Gini Values Research Articles

Spatial accessibility and equity of community healthcare: unraveling the impact of varying time and transport mode.

Achieving a higher level of accessibility and equity to community healthcare services has become a major concern for health service delivery from the perspectives of health planners and policy makers in China. In this study, we introduced a comprehensive door-to-door (D2D) model, integrating it with the open OD API results for precise computation of accessibility to community hospitals over different transport modes. For the D2D public transit mode, we computed the temporal variation and standard deviation of accessibility at different times of the day. Additionally, accessibility values for D2D riding mode, D2D driving mode, and simple driving mode were also computed for comparison. Moreover, we introduced Lorenz curve and Gini index to assess the differences in equity of community healthcare across different times and transport modes. The D2D public transit mode exhibits noticeable fluctuations in accessibility and equity based on the time of day. Accessibility and equity were notably influenced by traffic flow between 8 AM and 11 AM, while during the period from 12 PM to 10 PM, the open hours of community hospitals became a more significant determinant in Nanjing. The moments with the most equitable and inequitable overall spatial layouts were 10 AM and 10 PM, respectively. Among the four transport modes, the traditional simple driving mode exhibited the smallest equity index, with a Gini value of only 0.243. In contrast, the D2D riding mode, while widely preferred for accessing community healthcare services, had the highest Gini value, reaching 0.472. The proposed method combined the D2D model with the open OD API results is effective for accessibility computation of real transport modes. Spatial accessibility and equity of community healthcare experience significant fluctuations influenced by time variations. The transportation mode is also a significant factor affecting accessibility and equity level. These results are helpful to both planners and scholars that aim to build comprehensive spatial accessibility and equity models and optimize the location of public service facilities from the perspective of different temporal scales and a multi-mode transport system.

Income inequality as a determinant of neonatal mortality in the Americas during 2000–2019: implications for the attainment of Sustainable Development Goal target 3.2

BackgroundThe work of the WHO Commission on the Social Determinants of Health has been fundamental to provide a conceptual framework of the social determinants of health. Based on this framework, this study assesses the relationship of income inequality as a determinant of neonatal mortality in the Americas and relates it to the achievement of the Sustainable Development Goal target 3.2 (reduce neonatal mortality to at least as low as 12 deaths per 1,000 live births). The rationale is to evaluate if income inequality may be considered a social factor that influences neonatal mortality in the Americas.MethodsYearly data from 35 countries in the Americas during 2000–2019 was collected. Data sources include the United Nations Inter-agency Group for Child Mortality Estimation for the neonatal mortality rate (measured as neonatal deaths per 1,000 live births) and the United Nations University World Institute for Development Economics Research for the Gini index (measured in a scale from 0 to 100). This is an ecological study that employs a linear regression model that relates the neonatal mortality rate (dependent variable) to the Gini index (independent variable), while controlling for other factors that influence neonatal mortality. Coefficient estimates and their robust standard errors were obtained using panel data techniques.ResultsA positive relationship between income inequality and neonatal mortality is found in countries in the Americas during the period studied. In particular, the analysis suggests that a unit increase in a country’s Gini index during 2000–2019 is associated with a 0.27 (95% CI [− 0.04, 0.57], P =.09) increase in the neonatal mortality rate.ConclusionThe analysis suggests that income inequality may be positively associated with the neonatal mortality rate in the Americas. Nonetheless, given the modest magnitude of the estimates and Gini values and trends during 2000–2019, the findings suggest a potential limited scope for redistributive policies to support reductions in neonatal mortality in the region. Thus, policies and interventions that address higher coverage and quality of services provided by national health systems and reductions in socio-economic inequalities in health are of utmost importance.

