Risk Evaluation Approach Research Articles

Design prognostics for 4400 TEU container vessel by multi‐variate Gaidai reliability approach

AbstractThis case study introduces an innovative multivariate methodology for assessing the lifetime of marine engineering systems, specifically in cargo vessel transportation. The analysis focused on stress data collected onboard a 4400 TEU container vessel during multiple trans‐Atlantic voyages. One of the major challenges in marine cargo transport lies in mitigating the risk of container loss due to excessive whipping loads. Accurate prediction of extreme stress levels on vessel deck panels remains difficult, primarily because of the nonlinear and non‐stationary nature of wave and ship motion interactions. Higher‐order dynamic effects, such as second‐ and third‐order responses, often become significant when ships operate under adverse environmental conditions, amplifying nonlinear influences. Laboratory simulations, constrained by wave characteristics and scale similarity issues, may not always provide reliable results. Consequently, data collected from vessels navigating extreme weather conditions serves as a critical resource for comprehensive container ship risk assessment. The primary goal of this study was to validate and demonstrate the effectiveness of a novel multivariate risk evaluation approach, leveraging onboard measurements of dynamic areal pressure on cargo ship deck panels as the core dataset. The Gaidai methodology for multivariate risk evaluation proved to be a robust tool for assessing failure, hazard, and damage risks in complex, nonlinear vessel deck panel and ship hull stress systems.

RISK ASSESSMENT AND DECISION-MAKING IN GEOLOGY-DRIVEN PROJECTS: A MANAGEMENT PERSPECTIVE

Executing many civil projects is arduous due to the many associated dangers. Risk Assessment (RA) is crucial for selecting building projects and managing construction activities. RA examines adverse events in the preparation and scheduling phases of building projects. Construction risk refers to the vulnerability to possible losses. Given the construction sector's significance to a nation's economy, it is imperative to investigate and analyze diverse risk identification and management solutions. This paper presents a methodology developed through a real-time case study to assess risk in building projects. Risk can be described as determining the probability, intensity, and exposure of all hazards related to a building phase. The research advocates for a combined approach for risk evaluation in building projects utilizing RA and the Geological Information System (GIS) for decision-making. The research identifies various hazards in building projects via the study of the literature research and the formulation of surveys. The suggested study documents the replies from diverse building projects to the specified surveys. The findings of the proposed study identify the primary hazards in building projects. The research advocates for a comprehensive spatial risk evaluation and a GIS-based approach for diverse building projects.

A Data-Driven Approach for Battery System Safety Risk Evaluation Based on Real-World Electric Vehicle Operating Data

A Data-Driven Approach for Battery System Safety Risk Evaluation Based on Real-World Electric Vehicle Operating Data

Limit hypersurface state of the art Gaidai multivariate risk evaluation approach for offshore Jacket

Current study presents state of the art approach to risk and reliability assessment for multivariate nonlinear dynamic systems. Specifically, novel hypersurface Gaidai risks evaluation methodology has been presented for evaluating offshore structural risks. Advocated approach being particularly suitable for offshore engineering multidimensional dynamic systems, possessing large number of critical components, that have either been physically observed/measured, or numerically modeled over a representative timelapse. Advocated Gaidai hypersurface structural risks evaluation methodology is applicable for a wide range of engineering and industrial systems. This study demonstrates that given in situ environmental conditions, it is well possible to assess accurately risks of system’s failures, damages or hazards, caused by excessive structural dynamics. Multivariate dynamic systems, possessing nonlinear cross-correlations between critical system’s components often present design challenges, when utilizing classic structural risks evaluation approaches, as those are mostly only univariate or bivariate. Dynamic offshore Jacket hot spot stresses have been utilized as an example in this reliability investigation. Modeling system’s excessive dynamics is challenging because of the non-stationarity of the system and the intricacy of fluid-structural dynamic interactions, arising from in situ wave-induced loads, influencing Jacket’s structural dynamics. Nonlinearities greatly affect structural dynamics, e.g., 2nd, 3rd, higher order effects within fluid-structural interactions. Methodology presented in this work offers a straightforward, effective, yet precise means of assessing the risks of failure or hazard for multivariate, non-stationary, non-linear dynamic offshore systems. For bivariate case, verification note has been added.

