Anti-Japanese Sentiment among Chinese University Students: The Influence of Contemporary Nationalist Propaganda

This study looks at the sources of anti-Japanese sentiment in today’s China. Using original survey data collected in June 2014 from 1,458 students at three elite universities in Beijing, we quantitatively investigate which factors are associated with stronger anti-Japanese sentiment among elite university students. In particular, we examine the link between the Chinese Communist Party (CCP)’s nationalist propaganda (especially patriotic education) and university students’ anti-Japanese sentiment. We find that nationalist propaganda does indeed have a significant effect on negative sentiment towards Japan. Reliance on state-sanctioned textbooks for information about Japan, visiting museums and memorials or watching television programmes and movies relating to the War of Resistance against Japan are all associated with higher levels of anti-Japanese sentiment. The findings suggest the effectiveness of nationalist propaganda in promoting anti-Japanese sentiment. We also find that alternative sources of information, especially personal contact with Japan, can mitigate anti-Japanese sentiment. Thus, visiting Japan and knowing Japanese people in person can potentially offset some of the influences of nationalist propaganda

Research on Visualization of Multi-Dimensional Real-Time Traffic Data Stream Based on Cloud Computing

AbstractBased on efficient continuous parallel query series algorithm supporting multi-objective optimization, by using visual graphics technology for traffic data streams for efficient real-time graphical visualization, it improve human-computer interaction, to realize real-time and visual data analysis and to improve efficiency and accuracy of the analysis. This paper employs data mining processing and statistical analysis on real-time traffic data stream, based on the parameters standards of various data mining algorithms, and by using computer graphics and image processing technology, converts graphics or images and make them displayed on the screen according to the system requirements, in order to track, forecast and maintain the operating condition of all traffic service systems effectively

A Cloud-Computing-Based Data Placement Strategy in High-Speed Railway

As an important component of China’s transportation data sharing system, high-speed railway data sharing is a typical application of data-intensive computing. Currently, most high-speed railway data is shared in cloud computing environment. Thus, there is an urgent need for an effective cloud-computing-based data placement strategy in high-speed railway. In this paper, a new data placement strategy named hierarchical structure data placement strategy is proposed. The proposed method combines the semidefinite programming algorithm with the dynamic interval mapping algorithm. The semi-definite programming algorithm is suitable for the placement of files with various replications, ensuring that different replications of a file are placed on different storage devices, while the dynamic interval mapping algorithm ensures better self-adaptability of the data storage system. A hierarchical data placement strategy is proposed for large-scale networks. In this paper, a new theoretical analysis is provided, which is put in comparison with several other previous data placement approaches, showing the efficacy of the new analysis in several experiments

Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History

A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional), 10 CEC2005 benchmark functions (30-dimensional), and a real-world problem (multilevel image segmentation problems). Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests

Comparison of dynamic changes of endogenous hormones between calli derived from mature and immature embryos of maize

Mature and immature embryos of maize inbred lines 87-1 and 137 were used as explants to induce callus on improved N6 medium. The contents of endogenous hormones abscisic acid (ABA), indoleacetic acid (IAA), gibberellic acid (GA3) and cytokinins (ZR) of immature, mature embryos and their corresponding calli were detected by method of enzyme-linked immunosorbant assay (ELISA). At the beginning of culture, IAA and GA3 levels decreased rapidly and reached their lowest levels at day 7, indicating that large amounts of IAA and GA3 are needed for germination. Levels of IAA and GA3 were highest at the beginning of embryonic callus formation from immature embryos, suggesting high levels of IAA and GA3 were beneficial to induction of embryonic callus from immature embryos (CIME). The IAA, GA3 and ABA contents and ration of IAA to ABA (IAA/ABA), GA3 to ABA (GA3/ABA) in callus of mature embryos (CME) were higher than those of CIME after the 14th day from culture initiation and the changes of ratios IAA/ABA and GA3/ABA increased rapidly in CME while they remained low in CIME during the whole experimental period. This inferred that high levels of IAA, GA3 or ABA and large increases in IAA/ABA and GA3/ABA might hinder the induction and maintenance of embryonic calli from mature embryos

