GdVO 4 nano/microcrystals with different morphologies were successfully synthesized via an efficient and facile hydrothermal process using trisodium citrate (Na 3 Cit) as the chelating ligand. X-Ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectra (XPS), fourier transform infrared spectroscopy (FT-IR), photoluminescence (PL) and cathodoluminescence (CL) spectra were employed to characterize the samples. By tuning the reaction time, vanadium sources, and Na 3 Cit content, GdVO 4 samples with different morphologies and sizes have been successfully synthesised. The possible formation mechanism for these diverse architectures is proposed on the basis of time-dependent experiments. Under ultraviolet (UV) and low-voltage electron beam excitation, GdVO 4 : Ln 3+ (Ln 3+ = Eu, Dy, and Sm) phosphors show strong light emissions with different colors coming from various Ln 3+ ions due to an efficient energy transfer from vanadate groups to the dopants. The ability to generate GdVO 4 nano/microstructures with diverse shapes, and multicolor emission provides a great opportunity for systematically evaluating their luminescence properties, as well as fully exploring their applications in light emitting phosphors, advanced flat panel display, field emission display devices or biological labeling.

In longwall mining of coal mines, the large deformation of small pillar retaining roadways creates difficulties for the safe and efficient retreating of the mining panel. Based on the engineering background of a small coal pillar retaining roadway in Wangzhuang coal mine, pressure relief technology for non-penetrating directional pre-splitting blasting with a deep hole ahead was proposed. The influence of the non-penetrating fracture length on the pre-splitting effect was studied by numerical simulation. The results showed that the vertical stress in the coal pillar center, the small pillar retaining roadway deformation, and the energy accumulation on the pillar decreased with an increase in the non-penetrating fracture length. The vertical stress at the working face end increased with an increase in the non-penetrating fracture length. The field application and monitoring results indicated that non-penetrating directional pre-splitting blasting could effectively control the deformation of small pillar retaining roadways. The roof-to-floor and rib-to-rib maximum convergences of the 6208 tail entry were reduced by 53.66% and 52.62%, respectively, compared to the results with no blasting. The roadway section met the demands of mining panel high-efficiency retreating, thereby demonstrating the rationality of the technical and numerical simulation results. The research results shed light on the improvement of small coal pillar retaining roadway maintenance theory and technology.

<abstract> <p>Major emergencies cause massive financial risk and economic loss. In the context of major emergencies, we propose the GPD-CAViaR model to depict the extreme risks of financial sectors, and utilize the TVP-SV-VAR model to analyze their transmission effect. We find that (ⅰ) the securities sector has the highest extreme risks among the four financial sectors; (ⅱ) when major emergencies occur, the extreme risks of various financial sectors increase rapidly; (ⅲ) the transmission effect in short term is stronger than that in medium and long term; and (ⅳ) the transmission effects at different time points are relatively consistent.</p> </abstract>

Due to the continuous development of Natural Language Processing (NLP), the task of short text categorization has been paid more and more attention. In short text clustering, the high-dimensional sparseness of text representation matrix becomes a challenging problem. This paper proposes a deep embedded method for feature extraction and clustering allocation using auto encoder of sentence distributed embedding. This method maps from data space to low-dimensional feature space and iteratively optimizes clustering targets. Experimental results on three short Chinese text data sets verify the effectiveness of the method. Moreover, it is superior to the existing correlation clustering methods.

Outliers of ship trajectory from the Automatic Identification System (AIS) onboard a ship will affect the accuracy of maritime situation awareness, especially for a regular ship trajectory mixed with a spoofing ship, which has an unauthorized Maritime Mobile Service Identification code (MMSI) owned by a regular ship. As has been referred to in the literature, the trajectory of these spoofing ships would simply be removed, and more AIS data would be lost. The pre-processing of AIS data should aim to retain more information, which is more helpful in maritime situation awareness for the Maritime Safety Administration (MSA). Through trajectory feature mining, it has been found that there are obvious differences between the trajectory of a regular ship and that of a regular ship mixed with a spoofing ship, such as in terms of speed and distance between adjacent trajectory points. However, there can be a long update time interval in the results of severe missing trajectories of a ship, bringing challenges in terms of the identification of spoofing ships. In order to accurately divide the regular ship trajectory and spoofing ship trajectory, combined with trajectory segmentation by the update time interval threshold, the isolation forest was adopted in this work to train the labeled trajectory point of a regular ship mixed with a spoofing ship. The experimental results show that the average accuracy of the identification of spoofing ships using isolation forest is 88.4%, 91%, 93.1%, and 93.3%, corresponding to different trajectory segmentation by update time intervals (5 h, 10 h, 15 h, and 20 h). The research conducted in this study can almost eliminate the outliers of ship trajectory, and it also provides help for maritime situation awareness for the MSA.