Pseudomonas aeruginosa infection correlates with high MFI donor-specific antibody development following lung transplantation with consequential graft loss and shortened CLAD-free survival

BackgroundDonor-specific antibodies (DSAs) are common following lung transplantation (LuTx), yet their role in graft damage is inconclusive. Mean fluorescent intensity (MFI) is the main read-out of DSA diagnostics; however its value is often disregarded when analyzing unwanted post-transplant outcomes such as graft loss or chronic lung allograft dysfunction (CLAD). Here we aim to evaluate an MFI stratification method in these outcomes.MethodsA cohort of 87 LuTx recipients has been analyzed, in which a cutoff of 8000 MFI has been determined for high MFI based on clinically relevant data. Accordingly, recipients were divided into DSA-negative, DSA-low and DSA-high subgroups. Both graft survival and CLAD-free survival were evaluated. Among factors that may contribute to DSA development we analyzed Pseudomonas aeruginosa (P. aeruginosa) infection in bronchoalveolar lavage (BAL) specimens.ResultsHigh MFI DSAs contributed to clinical antibody-mediated rejection (AMR) and were associated with significantly worse graft (HR: 5.77, p < 0.0001) and CLAD-free survival (HR: 6.47, p = 0.019) compared to low or negative MFI DSA levels. Analysis of BAL specimens revealed a strong correlation between DSA status, P. aeruginosa infection and BAL neutrophilia. DSA-high status and clinical AMR were both independent prognosticators for decreased graft and CLAD-free survival in our multivariate Cox-regression models, whereas BAL neutrophilia was associated with worse graft survival.ConclusionsP. aeruginosa infection rates are elevated in recipients with a strong DSA response. Our results indicate that the simultaneous interpretation of MFI values and BAL neutrophilia is a feasible approach for risk evaluation and may help clinicians when to initiate DSA desensitization therapy, as early intervention could improve prognosis.

Unlocking the complete blood count as a risk stratification tool for breast cancer using machine learning: a large scale retrospective study

Optimizing early breast cancer (BC) detection requires effective risk assessment tools. This retrospective study from Brazil showcases the efficacy of machine learning in discerning complex patterns within routine blood tests, presenting a globally accessible and cost-effective approach for risk evaluation. We analyzed complete blood count (CBC) tests from 396,848 women aged 40–70, who underwent breast imaging or biopsies within six months after their CBC test. Of these, 2861 (0.72%) were identified as cases: 1882 with BC confirmed by anatomopathological tests, and 979 with highly suspicious imaging (BI-RADS 5). The remaining 393,987 participants (99.28%), with BI-RADS 1 or 2 results, were classified as controls. The database was divided into modeling (including training and validation) and testing sets based on diagnostic certainty. The testing set comprised cases confirmed by anatomopathology and controls cancer-free for 4.5–6.5 years post-CBC. Our ridge regression model, incorporating neutrophil–lymphocyte ratio, red blood cells, and age, achieved an AUC of 0.64 (95% CI 0.64–0.65). We also demonstrate that these results are slightly better than those from a boosting machine learning model, LightGBM, plus having the benefit of being fully interpretable. Using the probabilistic output from this model, we divided the study population into four risk groups: high, moderate, average, and low risk, which obtained relative ratios of BC of 1.99, 1.32, 1.02, and 0.42, respectively. The aim of this stratification was to streamline prioritization, potentially improving the early detection of breast cancer, particularly in resource-limited environments. As a risk stratification tool, this model offers the potential for personalized breast cancer screening by prioritizing women based on their individual risk, thereby indicating a shift from a broad population strategy.

