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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Grey Relational Analysis between Chlorophyll a and Environmental Factors in ShaHu Lake

[Objective] The aim was to explore effects of environmental factors on the content of Chlorophyll a in ShaHu Lake.[Method] Based on the data in Shahu Lake from November in 2007 to September in 2008,the relationship between chlorophyll a and environmental factors like water temperature,pH,secchi-depth （SD）,total nitrogen,total phosphorus and potassium permanganate index was studied by grey relational analysis method.[Result] The main environmental factors affecting the content of Chlorophyll a in ShaHu Lake were in order of water temperature potassium permanganate index 〉total nitrogen 〉pH〉 total phosphorus 〉SD.[Conclusion] The research provides reference for the control of eutrophication and the reasonable development and utilization of Shahu Lake.

Response of Ecosystem Service Value Based on Land Use Changes and Analysis of its Driving Factors in Typical Hilly Region with Red Soil

The contribution rate of ecosystem service value variation was used to analyze the effects of land use changes on the changes of ecosystem service value in Xingguo County during 1996-2005.Grey integrated correlation was employed to explore the contribution level of the indicators such as total population,urbanization level,proportion of primary industry and investment of social fixed assets on ecosystem service value,and the correlation analysis was also carried out.The results showed that the ecosystem service value in Xingguo County during 1996-2005 mainly was woodland,and the decrease of woodland area was the major reason for the sustained reduction of ecosystem service value.With the further increase of market demand and the incentives of local government,the garden area rapidly increased during 2001-2005,and the influence degree of garden towards the changes of ecosystem service value was only second to woodland,ranking No.2.Four socio-economic indicators had different correlation degree with ecosystem service value during the different research periods.Total population,urbanization level and proportion of primary industry had high correlation degree with ecosystem service value,whereas the influence degree of various socio-economic indicators on ecosystem service value was equal with each other day by day.Urbanization level,investment of social fixed assets and total population had significant negative correlation with ecosystem service value,while the proportion of primary industry had positive correlation with ecosystem service value.