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.

Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.

Latent profile analysis of perceived stress and influencing factors in colorectal cancer patients

Objective:To understand the latent categories of perceived stress in colorectal cancer patients and analyze the characteristics of different categories of patients.Methods:A total of 255 colorectal cancer patients receiving treatment in the gastrointestinal surgery and oncology depar tments of a ter tiary Grade A hospital in Sichuan Province,from January 2023 to June 2023,were selected as the study subjects.General information questionnaire,Chinese version of the Perceived Stress Scale(CPSS),and Comprehensive Score Table for Patient-Repor ted Outcome Measures of Economic Toxicity(COST-PROM)were used for data collection.Results:Perceived stress in colorectal cancer patients was classified into 3 latent categories:C1“Low stress-stable type”(19.2%),C2“Moderate stress-uncontrolled type”(23.9%),and C3“High stress-anxious type”(56.9%).The average score of perceived stress was(34.07±5.08).Compared with C1 type,patients with a monthly household income of≤3000 RMB were more likely to belong to the C2 and C3 types(P<0.05),and patients without a stoma were less likely to belong to C3 type(P<0.05).Compared with C2 type,male patients were more likely to belong to C3 type(P<0.05),and patients without a stoma were less likely to belong to C3 type(P<0.05).Compared with C3 type,patients with higher economic toxicity scores were more likely to be classified into C1 and C2 types(P<0.05).Conclusions:Perceived stress in colorectal cancer patients exhibits distinct categorical features.Male gender,lower income,presence of a stoma,and higher economic toxicity are associated with higher levels of perceived stress in colorectal cancer patients.

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.

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.

Risk factors for hepatocellular carcinoma in cirrhosis:A comprehensive analysis from a decade-long study

BACKGROUND Cirrhosis is a significant risk factor for the development of hepatocellular carcinoma(HCC).Variability in HCC risk among patients with cirrhosis is notable,particularly when considering the diverse etiologies of cirrhosis.AIM To identify specific risk factors contributing to HCC development in patients with cirrhosis.METHODS This retrospective study analyzed data from cirrhotic patients at Beijing Youan Hospital from January 1,2012 to September 30,2022 with at least 6 mo of followup.Patient demographics,medical histories,etiologies,and clinical characteristics were examined.Cox regression analysis was used to analyze correlations of the above parameters with hepatocarcinogenesis,while competing risk regression was used to estimate their adjusted hazard ratios accounting for death.The cumulative incidence was plotted over time.RESULTS Overall,5417 patients with cirrhosis(median age:54 years;65.8%males)were analyzed.Hepatitis B virus(HBV)was the most common etiology(23.3%),with 25%(n=1352)developing HCC over a 2.9-year follow-up period.Patients with multiple etiologies had the HCC highest incidence(30.3%),followed by those with HBV-related cirrhosis(29.5%).Significant risk factors included male sex,advanced age,hepatitis C virus(HCV)infection,elevated blood ammonia,and low platelet count.Men had a higher 5-year HCC risk than women(37.0%vs 31.5%).HBV,HCV,and HBV/HCV co-infected patients had 5-year risks of HCC of 45.8%,42.9%,and 48.1%,respectively,compared to 29.5%in nonviral hepatitis cases,highlighting the significant HCC risk from viral hepatitis,especially HBV,and underscores the importance of monitoring these high-risk groups.CONCLUSION In conclusion,HBV-related cirrhosis strongly correlates with HCC,with male sex,older age,viral hepatitis,elevated blood ammonia,and lower albumin and platelet levels increasing the risk of HCC.

Analysis of the Factors That Cause Environmental Degradation of Lake Santa Maria Tasi Tolu Dili Timor-Leste

