Risk Assessment Method Research Articles

Risk Assessment for Cyber Resilience of Critical Infrastructures: Methods, Governance, and Standards

As future infrastructures increasingly rely on digital systems, their exposure to cyber threats has grown significantly. The complex and hyper-connected nature of these systems presents challenges for enhancing cyber resilience against adverse conditions, stresses, attacks, or compromises on cybersecurity resources. Integrating risk assessment with cyber resilience allows for adaptive approaches that can effectively safeguard critical infrastructures (CIs) against evolving cyber risks. However, the wide range of methods, frameworks, and standards—some overlapping and others inadequately addressed in the literature—complicates the selection of an appropriate approach to cyber risk assessment for cyber resilience. To investigate this integration, this study conducts a systematic literature review (SLR) of relevant methodologies, standards, and regulations. After conducting the initial screening of 173 publications on risk assessment and cyber resilience, 40 papers were included for thorough review. The findings highlight risk assessment methods, standards, and guidelines used for cyber resilience and provide an overview of relevant regulations that strengthen cyber resilience through risk assessment practices. The results of this paper will offer cybersecurity researchers and decision-makers an illuminated understanding of how risk assessment enhances cyber resilience by extracting risk assessment best practices in the literature supported by relevant standards and regulations.

Assessment of Musculoskeletal Disorders in Brick Workers Using an Ergonomic Approach

Introduction: This study focuses on micro, small, and medium enterprises (MSMEs) engaged in the manual production of bricks. Specifically, it addresses the repetitive tasks of hoeing, mixing clay, and lifting and carrying bricks, which are performed for prolonged periods. The aim is to assess and mitigate risk factors associated with these manual tasks that may lead to musculoskeletal disorders (MSDs). Methods: The study involved 75 male workers with an average age of 43.52 ± 11.02 years, an average work experience of 14.18 ± 10.43 years, and an average body mass index (BMI) of 23.73 ± 2.59. The workers' manual material handling (MMH) activities were evaluated using the Workplace Ergonomic Risk Assessment (WERA) and Key Indicator Method (KIM). Additionally, the Nordic Body Map (NBM) questionnaire was used to identify areas of the body where workers experience pain related to their tasks. Results: The WERA assessment identified ten activities with a medium risk level, necessitating further analysis. The KIM assessment revealed six activities with a very high risk level and four with high risk. Recommendations were made for four high-risk activities, specifically suggesting the redesign of material transport carts. This intervention has the potential to reduce injury risk scores by up to 50%. Conclusion: The combined use of the WERA and KIM methods proves effective in assessing MMH risk in brick-production MSMEs, offering insights for targeted ergonomic interventions.

Risk Assessment of Small-Diameter Shield Construction in a Deep Drainage Tunnel Based on an ISM–CRITIC–Cloud Model

The deep drainage tunnel project is an important measure to alleviate urban waterlogging. The construction of a deep drainage tunnel is a complicated process, involving many influencing factors, and there are correlations among these influencing factors, so the risk assessment is difficult. In this study, the ISM method, CRITIC method and cloud model are combined to build a risk assessment model for the small-diameter shield construction of a deep drainage tunnel. Firstly, the risk index system of small-diameter shield construction in a deep drainage tunnel is put forward. Secondly, the ISM method is used to divide the risk indicators and extract the key risk factors. Then, these key risk factors are weighted with the CRITIC method, and the cloud model is used to evaluate the construction risk of a small-diameter shield of a deep drainage tunnel. Finally, the feasibility and accuracy of the proposed method are verified by a practical case. It was found that the risk assessment method proposed in this study can not only effectively assess the level of security risk, but also identify the key risk factors and rank the importance of these factors. The results of this study can reduce the interference items and workload of risk assessment to a certain extent, and help provide managers with an accurate decision-making basis.

