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.

Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning

BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.

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.

Natural Disaster Risk Monitoring for Immovable Cultural Relics Based on Digital Twin

Natural disaster risk monitoring is an important task for disaster prevention and reduction.In the case of immovable cultural relics,however,the feedback mechanism,risk factors,monitoring logic,and monitoring indicators of natural disaster risk monitoring are complex.How to achieve intelligent perception and monitoring of natural disaster risk for immovable cultural relics has always been a focus and a challenge for researchers.Based on the analysis of the concepts and issues related to the natural disaster risk of immovable cultural relics,this paper proposes a framework for natural disaster risk monitoring for immovable cultural relics based on the digital twin.This framework focuses on risk monitoring,including the physical entities of natural disaster risk for immovable cultural relics,monitoring indicators,and virtual entity construction.A platform for monitoring the natural disaster risk of immovable cultural relics is proposed.Using the Puzhou Ancient City Site as a test bed,the proposed concept can be used for monitoring the natural disaster risk of immovable cultural relics at different scales.

Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter

In traditional finite-temperature Kohn–Sham density functional theory(KSDFT),the partial occupation of a large number of high-energy KS eigenstates restricts the use of first-principles molecular dynamics methods at extremely high temperatures.However,stochastic density functional theory(SDFT)can overcome this limitation.Recently,SDFT and the related mixed stochastic–deterministic density functional theory,based on a plane-wave basis set,have been implemented in the first-principles electronic structure software ABACUS[Q.Liu and M.Chen,Phys.Rev.B 106,125132(2022)].In this study,we combine SDFT with the Born–Oppenheimer molecular dynamics method to investigate systems with temperatures ranging from a few tens of eV to 1000 eV.Importantly,we train machine-learning-based interatomic models using the SDFT data and employ these deep potential models to simulate large-scale systems with long trajectories.Subsequently,we compute and analyze the structural properties,dynamic properties,and transport coefficients of warm dense matter.

Analysis of risk factors leading to anxiety and depression in patients with prostate cancer after castration and the construction of a risk prediction model

BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.

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 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.

Risk stratification for radioactive iodine refractoriness using molecular alterations in distant metastatic differentiated thyroid cancer

Objective: Patients with radioactive iodine-refractory differentiated thyroid cancer(RAIR-DTC) are often diagnosed with delay and constrained to limited treatment options. The correlation between RAI refractoriness and the underlying genetic characteristics has not been extensively studied.Methods: Adult patients with distant metastatic DTC were enrolled and assigned to undergo next-generation sequencing of a customized 26-gene panel(Thyro Lead). Patients were classified into RAIR-DTC or non-RAIR groups to determine the differences in clinicopathological and molecular characteristics. Molecular risk stratification(MRS) was constructed based on the association between molecular alterations identified and RAI refractoriness, and the results were classified as high, intermediate or low MRS.Results: A total of 220 patients with distant metastases were included, 63.2% of whom were identified as RAIRDTC. Genetic alterations were identified in 90% of all the patients, with BRAF(59.7% vs. 17.3%), TERT promoter(43.9% vs. 7.4%), and TP53 mutations(11.5% vs. 3.7%) being more prevalent in the RAIR-DTC group than in the non-RAIR group, except for RET fusions(15.8% vs. 39.5%), which had the opposite pattern. BRAF and TERT promoter are independent predictors of RAIR-DTC, accounting for 67.6% of patients with RAIR-DTC. MRS was strongly associated with RAI refractoriness(P<0.001), with an odds ratio(OR) of high to low MRS of 7.52 [95%confidence interval(95% CI), 3.96-14.28;P<0.001] and an OR of intermediate to low MRS of 3.20(95% CI,1.01-10.14;P=0.041).Conclusions: Molecular alterations were associated with RAI refractoriness, with BRAF and TERT promoter mutations being the predominant contributors, followed by TP53 and DICER1 mutations. MRS might serve as a valuable tool for both prognosticating clinical outcomes and directing precision-based therapeutic interventions.

