Abstract:This paper studies the linear stability of Navier-Stokes-Poisson equation coupled with magnetic field by the principle of exchange of stabilities (PES). It is showed that the stability of the steady state solution is closely depend on the non-dimensional Mach number.

Abstract:Sotomayor and Teixeira proposed a method, namely S-T regularization, to regularize piecewise smooth systems. We in this paper study the dynamical behaviors of a fold-fold regularized system of type II with reverse direction. With the non-degenerate condition of divergence, we show that the equilibrium of regularized system is either a node or a focus. Moreover, we also show that the order and stability of the focus vary with the parameters and give the degenerate curve of the focus. Finally, we discuss the dependence of the order of focus on the regularized function.

Abstract:We construct a numerical method for computing the ground state solution of Logarithmic nonlinear Schrodinger equation. We firstly regularize the energy functional of the model and then compute the ground state solution by using the normalized gradient flow method. At each time step, an implicit numerical scheme based on the backward Euler Fourier spectral method is used and solved by fixed point iteration. Finally we analyze the energy error and provide a numerical simulation to verify th e reliability of the method.

Abstract:This paper investigates the properties of a sequential maximum likelihood estimator of the unknown drift parameter for a Logistic diffusion process. We derive the explicit formulas for the sequential estimator and its mean squared error. The estimator is showed to be closed, unbiased uniformly normally distributed and strongly consistent. Finally, a numerical example is provided to illustrate our theory.

Abstract:For a finite-dimension algebra A over a field k, let M be the morphism category of finitely generated projective A-module. We classify the maximal rigid objects of M by support t-tilting A-modules. As a consequence, each basic maximal rigid object of M has precisely 2|A| indecomposable direct summands.

Abstract:Most of the existing mainstream textual entailment models adopt recurrent neutral network to encode text, and various complex attention mechanisms or manually extracted text features are used to improve the accuracy of textual entailment recognition. The training and inference speed of the models is usually slow due to the complex network structure and the sequential nature of RNNs. In this paper, Lightweight Text Entailment Model is proposed. In the proposed model, the self-attentional encoder is adopted to encode text vectors; the dot product attention mechanism is adopted to interact two texts; the convolutional neural network is adopted to deduce interactive features, and the module number of the structure can be adjusted according to the reasoning difficulty of data. Experiments on multiple datasets show that the parameter size of single module in the model is only 665 K, and the inference speed of the model is at least twice as high as that of other mainstream models, under the condition of high accuracy.

Abstract:Foreground extraction is to separate the objects of interest based on the whole image cognition. In this paper, a foreground extraction model based on brightness perception is proposed by combining the visual brightness perception and level set method. Based on the brightness' visual correlation of pixel pairs, combining the similarity within the visual region and the difference among regions, the brightness perception energy functional is designed. The visual region is obtained by optimizing the energy function using Rayleigh entropy, and the initial curve evolves to the foreground contour by using the characteristics of the visual region. Compared with the traditional algorithm, the proposed model uses the image visual features to extract the foreground from the overall cognition of the image and improves the foreground extraction quality of the level set method.

Abstract:In order to achieve the dual goals of link prediction and sign prediction in signed social networks quickly and accurately, a link prediction algorithm is proposed based on the clustering coefficient of common neighbor nodes and the influence of the sign of edges. With the structural balance theory, the similarity of the two nodes based on their first-order common neighbors and the second-order common neighbors is defined respectively by using the degree, clustering coefficient, intermediate transitive nodes, and the influence of the sign of the edge, the total similarity score of the two nodes is finally obtained and its absolute value is used to measure the possibility to establish a link of the two nodes, then its sign is the sign prediction result of the link. Accordingly, the link prediction and sign prediction are realized in signed networks. Experiments have been carried out on six representative signed network datasets, with evaluation indicators such as AUC, adjusted precision' and accuracy. The experiment results are compared with several link prediction algorithms in signed networks the sensitivity of adjustable step size parameters is also analyzed. Experimental results show that the proposed algorithm can achieve good performance in both link prediction and sign prediction, and its accuracy is higher than other algorithms for both sparse networks and the prediction of negative links.

Abstract:The shared self－service check－in system can effectively alleviate the tension of limited airport resources in the future. The effective allocation of resources is the key to improving the performance of this collaborative work system. This paper establishes a static resource integer programming model and dynamic adjustment strategy. The model considers the collaborative working relationship between system counter and transportation equipment, and restricts the configuration scheme to provide a stable service level. the strategy improves the equipment utilization by increasing or decreasing the number of equipment according to the initial allocation scheme of different scales. The discrete event simulation results show that the static allocation scheme generated by the model applied to the shared self－service check－in system can not only improve the equipment utilization rate by 132%, but also shorten the queuing time of passengers by 62.5% respectively，compared with traditional check-in system Furth more, the dynamic adjustment strategy has increased the equipment utilization rate by 8%~15%, which can comprehensively optimize and improve the efficiency of future airport baggage handling systems.

