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.

The risks, degree of malignancy and clinical progression of prostate cancer associated with the MDM2 T309G polymorphism： a meta-analysis

To determine the risk, malignant degree and clinical progression of prostate cancer （PCa） associated with mouse double-minute 2 protein （MDM2） T309G variants, a meta-analysis was performed on all eligible published studies. Odds ratios （ORs） with 95% confidence intervals （CIs） were estimated to assess these associations in seven studies that included 5151 cases and 1003 controls. In the overall analysis, the 309G allele was significantly associated with a decreased PCa risk （0R=0.85, 95% CI： 0.74-0.97）; this was also the case for the homozygous comparison （0R--0.72, 95% Ch 0.55-0.95） and the dominant genetic model （0R=0.79, 95% Ch 0.65-0.96）. The 309G allele was also found to be significantly associated with lower degrees of PCa malignancy （0R=0.85, 95% Ch 0.75-0.96） in the overall analysis, as well as in the heterozygous comparison （0R=0.79, 95% Ch 0.65-0.96）, homozygous comparison （0R=0.76, 95% Ch 0.58-0.98） and dominant genetic model （0R=0.81, 95% CI： 0.68-0.96）. Furthermore, grouping analysis showed that the 309G allele in Caucasians was significantly correlated with a decreased PCa risk （0R=0.77, 95% Ch 0.61-0.96）; this was also the case in the homozygous comparison （0R=0.51, 95% Ch 0.31-0.86）. The grouping analysis also showed that the 309G variant in Caucasians was significantly associated with a lower degree of PCa malignancy in all of the genetic models. In addition, we found that the 309G variant in Caucasians was significantly associated with a slower PCa clinical progression in all of the genetic models. In summary, our meta-analysis showed that the MDM2 309G variant was significantly associated with a decreased PCa risk, lower malignant degree and slower clinical progression in Caucasians, but there was no obvious association in the Asian population.

Changes of crop climate risk degree:a case study on cotton in Henan Province

The agricultural climate risk in the geographical transitional zones is very sensitive to climate change. Based on previous researches, this article established the cotton climate suitability model and the risk index to analyze the cotton climate risk degree in Henan Province. The result shows that, in the last 40 years, the cotton climate risk degree has a gradual increasing trend in which the precipitation plays a very important role, and the climate suitability and its deviation have different performances in the influence on the risk change in different phases; the increase rate of risk degree has significant regional and transitional characteristics, for example, from the eastern plain to the western mountains, the risk change rate becomes slower and slower, so the contrast between the central-southern Huang-Huai plain and the Funiu mountains is the most remarkable; and in the north-south direction, the contrast between the fast increase belt from the central-southern Huang-Huai plain to the middle Yellow River and the slow increase belt near the Yellow River is particularly remarkable, which is possibly the results of topographic and climatic transitional belt; and the risk change process also has obvious regional difference. For instance, abrupt change is an important characteristic in eastern Henan where this sudden change type has notable latitudinal difference, while in the western mountainous area it is very rare. Although the temperature change over the 40 years is still increasing in this area, the trend has reduced gradually since the end of the 1980s.

Hesitant Fuzzy-Sets Based Decision-Making Model for Security Risk Assessment

Security is an important component in the process of developing healthcare web applications.We need to ensure security maintenance;therefore the analysis of healthcare web application’s security risk is of utmost importance.Properties must be considered to minimise the security risk.Additionally,security risk management activities are revised,prepared,implemented,tracked,and regularly set up efficiently to design the security of healthcare web applications.Managing the security risk of a healthcare web application must be considered as the key component.Security is,in specific,seen as an add-on during the development process of healthcare web applications,but not as the key problem.Researchers must ensure that security is taken into account right from the earlier developmental stages of the healthcare web application.In this row,the authors of this study have used the hesitant fuzzy-based AHP-TOPSIS technique to estimate the risks of various healthcare web applications for improving security-durability.This approach would help to design and incorporate security features in healthcare web applications that would be able to battle threats on their own,and not depend solely on the external security of healthcare web applications.Furthermore,in terms of healthcare web application’s security-durability,the security risk variable is measured,and vice versa.Hence,the findings of our study will also be useful in improving the durability of several web applications in healthcare.