Predictors of Growth Mindset among Ghanaian Classroom College of Education Teacher Trainees

The study aimed to examine the important predictors of Ghanaian classroom Diploma in Basic Education teacher trainees' growth mindset toward student learning. Participants included a purposive sample of 328 (177 male and 151 female) second- and third-year teacher trainees in one college of education in the northern part of Ghana. The predictor variables were the type of program, year in program, age, sex, and marital status. The student learning subscale of the Physical Education and Sports Ability Survey (Sofo et al., 2016) served as the main data source. The items were adapted for the classroom setting. Most of the trainees had a growth mindset but with some fixed ideas for student learning (80.18%). Approximately 19.21% of the trainees had a strong growth mindset in student learning. The mean decreased Gini (MDG) values for the year in the program, program type, and marital status (married vs. single) showed that these predictors were important for student learning. A logistic regression analysis showed that type of program, year in program, and marital status were significant predictors of growth mindset for student learning. Trainees in the early childhood program were approximately seven times more likely to have a growth mindset regarding student learning compared to those in the primary education program. Trainees in their third year were approximately 145 times more likely to have a growth mindset regarding student learning compared to trainees in their second year. Single teacher trainees were approximately 213 times more likely to have a growth mindset regarding student learning compared to married teacher trainees. The study provides insights for teacher educators regarding the profiles and factors that promote the development of a growth mindset in teacher education settings in Ghana

Gut Microbiota Alterations in Adolescent Idiopathic Scoliosis Are Associated with Aberrant Bone Homeostasis.

Low bone mineral density is the major prognostic factor for adolescent idiopathic scoliosis (AIS), but the underlying mechanisms remain unclear. Accumulating evidence suggests that gut microbiota (GM) have the potential to affect bone development, and the GM signatures are altered in AIS patients. However, the effect of GM alterations on aberrant bone homeostasis in AIS remains unclear. This study aims to investigate the GM profile in AIS patients with different bone mineral density (BMD) and explore the association between GM, osteopenia, and aberrant bone turnover. A total of 126 patients with AIS who received surgical treatment were retrospectively included in this study. We analyzed the composition of the GM by 16S rRNA sequencing and BMD by dual X-ray absorptiometry. Based on the BMD of the femur neck, the patients were divided into the osteopenia group (OPN) if the Z score < -1, and the normal (NOR) group if the Z score ≥ -1 SD compared to the healthy control. For the 16S rRNA sequencing, the raw reads were filtered to remove low-quality reads, and operational taxonomic units were identified with the Uparse program. Weighted UniFrac distance matrix for the beta-diversity metrics and principal coordinate analysis (PCoA) was performed, and the statistical comparisons were made with permutational multivariate analysis of variance (PERMANOVA) and analysis of similarity (ANONISM). Linear discriminant analysis effect size (LEfSe) was used to identify the enriched species in two groups. The "Random forest" was applied to determine the optimal biomarker for OPN according to the mean decrease in Gini value. The metabolic function was predicted by the Tax4Fun analysis. The Pearson correlation coefficient was used to evaluate the associations between GM species, bone turnover markers, and BMD. The serum β-CTX was increased in the OPN group (n = 67) compared to the NOR group (n = 59). Patients in OPN groups showed significantly decreased α diversity indicated by the Shannon index. Principal coordinate analysis (PCoA) analysis showed significant clustering of GM between OPN and NOR groups. At genus level, the Escherichia-Shigella and Faecalibacterium were significantly enriched in the OPN group compared to that in the NOR group (p < 0.05), whereas the abundance of Prevotella was significantly decreased (p = 0.0012). The relative abundance of Megamonas and Prevotella was positively correlated with the femur BMD. The abundance of Escherichia-Shigella was negatively correlated with femur BMD and positively correlated with serum β-CTX levels. Functional analysis revealed significant differences in starch and sucrose metabolism, pyruvate and cysteine, and methionine metabolism between NOR and OPN groups. The alterations of GM in AIS patients are correlated with osteopenia. The association between enriched species, BMD, and bone turnover markers provides novel diagnostic and therapeutic targets for the clinical management of AIS.

