Research on Architecture of Risk Assessment System Based on Block Chain

The risk assessment system has been applied to the information security,energy,medical and other industries.Through the risk assessment system,it is possible to quantify the possibility of the impact or loss caused by an event before or after an event,thereby avoiding the risk or reducing the loss.However,the existing risk assessment system architecture is mostly a centralized architecture,which could lead to problems such as data leakage,tampering,and central cheating.Combined with the technology of block chain,which has the characteristics of decentralization,security and credibility,collective maintenance,and untamperability.This paper proposes a new block chainbased risk assessment system architecture and a consensus mechanism algorithm based on DPOS improvement.This architecture uses an improved consensus mechanism to achieve a safe and efficient risk assessment solving the problem of data tampering in the risk assessment process,avoiding data leakage caused by improper data storage.A convenient,safe and fast risk assessment is achieved in conjunction with the improved consensus mechanism.In addition,by comparing existing risk assessment architecture,the advantages and impacts of the new block chain-based risk assessment system architecture are analyzed.

A risk assessment system for alien plant bio-invasion in Xiamen,China

Bio-invasion has caused serious ecosystem damage and enormous economic losses in China,and it has been the greatest factor to island bio-diversity reduction.Xiamen,as an important seaport with a relatively high conservation value,is particularly vulnerable to bio-invasion for its typical island ecosystem as well as frequent human-made disturbance.As a result of field surveys,literature review, and consultation with experts,a list of 67 alien plants identified as major invaders(12 species)and emerging invade...

Developing a risk assessment system for gas tunnel disasters in China

Gas disasters always restrict the tunnel constructions in mountain area, which becomes a major geological barrier against the development of underground engineering. China is rich in coal resources and has a large amount of gas with a wide range of distribution. However, China experienced not only adverse effects on coal mining but also gas disasters in underground engineering construction, such as tunnels and chambers. With the increased number of tunnels passing through coal-bearing strata, the incidence of gas accidents is also rising. Therefore, the significance of preventing and mitigating gas disasters should be emphasized, and an effective risk assessment method for gas disasters should be established. On the basis of research on over 100 gas tunnels in China, a relatively ideal gas disaster risk assessment method and system for tunnels are established through the following measures. Firstly, geo-environmental conditions and gas situations were analyzed during construction. Secondly, qualitative analysis was combined with quantitative analysis. Finally, the influencing factors of gas disasters, including geological conditions, gas,and human factors, were investigated. The gas tunnel risk assessment system includes three levels:(1) the grading assessment of a gas tunnel during the planning stage,(2) the risk assessment of gas tunnel construction during the design and construction stages,(3) the gas tunnel outburst risk assessment during the coal uncovering stage. This system was applied to the dynamic assessment of gas disaster during the construction of the Zipingpu tunnel of Dujiangyan–Wenchuan Highway(in Sichuan, Southwest China). The assessment results were consistent with the actual excavation, which verified the rationality and feasibility of the system. The developed system was believed to be back-up and applied for risk assessment of gas disaster in the underground engineering construction.

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable centralized traffic control(CTC)system risk assessment method.Design/methodologylapproach-First,system-theoretic process analysis(STPA)is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis.Then,to enhance the accuracy of weight calculation,the fuzzy analytical hierarchy process(FAHP),fuzzy decision-making trial and evaluation laboratory(FDEMATEL)and entropy weight method are employed to calculate the subjective weight,relative weight and objective weight of each index.These three types of weights are combined using game theory to obtain the combined weight for each index.To reduce subjectivity and uncertainty in the assessment process,the backward cloud generator method is utilized to obtain the numerical character(NC)of the cloud model for each index.The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system.This cloud model is used to obtain the CTC system's comprehensive risk assessment.The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud.Finally,this process yields the risk assessment results for the CTC system.Findings-The cloud model can handle the subjectivity and fuzziness in the risk assessment process well.The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/value-This study provides a cloud model-based method for risk assessment of CTC systems,which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment,achieving effective risk assessment of CTC systems.It can provide a reference and theoretical basis for risk management of the CTC system.