More than malware: unmasking the hidden risk of cybersecurity regulations

Cybersecurity investments are made within a complex and ever-evolving environment, where regulatory changes represent a significant risk factor. While cybersecurity regulations aim to minimize cyber risks and enhance protection, the uncertainty arising from frequent changes or new regulations can significantly impact organizational response strategies. This paper explores the determinants and implications of regulatory risks associated with cybersecurity, aiming to provide a deeper understanding of how these risks influence strategic decision-making. The study delves into the suggestion of preventive and mitigative controls that enable businesses to adapt to and mitigate potential disruptions caused by regulatory changes, thereby preserving their established cybersecurity practices. Another key contribution of this study is the introduction of a stochastic econometric model that illustrates how regulatory risks and uncertainties can affect investment behaviors, often prompting a “wait-and-see” stance. This model synthesizes the complex relationship among investment choices, regulatory changes, and cybersecurity risks, providing insights into the dynamic nature of cybersecurity investment strategies. The research findings offer valuable guidance for risk management and strategic planning in cybersecurity investments. By comprehensively understanding the drivers and impacts of regulatory risks, businesses and policymakers can develop more effective risk evaluation and management approaches. This is essential for sustaining a strong cybersecurity posture while navigating the changing regulatory environment.

Public Private Partnership to Brownfield Remediation Projects in China: A Combined Risk Evaluation Approach

Brownfields have been receiving significant attention all over the world because of their potential threats to the environment and public health. However, a funding shortage constitutes the main obstacle to the brownfield remediation (BR). In China, to ease financial dilemmas, the governments seek collaborations with private-sector companies, i.e., the Public Private Partnership (PPP) mode. Despite all the benefits, BR and PPP contain high risks, making stakeholders extremely cautious about investing in such projects. To support the decision-making process of the public and private parties, this paper designs a comprehensive approach to evaluate the risks of BR PPP projects in China. In more detail, several commonly used risk methods, such as TOPSIS, GRE, and FSE, are employed to construct a combined risk evaluation process, which applies multiple combined evaluation techniques to iteratively integrate individual results from those methods until a valid common result is achieved. To show the practical implementation procedure of the proposed combined approach, a hypothetical case study is performed to assess the risks of seven BR PPP projects. The analytical process also verifies that the consistency and reliability of the risk evaluation result can be achieved effectively and efficiently by jointly deploying multiple risk methods through combined techniques. The proposed decision framework facilitates a novel research idea in evaluating complicated risk situations, and can be applied to other similar scenarios where uncertainties and inconsistencies are inevitable.

A Multi-Hazard Risk Assessment Framework for Urban Disaster Prevention Planning: A Case Study of Xiamen, China

Understanding and measuring the relative risk level of a city facing multi-hazards is fundamental to improving its disaster prevention planning and schemes. A comprehensive risk evaluation approach stands at the intersection of risk management and disaster system theory. It is also an important interdisciplinary field of catastrophology, economics, and urban infrastructure planning. We believe that current attempts to define and measure comprehensive urban natural disaster risks have certain limitations. Therefore, we propose an Urban Multi-hazards Risk Assessment Framework (UMRAF) which draws on definitions, methods, and experience from risk management, evaluation of property, the value of statistical life, and disaster system theory. It contains local disaster identification, place-based risk assessment (taking into account more than one hazard at a time), urban anti-disaster capability assessment, and relative composite risk index measurement. In our case study of Xiamen, China, to check the feasibility of our UMRAF, we examined local multi-hazards risk distribution and urban anti-disaster capacity layout. We then expanded and visualised the spatial distribution of the relative composite risk index of each evaluation unit across the city via our analyst tool, thereby helping to tailor measures that can reduce risk at a local level.

