Risk Analysis Model Research Articles

Sentinel testing, analytical sigma metrics and a risk management approach as part of a simplified method verification/validation process.

Verification and validation of analytical methods are crucial aspects of quality assurance in a laboratory. This study aimed to develop a risk analysis and assessment tool to streamline the process of identifying so-called 'sentinel' tests. The Roche Cobas 8000 systems were evaluated to analyze 83 serum analytes, including routine chemistry, immunoassays, and therapeutic drugs. A failure mode and effects analysis were conducted to produce an analytic risk rating. This was achieved by multiplying the scores for Sigma metrics, the score for potential damage extent, and the score for environmental factors. Each test was assigned a typical risk priority number (RPN). Tests with an RPN of ≤9 were rated as low risk and ranked as 'B'. Tests with an RPN of >10 were considered high risk and graded as 'A'. Regarding the Cobas C701/ISE, 17 of 54 methods were rated as 'A' and subject to a systematic method review process. A total of 37 methods were assigned a rank of 'B' and hence were eligible for a selective verification process. Concerning the Cobas E801, 10 out of 29 methods were classified as 'A' and, therefore, require a systematic verification process. A further nineteen methods were assigned a rank of 'B' and hence eligible for a select verification. This study demonstrated the high effectiveness of the risk analysis and assessment model developed to identify sentinel tests in the lean management of the verification/validation process.

EXCAVATION AND EMPIRICAL STUDY OF RISK FACTORS ASSOCIATED WITH STROKE AND DEPRESSION

Stroke is an acute cerebrovascular disease characterized by a high incidence, disability rate, recurrence rate, and mortality rate. Depression, as one of the main types of mood disorders, can manifest as symptoms such as a loss of interest in daily activities, reduced energy, diminished self-esteem, insomnia, and decreased appetite. There exists a significant relationship between these two conditions in terms of their pathogenesis. To delve deeper into the factors associated with the occurrence of both diseases, particularly examining the influence of depression on stroke risk, the authors conducted a large-scale investigation and data collection in the population of Shanghai, obtained 5599 valid data points, including information on hypertension, abnormal blood lipid levels, diabetes, atrial fibrillation, body mass index (BMI), a history of stroke, family history of stroke, previous transient ischemic attacks, smoking history, and exercise habits. Ultimately, the authors employed the decision tree C4.5 algorithm to construct a risk analysis model for both stroke and depression, analyzing the risk factors contributing to their occurrence and inferring the correlation between depression and stroke. The results revealed a close association between depression and a history of stroke, BMI, gender, and physical activity. Additionally, in the nonelderly population, psychological factors such as anxiety, depression, mood disorders, and stress were found to be closely linked to stroke onset. In many instances, regular exercise may mitigate the adverse effects of depression on stroke risk.

A Grey Combined Prediction Model for Medical Treatment Risk Analysis during Pandemics

A Grey Combined Prediction Model for Medical Treatment Risk Analysis during Pandemics

Credit Scoring Model Weighted by Investment and Financing Decisions: Case Study in a Building Supply Store

Current literature provides various models for credit risk analysis, called Credit Scoring Models (CSMs). However, these models are not suited to the majority of Micro and Small Enterprises (MSEs). This is compounded by a lack of technical knowledge of microentrepreneurs linked to the high costs and complexity of the CSMs. These issues are significant as 99% of Brazilian companies are MSEs. Therefore, this paper aims to propose a CSM for an MSE that commercializes construction materials in São Paulo, Brazil. This research is quantitative and characterized as a case study whose CSM is based on the Naive Bayes algorithm implemented in Microsoft Office Excel 2016. This model calculates the probability of default and adherence by weighting the results based on the Modern Finance Theory with the Cost of Denying and the Cost of Granting. The application of the model demonstrates that the successes in approvals (70%) and disapprovals (66%) of Credit Sales were significant with a result of R$ 32.20 thousand and an increase in net profit by 124.2%. We have evidenced that the proposed CSM is able to weigh the risk in investment and financing decisions for the Credit Sales of an MSE. This paper provides a low-cost CSM, adapted to reality and easy to handle and implement in MSEs. This research is a reference to the development of CSMs focused on the credit concessions conducted by MSEs.