Risk Assessment of Diary Cattle Brucellosis Based on Systematic Multilevel Grey Relation Entropy Method

[Objective] The study was to explore the major factors affecting diary cattle brucellosis risk assessment,as well as their strong-to-weak sequence,so as to provide theoretical basis for assessing diary cattle brucellosis risk level in different regions.[Method] From 4 dimensions of feeding and importing,breeding,housing and polyculture situation,an evaluation index system was set up,and diary cattle brucellosis risk survey was conducted in 3 typical regions.Finally,systematic multilevel grey relation entropy method was applied to perform data analysis.[Result] The strong-to-weak sequence of Level 1 impact factor of diary cattle brucellosis was as follows：feeding and importinghousingpolyculture situationbreeding;the sequence of Level 2 impact factor was U32〉U12〉U11〉U31〉U21〉U42〉U43〉U23〉U22〉U41;the risk level sequence of 3 typical regions was Province A（County A1,A2,A3）Province B（County B1,B2,B3）Province C（County C1,C2,C3）.[Conclusion] According to the weight of Level 1 index strata,administrative departments at all levels and dairy cattle farmers should lay emphasis on the aspects of feeding,importing and housing;viewed from the perspective of Level 2 index strata,dairy cattle farmers should value the siting of cattle field,the brucellosis surveillance before importing and milking modes most.According to the diary cattle brucellosis risk level of 3 typical regions,if administrative departments at all levels strengthen peoples＇ awareness of their personal health and increase investment in this area,with new healthy cultured atmosphere built,the risk level of diary cattle brucellosis will surly decline.

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.

Risk Assessment of Deep-Water Horizontal X-Tree Installation

Due to the high potential risk and many influencing factors of subsea horizontal X-tree installation,to guarantee the successful completion of sea trials of domestic subsea horizontal X-trees,this paper established a modular risk evaluation model based on a fuzzy fault tree.First,through the analysis of the main process oftree down and combining the Offshore&Onshore Reliability Data(OREDA)failure statistics and the operation procedure and the data provided by the job,the fault tree model of risk analysis of the tree down installation was established.Then,by introducing the natural language of expert comprehensive evaluation and combining fuzzy principles,quantitative analysis was carried out,and the fuzzy number was used to calculate the failure probability of a basic event and the occurrence probability of a top event.Finally,through a sensitivity analysis of basic events,the basic events of top events significantly affected were determined,and risk control and prevention measures for the corresponding high-risk factors were proposed for subsea horizontal X-tree down installation.

Health risk assessment of trace metal(loid)s in agricultural soils based on Monte Carlo simulation coupled with positive matrix factorization model in Chongqing, southwest China

This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.

Importance of risk assessment,endoscopic hemostasis,and recent advancements in the management of acute non-variceal upper gastrointestinal bleeding

Acute non-variceal upper gastrointestinal bleeding(ANVUGIB)is a common medical emergency in clinical practice.While the incidence has significantly reduced,the mortality rates have not undergone a similar reduction in the last few decades,thus presenting a significant challenge.This editorial outlines the key causes and risk factors of ANVUGIB and explores the current standards and recent updates in risk assessment scoring systems for predicting mortality and endoscopic treatments for achieving hemostasis.Since ANUVGIB predominantly affects the elderly population,the impact of comorbidities may be responsible for the poor outcomes.A thorough drug history is important due to the increasing use of antiplatelet agents and anticoagulants in the elderly.Early risk stratification plays a crucial role in deciding the line of management and predicting mortality.Emerging scoring systems such as the ABC(age,blood tests,co-morbidities)score show promise in predicting mortality and guiding clinical decisions.While conventional endoscopic therapies remain cornerstone approaches,novel techniques like hemostatic powders and over-the-scope clips offer promising alternatives,particularly in cases refractory to traditional modalities.By integrating validated scoring systems and leveraging novel therapeutic modalities,clinicians can enhance patient care and mitigate the substantial morbidity and mortality associated with ANVUGIB.

