Risk Assessment System Research Articles

Probabilistic seismic risk analysis of electrical substations considering equipment-to-equipment seismic failure correlations

When an earthquake occurs, electrical equipment in a substation exhibits a certain level of seismic failure correlation since they suffer similar ground motions and share similar structural characteristics. However, this equipment-to-equipment seismic failure correlation (E2ESFC) was neglected in previous substation-level probabilistic seismic risk analyses due to the lack of awareness and practical approach. To investigate the effect of different degrees of the E2ESFC on the substation seismic risk, an efficient method for considering partially correlated seismic failure was proposed. The concepts of “damage demand probability” and “damage capacity probability” were derived from the equipment's fragility curve. Then the partial correlation of equipment's capacity probabilities can be easily introduced and incorporated into the substation-level risk analysis through the combination of Copula functions and the Monte Carlo simulation. A case study on a real-world 220/110 kV substation using an equi-correlation model demonstrated that ignoring the E2ESFC among the same type of equipment will lead to an underestimate of the probability of seeing high seismic loss. Furthermore, a general method to assess the E2ESFC coefficients between equipment was also proposed, laying the foundation to facilitate applications of the introduced E2ESFC simulation method and to generate a more reliable system risk assessment result.

Development of an Indicator System for Evaluating Risks in Trekking Tourism

In today’s fast-paced and and high-stress society, trekking tourism is a leisure activity for stress relief and closer nature engagement. Given the adventurous nature inherent in trekking tourism, risk assessment is deemed both crucial and imperative. This study aims to develop a risk assessment framework for trekking tourism based on the Delphi method and Analytic Hierarchy Process (AHP). Initially, we conducted a literature review and expert interviews grounded in the Systems Theory of Accident Causation and the Human Factors Theory to establish the risk assessment paradigm specific to trekking tourism. Subsequently, we ascertained specific weights for each evaluation criterion and delineated explicit data sources for each operational indicator. The findings indicate that the formulated risk assessment system for trekking tourism is both scientifically grounded and practical, furnishing stakeholders in the tourism industry, organizational teams, and management authorities with an efficacious tool for evaluating the safety conditions of trekking tourism activities.

The Influence of Big Data Analytics on Supply Chain Operations Efficiency

This paper aims to validate the feasibility of establishing a risk assessment system. Delphi and Entropy Weighting for Indicator Weighting was conducted by experts in the field of economics on the assessment data of Haier's supply chain indicators to analyze and derive a risk assessment system based on Haier's supply chain indicators. With a comprehensive analysis of risk assessment indicators using the Delphi and entropy weighting methods, the feasibility of the evaluation system for supply chain risk analysis in the context of big data gets demonstrated. The establishment of this assessment system will help Haier to analyze and respond to supply chain risks in the future, and will solve the related problems in avoiding supply chain market risk. These problems encompass difficulties related to managing substantial volumes of data and handling uncertainties in demand and supply. The template makes relevant conclusions and suggestions based on analysis, which could have effect on supply chain risk circumvention in a certain extent.

Safety of Hydrogen Storage Technologies

While hydrogen is regularly discussed as a possible option for storing regenerative energies, its low minimum ignition energy and broad range of explosive concentrations pose safety challenges regarding hydrogen storage, and there are also challenges related to hydrogen production and transport and at the point of use. A risk assessment of the whole hydrogen energy system is necessary to develop hydrogen utilization further. Here, we concentrate on the most important hydrogen storage technologies, especially high-pressure storage, liquid hydrogen in cryogenic tanks, methanol storage, and salt cavern storage. This review aims to study the most recent research results related to these storage techniques by describing typical sensors and explosion protection measures, thus allowing for a risk assessment of hydrogen storage through these technologies.