16S rRNA sequencing analysis of the oral and fecal microbiota in colorectal cancer positives versus colorectal cancer negatives in Iranian population

BackgroundColorectal cancer (CRC) poses a significant healthcare challenge, accounting for nearly 6.1% of global cancer cases. Early detection, facilitated by population screening utilizing innovative biomarkers, is pivotal for mitigating CRC incidence. This study aims to scrutinize the fecal and salivary microbiomes of CRC-positive individuals (CPs) in comparison to CRC-negative counterparts (CNs) to enhance early CRC diagnosis through microbial biomarkers.Material and methodsA total of 80 oral and stool samples were collected from Taleghani Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran, encompassing both CPs and CNs undergoing screening. Microbial profiling was conducted using 16S rRNA sequencing assays, employing the Nextera XT Index Kit on an Illumina NovaSeq platform.ResultsDistinct microbial profiles were observed in saliva and stool samples of CPs, diverging significantly from those of CNs at various taxonomic levels, including phylum, family, and species. Saliva samples from CPs exhibited abundance of Calothrix parietina, Granulicatella adiacens, Rothia dentocariosa, and Rothia mucilaginosa, absent in CNs. Additionally, Lachnospiraceae and Prevotellaceae were markedly higher in CPs' feces, while the Fusobacteria phylum was significantly elevated in CPs' saliva. Conversely, the non-pathogenic bacterium Akkermansia muciniphila exhibited a significant decrease in CPs' fecal samples compared to CNs.ConclusionThrough meticulous selection of saliva and stool microbes based on Mean Decrease GINI values and employing logistic regression for saliva and support vector machine models for stool, we successfully developed a microbiota test with heightened sensitivity and specificity for early CRC detection.

A precise coronary artery disease prediction using Boosted C5.0 decision tree model

&lt;p&gt;In coronary artery disease, plaque builds up in the arteries that carry oxygen-rich blood to the heart. Having plaque in the arteries can constrict or impede blood flow, leading to a heart attack. Shortness of breath and soreness in the chest are common symptoms. Lifestyle modifications, medication, and potentially surgery are all options for treatment. In coronary artery disease, plaque builds up in the arteries that carry oxygen-rich blood to the heart. Having plaque in the arteries can constrict or impede blood flow, leading to a heart attack. Shortness of breath and soreness in the chest are common symptoms. Lifestyle modifications, medication, and potentially surgery are all options for treatment. This paper presents a Hybrid Boosted C5.0 model to predict coronary artery disease more precisely. A Hybrid Boosted C5.0 model is formed by combining the C5.0 decision tree and boosting methods. Boosting is a supervised machine learning method that leverages numerous inadequate models to construct a more robust and powerful model. The proposed model and some well-known existing machine learning models, i.e., decision tree, AdaBoost, and random forest, were implemented using an online coronary artery disease dataset of 6611 patients and compared based on various performance measuring parameters. Experimental analysis shows that the proposed model achieved an accuracy of 91.62% at training and 81.33% at the testing phase. The AUC value achieved in the training and testing phase is 0.957 and 0.88, respectively. The Gini value achieved in the training and testing phase is 0.914 and 0.759, respectively, far better than the proposed method.&lt;/p&gt;

Research on gearbox fault detection model based on ridge regression and decision tree

Gearbox is widely used in mechanical equipment and plays an important role in mechanical transmission. Therefore, it is necessary to detect and diagnose the fault of the gearbox in time. This paper needs to establish a fault detection model of the gearbox to detect whether the gearbox is in a fault state. Because the result can only be yes or no, the ridge regression model is first established. However, because the difference between the original sample data is not obvious, the accuracy of the obtained ridge regression model is low. Therefore, this study extracts the features of the data of the four parts, and defines five indicators: effective value, pulse index, skewness index, margin index and kurtosis index. The decision tree model is established with 70 % of the sample data. Firstly, the depth of the largest tree is set to 5. Secondly, the importance of the feature is determined according to the size of the Gini value, and the fault detection decision tree model is constructed. Finally, the model is tested with 30 % of the test data, and the accuracy is 91.17 %. The precision rate is 88.9 %, and the recall rate is 94.12 %. It is considered that the model is more reliable.

