Intervention decision-making in MAV/UAV cooperative engagement based on human factors engineering

Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle（MAV/UAV） cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human factors engineering（HFE）.Firstly, based on the brief review of research status of HFE, it gives structural description to emergency in the process of cooperative engagement and analyzes intervention of commanders. After that,constraint conditions of intervention decision-making of commanders based on HFE（IDMCBHFE） are given, and the mathematical model, which takes the overall efficiency value of handling emergencies as the objective function, is established. Then, through combining K-best and variable neighborhood search（VNS） algorithm, a K-best optimization variable neighborhood search mixed algorithm（KBOVNSMA） is designed to solve the model. Finally,through three groups of simulation experiments, effectiveness and superiority of the proposed algorithm are verified.

Factors influencing the degree of participation in surgical decision-making among Chinese patients with prostate cancer:A qualitative research

Objective:There was increasingly demand of participation in surgical decision-making among Chinese patients with prostate cancer.However,due to the complex healthcare system and advanced care settings,it is quite challenging for the patients to gain sufficient support from the institute and the government.This research aimed to investigate the factors that impact the degree of participation in surgical decision-making among Chinese prostate cancer patients.Methods:A phenomenological approach of qualitative research based on the results of semi-structured interviews was adopted,to explore the influencing factors which hinder the participation in surgical decision-making.Consolidated Criteria for Reporting Qualitative Research were utilized.Up to 160 post-operative patients who had undergone radical prostatectomy along with 68 medical and nursing staffs,were purposively recruited in this research.This retrospective study was carried out from September 2018 to August 2019.After recording and transcribing the interviews,the interview materials were evaluated via the Colaizzi's seven step approach and the NVivo Version 10 software to analyze the interview content.Results:According to the analysis and summary of the interviews,there were three factors affecting the degree of participation in surgical decision-making.Firstly,insufficient information was provided by medical and nursing staffs because of their lack of time,proper communication skills,and career experience,as well as difficulties in the development of patient decision aid and inconsistent resource availability.Secondly,the cognitive level of decision-making among patients was relatively low due to poor psychological endurance,insufficient amount of education,senility,and less knowledge and information demand.Ultimately,decisions were constantly made by family members with/without patients.Conclusions:The degree of participation of Chinese prostate cancer patients in the surgical decision-making had much space for improvement.

Analysis of Influencing Factors Tourists1 Purchase Decision-making Behavior in Kunming

Based on the theory of consumer behavior,this paper analyzes the current situation of tourism shopping market in Kunming,and analyzes the decision-making behavior of tourists shopping in Kunming with the questionnaire survey,and clarifies the influencing factors of the decision-making behavior of visitors to Kunming.In the future,the influencing factors of Kunming tourists'shopping decision-making behavior are combined with the current situation of Kunming's tourism shopping market.The problems of cheating-induced shopping,the high price of shopping products,the low level of tourism shopping experience and the imperfect after-sales service are analyzed.Finally,the corresponding countermeasures and suggestions are proposed from four aspects:rectifying the tourism shopping market,establishing a sound price supervision mechanism,strengthening the tourism shopping experience,and improving after-sales service.

Factors of Marketing Toward Customer Decision-Making in Cross-Border E-Commerce:Case Study of QD Company

Taking small cross-border e-commerce enterprise QD company as an example,this study targets customers consuming on the platform of cross-border e-commerce enterprise QD company,and the purpose is to study the marketing factors affecting customer decision-making.By combining with relevant literature,this paper preliminarily confirmed that 7Ps,Technology Acceptance Model(TAM),and customer decision-making theory as the theoretical basis,and established the theoretical model.According to the mature scale of domestic and foreign scholars to measure the research variables,with 7Ps and TAM as the independent variable,TAM perceived usefulness and ease of use as the core of the independent variable,customer decision-making as the dependent variable,in the test before the project,forming a formal questionnaire,the main data are obtained through the design and distribution of the questionnaire.Based on a large number of relevant literature,this paper examines the marketing factors influencing customer decisions in cross-border e-commerce,then identifies goals,builds models,and proposes hypotheses.According to the technical acceptance model in consumer behavior theory,select the cross-border e-commerce enterprise quantum dot platform of consumers as the research object,finally through SPSS software on cross-border consumer feedback,including descriptive statistical analysis,factor analysis,correlation analysis,regression analysis test customer satisfaction,finally found a significant positive relationship between customer decision-making and marketing mix and TAM variables.

