Spatial Disparities and Correlated Variables of Community Care Facility Accessibility in Rural Areas of China

With the increasing aging of the world’s population, research on the equitable allocation of elderly care facilities has received increasing attention, but measuring the accessibility of community care facilities (CCFs) in rural areas has received little attention. In this study, which covered 7985 CCFs in 223,877 villages, we measured the accessibility of CCFs in rural areas of Hubei Province by using the nearest distance method. Based on the accessibility calculation, the spatial disparities and agglomeration characteristics of spatial accessibility were analyzed, and the correlated variables related to the accessibility were analyzed from both natural environment and socioeconomic aspects by employing a geographically weighted regression (GWR) model. Our results show that 87% of villages have a distance cost of less than 7121 m and 81% of townships have a distance cost of less than 5114 m; good spatial accessibility is present in the eastern and central regions, while poor spatial accessibility is shown in a small number of areas in the west. The results from the clustering analysis show that the hot spot areas are mainly clustered in the western mountainous areas and that the cold spot areas are mainly clustered around Wuhan city. We also observed that area, elevation, population aged 65 and above, and number of villages are significantly correlated with accessibility. The results of this study can be used to provide a reference for configuration optimization and layout planning of elderly care facilities in rural areas.

Depression Detection with DM v1

The study has used data from YMM. The Yes/No variables that had a low correlation with target variable have been removed. To extract the most relevant features , the high correlated variables with the target variable , the Boruta method was used in conjunction with a Random Forest( RF) Classifier. To select suitable supervised learning models, the Tree-based Pipeline Optimization Tool To select suitable supervised learning models, the Tree-based Pipeline Optimization Tool (TPOTclassifier) has been employed. RF, XGBoost (XGB), Decision Tree (DT), and Gaussian Naive Bayes (GaussianNB) have been employed in the depression identification step.has been employed. RF, XGBoost (XGB), Decision Tree (DT), and Gaussian Naive Bayes (GaussianNB) were employed in the depression identification step.

Depression Detection Algorithm v1

The study has used data from YMM. The Yes/No variables that had a low correlation with target variable have been removed. To extract the most relevant features , the high correlated variables with the target variable , the Boruta method was used in conjunction with a Random Forest( RF) Classifier. To select suitable supervised learning models, the Tree-based Pipeline Optimization Tool To select suitable supervised learning models, the Tree-based Pipeline Optimization Tool (TPOTclassifier) has been employed. RF, XGBoost (XGB), Decision Tree (DT), and Gaussian Naive Bayes (GaussianNB) have been employed in the depression identification step.has been employed. RF, XGBoost (XGB), Decision Tree (DT), and Gaussian Naive Bayes (GaussianNB) were employed in the depression identification step.

A step by step procedure for multivariate modeling

<p><span><span>In environmental sciences, it is very common to observe spatio-temporal multiple data concerning several correlated variables which are measured in time over a monitored spatial domain. In multivariate Geostatistics, the analysis of these correlated variables requires the estimation and modelling of the spatio-temporal multivariate covariance structure.<br>In the literature, the linear coregionalization model (LCM) has been widely used, in order to describe the spatio-temporal dependence which characterizes two or more variables. In particular, the LCM model requires the identification of the basic independent components underlying the analyzed phenomenon, and this represents a tough task. In order to overcome the aforementioned problem, this contribution provides a complete procedure where all the necessary steps to be followed for properly detect the basic space-time components for the phenomenon under study, together with some computational advances which support the selection of an ST-LCM.<br>The implemented procedure and the related algorithms are applied on a space-time air quality dataset.<br>Note that the proposed procedure can help practitioners to reproduce all the modeling stages and to replicate the analysis for different multivariate spatio-temporal data.</span></span></p>

Exploratory Factor Analysis of Rainbow Trout Serum Chemistry Variables

Clinical chemistry offers a valuable, affordable, moderately invasive, and nondisruptive way to assess animal physiological status and wellness within defined ranges and is widely used as a diagnostic clinical tool. Because of physiological differences between mammals, clinical correlates of blood chemistry variables are not known in detail in fish, in which tissue/organ function tests are inferred from mammal-derived clinical chemistry data. The aim of the present study was to apply exploratory factor analysis on a serum chemistry dataset from clinically healthy, reared rainbow trout Oncorhynchusmykiss (Walbaum, 1792) to select the most correlated variables and to test for possible underlying factors explaining the observed correlations as possible physiological status estimates in trout. The obtained factors were tested for correlation with hepatosomatic and splenosomatic indexes. Thirteen highly correlated variables were selected out of 18 original serum chemistry variables, and three underlying factors (Factors 1, 2, and 3) were identified that explained the observed correlations among variables. Moreover, Factor 1 correlated negatively with the hepatosomatic index and Factors 2 and 3 negatively with the splenosomatic index. The obtained factors were tentatively associated with: protein (liver) metabolism (Factor 1), cell turnover (Factor 2), and lipid (muscle) metabolism (Factor 3).