Risk Calculation Method Research Articles

Risk analysis and assessment method for infectious diseases based on information entropy theory

Infectious diseases risk is directly related to human life safety. After the COVID-19 pandemic, people have paid unprecedented attention to the risk of infectious diseases. Compared with treatment after the outbreak of the epidemic, identifying the influencing factors of infectious disease risk and quantitatively analyzing and assessing infectious disease risk before the outbreak of the epidemic plays an equally important role. This article focuses on the risk of irregular outbreaks of infectious diseases. On the one hand, a method based on information gain is proposed to calculate the weight of environmental factors directly related to infectious disease risk, to clarify the correlation between environmental factors and infectious disease risk. On the other hand, the risk calculation method based on risk weight number is proposed to calculate the risk level of different infectious diseases under the influence of specific environmental factors. Finally, the effectiveness and feasibility of the proposed method are verified through case analysis and discussion. By comparing it with other risk assessment methods, the advantages and disadvantages of the proposed method are demonstrated.

Multi-Criteria Model for Identifying and Ranking Risky Types of Maritime Accidents Using Integrated Ordinal Priority Approach and Grey Relational Analysis Approach

Accidents in marine operations are occurring consistently despite government safety initiatives and efforts to lower the number of accidents and the ensuing human casualties. Since each accident type has a different frequency and casualty rate, identifying risky accident types is important to determine the priority for taking necessary risk reduction actions. Usually, a risk is calculated using two criteria, i.e., the frequency and fatality of an accident. However, the accident statistics show that for the last 5 years from 2018 to 2022, the injury rate is more than three times the death rate in maritime accidents in Korean waters. Considering the importance of injury, unlike other previous studies, we perform a risk analysis with three criteria, i.e., frequency, death, and injury to complement the conventional risk calculation methods, which can help decision-makers allocate the limited resources to the riskiest types of accidents in order of priority. In doing so, we employed an integrated ordinal priority approach (OPA) and grey relational analysis (GRA) method to assign proper weight to each criterion and rank eight accident types. We categorized the accidents types into three different groups where safety accidents and collisions were ranked as the most dangerous types. The combined OPA and GRA technique has been effectively applied to other risky industries, as well as the maritime industry. Additionally, the proposed method is suitable for multi-criteria models when each criterion has a different importance. Finally, the method can be integrated into the framework of the risk ranking process to enhance the analysis results.

Probabilistic risk assessment: Hazard impact study of safety-critical space launch events onto world air traffic & creation of ADIONA software

Space and air traffic increase yearly, prompting concerns about their coexistence. Fallback and splashdown of rocket stages to earth into predefined areas is a usual scenario, explosions of rockets and fallback into unprecedented areas is a probable scenario. These events represent a threat to world air traffic. In the present study, a probabilistic risk assessment on the launcher's stages fallback onto world air traffic has been carried out. A novel method and software, ADIONA, were developed to assess risks of rocket explosions and debris impact on air traffic. This pioneering software, a venture of the Flight Safety Department of the French Space Agency (CNES), supervising European Space Agency (ESA) launches, calculates at every possible time instant, defined by the user, the estimated positions of airplanes with along their own trajectories, trajectories and positions of rockets and the corresponding areas where debris falls back on earth after an explosion or a deviation. It further assesses the risks of fall down of debris on air traffic in case of a launch mishap and the probability of fatality to an airplane regarding the risk boundaries set by the French Space Operations Act. The software has been implemented in a general way for broad future application in operations and continued studies and use. Real-world air traffic and rocket launch data were incorporated, particularly focusing on launches from the French Guiana Space Center (CSG) and air traffic over the North Atlantic Ocean. These real-world analyses represent realistic scenarios. The obtained results show a considerable high possible risk onto world air traffic in case of a rocket explosion after launch. It is concluded that the cohabitation of space and air traffic and the probabilistic risk assessment thereof is important to be monitored in order to eventually be diminished to reduce the risk. It is recommended to invest further resources into this observation type of analysis. Further investigations should take place on this probabilistic risk analysis, taking into account more details and fine-tuning in the risk calculation and assessment method. The study's findings and software lay the groundwork for future risk analysis and development, facilitating ongoing and future investigations.