Marketing Factors Influencing Customer Decision-Making in the Interior Decorations Industry in Guangxi,China

The purpose of this study was to empirically examine the influence of marketing mix towards customer decision-making in the interior decoration industry of Guangxi,China.The interior decoration industry in Guangxi is growing at rapid pace.Therefore,this study is specially helpful for the interior decoration industry in Guangxi,China,and the interior decoration industry in Guangxi can use marketing mix for their business gained.In order to carry out this study,the population was taken to be those customers of the interior decoration.A sample(N=425)customers were taken using simple random sampling from the interior decoration consumers of Guangxi,China.It was hypothesized that marketing mix had positive influence on customer decision-making.It was hypothesized that marketing mix predicts customer decision-making.The results were analyzed with help SPSS software.Descriptive statistical analysis,Pearson correlation test,and regression analysis were used to test hypothesis.The results showed significant positive relationship and marketing mix was a predictor of customer decision-making.This research is significant and useful for all the interior decoration enterprises in Guangxi.

Personality theory:New factors to incorporate in public decision-making in communities

Background:This study explored the effects of personality factors on public behavioral decision‐making.Methods:We examined the literature on personality theory based on triadic interaction decision theory,and summarized and compared the findings with studies of the Big Five personality characteristics.A literature review method was used to explore the implications of personality theory for public decisionmaking in Chinese communities.Results:Individuals with high neuroticism can be targeted by influential communicators.Individuals with high extraversion can influence decisionmaking through interpersonal relationships.Individuals with high levels of openness can be influenced by the development of novel activities.Conscientious individuals respond to scientific and rational knowledge.Individuals with high agreeableness can be influenced by groups.Conclusions:Personality traits can influence behavioral decisions and can have positive or negative effects on behavioral outcomes.For people with different personality traits,social actors and social activity communicators should formulate targeted measures according to the classification of personality traits.The current findings have implications for enriching research perspectives and approaches to public community decision‐making.

The Marketing Factors Affecting Customer Decision-Making of Xiaomi Smart Wearable Devices in Guangxi,China

From an empirical point of view,this paper proposes research hypotheses and models based on the market situation of Xiaomi smart wearable devices in Guangxi,as well as the research status of consumers’purchasing decisions,combined with the empirical research of some researchers.This paper designs questionnaires and scales.The sampling survey method is used to investigate and analyze the influencing factors of Guangxi consumers’decision to purchase Xiaomi smart wearable devices.Questionnaires were distributed through Questionnaire Star,and 385 valid questionnaires were collected for descriptive statistics and correlation analysis.Conclusions are as follow:(1)Consumers in Guangxi who purchase Xiaomi smart wearable devices are between 19 and 32 years old,and most of them have a bachelor’s degree.Among the five factors of demographic characteristics,only income and marketing mix satisfaction have a positive correlation,indicating that customers are sensitive to Xiaomi smart wearable products.And among the customers of Xiaomi smart wearable products,the monthly income of less than 5,000 yuan accounted for 30.91%of the total number of surveys;the monthly income was 5,000-7,000 yuan,accounting for 34.29%.(2)The satisfaction of the marketing mix is positively correlated with the satisfaction of customer decision-making.The satisfaction of the marketing mix varies with the age,gender,education,income,and working years of each population,and only the income is positively correlated with the satisfaction of the marketing mix.Relationships,age,gender,education,and years of employment were not associated with marketing mix satisfaction.According to the above conclusions,relevant and reasonable product development and marketing suggestions are put forward for the enterprise,which provides a reference for the enterprise’s brand building and market development.Therefore,on the basis of comparing with other scholars at home and abroad,through the 7P marketing theory and purchasing decision theory and the research on the current situation of influencing factors for customers to purchase Xiaomi smart wearable devices in Guangxi,this paper compiled a questionnaire for 385 private colleges and universities in Guangxi.A questionnaire survey was carried out with customers,and the current situation of customers’purchasing decision-making behavior was obtained and analyzed and the following suggestions were put forward:continuously innovating products,targeting target customers,reasonably setting product prices,improving marketing mix.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