Risk assessment of oil and gas investment environment in countries along the Belt and Road Initiative

With the implementation of the Belt and Road Initiative, China is deepening its cooperation in oil and gas resources with countries along the Initiative. In order to better mitigate risks and enhance the safety of investments, it is of significant importance to research the oil and gas investment environment in these countries for China's overseas investment macro-layout. This paper proposes an indicator system including 27 indicators from 6 dimensions. On this basis, game theory models combined with global entropy method and analytic hierarchy process are applied to determine the combined weights, and the TOPSIS-GRA model is utilized to assess the risks of oil and gas investment in 76 countries along the Initiative from 2014 to 2021. Finally, the GM(1,1) model is employed to predict risk values for 2022-2025. In conclusion, oil and gas resources and political factors have the greatest impact on investment environment risk, and 12 countries with greater investment potential are selected through cluster analysis in conjunction with the predicted results. The research findings may provide scientific decisionmaking recommendations for the Chinese government and oil enterprises to strengthen oil and gas investment cooperation with countries along the Belt and Road Initiative.

Comprehensive Risk Assessment of Sea Level Rise and Tropical Cyclones in Dongzhaigang Mangroves,China

Mangroves play a pivotal role in tropical and subtropical coastal ecosystem,yet they are highly vulnerable to the effects of climate change,particularly the accelerated global sea level rise(SLR)and stronger tropical cyclones(TCs).However,there is a lack of research addressing future simultaneous combined impacts of the slow-onset of SLR and rapid-onset of TCs on China's mangroves.In order to develop a comprehensive risk assessment method considering the superimposed effects of these two factors and analyze risk for mangroves in Dongzhaigang,Hainan Island,China,we used observational and climate model data to assess the risks to mangroves under low,intermediate,and very high greenhouse gas(GHG)emission scenarios(such as SSP1-2.6,SSP2-4.5,and SSP5-8.5)in 2030,2050,and 2100,and compiled a risk assessment scheme for mangroves in Dongzhaigang,China.The results showed that the combined risks from SLR and TCs will continue to rise;however,SLRs will increase in intensity,and TCs will decrease.The comprehensive risk of the Dongzhaigang mangroves posed by climate change will remain low under SSP1-2.6 and SSP2-4.5 scenarios by 2030,but it will increase substantially by 2100.While under SSP5-8.5 scenario,the risks to mangroves in Dongzhaigang are projected to increase considerably by 2050,and approximately 68.8%of mangroves will be at very high risk by 2100.The risk to the Dongzhaigang mangroves is not only influenced by the hazards but also closely linked to their exposure and vulnerability.We therefore propose climate resilience developmental responses for mangroves to address the effects of climate change.This study for the combined impact of TCs and SLR on mangroves in Dongzhaigang,China can enrich the method system of mangrove risk assessment and provide references for scientific management.

A risk assessment method considering risk attributes and work safety informational needs and its application

The technological revolution has spawned a new generation of industrial systems,but it has also put forward higher requirements for safety management accuracy,timeliness,and systematicness.Risk assessment needs to evolve to address the existing and future challenges by considering the new demands and advancements in safety management.The study aims to propose a systematic and comprehensive risk assessment method to meet the needs of process system safety management.The methodology first incorporates possibility,severity,and dynamicity(PSD)to structure the“51X”evaluation indicator system,including the inherent,management,and disturbance risk factors.Subsequently,the four-tier(risk point-unit-enterprise-region)risk assessment(RA)mathematical model has been established to consider supervision needs.And in conclusion,the application of the PSD-RA method in ammonia refrigeration workshop cases and safety risk monitoring systems is presented to illustrate the feasibility and effectiveness of the proposed PSD-RA method in safety management.The findings show that the PSD-RA method can be well integrated with the needs of safety work informatization,which is also helpful for implementing the enterprise's safety work responsibility and the government's safety supervision responsibility.