Heavy Metal Pollution Characteristics and Ecological Risk Assessment of Kafang Tin Tailings Pond

To study the pollution characteristics and ecological risks of heavy metals in tailings ponds is an important prerequisite for protecting the surrounding environment and human health. The total amount and morphology of five "toxic" heavy metals （As, Cr, Cd, Pb, and Hg） in Kafang tin tailings pond were determined. Based on the aforementioned, we analyzed the distribution characteristics of heavy metals. The potential ecological risks of heavy metals in tailings ponds were studied from the aspects of total amount and morphology. Sample data were analyzed using the geo-accumulation index method, potential ecological risk assessment method, and risk assessment criteria. The results showed that, from the perspective of the total amount of heavy metals, the spatial distribution of As was different, and the pollution was serious； the spatial distribution of Cd, Hg, Cr, and Pb was relatively uniform； the pollution of Cd and Hg was slight； and the pollution of Cr and Pb was controllable. From the perspective of the morphology of heavy metals, As, Cr, Pb, and Hg in tailings were mainly in the residual state and had low environmental pollution risk, whereas Cd in tailings was mainly in an oxidizable state and had high environmental pollution risks. Therefore, Kafang tin tailings pond should strengthen the control of As in tailings and pay attention to the potential pollution risks of Cd in tailings and the overall pollution risks of Hg, Pb, and Cr in the entire reservoir were controllable.

Dynamic Evaluation of the Safety Risk During Shield Construction near Existing Tunnels via a Pair[sbnd]Copula Bayesian Network

Shield tunnel construction near existing tunnels can lead to the deformation and settlement of adjacent tunnels, posing safety risks. To analyze and evaluate the key factors influencing such safety risks, a risk assessment method based on the pair-copula Bayesian network (PCBN) model is proposed. The PCBN model, which integrates a Bayesian network (BN) with a pair copula (PC), is applied to simulate a complex risk factor system and related risk factors. Using the constructed PCBN model, a case study was conducted focusing on excavation between Wuhan Metro Lines 8 and 3, and it was determined that the deformation and settlement risk during shield tunnel construction falls into the safe risk category (Level 3). The key risk factors that significantly impact deformation and settlement risk were identified through correlation analysis and were ranked by importance as follows: advance rate, total thrust, and grouting pressure. Compared with traditional risk assessment methods and BNs, the PCBN model can flexibly handle complex relationships among risk factors, especially nonlinear and tail dependencies, due to the integration of PCs. This approach not only enhances the accuracy of risk assessment but also improves the interpretability of the model, with transparent internal mechanisms, largely due to the use of the BN. On the basis of these findings, decision-making recommendations are proposed to reduce the risk of shield tunnel construction near existing tunnels. The implementation of these recommendations in the Wuhan Metro project has yielded positive outcomes.

A new holistic perspective to assess the ecological risk of microplastics: A case study in Baiyangdian Basin, China

By integrating probabilistic ecological risk assessment with the overall risk index method, which considers the multidimensional characteristics of the microplastome, the ecological risks of microplastic pollution were assessed more comprehensively. This study took the Baiyangdian Basin as an example to address the limitations of current risk assessment methods that rely on concentration data or the individual risk of microplastics. Using an exponential regression model, the acute and chronic ecological risk thresholds for the overall risk index method were determined to be 0.43 and 0.30, respectively. The acute and chronic ecological risks of the microplastome occupied 61% and 79% of the Baiyangdian Wetland and 0% and 14% of the Fu River, while the Xiaoyi River did not exhibit risk during the rainy season. Results indicated that intense human activities, poor hydrodynamics, low settling velocity and high levels of environmental chemical pollutants jointly contributed to the high risk of the microplastome in water bodies. Compared with the probabilistic ecological risk assessment method (risk characterization ratio), there was a significant difference in the area of acute and chronic ecological risks caused by the microplastome in the Baiyangdian Basin when using the overall risk index method. This proved that considering only concentration cannot truly reflect the toxicity of microplastics to aquatic organisms.

A knowledge graph-based method for intelligent risk assessment of power grid

Abstract With the advancement of science and technology, the material resources that support people’s lives are becoming increasingly abundant, leading to increasingly complex accidents in the electricity system caused by factors such as Natech. Currently, a superficial security check problem exists in analyzing potential hazards in China. Moreover, the evaluation work becomes superficial, resulting in inaccurate identification results and making it difficult to ensure the long-term safety and stability of the power system. Based on these issues, this paper proposes a power system safety assessment method based on a standardized digital knowledge graph (KG). In this method, firstly, through research on standardized digital transformation theory, a triplet dataset based on event accidents is established to support knowledge graph construction; secondly, through knowledge graph construction, hazard analysis and evaluation of the power system are achieved.

