Hui Li

Previous studies have offered a rationale for engaging students in small-group student talk for the planning of L2 individual writing. To further investigate whether such talk effectively promotes the quality of argument in the context of Chinese tertiary EFL learners’ argumentative writing and whether such effects could be retained, the current study adopted a quasi-experimental design with a pretest, a posttest, and a delayed posttest in two intact EFL classes. The performance of the intervention group and the comparison group were scrutinized to examine the effects of the intervention. The analytic scores on six components of the writing task and the holistic writing scores cumulated of all these components were measured to see the immediate and sustained effects. Significant changes of the holistic scores in both the immediate posttest and the delayed posttest indicated that such small-group student talk enabled students in the treatment class to achieve better performance in the overall quality of argumentation compared with those in the comparison class. Statistical analyses revealed immediate and sustained effects of small-group student talk on the quality of counterargument claim, counterargument data, and rebuttal claim. Counterargument claim was the only element in which students in both classes made significant improvement, but the treatment class demonstrated a larger effect size. No discernible differences were found either between or within the treatment class and the comparison class with respect to the quality of claim, data, and rebuttal data across tests. Possible explanations concerning the findings and limitations of the study were discussed.

BackgroundThe Chinese version of the Mini-Mental State Examination and the Beijing version of the Montreal Cognitive Assessment are the most commonly used scales to screen for Alzheimer’s disease among Chinese patients; however, their consistency varies according to populations and languages. Equivalent conversion of MMSE-C and MoCA-BJ scores is important for meta-analysis.Materials and methodsMMSE-C and MoCA-BJ scoring were performed on the enrolled patients with AD. Consistency analysis of MMSE-C and MoCA-BJ scores of patients in the conversion groups was performed. The circle-arc method was used to convert the MMSE-C scores of the conversion groups into MoCA-BJ scores, and the conversion formula was generated. The MMSE-C data of the verification group was converted to MoCA-BJ according to the formula, and the consistency analysis of the original MoCA-BJ of the verification group and the converted MoCA-BJ was performed to verify the conversion model.ResultsThe results of the consistency analysis of MMSE-C and MoCA-BJ in group A showed that the correlation coefficients of the total group, high education years subgroup, medium education years subgroup, and low education years subgroup were 0.905, 0.874, 0.949, and 0.874, respectively, with high consistency and statistical significance. After applying the circle-arc method for equivalent conversion, the consistency analysis results of the original and the converted MoCA-BJ of the patients in group B of the total group, high education years subgroup, medium education years subgroup, and low education years subgroup were 0.891, 0.894, 0.781, 0.909, respectively, with high consistency and statistical significance.ConclusionWe established and validated a model of MMSE-C and MoCA-BJ score conversion for Chinese patients with AD using the circle-arc method. This model could be useful for multi-centers clinical trials and meta-analysis.

At present, there is a wide variety of tourism resources on the Internet. Tourism management departments must monitor these resources. At the same time, tourists must also retrieve personalized information that they are interested in. This requires a lot of time and energy. This essay studies and implements the tourism network resource monitoring system. The main work completed in the thesis proposes and constructs a topic collection algorithm and establishes a starting point, topic keywords, and a prediction mechanism. The algorithm includes three stages: the first climbing stage, the learning stage, and the continuous climbing stage. Open category directory search is used for similarity judgment and result evaluation. The experimental results show that with the continuous execution of the crawling process, the collection speed of related pages is getting faster and faster. We propose an algorithm for the extraction of wood based on the density of Internet tourism resources. The algorithm calculates the ratio of Internet tourism resource labels by row and uses a threshold extraction algorithm to distinguish area from private non-Internet tourism resource area. Experimental results show that the algorithm can successfully extract the main content of the article from a wide variety of web pages. This thesis takes the monitoring of tourism network resources as the research object and establishes a tourism network resource monitoring system, which can provide users with customizable, all-round, and real-time tourism network resource collection, extraction, and retrieval services so as to monitor tourism resources. The research results of this article can promote the construction of tourism informatization and can help users grasp the latest tourism information, thereby bringing great convenience to tourism. The system only downloads travel-related information through the use of topic collection technology, reducing the interference of irrelevant redundant web pages.

Given a target area and a location-aware social network, the location-aware influence maximization problem aims to find a set of seed users such that the information spread from these users will reach the most users within the target area. We show that the problem is NP-hard and present an approximate algorithm framework, namely, TarIM-SF, which leverages on a popular sampling method as well as spatial filtering model working on arbitrary polygons. Besides, for the large-scale network we also present a coarsening strategy to further improve the efficiency. We theoretically show that our approximate algorithm can provide a guarantee on the seed quality. Experimental study over three real-world social networks verified the seed quality of our framework, and the coarsening-based algorithm can provide superior efficiency.