The Credit Risk Assessment Model of Internet Supply Chain Finance: Multi-Criteria Decision-Making Model with the Principle of Variable Weight

The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi-time points and multi-indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment.

The Credit Risk Assessment Model of Internet Supply Chain Finance: Multi-Criteria Decision-Making Model with the Principle of Variable Weight

The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the Electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi time points and multi indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the Multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment.

Method for Risky Multiobjective Group Decision-Making and Its Application

The multiobjective group decision-making problem under risk is common in reality. This paper focuses on the study about risky multiobjective group decision-making problem where the index value is not certain. We give indexes classifying method and index normalizing formula of this type problem. By building objective function that minimizes general weighted distance from every alternative to the relatively best and worst alternative, the optimal membership degree of every decision-maker to every alternative can be obtained, and by building another objective function that minimizes general weighted distance from the optimal membership degree of every decision-maker to every alternative to the group optimal alternative and the group inferior alternative, the optimal membership degree of every decision-maker to every alternative can be obtained, which are both based on probability theory and fuzzy theory. Aftermost a model is established which collects group preferences. This method provides a new idea and approach for solving multiobjective decision-making problem among uncertain system, which is applicable for practical problem. Finally a case study shows a satisfactory result.

Estimating the Degree of Earthquake Risk from Fault Characteristics

A theoretical model considering the relationship between fault characteristics and tectonic stress is proposed to quantify earthquake risk degree. The model is applied in practice to the fault along the southern margin of Weihe basin in Shaanxi Province, Northwestern China and fitted well with reality.

Research on Industrial Relevance Degree and Industrial Financial Situation Risk Matrix Construction——Based on Chinese Information Technology Industry

Based on Input-Output Table in 2010 issued by National Bureau of Statistics of China, with the help of input-output model and with the calculation of indexes of industrial relevance degree in Chinese information technology industry, the paper reveals the industrial relevance in Chinese information technology industry. The paper also selects the relevant industries which are highly associated with the development of Chinese information technology industry based on industrial relevance degree to analyze the influences of these industries on the financial situation risk fluctuation in information technology industry and to design the matrix of financial situation risk in information technology industry. Then, the paper offers countermeasures and suggestions for the development of our information technology industry.

Hydrological Modeling Using GIS for Mapping Flood Zones and Degree Flood Risk in Zeuss-Koutine Basin (South of Tunisia)

This study lies within the scope of a strategy of prevention from inundations by the contribution of new technology in stage of the hydrological and geomorphological modeling for protection against the floods in a medium of weak at the average risk in South-eastern Tunisia, starting from the catchment area of Zeuss-Koutine. Considering the lack of studies we were brought to extract the area catchment in question, and to deduce its geomorphological and hydrometric characteristics, starting from the digital terrain model. We could obtain, by overlaying maps of slopes, indices and flows, the hydrological zonation of the catchment area of Zeuss-Koutine. The hydrological study of the basin’s slopes of Zeuss-Koutine is not lying out that very little physical information rests primarily on cartographic processes. The use of the latter can be regarded as an allowing indicator, by the crossing of the explanatory factors of the surface flow (slopes and direction of flow), to define a set of homogeneous hydrological zones in the level of the hydrological characteristics (average slopes, altitudes, roughness, etc). It is mainly a question of better taking account of the physical properties of the basins slopes.

