered significant attention from researchers. However, accurate and comprehensive investigations of national-level MP pollution are still lacking. In this study, we systematically collated a national MP pollution dataset consisting of 7766 water, soil, and sediment sampling sites from 544 publicly published studies, revealing the spatiotemporal distribution and potential risks of MP pollution in China. The results indicate that MP distribution is influenced by various regional factors, including economic development level, population distribution, and geographical environment, exhibiting considerable range and complexity. MP concentrations are generally higher in economically prosperous areas, but the degree of pollution varies significantly across different environmental media. Given the uncertainty and lack of standardized data in traditional microplastic risk assessment methods, this article highlights the urgency of developing a comprehensive big data and artificial intelligence (AI)-based regulatory framework. This work provides a substantial amount of accurate MP pollution data and offers a fresh perspective on leveraging AI for microplastic pollution regulation.

Distribution, migration, and segmented risk assessment of the total petroleum hydrocarbons in soil, groundwater, and soil gas at an oil refinery

In this study, the concentrations of six petroleum hydrocarbons (n-alkane hydrocarbons (total petroleum hydrocarbons [TPH] (C6-C9) and (C10-C40)), BTEX(benzene, toluene, ethylbenzene, m & p-xylene)) in 261 soil, 31 groundwater, and 4 soil gas samples were measured to determine their spatial distribution，migration patterns and human health risks. To assess the rationality of parameter selection, GS-MS (gas chromatography-mass spectrometry) method was used to analyze the chemical composition of TPH (C6-C9), shanghai local regulation was referenced to assess TPH (C10-C40) various segmentation health risks. The results revealed that the contaminants were mainly concentrated in the refining workshop and the storage tank. The pathway of steam invasion in soil gas and the CR (carcinogenic risk) and HQ (hazard quotient) assessment of benzene in soil gas were 0–4 and 1–3 orders of magnitude higher than those in soil. TPH (C6-C9) was more similar to the Total Petroleum Hydrocarbons (Aromatic Low) in RSL (Regional Screening Level). Aliphatic hydrocarbons [AHCs]. (C10-C12) had the highest non-carcinogenic risk among all TPH (C10-C40) parts. The present study provided a reference to accurately determine the toxicological parameters of TPH (C6-C9) and TPH (C10-C40) and to improve the BTEX risk assessment methods.

The Nearshore Marine of ShenZhen Environmental Risk Assessment Method Based on the DPSIR Framework

The nearshore marine areas are the most intensely concentrated regions for human economic activities and serve as vital carriers for the development of the marine economy. Accurately assessing the environmental risks in these areas is of significant importance for the sustainable development of the marine economy. This study constructs an environmental risk assessment system for the nearshore marine areas of Shenzhen City based on the DPSIR framework and evaluates it using the comprehensive index method. Specifically, the study begins by selecting 27 indicators related to the marine environment to construct the evaluation index system. It employs the grey relational analysis and entropy weight method to weight the indicators and finally assesses the environmental risks of Shenzhen's nearshore marine areas from 2013 to 2020 using the comprehensive index method. The study finds that the environmental risks of Shenzhen's nearshore marine areas from 2013 to 2020 are generally low, with the risk score increasing from 0.3277 to 0.7308 year by year, shifting from a more dangerous to a safe risk level. The research indicates that the marine environment of the nearshore areas in Shenzhen is greatly influenced by local human economic activities and the industrial structure of Shenzhen. The study suggests that the environmental safety level of Shenzhen's nearshore marine areas could be improved by leveraging policy guidance, optimizing the industrial structure, and perfecting tourism management methods.

Risk analysis and assessment method for infectious diseases based on information entropy theory

Infectious diseases risk is directly related to human life safety. After the COVID-19 pandemic, people have paid unprecedented attention to the risk of infectious diseases. Compared with treatment after the outbreak of the epidemic, identifying the influencing factors of infectious disease risk and quantitatively analyzing and assessing infectious disease risk before the outbreak of the epidemic plays an equally important role. This article focuses on the risk of irregular outbreaks of infectious diseases. On the one hand, a method based on information gain is proposed to calculate the weight of environmental factors directly related to infectious disease risk, to clarify the correlation between environmental factors and infectious disease risk. On the other hand, the risk calculation method based on risk weight number is proposed to calculate the risk level of different infectious diseases under the influence of specific environmental factors. Finally, the effectiveness and feasibility of the proposed method are verified through case analysis and discussion. By comparing it with other risk assessment methods, the advantages and disadvantages of the proposed method are demonstrated.

