Risk Control Model Research Articles

Effect of Health Education on Schistosomiasis Control Knowledge, Attitude, and Practice after Schistosomiasis Blocking: Results of a Longitudinal Observational Study in the Field.

Schistosomiasis is a zoonotic infectious disease that seriously harms people's physical and mental health. As early as 1985, the WHO suggested that health education and health promotion should be the focus of schistosomiasis prevention work. This study aimed to explore the effect of health education on controlling the risk of schistosomiasis transmission after schistosomiasis blocking and to provide a scientific basis for the further improvement of intervention strategies after schistosomiasis interruption in China and other endemic countries. In Jiangling County, Hubei Province, China, one severe, one moderate, and one mildly endemic village were selected as the intervention group; two severe, two moderate, and two mildly endemic villages were selected as the control group. In towns with different epidemic types, a primary school was randomly selected for intervention. In September 2020, a baseline survey was carried out through a questionnaire survey to understand the knowledge, attitudes, and practices (KAP) of adults and students concerning schistosomiasis control. Next, two rounds of health education interventions for schistosomiasis control were carried out. The evaluation survey was conducted in September 2021 and the follow-up survey conducted in September 2022. Compared with the baseline survey, the qualified rate of the KAP on schistosomiasis prevention of the control group in the follow-up survey increased from 79.1% (584/738) to 81.0% (493/609) (p > 0.05); in the intervention group, the qualified rate of the KAP on schistosomiasis control increased from 74.9% (286/382) to 88.1% (260/295) (p < 0.001). In the baseline survey, the qualified rate of the KAP of the intervention group was lower than that of the control group, and in the follow-up survey, the qualified rate of the KAP of the intervention group was 7.2% higher than that of the control group (p < 0.05). Compared with the baseline survey, the accuracy rates of the KAP of the intervention group's adults were higher than those of the control group, with statistical significance (p < 0.001). Compared with the baseline survey, the qualified rate of the students' KAP in the follow-up survey increased from 83.8% (253/302) to 97.8% (304/311) (p < 0.001). In the follow-up survey, the accuracy rate of the knowledge, attitudes, and practices of the students was significantly different from the baseline accuracy (p < 0.001). a health education-led risk control model of schistosomiasis can significantly improve schistosomiasis control knowledge among adults and students, establishing correct attitudes and leading to the development of correct hygiene habits.

Study on Control of Cumulative Risk for Submarine Tunnel Shield Construction

Researching the control of cumulative risk for submarine tunnel shield construction is of great significance for improving risk management capability. Firstly, the concepts of cumulative risk, fragility, and the theory of system dynamics (SD) are introduced. Secondly, the risk conduction network is established, and the risk conduction mechanism based on fragility is analyzed. Then, the cumulative risk control model is established by SD. Finally, Dalian Metro Line 5 Cross-sea Large Diameter Shield Construction Project is used to verify the model. Simulations of cumulative risk with and without control measures are compared, and the result is in line with objective facts. The correctness and effectiveness of the model are verified.

Predicting of Credit Default by SVM and Decision Tree Model Based on Credit Card Data

With the global financial crisis and increased credit risk, default forecasting is playing an increasingly important role in every sector of the economy. Currently, there are linear models and machine learning models for predicting credit defaults. In recent years, big data risk control models are superior to traditional bank models in predicting default rates, and can also conduct business quickly and on a large scale. This paper compares the SVM and the decision tree model in the machine learning model based on the credit card loan data set, and finally evaluates the prediction effect between the two models. According to the study, the decision tree model outperforms the SVM in terms of prediction accuracy. The use of big data to conduct machine learning to predict credit conditions enables financial institutions to serve small, medium and micro enterprises that were difficult to cover by traditional finance on a large scale in the past. It is a world-class innovation in finance.

