Abstract:In this paper, we consider the real continuous solutions of a class of generalized Dhombres equations. By using the iterative theory of functional equations, characteristic theory of polynomial-like iterative equations and a technique of reducing order, we give all real continuous solutions of these equations.

Abstract:To satisfy the actual demand for interpretability and prediction accuracy in judicial practice, we in this paper propose an intelligent sentencing method based on the probabilistic graphical model (PGM). This model is built on the cornerstone of sentencing factors. The parameters are estimated by using the maximum likelihood criterion, and the predicted values are obtained by calculating the mathematical expectation of distribution. Experimental result on dangerous driving shows that the prediction accuracy of the method is better than that based on comparison models, such as decision tree and neural network. Meanwhile, this method has good interpretability as well.

Abstract:Moderate deviation principle is an important method for constructing asymptotic confidence intervals in statistical inference. This work aims at the moderate deviation principle for the stochastic Cahn-Hilliard equations driven by multiplicative Lévy noise. In these equations, the interaction of high order nonlinear term and jump noise results in the difficulty of dealing with the stochastic integral and deducing the exponential-type probability estimation. Nevertheless, with the help of classical weak convergence method, we estabish the moderate deviation principle underlying the verification of two moderate deviation conditions.

Abstract:In this paper, we consider the number of limit cycles bifurcated from the weak center of a class of piecewise smooth quadratic systems of focus-parabolic type. It is well known that these systems process five center conditions. Taking one of the center conditions as an example, we show that at least 6 limit cycles can bifurcate from the center by perturbing the system parameters up to the order of 8, thus improve the corresponding results.

Abstract:This paper aims at the existence of positive solutions for the third-order periodic boundary value problems with indefinite weight, the proof of the main results is based on the Leray-Schauder fixed point theorem.

Abstract:SQL injection attacks would cause significant harm because they can steal or destroy data by intruding target database. SQL injection attack detection can find out the potential security threat in time, and it is beneficial to the database security protection. However, in intelligent transportation system, due to its internal complexity and the emergence of new varieties of SQL injection attacks, the size of abnormal samples cannot meet the requirement of machine learning model training. This would carry a significant risk of model overfitting and performance degradation. In order to solve the problem, a SQL injection attack detection framework is designed based on bit coding, considering the characteristics of intelligent transportation systems and SQL injection attacks comprehensively. In the framework, pre-training word embedding model and parsing of grammatical rules are not needed. Then, a semi-supervised SQL injection attack detection model (ASDM) is proposed based on this framework, combined with the attention mechanism. In the model, the samples are reconstructed to learn the high-level features(such as the central trend and the dispersion degree of the features) and to express the feature posterior distribution and feature deviation. Then, these high-level features are fused with the data coding features to highlight the differences between different types of data. Finally, the attention mechanism and residual network are introduced to construct the detector, with the aim of exerting different attention intensity to the features according to their importance degree and guaranteeing the generalization ability of the model. The experimental results show that the proposed method has better detection performance compared with other SQL injection attack detection methods for the data with unbalanced labels, and can detect unknown SQL injection attacks.

Abstract:Since a lot of pattern matching in the process of dictionary set establishment and three-dimensional(3D) reconstruction, the super dimension algorithm takes a long time and is difficult in practical application. To solve this problem, a super-dimensional reconstruction algorithm based on porosity classification is proposed, which can greatly reduce the time cost of 3D reconstruction. First, the dictionary sets are classified by the porosity of dictionary elements. Then, the porosity classification dictionary is used to search according to porosity during reconstruction, and the corresponding dictionary interval is searched first. Based on the 3D reconstruction of different training images and porosity distribution, an adaptive search range determination method is proposed. Finally, the effectiveness of the proposed super dimension algorithm is verified by multiple reconstruction of the training images of high, medium and low porosity, a comparative analysis is made on the reconstruction results of the traditional Super dimension algorithm and the proposed algorithm in terms of the statistical characteristic function, pore-throat parameters and reconstruction time.