A hybrid method for the intelligent effectiveness management of production operators

AbstractThe paper presents a hybrid method for the effective management of human resources when executing production processes. The method was proposed for managing production operators in systems of relatively high complexity—with the possibility of multiple alternative ways of designating operators to production stations. The method combines a novel approach of assessing the effectiveness of production operators, the use of an intelligent algorithm and the risk assessment method. The concept of effectiveness was used in the sense of a praxeological approach. Availability, performance and quality were defined as its components, and were evaluated through the prism of losses occurring in production processes. The aim of the study is to develop a hybrid method in order to delegate production operators to tasks, not only on the basis of a competence metric, but also on the basis of an individual effectiveness indicator and the risk assessment of production process stages. For this purpose, a novel method of assessing the effectiveness of the use of available resources when implementing production processes (based on a set of quantitative indicators and taking into account the categorization of production losses) was juxtaposed using the Tabu Search algorithm and the FMEA method. The method enables a near-optimal solution to be generated—one in which operators are assigned to production tasks in the most effective way possible, which in turn minimizes both the risk of production delays and quality defects due to operator fault.

Knowledge infrastructure for integrated data management and analysis supporting new approach methods in predictive toxicology and risk assessment

The EU-ToxRisk project (2016–2021) was a large European project working towards shifting toxicological testing away from animal tests, towards a toxicological assessment based on comprehensive mechanistic understanding of cause-consequence relationships of chemical adverse effects. More than 40 partners from scientific institutions, industry and regulators coordinated their work towards this goal in a six-year long programme. The breadth and variety of data and knowledge generated, presented a challenging data management landscape.Here, we describe our approach to data management as developed under EU-ToxRisk. The main building blocks of the data infrastructure are: 1) An easy-to-use, extensible data and metadata format; 2) A flexible system with protocols for data capture and sharing from the entire consortium; 3) A methods database for describing and reviewing data generation and processing protocols; 4) Data archiving using a sustainable resource; 5) Data transformation from the archive to the system that provides granular access; 6) Application Programming Interface (API) for access to individual data points; 7) Data exploration and analysis modules, based on a «web notebook» approach to executable data processing documentation; and 8) Knowledge portal that ties together all of the above and provides a collaboration space for information exchange across the consortium. This knowledge infrastructure is being extended and refined for the support of follow-up projects (RISK-HUNT3R, ASPIS cluster, European Open Science Cloud (2021–2026)).

MiRNA-mediated insect-resistant transgenic rice poses no risk to a non-target parasitoid, Cotesia chilonis, via direct feeding or through its target host.

MicroRNAs (miRNAs) have started to play an important role in pest control, and novel miRNA-based transgenic insect-resistant plants are now emerging. However, an environmental risk assessment of these novel transgenic plants expressing insect miRNAs must be undertaken before promoting their application. Here, transgenic miR-14 rice, which has high resistance to the rice stem borer Chilo suppressalis, was used as an example for evaluation in this study. Taking the tier1 risk assessment method in Bacillus thuringiensis (Bt) crops as a reference, the effects of the direct exposure of a non-target parasitoid, Cotesia chilonis, to a high concentration of miRNA were evaluated. The results showed that direct feeding with miR-14 at high concentration had no significant effects on the biological parameters of Co.chilonis, whereas when miR-14 was injected into Ch.suppressalis-parasitized larvae, the development duration of Co.chilonis was significantly affected. In combination with the real conditions of the rice paddy field, it could be inferred that transgenic miR-14 rice has no significant negative effects on the important non-target parasitoid, Co.chilonis. These results will provide a foundation for the establishment of a new safety evaluation system for novel RNAi-based transgenic plants.