Explanatory model for the Control of the risk of obesity in Mexican adolescents

Objective: estimate the explanatory value of the Obesity Risk Control model in adolescents with normal weight and overweight, guided by the Health Promotion Model.Methods: descriptive, cross-sectional, analytical study. The sample was calculated for a simple linear regression model that included age, sex, body mass index, personality traits, economic level, appetite traits, emotional eating, social support through social networks, family social support, self-regulation and locus of control. Spearman's Rho coefficient (p&lt;0,05) and linear regression (p&lt;0,001) were used.Results: 518 adolescents participated. The average age was 16,8 years (SD 0,9). 55,8 % were women. The average body mass index was 23,1 kg/m2 (SD 2,7). 61,9 % of female and 59,0 % of male adolescents were of normal weight. Physical activity was poor in 74,3 % and regular eating habits in 54,6 %. The model was made up of seven explanatory variables that were: sex, personality traits of kindness, responsibility, squeamish attitude, self-regulation in physical activity and eating habits, as well as internal locus of control, which explained 27 %, Conclusions: the Obesity Risk Control model was made up of seven variables that explained 27 % of the behavior, preserving the Health Promotion Model constructs. It is important to conduct research that incorporates commitment to an action plan and immediate competition preferences, as these are constructs that take precedence over health-promoting behavior and that, if present, favor or impact behavior

Retracted: Design of Financial Risk Control Model Based on Deep Learning Neural Network.

[This retracts the article DOI: 10.1155/2022/5842039.].

Contract pre-review and performance risk control model analysis

Under the background of continuous development of Chinese science and technology economy, social residents' demand for power resources is getting higher and higher. In order to strengthen its core competitiveness, electric power enterprises begin to focus on the study of contract management. According to the analysis of the current situation of contract performance management in power enterprises in recent years, as the core content of overall project management, the interest relationship between the construction unit and the enterprise will eventually be reflected in the contract. Therefore, it is necessary to strengthen the pre-review of the contract and the management and control of the performance risk, and always maintain a high level of risk awareness. Only in this way can the parties to the contract use legal means to safeguard their rights and interests. On The Basis Of Understanding The Risk Of Contaect Review Of And Performance ，This Patper Deeply Discusses The Forward Contract Model Of a certain power Project according To The system Constructed By the current power enterprise, so as to determine the contract management measures of the power enterprise.

Risk control simulation of the closed-loop supply chain of waste electrical and electronic equipment based on system dynamics

Recycling and remanufacturing waste electrical and electronic equipment (WEEE) can promote the comprehensive utilization of resources and promote the development of the circular economy. However, there are associated risks involved. This study explored the effectiveness of risk control in the WEEE closed-loop supply chain to provide decision support in the sustainable development of WEEE. The catastrophe progression method was used for risk evaluation, and the WEEE risk control model was constructed using system dynamics to analyze the influence of risk control strategies. The results show that adopting risk control strategies positively promotes risk control. Risk mitigation is most pronounced when both the fund subsidy and the green and low-carbon strategies are adopted. When only one approach is implemented, the green and low-carbon strategy has a more considerable effect. When the intensities of the mitigation measures are improved to the same extent, the risk control effect generated by the fund subsidy strategy was found to have a greater impact. The findings provide a useful reference for formulating subsequent risk control strategies in the WEEE closed-loop supply chain.

Enterprise Financing Risk Control of Machine Learning Combined with Blockchain Technology

In order to scientifically and reasonably control the financing risk of supply chain enterprises and minimize the financing risk of the enterprises, this paper introduces the relevant theories of machine learning algorithm and blockchain technology, proposes an enterprise financing risk control model based on the combination of machine learning algorithm and blockchain technology, and uses system dynamics to dynamically evaluate the system risk. Finally, the specific changes of each subsystem over time are obtained. Through the risk evaluation results, it can be seen that the risk level of the blockchain system has decreased from the initial 4.3 to 0.3 after two months of transition, and the risk control effect is very significant.

Performance Verification of Bayesian Network–Based Security Risk Management and Control System for Power Trading Institutions

Risk in power trading is unavoidable for various reasons. The impact of this risk would vary based on the trading characteristics that mainly depend on the market design and power purchase agreements. So, a security risk management and control system for power trading institutions based on a Bayesian network is designed to reduce the risk of power trading projects. As a part of the network, we first provided the overall architecture of the risk management and control system, which includes a malicious network behaviour detection module, controller selection module, data transmission module, and management and control result visualisation module. Second, the hardware test design was implemented by analysing each module’s working principle and function. Based on the hardware design of the system, the regression analysis method is used to evaluate the risk of power transactions, followed by market fluctuation prediction to obtain the prediction result induced risks. The relationship between security risks and risk-influencing factors is analysed using the Bayesian network. The initial list of risks is established, the uncertain risk factors are reasoned, and the security risk management and control model of power trading institutions is tested to achieve the goal of risk management and control. The experimental results show that this method’s risk management and control efficiency are high. At the same time, this method effectively realised comprehensive risk identification by reducing the loss to power enterprises and has near-practical application value.