This research aims to understand more closely the damage to the lake environment and the factors that cause pollution in Lake Santa Maria are the first factor of increasing urbanization, the use of the land around the lake as a place to live, the absence of maximum control from the local government this case, the Dili municipal authority. Types of solid waste consist of iron from car accidents, motorcycles, used building materials, plastic, used drink bottles and clothes, mosquito nets, food scraps from household waste, as well as old fishing nets from residents. In addition, household waste such as bath and bath, dishwashing, detergents, and waste from tempeh and tofu factories, including burnt oil from cars and motorcycles, are thrown into the lake. Municipal waste management is based on environmental standards to determine the quality of waste management in Dili Municipality. It is possible to identify the composition of waste and waste, as well as predict its environmental impact. Human (Anthropic) factors Domestic Liquid Waste, Domestic Solid Waste: Composed of organic and inorganic waste. Synthetic Waste, Disposal of Used Oil, Disposal of Domestic Animal Waste, Shallow Drains and Septic Tanks, Mountain Garbage, Garbage Thrown by Visitors, Natural Factors, Climate change, Prolonged drought, Low rainfall, El Niño and La Niña factors, Wind speed, Heat (high daily temperature pressure), Greater water evaporation, Dry wind. The occurrence of contamination necessarily implies an ecological imbalance. The impact introduced by residual compounds and waste that may be associated with the toxicity.

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.

Correlation Analysis between Fruit Cracking and Influencing Factors in Apricot

In this study, 14 representative apricot cultivars in the production were selected as the experimental materials, and their fruit cracking characteristics, as well as the correlations between fruit cracking and influencing factors （e.g., pedcarp structure, mineral elements contents, DW/FW ratio and soluble sugar content） were analyzed to provide some reference for systematic study on fruit cracking mecha- nism of apricot. The results showed the cultivars with small orderly-and compactlyarranged epidermal cells were difficult to crack, while the cultivars with big disorderly-and loosely-arranged epidermal cells were easy to crack. There was no significant correlation between pericarp thickness and cracking index. The correlations between cracking and mineral elements contents of apricot fruit were in the order as Ca 〉 Zn 〉 Mn 〉 Fe 〉 K 〉 Mg 〉 Cu. The cracking index of apricot fruit was significantly negatively correlated with Ca content, was weakly correlated with Zn and Mn contents, and was uncorrelated with Fe, K, Mg and Cu contents. Ca deficiency was the main factor affecting the fruit cracking in apricot. Under the same conditions, the higher the water content is, the lower the cracking index is; and the higher the soluble sugar content is, the higher the cracking index is.

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.

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.

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.

Analysis of Preoperative Factors Influencing Hypoglossal-facial ‘Side’-to-side Neurorrhaphy for Facial Paralysis after Excision of Acoustic Neuroma

Objective Hypoglossal nerve-facial nerve‘side’-to-side neurorrhaphy is a new method for the treatment of potential incomplete facial paralysis after acoustic neuroma.However,there are differences in postoperative outcomes among patients.This study analysed preoperative factors that may influence the treatment outcomes of neurorrhaphy.Methods We performed a retrospective study of 53 patients who were treated by neurorrhaphy for facial paralysis after acoustic neuroma resection.After a one-year follow-up period,the patients were divided into two groups according to facial functional outcome:better recovery or ordinary recovery.We analysed the following factors:gender,age,tumour size,and characteristics,tumour adhesion to the facial nerve,the duration of facial paralysis(DFP)and F wave appearance prior to neurorrhaphy(F wave).Results Univariate analysis showed significant differences between the two groups in DFP(P=0.0002),tumour adhesion to the facial nerve(P=0.0079)and F waves(P=0.0048).Logistic regression analysis of these factors also showed statistical significance with P values of 0.042 for the DFP,0.043 for F waves,and 0.031 for tumour adhesion to the facial nerve.Conclusions Tumour adhesion to the facial nerve,F waves appearance and DFP prior to neurorrhaphy are the predominant factors that influence treatment outcomes.

Spatial-temporal Evolution Characteristics and Decoupling Analysis of Influencing Factors of China’s Aviation Carbon Emissions

