Identifying the best common factor model via exploratory eactor analysis

Currently,there is no solid criterion for judging the quality of the estimators in factor analysis.This paper presents a new evaluation method for exploratory factor analysis that pinpoints an appropriate number of factors along with the best method for factor extraction.The proposed technique consists of two steps:testing the normality of the residuals from the fitted model via the Shapiro-Wilk test and using an empirical quantified index to judge the quality of the factor model.Examples are presented to demonstrate how the method is implemented and to verify its effectiveness.

Modeling Analysis of Factors Influencing Wind-Borne Seed Dispersal: A Case Study on Dandelion

A weed is a plant that thrives in areas of human disturbance, such as gardens, fields, pastures, waysides, and waste places where it is not intentionally cultivated. Dispersal affects community dynamics and vegetation response to global change. The process of seed disposal is influenced by wind, which plays a crucial role in determining the distance and probability of seed dispersal. Existing models of seed dispersal consider wind direction but fail to incorporate wind intensity. In this paper, a novel seed disposal model was proposed in this paper, incorporating wind intensity based on relevant references. According to various climatic conditions, including temperate, arid, and tropical regions, three specific regions were selected to establish a wind dispersal model that accurately reflects the density function distribution of dispersal distance. Additionally, dandelions growth is influenced by a multitude of factors, encompassing temperature, humidity, climate, and various environmental variables that necessitate meticulous consideration. Based on Factor Analysis model, which completely considers temperature, precipitation, solar radiation, wind, and land carrying capacity, a conclusion is presented, indicating that the growth of seeds is primarily influenced by plant attributes and climate conditions, with the former exerting a relatively stronger impact. Subsequently, the remaining two plants were chosen based on seed weight, yielding consistent conclusion.

R-Factor Analysis of Data Based on Population Models Comprising R- and Q-Factors Leads to Biased Loading Estimates

Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.

A growth-regulating factor 7(GRF7)-mediated gene regulatory network promotes leaf growth and expansion in sugarcane

Knowledge of the function of growth-regulating factors(GRFs)in sugarcane(Saccharum officinarum and S.spontaneum)growth and development could assist breeders in selecting desirable plant architectures.However,limited information about GRFs is available in Saccharum due to their polyploidy.In this study,22 GRFs were identified in the two species and their conserved domains,gene structures,chromosome location,and synteny were characterized.GRF7 expression varied among tissues and responded to diurnal rhythm.SsGRF7-YFP was localized preferentially in the nucleus and appears to act as a transcriptional cofactor.SsGRF7 positively regulated the size and length of rice leaves,possibly by regulating cell size and plant hormones.Of seven potential transcription factors binding to the SsGRF7 promoter in S.spontaneum,four showed positive expression patterns,and two showed negative expression patterns relative to SsGRF7.

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.

Sugarcane transcription factor ScWRKY4 negatively regulates resistance to pathogen infection through the JA signaling pathway

WRKY transcription factors,transcriptional regulators unique to plants,play an important role in defense response to pathogen infection.However,the resistance mechanisms of WRKY genes in sugarcane remain unclear.In the present study,gene ontology(GO)enrichment analysis revealed that WRKY gene family in sugarcane was extensively involved in the response to biotic stress and in defense response.We identified gene ScWRKY4,a classⅡc member of the WRKY gene family,in sugarcane cultivar ROC22.This gene was induced by salicylic acid(SA)and methyl jasmonate(MeJA)stress.Interestingly,expression of ScWRKY4 was down-regulated in smut-resistant sugarcane cultivars but up-regulated in smutsusceptible sugarcane cultivars infected with Sporisorium scitamineum.Moreover,stable overexpression of the ScWRKY4 gene in Nicotiana benthamiana enhanced susceptibility to Fusarium solani var.coeruleum and caused down-regulated expression of immune marker-related genes.Transcriptome analysis indicated suppressed expression of most JAZ genes in the signal transduction pathway.ScWRKY4 interacted with ScJAZ13 to repress its expression.We thus hypothesized that the ScWRKY4 gene was involved in the regulatory network of plant disease resistance,most likely through the JA signaling pathway.The present study depicting the molecular involvement of ScWRKY4 in sugarcane disease resistance lays a foundation for future investigation.

