Risk Analysis Research Articles

Integration of fuzzy AHP and explainable AI for effective coastal risk management: A micro-scale risk analysis of tropical cyclones

The east coast of India, especially the coastal region of Odisha, is highly threatened by tropical cyclones. This study develops a detailed risk map for tropical cyclones in the coastal districts of Odisha at the micro level, focusing on the assessment of risk factors at the block level. Using a multi-criteria decision making (MCDM) approach, the study considers four primary risk components: Exposure, vulnerability, susceptibility, and mitigation options. The Explainable Artificial Intelligence (XAI) framework, which uses the XGBoost model in conjunction with SHAP values, is applied to identify and elucidate the factors influencing risk levels in 69 blocks. Results indicate that about 65% of the area is at high risk to tropical cyclone, especially in the northeastern and central regions. In particular, 32 blocks are classified as high to very high-risk zones. The study shows a contrast in risk levels, with blocks in the northeast and southeast at higher risk, while blocks in the southern regions such as Ganjam and Puri and in the central parts of Kendrapara and Baleswar districts are at lower risk. The findings from this study are crucial for local authorities to identify vulnerable areas and improve cyclone preparedness and risk management strategies in Odisha.

Transfer factors of 238U, 232Th, 40K, 210Pb to crops and radiation impact assessment in semi-arid environment

AbstractResearch on the safety of staple agricultural food products has always been one of humanity’s priorities and provides input for dose assessment models. Within this important priority, activity concentrations, transfer factors, and radiological effects of 238U, 232Th, 210Pb, and 40K were studied for selected crops in a village close to the NORM area, located in Central Anatolia region of Turkey. The RESRAD-onsite code has been used to assess the total dose rate. The simulation of the risk analysis covered 80 years. The maximum total dose of 0.5 mSv y−1 was obtained at t = 30 years.

Corporate Accounting Management Risks Integrating Improved Association Rules and Data Mining

With the development of the times, enterprises need to face more data in operational decision-making. Traditional data analysis strategies cannot handle the growing amount of data well, and the accuracy of analysis will also decrease when faced with uneven data types. The research uses a corporate accounting management risk analysis technology that combines big data algorithms and improved clustering algorithms. This method combines big data processing ideas with a clustering algorithm that incorporates improved weighting parameters. The results show that on the data sets DS1, DS2, and DS3, the NMI values of the GMM algorithm are all 0; while the NMI values of the MCM algorithm correspond to 0.9291, 0.9088 and 0.8881 respectively. At the same time, the Macro-F1 values of the Verify2 algorithm correspond to 0.9979, 0.9501, and 0.9375 respectively, and the recognition accuracy of the data remains above 85%. In the running time comparison, when the number of samples in the data set reaches 5,000, the calculation time of the Verify2 algorithm remains within 5 seconds. In terms of practical application results, the study selected the profitability risk indicators of 40 companies for analysis. After conducting risk ratings, it can be seen that companies No. 5, 6, 7, and 39 have the highest risk levels, and companies No. 33 and 34 have the highest risk levels. The lowest level. After conducting risk assessments on the 40 selected listed companies, the risk level of net asset income of each company remained at level 5, and the risk level of earnings per share remained at level 3. The above results show that this technology has good performance in terms of calculation accuracy and calculation time, can assess enterprise risks, and can provide data support for enterprise operation decisions.

Enhancing maritime transportation security: A data-driven Bayesian network analysis of terrorist attack risks.

Maritime terrorist accidents have a significant low-frequency-high-consequence feature and, thus, require new research to address the associated inherent uncertainty and the scarce literature in the field. This article aims to develop a novel method for maritime security risk analysis. It employs real accident data from maritime terrorist attacks over the past two decades to train a data-driven Bayesian network (DDBN) model. The findings help pinpoint key contributing factors, scrutinize their interdependencies, ascertain the probability of different terrorist scenarios, and describe their impact on different manifestations of maritime terrorism. The established DDBN model undergoes a thorough verification and validation process employing various techniques, such as sensitivity, metrics, and comparative analyses. Additionally, it is tested against recent real-world cases to demonstrate its effectiveness in both retrospective and prospective risk propagation, encompassing both diagnostic and predictive capabilities. These findings provide valuable insights for the various stakeholders, including companies and government bodies, fostering comprehension of maritime terrorism and potentially fortifying preventive measures and emergency management.