Developing an emission risk control model in coal‐fired power plants for investigating CO2 reduction strategies for sustainable business development

AbstractThe climatic risk consists of financial and environmental risks, predominantly evolutes from carbon dioxide emissions from coal‐fired power plants. We develop a climatic risk control model to investigate the coexistence of renewable energy and the post‐combustion carbon capture technology as CO2 reduction strategies. Additionally, our proposed framework explicitly considers the acceptance of such strategies based on a balance of electricity supply according to demand. This paper adopts an additive fuzzy mixed‐integer optimization approach to model uncertain parameters and determines the optimal solution focusing on business and the environment. Furthermore, we investigate the feasibility of such emission control strategies with scenario analysis that help to execute the country's emission reduction policies. The usefulness of our methodology is demonstrated using data from the coal‐based power sector in the Eastern part of India. Overall, with the proposed model, we can achieve 30% reduction in emission release, which provides strategies to the decision‐maker for investment towards sustainable development.

Model of Potential Strengthening and The Role of Family in Control and Modification of Risk Factors in Adult Women with Coronary Heart Disease

Coronary heart disease is one of the non-communicable diseases with the highest prevalence in Indonesia. Coronary heart disease incidents can be prevented by controlling modifiable Coronary heart disease risk factors. Family is the strength for a person to survive pressure or keep life in balance. The purpose was to analyze the effect of strengthening family potency on the control of modifiable coronary heart disease risk factors in adult women. The research design used a quasi-experimental. The research variable was Coronary heart disease risk control models. The population in this study were families who had adult female family members with coronary heart disease in the work area of Arosbaya Public Health Center in 2019. In the sample in this study, as many as 30 people in each group were taken by simple random sampling. The research instrument used a simulation model for controlling Coronary heart disease risk factors that can be modified for women by the family using modules. Analysis used the Kolmogorov Smirnov and pair t-tes. The results showed that the mean modification of Coronary heart disease risk factors in adult women increased, except for avoidance of cigarette smoke (p &lt;0.05). The pair t-test results show that there is a difference in controlling the risk factors for Coronary heart disease that can be modified after strengthening family potential, except for the avoidance of cigarette smoke. It can be concluded that the implementation of the family potential strengthening model is proven to significantly increase the family's ability to control the risk factors for Coronary heart disease can be modified in adult women. It is recommended that Coronary heart disease in adult women can be carried out by controlling risk factors that can be modified by involving all family components

Design of Financial Risk Control Model Based on Deep Learning Neural Network.

In recent years, with the continuous increase of financial business, the risk of business is on the rise. Among them, major risk cases are frequent, the cases are increasingly complex, and the means of committing crimes are concealed. The main research contents of this paper include the preprocessing of internal and external financial data and the structure design of recurrent NNs. Its purpose is to design a financial risk control model based on a deep learning NNs, thereby reducing financial risk. The Borderline-SMOTE algorithm is used first to preprocess the sample data, and the oversampling method is used to eliminate the imbalance of the data, and then, the long short-term memory deep NNs algorithm is introduced to process the sample data with time series characteristics. The final experiment shows that LSTM has a better accuracy, reaching 0.9715, compared with traditional methods; the sample preprocessing method and risk control model proposed in this paper have better ability to identify fraudulent customers, and the model itself has faster iteration efficiency.