Real-time risk assessment of distribution systems based on Unscented Kalman Filter

The continuous growth of renewable energy and the load level has posed increasingly severe operational risks to distribution systems. In view of this, this paper combines state estimation with risk assessment, and uses the results of distribution system state estimation based on Unscented Kalman Filter as the input of risk assessment. With the combination, the sampling based on probability distributions in traditional risk assessment methods is no longer needed, thus avoiding the difficulty of updating probability distributions timely according to the latest information in real-time operation. By applying the proposed risk assessment method, the real-time assessment of operational risks in perspectives of bus voltage, branch power, and renewable energy utilization is achieved. Meanwhile, the weight of each risk index is properly determined according to both subjective and objective knowledge by using Analytic Hierarchy Process method and entropy weight method. Case studies show that the proposed method achieves effective assessment of comprehensive risks in the operation of distribution system.

A dynamic importance function for accidental scenarios generation by RESTART in the computational risk assessment of cyber-physical infrastructures

The Computational Risk Assessment (CRA) of Cyber-Physical Systems (CPSs) calls for the analysis of accidental scenarios emerging from the complexities and interdependencies typical of CPSs. Generating these scenarios via crude Monte Carlo Simulation (MCS) is impractical due to the high computational demand of simulation codes of CPSs, considering the combinatorial number of possible scenarios. In this paper, we tailor the use of Repetitive Simulation Trials After Reaching Thresholds (RESTART), a rare-event simulation method of literature, to efficiently generate relevant accidental scenarios. The tailored RESTART is guided by a dynamic Importance Function (IF) originally introduced here to dynamically characterize the relevance of the scenarios with reference to the current topology of the CPS and the susceptibility of its components. Two case studies of increasing complexity are considered: a single power grid and a CPS consisting of an Integrated Power and Telecommunication (IP&TLC) infrastructure. Results show that RESTART mines out more relevant scenarios than crude MCS for a number of different IFs based on vulnerability metrics of literature, and thus particularly efficiently when the novel IF is adopted.

RESEARCH METHODS AND GEOINFORMATION MODELS OF CHANGES IN LAND RESOURCES OF ALMATY REGION UNDER THE INFLUENCE OF HOUSEHOLD WASTE

This article deals with the solution of scientific problems, including the development of holistic conceptual models for presenting, processing and analyzing geographic information, and comprehensively assesses the extent of the impact of regional systems on many dangerous natural and artificial processes. This makes it possible to resolve the contradiction between the need to analyze the state of various territorial systems in order to prevent the occurrence of emergencies due to the influence of natural and man-made processes.On the one hand, providing information and support in decision-making, and on the other, ensuring the provision of cartographic models of transport systems risk assessment of dangerous natural and man-made processes and their mapping. Integrated environmental assessment and modeling of industrial systems. A method for mapping payment systems and assessing the health of the population. Since the city of Almaty is a large metropolis, the ecological situation is very poor. This directly affects the ecology of the City, Public Health. We manage, process data by processing GIS data. In solving upcoming environmental problems, in developing new solutions, a special data card will come to the rescue. This will lead to solving problems from this point of view. It is possible to create monitoring maps by combining space surveys and statistical data. One of the most effective aspects of GIS data is that it allows you to optimally consider problems and work in any area. From this point of view, the GIS industry is developing rapidly. In the country, this industry is developing rapidly and is gaining a lot of demand.

Projecting urban flood risk through hydrodynamic modeling under shared socioeconomic pathways

Under future climate change, accurate risk assessment of urban flooding disasters is paramount for effective adaptation and mitigation strategies. However, conventional indicator-based assessment methods often fall short of accurately capturing the complexity of flooding dynamics. Current research predominantly focuses on predicting future hazard shifts while overlooking changes in other critical indicators. In this study, we establish a comprehensive index system for risk assessment, and quantified future changes in most indicators, utilizing the InfoWorks ICM model for hazard simulation and the CLUMondo model for land use predictions. Based on risk assessment results and regional characteristics, we further analyze the key factors driving future risk and discuss corresponding measures. The results indicate an exacerbation of future urban flood risk, with an 18% increase in high risk areas, primarily concentrated in the center of the study area. The dominant indicators are inundation depth and land use over the whole study area. However microtopography significantly affects risk in low-lying areas. Overall, under higher emission scenarios, the influence of GDP and population rises. These findings offer methodological insights for future urban flood risk assessment research and provide policymakers with valuable guidance to develop targeted adaptation measures in response to climate change.