Improvement of working conditions assessment and risk calculation methods taking into account the specific features of pesticide application with unmanned aerial vehicles (UAV)

Improvement of working conditions assessment and risk calculation methods taking into account the specific features of pesticide application with unmanned aerial vehicles (UAV)

Risk assessment of distribution network operation with scale distributed photovoltaics

In the context of large-scale photovoltaic integration into the distribution network, it is of great significance to comprehensively consider the uncertainty of photovoltaic output and load and scientifically and effectively evaluate the operational risks of the distribution network, in order to ensure the reliable and economic operation of the distribution network. At the same time, with the increase of distributed photovoltaic installed capacity, the small interference stability caused by it has an increasing impact on the operation of the distribution network. In this paper, we first establish a probabilistic model of photovoltaic output and load. Then, we propose a risk calculation method based on the non-sequential Monte Carlo method and probabilistic power flow calculation. Then, a risk analysis of large-scale photovoltaic access to the distribution network was carried out. A new small interference stability risk index was constructed to evaluate the risk of system oscillation caused by photovoltaic access. On this basis, the comprehensive risk value is calculated, and the risk levels are divided based on the voltage over-limit risk and power flow over-limit risk. Finally, the feasibility and effectiveness of the risk assessment model are verified through case analysis.

Research on the risk evaluation of enterprises' carbon compliance failure

In order to mitigate global warming and help the country achieve its carbon peaking and carbon neutrality targets at the earliest possible time, the emission-control companies should accomplish the carbon compliance in accordance with relevant national policies and regulations. However, these companies frequently face the failure risk of carbon compliance subjected to various factors, including the national carbon quota policy, local carbon market situation, the verification of carbon offset projects, as well as the effectiveness of carbon reduction technologies. To help the enterprises avoid the risk of carbon-compliance failure and design rational carbon asset management strategy, in this research, the innovative combination of interpretive structural modeling (ISM), Bayesian network model, risk calculation and sensitivity analysis method was formulated. Firstly, the ISM method was used to establish a hierarchical relationship of risk factors that contribute to the failure of carbon compliance. Secondly, the probability prediction model of carbon-compliance failure risk based on the Bayesian network model was established by aid of the Netica software. Thirdly, the risk value of enterprise's carbon compliance failure was quantitatively calculated based on its production operation and carbon asset management. Finally, the sensitivity analysis method was used to identify critical risk factors and design risk control measures for six well-known domestic enterprises, laying good foundation for improving the success rate of carbon compliance and facilitating low-carbon green transformation. Compared to traditional qualitative risk assessment method, this combined approach is capable of realizing the quantitative evaluation of failure risk based on comprehensive investigation and analysis of the production and operational situation, which provides effective technical support to enhance enterprise's compliance awareness and improve low carbon competitiveness.

Which first-trimester risk assessment method for preeclampsia is most suitable? A model–based impact study

Low-dose aspirin treatment reduces the risk of preeclampsia among high-risk pregnant women. Internationally, several first-trimester risk-calculation methods are applied. This study aimed to assess the costs and benefits of different first-trimester preeclampsia risk estimation algorithms: EXPECT (an algorithmic prediction model based on maternal characteristics), National Institute for Health and Care Excellence (a checklist of risk factors), and the Fetal Medicine Foundation (a prediction model using additional uterine artery Doppler measurement and laboratory testing) models, coupled with low-dose aspirin treatment, in comparison with no risk assessment. We constructed a decision analytical model estimating the number of cases of preeclampsia with each strategy and the costs of risk assessment for preeclampsia and early aspirin treatment, expressed in euros (€) in a hypothetical population of 100,000 women. We performed 1-way sensitivity analyses to assess the impact of adherence rates on model outcomes. Application of the EXPECT, National Institute for Health and Care Excellence, and Fetal Medicine Foundation models results in respectively 1.98%, 2.55%, and 1.90% of the women developing preeclampsia, as opposed to 3.00% of women in the case of no risk assessment. Overall, the net financial benefits of the EXPECT, National Institute for Health and Care Excellence, and Fetal Medicine Foundation models relative to no risk assessment are €144, €43, and €38 per patient, respectively. The respective percentages of women receiving aspirin treatment are 18.6%, 10.2%, and 6.0% for the 3 risk assessment methods. The EXPECT and Fetal Medicine Foundation model are comparable with regard to numbers of prevented preeclampsia cases, and both are superior to the National Institute for Health and Care Excellence model and to no risk assessment. EXPECT is less resource-demanding and results in the highest cost savings, but also requires the highest number of women to be treated with aspirin. When deciding which strategy is preferable, cost savings and easier use have to be weighed against the degree of overtreatment, although low-dose aspirin has no clear disadvantages during pregnancy.