Credit risk assessment method driven by asymmetric loss function

Credit risk assessment is significantly hindered by the problem of class imbalance, and cost-sensitive methods represent an effective strategy to address this issue. However, most algorithms tend to approach the imbalance from a class perspective, overlooking the finer details at the sample level. Moreover, such methods are susceptible to interference from noise and outliers. In response to these challenges, this paper proposes an asymmetric cost-sensitive support vector machine (QTSVM) that combines the quadratic type squared error loss function (QTSELF) with support vector machine (SVM). It not only leverages the asymmetry of the loss function by applying varying penalties based on classification errors but also employs different processing measures for samples from different classes. Additionally, it enhances model robustness by imposing a tiny penalty on noise or outliers. The adaptive moment estimation (Adam) algorithm is employed to optimize QTSVM. Extensive experiments and statistical tests profoundly demonstrate the superior performance of QTSVM.

The Influence of Cyber Security Knowledge, Cyber Security Awareness, and Behaviour Protection on Intention to Use Among Mobile Banking Users in Jakarta

Businesses and individuals, especially banks, must use risk assessment methods and preventive measures to protect mobile banking application users. Looking at these events, the researcher aims to see whether cyber security awareness, knowledge & behavior protection of users affect the intention to use mobile banking applications and whether these three variables are important for mobile banking users to understand in maintaining the security of transactions using mobile banking which will test the influence of these 3 variables on users' interest in continuing to use mobile banking even though users know the high risk if there is negligence in carrying out these 3 things and believing in the security of the mobile banking system in Indonesia. The population of this study is people who work in Jakarta. The respondents' answers were taken using Google Docs and distributed in general. The researcher obtained a total of 130 respondents. The data was processed using SPSS 27 with the analysis method of validity, reliability, and hypothesis test including the R2 test, F test, and T-test.

Markers of Endothelial Injury in Extracorporeal Membrane Oxygenation: A New Risk Assessment Method

IntroductionExtracorporeal membrane oxygenation (ECMO) has become more widely used in recent years. However, ECMO remains a resource-intensive modality, and identifying patients most likely to benefit from it can be a complex task. Few methods exist to help risk stratify potential ECMO patients. Syndecan-1 (SDC-1) and soluble thrombomodulin (sTM) are markers of endothelial dysfunction and are used as a sign of disease severity in various forms of trauma. Our study aims to evaluate the association between precannulation levels of SDC-1 and sTM with mortality, current scoring systems, and their ability to predict mortality on ECMO. MethodsPatients initiated on venoarterial ECMO were retrospectively analyzed. Clinical data were collected, and precannulation Acute Physiology and Chronic Health Evaluation scores were calculated. Blood samples from precannulation collection were assayed for SDC-1 and sTM by enzyme linked immunosorbent assay. The primary outcome was mortality on ECMO. ResultsThirty-four patients were included in the analysis. Most were male (76.5%), with a median age of 61.5 y and body mass index of 28.2. Overall mortality was 61.7%. sTM was significantly higher in patients who died on venoarterial ECMO compared to those who lived. Pre–SDC-1 level of ≥951 ng/mL is marginally predictive of a higher mortality risk (area under the receiver operating characteristic curve 0.70; P = 0.070). Pre-sTM levels of ≥5348 pg/mL predicted mortality (area under the receiver operating characteristic curve 0.89; P = 0.003). ConclusionsSDC-1 and sTM are associated with a higher mortality risk in patients on ECMO. These biomarkers may be a valuable addition to current scoring systems. Furthermore, more work should focus on characterizing the effects of cardiogenic shock on the endothelium.

Influencing Factors and Ecological Risks of Organochlorine Pesticide Adsorption in Surface Sediments of Qingshui River