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.

Spatial-temporal differentiation and influencing factors of rural settlements in mountainous areas: an example of Liangshan Yi Autonomous Prefecture, Southwestern China

Rural settlement is the basic spatial unit for compact communities in rural area. Scientific exploration of spatial-temporal differentiation and its influencing factors is the premise of spatial layout rationalization. Based on land use data of Liangshan Yi Autonomous Prefecture(hereinafter referred to as Liangshan Prefecture) in Sichuan Province, China from 1980 to 2020, compactness index, fractal dimension, imbalance index, location entropy and the optimal parameters-based geographical detector(OPGD) model are used to analyze the spatial-temporal evolution of the morphological characteristics of rural settlements, and to explore the influence of natural geographical factors, socioeconomic factors, and policy factors on the spatial differentiation of rural settlements. The results show that:(1) From 1980 to 2020, the rural settlements area in Liangshan Prefecture increased by 15.96 km^(2). In space, the rural settlements are generally distributed in a local aggregation, dense in the middle and sparse around the periphery. In 2015, the spatial density and expansion index of rural settlements reached the peak.(2) From 1980 to 2020, the compactness index decreased from 0.7636 to 0.7496, the fractal dimension increased from 1.0283 to 1.0314, and the fragmentation index decreased from 0.1183 to 0.1047. The spatial morphological structure of rural settlements tended to be loose, the shape contour tended to be complex, the degree of fragmentation decreased, and the spatial distribution was significantly imbalanced.(3) The results of OPGD detection in 2015 show that the influence of each factor is slope(0.2371) > traffic accessibility(0.2098) > population(0.1403) > regional GDP(0.1325) > elevation(0.0987) > poverty alleviation(0). The results of OPGD detection in 2020 show that the influence of each factor is slope(0.2339) > traffic accessibility(0.2198) > population(0.1432) > regional GDP(0.1219) > poverty alleviation(0.0992) > elevation(0.093). Natural geographical factors(slope and elevation) are the basic factors affecting the spatial distribution of rural settlements, and rural settlements are widely distributed in the river valley plain and the second half mountain area. Socioeconomic factors(traffic accessibility, population, and regional GDP) have a greater impact on the spatial distribution of rural settlements, which is an important factor affecting the spatial distribution of rural settlements. Policy factors such as poverty alleviation relocation have an indispensable impact on the spatial distribution of rural settlements. The research results can provide decisionmaking basis for the spatial arrangement of rural settlements in Liangshan Prefecture, and optimize the implementation of rural revitalization policies.