A decision support method for credit risk based on the dynamic Bayesian network

PurposeCredit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.Design/methodology/approachFirst, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.FindingsCompared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.Originality/valueThe authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

Research on the risk evaluation of urban wastewater treatment projects based on an improved fuzzy cognitive map

To address the issues of ambiguous uncertainty and conflicting evidence in risk evaluation of wastewater treatment public-private partnership (PPP) project, a risk evaluation approach based on an evidence-based reasoning (ER)-improved fuzzy cognitive map (FCM) and 15 secondary evaluation indexes system are proposed. Then, ER is used to fuse expert opinions to obtain a correlation matrix. Finally, the rank evaluation results are obtained by FCM inference. External environmental risk (S), investment and financing risk (F) are the risk factors that have the greatest impact and sensitiveness on PPP wastewater treatment projects. For predictive analysis, the maximum project risk value was 0.5523 when the S value was set to 1, followed by the project risk value when the F value was set to 1. For diagnostic analysis, the greatest change in S value converged to 0.4978 when all risk factors were set to a neutral value of 0.5, while the greatest change in project risk was observed when all risk factors were set to 1. In addition, policy and legal risk, economic risk and market risk are the most sensitive risk factors among all secondary risk factors. The results are able to provide reliable project risk assessment solutions for project decision makers.

Fine-Kinney-Based Occupational Risk Assessment using Pythagorean Fuzzy AHP-COPRAS for the Lifting Equipment in the Energy Distribution and Investment Sector

The initial risk assessment, especially when using a risk score system, is the main step in a risk assessment process that comes after determining the scope of the risks and assessment. The Fine-Kinney method, a thorough approach to quantitative assessments to help keep risks under control, is commonly used in risk assessment. A risk score (RS) is determined using the standard version of Fine-Kinney by mathematically multiplication of probability (P), exposure (E), and consequence (C) parameters. The Fine-Kinney-based risk evaluation approach has the disadvantage of not accounting for the relationships among the risk parameters' interaction and determining the risk precedence of work-related hazards. Hence, a new hazard evaluation method for occupational health and safety (OHS) is required to lessen the adverse effects of rising dangers. In this paper, a novel approach is proposed for integrating Fine–Kinney-based occupational hazard evaluation and AHP-COPRAS for the energy distribution and investment sector under the Pythagorean fuzzy environment. A lifting equipment case study is used to demonstrate practicality and efficacy of the suggested integrated approach. To verify the novel method to risk assessment, a comparative study and sensitivity analysis are also provided. As a result, using the benefit of Pythagorean fuzzy sets, which more effectively express uncertainty, the integrated approach yields more logical conclusions for assessing work-related hazards in the energy distribution and investment sector.

Development, stability-Indicating assessment, and evaluation of influential method conditions using a full factorial design for the determination of Nintedanib Esylate related impurities.

The design of an appropriate analytical method for assessing the quality of pharmaceuticals requires a deep understanding of science, and risk evaluation approaches are appreciated. The current study discusses how a related substance method was developed for Nintedanib esylate. The best possible separation between the critical peak pairs was achieved using an X-Select CSH Phenyl Hexyl (150 × 4.6) mm, 3.5 μm column. A mixture of water, acetonitrile, and methanol in mobile phase-A (70:20:10) and mobile phase-B (20:70:10), with 0.1% trifluoroacetic acid and 0.05% formic acid in both eluents. The set flow rate, wavelength, and injection volumes were 1.0 mL/min, 285 nm, and 5 μL, respectively, with gradient elution. The method conditions were validated as per regulatory requirements and United States Pharmacopeia general chapter <1225>. The correlation coefficient for all impurities from the linearity experiment was found to be >0.999. The % relative standard deviation from the precision experiments ranged from 0.4 to 3.6. The mean % recovery from the accuracy study ranged from 92.5 to 106.5. Demonstrated the power of the stability-indicating method through degradation studies; the active drug component is more vulnerable to oxidation than other conditions. Final method conditions were further evaluated using a full-factorial design. The robust method conditions were identified using the graphical optimization from the design space. This article is protected by copyright. All rights reserved.