Classification of hundreds of classes: A case study in a bank internal control department

The main function of the internal control department of a bank is to inspect the banking operations to see if they are performed in accordance with the regulations and bank policies. To accomplish this, they pick up a number of operations that are selected randomly or by some rule and, inspect those operations according to some predetermined check lists. If they find any discrepancies where the number of such discrepancies are in the magnitude of several hundreds, they inform the corresponding department (usually bank branches) and ask them for a correction (if it can be done) or an explanation. In this study, we take up a real-life project carried out under our supervisory where the aim was to develop a set of predictive models that would highlight which operations of the credit department are more likely to bear some problems. This multi-classification problem was very challenging since the number of classes were enormous and some class values were observed only a few times. After providing a detailed description of the problem we attacked, we describe the detailed discussions which in the end made us to develop six different models. For the modeling, we used the logistic regression algorithm as it was preferred by our partner bank. We show that these models have Gini values of 51 per cent on the average which is quite satisfactory as compared to sector practices. We also show that the average lift of the models is 3.32 if the inspectors were to inspect as many credits as the number of actual problematic credits.

Machine-Learning Classification Models to Predict Liver Cancer with Explainable AI to Discover Associated Genes

Hepatocellular carcinoma (HCC) is the primary liver cancer that occurs the most frequently. The risk of developing HCC is highest in those with chronic liver diseases, such as cirrhosis brought on by hepatitis B or C infection and the most common type of liver cancer. Knowledge-based interpretations are essential for understanding the HCC microarray dataset due to its nature, which includes high dimensions and hidden biological information in genes. When analyzing gene expression data with many genes and few samples, the main problem is to separate disease-related information from a vast quantity of redundant gene expression data and their noise. Clinicians are interested in identifying the specific genes responsible for HCC in individual patients. These responsible genes may differ between patients, leading to variability in gene selection. Moreover, ML approaches, such as classification algorithms, are similar to black boxes, and it is important to interpret the ML model outcomes. In this paper, we use a reliable pipeline to determine important genes for discovering HCC from microarray analysis. We eliminate redundant and unnecessary genes through gene selection using principal component analysis (PCA). Moreover, we detect responsible genes with the random forest algorithm through variable importance ranking calculated from the Gini index. Classification algorithms, such as random forest (RF), naïve Bayes classifier (NBC), logistic regression, and k-nearest neighbor (kNN) are used to classify HCC from responsible genes. However, classification algorithms produce outcomes based on selected genes for a large group of patients rather than for specific patients. Thus, we apply the local interpretable model-agnostic explanations (LIME) method to uncover the AI-generated forecasts as well as recommendations for patient-specific responsible genes. Moreover, we show our pathway analysis and a dendrogram of the pathway through hierarchical clustering of the responsible genes. There are 16 responsible genes found using the Gini index, and CCT3 and KPNA2 show the highest mean decrease in Gini values. Among four classification algorithms, random forest showed 96.53% accuracy with a precision of 97.30%. Five-fold cross-validation was used in order to collect multiple estimates and assess the variability for the RF model with a mean ROC of 0.95±0.2. LIME outcomes were interpreted for two random patients with positive and negative effects. Therefore, we identified 16 responsible genes that can be used to improve HCC diagnosis or treatment. The proposed framework using machine-learning-classification algorithms with the LIME method can be applied to find responsible genes to diagnose and treat HCC patients.

Early Results from GLASS-JWST. XVIII. A First Morphological Atlas of the 1 < z < 5 Universe in the Rest-frame Optical

We present a rest-frame optical morphological analysis of galaxies observed with the NIRCam imager on the James Webb Space Telescope (JWST) as part of the GLASS-JWST Early Release Science program. We select 388 sources at redshifts 0.8 < z < 5.4 and use the seven 0.9–5 μm NIRCam filters to generate rest-frame gri composite color images, and conduct visual morphological classification. Compared to Hubble Space Telescope (HST)–based work we find a higher incidence of disks and bulges than expected at z > 1.5, revealed by rest-frame optical imaging. We detect 123 clear disks (58 at z > 1.5) of which 76 have bulges. No evolution of bulge fraction with redshift is evident: 61% at z < 2 (N = 110) versus 60% at z ≥ 2 (N = 13). A stellar mass dependence is evident, with bulges visible in 80% of all disk galaxies with mass >109.5 M ⊙ (N = 41) but only 52% at M < 109.5 M ⊙ (N = 82). We supplement visual morphologies with nonparametric measurements of Gini and asymmetry coefficients in the rest-frame i band. Our sources are more asymmetric than local galaxies, with slightly higher Gini values. When compared to high-z rest-frame ultraviolet measurements with HST, JWST shows more regular morphological types such as disks, bulges, and spiral arms at z > 1.5, with smoother (i.e., lower Gini) and more symmetrical light distributions.