Abstract:Aiming at the problems about poor real-time monitoring of rolling bearing condition and low accuracy of fault diagnosis, a feature extraction algorithm based on improved local mean decomposition (ILMD) and mathematical morphology fractal theory is proposed, and making it combine with probabilistic neural network (PNN) to complete the intelligent recognition and classification of bearing status. Firstly, the algorithm decomposes the original signal of the bearing through ILMD, selects the two-order component with the largest correlation coefficient and finds its fractal dimension as the feature vector. Secondly, the “morphological coverage area” is used as the third-dimensional feature vector. At the same time, a three-dimensional feature matrix is constructed. Finally, the feature matrix is input into PNN to complete the state recognition and classification. Using the actual bearing data of CWRU，the experiment results show that the proposed algorithm can not only accurately identify bearings in different states, but also effectively classify bearing states with different damage levels under the same failure. The average recognition rate exceeds 99.6%, and the average recognition time is 0.21 s.

Abstract:Fault diagnosis for intelligent vehicles is of great significance to ensure the safe driving. This paper proposes a series of fault diagnosis methods aiming at anomaly detection for sensor data and vehicle motion of intelligent vehicles. For the non-sequential sensor data, extreme learning machine based autoencoder is utilized to compress the normal data instances to learn the feature representation, and then reconstruct the data using the compressed feature. Whether an instance is normal or not is decided in accordance with the reconstruction error. To detect the anomaly in the sequential sensor data, multi-layer long-short time memory network is adopted to learn the time adherence of the sequential data to predict the current data value, and whether the data is normal or not is judged according to the prediction error. Besides, an adaptive threshold calculation method is proposed, where the threshold dynamically changes with the reconstruction error or prediction error and enable the decision variable sensitive to the anomaly. Furthermore, to detect whether the vehicle motion is abnormal, the vehicle bicycle kinematic model and Kalman filter are adopted and the normality of the residuals between the estimated and measured values is checked using Jarque-Berra test. The experiments verifies that the methods proposed in this paper can effectively detect the anomaly in the non-sequential or sequential sensor data, and detect the abnormality of the vehicle motion.

Abstract:The developmental rules of hydrogen plasma properties with the changes of discharging current, magnetic field and gas flow were studied using the facility of Sichuan University Plasma Surface Investigation (SCU-PSI). The performance of regulated plasma was characterized by Langmuir double probes and the irradiation of pure tungsten under above plasma environment was researched. The experimental results show that, in the range of magnetic field 0.1~0.2 T, gas flow 1 000~2 000 sccm and discharging current 180~228 A, the electron temperature and density are positively correlated with magnetic field and current and discharging current, and negatively correlated with gas flow. And the greatest electron density, the flux density and the heat load obtained are 2.6×1019 m-3, 8.8×1022 m-2?s-1, 6.5×104 W/m2, respectively. In the irradiation experiment of pure tungsten using above achieved hydrogen plasma environment, the particle was found due to the irradiation damage and the scale of particles increased with the increase of plasma flux. Therefore, from this paper, it reveals that the SCU-PSI could produce satisfied plasma environment and could be used as the effective facility to study the interaction between hydrogen plasma and plasma facing materials, like tungsten, in Tokamak device of the field of nuclear fusion.

Abstract:Fission gas release (FGR) has an extremely important influence on the thermo-mechanical behavior of a fuel element. Accurate simulation of FGR in reactors is one of the most basic contents in the development of fuel element performance analysis program, and it is also an important evaluation criterion. In this paper, we discuss the FGR behaviors in fast reactors in detail. The FGR model is based on the equations modeling the behavior of gas atoms and bubbles. The behaviors such as the generation, free diffusion, capture and re-dissolution of gas atoms in the grain are modeled in detail, and the nucleation, growth, coalescence, and migration of bubbles in the grain are simulated. Moreover, the growth, connection, channel formation, and gas release of intergranular bubbles are modeled. We develop a model for FGR calculation, using a method of numerical discrete and iteration calculation. The calculated results are compared with those of experiments and physical theories, which shows very good agreement. Therefore, the model can predict the gas release and swelling in the fuel and the concentration and size of intragranular bubbles, indicating that this model is appliable in a fuel element analysis code.