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.

Variation Characteristics of Root Traits of Different Alfalfa Cultivars under Saline-Alkaline Stress and their Relationship with Soil Environmental Factors

Soil salinization is the main factor that threatens the growth and development of plants and limits the increase of yield.It is of great significance to study the key soil environmental factors affecting plant root traits to reveal the adaptation strategies of plants to saline-alkaline-stressed soil environments.In this study,the root biomass,root morphological parameters and root mineral nutrient content of two alfalfa cultivars with different sensitivities to alkaline stress were analyzed with black soil as the control group and the mixed saline-alkaline soil with a ratio of 7:3 between black soil and saline-alkaline soil as the saline-alkaline treatment group.At the same time,the correlation analysis of soil salinity indexes,soil nutrient indexes and the activities of key enzymes involved in soil carbon,nitrogen and phosphorus cycles was carried out.The results showed that compared with the control group,the pH,EC,and urease(URE)of the soil surrounding the roots of two alfalfa cultivars were significantly increased,while soil total nitrogen(TN),total phosphorus(TP),organic carbon(SOC),andα-glucosidase activity(AGC)were significantly decreased under saline-alkaline stress.There was no significant difference in root biomass and root morphological parameters of saline-alkaline tolerant cultivar GN under saline-alkaline stress.The number of root tips(RT),root surface area(RS)and root volume(RV)of AG were reduced by 61.16%,44.54%,and 45.31%,respectively,compared with control group.The ratios of K^(+)/Na^(+),Ca^(2+)/Na^(+)and Mg^(2+)/Na^(+)of GN were significantly higher than those of AG(p<0.05).The root fresh weight(RFW)and dry weight(RDW),root length(RL),RV and RT of alfalfa were positively regulated by soil SOC and TN,but negatively regulated by soil pH,EC,and URE(p<0.01).Root Ca^(2+)/Na+ratio was significantly positively correlated with soil TN,TP and SOC(p<0.01).The absorption of Mg and Ca ions in roots is significantly negatively regulated by soilβ-glucosidase activity(BGC)and acid phosphatase activity(APC)(p<0.05).This study improved knowledge of the relationship between root traits and soil environmental factors and offered a theoretical framework for elucidating how plant roots adapt to saline-alkaline stressed soil environments.

Status quo and factors of depression and anxiety in patients with non-muscle invasive bladder cancer after plasma electrocision

BACKGROUND Bladder cancer is a type of cancer with a high incidence in men.Plasma electrosurgery(PES)is often used in the treatment of bladder cancer.Postoperative complications often cause depression and anxiety in patients after surgery.AIM To investigate the current state of depression and anxiety after PES in patients with non-muscle-invasive bladder cancer and analyze the factors affecting them.METHODS A retrospective study was conducted to compare the baseline data of patients by collecting their medical history and grouping them according to their mental status into negative and normal groups.Logistic regression analysis was used to explore the risk factors affecting the occurrence of anxiety and depression after surgery in patients with bladder cancer.RESULTS Comparative analyses of baseline differences showed that the patients in the negative and normal groups differed in terms of their first surgery,economic status,educational level,and marital status.A logistic regression analysis showed that it affected the occurrence of anxiety in patients with bladder cancer,and the results showed that whether the risk factors were whether or not it was the first surgery,monthly income between 3000 and 3000-6000,secondary or junior high school education level,single,divorced,and widowed statuses.CONCLUSION The risk factors affecting the onset of anxiety and depression in bladder cancer patients after PES are the number of surgeries,economic status,level of education,and marital status.This study provides a reference for the clinical treatment and prognosis of bladder cancer patients in the future.