Quantitative analysis of toxicity risks in the operation of ammonia-fueled tugboats

As efforts to conserve energy and reduce emissions in the global shipping industry intensify, people are constantly looking for new types of energy to replace traditional fuels. Ammonia is considered a novel energy source due to its potential for carbon-neutral shipping when produced from renewable energy sources and is extremely promising for the shipping industry. In this study, the fuel toxicity risk of ammonia-fueled ships under operating conditions was quantified for the first time. The assessment process adopts the traditional quantitative risk assessment (QRA) framework with accident probability calculations based on Bayesian theory, and the process of partial quantification of consequences also guides the delineation of safety zones. The results of the study suggest that ammonia-powered tugboats are at a higher risk of toxic accidents during operation than accidents at sea, but both are within the acceptable limits of current international maritime safety and environmental standards. Finally, safety measures and risk mitigation strategies are proposed based on the study results. This provides important support in safety risk analysis for the design, production and operation of subsequent ammonia fueled vessels and is of great theoretical and practical importance.

Effect of temperature-dependent and hysteretic sorption in computational mould risk analyses of wood fibreboard sheathing

Wood is known for its complex hygroscopic properties. Previous studies show that both the temperature-dependency and the hysteresis in sorption should be included in the physical models, if the most accurate results are desired from simulations of building assemblies with wood-based materials. Literature is scarce on the topic, where the significance of these aspects on the reliability of mould-risk assessments is inspected. Exterior walls with glass wool insulation and LDF-sheathing were studied by using two different models with previously measured material data. Over 300 hygrothermal simulations were carried out, where mould-indices were computed on the warm side of the sheathing. According to both conventional and more sophisticated model, the studied walls perform well in cold climate if no driving rain leaks in. Temperature-dependent and hysteretic model yields slightly smaller mould-indices in general, but for certain leaky cases the difference is significant, which indicates the importance of sophisticated sorption-properties in modelling.

Gaussian process regression driven rapid life-cycle based seismic fragility and risk assessment of laminated rubber bearings supported highway bridges subjected to multiple uncertainty sources

Structural attributes, corrosion of steel rebars in reinforced concrete (RC) columns, thermal oxidative aging of inherent rubber in laminated rubber bearings (LRBs), replacement of LRBs, and the time-dependent seismic hazard level at the bridge site are well recognized as the main factors that affect the accurate assessment of the seismic performance of RC bridges implemented with LRBs. However, the probabilistic seismic demand model (PSDM) and probabilistic structural capacity model (PSCM) of deteriorated bridge components may not exhibit homoscedastic log-linearity. And the expansion of seismic fragility surfaces and seismic risk curves in the temporal dimension requires a considerable computational cost on the nonlinear analysis, which regretfully has not been well addressed in current studies. To fill in this gap, this study proposes a life-cycle based seismic fragility and risk assessment framework for LRBs supported RC bridges considering deterioration mechanisms of bridge components during their life cycles demonstrated by a three-span LRBs supported highway bridge considering multiple uncertainties such as the replacement interval for LRBs. In this framework, Gaussian process regression (GPR) is employed to effectively construct the PSDM and PSCM of deteriorated bridge components, and then the time-dependent seismic fragility surfaces of bridge components and bridge system are established. Life-cycle based seismic risk analysis is conducted to quantify the coupled time-dependent effects of deterioration and site seismic hazard on a newly built bridge during its design service life and a deteriorated bridge within its remaining service life, respectively. Results indicate that GPR can successfully capture the nonlinearity and heteroscedasticity characteristics of PSDM and PSCM, leading to a significant reduced number of nonlinear analysis cases to generate the time-dependent seismic fragility surfaces and seismic risk curves. Consequently, the proposed framework using GPR can significantly improve the accuracy and efficiency of life-cycle based seismic performance assessment when uncertainties of structural attributes, deterioration progress and seismic hazard are taken into consideration.