Enhancing landslide management with hyper-tuned machine learning and deep learning models: Predicting susceptibility and analyzing sensitivity and uncertainty

IntroductionNatural hazards such as landslides and floods have caused significant damage to properties, natural resources, and human lives. The increased anthropogenic activities in weak geological areas have led to a rise in the frequency of landslides, making landslide management an urgent task to minimize the negative impact. This study aimed to use hyper-tuned machine learning and deep learning algorithms to predict landslide susceptibility model (LSM) and provide sensitivity and uncertainty analysis in Aqabat Al-Sulbat Asir region of Saudi Arabia.MethodsRandom forest (RF) was used as the machine learning model, while deep neural network (DNN) was used as the deep learning model. The models were hyper-tuned using the grid search technique, and the best hypertuned models were used for predicting LSM. The generated models were validated using receiver operating characteristics (ROC), F1 and F2 scores, gini value, and precision and recall curve. The DNN based sensitivity and uncertainty analysis was conducted to analyze the influence and uncertainty of the parameters to the landslide.ResultsResults showed that the RF and DNN models predicted 35.1–41.32 and 15.14–16.2 km2 areas as high and very high landslide susceptibility zones, respectively. The area under the curve (AUC) of ROC curve showed that the LSM by the DNN model achieved 0.96 of AUC, while the LSM by RF model achieved 0.93 of AUC. The sensitivity analysis results showed that rainfall had the highest sensitivity to the landslide, followed by Topographic Wetness Index (TWI), curvature, slope, soil texture, and lineament density.DiscussionRoad density and geology map had the highest uncertainty to the landslide prediction. This study may be helpful to the authorities and stakeholders in proposing management plans for landslides by considering potential areas for landslide and sensitive parameters.

Frekans Alanı Öznitelik Çıkarımına Dayalı Makine Öğrenme Teknikleri ile Trapezius Yüzey EMG Sinyallerini Kullanarak Servikal Disk Fıtığı Hastalığının Tahmini

Cervical disk herniation (CDH) is a disease that affects the quality of life of many people due to the neck pain it causes. The aim of this study was to develop an automatic prediction system to aid in diagnosis by evaluating the change in the surface electrical activity of the trapezius muscle in SDH disease in order to find an answer to the question: 'Can the surface electromyogram (sEMG) recorded from the trapezius muscle be an effective indicator for the diagnosis of SDH disease?'. To this end, a dataset will be created using preprocessing and feature extraction methods from sEMG signals from CDH patients and healthy individuals. In the first step, the Savitsky-Golay filter is used to denoise the sEMG signals and the dominant frequency signals between 20 and 150 Hz are included in the study using the Butterworth filter design. Twenty PSD-based features in the frequency domain were then obtained from the signals to which we applied the Burg method. Eleven of the most significant features based on the information gain, gain ratio, and Gini values are selected to be submitted to the classifiers. 80% of all new feature areas are used for classification and the rest for prediction. The best classification accuracy of 91.6% was obtained with the Tree classifier using 10-fold cross-validation for classification. In addition, neural networks and CN2 rule inducer provided 87.5% classification accuracy for prediction using 20% of the remaining data that the classifiers had not seen before. The experimental results demonstrate that the trapezius muscle has different surface electrical activity in CDH patients and healthy subjects and that the frequency domain characteristics of this activity are important for disease prediction.