ABSTRACT Taking the first-level tributary of Ningxia section of the Yellow River (Qingshui River) as the study area, surface sediment samples were collected during the seasons in 2018. The distribution characteristics, pollution levels, possible sources, influencing factors, and ecological risks of organochloride pesticides (OCPs) in the surface sediments were studied. The detected mean amounts were n.d. ~10.640 (1.474) ng·g−1, and the detection rate was 80.95%. The content (mean values) of Hexachlorocyclohexanes (HCHs), Endosulfan, Chlordane, and Dichlorodiphenyltrichloroethane (DDT) with higher detection amounts and detection rates were n.d. ~0.949 (0.153), n.d. ~1.973 (0.331), n.d. ~0.271 (0.052), and n.d. ~10.583 (0.819) ng·g−1, respectively. The total OCP amounts were detected in Changshantou and Liwang research sections, which have surrounding farmlands and low water flow. The total organic carbon plays a vital role in the residue and distribution of β-HCH and Dieldrin. In a high-salinity environment, the lower-layer sediments had higher adsorption efficiencies for DDT and γ-HCH and the HCH content decreased with the increase in pH. The total nitrogen and phosphorus in sediments were among the controlling factors affecting the distribution of Endosulfan II and p,p’-DDT, respectively, but their effects on Dieldrin only appeared in the spring flood season. Further, a functional model was established with respect to the adsorption amount of various OCPs and the influencing factors. The component analysis results showed that OCPs primarily originated from historical residues; however, only small amounts of Lindane, Dicofol, Endosulfan, and Chlordane compounds have been observed recently in the local environment. Using the low-effect interval and median method for potential ecological risk assessment, Endosulfan was noted to have potential ecological risks, mainly distributed in the middle and lower reaches of the river. The reasons for the potential harm require attention and further research.

Risk Evaluation of Urban Subway Site Selection: Balance, Attractiveness, and Financing Models

As a crucial form of public transportation, subways are becoming essential infrastructure that cities in China increasingly prioritize for development. However, there is a lack of effective risk assessment methods for subway station and line siting. To address this gap, this paper uses the subway system in Kunming, China, as a case study, establishing a subway site risk evaluation framework (SIRE-BAF) that integrates three dimensions: balance (B), attractiveness (A), and financing mode (F). An extended NP-RV model is proposed to assess the balance (or imbalance) characteristics of subway stations based on sub-dimensions of traffic supply, land use, and urban vitality. Findings indicate that (1) the balance (or imbalance) of subway stations is distinctly distributed along the line and simultaneously exhibits a spatial pattern radiating from the urban core to the periphery. (2) Stations with high urban vitality and minimal imbalance are highly attractive and tend to face “undersupply” during operation, whereas stations with lower attractiveness are more prone to “oversupply”. A higher level of BAF coupling coordination suggests a more suitable subway site selection and lower investment risk, while lower coupling coordination indicates increased risk. (3) Excessive reliance on the “subway + real estate” model, without considering urban vitality, may lead to high vacancy rates and reduced efficiency in subway service. This paper further assesses the site selection risks for the proposed Kunming subway. This study contributes to risk assessments of existing subway operations and maintenance in Chinese cities, enhances planning rationality and site selection for proposed subways, and holds potential applicability for other cities.

Risk assessment of domino effects under fire and explosion accidents in LNG storage tank farms based on Bayesian network

With the increase of liquefied natural gas (LNG) storage capacity, the number of storage tanks in LNG tank farms increases, which makes the possibility of domino effect higher, so it is important to analyze the risk of domino effect in LNG tank farms. However, the traditional domino effect risk assessment only considers a single accident type, which is not consistent with reality. Therefore, a Bayesian network-based risk assessment method for the domino effect of fire and explosion accidents in LNG storage tank farm is proposed in this study. The method utilizes the Bayesian network approach to model the propagation of domino accidents and calculate the failure probability of domino effects under fire and explosion accidents. The most likely sequence of accidents and the critical tank in the LNG storage tank farm can also be defined through the Bayesian network inference. Finally, the reliability of the proposed method is proved by a real case. Compared with only considering a single accident type, mixed accident types lead to a more reasonable result in the domino effect probabilistic analysis. It is believed that the developed approach has great theoretical significance for domino accident prevention and control in LNG storage tank farms.