Multidimensional classification matrix for information security risk assessment

In this study, we address one of the key challenges related to a comprehensive risk assessment system for information security concerning personnel during access delineation to company information resources. The relevance of this research is confirmed by numerous instances of information leaks, which highlight the insufficient effectiveness of traditional classification and access control methods. The research aims to analyze existing classification strategies for company information resources and develop an additional method based on continuous access analysis and dynamic adjustment of resource classification. To achieve this goal, we employed methods such as analyzing current information classification strategies, combining various classification techniques, and implementing a graphical method that combines traditional resource classification with a dynamic component using a multidimensional matrix. The main results of the study involve the development of an enhanced method that allows continuous analysis of personnel access to company information resources and dynamic adjustments to resource classification based on access delineation rules. The proposed approach allows for the inclusion of any number of indicators in a graph as a set of vectors, subsequently calculating overall risk assessments based on the sum or difference of these vectors. The practical value of this work lies in its ability to fully utilize modern access control technologies and serve as a foundation for further research, such as automated information classification using neural network training. Additionally, within this study, we conducted a detailed review of existing risk assessment methods for company information resources, identifying key limitations inherent in traditional approaches. Specifically, we analyzed methods based on fixed access levels and the use of static rules for access control. It became evident that such methods are inadequate in responding to dynamic changes in user behavior and the evolving importance of information resources. Thus, the proposed approach allows for more flexible and adaptive access control to information resources, achieved through continuous access monitoring and automatic adjustments based on behavioral user data and contextual changes in resource utilization.

Development of a COVID-19 early risk assessment system based on multiple machine learning algorithms and routine blood tests: a real-world study.

During the Coronavirus Disease 2019 (COVID-19) epidemic, the massive spread of the disease has placed an enormous burden on the world's healthcare and economy. The early risk assessment system based on a variety of machine learning (ML) algorithms may be able to provide more accurate advice on the classification of COVID-19 patients, offering predictive, preventive, and personalized medicine (PPPM) solutions in the future. In this retrospective study, we divided a portion of the data into training and validation cohorts in a 7:3 ratio and established a model based on a combination of two ML algorithms first. Then, we used another portion of the data as an independent testing cohort to determine the most accurate and stable model and compared it with other scoring systems. Finally, patients were categorized according to risk scores and then the correlation between their clinical data and risk scores was studied. The elderly accounted for the majority of hospitalized patients with COVID-19. The C-index of the model constructed by combining the stepcox[both] and survivalSVM algorithms was 0.840 in the training cohort and 0.815 in the validation cohort, which was calculated to have the highest C-index in the testing cohort compared to the other 119 ML model combinations. Compared with current scoring systems, including the CURB-65 and several reported prognosis models previously, our model had the highest AUC value of 0.778, representing an even higher predictive performance. In addition, the model's AUC values for specific time intervals, including days 7,14 and 28, demonstrate excellent predictive performance. Most importantly, we stratified patients according to the model's risk score and demonstrated a difference in survival status between the high-risk, median-risk, and low-risk groups, which means a new and stable risk assessment system was built. Finally, we found that COVID-19 patients with a history of cerebral infarction had a significantly higher risk of death. This novel risk assessment system is highly accurate in predicting the prognosis of patients with COVID-19, especially elderly patients with COVID-19, and can be well applied within the PPPM framework. Our ML model facilitates stratified patient management, meanwhile promoting the optimal use of healthcare resources.

Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction

This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns.

Methods used for caries detection and diagnosis in Ontario dental practices: a cross-sectional survey