Chemokine platelet factor 4 accelerates peripheral nerve regeneration by regulating Schwann cell activation and axon elongation

Schwann cells in peripheral nerves react to traumatic nerve injury by attempting to grow and regenerate.Howeve r,it is unclear what factors play a role in this process.In this study,we searched a GEO database and found that expression of platelet factor 4 was markedly up-regulated after sciatic nerve injury.Platelet factor is an important molecule in cell apoptosis,diffe rentiation,survival,and proliferation.Further,polymerase chain reaction and immunohistochemical staining confirmed the change in platelet factor 4 in the sciatic nerve at different time points after injury.Enzyme-linked immunosorbent assay confirmed that platelet factor 4 was secreted by Schwann cells.We also found that silencing platelet factor 4 decreased the proliferation and migration of primary cultured Schwann cells,while exogenously applied platelet factor 4 stimulated Schwann cell prolife ration and migration and neuronal axon growth.Furthermore,knocking out platelet factor 4 inhibited the prolife ration of Schwann cells in injured rat sciatic nerve.These findings suggest that Schwann cell-secreted platelet factor 4 may facilitate peripheral nerve repair and regeneration by regulating Schwann cell activation and axon growth.Thus,platelet factor 4 may be a potential therapeutic target for traumatic peripheral nerve injury.

Analysis of clinicopathological features and prognostic factors of breast cancer brain metastasis

BACKGROUND Breast cancer(BC)has become the most common malignancy in women.The incidence and detection rates of BC brain metastasis(BCBM)have increased with the progress of imaging,multidisciplinary treatment techniques and the extension of survival time of BC patients.BM seriously affects the quality of life and survival prognosis of BC patients.Therefore,clinical research on the clinicopathological features and prognostic factors of BCBM is valuable.By analyzing the clinicopathological parameters of BCBM patients,and assessing the risk factors and prognostic indicators,we can perform hierarchical diagnosis and treatment on the high-risk population of BCBM,and achieve clinical benefits of early diagnosis and treatment.AIM To explore the clinicopathological features and prognostic factors of BCBM,and provide references for diagnosis,treatment and management of BCBM.METHODS The clinicopathological data of 68 BCBM patients admitted to the Air Force Medical Center,Chinese People’s Liberation Army(formerly Air Force General Hospital)from 2000 to 2022 were collected.Another 136 BC patients without BM were matched at a ratio of 1:2 based on the age and site of onset for retrospective analysis.Categorical data were subjected to χ^(2) test or Fisher’s exact probability test,and the variables with P<0.05 in the univariate Cox proportional hazards model were incorporated into the multivariate model to identify high-risk factors and independent prognostic factors of BCBM,with a hazard ratio(HR)>1 suggesting poor prognostic factors.The survival time of patients was estimated by the Kaplan-Meier method,and overall survival was compared between groups by log-rank test.RESULTS Multivariate Cox regression analysis showed that patients with stage Ⅲ/Ⅳ tumor at initial diagnosis[HR:5.58,95% confidence interval(CI):1.99–15.68],lung metastasis(HR:24.18,95%CI:6.40-91.43),human epidermal growth factor receptor 2(HER2)-overexpressing BC and triple-negative BC were more prone to BM.As can be seen from the prognostic data,52 of the 68 BCBM patients had died by the end of follow-up,and the median time from diagnosis of BC to the occurrence of BM and from the occurrence of BM to death or last follow-up was 33.5 and 14 mo,respectively.It was confirmed by multivariate Cox regression analysis that patients with neurological symptoms(HR:1.923,95%CI:1.005-3.680),with bone metastasis(HR:2.011,95%CI:1.056-3.831),and BM of HER2-overexpressing and triple-negative BC had shorter survival time.CONCLUSION HER2-overexpressing,triple-negative BC,late tumor stage and lung metastasis are risk factors of BM.The presence of neurological symptoms,bone metastasis,and molecular type are influencing prognosis factors of BCBM.

A comprehensive approach to a variability analysis between earthquake activity and hydro-environmental factors on the Korean Peninsula

Following the Pohang and Gyeongju earthquakes and their aftershocks,there is no longer any zone that is safe from earthquake-related disasters in the Korean Peninsula.In order to monitor and predict earthquakes,correlation analysis of earthquakes and hydro-environmental factors are insufficient,and the development and application of hydro-environmental factor measurement equipment is still in the early stages.This study developes and verifies a more precise radon measurement device.Four specific earthquake cases(2019–2020)were selected,and the correlation of the analyses of the earthquakes and hydro-environmental factors(radon,electric conductivity(EC),water-level(WL),and water-temperature(WT))was conducted at the three specific groundwater stations.Accordingly,was confirmed that four factors are affected by earthquakes or seismic movement.Furthermore,the variability of the EC showed an identical tendency for a certain period before an earthquake occurred,and,in particular,the variability trends for radon,WL,and EC coincided at the time of the earthquake′s occurrence.