Occurrence,Spatial Distribution,Sources and Risk Assessment of Per-and Polyfluoroalkyl Substances in Surface Sediments of the Yellow River Delta Wetland

Per-and polyfluoroalkyl substances(PFASs)are emerging persistent organic pollutants(POPs).In this study,47 surface sediment samples were collected from the Yellow River Delta wetland(YRDW)to investigate the occurrence,spatial distribution,potential sources,and ecological risks of PFASs.Twenty-three out of 26 targeted PFASs were detected in surface sediment samples from the YRDW,with totalΣ23PFASs concentrations ranging from 0.23 to 16.30 ng g^(-1) dw and a median value of 2.27 ng g^(-1) dw.Perfluorooctanoic acid(PFOA),perfluorobutanoic acid(PFBA)and perfluorooctanesulfonic acid(PFOS)were the main contaminants.The detection frequency and concentration of perfluoroalkyl carboxylic acids(PFCAs)were higher than those of perfluoroal-kanesulfonic acids(PFSAs),while those of long-chain PFASs were higher than those of short-chain PFASs.The emerging PFASs substitutes were dominated by 6:2 chlorinated polyfluoroalkyl ether sulfonic acid(6:2 Cl-PFESA).The distribution of PFASs is significantly influenced by the total organic carbon content in the sediments.The concentration of PFASs seems to be related to human activities,with high concentration levels of PFASs near locations such as beaches and villages.By using a positive matrix factorization model,the potential sources of PFASs in the region were identified as metal plating mist inhibitor and fluoropolymer manufacturing sources,metal plating industry and firefighting foam and textile treatment sources,and food packaging material sources.The risk assessment indicated that PFASs in YRDW sediments do not pose a significant ecological risk to benthic organisms in the region overall,but PFOA and PFOS exert a low to moderate risk at individual stations.

Forecast Method of Multimode System for Debris Flow Risk Assessment in Qingping Town,Sichuan Province,China

The Wenchuan Earthquake of May 12,2008 triggered large numbers of geo-hazards.The heavy rain on 13 August 2010 triggered debris flows with total volume of more than 6 million cubic meters and the debris flows destroyed 500 houses and infrastructure built after the Wenchuan Earthquake.The study area Qingping Town was located in the northwestern part of the Sichuan Basin of China,which needs the second reconstructions and the critical evaluation of debris flow.This study takes basin as the study unit and defines collapse,landslide and debris flow hazard as a geo-hazard system.A multimode system composed of principal series system and secondary parallel system were established to evaluate the hazard grade of debris flow in 138 drainage basins of Qingping Town.The evaluation result shows that 30.43% of study basins(42 basins) and 24.58% of study area,are in extremely high or high hazard grades,and both percentage of basin quantity and percentage of area in different hazard grades decrease with the increase of hazard grade.According to the geo-hazard data from the interpretation of unmanned plane image with a 0.5-m resolution and field investigation after the Wenchuan Earthquake and 8.13 Big Debris Flow,the ratio of landslides and collapses increased after the Wenchuan Earthquake and the ratios of extremely high or high hazard grades were more than moderate or low hazard grades obviously.23 geo-hazards after8.13 Big Debris Flow in Qingping town region all occurred in basins with extremely high or high hazard grades,and 9 debris flows were in basins with extremely high hazard grade.The model of multimode system for critical evaluation could forecast not only the collapse and landslide but also the debris flow precisely when the basin was taken as the study unit.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Distribution, health and ecological risk assessments of trace elements in Nigerian oil sands

The Nigerian oil sands represent the largest oil sand deposit in Africa, yet there is little published information on the distribution and potential health and ecological risks of trace elements in the oil resource. In the present study, we investigated the distribution pattern of 18trace elements(including biophile and chalcophile elements) as well as the estimated risks associated with exposure to these elements. The results of the study indicated that Fe was the most abundant element, with a mean concentration of 22,131 mg/kg while Br had the lowest mean concentration of 48 mg/kg. The high occurrence of Fe and Ti suggested a possible occurrence of ilmenite(Fe TiO_(3)) in the oil sands. Source apportionment using positive matrix factorization showed that the possible sources of detected elements in the oil sands were geogenic, metal production, and crustal. The contamination factor, geo-accumulation index, modified degree of contamination, pollution load index, and Nemerow pollution index indicated that the oil sands are heavily polluted by the elements. Health risk assessment showed that children were relatively more susceptible to the potentially toxic elements in the oil sands principally via ingestion exposure route(HQ > 1E-04). Cancer risks from inhalation are unlikely due to CR < 1E-06 but ingestion and dermal contact pose severe risks(CR > 1E-04). The high concentrations of the elements pose serious threats due to the potential for atmospheric transport, bioaccessibility, and bioavailability.