Abstract:Existing session-based recommendations with graph neural networks could capture the item's transition relationship by constructing graph structures from sessions. However, most graph neural networks and their improved models only consider the single transition relationship of items in the session when modeling sessions. As a result, a large amount of effective information is ignored, and the analysis of hidden correlations between items is lacking. Therefore, a session-based recommendation algorithm with graph neural network and multi-source graph information is proposed. In the algorithm, the users' repeat behavior information and item content related information are incorporated into the session graph modeling process, which effectively extracts the deeper complex transformation relationship of items, and aggregates it through linear transformations. In addition, an external attention mechanism is used to obtain the hidden association information of the session sequence items, making the generated session vectors more accurate. The experiments were performed on the real datasets: Yoochoose and Diginetica, and the results showed that the model outperformed the benchmark model. In particularly, it outperforms the state-of-the-art benchmark model GC-SAN, on average by 12.50% in terms of the MRR@20 evaluation metric, and can better predict user's next click items.

Abstract:When applying blockchain in the Internet of things with mobile nodes, these nodes are called blockchain mobile nodes. Blockchain mobile nodes have problems such as short communication connection time, insufficient computing power and storage capacity, resulting in low security and throughput when the network reaches a consensus. To tackle these drawbacks, a trusted incentive algorithm is proposed to optimize the consensus process. First the blockchain mobile node receives the information required by the consensus and completes the initialization. Secondly, each blockchain mobile node generates a decision block, which contains the voting result of the verification message, its own credibility factor and the generation time of the decision block. A relay node is selected from the cluster composed of adjacent blockchain mobile nodes by the decision block. The relay node propagates the verification message to the next cluster and the generated blocks are stored in the edge server. One relay is one hop, the consensus is completed when the number of hops is greater than the network threshold number of hops. Finally, the network rewards or punishes the nodes according to the incentive mechanism, and updates the behavior identifications of the nodes according to the incentive situation, the behavior identifications is then fed back to the consensus. The simulation results show that compared with the POET and POS algorithms applied in the same network, the trusted incentive algorithm can effectively reduce the verification failure rate of verification messages and improve the consensus security under the condition of ensuring a certain throughput. It is more suitable for the Internet of things with mobile nodes.

Abstract:Human behavior is complex and diverse, and the information such as scene, appearance and location are closely related to human behavior. Aiming at the problem of how to make efficient comprehensive use of these information, a multi-person behavior recognition method integrating scene and interactive features was proposed, and the individual appearance features and scene features were extracted by two channels. For the individual channel, the attention mechanism module was used to focus on the areas with greater correlation with behavior, and the extracted individual appearance features combined with location features were input into the graph convolution network for relational reasoning. Among them, the graph convolution network used the cosine similarity method to measure the correlation between individual features, and combined the position features between individuals for relationship reasoning; For the scene channel, scene features were extracted by using ResNet-50 pretrained on place365 dataset. Finally, the final features obtained from individual channels and scene channels were weighted and fused to obtain the behavior recognition results of groups and all individuals. The experimental results on the Collective Activity Dataset (CAD) show that this method can improve the accuracy of behavior recognition, and the accuracy of group behavior and individual behavior reaches 92.29% and 78.19%.

Abstract:To save transmission bandwidth and storage resources, imaging devices and systems generally perform lossy compression on images and videos. JPEG images usually suffer from obvious blocking effect due to block quantization coding. Removing the blocking effect of the image can not only improve the visual experience of users, but also facilitate other computer vision tasks. Therefore, an image deblocking method based on multi-scale wide-activated residual attention network (MWRAN) is proposed. The MWRAN is mainly constructed by the multi-scale wide-activated residual attention block (MWRAB). The MWRAB can not only activate more non-linear features to promote the flow of information in the network, but also capture rich image multi-scale features. In addition, the MWRAB can adaptively adjust the learned features to focus on more important information via the proposed lightweight contrast-aware channel attention (LCCA). The ablation experiment is conducted to verify the effectiveness of the proposed MWRAB. The MWRAN achieves higher objective evaluation indices and produces subjective perceptual effects closer to the original image than several state-of-the-art image deblocking methods on common benchmark datasets.

Abstract:With the rapid development of 5G communications technology, IoT and big data technology, traditional cloud computing models have become increasingly unable to keep up with the growth rate of data, as a new computing model, edge computing has demonstrated a strong ability to handle big data and high-speed computing. This paper propose an edge computing framework suitable for video image processing, and two improvements to the traditional moving target tracking algorithm: (1) Raspberry Pi is used as the video front-end processor, which has the characteristics of small size, low cost, and strong computing power; (2） a step-by-step image sampling method with a smaller step size as a sliding window is used to improve the original compression tracking algorithm, thereby reducing the amount of calculation. The results of computer simulation experiments show that the algorithm improves the operation speed without affecting the tracking accuracy.