Methodical aspects of analysis and risk management of an insurance company

The object of research is the risks of the insurance company. The paper is devoted to the study of the risks of the insurance company, which generate threats to the level of its solvency. The general state of economic development in general and the insurance market in particular determine the operating conditions of any insurer. The insurance company's ability to regulate such conditions is limited, but the formation of a stable state of the internal environment creates opportunities to adapt to threats from the external environment. Timely detection of potential risks in the activities of the insurer, provision of reserves to cover possible losses related to risks, enable companies to overcome the consequences of risks with minimal losses. The methodical basis for evaluating potential risks in the insurance company's activities is strategic analysis methods, such as PEST and SWOT analysis. Their results give an idea of the state of threats in the external environment and the potential opportunities of the insurer's internal reserves. The methods of economic-mathematical modeling and forecasting make it possible to assess the current state of the company and investigate its behavior under the influence of external and internal factors. The basis of the forecast model of risk analysis of an insurance company is formed by the coefficient of risk of loss of solvency, calculated according to the key parameters of the assessment of its financial condition. The reliability of the forecast of the possible risk of loss of the insurer's solvency is ensured by constructing a trend line and forming a forecast sheet in the MS Excel software environment. The forecast sheet forms the probabilities of the values of the indicators with a distinction between optimistic and pessimistic levels of the forecast. Using the trend line and finding the value of the coefficient of determination (R2), the probability of realizing the forecast of growth or reduction in the value of each individual parameter of the model is determined. The practical significance of the obtained results lies in the possibility of increasing the efficiency of the use of available resources and reserves in the process of risk management. The proposed methodical approach can be used to assess the impact of risks in the insurer's activities, which will increase the accuracy of the obtained results.

An analytical two-stage risk analysis model in the real-time reservoir operation

Flood risk analysis is vital for real-time reservoir operation. The reservoir operation horizon (OH) is generally longer than the streamflow forecast horizon (FH), while the gap between the FH and the OH was seldom considered in analytical flood risk models. This study develops an analytical two-stage risk model covering the period within the FH (the first stage) and from the FH to the OH (the second stage). In the first stage, the errors in forecasted streamflow and reservoir water level-storage relationships are considered using the multinormal distribution within the FH. In the second stage, design flood hydrographs are used to estimate the flood risk by conducting reservoir routing. Finally, the total flood risk is calculated by using the copula method to combine the flood risks from the first and second stages. Results for a case study using China’s Three Gorges Reservoir indicate that an effective FH can be identified to minimize flood risk, and balance between the forecast accuracy and length of the horizon. The length of the effective FH is not fixed and depends on reservoir inflow. Moreover, larger errors will not always lead to greater flood risks. The proposed method provides useful information on flood risk for real-time reservoir operation.

Inference for bivariate extremes via a semi-parametric angular-radial model

AbstractThe modelling of multivariate extreme events is important in a wide variety of applications, including flood risk analysis, metocean engineering and financial modelling. A wide variety of statistical techniques have been proposed in the literature; however, many such methods are limited in the forms of dependence they can capture, or make strong parametric assumptions about data structures. In this article, we introduce a novel inference framework for bivariate extremes based on a semi-parametric angular-radial model. This model overcomes the limitations of many existing approaches and provides a unified paradigm for assessing joint tail behaviour. Alongside inferential tools, we also introduce techniques for assessing uncertainty and goodness of fit. Our proposed technique is tested on simulated data sets alongside observed metocean time series’, with results indicating generally good performance.

Prepare for the sustainability era: A quantitative risk analysis model for container shipping sustainability-related risks

Environmental, social, and governance (ESG) issues have attracted global stakeholders' attention, reflecting the focus of sustainability development in this sustainability era. Container shipping is confronted with a wide spectrum of sustainability-related risks during the ESG transformation. Theoretically, this research identifies 36 sustainability risks of container shipping, establishes a customised evaluation framework for container shipping sustainability risks, and quantifies the risks regarding their threatening levels. The five most threatening risks are ‘reinforcement of international emission control policy’, ‘global climate change’, ‘shipping equipment failure’, ‘fuel price volatility’, and ‘ship violates the emission control policy in port area’. The results show that the long-term likelihood as well as environmental and profit impacts of sustainability risks are the most important criteria. Managerially, this research shows that environmental risks are generally the most prioritised risks, while social risks are not significantly impactful to container shipping companies. However, among the social risks, seafarer-related risks are worth paying attention.