Charting new waters with CRAMMTS: A survey-driven cybersecurity risk analysis method for maritime stakeholders

This article presents a novel survey-based cybersecurity risk assessment model, CRAMMTS (Cyber Risk Analysis Method for Maritime Transportation Systems), specifically designed for the maritime sector, addressing a critical gap in the literature. Our study contributes significantly in three ways: firstly, through a comprehensive critical literature review of 31 maritime guidelines and 95 scholarly articles, identifying the need for a new cybersecurity risk assessment method; secondly, by developing CRAMMTS, an adaptation of the ISRAM risk analysis method, incorporating the International Maritime Organization's criteria and enabling participation from maritime professionals, especially policymakers and leaders. The third contribution is a case study, the practical application of CRAMMTS in surveying 80 maritime professionals, assessing their perception of cybersecurity risks, and identifying varying risk levels, with the highest associated with cyber threat actors. This approach proved effective in assessing risks at both tactical and strategic levels and providing a clear, quantitative risk metric for decision-making. Our research underscores the maritime sector's need for a holistic, easily implementable cybersecurity risk analysis method that engages leaders and adapts to various Maritime Transportation System scopes, thereby enhancing cybersecurity risk assessment in this crucial domain.

A fire risk assessment method for offshore platforms: applications of 24Model with BN

ABSTRACT This paper integrates the 24Model with a BN and proposes a fire risk assessment method for offshore platforms. Triangular fuzzy membership function fuzzy set theory, coefficient of variation method and the combination of node state distance function and fuzzy inference are used to address the subjectivity and uncertainty due to insufficient objective data and conflicting opinions of multiple experts. Based on the proposed method, the human, organisational and technical factors of an offshore platform are comprehensively analysed from the perspective of the system. Based on diagnostic inference and sensitivity analysis, the main affecting factors and causal path of the risk scenario are determined. The results show that the proposed method could help in identifying the root causes of the failures and provide new insights into the probabilities and relationships among different factors.

Assessing the infringement risk of patent portfolios using network analysis and IF-TOPSIS: A case of standard-essential patent portfolios in the ICT industry

With the intensification of technological innovation competition and the surge in the number of patents in modern society, companies' technology activities such as technology R&D, technology licensing and technology transfer now regularly involve large patent portfolios, as well as a rising risk of patent infringement. However, the existing patent infringement risk assessment methods are only designed for individual patents, which is not enough to meet the companies' practical need for assessing and managing the infringement risk of large patent portfolios. Therefore, this study proposes a combined approach using network analysis and intuitionistic fuzzy technique for order preference by similarity to ideal solution (IF-TOPSIS), which can assess and rank the infringement risk of a company's patent portfolio relative to each of its competitors' patent portfolios, thereby providing a scientific base for the company's management decisions on patent portfolios. In this approach, one-mode weighted patent proximity networks are constructed to extract topology information of patent portfolios, and the IF-TOPSIS method is combined to obtain and rank the risk results under a proper fuzzy framework. The case study uses patent portfolios containing a total of 6736 standard-essential patents (SEPs) in the information and communications technology (ICT) industry as test data, and verifies the practicality and validity of this intelligent approach. Managerial implications for technology managers and patent attorneys are elaborated.

To Feel the Spatial: Graph Neural Network-Based Method for Leakage Risk Assessment in Water Distribution Networks

As water distribution networks expand, evaluating pipeline network leakage risk has become increasingly crucial. Contrary to traditional evaluation methods, which are often hampered by subjective weight assignment, data scarcity, and high expenses, data-driven models provide advantages like autonomous weight learning, comprehensive coverage, and cost-efficiency. This study introduces a data-driven framework leveraging graph neural networks to assess leakage risk in water distribution networks. Employing geographic information system (GIS) data from a central Chinese city, encompassing pipeline network details and historical repair records, the model achieved superior performance compared to other data-driven approaches, evidenced by metrics such as precision, accuracy, recall, and the Matthews correlation coefficient. Further analysis of risk factors underscores the importance of factors like pipe age, material, prior failures, and length. This approach demonstrates robust predictive accuracy and offers significant reference value for leakage risk evaluation.