Multiple regression analysis of risk factors related to radiation pneumonitis

BACKGROUND Radiation pneumonitis(RP)is a severe complication of thoracic radiotherapy that may lead to dyspnea and lung fibrosis,and negatively affects patients’quality of life.AIM To carry out multiple regression analysis on the influencing factors of radiation pneumonitis.METHODS Records of 234 patients receiving chest radiotherapy in Huzhou Central Hospital(Huzhou,Zhejiang Province,China)from January 2018 to February 2021,and the patients were divided into either a study group or a control group based on the presence of radiation pneumonitis or not.Among them,93 patients with radiation pneumonitis were included in the study group and 141 without radiation pneumonitis were included in the control group.General characteristics,and radiation and imaging examination data of the two groups were collected and compared.Due to the statistical significance observed,multiple regression analysis was performed on age,tumor type,chemotherapy history,forced vital capacity(FVC),forced expiratory volume in the first second(FEV1),carbon monoxide diffusion volume(DLCO),FEV1/FVC ratio,planned target area(PTV),mean lung dose(MLD),total number of radiation fields,percentage of lung tissue in total lung volume(vdose),probability of normal tissue complications(NTCP),and other factors.RESULTS The proportions of patients aged≥60 years and those with the diagnosis of lung cancer and a history of chemotherapy in the study group were higher than those in the control group(P<0.05);FEV1,DLCO,and FEV1/FVC ratio in the study group were lower than those in the control group(P<0.05),while PTV,MLD,total field number,vdose,and NTCP were higher than in the control group(P<0.05).Logistic regression analysis showed that age,lung cancer diagnosis,chemotherapy history,FEV1,FEV1/FVC ratio,PTV,MLD,total number of radiation fields,vdose,and NTCP were risk factors for radiation pneumonitis.CONCLUSION We have identified patient age,type of lung cancer,history of chemotherapy,lung function,and radiotherapy parameters as risk factors for radiation pneumonitis.Comprehensive evaluation and examination should be carried out before radiotherapy to effectively prevent radiation pneumonitis.

Influencing factor analysis of interception probability and classification-regression neural network based estimation

The interception probability of a single missile is the basis for combat plan design and weapon performance evaluation,while its influencing factors are complex and mutually coupled.Existing calculation methods have very limited analysis of the influence mechanism of influencing factors,and none of them has analyzed the influence of the guidance law.This paper considers the influencing factors of both the interceptor and the target more comprehensively.Interceptor parameters include speed,guidance law,guidance error,fuze error,and fragment killing ability,while target performance includes speed,maneuverability,and vulnerability.In this paper,an interception model is established,Monte Carlo simulation is carried out,and the influence mechanism of each factor is analyzed based on the model and simulation results.Finally,this paper proposes a classification-regression neural network to quickly estimate the interception probability based on the value of influencing factors.The proposed method reduces the interference of invalid interception data to valid data,so its prediction accuracy is significantly better than that of pure regression neural networks.

Dealing with Multicollinearity in Factor Analysis: The Problem, Detections, and Solutions

Multicollinearity in factor analysis has negative effects, including unreliable factor structure, inconsistent loadings, inflated standard errors, reduced discriminant validity, and difficulties in interpreting factors. It also leads to reduced stability, hindered factor replication, misinterpretation of factor importance, increased parameter estimation instability, reduced power to detect the true factor structure, compromised model fit indices, and biased factor loadings. Multicollinearity introduces uncertainty, complexity, and limited generalizability, hampering factor analysis. To address multicollinearity, researchers can examine the correlation matrix to identify variables with high correlation coefficients. The Variance Inflation Factor (VIF) measures the inflation of regression coefficients due to multicollinearity. Tolerance, the reciprocal of VIF, indicates the proportion of variance in a predictor variable not shared with others. Eigenvalues help assess multicollinearity, with values greater than 1 suggesting the retention of factors. Principal Component Analysis (PCA) reduces dimensionality and identifies highly correlated variables. Other diagnostic measures include the condition number and Cook’s distance. Researchers can center or standardize data, perform variable filtering, use PCA instead of factor analysis, employ factor scores, merge correlated variables, or apply clustering techniques for the solution of the multicollinearity problem. Further research is needed to explore different types of multicollinearity, assess method effectiveness, and investigate the relationship with other factor analysis issues.