Health Risk Assessment of Employees Exposed to Chlorination By-products of Recreational Water in Large Amusement Parks in Shanghai

Objective Chlorination is often used to disinfect recreational water in large amusement parks;however,the health hazards of chlorination disinfection by-products(DBPs)to occupational populations are unknown.This study aimed to assess the exposure status of chlorinated DBPs in recreational water and the health risks to employees of large amusement parks.Methods Exposure parameters of employees of three large amusement parks in Shanghai were investigated using a questionnaire.Seven typical chlorinated DBPs in recreational water and spray samples were quantified by gas chromatography,and the health risks to amusement park employees exposed to chlorinated DBPs were evaluated according to the WHO's risk assessment framework.Results Trichloroacetic acid,dibromochloromethane,bromodichloromethane,and dichloroacetic acid were detected predominantly in recreational water.The carcinogenic and non-carcinogenic risks of the five DBPs did not exceed the risk thresholds.In addition,the carcinogenic and non-carcinogenic risks of mixed exposure to DBPs were within the acceptable risk limits.Conclusion Typical DBPs were widely detected in recreational water collected from three large amusement parks in Shanghai;however,the health risks of DBPs and their mixtures were within acceptable limits.

An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data

With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management,but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry.Therefore,an improved risk assessment algorithm(PS-AE-LSTM)based on long short-term memory network(LSTM)with autoencoder(AE)is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels.Firstly,based on the normal distribution characteristics of flight data,a probability severity(PS)model is established to enhance the quality of risk assessment labels.Secondly,autoencoder is introduced to reconstruct the flight parameter data to improve the data quality.Finally,utilizing the time-series nature of flight data,a long and short-termmemory network is used to classify the risk level and improve the accuracy of risk assessment.Thus,a risk assessment experimentwas conducted to analyze a fleet landing phase dataset using the PS-AE-LSTMalgorithm to assess the risk level associated with aircraft hard landing events.The results show that the proposed algorithm achieves an accuracy of 86.45%compared with seven baseline models and has excellent risk assessment capability.

IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection methods.This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT botnets.Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically.Fuzzy component settings are optimized using PSO to improve accuracy.The methodology allows for more complex thinking by transitioning from binary to continuous assessment.Instead of expert inputs,PSO data-driven tunes rules and membership functions.This study presents a complete IoT botnet risk assessment system.The methodology helps security teams allocate resources by categorizing threats as high,medium,or low severity.This study shows how CICIoT2023 can assess cyber risks.Our research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

Fuzzy Risk Assessment Method for Airborne Network Security Based on AHP-TOPSIS

With the exponential increase in information security risks,ensuring the safety of aircraft heavily relies on the accurate performance of risk assessment.However,experts possess a limited understanding of fundamental security elements,such as assets,threats,and vulnerabilities,due to the confidentiality of airborne networks,resulting in cognitive uncertainty.Therefore,the Pythagorean fuzzy Analytic Hierarchy Process(AHP)Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)is proposed to address the expert cognitive uncertainty during information security risk assessment for airborne networks.First,Pythagorean fuzzy AHP is employed to construct an index system and quantify the pairwise comparison matrix for determining the index weights,which is used to solve the expert cognitive uncertainty in the process of evaluating the index system weight of airborne networks.Second,Pythagorean fuzzy the TOPSIS to an Ideal Solution is utilized to assess the risk prioritization of airborne networks using the Pythagorean fuzzy weighted distance measure,which is used to address the cognitive uncertainty in the evaluation process of various indicators in airborne network threat scenarios.Finally,a comparative analysis was conducted.The proposed method demonstrated the highest Kendall coordination coefficient of 0.952.This finding indicates superior consistency and confirms the efficacy of the method in addressing expert cognition during information security risk assessment for airborne networks.