Abstract:The graphene nanostructure model under the joint modulation of ferromagnetic stripes and hard barriers is established. The effects of the magnitude of magnetic field generated by ferromagnetic stripes and the width of ferromagnetic stripes on the valley-dependent electron transport properties in graphene are calculated, and the electron conductance and the valley polarization in the graphene nanostructure are studied. The numerical results show that the significant valley polarization effect can be realized in such a nanostructure, and the strength of the magnetic field and the width of the ferromagnetic stripes will have a great influence on the electron conductance and valley polarization. Therefore, the valley polarization intensity actually required can be obtained by controlling the width of the ferromagnetic stripes and the strength of the magnetic field generated by it. This study is very helpful for understanding and designing valleytronic devices.

Abstract:Dispersion properties are an important feature of electrical parameters of mixed materials, and have always attracted the attention of many scholars, but there are few researches on dispersion-free properties of periodic mixed materials. Based on the empirical formulas describing the equivalent electrical parameters of periodic mixed materials, this paper deduces the conditions that each component should satisfy when the equivalent electrical parameters of periodic mixed materials have no dispersion. The finite element method is used to extract the effective permittivity and electrical conductivity of the periodic mixed materials. It is found that the numerical simulation results are highly consistent with the deduced results, which shows the effectiveness of the dispersion-free theory for periodic mixed materials proposed in this paper. On the premise that the effective electrical parameters meet the dispersion-free condition, the variations of the effective electrical parameters of the mixed materials with the volume ratio and the electrical parameter ratio of each component, and the influence of the shape and spatial distribution of the inclusions on the dispersion-free effective electrical parameters of the mixed materials are analyzed. Based on the crystal cell structure, a dispersion-free theory of periodic mixed materials is proposed in this paper, which can provide a theoretical basis for the design of dispersion-free components and sample preparation and measurement result calibration of effective electrical parameter measurement equipment at various frequency bands.

Abstract:Based on the first-principles of density functional theory, we use the PBEsol + U method to calculate the lattice and electronic structures, as well as mechanical and optical properties of ThO2, PaO2 and UO2 and (Th, Pa)O2 and (U, Pa)O2. The results of structural optimization indicate that the PBEsol + U method can provide more accurate lattice and mechanical parameters for actinide-based oxide ThO2, PaO2, and UO2. The calculation results show that the lattice parameters and the bandgaps of ThO2, PaO2, and UO2 are consistent with the experimental values and other related theoretical values. In the meanwhile, the lattice parameters of (Th, Pa)O2 and (U, Pa)O2 are between ThO2 and UO2, while predicting the band gaps of (Th, Pa)O2 and (U, Pa) O2 are also between ThO2 and UO2. Electronic properties calculations indicate that PaO2 and UO2 are Mott insulators, while ThO2 is a charge-transfer insulator. These are consistent with experiments and theoretical conclusions. (Th, Pa)O2 and (U, Pa)O2 have significant spin polarization effects near the Fermi energy level, and the bands are mainly occupied by Pa-5f and U-5f electronic state, respectively. Lastly, the real and imaginary parts and optical parameters of the optical dielectric functions of these systems are compared and analyzed.

Abstract:Using bentonite as the main raw material, zeolite A was prepared by alkaline melting. The static adsorption experiments were carried out to explore the adsorption kinetics and thermodynamics of Sr2+ on bentonite and zeolite A. The results show that the adsorption capacity of bentonite and zeolite depends on the initial concentration of Sr2+, and the trends of adsorption behavior with respect to the initial concentration and adsorption time is roughly the same for bentonite and zeolite. The isotherm adsorption process of Sr2+ on bentonite and zeolite conforms to the Langmuir isotherm adsorption model. The adsorption mechanism of Sr2+ on bentonite and zeolite is similar, both based on cation exchange adsorption in monolayer. The kinetics of adsorption follows a pseudo-second-order kinetic adsorption process controlled by chemical reaction. The bentonite and zeolite before and after adsorption were characterized by XRD and SEM. The adsorption of bentonite and zeolite in acidic solution did not severely damage or affect their intrinsic framework structures. The adsorption capacity of A zeolite for Sr2+ is about 3.9 times of that of bentonite, showing a better adsorption capability than bentonite.