Effects of Leymus chinensis hay and alfalfa hay on growth performance, rumen microbiota, and untargeted metabolomics of meat in lambs

ObjectiveThe objective of this study was to compare the effects of Leymus chinensis hay and alfalfa hay as the roughage on the rumen bacterial and the meat metabolomics in lambs.MethodsFourteen male lambs were randomly assigned to two dietary treatments (one group was fed with concentrate and Leymus chinensis hay; another was fed with concentrate and alfalfa hay) with seven replicates per treatment. The feeding experiment lasted for 60 days. Lambs were slaughtered at the end of the feeding experiment. Growth performance, carcass performance, and weights of various viscera were determined. The longissimus dorsi and rumen contents were collected for untargeted metabolomics and 16S rDNA amplicon sequencing analysis, respectively.ResultsThe lambs fed with alfalfa hay showed a significantly increased in average daily gain, carcass weight, dressing percentage, loin-eye area, and kidney weight. Feeding Leymus chinensis hay and alfalfa hay diets resulted in different meat metabolite deposition and rumen bacterial communities in the lambs. The relative abundance of phyla Fibrobacteres, Bacteroidetes, and Spirochaetes were greater in the Leymus Chinensis hay group, while, the relative abundance of Firmicutes, Proteobacteria, Fusobacteria, and Verrucomicrobia were greater in the alfalfa hay group. Based on untargeted metabolomics, the main altered metabolic pathways included alanine, aspartate and glutamate metabolism, D-glutamine and D-glutamate metabolism, phenylalanine metabolism, nitrogen metabolism, and tyrosine metabolism. Several bacteria genera including BF31, Alistipes, Faecalibacterium, Eggerthella, and Anaeroplasma were significantly correlated with growth performance and meat metabolites.ConclusionAlfalfa hay improved growth performance and carcass characteristics in lambs. Leymus chinensis hay and alfalfa hay caused different meat metabolite deposition by modifying the rumen bacterial community. These findings will be beneficial to future forage utilization for sheep growth, carcass performance, and meat quality improvement

Phenylboronic ester-modified polymeric nanoparticles for promoting TRP2 peptide antigen delivery in cancer immunotherapy

The tremendous development of peptide-based cancer vaccine has attracted incremental interest as a powerful approach in cancer management, prevention and treatment. As successful as tumor vaccine has been, major challenges associated with achieving efficient immune response against cancer are (1) drainage to and retention in lymph nodes; (2) uptake by dendritic cells (DCs); (3) activation of DCs. In order to overcome these barriers, here we construct PBE-modified TRP2 nanovaccine, which comprises TRP2 peptide tumor antigen and diblock copolymer PEG-b-PAsp grafted with phenylboronic ester (PBE). We confirmed that this TRP2 nanovaccine can be effectively trapped into lymph node, uptake by dendritic cells and induce DC maturation, relying on increased negative charge, ROS response and pH response. Consistently, this vehicle loaded with TRP2 peptide could boost the strongest T cell immune response against melanoma in vivo and potentiate antitumor efficacy both in tumor prevention and tumor treatment without any exogenous adjuvant. Furthermore, the TRP2 nanovaccine can suppress the tumor growth and prolong animal survival time, which may result from its synergistic effect of inhibiting tumor immunosuppression and increasing cytotoxic lymphocyte (CTL) response. Hence this type of PBE-modified nanovaccine would be widely used as a simple, safe and robust platform to deliver other antigen in cancer immunotherapy

A Hybrid Model Based on Wavelet Decomposition-Reconstruction in Track Irregularity State Forecasting

Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR) and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR) models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy

Study of Railway Track Irregularity Standard Deviation Time Series Based on Data Mining and Linear Model

Good track geometry state ensures the safe operation of the railway passenger service and freight service. Railway transportation plays an important role in the Chinese economic and social development. This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis. Linear recursive model and linear-ARMA model based on wavelet decomposition reconstruction are proposed, and all they offer supports for the safe management of railway transportation