Quantitative risk analysis of road transportation of hazardous materials in coastal areas

Given the complex climate conditions in coastal areas and their role as key transportation hubs for hazardous chemicals, this study proposes a method to quantitatively and comprehensively evaluate transportation risks. Initially, accident data were analyzed to identify risk factors from five aspects: human, vehicle, materials, environment, and management, based on system safety theory. Subsequently, a risk analysis model was developed using Decision-making Trial and Evaluation Laboratory, interpretive structural model theory, and Bayesian theory to quantitatively assess accident risk levels. The model was applied to a case involving a hazardous chemical accident on a cross-sea bridge, where Bayesian backward reasoning was used to analyze the sensitivity and importance of risk factors. This approach facilitated the key risk factors affecting the safety of hazardous chemical transportation systems. Notably, the study incorporated scenarios involving hazardous material transport vehicles crossing sea bridges into the risk assessment framework, offering valuable insights for management authorities. It also considered the impact of strong side winds-a factor often overlooked-in hazardous material transport. The validation process demonstrated that the method accurately quantifies the risk of hazardous chemical transportation and identifies the key factors influencing accident occurrence. The research highlights that strong gusts of wind significantly impact safety, and human factors are crucial in the overall risk system.

Cascade failure risk analysis of mixed reservoirs based on disaster spreading theory

The reservoirs in the basin are connected in series or in parallel, forming a mixed reservoir group. Failure of a single dam in a mixed reservoir group may lead to subsequent failures of downstream dams, resulting in a cascading failure of the reservoir group. However, existing studies mainly focus on the impact of the upstream dam failure on the downstream dams, and lack a comprehensive consideration of the risk level of the upstream dams, the dam's own risk, and its own warning and flood release capacity. This limitation affects the accuracy of the actual risk calculation results of mixed reservoir dams. In this paper, the cascading failure risk chain is determined using graph theory, and a cascade failure risk analysis model based on disaster spreading theory is developed by combining dam break flood simulation and reservoir flood routing theory. The cascade failure risk is assessed in terms of the probability and the duration of a cascading failure. The model is applied to six mixed reservoirs in the Wujiang River Basin, China. The sensitivity of the model parameters is analyzed by examining a specific node, and the feasibility and effectiveness of the model are verified.

Future security threats arising from the UK’s deprivation of citizenship: a model to understand the human rights-security risk landscape

ABSTRACT Following the collapse of the Islamic State, the issue of “Returning Foreign Fighters” became a dominant global problem. The securitised response adopted by many states is a cause for concern, particularly in relation to human rights. Men, women and children with a range of physical and psychological trauma are now situated in camps across northeastern Syria, posing a complex security threat. The situation in these camps is far from secure, and the risk of radicalisation and indoctrination (among the children in particular) is high. Through the development of a model of risk analysis which emphasises both security risks and human rights risks, this paper presents an innovative exploration of the phenomenon of women and children associated with ISIS in north-east Syria. The impact of this study is wide-reaching and is intended to provide a foundation for resolving the heightened security threat these individuals pose in the medium and long terms.

Integrating STPA Extended for Coordination into SysML Using RAAML

AbstractAs Model‐Based Systems Engineering (MBSE) becomes prevalent in engineering practice, the Department of Defense (DoD) requires a consistent methodology to conduct and record system safety analyses in the system model. Systems Theoretic Process Analysis (STPA) is a relatively new safety and hazard analysis method that utilizes the principles of Systems Theory and abstraction to analyze today's complex systems. Systems Theoretic Process Analysis Extended for Coordination (STPA‐Coord) provides a framework to design safe coordination among a system‐of‐systems architecture, which is needed for next‐generation integrated military systems. This research presents results from conducting an STPA‐Coord in Systems Modeling Language (SysML) using Risk Analysis and Assessment Modeling Language (RAAML), a recent extension to SysML that provides tools and guidance for multiple safety analyses. Results describe deviations from the RAAML standard and suggest modifications to RAAML for STPA‐Coord. Results include qualitative and quantitative observations conducting an STPA‐Coord using SysML, including time required for the effort and observed benefit over document‐based methodologies.