Energy inequality in climate hazards: Empirical evidence of social and spatial disparities in managed and hazard-induced power outages

Due to the effects of climate change and urbanization, the severity and frequency of hazard events is expected to increase. The energy sector in the United States is ever more vulnerable to extreme climatic hazards. Hurricane winds can damage electrical lines, causing hazard-induced power outages. Extreme heat and freezing temperatures can imbalance the supply and demand for energy resulting in managed power outages. Utility companies reportedly prioritize the restoration of power systems based on the number of outages and the size of affected populations. This approach fails to account for unequal impacts of hazard-induced and managed power outages. Research in equitable infrastructure emphasizes that certain populations, such as lower income and racial-ethnic minority households, are disproportionately impacted by disruptions in the power system. Moreover, the connected network qualities of the power system suggests an element of spatial vulnerabilities. However, little empirical evidence exists regarding the presence and extent of energy inequality. A main roadblock is the data collection process, in that outage data is often perishable and not found at granular spatial scales to allow the undertaking of a comprehensive analysis on impacts of power losses. Recognizing this important gap, this study collected and analyzed observational data related to the managed power outages during Winter Storm Uri (2021) and the hazard-induced outages during Hurricane Ida (2021). The research quantified the period of recovery at a granular spatial scale using an equitable-focused analysis to detect social and spatial inequalities through an exploratory lens. In extreme cases of power outage, census tracts of lower income and higher percentage of Hispanic population had longer median durations of recovery during Winter Storm Uri. In the hazard-induced outages of Hurricane Ida, non-coastal zip codes with lower income had a 1.00-day longer median duration of recovery and higher percentage of Black population had a 2.00-day longer median duration of recovery while coastal zip codes with higher percentage of Black population had a 1.00-day longer median of recovery. Non-coastal regions had 63% greater spatial Gini values and 16% greater value in infrastructure inequality when compared to coastal regions. The managed power outages resulted in a 3% to 19% greater value of infrastructure inequality to the hazard-induced power outages. The findings provide evidence of pervasive social and spatial inequality in power outages during climate hazards and highlight the importance of integrating equity into the manner in which utility managers and emergency planners restore power outages.

Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes

SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.

A Contribution to the Study of Social Inequality in the Late Neolithic of the Central Balkans

The social inequality represents one of the major problems of the contemporary world and an important anthropological subject. The transition to agriculture is often viewed as an important turning point for the development of social inequality in (pre)history. For this reason, the study of the social inequality in the Neolithic communities is of particular importance for the understanding of the origins and the development of this phenomenon. This paper represents an empirical contribution to the study of social inequality in the Late Neolithic in the Central Balkans (5300-4500 BCE). The aim is to quanitfy and estimate the levels of inequality between households in three Late Neolithic settlements: Belovode, Pločnik, and Drenovac. In line with the current methods for the estimation of social inequality from archaeological remains, this study relies on the house floor area as a proxy for household wealth. The house floor area measurements are based on the geophysical surveys data published in the literature with around 1000 house floor area measurements available for the analysis. The Gini index is calculated for each site based in the distribution of house floor area. The results suggest that the social inequality on all three sites was relatively low, as the Gini values range from 0.18 to 0.22. These values fit well with the Gini estimates based on the previous research of the social inequality in the Neolithic and Eneolithic period in the Central Balkans. When compared to the cross-cultural variation based on the ethnographic, historical and archaeological sources from the literature, these values are low compared to other horticultural and agricultural communities. One potential explanation for such low values is that the agricultural production in the Late Neolithic of the Central Balkans was labor-limited rather than land-limited, which usually results in the low potential for social inequality, as hypothesized and shown by Bogaard et al. (2019). On the other hand, we must keep in mind that the wealth is measured by proxy which is suitable for the detection of statistical trends in cross-cultural comparisons, but may be less reliable for individual cases. Moreover, it is not certain that the individual house is the basic social unit, as it is possible that the basic social unit for a corporate group which includes extended families living in several houses. Therefore, the result which suggests low levels of social inequality should be taken as a hypothesis which needs to be tested further with other classes of evidence.