System dynamics tool for entropy-based risk control on sleeve grouting in prefabricated buildings

PurposeThe production of sleeve grouting in prefabricated construction is routinely plagued by a variety of factors, and lack of mass data and complex environmental conditions over time make problems inevitable. Thus, a dynamic risk control system is a valuable support for the successful completion of the sleeve grouting process. This study aims to develop an entropy-based sleeve grouting risk dynamic control system.Design/methodology/approachFirst, static risk assessment was conducted through the structured interview survey using the entropy weight method, followed by a dynamic risk control technique, where indicators were simulated through system dynamics containing causal loop diagrams and stock-and-flow diagrams.FindingsFinally, three types of risk control models, namely, “tortuous type”, “stable type” and “peak loop type”, were developed in the entropy-based sleeve grouting risk dynamic control system and simulated using system dynamics in a real case.Originality/valueCompared to traditional sleeve grouting risk management, the developed system enabled dynamic control over time.

Financial Risk Control Model Based on Deep Neural Networks

Under the background of global economic integration, commercial banks are facing more and more complex business environment. As one of the major financial risks faced by commercial banks, liquidity risk determines and reflects the safety and profitability of bank operation. Based on joint-stock commercial banks as the research object, this paper, respectively, from the angle of the static and dynamic measurements and projections for liquidity risk and based on the current situation of four joint-stock commercial banks liquidity level study, tries to explore the change law of commercial banks liquidity risk and financial risk control of commercial bank and puts forward reasonable suggestions. In this paper, an AHP neural network model combining subjective and objective methods is proposed. This method can not only overcome the defects of the single evaluation method but also improve the data accessibility by using the qualitative data and quantitative data of AHP. In the aspect of financial risk control system, this paper tries to establish a more comprehensive and practical financial risk control model by combining the previous research of scholars, the business model process, and the experience of practical workers.

Invested Costs and Risk Control Model of Social Governance Based on Fuzzy Algorithm

In view of the fact that the current methods cannot effectively and correctly control the invested costs and risk of social governance, resulting in a higher risk rate in the invested costs, a fuzzy algorithm-based invested costs and risk control model for social governance is proposed. This paper analyzes the classification and causes of risk, expounds the methods of risk identification and risk assessment, constructs and studies the invested costs and risk control model of social governance, establishes the fuzzy judgment matrix of risk control, calculates the single-layer ranking weight vector of risk fuzzy judgment matrix, and determines the fuzzy judgment matrix. The membership degree of risk influencing factors to the risk level is input into the social governance invested cost and risk control model to obtain the corresponding risk assessment results and predict the invested cost. According to the comparison of experimental results, through the test of risk degree and risk rate, it is verified that the maximum risk level value of the model is 20, which can minimize the risk degree; the risk control coefficient of the model is between 0.6 and 1.0, which can effectively reduce the probability of risk and achieve the purpose of design.

A novel risk-control model for the online portfolio selection of high-frequency transactions

Online portfolio selection (OPS) is an active area of study that has recently attracted the attention of researchers from a diverse range of fields. However, the existing models applied to financial markets typically do not consider incremental learning, asset volatility, and the removal of system noise at the eigenvector level. We first propose a bidirectional incremental learning method, the bidirectional-broad learning system (Bi-BLS), which is more flexible and can conduct OPS in two incremental directions. Then, we propose a new method for removing the noise based on random matrix theory and principal component analysis, which can remove the noise at the eigenvalue and eigenvector levels. Furthermore, two novel risk-control metrics, the Adjusted Sharpe Ratio and the Adjusted Information Ratio, are also proposed to treat asset volatility as risk. These ratios first use asset volatility as the denominator other than the standard deviation of portfolio weights. Finally, a series of experiments are conducted to assess model performance. The results show that the proposed model produces a steady increase in cumulative return and achieves the best performance in terms of final cumulative return. This model also outperforms other models (Best Stork, UBAH, BCRP, UCRP, ONS, PAMR, CWMR, OLMAR, CORN, LOAD, NEW, ALAIT and SNN) as measured by risk-control metrics, such as the Sharpe Ratio, Adjusted Sharpe Ratio, Information Ratio, and Calmar Ratio. Therefore, the proposed model can produce excess returns while effectively controlling risk.