Automatic detection of small bowel lesions with different bleeding risks based on deep learning models

BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel(SB)capsule endoscopy(CE)that can assist physicians in diagnosis.However,the existing deep learning models present some unresolved challenges.AIM To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks,and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups.METHODS The proposed model represents a two-stage method that combined image classification with object detection.First,we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images,normal SB mucosa images,and invalid images.Then,the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding,and the location of the lesion was marked.We constructed training and testing sets and compared model-assisted reading with physician reading.RESULTS The accuracy of the model constructed in this study reached 98.96%,which was higher than the accuracy of other systems using only a single module.The sensitivity,specificity,and accuracy of the model-assisted reading detection of all images were 99.17%,99.92%,and 99.86%,which were significantly higher than those of the endoscopists’diagnoses.The image processing time of the model was 48 ms/image,and the image processing time of the physicians was 0.40±0.24 s/image(P<0.001).CONCLUSION The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images,which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.

Hazard risk assessment of tropical cyclones based on joint probability theory

The main hazard-causing factors of tropical cyclones are strong wind,heavy rainfall,and storm surge.Evaluation of the hazard-causing degree of a tropical cyclone requires a joint intensity analysis of these hazard-causing factors.According to the maximum hourly mean wind speed,total rainfall,and maximum tide level at various observation stations in Hong Kong during these tropical cyclones,three hazard-causing indices for tropical cyclones are introduced:the strong-wind index(VI),total-rainfall index(RI),and tide-level index(LI).Through a joint probability analysis of VI,RI,and LI for a tropical cyclone affecting Hong Kong,the joint return period is calculated to evaluate its joint hazard-causing intensity.A limit state function of Hong Kong’s resistance to tropical cyclones is developed and used to evaluate the regional risk of tropical cyclones affecting Hong Kong.The results indicate that the joint return period of VI,RI,and LI can reflect the joint hazard-causing intensity of strong wind,heavy rain,and storm surge caused by tropical cyclones;if the overall design return periods of the regional structures decrease,the regional ability to defend against tropical cyclone disasters is degraded.

Prevalence and risk factors of diabetes mellitus among elderly patients in the Lugu community

BACKGROUND Age is a significant risk factor of diabetes mellitus(DM).With the develop of population aging,the incidence of DM remains increasing.Understanding the epidemiology of DM among elderly individuals in a certain area contributes to the DM interventions for the local elderly individuals with high risk of DM.AIM To explore the prevalence of DM among elderly individuals in the Lugu community and analyze the related risk factors to provide a valid scientific basis for the health management of elderly individuals.METHODS A total of 4816 elderly people who came to the community for physical examination were retrospectively analyzed.The prevalence of DM among the elderly was calculated.The individuals were divided into a DM group and a non-DM group according to the diagnosis of DM to compare the differences in diastolic blood pressure(DBP)and systolic blood pressure(SBP),fasting blood glucose,body mass index(BMI),waist-to-hip ratio(WHR)and incidence of hypertension(HT),coronary heart disease(CHD),and chronic kidney disease(CKD).RESULTS DM was diagnosed in 32.70%of the 4816 elderly people.The BMI of the DM group(25.16±3.35)was greater than that of the non-DM group(24.61±3.78).The WHR was 0.90±0.04 in the non-DM group and 0.90±0.03 in the DM group,with no significant difference.The left SBP and SBP in the DM group were 137.9 mmHg±11.92 mmHg and 69.95 mmHg±7.75 mmHg,respectively,while they were 126.6 mmHg±12.44 mmHg and 71.15 mmHg±12.55 mmHg,respectively,in the non-DM group.These findings indicate higher SBP and lower DBP in DM patients than in those without DM.In the DM group,1274 patients were diagnosed with HT,accounting for 80.89%.Among the 3241 non-DM patients,1743(53.78%)were hypertensive and 1498(46.22%)were nonhypertensive.The DM group had more cases of HT than did the non-DM group.There were more patients with CHD or CKD in the DM group than in the non-DM group.There were more patients who drank alcohol more frequently(≥3 times)in the DM group than in the non-DM group.CONCLUSION Older adults in the Lugu community are at a greater risk of DM.In elderly individuals,DM is closely related to high BMI and HT,CHD,and CKD.Physical examinations should be actively carried out for elderly people to determine their BMI,SBP,DBP,and other signs,and sufficient attention should be given to abnormalities in the above signs before further diagnosis.