BackgroundEarly detection of caries is essential for applying non-surgical treatment procedures and preventing the formation of cavitated lesions leading to unnecessary removal of tooth structure. Understanding dentists’ preferences for caries detection tools can inform stakeholders about their strategies and knowledge of contemporary, evidence-based caries management approaches. However, there is a lack of research exploring the detection methods of caries commonly used by dentists in Ontario, Canada. The objective of this study was to investigate the methods of caries detection and diagnosis preferred by dentists in Ontario.MethodsA 21-item self-reported survey was mailed to one thousand Ontario dental practices in the Winter of 2022. Descriptive and bivariate data analysis were performed to determine the associations between: demographics and professional practice characteristics (explanatory variables), and methods for detecting and diagnosing dental caries (outcome variables) using SPSS Statistics 29.0.ResultsA total of 325 dentists (33%) responded to the survey, with 274 answering all of the questions completely. The highest proportion of respondents were 35–44 years of age (32.8%) and male (53.4%). More than half of the respondents reported using a dental explorer to assess primary occlusal caries (57.6%), secondary caries (57.1%), and cervical caries (57.5%). Likewise, 57.9% of the participants reported using dental radiographs to diagnose proximal caries. Among additional caries detection tools, digital radiography (89.8%) and traditional radiography (84.7%) were the most used methods/modalities, while cone beam computed tomography was the least (12.8%). Most study participants did not use any caries classification system (77.7%) or caries risk assessment tool (85.3%).ConclusionsParticipants preferred conventional methods for caries detection, instead of contemporary visual-tactile caries lesions classification and/or caries risk assessment systems. These findings indicate a need for continuing dental education programs tailored to evidence-based caries management approaches.

External validation of the DIAFORA system to predict lower-extremity amputations in a prospective Danish cohort.

A diabetes-related foot ulcer (DFU) is a major risk factor for lower-extremity amputation (LEA). To help clinicians predict the risk of LEA in people with DFU, the Diabetic Foot Risk Assessment (DIAFORA) system was developed but has never been externally validated. In this study, 317 people presenting with a new DFU were included. At baseline, participants were grouped into three groups based on their DIAFORA score: low-risk (<15), medium-risk (15-25), and high-risk (>25). Participants were followed until healing, LEA, death, or at least 3 months. Discriminative accuracy was evaluated using sensitivity, specificity, likelihood ratios (LRs) and the area under the curve (AUC). All 317 participants completed at least 3 months of follow-up for a median duration of 146 days, during which 12.6% underwent minor amputation and 2.5% major amputation. People in the low- and medium-risk categories had major amputation rates of 0.9% and 2.1%, respectively, and negative LR of major LEA of 0.10 and 0.38, respectively, while the people in the high-risk category had an amputation rate of 25.0% and a positive LR of 12.9. The DIAFORA risk groups had a sensitivity of 75.0% and a specificity of 65.7%, with a corresponding AUC of 0.78 (95% CI 0.68-0.87) for the prediction of major LEA. The DIAFORA score is a useful tool for risk stratification of people presenting with a newly occurred DFU, with the external validation presenting results similar to those presented in the original study. The DIAFORA score may guide clinicians towards more individualized DFU treatment regimens.

A risk assessment of a gas pressure reduction station system with confidence for dealing with imprecisions and unknowns

Process systems are sensitive and vital industrial facilities. Disturbances in their performance may cause harm to the environment,humans,or significant economic damage. In risk assessment of chemical process industries, the available data, information, and knowledge are typically rare, limited, and often unrealistic. This issue poses a challenge to conducting a credible quantitative risk assessment and effects the robustness of the results. To address these challenges, this work proposes a methodology based on the Dempster-Shafer theory of evidence as the reasoning framework. It incorporates risk identification, analysis, and mitigation phases to ensure a thorough analysis of risks and the integration of proactive risk reduction strategies. The approach aims to model the worst-case hazard scenario and assess associated risks using various methods such as FMECA, Bow-Tie, Credal Network, and Dempster-Shafer theory. The proposed approach models imprecision and data ambiguity using intervals and associated belief mass. This extension provides a basis for addressing the fundamental problem of prior ignorance about the distribution of the observed data, which is prevalent in data mining applications. A new approach is proposed that utilizes Belief and Plausibility curves, similar to a Cumulative Distribution Function, to propagate uncertainty, enhance criticality discrimination, and determine cumulated belief measures. This approach is applied in analyzing the failure modes identified in FMECA and is further extended through the credal network for comprehensive risk assessment. Results show how to express irrelevant and independent judgments, and how to work out with inferences in credal networks. This issue is often overlooked, but if properly addressed it represents the key to ultimately drawing reliable conclusions and fully utilizing the system's available data. A case study of the City Gate Station system was used to verify the application potential of the proposed approach.