Equity assessment of the distribution of mental health beds in China: based on longitudinal data from 2011 to 2020

BackgroundMental health problems have become a public health problem that needs to be solved in China. However, medical resources for mental healthcare remain insufficient and unevenly distributed. The Chinese central government has taken many measures to address this issue over the last decade. This study aimed to evaluate the changes in equity in mental health bed allocation from 2011 to 2020.MethodsThe data of this study came from the China Health Statistical Yearbook (2012–2021) and the China National Administrative Division Information Platform. The annual growth rate was used to evaluate the time trends of mental health beds. The Lorenz curve, Gini coefficient and Theil index were used to assess equity in the demographic and geographical dimensions. The distribution of mental health beds was visualized on a map using geographic information system (GIS) software.ResultsThe total number of mental health beds in China increased steadily from 2011 to 2020. At the national level, the Gini coefficient and Theil index all exhibited downward trends over time. The mental health bed allocation in terms of the demographic dimension was relatively equitable, with Gini values all less than 0.3; however, the Gini coefficients by geographical area were all more than 0.6, indicating inequity. Intraregional contribution rates were higher than interregional contribution rates, which were all above 60%.ConclusionThe overall distribution equity of mental health beds improved from 2011 to 2020. The equity of mental health beds in terms of population size is superior to that in terms of geographical area. Intraregional differences are the main source of inequity. In particular, differences within the western region need to be given attention. Thus, the findings from this study emphasize that the demographic and geographical distributions and all influencing factors should be considered when the government makes mental health resource allocation policies.

Mental health resources and its equity in Central South of China: A case study of Hunan Province.

ObjectivesMental health resources are an important basis for coping with mental health services. The equity is an important index of a reasonable allocation of health resources. This study aims to evaluate the mental health resources and its equity allocation in Hunan Province, which is one of the typical central south areas of China, so as to provide reference for the development of mental health in China and other areas.MethodsData related to mental health resources was obtained from the Project of Mental Health Resources in Hunan Province, which was conducted by the Department of Hunan Mental Health Center in 2019. The Gini coefficient, the Theil index and other indicators were employed to quantitatively evaluate the equity of mental health resources’ allocation.ResultsBy the end of 2018, there were a total of 141 mental health institutions in Hunan Province of China, the bed density was 5.31 beds per 10,000 people, the ratio of doctors to nurses was 2.20, the number of outpatients of mental health institutions was 1288,047 per year. The mental health resources’ allocation in terms of demographic dimension were in a preferred status with the Gini values all less than 0.3, and the Gini values for mental health resources`allocation in terms of geographical dimension ranged from 0.24 to 0.35. The Theil index for mental health allocation in terms of demographic dimension was lower than 0.05, and the Theil index for mental health allocation in terms of geographical dimension ranged from 0.04 to 0.11.ConclusionsThe shortage of mental health resources is still the priority issue to be increased and optimized by policy-makers in Hunan in the future, especially the human resources. Moreover, the utilization of mental health resources was low though its equity was fair. Policy-makers need to consider the high utilization and geographical accessibility of health resources among different regions to ensure people in different regions could get access to available health services.

Spatio-Temporal Patterns of Ecosystem Services Provided by Urban Green Spaces and Their Equity along Urban–Rural Gradients in the Xi’an Metropolitan Area, China

Urban green spaces (UGSs) are indispensable for providing various ecosystem services (ESs) to society and city-dwellers. The equitable allocation of ESs provided by UGSs is a prerequisite for maintaining urban sustainability and human well-being. However, empirical studies have assessed the dynamics of UGS equity from ES facets, while neglecting to strengthen integrated evaluation indicators and coupled spatially explicit models. A framework was developed in this study to assess the spatio-temporal patterns of ESs provided by UGSs and their equity based on models of the Intelligent Urban Ecosystem Management System, location entropy, and the Gini coefficient. The results indicated that with urban spatial expansion, in each concentric circle, the values of three ES averages were generally the lowest in the megacity (Xi’an), and the values of carbon sequestration and the recreation averages in the medium-sized cities (Baoji and Xianyang) were also lower than in the small-sized city (Tongchuan). The inequity of ESs provided by UGSs was exacerbated from 2000 to 2020 in the Xi’an Metropolitan Area. The ES equity was the worst in Xi’an and the best in Tongchuan. Areas with extremely low CR location entropy occupied 67.01% in 2020. As cities spatially expanded, areas with extremely low ES location entropy generally increased in 2020. Additionally, the zero-value area of the ES Gini coefficient in Xi’an was the largest from 2000 to 2020. The ES Gini values mostly increased in the same concentric circle of each city in 2020, which demonstrates that equity declined. Spatial equity could be improved by diversifying UGS structures, promoting spatial matching between the ES supply and beneficiaries’ demands, and integrating UGSs into any available space in future UGS planning. The findings of the study could offer insights for optimizing the equitable provision of UGS resources and the targeted planning of urban greening.