Optimal Decision-Making of Trans-Provincial Electricity Market Subjects with Risks under Renewable Portfolio Standards

The randomness and uncertainty of renewable energy generation are expected to significantly change the optimal decision-making of trans-provincial electricity market subjects.Therefore,it is beneficial to optimize the interests of each of these subjects,considering the unpredictable risks of renewable energy under the renewable portfolio standards(RPS)and researching their effects on the optimal decision-making of transprovincial electricity market multi-subjects.First,we develop a trans-provincial trading market mechanism for renewable energy and clarify the electricity supply and demand relation and the green certificates supply and demand relation of trans-provincial electricitymarketmulti-subjects.Then,under the RPS,we construct a multi-subject game model of the power supply chain that recognizes the risks,and adopt the reverse induction method to discuss the optimum risk-taking judgment of each subject in the trans-provincial electricity market.Finally,we useMATLAB to verify the viability and efficacy of the proposed gamemodel,and obtain a certain reference value for the optimal decision-making of trans-provincial electricity market subjects.In summary,we consider the uncertainty risks of renewable energy under RPS,study the effects of the green certificate price and risk aversion coefficient in the RPS mechanism on the optimal decisionmaking of trans-provincial electricity market subjects,and obtain the changing trends of two different power products and those of different electricity market subjects under the influence of the green certificate price and risk aversion coefficient,which have a certain reference value for studying the factors affecting the optimal decision-making of trans-provincial electricity market subjects.

APPROACH FOR FUZZY MULTI-ATTRIBUTE DECISION-MAKING WITH FUZZY COMPLEMENTARY PREFERENCE RELATION ON ALTERNATIVES

In presented fuzzy multi-attribute decision-making （FMADM） problems, the information about attribute weights is interval numbers and the decision maker （DM） has fuzzy complementary preference relation on alternatives. Firstly, the decision-making information based on the subjective preference information in the form of the fuzzy complementary judgment matrix is uniform by using a translation function. Then an objective programming model is established. Attribute weights are obtained by solving the model, thus the fuzzy overall values of alternatives are derived by using the additive weighting method. Secondly, the ranking approach of alternatives is proposed based on the degree of similarity between the fuzzy positive ideal solution of alternatives （FPISA） and the fuzzy overall values. The method can sufficiently utilize the objective information of alternatives and meet the subjective requirements of the DM as much as possible. It is easy to be operated and implemented on a computer. Finally, the proposed method is applied to the project evaluation in the venture investment.

Two-phase TOPSIS of uncertain multi-attribute group decision-making

To solve the uncertain multi-attribute group decision-making of unknown attribute weights,three optimal models are built to decide the corresponding ideal solution weights,standard deviation weights and mean deviation weights.The comprehensive attribute weights are gotten through the product of the above three kinds of weights.And each decision maker＇s weighted decision matrices are also received by using the integrated attribute weights.The closeness degrees are also gotten by use of technique for order preference by similarity to ideal solution（TOPSIS） through dealing with the weighted decision matrices.At the same time the group decision matrix and weighted group decision matrix are gotten by using each decision-maker＇s closeness degree to every project.Then the vertical TOPSIS method is used to calculate the closeness degree of each project.So these projects can be ranked according to their values of the closeness degree.The process of the method is also given step by step.Finally,a numerical example demonstrates the feasibility and effectiveness of the approach.

A quantitative model for danger degree evaluation of staged operation of earth dam reservoir in flood season and its application

Based on the natural disaster risk evaluation mode, a quantitative danger degree evaluation model was developed to evaluate the danger degree of earth dam reservoir staged operation in the flood season. A formula for the overtopping risk rate of the earth dam reservoir staged operation was established, with consideration of the joint effect of flood and wind waves in the flood sub-seasons with the Monte Carlo method, and the integrated overtopping risk rate for the whole flood season was obtained via the total probability approach. A composite normalized function was used to transform the dam overtopping risk rate into the danger degree, on a scale of 0-1. Danger degree gradating criteria were divided by four significant characteristic values of the dam overtopping rate, and corresponding guidelines for danger evaluation are explained in detail in this paper. Examples indicated that the dam overtopping danger degree of the Chengbihe Reservoir in China was 0.33-0.57, within the range of moderate danger level, and the flood-limiting water level （FLWL） can be adjusted to 185.00 m for the early and main flood seasons, and 185.00-187.50 m for the late flood season. The proposed quantitative model offers a theoretical basis for determination of the value of the danger degree of an earth dam reservoir under normal operation as well as the optimal scheduling scheme for the reservoir in each stage of the flood season.