Abstract:Based on CC-PVDZ, the effects of different external electric fields on the molecular structure and properties of CO2 were studied by using the density functional theory B1B95 method. It is found that the geometric parameters and charge distribution of CO2 are obviously dependent on the electric field. The total energy, dipole moment, and the HOMO/LUMO energy of CO2 are influenced by the variation of the external electric field. Based on the influence of external electric field on the energy gaps of CO2, it can be found that external electric field will affect the ability of CO2 to participate in chemical reaction. The Infrared spectrum can also be affected by the external electric field. Thus the electric field can be used to regulate the Infrared spectra of molecules, and facilitate the capture of the spectral information. Moreover, the influenced of different external electric fields to the dissociation energy of 1C-2O bond in CO2 molecular is discussed, and the dissociation energy of 1C-2O bond is highly correlated with the external electric field. The results provide an important theoretical basis for the decomposition of CO2 by external electric field.

Abstract:Vehicle explosion is becoming more and more common in explosion-related incidents. In order to combat domestic and foreign terrorists, and to support the criminal investigation and prevention about vehicle explosions, experimental research on the damage features of car under blast loading has been performed in this paper. In our experiments, explosives were detonated in the center of the bottom of the car, backup box center and under the driver’s seat respectively. The dynamic process of experiments was recorded by two high-speed photographic channels. Free field overpressure distributions in different directions were analyzed by using the shock wave overpressure test system, and the overpressure on the car body was obtained as well. A large number of automobile damage images and debris distribution data were achieved by camera. The effects of different explosion positions on blast damage features as well as the overpressure distribution of vehicles were obtained in the experiments, which are of significance to the scene restoration and detection of car explosion cases.

Abstract:In this paper, the structural properties and the mechanical stability of Ce-based intermetallic CeAuGa3 are studied based on the first-principles calculations. The full sets of elastic constants, the associated macroscopic elastic parameters (bulk, shear and Young’s moduli, Poisson ratio), and phonon dispersion have been determined, indicating that the I4mm structure is both mechanically and dynamically stable. By calculating the spin exchange interaction between cerium ions, we have analyzed several possible ordered spin states of CeAuGa3, and found that the ground state of CeAuGa3 prefers to be ferromagnetic ordering. The positive (ferromagnetic) exchange coupling between the first and second nearest neighbors within each ab layer plays a dominant role, while the third nearest neighbour ferromagnetic interaction between adjacent ab layers of Ce ions is relatively weak.

Abstract:Ceramic-based Thick Gaseous Electron Multipliers (THGEMs) have been successfully applied to neutron detection. The influences of ceramic, FR4, kapton, and PTFE substrates on the thermal neutron scattering and absorption were studied and compared by Geant4 software. The ceramic-THGEMs, FR4-THGEMs, kapton-THGEMs and PTFE-THGEMs are homemade in PCB factory, which have the same geometric parameters such as aperture of 200 μm, hole spacing of 600 μm, thickness of 200 μm, insulation ring width of 80 μm, and sensitive area of 50 mm × 50 mm. The gas gain, energy resolution and gain stability were measured using 5.9 keV X-rays, four different type substrates THGEM can work normally. The maximum gain of a single and double layer ceramic THGEMs reach 1.2×104 and 4×104 in Ar + CO2 =80:20, respectively. The energy resolution is better than 24.4% in Ar + iC4H10 = 97:3. Good gain stability was obtained during a more than 100 hours continuous test. By using a 239Pu source, the alpha deposited energy spectrum and gain curve of the ceramic THGEM were measured.

Abstract:Bismuth telluride compounds are thermoelectric materials with the best performance near room temperature, which have important application value in waste heat recovery and solid state refrigeration. The main preparation method is ball grind milling, and slight changes of various parameters may affect the microstructure and thermoelectric properties of materials. Ball milling time, as an important ball grinding parameter, can not only affect the refinement of powder particle size, but also regulate the thermoelectric properties of materials. Therefore, it is urgent to analyze the influence of ball milling time on the crystal structure, particle size and the thermoelectric performance of products. In this paper, bismuth telluride compounds were prepared by adjusting different ball milling time with constant ball grinding speed. The response of grains to ball milling time is found through the analysis of crystal structure and powder particle size. The result of subsequent thermoelectric performance tests shows that, with the increase of ball milling time, the change of particle size leads to the synergistic tuning of electron and phonon transport. Finally, the maximum ZT merit of n-type and p-type bismuth telluride were increased to 0.91 and 1.11, respectively. This study systematically summarizes the influence of ball milling time on the microstructure and the thermoelectric capability of bismuth telluride. Our work shows the significance in the mergence of powder metallurgy and thermoelectrics, which may play some roles in the commercial application of thermoelectric conversion technology.