The aviation industry has become one of the top ten greenhouse gas emission industries in the world. China’s aviation carbon emissions continue to increase, but the analysis of its influencing factors at the provincial level is still incomplete. This paper firstly uses Stochastic Impacts by Regression on Population, Affluence and Technology model(STIRPAT) model to analyze the time series evolution of China’s aviation carbon emissions from 2000 to 2019. Secondly, it uses the Logarithmic Mean Divisia Index(LDMI) model to analyze the influencing characteristics and degree of four factors on China’s aviation carbon emissions, which are air transportation revenue, aviation route structure, air transportation intensity and aviation energy intensity. Thirdly, it determines the various factors’ influencing direction and evolution trend of 31 provinces’ aviation carbon emissions in China(not including Hong Kong, Macao, Taiwan of China due to incomplete data). Finally, it derives the decoupling effort model and analyzes the decoupling relationship and decoupling effort degree between air carbon emissions and air transportation revenue in different provinces. The study found that from 2000 to2019, China’s total aviation carbon emissions continued to grow, while the growth rate of aviation carbon emissions showed a fluctuating downward trend. Air transportation revenue and aviation route structure promote the growth of total aviation carbon emissions, and air transportation intensity and aviation energy intensity have a restraining effect on the growth of total aviation carbon emissions. The scope of negative driving effect of air transportation revenue and air transportation intensity on total aviation carbon emissions in various provinces has increased. While the scope of positive driving influence of aviation route structure on total aviation carbon emissions of various provinces has increased, aviation energy intensity mainly has negative driving influence on total aviation carbon emissions of each province. Overall, the emission reduction trend in the areas to the west and north of the Qinling-Huaihe River Line is obvious. The decoupling mode between air carbon emissions and air transportation revenue in 31 provinces is mainly expansion negative decoupling.The air transportation intensity effect shows strong decoupling efforts in most provinces, the decoupling effort of aviation route structure effect and aviation energy intensity effect is not prominent.

Rutting influencing factors and prediction model for asphalt pavements based on the factor analysis method

To clarify the importance of various influencing factors on asphalt pavement rutting deformation and determine a screening method of model indicators,the data of the RIOHTrack full-scale track were examined using the factor analysis method(FAM).Taking the standard test pavement structure of RIOHTrack as an example,four rutting influencing factors from different aspects were determined through statistical analysis.Furthermore,the common influencing factors among the rutting influencing factors were studied based on FAM.Results show that the common factor can well characterize accumulative ESALs,center-point deflection,and temperature,besides humidity,which indicates that these three influencing factors can have an important impact on rutting.Moreover,an empirical rutting prediction model was established based on the selected influencing factors,which proved to exhibit high prediction accuracy.These analysis results demonstrate that the FAM is an effective screening method for rutting prediction model indicators,which provides a reference for the selection of independent model indicators in other rutting prediction model research when used in other areas and is of great significance for the prediction and control of rutting distress.

Analysis on the Influencing Factors of Low-carbon Economy and Its Mitigation Countermeasures in Sichuan Province

[Objective]The study aimed to analyze the influencing factors of low-carbon economy and its mitigation countermeasures in Sichuan Province.[Method]Taking Sichuan Province as an example,an extended STIRPAT model was established firstly,then the impacts of population,economy and technology on carbon emissions from 2000 to 2009 were analyzed econometrically by using the principal component analysis method.Finally,some corresponding countermeasures to reduce carbon dioxide emissions were put forward.[Result]At present,population scale had the greatest influence on carbon emissions in Sichuan Province,then energy consumption per industrial added value and the proportion of industrial added value to GDP.In addition,the influence of population scale on carbon emissions was still greater than that of population structure,and technical factor also has certain explanatory power on carbon emissions.Some countermeasures,like controlling population growth,advocating low-carbon life style and consumption model,paying more attention to the strategic adjustment of industrial structure to gradually reduce the proportion of high-carbon industries,encouraging energy consumption and emissions reduction plus scientific and technological innovation in a new energy technology filed,could be adopted to reduce carbon dioxide emissions,so as to adjust to the development of low-carbon economy in Sichuan Province.[Conclusion]The research could provide references for the establishment of policies for reducing carbon emissions.

Analysis of factors influencing the Henry Hub natural gas price based on factor analysis

Henry Hub as an important transaction hub for natural gas sets the gas price standard in the USA. In this paper, the factors influencing Henry Hub natural gas prices are analyzed, and the major factors determining the price levels in the period between January 1997 and December 2016 are studied. It is found that economic conditions, total energy demand, US dollar exchange rate and gas consumption are the major factors. The mechanism of each factor influencing the Henry Hub natural gas price is also explored in the paper.