Prerequisites for applying the risk-based approach to assessing the explosive and fire hazardous properties of underground mining materials

The relevance of the work is due to the introduction of new high-intensity mining technologies underground, the conduct of mining operations at ever greater depths of coal and ore, changes in the explosive properties of the extracted raw materials, as well as incidents and accidents of varying severity occurring at mining facilities. There is an opinion that one of the causes of accidents and emergencies in mines is the inability to take into account both the features of the manifestation of the properties of the rock mass and the change in the explosive properties of minerals. The problem of applying a risk-oriented approach, namely a comprehensive assessment of the risk of accidents, in this case fires and/or explosions, has not been resolved. The applied risk calculation methods are diverse, as a rule, the accuracy of the results is low, and none of them allows taking into account all the identifiable hazards that lead to the occurrence of endogenous and exogenous fires, as well as explosions of hybrid mixtures in the space of mine workings. This article attempts to study in detail the prerequisites for applying a risk-based approach to solving this problem. Theoretical approaches and risk assessment in mines and mines are considered and methods are given that allow in express mode to obtain up-to-date information on the explosive and fire hazardous properties of the extracted raw materials.

Study on Dynamic Probability and Quantitative Risk Calculation Method of Domino Accident in Pool Fire in Chemical Storage Tank Area.

The domino event caused by fire is one of the common accidents in hydrocarbon storage tank farms, which further expands the severity and scope of the accident. Due to the different failure sequence of the storage tanks in a domino accident, the radiant heat generated by the failed storage tank to the target tank is different. Based on the influence of this synergistic effect, this study combined the Monte Carlo algorithm and FSEM, and proposed a fast real-time probability calculation method for a fire domino accident in a storage tank area, for the first time. This method uses the Monte Carlo algorithm to simulate all accident scenarios, and obtains the evolution of multiple escalation fire domino accidents under the synergistic effect according to FSEM, and then calculates the real-time failure probability and risk. Based on a comprehensive analysis of the accident propagation path, this method avoids the problem of a large amount of calculation, and is conducive to the rapid and effective analysis of the fire risk in a storage tank area and the formulation of corresponding risk reduction measures. The effectiveness and superiority of the proposed method were proved by a case study.

Using risk meshing to improve three-dimensional risk assessment of chemical industry

With the development of three-dimensional (3D) risk assessment, the accuracy of 3D risk calculations plays an important role in ensuring the quality of risk assessment results for the chemical industry. A more accurate risk calculation is significant to improving the accuracy and reasonability of 3D risk assessment results. Therefore, a novel risk calculation method called risk meshing (RM) for field theory-based 3D risk assessment is proposed in this study. For the proposed RM, the 3D risk is evaluated and calculated based on the possibility, vulnerability, and consequence with respect to the risk. An assessed elementary unit (AEU) is then introduced to compute the 3D risk in different partitioned blocks by taking advantage of the regional evaluation ability with respect to the vulnerability assessment. Fire and explosion accidents caused by storage tanks in a coal chemical industry plant are employed to make a case study, and the evaluation results show that RM can provide more accurate results because building and road layouts are considered for the risk calculation. Meanwhile, the optimal risk reduction route (ORRR) is confirmed by RM and using traditional methods. Hence, the validity of RM is verified based on the comparison of ORRRs obtained by RM and those by traditional methods. The paper indicates the significance of RM in improving the accuracy and reasonability of 3D risk assessments by analyzing the evaluation and comparison results, which lays a theoretical foundation for further development of field theory-based 3D risk assessments of chemical industry.