Adaptive edge security framework for dynamic IoT security policies in diverse environments

The rapid expansion of Internet of Things (IoT) technologies has introduced significant cybersecurity challenges, particularly at the network edge where IoT devices operate. Traditional security policies designed for static environments fall short of addressing the dynamic, heterogeneous, and resource-constrained nature of IoT ecosystems. Existing dynamic security policy models lack versatility and fail to fully integrate comprehensive risk assessments, regulatory compliance, and AI/ML (artificial intelligence/machine learning)-driven adaptability. We develop a novel adaptive edge security framework that dynamically generates and adjusts security policies for IoT edge devices. Our framework integrates a dynamic security policy generator, a conflict detection and resolution in policy generator, a bias-aware risk assessment system, a regulatory compliance analysis system, and an AI-driven adaptability integration system. This approach produces tailored security policies that adapt to changes in the threat landscape, regulatory requirements, and device statuses. Our study identifies critical security challenges in diverse IoT environments and demonstrates the effectiveness of our framework through simulations and real-world scenarios. We found that our framework significantly enhances the adaptability and resilience of IoT security policies. Our results demonstrate the potential of AI/ML integration in creating responsive and robust security measures for IoT ecosystems. The implications of our findings suggest that dynamic and adaptive security frameworks are essential for protecting IoT devices against evolving cyber threats, ensuring compliance with regulatory standards, and maintaining the integrity and availability of IoT services across various applications.

Study on the rapid decompression rate of PA6 liner material of type IV on-board hydrogen storage cylinders

Investigations were carried out in this paper to study the influence of rapid decompression rate on the polyamide 6 (PA6) liner materials through experimental and numerical methods. Firstly, hydrogen permeation tests at different pressures were conducted to obtain the functions representing the trend of permeation parameters with the pressure. Then decompression tests at different decompression rates had been carried out to get whitening specimens which would be observed using scanning electron microscopy (SEM). Cavitation was found and the variety of the number of cavities with the decompression rate was recorded. It was found that the faster the decompression rate was, the higher the number of cavities in the specimen was. Thirdly, to determine the hydrogen concentration throughout the specimen and numerically check if cavitation occurred at various decompression rates, a 2D finite element model was established which introduced the permeation parameters as functions of pressure but not temperature, as another numerical model indicated the temperature field of specimens changed little. Then an approach was proposed to successfully implement the risk assessment of cavitation, and the liner material was considered as elastoplastic neglecting its viscoelasticity-viscoplasticity. Based on the assumptions and experimental conditions in the study, the Mises yield criterion was successful to assess the cavitation risk, and the approach was verified as the analysis results about the cavitation agree well with the SEM observations. In the end, based on the approach, it could be concluded that 11h was the critical safe decompression time for the PA6 material in this study, and the decompression rate was 7.95 MPa/h. The approach in this study can also be used as guidelines for the cavitation risk assessment of other systems and materials undergoing rapid decompression.

Making Systemic Risk Assessments Work: How the DSA Creates a Virtuous Loop to Address the Societal Harms of Content Moderation

Abstract The European Union’s Digital Services Act (DSA) introduces a new regulatory approach to address the societal harms of online platforms: Systemic risk assessments. While a core component of the DSA, the regulation only outlines the standards and processes governing systemic risk assessments in broad strokes. It remains unclear what these systemic risk assesments will entail in practice. This Article develops a proposal of how systemic risk assessments should be implemented. It situates systemic risk assessments as a critical step toward platform accountability as they address societal harms, while existing approaches, such as remedy mechanisms, only protect user rights. Engaging with intangible harms and regulating speech and public discourse, risk assessments also entail significant challenges. Conventional reference points for content moderation regulation, such as terms and conditions, contractual freedom, fundamental rights and expertise, do not provide practical and legitimate bases to concretize risk assessment obligations. Public actors, such as the European Commission, should refrain from defining substantive standards, too, as they are directly bound by freedom of expression guarantees. Instead, the Article argues, the Commission should foster a procedural framework, a “virtuous loop,” which empowers civil society and allows it to specify and refine the standards governing systemic risks over time. Developing this framework, the Article explains how systemic risk assessment can fix “multistakeholderism,” and “multistakeholderism,” in turn, can help make systemic risk assessments work.