Biological factors driving colorectal cancer metastasis

Approximately 20%of colorectal cancer(CRC)patients present with metastasis at diagnosis.Among Stage I-III CRC patients who undergo surgical resection,18%typically suffer from distal metastasis within the first three years following initial treatment.The median survival duration after the diagnosis of metastatic CRC(mCRC)is only 9 mo.mCRC is traditionally considered to be an advanced stage malignancy or is thought to be caused by incomplete resection of tumor tissue,allowing cancer cells to spread from primary to distant organs;however,increa-sing evidence suggests that the mCRC process can begin early in tumor development.CRC patients present with high heterogeneity and diverse cancer phenotypes that are classified on the basis of molecular and morphological alterations.Different genomic and nongenomic events can induce subclone diversity,which leads to cancer and metastasis.Throughout the course of mCRC,metastatic cascades are associated with invasive cancer cell migration through the circulatory system,extravasation,distal seeding,dormancy,and reactivation,with each step requiring specific molecular functions.However,cancer cells presenting neoantigens can be recognized and eliminated by the immune system.In this review,we explain the biological factors that drive CRC metastasis,namely,genomic instability,epigenetic instability,the metastatic cascade,the cancer-immunity cycle,and external lifestyle factors.Despite remarkable progress in CRC research,the role of molecular classification in therapeutic intervention remains unclear.This review shows the driving factors of mCRC which may help in identifying potential candidate biomarkers that can improve the diagnosis and early detection of mCRC cases.

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.

Analysis of the influencing factors and clinical related characteristics of pulmonary tuberculosis in patients with type 2 diabetes mellitus

BACKGROUND In China,the prevalence of type 2 diabetes mellitus(T2DM)among diabetic patients is estimated to be between 90%-95%.Additionally,China is among the 22 countries burdened by a high number of tuberculosis cases,with approximately 4.5 million individuals affected by active tuberculosis.Notably,T2DM poses a significant risk factor for the development of tuberculosis,as evidenced by the increased incidence of T2DM coexisting with pulmonary tuberculosis(T2DMPTB),which has risen from 19.3%to 24.1%.It is evident that these two diseases are intricately interconnected and mutually reinforcing in nature.AIM To elucidate the clinical features of individuals diagnosed with both T2DM and tuberculosis(T2DM-PTB),as well as to investigate the potential risk factors associated with active tuberculosis in patients with T2DM.METHODS T2DM-PTB patients who visited our hospital between January 2020 and January 2023 were selected as the observation group,Simple DM patients presenting to our hospital in the same period were the control group,Controls and case groups were matched 1:2 according to the principle of the same sex,age difference(±3)years and disease duration difference(±5)years,patients were investigated for general demographic characteristics,diabetes-related characteristics,body immune status,lifestyle and behavioral habits,univariate and multivariate analysis of the data using conditional logistic regression,calculate the odds ratio(OR)values and 95%CI of OR values.RESULTS A total of 315 study subjects were included in this study,including 105 subjects in the observation group and 210 subjects in the control group.Comparison of the results of both anthropometric and biochemical measures showed that the constitution index,systolic blood pressure,diastolic blood pressure and lymphocyte count were significantly lower in the case group,while fasting blood glucose and high-density lipoprotein cholesterol levels were significantly higher than those in the control group.The results of univariate analysis showed that poor glucose control,hypoproteinemia,lymphopenia,TB contact history,high infection,smoking and alcohol consumption were positively associated with PTB in T2DM patients;married,history of hypertension,treatment of oral hypoglycemic drugs plus insulin,overweight,obesity and regular exercise were negatively associated with PTB in T2DM patients.Results of multivariate stepwise regression analysis found lymphopenia(OR=17.75,95%CI:3.40-92.74),smoking(OR=12.25,95%CI:2.53-59.37),history of TB contact(OR=6.56,95%CI:1.23-35.03)and poor glycemic control(OR=3.37,95%CI:1.11-10.25)was associated with an increased risk of developing PTB in patients with T2DM,While being overweight(OR=0.23,95%CI:0.08-0.72)and obesity(OR=0.11,95%CI:0.02-0.72)was associated with a reduced risk of developing PTB in patients with T2DM.CONCLUSION T2DM-PTB patients are prone to worse glycemic control,higher infection frequency,and a higher proportion of people smoking,drinking alcohol,and lack of exercise.Lymphopenia,smoking,history of TB exposure,poor glycemic control were independent risk factors for T2DM-PTB,and overweight and obesity were associated with reduced risk of concurrent PTB in patients with T2DM.