Portfolio construction by using Markowitz Model and Index Model during pandemic of COVID-19

During the epidemic, many industries have been hit to varying degrees, social productivity has sharply decreased, and economic development has stagnated or even regressed. It is estimated that global GDP declined by 4.3% in 2020 due to the impact of the pandemic. The same thing happened in the stock market, where the epidemic increased the vulnerability of it, causing stock prices to fluctuate greatly and investors to suffer losses. Recently a bunch of studies have focused on how to make successful investments by modeling and data analysis under the difficult circumstances. This paper will continue to use the MM model and the IM model to construct optimal portfolios under different constraints in order to simulate various situations and provide the investors with advice of all aspects.10 different companies in 3-4 staple fields will be randomly selected as target in this paper as the main factors of MM model and IM model in order to prove the wide compatibility of the two models in the real stock market.In addition, this paper will use both the minimum risk calculation method and the maximum efficiency calculation method as the criterion to measure a portfolio’s performance with the purpose of presenting the investors two different standpoints to view their portfolios.

Health Risk Assessment for Pest Control Workers Using Biocidal Products after COVID-19 Outbreak: A Comparison of EU and US

After the COVID-19 outbreak, the workloads of disinfection have remarkably increased. As disinfectants are harmful to human, health risk assessment of disinfection workers is needed to ensure their safety. However, the acceptable operator exposure level (AOEL) for most disinfectants are not established. Moreover, the method of calculating the AOEL varies across the countries. This study aims to identify the health risks of disinfection workers based on two different risk assessment methods of EU and US after the COVID-19 outbreak. We surveyed 533 disinfection workers to identify exposure patterns of disinfectants. The inhalation and dermal exposure were calculated based on different work types. The AOEL was calculated and compared using the EU and US methods. The health risks of workers are determined by hazard index based on whether the exposure exceeds AOEL. Workers used 53 chemicals from 175 biocidal products, and only 34 chemicals (65%) had AOEL. 17 Chemicals with absorption factors for each route or AOEL were selected for risk assessment. When compared the estimated AOEL was calusing EU and US methods, only 2 (4%) were the same. The result of health risk assessment showed 10 (19%) were exceed 1 (acceptable level) using EU method, while 9 (17%) were exceed 1 using US method. This study performed the health risk assessment of disinfection workers based on EU and US methods after the COVID-19 outbreak. Only 65% had an AOEL, and health risks of workers were differd according to risk calculation methods of EU and US. Given the increasing use of biocides after the COVID-19 outbreak, AOEL need to be established for health risk assessment of workers. Biocides, Risk assessment, Reference dose, AOEL, COVID-19, pest control worker This study was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1F1A1063967).

Calculation of dam risk probability of cascade reservoirs considering risk transmission and superposition

Because of the risk transmission and superposition among dams in cascade reservoirs, the analysis and probability calculation of dam risk become more complex compared with a single reservoir. By analyzing the main risk sources and actionmechanisms, the disaster-causing factors, disaster-transmitting body and disaster-bearing body in the cascade reservoirs system were determined. By defining the influence coefficient (IC) to express the transmission and superposition degree of dam risk among cascade reservoirs, dam risk was divided into two parts: own risk (OR) and additional risk (AR). On this basis, the relevant concepts and equations for the calculation of the dam risk probability of cascade reservoirs were proposed. The numerical simulation was carried out to quantify the IC, and a Bayesian network analysis model was constructed to calculate the OR. Finally, taking five cascade reservoir dams in the Dadu River basin as examples, the value of their ORs, ARs and risk probabilities were calculated, and thereafter the weakest cascade, controlling cascade and general cascade in the system were divided. The proposed methodology realizes an effective connection with the dam risk calculation method of a single reservoir, which can provide a reference for the risk assessment and management of cascade reservoirs in the basin.