Analysis of inhomogeneity of solidified microstructure of continuous casting copper tubular billet based on factor analysis

The horizontal continuous casting process,the initial step in TP2 copper tubular processing,directly determines the microstructure and properties of copper tubular.However,the process parameters of the continuous casting characterize time variation,multiple disturbances and strong coupling.As a consequence,their influence on a casting billet is difficult to be determined.Due to the above issues,the common factor and special factor analysis of the factor analysis model were used in this study,and the casting experiment and billet metallographic experiment were carried out to diagnose and analyze the reason of the microstructure inhomogeneity.The multiple process parameters were studied and classified using common factor analysis,2 the cast billets with abnormal microstructures were identified by GT^(2) statistics,and the most important factors affecting the microstructural homogeneity were found by special factor analysis.The calculated and experimental results show that the principal parameters influencing the inhomogeneity of solidified microstructure are the primary inlet water pressure and the primary outlet water temperature.According to the consequence of the above investigation,the inhomogeneity of the copper billet microstructure can be effectively improved when the process parameters are controlled and adjusted.

Using Factor Analysis to Determine the Factors Impacting Learning Python for Non-Technical Business Analytics Graduate Students

This pioneering research represents a unique and singular study conducted within the United States, with a specific focus on non-technical graduate students pursuing degrees in business analytics. The primary impetus behind this study stems from the escalating demand for data-driven professionals, the diverse academic backgrounds of students, the imperative for adaptable pedagogical methods, the ever-evolving landscape of curriculum designs, and the overarching commitment to fostering educational equity. To investigate these multifaceted dynamics, we employed a data collection method that included the distribution of an online survey on platforms such as LinkedIn. Our survey reached and engaged 74 graduate students actively pursuing degrees in Business Analytics within the United States. This comprehensive research is the first and only one of its kind conducted in this context, and it serves as a vanguard exploration into the challenges and influences that shape the learning journey of Python among non-technical graduate Business Analytics students. The analytical insights derived from this research underscore the pivotal role of hands-on learning strategies, exemplified by practice exercises and assignments. Moreover, the study highlights the positive and constructive influence of collaboration and peer support in the process of learning Python. These invaluable findings significantly augment the existing body of knowledge in the field of business analytics. Furthermore, they offer an essential resource for educators and institutions seeking to optimize the educational experiences of non-technical students as they acquire essential Python skills.

Quality of life of hospitalized patients after lung cancer operation and analysis of influencing factors

Objective:To explore the current status and influencing factors of quality of life in patients with lung cancer after surgery in a tertiary hospital in Hainan province.Methods:To investigate the influencing factors of quality of life of lung cancer patients after surgery in a tertiary hospital in Hainan province by cross‑sectional survey method.Results:The scores of insomnia,appetite loss,constipation and pain in 186 lung cancer patients after surgery in a tertiary hospital in Hainan Province were significantly higher than the reference value.Multiple linear regression analysis showed that older patients(>60 years)had lower scores in physical function domain(β=-0.193),and female patients had more appetite loss symptoms(β=0.245).Compared with other minority ethnic groups,Han ethnic group had lower scores in role function domain(β=0.179),more severe fatigue symptoms(β=-0.162),and higher general health level(β=0.166).Patients with employee medical insurance had lower scores of emotional function(β=0.194),cognitive function(β=0.281),the lowest score in social function(β=0.188),and severe pain in other parts(β=-0.227).Smokers had less cough symptoms(β=0.175)and more arm and shoulder pain symptoms(β=-0.21)than non‑smokers.Patients with secondhand smoke exposure had lower cognitive function scores(β=-0.158)and more obvious symptoms of oral ulcer(β=0.185).Patients who drank alcohol frequently(drinking frequency>1 time/day)had more severe cough symptoms(β=0.27).Patients with small number of children(0‑1)had milder cough symptoms(β=0.178).Patients who did not understand the disease had obvious symptoms of arm and shoulder pain(β=0.151).Patients with early pathological stage(stageⅠ‑Ⅱ)had more severe shortness of breath(β=-0.159)and pain(β=-0.181).The symptoms of appetite loss were more obvious in patients living in cities(β=0.192).The symptoms of peripheral neuropathy were more obvious(β=0.174).Patients who often consumed pickulated food had severe pain symptoms(β=-0.219),and pain in other parts was obvious(β=-0.149).Male patients had obvious alopecia symptoms(β=-0.306).Conclusion:Age,ethnicity,residence,type of medical insurance,number of children,pathological stage of lung cancer,smoking,second‑hand smoke exposure,alcohol consumption,and frequent consumption of pickled food were related to the quality of life of lung cancer patients in hospital after surgery.Medical staff and family members should pay attention to the emotional communication of patients during the treatment of lung cancer patients in hospital after surgery.Patients should avoid exposure to smoking,alcohol and second‑hand smoke,and reduce consumption of pickled food.