Abstract:The compositions and contents of precipitates in metallurgical grade silicon (MG-Si) directly affect the activity and selectivity of silicone monomer synthesis. Therefore, it is particularly significant to investigate the relationship between impurity content and precipitates correspondence in MG-Si. In this study, the microstructures and distributions of precipitates in MG-Si before and after refining are investigated by using scanning electron microscope (SEM) and energy dispersive X-Ray spectroscopy (EDX). It is found that due to the high content of Ca and Al contents in MG-Si before refining, its impurity phases are mainly Si2Al2Ca, Si-Al-Ca, Si8Al6Fe4Ca etc., but after refining, Fe is the main element in MG-Si, so its phases mainly include FeSi2(Al), FeSi2Ti, Si8Al6Fe4Ca, etc. In addition, the statistics conducted by Image Pro Plus show that the Si2Al2Ca and Si8Al6Fe4Ca phases account for respectively 48% and 30% of the precipitates composition before refining, while the FeSi2(Al) phase accounts for 68% after refining, and the only Ca-containing phase (Si8Al6Fe4Ca) accounts for 7% of the precipitates content. Here we compare the compositions and proportions of precipitates in MG-Si before and after refining, in order to explore the law of impurity content on precipitated phases, so as to prepare the best downstream MG-Si raw material for silicones.

Abstract:In order to improve the detection efficiency of harmful substances in the textile production process, this paper applies the compression sensing method to the detection process of harmful substances. The observation matrix is used as the sample mixing scheme, and the detection times that are far less than that of the samples to be tested are obtained through the mixed detection. After the mixed detection is completed, the content of harmful substances in the original samples is reconstructed from the mixed detection values according to the corresponding reconstruction algorithm, and then the number and detection rate of unqualified samples are obtained. Finally, the influence of the mixed detection matrices generated by different parameters on the reconstruction effect is explored by simulation experiments, and it is verified by the real detection project of detecting harmful substances bisphenol A, aromatic amines and formaldehyde in fiber textiles. The verification results show that the mixed sample detection scheme based on compression perception proposed in this paper can not only ensure the accuracy of the detection, but also can reduce the detection cost and improve the detection efficiency.

Abstract:In order to explore the molecular mechanism of Arabidopsis SnRK2.2 and SnRK2.3 genes in response to Cd stress. Wild-type (WT), double mutant SnRK2.2/2.3, overexpressing SnRK2.2 and overexpressing SnRK2.3 transgenic plants were used as materials to study the relationship between SnRK2.2 and SnRK2.3 genes and Cd stress response. The study found that the overexpression of SnRK2.2 and SnRK2.3 genes can improve the tolerance of Arabidopsis Cd, which showed that the overexpression of the two genes can reduce the accumulation of Cd, malondialdehyde (MDA) and reactive oxygen species (ROS) and increase the activities of antioxidant enzymes CAT, POD and SOD. qRT-PCR results showed that under Cd stress, the expression levels of iron-regulated transporter IRT1 transcription factors FIT, bHLH038 and bHLH039 were significantly inhibited, and the expressions levels of ABA synthesis-related genes AAO3 and NCED3 were significantly up-regulated in the two overexpressed plants. Under Cd stress, the ABA content of the two overexpressed plants was significantly higher than that of the WT and double mutant. These results suggested that when Arabidopsis was under Cd stress, SnRK2.2 and SnRK2.3 genes reduced the uptake of Cd by down-regulating the expression of the IRT1 gene, and alleviated the toxicity of Cd to plants by increasing the content of endogenous ABA.