Risk Analysis in Building Renovations: Strategies for Investors

Risk Analysis in Building Renovations: Strategies for Investors

Extending X-reality technologies to digital twin in cultural heritage risk management: a comparative evaluation from the perspective of situation awareness

DT systems, characterized by real-time capabilities, high precision, and high integration, have become essential in various domains. In the context of cultural heritage, a DT system encompasses comprehensive information about heritage sites, contextual data, and expert knowledge, forming a complex dynamic system. The substantial volume of information and diverse sources significantly increases the cognitive load for management personnel in understanding on-site situations. This study, from the perspective of situational awareness, introduces X-reality technologies (VR and AR) into DT systems for cultural heritage risk management. The aim is to evaluate the effectiveness of different X-reality technologies in cultural heritage risk perception and their impact mechanisms. A total of 184 participants were divided into two groups and experienced three different applications (2D desktop, VR, and AR). Using situational awareness rating techniques, participants' responses were measured across three dimensions: attention resource demand, attention resource supply, and understanding of the situation. SEM was employed to estimate the stability of the scale data. The results indicate that, compared to traditional 2D desktops, both VR and AR demonstrate advantages in enhancing heritage risk situational awareness. However, in AR mode, no significant advantages were found in the dimensions of attention resource demand and attention resource supply compared to traditional 2D desktop applications. Furthermore, a significant difference in immersion between VR and AR was found to affect the attention resource demand dimension. Although the results suggest differences in the interactivity of VR and AR in affecting the attention resource demand dimension, no significant differences were found. By comprehensively understanding the functional mechanisms of X-reality technologies in influencing cultural heritage risk situational awareness, this study provides design references for constructing DT systems for cultural heritage risk management. Additionally, it offers insights for heritage site managers, experts, and stakeholders to enhance risk perception efficiency, promoting more effective risk assessment, analysis, and strategic decision-making, thereby reducing damage to cultural heritage.

Hepatic decompensation is the major driver of mortality in hepatocellular carcinoma patients treated with atezolizumab plus bevacizumab: The impact of successful antiviral treatment.

Unlike other malignancies, hepatic functional reserve competes with tumour progression in determining the risk of mortality from hepatocellular carcinoma (HCC). However, the relative contribution of hepatic decompensation over tumour progression in influencing overall survival (OS) has not been assessed in combination immunotherapy recipients. From the AB-real observational study(n=898), we accrued 571 patients with advanced/unresectable HCC, Child-Pugh A class treated with frontline atezolizumab+bevacizumab(AB). Hepatic decompensation and tumour progression during follow-up were studied in relationship to patients' OS using time-dependent Cox model. Baseline characteristics were evaluated as predictors of decompensation in competing risks analysis. During a median follow-up of 11.0 months (95%CI 5.1-19.7), 293 patients(51.3%) developed tumour progression without decompensation and 94(16.5%) developed decompensation. In multivariable time-dependent analysis, decompensation(hazard ratio[HR] 19.04, 95%CI 9.75-37.19), HCC progression(HR 9.91, 95%CI 5.85-16.78), albumin-bilirubin(ALBI) grade 2/3(HR 2.16, 95%CI 1.69-2.77) and number of nodules>3(HR 1.63, 95%CI 1.28-2.08) were independently associated with OS. Pre-treatment ALBI grade 2/3(subdistribution HR [sHR] 3.35, 95%CI 1.98-5.67) was independently associated with decompensation, whereas viral aetiology was protective(sHR 0.55, 95%CI 0.34-0.87). Among patients with viral aetiology, effective antiviral treatment was significantly associated with lower risk of decompensation (sHR 0.48, 95%CI 0.25-0.93). Hepatic decompensation identifies patients with the worst prognosis following AB and is more common in patients with baseline ALBI>1 and non-viral aetiology. Effective antiviral treatment may protect from decompensation, highlighting the prognostic disadvantage of patients with non-viral aetiologies and the importance of multi-disciplinary management to maximise OS.

Path-Sampling and Machine Learning for Rare Abnormal Events: Application to Polymerization CSTRs

Previously, we developed novel, unidirectional, static, multivariate, alarm systems for rare un-postulated abnormal events, demonstrated successfully for an exothermic CSTR. Herein, our techniques are improved significantly for a more-complex polystyrene CSTR, operating in its unstable region, capable of abnormal shifts to two undesirable regions; i.e., unsafe and unreliable regions. BG-FFS, a path-sampling algorithm, is utilized to simulate efficiently multiple rare abnormal trajectories; then, the XGBoost machine learning algorithm is utilized to develop accurate predictive models for committer probabilities; i.e., pB as a function of key process variables – such models, when deployed in real-time, result in improved bidirectional dynamic multivariate alarm systems, capable of response actions using real-time pB predictions. Then, using our rationalization strategies, the initial alarm systems are evaluated and modified, followed by DRAn (Dynamic Risk Analysis) studies and sensitivity analyses to investigate the effects of varying other process parameters to achieve more effective response actions.