Social Sustainability in Practice: Bridging the Gap from Declarations to Real-World Scenarios on Sustainability Driven by Ergonomics

In the paper, the role of ergonomics in promoting social sustainability within the workplace has been investigated and supported by evidence from the literature. Empirical research was conducted among 147 assembly line workers to gain insights into work-related discomfort and fatigue. This study used an ergonomic risk assessment (ERA) method to evaluate the ergonomic quality of workstations and correlated these findings with employees’ subjective experiences. The results showed a relationship between objective ergonomic assessments and subjective perceptions of work itself and company organizational factors, which were orally stated to the assessors, emphasizing the critical role of ergonomics in advancing social sustainability goals. The proposed DMAIC model was implemented to integrate ergonomic innovations into business processes to achieve social sustainability goals. Further research is needed to operationalize ergonomics in sustainability efforts.

Enhanced Condition Monitoring of Gearboxes using Fuzzy Comprehensive Evaluation and Group Decision-making: A Case Study

In this study, we introduce a novel methodology for the condition monitoring of gearboxes by integrating fuzzy comprehensive evaluation and group decision-making. Traditional risk assessment methods often suffer from subjective biases and uncertainties inherent in expert evaluations. Our approach mitigates these limitations by aggregating expert opinions using fuzzy membership functions and assigning weights based on the similarity of individual evaluations to the group consensus. This converts qualitative judgments into quantitative measures, resulting in more precise and objective Risk Priority Numbers (RPNs). We validate the efficacy of our methodology through a case study involving a gearbox. The primary failure modes identified include gear tooth wear, misalignment, bearing failure, lubrication failure, and thermal overload. Our results indicate a significant improvement in condition monitoring accuracy, with calculated fuzzy RPN values closely aligning with historical data and expert feedback. Comparative analysis highlights the advantages of our methodology over conventional RPN calculations, particularly in reducing subjective biases and enhancing the reliability of risk assessments. Our findings demonstrate that this methodology can be effectively applied in various industrial settings, establishing a robust framework for mechanical system condition monitoring. Future research should explore integrating advanced data analytics and machine learning techniques to enhance the methodology's accuracy and efficiency.

Application of m6A regulators to predict transformation from myelodysplastic syndrome to acute myeloid leukemia via machine learning.

Myelodysplastic syndrome (MDS) frequently transforms into acute myeloid leukemia (AML). Predicting the risk of its transformation will help to make the treatment plan. Levels of expression of N6-methyladenosine (m6A) regulators is difference in patients with AML, MDS, and MDS transformed into AML. Seven machine learning algorithms were established based on all of 26 m6A or main differentially expressed m6A regulator genes, and attempted to establish a risk assessment method to distinguish AML from MDS and predict the transformation of MDS into AML. In collective of m6A regulators sets, support vector machine (SVM) and neural network (NNK) model best distinguished AML or MDS from control, with area under the ROC curve (AUROC) 0.966 and 0.785 respectively. The SVM model best distinguished MDS from AML, with AUROC 0.943, sensitivity 0.862, specificity 0.864, and accuracy 0.864. In differentially expressed gene sets, SVM and logistic regression (LR) model best distinguished AML or MDS from control, with AUROC 0.945 and 0.801 respectively. The random forest (RF) model best distinguished between MDS and AML, with AUROC 0.928, sensitivity 0.725, specificity 0.898, and accuracy 0.818. For predictive capacity of MDS transformed into AML, SVM model showed the best predicted in collective m6A regulators sets, with AUROC 0.781 and accuracy 0.740. The LR model showed the best predicted in differential expression m6A regulators sets, with AUROC 0.820 and accuracy 0.760. All results suggested that machine learning model established by m6A regulators can be used to distinguished AML or MDS from control, distinguished AML from MDS and predicted the transformation of MDS into AML.