Abstract:Cypripedium palangshanense is a rare and endangered protected plant unique to China. It is an extremely small population of wild distributed in a narrow area. To study its genetic diversity CDDP molecular marker technology was used to evaluate the genetic diversity of 92 materials from 6 experimental sites in 2 wild populations of C. palangshanense in Wolong and Wanglang Nature Reserves, and further explore the relationship between genetic diversity and environmental factors. The results of the genetic analysis showed that a total of 131 locus were detected at the species level with 12 primers, the percentage of polymorphic loci (PPL) was 100%, the observed allele (Na) was 2, the effective allele (Ne) was 1.5026, the Nei′s genetic diversity index (H) was 0.3141, and the Shannon diversity index (I) was 0.4856; At the population level, the genetic diversity of Wolong population was higher than that of Wanglang population, and the variation range of Na was 1.4504 ~ 1.9160; the variation range of Ne was 1.2446 ~ 1.4336; H was between 0.1464 and 0.2679; I was between 0.2231 and 0.4153; the variation range of PPL was 45.04% ~ 91.60%. The genetic differentiation coefficient (Gst) of C. palangshanense was 02863. The genetic structure and AMOVA analysis showed that there was genetic differentiation among the populations of C. palangshanense. Mantel test showed that there was a significant correlation between genetic distance and geographical distance among Cypripedium palang shanense populations in Balang mountain (R2 = 0.3830, P<0.05). According to UPGMA cluster analysis, Wolong population and Wanglang population are clustered into one branch. The correlation analysis between genetic diversity and environmental factors showed that there was a significant positive correlation among observed allele (Na), the number of polymorphic loci (Np), percentage of polymorphic loci (PPL) and available potassium content (AK); The number of effective alleles (Ne), Nei′s genetic diversity index (H) and Shannon information index (I) were significantly positively correlated with altitude (Alt); There was no significant correlation between remaining environmental factors and genetic diversity index. The results showed that CDDP molecular markers technology was suitable for the study of the genetic diversity of C. palangshanense, and was highly polymorphic.

Abstract:Macaca mulatta, as the most important non-human primate model animal, is widely used in biomedical research, so there are a large number of captive populations in the country. Diarrhea is more common in captive populations and is extremely harmful to macaques, especially juvenile macaques. Based on the changes in intestinal microbial composition, function and drug resistance of macaques with diarrhea, this study combined high-throughput sequencing technology and plate counting to explore the effect of adding active Bifidobacterium to the feed on the intestinal microbial diversity of macaques with chronic diarrhea and the improvement of diarrhea symptoms. Firstly, we performed 16S rRNA sequencing through a comparative analysis of the composition of the flora on the feed with and without Bifidobacterium added. We found that the relative abundance of Bifidobacterium in the feed added to Bifidobacteria was significantly increased compared with that of the unaddressed feed group (P<0.05). Then, by using diet with Bifidobacterium added to the chronic diarrhea macaques for one month for adjuvant treatment, the diarrhea symptoms were found to be significantly better than those in the unaddressed group. The feces of the chronic diarrhea macaques in the added group were collected for metagenomic sequencing, and the intestinal metagenomic data of the chronic diarrhea macaques and healthy macaques in the unaddressed group were compared with the intestinal metagenomic data of the unaddressed group of chronic diarrhea macaques and healthy macaques, and the relative abundance of Lactobacillus in the intestinal flora of the added diarrhea macaque was significantly upregulated compared with that of the unrestricted group (P>0.05). There was no significant difference from the healthy group (P>0.05). Finally, the Lactobacillus plate counting experiment was carried out on the macaques who improved after feeding Bifidobacterium and the macaques with persistent diarrhea. It was found that the number of Lactobacillus macaques with improved diarrhea after feeding Bifidobacterium was higher than that in the continuous diarrhea group, which verified the results of metagenomic sequencing analysis. The above results show that the level of Lactobacillus in the intestinal flora of the diarrhea macaque monkey after feeding Bifidobacterium is elevated, and the intestinal flora structure is restored to a healthy level, which is conducive to the improvement of diarrhea symptoms. This study provides data support for captive macaques in the adjuvant treatment of diarrheal diseases by using probiotic feeding to modulate gut microbes.

Abstract:To study the important role of phenylalanine ammonia-lyase, as a key enzyme in the phenylpropanoid metabolic pathway, in plant response to environmental stresses, the genomic information of Arabidopsis thaliana was downloaded from Phytozome and the PAL members were further identified by HMMER, Pfam and SMART tools. The physicochemical properties, secondary structure and conserved motifs of nine PAL proteins were analyzed. The results showed that the secondary structure of PAL members in Arabidopsis thaliana was mainly composed of α -helix and random curl. Leu and Ala were the most abundant amino acid residues. There were 20 different conserved motifs in the PAL gene family of Arabidopsis thaliana, among which motif 6 containing active site ASG appeared in all PAL genes. The analysis of cis-acting elements showed that the PAL gene family contained many elements related to light response, hormone response, stress response and growth and development. The mRNA expression levels of AT3G53260.1, AT3G53260.2 and AT3G47660.1 showed obvious changes based on transcriptome analysis, and the corresponding tendency was verified by further real-time PCR. Therefore, these 3 genes might be involved in the response to drought and salt stress of Arabidopsis thaliana. These results indicate that PAL genes play an important role in regulating plant growth and development and abiotic stress response.