Sampling plan for socioeconomic development indicators in Brazil: practical implications when considering precision and cost

Socioeconomic development indicators such as Income, Human Development Index, and Gini Index have explained recent deforestation events in Brazil. These indicators, obtained from the demographic census, are expensive and are released every ten years. Despite being costly, they are important descriptors of the phenomenon of deforestation that occurs in one of the most relevant ecosystems in the world, the Brazilian forests. Therefore, the objective in this study was to detect the most representative and the lowest costly sample in order to obtain the referred indicators, from Brazilian municipalities from 2000 to 2010. The sample mean and confidence interval were obtained to be compared with the population average. The mean and confidence interval estimators come from 1,000 resamples according to simple random sampling and stratified random by geographic region. We evaluated the following sample intensities: 10%, 20%, 30%, 40% and 50% of the Brazilian municipalities. The results indicated that: a) 10% of Brazilian municipalities are sufficient to represent Income, HDI and Gini; b) We recommend both random and stratified sampling when selecting the municipalities that will provide the HDI and Gini values, on the contrary, for Income, the indicated sampling is the stratified one. These results show the importance of applied research in sampling and simulation to optimize the survey of demographic data collection, in a situation of budgetary constraint, in countries of vast dimensions such as Brazil. This study can increase understanding of the importance of socioeconomic development indicators to estimating progressive forest loss in Brazil, the country with the largest rainforest in the world.

Information-based massive data retrieval method based on distributed decision tree algorithm

Based on the distributed decision tree algorithm, this paper first proposes a method of vertically partitioning datasets and synchronously updating the hash table to establish an information-based mass data retrieval method in a heterogeneous distributed environment, as well as using interval segmentation and interval filtering technologies for improved algorithm of distributed decision tree. The distributed decision tree algorithm uses the attribute histogram data structure to merge the category list into each attribute list, reducing the amount of data that needs to reside in the memory. Second, we adopt the strategy of vertically dividing the dataset and synchronously updating the hash table, select the hash table entries that can be used to update according to the minimum Gini value, modify the corresponding entries and use the hash table to record and control each sub-site. In the case of node splitting, it has a high accuracy rate. In addition, for classification problems that meet monotonic constraints in a distributed environment, this paper will extend the idea of building a monotonic decision tree in a distributed environment, supplementing the distributed decision tree algorithm, adding a modification rule and modifying the generated nonmonotonic decision tree to monotonicity. In order to solve the high load problem of the privacy-protected data stream classification mining algorithm under a single node, a Storm platform for the parallel algorithm PPFDT_P based on the distributed decision tree algorithm is designed and implemented. At the same time, considering that the word vector model improves the deep representation of features and solves the problem of feature high-dimensional sparseness, and the iterative decision tree algorithm GBDT model is more suitable for non-high-dimensional dense features, the iterative decision tree algorithm will be integrated into the word vector model (GBDT) in the data retrieval application, using the distributed representation of words, namely word vectors, to classify short messages on the GBDT model. Experimental results show that the distributed decision tree algorithm has high efficiency, good speed-up and good scalability, so that there is no need to increase the number of datasets at each sub-site at any time. Only a small number of data items are inserted. By splitting some leaf nodes, a small amount is added by branching to achieve a monotonic decision tree. The proposed system achieves a massive data ratio of 54.1% while compared with other networks of massive data ratio.