Hydrogen leakage risk analysis of hydrogen emergency power supply vehicles using the AET-RM method

In recent years, research and application of hydrogen fuel emergency power supply vehicle technologies have been steadily advancing. However, due to the highly flammable and explosive nature of hydrogen gas, the safety risk of hydrogen leakage has been taken seriously. Addressing the current inadequacies in the understanding of hydrogen leakage risks, unclear sources of risk, incomplete coverage of risk scenarios, and insufficiencies in risk assessment methods associated with hydrogen fuel emergency power supply vehicles, this study firstly establishes a multi-scenario and multi-factor coupled hydrogen leakage risk event database and an accident risk system. This effort focuses on the diversity of application scenarios, types of hydrogen leakage hazards and complexity of interfering factors. Subsequently, by refining existing risk assessment methods, a more balanced subjective-objective evaluation and more granular risk ratings approach, termed AET-RM, is proposed, which ensures finer risk grading. Finally, through this method, the types of hydrogen leakage hazards and their harm boundaries are clarified, achieving a systematic and precise graded quantitative assessment of risk sources, probabilities, and consequences. The study results indicate that the established event database contains no risk events rated as I (acceptable) or V (unacceptable). There are 37 events classified as low risk (rating II), accounting for 17.54% of the total. Medium-risk events (rating III) comprise 142 incidents, making up 67.30%, while high-risk events (rating IV) include 32 cases, representing 15.17%. The highest risk score, determined by the AET-RM method, pertains to a risk event in the power generation scenario, Contingencies: metal corrosion within the battery.

A Lab-Scale Experiment on the Spontaneous Combustion Behavior of Chronic Naturally–Oxidized Coal in Re-Mining Coal Mine

ABSTRACT Spontaneous combustion of coal in re-mining coal mine is a serious threat to the safety recovery of coal resources. In this paper, six coal samples, including two raw bituminous coal samples from the new mining working face and four oxidized bituminous coal samples from the re-mining working face, are selected. The pore structures, thermogravimetric properties, and spontaneous combustion characteristics of raw coal and oxidized coal are studied. The pore structures show that the surface of oxidized coals has more cracks and higher macropore volume and porosity as compared with raw coal. The thermogravimetric experiments demonstrate that the oxidized coal has a higher combustion rate and turns to the combustion reaction zone in advance. Moreover, the oxidized coals perform a better combustion property by showing a comprehensive combustion index of 6.7 × 10−9 min−2·°C−3 and 8.9 × 10−9 min−2·°C−3, higher than the raw coal of 4.0 × 10−9 min−2·°C−3. The spontaneous combustion characteristics show that the oxidized coal performs a greater rate of oxygen consumption and more oxidation products released than raw coal. The CPT of raw coal is higher than 157°C, while the CPT of oxidized coal is between 132°C and 140°C. The research results can provide a risk assessment method for coal spontaneous combustion in the re-mining coal mine, and provide guidance for avoiding the occurrence of coal fires.

Radiation to Chemicals Unpacking Occupational Safety Hazards in Educational Hospital through the HOSHRA Lens

Objective: The current study evaluates occupational safety and health risks in educational hospitals using the Hoshra index by concentrating on the detection and control of frequent hazards.Methods: Using a structured approach, the HOSHRA index classifies risks in to physical, chemical, biological, ergonomic and psychological domains. The framework supports targeted risk scoring, improving the efficiency with which hospitals can allocate resourcesFindings: The analysis uncovers important types of healthcare worker hazards. Biological and psychological risks appear to be particularly suboptimal, emphasizing the importance of effective infection control interventions, as well as psychological care. The study underscores the need to have a culture of safety that supports hazard reporting and management.Novelty: This is one of the first to use the HOSHRA index in many educational hospital, introducing new methods for risk analysis and assessment beyond traditional classic styles.Research Implications: The results highlight the need for adapted and risk based strategies in healthcare settings. Healthcare organizations can improve the well-being of staff and, by extension, patient care outcomes, by aligning safety protocols with the unique features of wards.

