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BackgroundIndividuals living in rural mountain areas tend to use health services less to manage discomfort or illness. This study aims to identify the variables that best explain the health service utilization of a sample of the rural population in the Dabie Mountains in China.MethodsTo obtain information about health service utilization, a cross-sectional household survey was conducted using face-to-face interviews among the residents of a poor town in the Dabie Mountains. A total of 1,003 residents aged 15 or more, who had felt unwell in the last two weeks before the survey, were included in the analysis. The χ2 test and binary logistic regression were used to analyze the factors influencing health service utilization.ResultsA total of 51.2% of those surveyed had not used health services when they felt unwell, higher than the data reported in the 4th National Health Services Survey of China. Enabling variables played an important role in predicting the utilization of health services. Factors associated with increased health service utilization included being younger, travelling longer to the nearest clinic, and higher household net income.ConclusionTo reduce disparities in health service utilization, (1) some effort should be made to change the perceptions and attitudes of older people; (2) reimbursement levels of the New Rural Cooperative Medical Care System should be improved to reduce economic barriers to health service utilization.

[1] In order to improve the spatial resolution of GPS (Global Positioning System) derived zenith total delays (ZTD), existing GPS networks must be considerably densified. Due to economic reasons, this densification is recommended with single frequency receivers. We prove the epoch-differenced ionospheric delay is sufficient for estimating ZTD from single frequency GPS data. Based on this result, the Satellite-specific Epoch-differenced Ionospheric Delay model (SEID) is developed which uses the observations from surrounding dual frequency stations. With the derived ionospheric corrections, single frequency data is converted to dual frequency data which can be processed using any existing GPS processing software package. The approach is validated with regional GPS networks by comparing ZTDs from the converted and the observed dual frequency data. Their differences in RMS is about 3 mm which is negligible compared to the differences among various state-ofthe-art software packages. The easy implementation and the accuracy of the new approach may speed up the densification of existing networks with single frequency receivers. Citation: Deng, Z., M. Bender, G. Dick, M. Ge, J. Wickert, M. Ramatschi, and X. Zou (2009), Retrieving tropospheric delays from GPS networks densified with single frequency receivers, Geophys. Res. Lett., 36, L19802,

This paper proposes an attention-based LSTM (AT-LSTM) model for financial time series prediction. We divide the prediction process into two stages. For the first stage, we apply an attention model to assign different weights to the input features of the financial time series at each time step. In the second stage, the attention feature is utilized to effectively select the relevant feature sequences as input to the LSTM neural network for the prediction in the next time frame. Our proposed framework not only solves the long-term dependence problem of time series prediction effectively, but also improves the interpretability of the time series prediction methods based on the neural network. In the end of this paper, we conducted experiments on financial time series prediction task with three real-world data sets. The experimental results show that our framework for time series pre-diction is state-of-the-art against the baselines.

The application of gene expression data to the diagnosis and classification of cancer has become a hot issue in the field of cancer classification. Gene expression data usually contains a large number of tumor-free data and has the characteristics of high dimensions. In order to select determinant genes related to breast cancer from the initial gene expression data, we propose a new feature selection method, namely, support vector machine based on recursive feature elimination and parameter optimization (SVM-RFE-PO). The grid search (GS) algorithm, the particle swarm optimization (PSO) algorithm, and the genetic algorithm (GA) are applied to search the optimal parameters in the feature selection process. Herein, the new feature selection method contains three kinds of algorithms: support vector machine based on recursive feature elimination and grid search (SVM-RFE-GS), support vector machine based on recursive feature elimination and particle swarm optimization (SVM-RFE-PSO), and support vector machine based on recursive feature elimination and genetic algorithm (SVM-RFE-GA). Then the selected optimal feature subsets are used to train the SVM classifier for cancer classification. We also use random forest feature selection (RFFS), random forest feature selection and grid search (RFFS-GS), and minimal redundancy maximal relevance (mRMR) algorithm as feature selection methods to compare the effects of the SVM-RFE-PO algorithm. The results showed that the feature subset obtained by feature selection using SVM-RFE-PSO algorithm results has a better prediction performance of Area Under Curve (AUC) in the testing data set. This algorithm not only is time-saving, but also is capable of extracting more representative and useful genes.