Acupuncture for constipation in Parkinson's disease: A systematic review and meta-analysis of randomized controlled trials.

Parkinson's disease (PD) is the second most common neurological disease worldwide, and there is a potential interaction between PD and constipation. PD constipation often causes significant trouble for patients and seriously affects their quality of life. Acupuncture is widely used for treating constipation and has been clinically proven. However, it is unclear whether the current evidence is sufficient to support acupuncture to improve PD constipation. We searched the Cochrane Central Register of Controlled Trials, Embase, PubMed, Web of Science, China National Knowledge Infrastructure, Wan Fang Data Knowledge Service Platform, and Chinese Scientific Journal Database (VIP database) for randomized controlled trials from inception through July 1, 2023. Randomized controlled trials (RCTs) included acupuncture, sham acupuncture, and medication for PD constipation. Stata 16.0 software and Cochrane RoB2.0 were used for data processing and migration risk analysis. The 11 studies included a total of 960 patients. The results showed that acupuncture or acupuncture combined with conventional treatment seemed to have advantages in improving complete spontaneous bowel movements (WMD: 1.49, 95% CI: 0.86, 2.11; P < .00001), Patient-Assessment of Constipation Quality of Life questionnaire (WMD: -11.83, 95% CI: -15.67, -7.99; P < .00001), the chronic constipation severity scale (CCS) (SMD: -0.99, 95% CI: -1.40, -0.58; P < .01), and c(RRP) (WMD: 2.13, 95% CI: 0.44, 3.82; P < .05). The present results show that compared with conventional treatment, acupuncture combined with conventional treatment seems to increase the number of spontaneous defecations in PD patients, improve quality of life, increase rectal resting pressure, and alleviate the severity of chronic constipation. Thus, acupuncture has the potential to treat PD constipation. However, due to the study's limitations, higher-quality RCTs are needed for verification.

K-Nearest Neighbors Estimator for Functional Asymmetry Shortfall Regression

This paper deals with the problem of financial risk management using a new expected shortfall regression. The latter is based on the expectile model for financial risk-threshold. Unlike the VaR model, the expectile threshold is constructed by an asymmetric least square loss function. We construct an estimator of this new model using the k-nearest neighbors (kNN) smoothing approach. The mathematical properties of the constructed estimator are stated through the establishment of the pointwise complete convergence. Additionally, we prove that the constructed estimator is uniformly consistent over the nearest neighbors (UCNN). Such asymptotic results constitute a good mathematical support of the proposed financial risk process. Thus, we examine the easy implantation of this process through an artificial and real data. Our empirical analysis confirms the superiority of the kNN-approach over the kernel method as well as the superiority of the expectile over the quantile in financial risk analysis.

Enhancing global maritime traffic network forecasting with gravity-inspired deep learning models

Aquatic non-indigenous species (NIS) pose significant threats to biodiversity, disrupting ecosystems and inflicting substantial economic damages across agriculture, forestry, and fisheries. Due to the fast growth of global trade and transportation networks, NIS has been introduced and spread unintentionally in new environments. This study develops a new physics-informed model to forecast maritime shipping traffic between port regions worldwide. The predicted information provided by these models, in turn, is used as input for risk assessment of NIS spread through transportation networks to evaluate the capability of our solution. Inspired by the gravity model for international trades, our model considers various factors that influence the likelihood and impact of vessel activities, such as shipping flux density, distance between ports, trade flow, and centrality measures of transportation hubs. Accordingly, this paper introduces transformers to gravity models to rebuild the short- and long-term dependencies that make the risk analysis feasible. Thus, we introduce a physics-inspired framework that achieves an 89% binary accuracy for existing and non-existing trajectories and an 84.8% accuracy for the number of vessels flowing between key port areas, representing more than 10% improvement over the traditional deep-gravity model. Along these lines, this research contributes to a better understanding of NIS risk assessment. It allows policymakers, conservationists, and stakeholders to prioritize management actions by identifying high-risk invasion pathways. Besides, our model is versatile and can include new data sources, making it suitable for assessing international vessel traffic flow in a changing global landscape.