Adolescent suicide risk factors and the integration of socialemotional skills in school-based prevention programs

Adolescents are considered one of the most vulnerable groups affected by suicide.Rapid changes in adolescents’physical and mental states,as well as in their lives,significantly and undeniably increase the risk of suicide.Psychological,social,family,individual,and environmental factors are important risk factors for suicidal behavior among teenagers and may contribute to suicide risk through various direct,indirect,or combined pathways.Social-emotional learning is considered a powerful intervention measure for addressing the crisis of adolescent suicide.When deliberately cultivated,fostered,and enhanced,selfawareness,self-management,social awareness,interpersonal skills,and responsible decision-making,as the five core competencies of social-emotional learning,can be used to effectively target various risk factors for adolescent suicide and provide necessary mental and interpersonal support.Among numerous suicide intervention methods,school-based interventions based on social-emotional competence have shown great potential in preventing and addressing suicide risk factors in adolescents.The characteristics of school-based interventions based on social-emotional competence,including their appropriateness,necessity,cost-effectiveness,comprehensiveness,and effectiveness,make these interventions an important means of addressing the crisis of adolescent suicide.To further determine the potential of school-based interventions based on social-emotional competence and better address the issue of adolescent suicide,additional financial support should be provided,the combination of socialemotional learning and other suicide prevention programs within schools should be fully leveraged,and cooperation between schools and families,society,and other environments should be maximized.These efforts should be considered future research directions.

Prediabetes: An overlooked risk factor for major adverse cardiac and cerebrovascular events in atrial fibrillation patients

BACKGROUND Prediabetes is a well-established risk factor for major adverse cardiac and cerebrovascular events(MACCE).However,the relationship between prediabetes and MACCE in atrial fibrillation(AF)patients has not been extensively studied.Therefore,this study aimed to establish a link between prediabetes and MACCE in AF patients.AIM To investigate a link between prediabetes and MACCE in AF patients.METHODS We used the National Inpatient Sample(2019)and relevant ICD-10 CM codes to identify hospitalizations with AF and categorized them into groups with and without prediabetes,excluding diabetics.The primary outcome was MACCE(all-cause inpatient mortality,cardiac arrest including ventricular fibrillation,and stroke)in AF-related hospitalizations.RESULTS Of the 2965875 AF-related hospitalizations for MACCE,47505(1.6%)were among patients with prediabetes.The prediabetes cohort was relatively younger(median 75 vs 78 years),and often consisted of males(56.3%vs 51.4%),blacks(9.8%vs 7.9%),Hispanics(7.3%vs 4.3%),and Asians(4.7%vs 1.6%)than the non-prediabetic cohort(P<0.001).The prediabetes group had significantly higher rates of hypertension,hyperlipidemia,smoking,obesity,drug abuse,prior myocardial infarction,peripheral vascular disease,and hyperthyroidism(all P<0.05).The prediabetes cohort was often discharged routinely(51.1%vs 41.1%),but more frequently required home health care(23.6%vs 21.0%)and had higher costs.After adjusting for baseline characteristics or comorbidities,the prediabetes cohort with AF admissions showed a higher rate and significantly higher odds of MACCE compared to the non-prediabetic cohort[18.6%vs 14.7%,odds ratio(OR)1.34,95%confidence interval 1.26-1.42,P<0.001].On subgroup analyses,males had a stronger association(aOR 1.43)compared to females(aOR 1.22),whereas on the race-wise comparison,Hispanics(aOR 1.43)and Asians(aOR 1.36)had a stronger association with MACCE with prediabetes vs whites(aOR 1.33)and blacks(aOR 1.21).CONCLUSION This population-based study found a significant association between prediabetes and MACCE in AF patients.Therefore,there is a need for further research to actively screen and manage prediabetes in AF to prevent MACCE.