OMG Standard to Extend SysML to Reliability Engineering

AbstractThis paper describes a Reliability Block Diagram (RBD) Library that has been added to the Risk Analysis and Assessment Modeling Language (RAAML) version 1.1, the Object Management Group (OMG) standard for safety and reliability extensions for SysML. A discussion of relevant reliability concepts is provided followed by a description of the RBD library. An example of the library using an example system is described, followed by plans for further reliability enhancements in future versions of RAAML.

Effect of COVID-19 on Thrombosis Incidence and Patient Prognosis in Kidney Transplant Recipients.

BACKGROUND Thrombosis poses a grave threat to patients undergoing kidney transplants, with a heightened risk of mortality. While previous studies have established a link between COVID-19 and thrombosis, the specific association between COVID-19 and thrombosis in this patient population remains unexplored. MATERIAL AND METHODS We conducted a retrospective analysis utilizing data from 394 individuals who underwent kidney transplantation within the period of September 1, 2015, to April 1, 2023. To evaluate overall survival, we employed Kaplan-Meier analysis and utilized a logistic regression model for risk analysis. Furthermore, we developed a prediction model and assessed its accuracy through calibration curves. RESULTS Out of the 394 patients included in our study, a total of 51 individuals experienced thrombosis, resulting in 2 deaths. Our analysis revealed that COVID-19 infection significantly increased the risk of thrombosis (odds ratio [OR] 8.60, 95% confidence interval 3.13-24.74, P<0.01). Additionally, the use of cyclosporine was found to elevate the risk of death (OR 20.86, 95% CI 7.93-59.24, P<0.01) according to multifactorial analysis. Logistic models were employed to screen variables, and predictive models were constructed based on the presence of COVID-19 infection and the usage of cyclosporine. A nomogram was developed, demonstrating promising accuracy in estimating the risk of thrombosis during internal validation, with a corrected C-index of 0.869. CONCLUSIONS Our study suggests that both COVID-19 infection and the use of cyclosporine can serve as reliable predictors of thrombosis risk in patients undergoing renal transplantation. Furthermore, we developed a mortality risk prediction model based on COVID-19 in assessing thrombosis.

Risk analysis of lithium-ion battery accidents based on physics-informed data-driven Bayesian networks

The catastrophic consequences of lithium-ion battery (LIB) accidents have attracted high attention from society and industry. Accordingly, risk analysis is indispensable for the risk prevention and control of LIBs. Nevertheless, it is difficult to establish a physics-informed risk analysis model due to the complex material characteristics and aging mechanisms of LIBs. Meanwhile, the data-driven approach requires historical information of LIBs and does not merely rely on knowledge of the internal mechanisms of LIBs. This study proposes a method integrating the physics-informed Bayesian network (BN) (i.e., mapping from fault tree) and data-driven BN (i.e., learning from data) to conduct risk analysis of LIBs. First, we establish physics-informed and data-driven BNs. Subsequently, we bridge physics-informed and data-driven BNs to establish a Bayesian network for risk analysis of LIB accidents. Second, we set up safety barriers in the system, including detectors, emergency response, and firefighting facilities. Third, we evaluate the performance of safety barriers. We validate the proposed model using data from LIBs in air transportation. The results indicate that safety barriers can reduce the accidental risk of LIBs. Eventually, we propose suggestions for the risk control of LIBs in air transportation. This study is supposed to provide theoretical basis for the risk prevention and control of LIB accidents.

Framework for process risk analysis of maritime accidents based on resilience theory: A case study of grounding accidents in Arctic waters

The complexity and non-linear interactions involved in maritime accidents make it challenging to apply traditional models of risk analysis to them. Resilience is adopting a perspective focuses on holistic aspects of safety across the pre-accident, mid-accident, and post-accident phases. This paper proposes a framework for process risk analysis of maritime accidents based on resilience theory by applying the function resonance analysis method (FRAM). We first identify a three-layer maritime accident based on maritime accident investigation reports and then analyze the potential variability of all relevant functions. Subsequently, a FRAM model is constructed to analyze the accident's functional resonance of accidents by aggregating variations in the functions. Finally, we apply Monte Carlo simulations to compute the values of coupling of all interconnected functions. The ship grounding accidents in Arctic waters is chosen as the case to demonstrate the proposed framework. The relationships of influence between the functions were analyzed to obtain critical coupling pathways of coupling between them, and a time-weighted PageRank algorithm was used to identify key functions. The results show that the proposed approach can reveal vulnerabilities and instabilities in the system, and enables the proposal of targeted, long-term measures of risk control to enhance its resilience and safety.