Probabilistic Lane-Change Decision-Making and Planning for Autonomous Heavy Vehicles

To improve the safety and driving stability of the autonomous heavy truck, it is necessary to consider the differences of driving behavior and drivable trajectories between the heavy trucks and passenger cars. This study proposes a probabilistic decision-making and trajectory planning framework for the autonomous heavy trucks. Firstly, the driving decision process is divided into intention generation and feasibility evaluations, which are realized using the utility theory and risk assessment, respectively. Subsequently the driving decision is made and sent to the trajectory planning module. In order to reflect the greater risks of the truck to other surrounding vehicles, the aggressiveness index(AI) is proposed and quantified to infer the asymmetrical risk level of lane-change maneuver. In the planning stage, the lateral and roll dynamics stability domains are developed as the constraints to exclude the candidate trajectories that would cause vehicle instability. Finally, the simulation results are compared between the proposed model and the artificial potential filed model in the scenarios extracted from the naturalistic driving data. It is shown that the proposed framework can provide the human-like lane-change decisions and truck-friendly trajectories, and performs well in dynamic driving environments.

Severity of the metabolic syndrome as a predictor of prediabetes and type 2 diabetes in first degree relatives of type 2 diabetic patients:A 15-year prospective cohort study

BACKGROUND Type 2 diabetes mellitus(T2DM)has high morbidity and mortality worldwide,therefore there is of paramount importance to identify the risk factors in the populations at risk early in the course of illness.A strong correlation between severity of metabolic syndrome(MetS)and HbA1c,fasting insulin and insulin resistance has been reported.Accordingly,the MetS severity score(or MestS Zscore)can potentially be used to predict the risk of T2DM progression over time.AIM To evaluate the association the of MestS Z-score in first degree relatives(FDRs)of T2DM with the risk of prediabetes and type 2 diabetes in future.METHODS A prospective open cohort study was conducted between 2003-2018.At baseline,the sample comprised of 1766 FDRs of patients with T2DM who had a normal glucose tolerance test.Relative risk(RR)and 95%confidence interval were calculated based on logistic regression.The receiver-operator characteristic analysis and area under the curve based on MetS Z-score were used to evaluate the risk of prediabetes and diabetes among the FDR population.RESULTS Baseline MetS Z-scores were associated with the its latest values(P<0.0001).Compared with individuals who were T2DM free at the end of follow up,those who developed T2DM had higher MetS Z-score at baseline(P<0.001).In multivariable logistic regression analyses for every unit elevation in MetS Z-score at the baseline,the RR for developing future T2DM and prediabetes was(RR=1.94,RR=3.84),(RR=1.5,RR=2.17)in total population and female group,respectively(P<0.05).The associations remained significant after adjusting the potential confounding variables.A cut off value of 0.97 and 0.94 was defined in the receiver-operator characteristic curve based on the MetS Z-score for differentiating female patients with diabetes and prediabetes from the normal population,respectively.CONCLUSION The MetS Z-score was associated with an increased risk of future T2DM.Appropriate interventions at earlier stages for preventing and attenuating MetS effects may be considered as an effective strategy for FDR as at-risk population.