An Evidential Reasoning Rule-Based Ensemble Learning Approach for Evaluating Credit Risks with Customer Heterogeneity

Credit risk evaluation has been vital for financial institutions to identify default customers and to avoid financial loss. Machine learning and data mining techniques have been adopted to develop scoring models for enhancing the prediction performance of default customers. However, it is difficult for these machine learning models for explaining the rejection or approval decision-making process to customers and other non-technical personnel. This paper presents an evidence reasoning (ER) rule-based ensemble learning approach for credit risk evaluation considering customer heterogeneity. Firstly, customers are segmented into different groups by [Formula: see text]-means clustering algorithms and a two-stage weighting method is proposed to determine the significances of attributes by their discriminating powers between groups and within groups. Then, the attribute-related evidence is obtained by Bayesian statistics to represent the relationships between the attributes and credit risks, and a two-stage weighting evidential reasoning (TER) is developed as a base learner for credit scoring. Lastly, multiple base learners TERs are aggregated for evaluating customers’ credit risks. An empirical study on three credit datasets demonstrated that the proposed approach can achieve high performance with good explainability. The predicted results of the model can be well comprehended by providing the contribution of attributes and the activated rules in evidential reasoning processes.

System Economy Improvement and Risk Shortening by Fuel Cell-UPFC Placement in a Wind-Combined System

It is important to understand the features of an integrated renewable energy power system, especially for deregulated systems. The greatest obstacle to assimilating renewable energy generators with the existing electrical system is their unpredictability. Because wind energy is inconsistent, incorporating it into an established power system necessitates more planning. The effects of wind farm (WF) incorporation with fuel cells and a unified power flow controller (UPFC) on electric losses, voltage profile, generating price, and the economics of the system in a deregulated power market are examined in this paper. An impact analysis of integrating wind farms into controlled and uncontrolled situations is conducted. At two randomly selected locations in India, the real-time statistics of the actual wind speed (AWS) and forecasted wind speed (FWS) were merged for this study. The surplus charge rate and deficit charge rate are intended to evaluate the imbalance cost which is arising from the difference between anticipated and true wind speeds to determine the economics of the system. Customers are always trying to find electricity that is reliable, inexpensive, and efficient due to the reconfiguration of the power system. As a consequence, the security limitations of the system may be surpassed or might function beyond the safety limit, which is undesirable. In the last section, heuristic algorithms, such as sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth-flame optimization algorithms (MFO), are employed to analyze economic risk. In the real-time energy market, it also covers how the fuel cells and UPFC are utilized to rectify the WF integration’s deviation. Economic risk evaluation approaches include value-at-risk (VaR) and conditional value-at-risk (CVaR). A modified IEEE 30-bus test system is used throughout the whole project.

A novel approach for fine-grained traffic risk characterization and evaluation of urban road intersections

An urban road intersection environment with low traffic risk is an essential component of sustainable cities. Various risk analysis and simulation techniques have been developed to measure and evaluate traffic risks. However, flexible assessment of intersection risk at a fine resolution remains a great challenge. This study proposes a novel approach for characterizing the field distribution of traffic risks at an urban road intersection and assessing traffic risks in multiple dimensions to increase the flexibility of risk assessment. First, we define a new traffic risk metric to quantify the risk of intersection traffic conflict points by combining their frequency and severity. Second, the plume diffusion model is introduced to model the non-linear relationship between traffic conflict points by considering the propagation of the traffic risk in the intersection. In this way, the entire intersection risk distribution map is derived for real-time risk analysis. Finally, a multidimensional hierarchical evaluation system of traffic risk at intersections is designed, including the traffic risk indicators at the intersection, path, and turn mode levels. The results of a case study in Wuhan, China, demonstrate the continuous characterization results of the risk field and provide multidimensional risk assessment index results, which are highly consistent with the cognitive results of manual risk scoring (up to 90% similarity). It also allows for risk assessment in a single path and turn modes through the risk field characterization and reveals the spatiotemporal dynamic risk distribution, thus providing an alternative tool for risk decision-making at urban road intersections.

State-of-the-Art in Evaluation Approaches for Risk Assessment of Insider Threats to Nuclear Facility Physical Protection Systems

Insiders present a challenging threat to physical protection systems at nuclear facilities. This paper evaluates the efficacy of traditional nuclear security risk evaluation approaches with respect to evaluating insider threat risk.