Using soil erosion to locate nonpoint source pollution risks in coastal zones: A case study in the Yellow River Delta, China

Soil erosion contributes greatly to nonpoint source pollution (NSP). We built a coastal NSP risk calculation method (CNSPRI) based on the Revised Universal Soil Loss Equation (RUSLE) and geospatial methods. In studies on the formation and transport of coastal NSP, we analysed the pollution impacts on the sea by dividing subbasins into the sea and monitoring the pollutant flux. In this paper, a case study in the Yellow River Delta showed that the CNSPRI could better predict the total nitrogen (TN) and total phosphorus (TP) NSP risks. The value of the soil erodibility factor (K) was 0.0377 t h·MJ−1·mm−1, indicating higher soil erodibility levels, and presented an increased trend from the west to the east coast. The NSP risk also showed an increased trend from west to east, and the worst status was found near the Guangli River of the south-eastern region. The contributions of the seven influencing factors to CNSPRI presented an order of vegetation cover > rainfall erosivity > soil content > soil erodibility > flow > flow path > slope. The different roles of source and sink landscapes influenced the pollutant outputs on a subbasin scale. Arable land and saline-alkali land were the two land-use types with the greatest NSP risks. Therefore, in coastal zones, to reduce NSP output risks, we should pay more attention to the spatial distribution of vegetation cover, increase its interception effect on soil loss, and prioritize the improvement of saline-alkali land to reduce the amount of bare land.

Value at risk for risk management with normal-logDagum mixture distribution

In this article, we aimed to calculate the value at risk (VaR), which is one of the financial risk calculation methods, by using mixture of two different distributions when the financial data does not fit the normal distribution. The normal-logDagum distribution consisting of mixture of Normal and log-Dagum distributions is proposed to calculate the VaR for non-normal financial data in the study. The expected-maximization (EM) algorithm for the maximum likelihood estimates of the parameters of normal-logDagum was defined. In application, the stocks of bank and telecommunication companies were examined. VaR values obtained with different distributions are compared numerically. As a result of the comparison, it was seen that the modeling based on normal-logDagum distribution is more successful in the statistical modeling of financial data.

A Modified FlowDroid Based on Chi-Square Test of Permissions.

Android devices are currently widely used in many fields, such as automatic control, embedded systems, the Internet of Things and so on. At the same time, Android applications (apps) always use multiple permissions, and permissions can be abused by malicious apps that disclose users’ privacy or breach the secure storage of information. FlowDroid has been extensively studied as a novel and highly precise static taint analysis for Android applications. Aiming at the problem of complex detection and false alarms in FlowDroid, an improved static detection method based on feature permission and risk rating is proposed. Firstly, the Chi-square test is used to extract correlated permissions related to malicious apps, and mutual information is used to cluster the permissions to generate feature permission clusters. Secondly, risk calculation method based on permissions and combinations of permissions are proposed to identify dangerous data flows. Experiments show that this method can significantly improve detection efficiency while maintaining the accuracy of dangerous data flow detection.

De-risking primary prevention: role of imaging.

Atherosclerotic cardiovascular disease (ASCVD) is a common disease among the general population, and includes four major areas: (1) coronary heart disease (CHD), manifested by stable angina, unstable angina, myocardial infarction (MI), heart failure, and coronary death; (2) cerebrovascular disease, manifested by transient ischemia attack and stroke; (3) peripheral vascular disease, manifested by claudication and critical limb ischemia; and (4) aortic atherosclerosis and aortic aneurysm (thoracic and abdominal). CHD remains the leading cause of death for both men and women in the United States. So, it is imperative to identify people at risk of CHD and provide appropriate medical treatment or intervention to prevent serious complications and outcomes including sudden cardiac death. Coronary artery calcification (CAC) is a marker of subclinical coronary artery disease. Therefore, coronary artery calcium score is an important screening method for Coronary artery disease (CAD). In this article, we performed a comprehensive review of current literatures and studies assessing the prognostic value of CAC for future cardiovascular disease (CVD) events. We searched PubMed, MEDLINE, Google Scholar, and Cochrane library. We also reviewed the 2018 American College of Cardiology (ACC)/American Heart Association (AHA) guideline on the assessment of CVD risk. A CAC score of zero corresponds to very low CVD event rates (∼1% per year) and hence a potent negative risk marker. This has been referred to as the ‘power of zero’ and affords the lowest risk of any method of risk calculation. It is now indicated in the 2018 ACC/AHA Cholesterol guidelines to be used to avoid statins for 5–10 years after a score of zero, and then re-assess the patient.