Safety Risk Assessment Method of In-Service Stage Suspension Equipment Based on Grey Fuzzy Comprehensive Evaluation

Performance safety is one of the important goals for the high-quality development of modern performance services. The in-service stage suspension equipment that has been put into use is one of the most frequently used and most closely related stage machinery in performances, and there are often significant safety hazards during its use. In response to the current lack of safety risk assessment methods and incomplete assessment techniques for in-service stage suspension equipment, this paper proposes a safety risk assessment method for in-service stage suspension equipment based on grey fuzzy comprehensive evaluation, with professional theaters as the target scenario. This method first identifies risk factors based on Failure Mode and Effects Analysis (FMEA), then uses the grey relational analysis (GRA) method for risk factor analysis, and finally adopts the fuzzy comprehensive evaluation (FCE) method to achieve safety risk level assessment. By constructing and analyzing an evaluation model for professional theater stage suspension equipment, the safety risk levels and corresponding safety risk factor rankings of performance accidents such as electric shock, falling, and failure can be obtained, and measures to reduce risks can be provided based on the most important risk factors. The research results show that more attention should be paid to the influence of human factors in the safety assessment and detection of in-service stage suspension systems. The research in this article is of great significance for improving the safe use of in-service stage suspension equipment, enhancing the level of performance safety management, and improving the quality of performance equipment services, laying the foundation for the formation of relevant regulatory systems and standards.

Quantitative Risk Assessment for Autonomous Vehicles: Integrating Functional Resonance Analysis Method and Bayesian Network

ABSTRACTIn autonomous vehicles (AVs), intricate functional‐level couplings exist among the components. Accidents can occur even when all functions are operating normally, as subtle performance variabilities in these functions can aggregate through these couplings, leading to functional resonance. The aim of this study is to identify, analyze and quantitatively assess the safety issues caused by these complex interactions in AVs and to propose appropriate risk management strategies to improve vehicle safety. Commonly used modern methods of risk assessment, such as system‐theoretical process analysis and accident mapping, struggle to capture this resonance in AVs and lack quantitative analysis. To this end, this paper proposes a quantitative risk assessment method that integrates functional resonance analysis method (FRAM) with Bayesian network (BN) to reveal the complex interactions and quantify risks within AVs. Initially, a FRAM model is constructed to characterize the function couplings of a system, which are subsequently aggregated into functional resonance chains to identify potential hazards. Then, these functional resonance chains are used to develop a BN model for quantitative assessment of system risk. A case study of an automatic emergency braking (AEB) system on an open‐source vehicle is conducted to verify its effectiveness. The results demonstrate that the proposed approach not only identifies functional resonance but also effectively quantifies risks in the AEB system.

Network-based investigation of petroleum hydrocarbons-induced ecotoxicological effects and their risk assessment

Petroleum hydrocarbons (PHs) are compounds composed mostly of carbon and hydrogen, originating from crude oil and its derivatives. PHs are primarily released into the environment through the diffusion of oils, resulting from anthropogenic activities like transportation and offshore drilling, and accidental incidents such as oil spills. Once released, these PHs can persist in different ecosystems and cause long-term detrimental ecological impacts. While the hazards associated with such PH contaminations are often assessed by the concentrations of total petroleum hydrocarbons in the environment, studies focusing on the risks associated with individual PHs are limited. In this study, different network-based frameworks were utilized to explore and understand the adverse ecological effects associated with PH exposure. First, a list of 320 PHs was systematically curated from published reports. Next, biological endpoint data from toxicological databases was systematically integrated, and a stressor-centric adverse outcome pathway (AOP) network, linking 75 PHs with 177 ecotoxicologically-relevant high confidence AOPs within AOP-Wiki, was constructed. Further, stressor-species networks, based on reported toxicity concentrations and bioconcentration factors data within ECOTOX, were constructed for 80 PHs and 28 PHs, respectively. It was found that crustaceans are documented to be affected by many of these PHs. Finally, the aquatic toxicity data within ECOTOX was used to construct species sensitivity distributions for PHs, and their corresponding hazard concentrations (HC05), that are harmful to 5% of species in the aquatic ecosystem, were derived. Further, the predicted no-effect concentrations (PNECs) and risk quotients for the US EPA priority polycyclic aromatic hydrocarbons (PAHs) were computed by using their environmental concentration data for Indian coastal and river waters. Overall, this study highlights the importance of using network-based approaches and risk assessment methods to understand the PH-induced toxicities effectively.