Evaluating the Texas Risk Assessment System (TRAS) Predictors of Revocation and Early Release in Adult Felony Probation

ABSTRACT Although much is known about recidivism risk, less is known about factors predicting early release. The current study analyses a sample of 2,070 adult felony probation clients to see if offense characteristics, domains from the Texas Risk Assessment System (TRAS), and demographic variables impact both revocation and successful early release. Results revealed that predictors of early release mirrored those of revocation with one exception—race. Specifically, Black clients were 27% less likely to be granted early release. The findings highlight the TRAS is an effective tool to help mitigate bias for revocation of probation but not for granting early release.

Observational cohort study on safety and efficacy of robotic thyroidectomy with super-meticulous capsular dissection versus open surgery for thyroid cancer: Postoperative dynamic risk assessment of radioactive iodine therapy.

To assess the efficacy and safety of robotic thyroidectomy (RT) with super-meticulous capsular dissection (SMCD) versus open thyroidectomy (OT) we used a dynamic risk assessment system incorporating 131I-WBS along with radioactive iodine (RAI) efficacy evaluation. Currently, the therapeutic efficacy of robotic surgery remains controversial. The 131I whole-body scan (131I-WBS) dynamic risk assessment system can detect small residual thyroid tissues and lesions, which may be used as indicators for the surgical efficacy of RT or OT thyroidectomy in differentiated thyroid cancer (DTC). This retrospective cohort study included 2,349 patients who underwent total thyroidectomy followed by RAI therapy in our department between August 2017 and June 2023. Propensity score matching was performed at a ratio of 1:3 based on surgical type and mean follow-up duration to minimize selection bias after excluding those lost to follow-up. The primary outcome was surgical completeness, assessed using a dynamic risk system incorporating 131I-WBS along with RAI efficacy evaluation. There was no significant difference in the number of metastatic lymph nodes removed between the two groups (P=0.45). The incidence rate of parathyroid gland transplantation was 395 (68.7%) in the OT group and 8 (3.8%) in the RT group (P<0.001). There were no differences in the thyroidectomy completeness based on the 3-hour iodine uptake rate and 99mTcO4- thyroid imaging between the two groups. The dynamic risk assessment with and without 131I-WBS showed significant differences (P<0.001). The postoperative and post-RAI dynamic risk scores, evaluated at the time of RAI and 6 months after RAI, did not differ significantly between the two groups (P>0.05). The rates of transient and permanent hypoparathyroidism were higher in the OT group than in the RT group (P<0.05). The local recurrence rates showed no significant difference between the groups. This study demonstrates that RT with SMCD can achieve outcomes equivalent to those of traditional open surgery when integrating the 131I-WBS dynamic evaluation system and the therapeutic effects of RAI. Additionally, robot surgery demonstrated a notable advantage in protecting parathyroid function.

Voltage over-limit risk assessment of wind power and photovoltaic access distribution system based on day-night segmentation and Gaussian mixture model

The access of distributed power sources such as wind power and photovoltaic (PV) with randomness and uncertainty makes the operation of distribution system more complicated. It is particularly necessary to comprehensively and efficiently evaluate the risk of voltage over-limit in distribution systems with wind power and photovoltaic. Firstly, aiming at the problem of centralized sampling of Monte Carlo method in risk assessment of distribution system, a day-night segmented Monte Carlo (MC) sampling method is proposed to generate a large number of day-night sampling scenarios. Secondly, the Gaussian mixture model is used to fit the probability density of the voltage obtained from probabilistic power flow calculations, concurrently, the utility preference exponential function characterizes the severity of the over-limit, and a comprehensive evaluation model of voltage over-limit risk is constructed. Finally, the effectiveness and accuracy of the proposed method are verified by taking the improved IEEE 33-bus distribution system as an example. These results offer actionable insights for future risk assessments of new energy grid connections.