Risk evaluation for C2C E‐commerce via an improved credit counting method

E‐commerce has become a popular way for shopping, especially during the Covid‐19 pandemic. The key of the E‐commerce is the credit evaluation, for instance the client to client (C2C) mode E‐commerce. In this paper, an improved credit scoring method is proposed. The improved credit scoring method uses the interval distribution of commodity prices to add points for successful transactions and deduct points for failed transactions by using credit grade deduction coefficients and credit level, which can solve credit hype and periodic deception. Through the analysis of credit risk, a risk calculation method for online transactions is designed by using the improved credit scoring model. The proposed risk calculation method can evaluate the risk of current transactions based on historical transaction conditions and current transaction prices. The simulated experiments show that the improved credit scoring method has better anti‐credit speculation and periodic deception effects, and the risk assessment method is more accurate and effective for risk analysis and prediction in C2C E‐commerce transactions.

РОЗРОБКА МЕТОДИЧНОГО ПІДХОДУ ЩОДО ПРОЦЕСУ УПРАВЛІННЯ РИЗИКОМ ВТРАТИ ІННОВАЦІЙНИХ МОЖЛИВОСТЕЙ

UDC 330.342; JEL Classification: O31, L26 The purpose of the study is to study the development of a methodological approach to the process of risk management of the loss of innovative opportunities. Risks are elements of the results of the implementation of any business decisions due to the fact that it is the main condition of the business process. Innovative activity is always associated with risk, which is characterized by the influence of certain factors on the results of activities that cannot be accurately calculated in advance. The object of research is the process of managing the risk of loss of innovative opportunities. Constant increase in competition, instability of supply and demand levels, outstripping the pace of development of engineering and technology, sharp changes in exchange rates, as well as other negative factors characteristic of the modern economy, create conditions in which there is a risk of losing the innovative ability of the enterprise to implement innovative projects, negatively affects on the economic efficiency of innovation. It is proved that the innovative activity of enterprises should be carried out under the influence of the external and internal environment, which is not always favorable in relation to it. Research Methodology. The theoretical and methodological basis of the study was steel, publications, monographs and scientific (analytical) developments of scientists on the problems of risk management of the metallurgical complex, and abstract-logical, monographic methods will also be used. The results of the study. Enterprises introducing innovations may face various types of risks, caused, first of all, by the time gap between the introduction of innovations and obtaining a useful result, the financial costs of events and the likely losses when introducing innovations. Therefore, the possibility of leveling a positive result of the development of innovations is not ruled out. Considering that the main source of innovation is still the own funds of enterprises, and innovative projects, as a rule, are rather risky measures, therefore it is necessary to formulate new approaches to the problem of managing the risk of losing innovative opportunities by creating a risk map and a program to minimize and neutralize them, use of risk calculation methods. This is especially important for the metallurgical industry, which is a strategically important branch of the metallurgical complex, and therefore requires careful study of development prospects, taking into account security and the ability to withstand threats.

Linking health impact and Post-environmental impact assessments: a case of municipal sewage treatment plant volatile organic compounds

Deficiencies remain in current health impact assessment (HIA) and environmental impact assessment (EIA) projects. To address the shortcomings in EIA theory, a case of odors from a municipal sewage treatment plant (MSTP) was examined and geographic factors were employed to associate the spatial diffusion of the pollutants with the population’s activities based on land-use attributes. After screening the MSTP priority control pollutants, odors, hydrogen sulfide, and ammonia were selected for this study. Then, the spatial parameters for the pollutant simulation were surveyed, including parameters concerning the meteorological analysis, environmental emission monitoring, and emission source analysis, and a prediction of the pollutant diffusion as imaged and identified. The types of human social activity and exposure patterns were sorted as land-use attributes. An integration of the spatial diffusion of the pollutants with the exposure profiles of the scenario population according to the land-use attributes was achieved using counterpart spatial coordination factors. In our study, the commonly applied method of HIA risk calculation was followed and then extended by the spatial techniques introduced. The results of the scenario HIA contours are presented here, making it easy to determine the acceptable levels of the MSTP odor pollutants on a geographic scale. This study examines a significant approach to associate HIA with post-EIA via spatial factors and addresses the deficiencies of HIA in EIA empirical applications.