Design and Visual Implementation of a Regional Energy Risk Superposition Model for Oil Tank Farms

Ensuring the safety of oil tank farms is essential to maintaining energy security and minimizing the impact of potential accidents. This paper develops a quantitative regional risk model designed to assess both individual and societal risks in oil tank farms, with particular attention to energy-related risks such as leaks, fires, and explosions. The model integrates factors like day–night operational variations, weather conditions, and risk superposition to provide a comprehensive and accurate evaluation of regional risks. By considering the cumulative effects of multiple hazards, including those tied to energy dynamics, and the stability and validity of the model are researched through Monte Carlo simulations and case application. The results show that the model enhances the reliability of traditional risk assessment methods, making it more applicable to oil tank farm safety concerns. Furthermore, this study introduces a practical tool that simplifies the risk assessment process, allowing operators and decision-makers to evaluate risks without requiring in-depth technical expertise. The methodology improves the ability to safeguard oil tank farms, ensuring the stability of energy supply chains and contributing to broader energy security efforts. This study provides a valuable method for researchers and engineers seeking to enhance regional risk calculation efficiency, with a specific focus on energy risks.

Harnessing Risks with Data: A Leakage Assessment Framework for WDN Using Multi-Attention Mechanisms and Conditional GAN-Based Data Balancing

Assessing leakage risks in water distribution networks (WDNs) and implementing preventive monitoring for high-risk pipelines has become a widely accepted approach for leakage control. However, existing methods face significant data barriers between Geographic Information System (GIS) and leakage prediction systems. These barriers hinder traditional pipeline risk assessment methods, particularly when addressing challenges such as data imbalance, poor model interpretability, and lack of intuitive prediction results. To overcome these limitations, this study proposes a leakage assessment framework for water distribution networks based on multiple attention mechanisms and a generative model-based data balancing method. Extensive comparative experiments were conducted using water distribution network data from B2 and B3 District Metered Areas in Zhengzhou. The results show that the proposed model, optimized with a balanced data method, achieved a 40.76% improvement in the recall rate for leakage segment assessments, outperforming the second-best model using the same strategy by 1.7%. Furthermore, the strategy effectively enhanced the performance of all models, further proving that incorporating more valid data contributes to improved assessment results. This study comprehensively demonstrates the application of data-driven models in the field of “smart water management”, providing practical guidance and reference cases for advancing the development of intelligent water infrastructure.

Evaluating the use of systems thinking methods in healthcare: a RE-AIM analysis of AcciMap and Net-HARMS

There are increasing calls for the application of systems ergonomics methods in healthcare, although evidence for their utility and uptake is limited. In this study, 67 Australian healthcare workers participated in a six-month longitudinal study where they were trained to apply the AcciMap adverse event analysis and Net-HARMS risk assessment methods. Data were gathered in line with the RE-AIM (Reach, Efficacy, Adoption, Implementation, and Maintenance) evaluation framework, including rates of organisational uptake and method validity, perceived workload, usability, and barriers and facilitators to use in practice. Overall RE-AIM ratings for AcciMap were relatively high, and more moderate for Net-HARMS. Time constraints was the most frequently identified barrier to the use of both methods in practice, while there was more organisational resistance to Net-HARMS uptake. Facilitators for the use of both methods include providing quality training and mentorship, additional time and software resources, and dedicated job roles.

Microplastics in Urban Ambient Air: A Rapid Review of Active Sampling and Analytical Methods for Human Risk Assessment

This study conducted a rapid review to evaluate active air sampling and analytical methods for characterizing outdoor air microplastics in urban areas. We synthesized information from 35 peer-reviewed journal articles. Studies utilizing active sampling methods were able to provide detailed data on inhalation concentrations and doses. The analytical techniques reviewed were categorized into microscopy, Fourier Transform Infrared (FTIR) spectroscopy, Raman spectroscopy, scanning electron microscopy (SEM), and mass spectrometry, including pyrolysis–gas chromatography (Py-GC). While conventional FTIR and Raman spectroscopy can identify microplastics in total suspended particles, advanced instruments such as µRaman and SEM are crucial for analyzing inhalable microplastics (e.g., particles smaller than 10 µm). Characterizing the shapes and colours of microplastics can provide qualitative estimates of their sources, with fibres and the colour black being the most predominant characteristics. Establishing dose–response relationships for health effects requires quantitative analyses; thus, combining techniques like µRaman with Py-GC is essential for comprehensive human risk assessments. Future studies should focus on identifying and quantifying inhalable microplastic compounds that are relevant to human health.