Development of an ensemble Bayesian inference‐based copula approach for bivariate risk evaluation of extreme precipitation under climate change

AbstractIn this study, an ensemble Bayesian inference‐based copula approach (EBICA) has been proposed to evaluate the joint risk of extreme precipitation indices under climate change. EBICA incorporates GCMs, quantile delta mapping‐based bias correction method, Monte Carlo Markov Chain (MCMC)‐based Bayesian inference method, and copula functions into an integration, where 17 GCMs and 3 copulas are explored EBICA can reflect the inherent uncertainties in GCM and copula selections, as well as deal with the uncertainty caused by copula parameters when modelling the dependent structures. Ensemble results of GCMs and copulas are also analysed to promote the projection accuracy of dynamic features of extreme precipitation (characterized by precipitation intensity, very heavy precipitation amount and frequency, and maximum 1 or 5‐day precipitation amount). EBICA is then applied to Fujian Province to investigate climate change impacts on extreme precipitation during 2021–2099. Major findings can be summarized as (a) GCM is the primary source of uncertainties that impacts extreme precipitation projection, and the ensemble of GCMs suggests that climate change would exacerbate future precipitation intensity, amount, and frequency over Fujian Province; (b) mountainous area in Fujian is more easily encountered with very heavy precipitation amount and coastal area is more easily faced with maximum 1‐day or consecutive 5‐day precipitation under climate change; (c) Fujian Province would averagely encounter with shorter co‐occurrence period of [SDII, R95p] and [R20mm, R95p], corresponding to higher risk level in very heavy precipitation amount and frequency, especially in the north part. Generally, these findings could help lay the foundation for forecasting and warning of extreme precipitation events and reducing associated risk losses.

A Dissolved Gas Assessment Model for Power Transformers According to Weighted Association Rule Mining

As one indispensable part of power systems, the reliable-operated power transformers are vital for energy transmission, whereas they are remarkably threatened by potential fault events. To achieve the satisfying and valid operation of power transformers, any fault events that may impact their health ought to be evaluated and early warned. With such motivations, this paper presents original insights on the assessment of power transmission health states via their internal dissolved gas, and an enhanced Association Rule Mining (ARM) model incorporating the analysis of High-Impact-Low-Probability (HILP) components, as well as a dynamic fault event risk evaluation approach, is proposed. The first step is to differentiate the risky components. Unlike the standard ARM, the rarely occurred components in each feature can also be assessed explicitly as the common components to explore the underlying HILP components in the proposed model, rather than just being viewed as trivial data and directly omitted. The second step is to rate the risk level of each risky component. A component importance measure-based evaluation approach is deployed to assess the corresponding risk weights of distinguished risky components. In this approach, the risk weight is determined straightforwardly via the impacts of each component on the variation level of total risks in the system, rather than simply by its frequency of occurrence or data share. Finally, the parameters of the risk weight evaluation approach can be dynamically adapted in an adjustment framework as well. This model is testified through an empirical case study, and the leading results can demonstrate its flexibility and robustness during real applications.

Financial Risk Management of Listed Companies Based on Mobile Edge Computing

As more and more listed companies have more and more operating experience, they are all paying more attention to the importance of financial risks, and they all hope to detect financial risks in a timely manner and intervene in the company’s operation process. However, the existing business risk management cannot fully identify the more and more hidden risks in modern times. Based on this, the purpose of this article is to assess the financial risk of publicly traded corporations and summarizes a suitable risk evaluation model. For the identification of financial risks, this study presents a mobile edge computing-based financial risk evaluation approach for publicly traded corporations. The evaluation indicators are stratified into three levels, and mobile edge computing is used to optimize the weights. The download calculation for mobile edge computing is also optimized and analyzed. Experiments show that the indicators selected in this paper are very suitable for the study of financial risk management of listed companies. The three-year comprehensive index scores of Company X are 0.60, 0.42, and 0.42, respectively. It can be seen that the index is more suitable for the actual risk. It can be considered that the financial risk evaluation model of listed companies in this paper has better performance.