Research on risk management and prevention and control system of major disasters in coal mine based on fish-bone diagram and STAMP/STPA model

Fishbone diagram and STAMP/STPA model is a problem of control safety from the perspective of system theory, caused by the lack of sufficient control and safety-related constraints. There is a certain relationship between the model and coal mine construction projects. Based on the introduction of the basic concepts of the fishbone diagram and the STAMP /STPA model, this paper combines the construction of coal mine projects in China with frequent accidents, and based on the fishbone diagram model, establishes a coal mine stamping accident cause model. At the same time, the empirical analysis of typical rock burst accidents is carried out, and the starting point of the analysis is the theoretical point of view caused by system accidents. According to the fishbone diagram and STAMP /STPA model, the accident process is analyzed, the cause of the accident is analyzed, the accident disaster is described, and the safety constraints and fault behaviors are identified. Finally, the basic causes of the accident are analyzed and compared. The results show that the fishbone diagram and the STAMP /STPA model can be well used in the risk management of major disasters in coal mines. Through the safety control structure of the risk control model, the constraints and failure behaviors of each control level can be effectively identified. According to the interaction and feedback at all levels, the cause of the accident can be analyzed and obtained, which is of great significance for preventing and controlling major disasters in coal mines. <br/>

Research on a Risk Control Model Based on Selective Ensemble Algorithm of Hierarchical Clustering and Simulated Annealing

In order to solve the problems in the traditional ensemble learning model, selective ensemble algorithms are often used to optimize the ensemble learning model. To settle the shortcomings of the current selective ensemble algorithm existing in the selection efficiency and predict outcomes, this paper proposes selective ensemble algorithm basing on stacking ensemble framework. The algorithm mainly uses the agglomerated hierarchical clustering (AHC) algorithm and the metropolis criterion of simulated annealing to select the type and number of base learners. In terms of empirical analysis, Lending-Club data is used to build a multi-classification model. The experimental results show that compared with the single learner model, the AHC-Metropolis selective ensemble algorithm has better performance and stability in predicting multi-classification problems, and can provide a basis for improving the financial risk control system and ensuring national security.

Analysis of Internet Financial Risk Control Model Based on Machine Learning Algorithms

On the basis of traditional credit risk control, this paper proposes the demand and direction of a new credit risk control strategy based on machine learning and relying on big data. First, on the basis of introducing the basic algorithmic principles of machine learning, we give reasons for choosing machine learning models and build a machine learning-based Internet consumer finance credit risk control strategy model to provide theoretical support for the empirical analysis later. Second, we take the test data of Internet consumer finance S company as the research sample and carry out empirical analysis according to the machine learning-based Internet consumer finance credit risk control strategy model. The comparison of the training results is based on the comprehensive consideration of training time, validation set accuracy, TPR evaluation indicators, and interpretability of the results; it verifies the advantages of the machine learning model in screening the key influencing factors that cause the overdue performance of credit customers. According to the optimized credit risk control strategy, corresponding strategy suggestions are provided for the credit risk control of S company. The research results show that the prediction effect of the classification model based on traditional linear regression is generally lower than that of the model based on the classification algorithm based on machine learning, and there is a complex nonlinear relationship between platform default and its related influencing factors. The accuracy of classification and early warning results of the random forest algorithm is relatively high, and the detection rate of the decision tree model is relatively high, but the cost is also the highest. In addition, the accuracy of the four types of early warning models is relatively stable, reaching an average of 80%. This paper proposes a machine learning-based Internet consumer finance credit risk control strategy model. Its system, timeliness, and risk prediction capabilities provide new ideas and suggestions for Internet consumer finance companies to design risk control strategies.

Supply Chain Risk Prevention and Control Based on Fuzzy Influence Diagram and Discrete Hopfield Neural Network

Efficient and reasonable supply chain management helps enterprises improve their efficiency, reduce costs, shorten cash flow times, and reduce enterprise risks. Risk prevention and control is a safety symbol for supply chains. To explore different influence degrees of multirisk factors and multilinks on enterprises, we propose a supply chain risk prevention and control model based on a fuzzy influence diagram and Hopfield neural network. Using the model that both calculates the risk size and occurrence probability of the supply chain and allows identifying various risk prevention and control levels, the supply chain risk is evaluated both objectively and fairly. We analyzed the theoretical and practical properties of supply chain risk prevention and control models and used it in the H company to illustrate this model.