Abstract:In order to explore the mechanism of factory cultivation of Tremella fuciformis with fresh juncao as the main substrate, GC-MS was used to analyze the substance transformation characteristics during 3 different stages，which were G1 (hypha growth phase, 12 d after inoculation), G2 (primordium differentiation stage, 24 d after inoculation) and G3 (fruit body growth phase, 36 d after inoculated). After main component analysis, partial least squares discriminant analysis, and orthogonal partial least squares discriminant analysis, the results showed that there were 426, 423, and 466 different metabolites in G2 than G1, G3 than G2, and G3 compared to G1, respectively. The carbohydrate content in G2 and G1 was higher than that in G3. The content of fat and terpenoids increased in G3 than that in G1. Differential metabolites were screened and metabolic pathways were matched. The differential pathways were fatty acid synthesis, glyoxylic acid and dicarboxylic acid metabolism, and the citric acid cycle. The synthesis pathways of unsaturated fatty acids were significantly different between G2 and G1. The metabolic pathways of alanine, aspartic acid and glutamate were significantly different between G3 and G2.The metabolic pathways of glycerol metabolism were significantly different between G3 and G1. All the results indicated that there were significant differences in substance transformation and metabolites in different growth stages of Tremella fuciformis.

Abstract:In order to deal with the large-scale water pollution caused by uranium, a strategy of biological treatment, and a method of microbial adsorption is used to achieve the purpose of removing uranium pollution in this research. In this study, a strain H002 with high tolerance to heavy metal uranium was selected from 142 isolates of radiation-resistant bacteria, which can stably tolerate up to 150 mg/L U (VI). After 16s rRNA, physiological and biochemical identification, it was determined the H002 belonged to Stenotrophomonas maltophilia. Adsorption experiments under different parameters showed that the maximum adsorption capacity of uranium by H002 cells reached 240 mg/g (dry weight cells) when the cells were collected from 12 hculture in TGY. The carboxyl group on the cell surface was shown to contribute to uranium adsorption by H002. In addition, different anions, cations and surfactants could affect the adsorption of uranium by H002 cells. Among them, sodium dodecyl sulfate could promote the adsorption of uranium, while high concentrations of CO32- and HCO3- ions inhibited adsorption. This study finally obtained a Stenotrophomonas maltophilia H002 that can tolerate and adsorb uranium, which has the potential to be a biological material for clearing uranium pollution.

Abstract:Exploring the structure composition of the intestinal microflora of yaks in different altitudes will be to provide help for the breeding and communication of yaks in different altitudes. In this study, fresh fecal samples of 12 yaks were randomly collected at an altitude of 2 897 to 4 717 m. Using 16S rDNA technology to determine the metagenome of samples, and the OTU abundance of intestinal microflora of yaks at high and low altitudes was compared and analyzed, and then the structural composition of the intestinal microflora of yaks at high and low altitudes also be studied. The results showed that the composition of the intestinal microflora of yak in the phylum, class, order, family, genus and species levels is different between high and low altitude areas. The intestinal flora of yak in high and low altitude areas had 8 bacteria that had significant differences at the genus level (P< 0.05). In conclusion, there were certain differences in the composition of the intestinal microflora of yaks at high and low altitudes, and the OTU abundances of intestinal microflora of yaks at low altitude displayed higher species richness than those in high altitude.

Abstract:In order to explore the gene silencing activity of diastereomeric pure PS-siRNA (pPS-siRNA) synthesized enzymatically by RNA polymerase, its silencing activity on Yes-associated protein (YAP), a potential cancer treatment target, and cytotoxicity to HeLa cells were studied by qPCR and Western blot in this study. The data showed that compared with unmodified siRNAs (PO-siRNAs), pPS-siRNAs can better reduce YAP gene expression without significant cytotoxicity (approximately 30% more efficient). In addition, MTT cell proliferation assay and flow cytometry showed that the proliferation of HeLa cells was inhibited after YAP down-regulated by the pPS-siRNAs. This research proved the superiority of pPS-siRNAs in gene therapy and the potential of YAP as a tumor therapeutic target.

Abstract:With the development of economy, the market share of internet consumer credit products is also expanding rapidly. With college students' holding more disposable funds, the base and frequency of using on-line credit products are also rising, which leads to various kinds of college students' financial risk events. Under the background of the new-raised national strategy "stabilizing finance", this paper analyses the current situation of internet ecology and general education of finance and economics, investigates the status quo of college students' use of internet consumer credit products through a survey, and finds that the lack of financial literacy education may bring severe challenges to the sustainability of national financial stability. Therefore, this paper puts forward the optimization scheme of "three elements, three layers and three guides" for general education of financial literacy in colleges and universities.

Abstract:How to perform individualized programming teaching is an important issue in educational practice, and how to recognize the personality of learners is the key for this issue. In this paper, the method of recognizing personality is proposed based on the programming behaviors of learners. Specifically, the programming behavior features of learners are first extracted from multiple aspects; then the classification models are established using the support vector machine; finally, the multi-task voting strategy is used to comprehensively identify learners' personality traits. The results show that the traits in the Big Five Model can be predicted using the proposed method, verifying the feasibility of using programming behaviors to identify learners’ personality to a certain extent; in addition, this paper also discusses the applications of the proposed method in future programming education.