China\u27s image as perceived by the American public after the 2008 Beijing Olympic Games

This study focused on the influence of hosting the Olympics on the country\u27s national image. Based on the 2008 Beijing Olympics, it investigated the relationships among people\u27s perception of the national image dimensions and those of the image that the host country tried to project during the Olympics. This study also examined the routes for the formation of these perceptions. An online survey was conducted. It was indicated that while majority of the respondents indicated different degrees of attitude change, only small portion reported a complete change of attitude. Also, more than thirty percent of those whose attitude has changed reported a more negative view toward China. It was found that although respondents were generally favorable toward the 2008 Olympics, the three promoted Olympic concepts were not well received; especially for the concepts of green Olympics and people\u27s Olympics . A lack of clear understanding of the three concepts was also identified among the respondents. Strong correlations were found among most of the national image dimensions and the projected Olympic image. The two lowest rated national image dimensions, government and exports, were found more correlated to the other dimensions and the projected Olympic image. Traditional media was found to be the dominant information source for both information about China and the Olympics. But personal experience with the Chinese people had become a very important route for information about China. And the internet played a big part in the distribution of information about the 2008 Olympics

Research on Personalized Learning Resource Recommendation Based on Knowledge Graph Technology

In the face of the dilemma of learners\u27 learning loss and information overload in information resources, a personalized learning resource recommendation algorithm is proposed by conducting in-depth and extensive research on the knowledge graph. This algorithm relies on the similarity or correlation between learners\u27 characteristics and course knowledge (learning resources) for recommendation. It analyzes learners\u27 characteristics in depth from four aspects: data collection and processing, model construction, resource and path recommendation, and model application, and establishes a multi layered dynamic feature model for learners; Analyze the core elements of the curriculum knowledge graph, decompose the curriculum knowledge into nanoscale knowledge granularity, and construct a curriculum knowledge graph model. The experimental results indicate that this algorithm improves learners\u27 learning efficiency and promotes their personalized development

Research on Accurate Recommendation of Learning Resources based on Graph Neural Networks and Convolutional Algorithms

In response to the challenges of learning confusion and information overload in online learning, a personalized learning resource recommendation algorithm based on graph neural networks and convolution is proposed to address the cold start and data scarcity issues of existing traditional recommendation algorithms. Analyze the characteristics of the Knowledge graph of learners and curriculum resources in depth, use the graph Auto encoder to extract the auxiliary information and features in the Knowledge graph and establish the corresponding feature matrix, and use Convolutional neural network for classification and prediction. The experimental results show that this algorithm improves the performance of recommendation systems, improves learners\u27 learning efficiency, and promotes personalized development

Multi-Attribute Utility Preference Robust Optimization: A Continuous Piecewise Linear Approximation Approach

In this paper, we consider a multi-attribute decision making problem where the decision maker's (DM's) objective is to maximize the expected utility of outcomes but the true utility function which captures the DM's risk preference is ambiguous. We propose a maximin multi-attribute utility preference robust optimization (UPRO) model where the optimal decision is based on the worst-case utility function in an ambiguity set of plausible utility functions constructed using partially available information such as the DM's specific preferences between some lotteries. Specifically, we consider a UPRO model with two attributes, where the DM's risk attitude is multivariate risk-averse and the ambiguity set is defined by a linear system of inequalities represented by the Lebesgue-Stieltjes (LS) integrals of the DM's utility functions. To solve the maximin problem, we propose an explicit piecewise linear approximation (EPLA) scheme to approximate the DM's true unknown utility so that the inner minimization problem reduces to a linear program, and we solve the approximate maximin problem by a derivative-free (Dfree) method. Moreover, by introducing binary variables to locate the position of the reward function in a family of simplices, we propose an implicit piecewise linear approximation (IPLA) representation of the approximate UPRO and solve it using the Dfree method. Such IPLA technique prompts us to reformulate the approximate UPRO as a single mixed-integer program (MIP) and extend the tractability of the approximate UPRO to the multi-attribute case. Furthermore, we extend the model to the expected utility maximization problem with expected utility constraints where the worst-case utility functions in the objective and constraints are considered simultaneously. Finally, we report the numerical results about performances of the proposed models.Comment: 50 pages,18 figure

Effect of Gelsemium elegans

Gelsemium elegans (GE) is a kind of well-known toxic plant. It can be detoxified by Mussaenda pubescens (MP), but the detoxification mechanism is still unclear. Thus, a detoxification herbal formula (GM) comprising GE and MP was derived. The Caco-2 cells monolayer model was used to evaluate GM effects on transporting six kinds of indole alkaloids of GE. The bidirectional transport studies demonstrated that absorbance percentage of indole alkaloids in GE increased linearly over time. But in GM, Papp (AP→BL) values of the most toxic members, gelsenicine, humantenidine, and gelsevirine, were lower than that of Papp (BL→AP) (P<0.05). The prominent analgesic effect members, gelsemine and koumine, were approximately 1.00 in γ values. Nowhere was this increasing efflux more pronounced than in the case of indole alkaloids with N-O structure. In the presence of verapamil, the γ values of humantenidine, gelsenicine, gelsevirine, and humantenine were decreased by 43.69, 41.42, 36.00, and 8.90 percent, respectively. The γ values in presence of ciclosporin were homologous with a decrease of 42.32, 40.59, 34.00, and 15.07 percent. It suggested that the efflux transport was affected by transporters. Taken together, due to the efflux transporters participation, the increasing efflux of indole alkaloids from GM was found in Caco-2 cells

Formation of Highly Oxidized Radicals and Multifunctional Products from the Atmospheric Oxidation of Alkylbenzenes

Aromatic hydrocarbons contribute significantly to tropospheric ozone and secondary organic aerosols (SOA). Despite large efforts in elucidating the formation mechanism of aromatic-derived SOA, current models still substantially underestimate the SOA yields when comparing to field measurements. Here we present a new, up to now undiscovered pathway for the formation of highly oxidized products from the OH-initiated oxidation of alkyl benzenes based on theoretical and experimental investigations. We propose that unimolecular H-migration followed by O-2-addition, a so-called autoxidation step, can take place in bicyclic peroxy radicals (BPRs), which are important intermediates of the OH -initiated oxidation of aromatic compounds. These autoxidation steps lead to the formation of highly oxidized multifunctional compounds (HOMs), which are able to form SOA. Our theoretical calculations suggest that the intramolecular H-migration in BPRs of substituted benzenes could be fast enough to compete with bimolecular reactions with HO2 radicals or NO under atmospheric conditions. The theoretical findings are experimentally supported by flow tube studies using chemical ionization mass spectrometry to detect the highly oxidized peroxy radical intermediates and closed-shell products. This new unimolecular BPR route to form HOMs in the gas phase enhances our understanding of the aromatic oxidation mechanism, and contributes significantly to a better understanding of aromatic-derived SOA in urban areas.Peer reviewe

Sieve Estimation of Time-Varying Panel Data Models with Latent Structures

Published in Journal of Business and Economic Statistics. 37, (2), 334-349. https://doi.org/10.1080/07350015.2017.1340299</p

Sieve estimation of time-varying panel data models with latent structures

Ministry of Education, Singapore under its Academic Research Funding Tier