The Evaluation of Alternative Risk Control Schemes Based on Cumulative Prospect Theory

Based on the analysis of the evaluation problems associated with the risk control scheme for major engineering projects,the evaluation method of the risk control scheme considering the irrational behavior of evaluation members in fuzzy random environment is proposed.Firstly,a maximum entropy model corresponding to any evaluation member is es-tablished by using triangular fuzzy random variables and grey correlation coefficient in order to obtain the weight of each risk factor of the member.Secondly,a nonlinear programming model is established according to the principle of minimiz-ing deviation to estimate the weight of different evaluation members on the evaluation of alternative risk control schemes.Lastly,the cumulative entropy model is used to calculate the weight of risk control schemes.Cumulative prospect theory obtains the comprehensive prospect utility value of each alternative to determine the optimal alternative.

Development of Localized Assessment of Municipal Wastewater Disposal Risks

A means to develop a comparative assessment of the risks of available wastewater effluent disposal options on a local scale needs to be developed to help local decision-makers make decisions on options such as direct or indirect potable reuse options. These options have garnered more interest as a result of water supply limitations in many urban areas. This risk assessment was developed from a risk assessment developed at the University of Miami in 2001 and Florida Atlantic University (FAU) in 2023. Direct potable reuse and injection wells were deemed to have the lowest risk in the most recent study by FAU. However, the injection well option may not be available everywhere. As a result, a more local means to assess exposure risk is needed. This paper outlines the process to evaluate the public health risks associated with available disposal alternatives which may be very limited in some areas. The development of exposure pathways can help local decision-makers define the challenges, and support later expert level analysis upon which public health decisions are based.

Survey and Prospect for Applying Knowledge Graph in Enterprise Risk Management

Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order.Amidst the challenges posed by intricate and unpredictable risk factors,knowledge graph technology is effectively driving risk management,leveraging its ability to associate and infer knowledge from diverse sources.This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios.Firstly,employing bibliometric methods,the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge graphs.In the succeeding section,systematically delineate the technical methods for knowledge extraction and fusion in the standardized construction process of enterprise risk knowledge graphs.Objectively comparing and summarizing the strengths and weaknesses of each method,we provide recommendations for addressing the existing challenges in the construction process.Subsequently,categorizing the applied research of enterprise risk knowledge graphs based on research hotspots and risk category standards,and furnishing a detailed exposition on the applicability of technical routes and methods.Finally,the future research directions that still need to be explored in enterprise risk knowledge graphs were discussed,and relevant improvement suggestions were proposed.Practitioners and researchers can gain insights into the construction of technical theories and practical guidance of enterprise risk knowledge graphs based on this foundation.

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.

DeepRisk:A deep learning approach for genome-wide assessment of common disease risk

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant interest.Although widely applied,traditional polygenic risk scoring methods fall short,as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms(SNPs).This presents a limitation,as genetic diseases often arise from complex interactions between multiple SNPs.To address this challenge,we developed DeepRisk,a biological knowledge-driven deep learning method for modeling these complex,nonlinear associations among SNPs,to provide a more effective method for scoring the risk of common diseases with genome-wide genotype data.Evaluations demonstrated that DeepRisk outperforms existing PRs-based methods in identifying individuals at high risk for four common diseases:Alzheimer's disease,inflammatory bowel disease,type 2diabetes,and breast cancer.

Climate change drives flooding risk increases in the Yellow River Basin

The Yellow River Basin(YRB)has experienced severe floods and continuous riverbed elevation throughout history.Global climate change has been suggested to be driving a worldwide increase in flooding risk.However,owing to insufficient evidence,the quantitative correlation between flooding and climate change remains illdefined.We present a long time series of maximum flood discharge in the YRB dating back to 1843 compiled from historical documents and instrument measurements.Variations in yearly maximum flood discharge show distinct periods:a dramatic decreasing period from 1843 to 1950,and an oscillating gentle decreasing from 1950 to 2021,with the latter period also showing increasing more extreme floods.A Mann-Kendall test analysis suggests that the latter period can be further split into two distinct sub-periods:an oscillating gentle decreasing period from 1950 to 2000,and a clear recent increasing period from 2000 to 2021.We further predict that climate change will cause an ongoing remarkable increase in future flooding risk and an∼44.4 billion US dollars loss of floods in the YRB in 2100.