Surge-NF: Neural Fields inspired peak storm surge surrogate modeling with multi-task learning and positional encoding

Storm surges pose a significant threat to coastal communities, necessitating rapid and precise storm surge prediction methods for long-time risk assessment and emergency management. High-fidelity numerical models such as ADCIRC provide accurate storm surge simulations but are computationally expensive. Surrogate models have emerged as an alternative option to alleviate the computational burden by learning from available numerical datasets. However, existing surrogate models face challenges in capturing the highly non-stationary and non-linear patterns of storm surges, resulting in over-smoothed response surfaces. Moreover, the dry–wet status of nearshore nodes has not been informatively considered in the training process.This study proposes Surge-NF, a novel point-based surrogate model inspired by Neural Fields (NF) from computer graphics. Surge-NF introduces two key innovations. A positional encoding module is proposed to mitigate over-smoothing of high-frequency peak storm surge spatial dependencies. A multi-task learning framework is proposed to simultaneously learn and predict the dry–wet status and peak surge values, leveraging task dependencies to improve prediction accuracy and data efficiency. We evaluate Surge-NF on the NACCS database with comparison to state-of-the-art alternative surrogate models. Surge-NF consistently reduces RMSE/MAE by 50% and achieves 4–5 times computational cost gain over baselines, requiring only 50 training storms to produce accurate predictions. The complementary benefits of the positional encoding and multi-task learning modules are evident from the improved prediction capability with their combined use.Overall, Surge-NF represents a significant advancement in storm surge surrogate modeling, offering its novel and unique ability to capture high-frequency spatial variations and leverage task dependencies. It has the potential to greatly enhance storm surge risk assessment and emergency response management, enabling effective decision-making and mitigation strategies to safeguard coastal communities from the devastating impacts of storm surges.

Delirium in psychiatric settings: risk factors and assessment tools in patients with psychiatric illness: a scoping review

BackgroundDelirium is a common disorder affecting patients’ psychiatric illness, characterized by a high rate of underdiagnosis, misdiagnosis, and high risks. However, previous studies frequently excluded patients with psychiatric illness, leading to limited knowledge about risk factors and optimal assessment tools for delirium in psychiatric settings.ObjectivesThe scoping review was carried out to (1) identify the risk factors associated with delirium in patients with psychiatric illness; (2) synthesize the performance of assessment tools for detecting delirium in patients with psychiatric illness in psychiatric settings.DesignScoping review.Data sourcesPubMed, Web of Science, and Embase were searched to identify primary studies on delirium in psychiatric settings from inception to Dec 2023 inclusive. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted.ResultsA final set of 36 articles meeting the inclusion criteria, two main themes were extracted: risk factors associated with delirium in patients with psychiatric illness and assessment tools for detecting delirium in psychiatric settings. The risk factors associated with delirium primarily included advanced age, physical comorbid, types of psychiatric illness, antipsychotics, anticholinergic drug, Electroconvulsive therapy, and the combination of lithium and Electroconvulsive therapy. Delirium Rating Scale-Revised-98, Memorial Delirium Assessment Scale, and Delirium Diagnostic Tool-Provisional might be valuable for delirium assessment in patients with psychiatric illness in psychiatric settings.ConclusionsDelirium diagnosis in psychiatric settings is complex due to the overlapping clinical manifestations between psychiatric illness and delirium, as well as their potential co-occurrence. It is imperative to understand the risk factors and assessment methods related to delirium in this population to address diagnostic delays, establish effective prevention and screening strategies. Future research should focus on designing, implementing, and evaluating interventions that target modifiable risk factors, to prevent and manage delirium in patients with psychiatric illness.

Asset risk assessment and management of large-scale electricity enterprises under the concept of financial sharing

The power grid is an important industry that is crucial to national security and economic development, and its importance in society continues to grow. As an emerging concept, financial sharing enables internal resource sharing and optimization, thereby improving the efficiency and effectiveness of asset management. This study investigates and analyzes the current situation of asset management in large-scale electricity enterprises in X Province, China, and proposes a comprehensive asset management strategy optimization plan based on the concept of financial sharing. The proposed plan integrates management models such as PDCA and designs an entire information management architecture to enhance resource utilization efficiency, reduce environmental pollution risks, and optimize asset allocation and operational decisions. In addition, it also utilizes the status of assets to assess the risks associated with fixed assets in the power grid. The results indicate that the asset risk assessment method under the concept of financial sharing can reduce power grid asset losses, effectively enhance the competitiveness and sustainable development capabilities of electricity enterprises.