Analysis of mental health status and related factors in patients with acute cerebral infarction

BACKGROUND Acute cerebral infarction(ACI)is characterized by a high incidence of morbidity,disability,recurrence,death and heavy economic burden,and has become a disease of concern in global researchers.As ACI has serious effects on patients’physical status,life and economy,often causing anxiety,depression and other psychological problems,these problems can lead to the aggravation of physical symptoms;thus,it is very important to understand the factors affecting the mental health of these patients.AIM To understand the elements that affect the mental health of patients who have suffered an ACI.METHODS A questionnaire survey was conducted among patients with ACI admitted to three tertiary hospitals(Quanzhou First Hospital,Fuqing City Hospital Affiliated to Fujian Medical University,and the 900 Hospital of the Joint Service Support Force of the People’s Liberation Army of China)in Fujian Province from January 2022 to December 2022 using the convenience sampling method.ACI inpatients who met the inclusion criteria were selected.Informed consent was obtained from the patients before the investigation,and a face-to-face questionnaire survey was conducted using a unified scale.The questionnaire included a general situation questionnaire,Zung’s self-rating depression scale and Zung’s self-rating anxiety scale.All questionnaires were checked by two researchers and then the data were input and sorted using Excel software.The general situation of patients with ACI was analyzed by descriptive statistics,the influence of variables on mental health by the independent sample t test and variance analysis,and the influencing factors on psychological distress were analyzed by multiple stepwise regression.RESULTS The average age of the 220 patients with ACI was 68.64±10.74 years,including 142 males and 78 females.Most of the patients were between 60 and 74 years old,the majority had high school or technical secondary school education,most lived with their spouse,and most lived in cities.The majority of patients had a personal income of 3001 to 5000 RMB yuan per month.The new rural cooperative medical insurance system had the largest number of participants.Most stroke patients were cared for by their spouses and of these patients,52.3%had previously smoked.Univariate analysis showed that gender,age,residence,course of disease,number of previous chronic diseases and smoking history were the main factors affecting the anxiety scores of patients with ACI.Age,living conditions,monthly income,course of disease and knowledge of disease were the primary variables influencing the depression score in patients with ACI.The findings of multivariate analysis revealed that the course of disease and gender were the most important factors influencing patients’anxiety scores,and the course of disease was also the most important factor influencing patients’depression scores.CONCLUSION Long disease course and female patients with ACI were more likely to have psychological problems such as a high incidence of emotional disorders.These groups require more attention and counseling.

Weight Analysis of the Influencing Factors of Homestay Competitiveness in Rural Guangzhou,China

At present,Guangzhou homestay industry is facing a bottleneck.Therefore,it is particularly important to analyze the factors that influence the competitiveness of rural homestays in Guangzhou,determine the evaluation system of competitiveness,and determine the weight of each factor.Based on Porter’s diamond theory,this paper analyzes and summarizes the influencing factors of homestay competitiveness,and divides the influencing factors into 5 primary factors and 34 secondary factors.The analytic hierarchy process(AHP)was used to determine the judgment matrix to form the weight results of each factor,and the results show that product characteristics account for the largest proportion among first level factors.Secondary factors such as theme creativity,personalized brand and the overall score account for a large proportion.The research results can act as a reference for the construction of competitiveness evaluation mechanism and model of local rural quality homestays.