Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.

The saturation magnetic density is lower due to the silicon content of the conventional motor core made of Non Grain-Oriented (NGO) silicon steel. It is increasingly difficult to meet the demand for high power density and high torque density of modern motors. Based on the above reasons, this paper proposed the idea of replacing the NGO silicon steel core by the Grain-Oriented (GO) silicon steel core part through a combined design. This paper mainly presents a novel thin an-isotropic GO silicon steel core used in high-speed (up to 14500rpm) traction motors. Magnetic saturation, power loss, thermal conductivity, and mechanical property analysis of the motor have been completed. Numerical modeling and experimental results have been given. The new prototype is being fabricated and it can be produced in mass production in the next few years.

Nighttime light (NTL) data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar Orbiting Partnership (NPP) satellite, has been widely used to evaluate gross domestic product (GDP). Nevertheless, due to the monthly VIIRS data fluctuation and missing data (excluded by producers) over high-latitude regions, the suitability of VIIRS data for longitudinal city-level economic estimation needs to be examined. While GDP distribution in China is always accompanied by regional disparity, previous studies have hardly considered the spatial autocorrelation of the GDP distribution when using NTL imagery. Thus, this paper aims to enhance the precision of the longitudinal GDP estimation using spatial methods. The NTL images are used with road networks and permanent resident population data to estimate the 2013, 2015, and 2017 3-year prefecture-level (342 regions) GDP in mainland China, based on eigenvector spatial filtering (ESF) regression (mean R² = 0.98). The ordinary least squares (OLS) (mean R² = 0.86) and spatial error model (SEM) (mean pseudo R² = 0.89) were chosen as reference models. The ESF regression exhibits better performance for root-mean-square error (RMSE), mean absolute relative error (MARE), and Akaike information criterion (AIC) than the reference models and effectively eliminated the spatial autocorrelation in the residuals in all 3 years. The results indicate that the spatial economic disparity, as well as population distribution across China’s prefectures, is decreasing. The ESF regression also demonstrates that the population is crucial to the local economy and that the contribution of urbanization is growing.

A segmented processing approach of eigenvector spatial filtering (ESF) regression is proposed to detect the relationship between NDVI and its environmental factors like DEM, precipitation, relative humidity, precipitation days, soil organic carbon, and soil base saturation in central China. An optimum size of 32 × 32 is selected through experiments as the basic unit for image segmentation to resolve the large datasets to smaller ones that can be performed in parallel and processed more efficiently. The eigenvectors from the spatial weights matrix (SWM) of each segmented image block are selected as synthetic proxy variables accounting for the spatial effects and aggregated to construct a global ESF regression model. Results show precipitation and humidity are more influential than other factors and spatial autocorrelation plays a vital role in vegetation cover in central China. Despite the increase in model complexity; the parallel ESF regression model performs best across all performance criteria compared to the ordinary least squared linear regression (OLS) and spatial autoregressive (SAR) models. The proposed parallel ESF approach overcomes the computational barrier for large data sets and is very promising in applying spatial regression modeling to a wide range of real world problem solving and forecasting.

This paper addresses the problem of secure amplify-and-forward (AF) relaying for multiple-input multiple output (MIMO) relaying networks in the presence of multiple eavesdroppers. Assuming practical imperfect eavesdroppers' channel state information (ECSI), we propose a robust approach to optimize the relay AF matrix, subject to power constraint, in order to maximize the received signal-to-interference-plus-noise ratio (SINR) at the destination while satisfying a set of secrecy constraints. The ECSI errors are assumed to fall within some predefined bounded sets. Since the resultant optimization problem is non-convex and semi-infinite, we transform it into a form constituted by the differences of convex functions (DC) using suitable matrix transformation techniques. Then an algorithmic solution with proven convergence is proposed by resorting to the penalty-DC algorithm (P-DCA). Experimental results show the security of the proposed transceiver design against eavesdropping and the robustness against the channel uncertainties.