Dynamic hesitant fuzzy linguistic group decision-making from a reliability perspective

A dynamic hesitant fuzzy linguistic group decisionmaking(DHFLGDM) problem is studied from the perspective of information reliability based on the theory of hesitant fuzzy linguistic term sets(HFLTSs). First, an approach is applied to transform the dynamic HFLTSs(DHFLTSs) into a set of proportional linguistic terms to eliminate the time dimension. Second, expert reliability is measured by considering both group similarity and degree of certainty, and an optimization method is employed to quantify the linguistic terms by maximizing the group similarity. Third, through computing the attribute stability as well as its reliability, a combination rule which considers both reliability and weight is proposed to aggregate the information, and then the aggregated grade values and degree of stability are used to make a selection. Finally,the application and feasibility of the proposed method are verified through a case study and method comparison.

Heavy Metal Levels and Ecological Risk in Crude Oil-Contaminated Soils from Okpare-Olomu, Niger Delta, Nigeria

Crude oil spills have inflicted extensive disruption upon the Niger Delta ecosystem, resulting in crop loss and severe environmental damage. Such spills exacerbate heavy metal concentration within soil due to the presence of metallic ions. The Okpare-Olomu community has borne the brunt of crude oil pollution from illicit bunkering, sabotage, and equipment malfunction. This study targets an evaluation of ecological hazards linked to heavy metals (HMs) in crude oil impacted agriculturally soils within Okpare-Olomu in Ughelli South LGA of Delta State. In this study, 24 topsoil samples were obtained from areas affected by crude oil pollution;the heavy metal content was evaluated through atomic absorption spectrometry. The concentration ranges for HMs (mg/kg) in soil were: 24.1 - 23,174 (Cu);0.54 - 37.1 (Cd);9.05 - 54 (Cr);12 - 174 (Ni);18.5 - 8611 (Pb);and 148 - 9078 (Zn) at a soil depth of 0 - 15 cm. Notably, metal concentrations were recorded to be above permissible World Health Organization limits. Predominantly, Zn and Pb recorded higher heavy metal concentration when compared to other heavy metals analysed, notably at sampling points PT7 through PT24. Zinc and Pb contamination exhibited highly significant contamination factors, and contamination severity was evidenced across all sample points, signifying a grave risk level. Pollution load indices indicated pervasive extreme pollution levels. Geoaccumulation indices signaled moderate to strong pollution, mainly by Pb and Zn. Ecological risk assessments revealed variable levels of heavy metal contamination, from low to very high, with potential ecological risk reflecting markedly elevated levels. This study underscores the imperative for soil remediation to rectify ecological imbalances in agriculturally affected soil constituents.

GIS Application in Urban Flood Risk Analysis: Midar as a Case Study

The significance of this study lies in its exploration of the advanced applications of Geographic Information Systems (GIS) in assessing urban flood risks, with a specific focus on Midar, Morocco. This research is pivotal as it showcases that GIS technology is not just a tool for mapping, but a critical component in urban planning and emergency management strategies. By meticulously identifying and mapping flood-prone areas in Midar, the study provides invaluable insights into the potential vulnerabilities of urban landscapes to flooding. Moreover, this research demonstrates the practical utility of GIS in mitigating material losses, a significant concern in flood-prone urban areas. The proactive approach proposed in this study, centered around the use of GIS, aims to safeguard Midar’s population and infrastructure from the devastating impacts of floods. This approach serves as a model for other urban areas facing similar challenges, highlighting the indispensable role of GIS in disaster preparedness and response. Overall, the study underscores the transformative potential of GIS in enhancing urban resilience, making it a crucial tool in the fight against natural disasters like floods.

Soil Contamination by Heavy Metals and Evaluations on Potential Ecological Risks in an Antimony Mine

To study the status of soil quality in an antimony mine, soil samples were collected from different regions and the elements＇ contents of Sb, Cd, Cr, Cu, Zn, Pb, Hg, Ni and As were analyzed using single factor pollution index, Nemerow index and potential ecological risk index. The heavy metal contamination of soils were evaluated. The results showed that Nemerow index for each sampled point was less than 0.7, meaning a clean state. When potential ecological risk assessment was conducted, the sampled point was less than 150, belonging to light pollution.