Making The Best Investment Strategy for Insurance Companies Under The Background of Extreme Weather

In the face of the negative impacts of the frequent occurrence of extreme weather disasters around the world, we have developed natural disaster risk prediction and analysis models for insurance companies to maximize their benefits. This paper first selects six risk indicators, such as flood, cold wave, drought and other disasters, and combines the analytic hierarchy process (AHP) and grey prediction model (GM) to calculate the impact weights of six kinds of natural disasters, and predicts the probability of future occurrence, so as to realize the comprehensive assessment of natural disaster risk. This paper also analyzes the effect of the owner's intention to buy insurance on the insurance strategy through the function model, and solves the optimal insurance strategy through the technique for order preferenceby similarity to ideal solution (TOPSIS) and neural network. We define the ratio of the total profit of natural disaster insurance to the total cost as the decision coefficient K. When K is greater than 35%, the insurance company chooses to write the policy, and when K is greater than 5% and less than 35%, the insurance company chooses to bear the risk. Finally, this paper collected the data of Florida and Kyushu, Japan. Through the insurance model, it calculated that the total profit of natural disaster insurance in Florida was 635.8 million US dollars, while that in Kyushu was -61.51 million US dollars. Therefore, insurance companies should invest in Florida.

Case-based risk analysis model for rainstorm inundation in metro systems based on a bayesian network

Case-based risk analysis model for rainstorm inundation in metro systems based on a bayesian network

Risk assessment of unmanned aerial vehicle accidents based on data-driven Bayesian networks

With the popularity of unmanned aerial vehicles (UAVs) in different application scenarios, a series of potential risks and challenges have emerged. Few quantitative studies in the existing literature analyze the risk factors that affect and cause UAV accidents. Therefore, this study aims to reveal the dependency relationships among the risk factors associated with UAV accidents by using a data-driven tree-augmented naïve Bayesian network (TAN-BN) method. Firstly, UAV accident reports from transportation departments of various countries are collected and analyzed to establish a dataset on the risk factors inducing different UAV accidents. Then, a data-driven UAV risk analysis model is constructed through data learning and training approaches to reveal the interdependency of risk factors and their comprehensive impacts on the severity of UAV accidents. Sensitivity analysis is comprehensively conducted to show the rationality of the proposed TAN-BN. The results reveal that the risk factors “other aircraft”, “observer errors”, “non-powerplant”, “preconditions for unsafe acts”, “powerplant”, and “adverse weather” are the key factors resulting in UAV accidents. In addition, “observer errors”, “other aircraft”, “non-powerplant” and “adverse weather” are more likely to incur severe “accidents”. Some potential proactive plans and measures are proposed to help prevent the occurrence of UAV accidents.

A qualitative risk assessment model for water reuse: Risks related to agricultural irrigation in Brazil

Urbanization and industrialization are increasing extreme weather events, causing water quantity and quality reduction. Global water scarcity impacts 32.5 % of the urban population and is growing. Brazil has also witnessed water scarcity, notably in the southeast (2014–2015) and south (2019–2020), with reservoirs dropping below 20 % capacity. Water reuse is vital for mitigating scarcity, though it presents risks due to contaminants. Risk analysis studies are crucial for evaluating contamination sources, pathways, and exposure scenarios in water reuse practices. Various methodologies, including quantitative, semi-quantitative, and qualitative analyses, can be employed. Given the uncertainty and diverse factors, qualitative methods are recommended for non-potable water reuse risk analysis. This work presents a qualitative risk analysis methodology that allows to evaluate non-potable water reuse categories. It assesses factors affecting human health and the environment, considering exposure scenarios, characteristics of the receptors, and sources of reused water. The risk analysis of water reuse was carried out focusing on agricultural reuse, considering as alternatives the irrigation of soybean and sugarcane crops. By reviewing literature, the probability of occurrence and the magnitude of impact of the risk factors were identified and rated, using an increasing relative numeric scale. This process resulted in an overall risk value for comparing agricultural irrigation alternatives. The obtained results indicate a promising risk analysis model that can be adjusted and applied to various water reuse modalities and key factors. This adaptable risk analysis model is mainly related to water treatment methods, prompting the proposal of risk control measures.