Two-stage data-driven optimal energy management and dynamic real-time operation in networked microgrid based on a deep reinforcement learning approach

Given the significant challenges posed by the vast and diverse data in energy management, this study introduces a two-stage approach: optimal energy management system (OEMS) and dynamic real-time operation (DRTOP). These stages employ a multi-agent policy-oriented deep reinforcement learning (DRL) approach, aiming to minimize operating and energy exchange costs through interactions in the networked microgrid (NMG) energy market. The primary objectives include minimizing the distribution system operator (DSO) cost and optimizing the exchanged power between DSO and NMG, and the power transmission losses and the secondary include minimizing MG’s operating cost, optimal use of renewable energy resources (RER) and energy storage systems (ESS), minimizing the exchanged power cost with the main grid and, risk analysis. The OEMS&DRTOP model is developed based on the Stackelberg game theory and the DRL structure. The DRL model is developed in two offline learning and online distributed operation phases to minimize the computational burden, time, and DRL operation process. This study’s results show the high efficiency of the presented approach to minimizing the operating cost, the exchanged power based on the price uncertainty, power transmission losses, and, RER and ESSs optimal participation. In addition, regarding computational load, the proposed concept demonstrates a 12.9% reduction compared to the dueling deep Q-network method and a 17% reduction compared to the deep Q-network method. Also regarding computational time, the proposed concept demonstrates a 17.13% reduction compared to the dueling deep Q-network method and a 25.6% reduction compared to the deep Q-network method.

A Spatial Risk Analysis of Springtime Daily Minimum Surface Air Temperature Values for Vineyard Site Selection: Applications to Pinot noir Grapevines throughout the Willamette Valley American Viticultural Area

A Spatial Risk Analysis of Springtime Daily Minimum Surface Air Temperature Values for Vineyard Site Selection: Applications to Pinot noir Grapevines throughout the Willamette Valley American Viticultural Area

Mechanical characterization of spectrin at the molecular level

Spectrin, a large cytoskeletal protein, consists of a heterodimeric structure comprising α and β subunits. Here, we have studied the mechanics of spectrin filament as a major constituent of dendrites and dendritic spines. Given the intricate biological details and compact biological construction of spectrin, we've developed a constitutive model of spectrin that describes its continuous deformation over three distinct stages and it’s progressive failure mechanisms. Our model closely predicts both the force at which uncoiling begins and the ultimate force at which spectrin fails, measuring approximately 93 ~ 100 pN. Remarkably, our predicted failure force closely matches the findings from AFM experiments focused on the uncoiling of spectrin repeats, which reported a force of 90 pN. Our theoretical model proposes a plausible pathway for the potential failure of dendrites and the intricate connection between strain and strain rate. These findings deepen our understanding of how spectrin can contribute to traumatic brain injury risk analysis.

Teaching and learning with older adults: Research ethics, risk analysis, and covert research

ABSTRACT This article reports a case study of older adults learning English in China. It indicates how, founded on consequentialist ethics, risk analysis, and safeguarding, it was decided to use covert research, drawing on the confluence of risk analysis, risk evaluation, risk management, safeguarding, research ethics, and important contextual and cultural features. Ethical principles of nonmaleficence, beneficence, safeguarding, and protection were addressed, and account was taken of the strength, likelihood, and consequences of risks, safeguards, and benefits, informed by Chinese cultural contexts, values, behaviors, and features of teaching and learning based on andragogy and geragogy. Implications are drawn for teaching and learning with older adults, advocating significant account to be taken of contextual factors.

Carlos Estremera - Analysis of Migrant Compounds from Food Packaging: Application to Disposable Tableware and Risk Analysis

Carlos Estremera - Analysis of Migrant Compounds from Food Packaging: Application to Disposable Tableware and Risk Analysis

Analysis of Migrant Compounds from Food Packaging: Application to Disposable Tableware and Risk Analysis

Substance migration from nine types of disposable plates made from cardboard, polystyrene (PS), and polypropylene (PP) have been studied. Migration tests were performed in four food simulants over 10 days at 40°C. Simulant extracts were analyzed using SPME-GC-MS and UPLC-QTOF-MS to identify and (semi)quantify volatile and non-volatile migrants, respectively.