Analysis on Nutritional Risk Screening and Influencing Factors of Hospitalized Patients in Central Urban Area

Rational nutritional support shall be based on nutritional screening and nutritional assessment. This study is aimed to explore nutritional risk screening and its influencing factors of hospitalized patients in central urban area. It is helpful for the early detection of problems in nutritional supports, nutrition management and the implementation of intervention measures, which will contribute a lot to improving the patient＇s poor clinical outcome. A total of three tertiary medical institutions were enrolled in this study. From October 2015 to June 2016, 1202 hospitalized patients aged ≥18 years were enrolled in Nutrition Risk Screening 2002（NRS2002） for nutritional risk screening, including 8 cases who refused to participate, 5 cases of same-day surgery and 5 cases of coma. A single-factor chi-square test was performed on 312 patients with nutritional risk and 872 hospitalized patients without nutritional risk. Logistic regression analysis was performed with univariate analysis（P〈0.05）, to investigate the incidence of nutritional risk and influencing factors. The incidence of nutritional risk was 26.35% in the inpatients, 25.90% in male and 26.84% in female, respectively. The single-factor analysis showed that the age ≥60, sleeping disorder, fasting, intraoperative bleeding, the surgery in recent month, digestive diseases, metabolic diseases and endocrine system diseases had significant effects on nutritional risk（P〈0.05）. Having considered the above-mentioned factors as independent variables and nutritional risk（Y=1, N=0） as dependent variable, logistic regression analysis revealed that the age ≥60, fasting, sleeping disorders, the surgery in recent month and digestive diseases are hazardous factors for nutritional risk. Nutritional risk exists in hospitalized patients in central urban areas. Nutritional risk screening should be conducted for inpatients. Nutritional intervention programs should be formulated in consideration of those influencing factors, which enable to reduce the nutritional risk and to promote the rehabilitation of inpatients.

Analysis of the Current Status and Influencing Factors of the Readiness for Discharge of Patients after Prostate Cancer Surgery Based on the Concept of Enhanced Recovery after Surgery (ERAS)

Objective To investigate the level of readiness for discharge of patients after prostate cancer surgery based on the concept of Enhanced Recovery After Surgery (ERAS), and to explore its influencing factors, so as to provide references for improving the readiness for discharge of patients after prostate cancer surgery. Methods The general information questionnaire, the discharge preparation scale, and the discharge guidance quality scale were used to investigate 119 patients discharged from the urological surgery department of a tertiary A-level hospital in Guangzhou after radical prostatectomy. Results The total score of discharge readiness of patients after radical prostatectomy was 147.74 ± 35.71 points, which was at a lower middle level and the total score of discharge guidance quality was 180.68 ± 38.91 points, which was at a medium level. Multiple linear regression analysis showed that education level, family monthly income, Gleason score, whether to perform lymphatic dissection, whether to discharge with a urinary catheter, and the quality of discharge guidance were the main factors influencing the readiness for discharge of patients after prostate cancer surgery. Conclusion In clinical nursing work, it is necessary to implement individualized health education according to the characteristics and needs of different patients to improve the level of preparation for discharge of patients after prostate cancer surgery.

Analysis on the Influencing Factors of Job Burnout of Nurses in Haikou Tertiary Hospital

Objective: To understand the influencing factors of job burnout among nurses in Haikou 3A hospital and explore its direct and indirect effects, so as to provide a scientific basis for the work efficiency of nursing staff. Methods: Between November 2, 2015 and November 2015, using multi stage random sampling, self-administered questionnaire survey was conducted among 1049 nursing staff, using the path analysis method to study the effect of direct and indirect factors effect. Results: The total score of job burnout of nurses was 38.44 ± 7.55, high occupational burnout was 0.9%, moderate occupational burnout was 66.5%, and low occupational burnout was 32.6%. The scores of job burnout were compared among the nurses with different titles, and less achievement (F = 8.342, P < 0.001) and depersonalization (F = 3.12, P = 0.025) were statistically significant. Nurses’ Job Burnout and job stressors were the first, and the canonical correlation coefficient was 0.4397 (F = 20.54, P < 0.0001), indicating that the more problems existed in patient care, the greater the degree of emotional exhaustion. The first canonical correlation coefficient of job burnout and job satisfaction of nurses was 0.3791 (F = 12.8, P < 0.0001), indicating that the better the family and work balance, the less individualized nurses were. The path analysis results showed that the 4 dimensions of job stressors (management and interpersonal problems) is positive, the direct effect of the strongest (0.219), the total effect of sort of work pressure source of 4 dimensions (0.245) > 5 dimensions of work pressure source (0.125) > title (−0.112) job satisfaction scores (−0.097). Conclusion: Job stress, job satisfaction and job title are the factors that affect job burnout. The 4 and the direct and indirect effects of job stressors are the strongest, and measures should be taken to solve these problems.