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.

Spatiotemporal deformation characteristics of Outang landslide and identification of triggering factors using data mining

Since the impoundment of Three Gorges Reservoir(TGR)in 2003,numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall.One case is the Outang landslide,a large-scale and active landslide,on the south bank of the Yangtze River.The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics.Data mining technology,including the two-step clustering and Apriori algorithm,is then used to identify the dominant triggers of landslide movement.In the data mining process,the two-step clustering method clusters the candidate triggers and displacement rate into several groups,and the Apriori algorithm generates correlation criteria for the cause-and-effect.The analysis considers multiple locations of the landslide and incorporates two types of time scales:longterm deformation on a monthly basis and short-term deformation on a daily basis.This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors.The data mining results reveal different dominant triggering factors depending on the monitoring frequency:the monthly and bi-monthly cumulative rainfall control the monthly deformation,and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide.It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslideprone areas.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Incidence and risk factors of depression in patients with metabolic syndrome

BACKGROUND Many studies have explored the relationship between depression and metabolic syndrome(MetS),especially in older people.China has entered an aging society.However,there are still few studies on the elderly in Chinese communities.AIM To investigate the incidence and risk factors of depression in MetS patients in China's Mainland and to construct a predictive model.METHODS Data from four waves of the China Health and Retirement Longitudinal Study were selected,and middle-aged and elderly patients with MetS(n=2533)were included based on the first wave.According to the center for epidemiological survey-depression scale(CESD),participants with MetS were divided into depression(n=938)and non-depression groups(n=1595),and factors related to depression were screened out.Subsequently,the 2-,4-,and 7-year follow-up data were analyzed,and a prediction model for depression in MetS patients was constructed.RESULTS The prevalence of depression in middle-aged and elderly patients with MetS was 37.02%.The prevalence of depression at the 2-,4-,and 7-year follow-up was 29.55%,34.53%,and 38.15%,respectively.The prediction model,constructed using baseline CESD and Physical Self-Maintenance Scale scores,average sleep duration,number of chronic diseases,age,and weight had a good predictive effect on the risk of depression in MetS patients at the 2-year follow-up(area under the curve=0.775,95%confidence interval:0.750-0.800,P<0.001),with a sensitivity of 68%and a specificity of 74%.CONCLUSION The prevalence of depression in middle-aged and elderly patients with MetS has increased over time.The early identification of and intervention for depressive symptoms requires greater attention in MetS patients.

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.

Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient

With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.

Interplay between microglia and environmental risk factors in Alzheimer's disease

Alzheimer s disease,among the most common neurodegenerative disorders,is chara cterized by progressive cognitive impairment.At present,the Alzheimer’s disease main risk remains genetic ris ks,but major environmental fa ctors are increasingly shown to impact Alzheimer’s disease development and progression.Microglia,the most important brain immune cells,play a central role in Alzheimer’s disease pathogenesis and are considered environmental and lifestyle"sensors."Factors like environmental pollution and modern lifestyles(e.g.,chronic stress,poor dietary habits,sleep,and circadian rhythm disorde rs)can cause neuroinflammato ry responses that lead to cognitive impairment via microglial functioning and phenotypic regulation.However,the specific mechanisms underlying interactions among these facto rs and microglia in Alzheimer’s disease are unclear.Herein,we:discuss the biological effects of air pollution,chronic stress,gut micro biota,sleep patterns,physical exercise,cigarette smoking,and caffeine consumption on microglia;consider how unhealthy lifestyle factors influence individual susceptibility to Alzheimer’s disease;and present the neuroprotective effects of a healthy lifestyle.Toward intervening and controlling these environmental risk fa ctors at an early Alzheimer’s disease stage,understanding the role of microglia in Alzheimer’s disease development,and to rgeting strategies to to rget microglia,co uld be essential to future Alzheimer’s disease treatments.