471 resultados para posts


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Shipping list no.: 92-501-P.

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Superseded by its Report: Union Government (Posts and Telegraphs)

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This study investigates the degree to which textual complexity indices applied on students’ online contributions, corroborated with a longitudinal analysis performed on their weekly posts, predict academic performance. The source of student writing consists of blog and microblog posts, created in the context of a project-based learning scenario run on our eMUSE platform. Data is collected from six student cohorts, from six consecutive installments of the Web Applications Design course, comprising of 343 students. A significant model was obtained by relying on the textual complexity and longitudinal analysis indices, applied on the English contributions of 148 students that were actively involved in the undertaken projects.

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Social media tools are increasingly popular in Computer Supported Collaborative Learning and the analysis of students' contributions on these tools is an emerging research direction. Previous studies have mainly focused on examining quantitative behavior indicators on social media tools. In contrast, the approach proposed in this paper relies on the actual content analysis of each student's contributions in a learning environment. More specifically, in this study, textual complexity analysis is applied to investigate how student's writing style on social media tools can be used to predict their academic performance and their learning style. Multiple textual complexity indices are used for analyzing the blog and microblog posts of 27 students engaged in a project-based learning activity. The preliminary results of this pilot study are encouraging, with several indexes predictive of student grades and/or learning styles.

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Poster presented at the “From Basic Sciences to Clinical Research” - First International Congress of CiiEM. Caparica, Portugal, 27-28 November 2015

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Conventional topic models are ineffective for topic extraction from microblog messages since the lack of structure and context among the posts renders poor message-level word co-occurrence patterns. In this work, we organize microblog posts as conversation trees based on reposting and replying relations, which enrich context information to alleviate data sparseness. Our model generates words according to topic dependencies derived from the conversation structures. In specific, we differentiate messages as leader messages, which initiate key aspects of previously focused topics or shift the focus to different topics, and follower messages that do not introduce any new information but simply echo topics from the messages that they repost or reply. Our model captures the different extents that leader and follower messages may contain the key topical words, thus further enhances the quality of the induced topics. The results of thorough experiments demonstrate the effectiveness of our proposed model.

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This study investigated the effects of the cement type and the water storage time on the push-out bond strength of a glass fiber post. Glass fiber posts (Fibrekor, Jeneric Pentron) were luted to post spaces using a self-cured resin cement (C&B Cement [CB]), a glass ionomer cement (Ketac Cem [KC]) or a resin-modified glass ionomer cement (GC FujiCEM [FC]) according to the manufacturers’ instructions. For each luting agent, the specimens were exposed to one of the following water storage times (n=5): 1 day (T1), 7 days (T7), 90 days (T90) and 180 days (T180). Push-out tests were performed after the storage times. Control specimens were not exposed to water storage, but subjected to the push-out test 10 min after post cementation. Data (in MPa) were analyzed by Kruskal-Wallis and Dunn`s test (α=0.05). Cement type and water storage time had a significant effect (p<0.05) on the push-out bond strength. CB showed significantly higher values of retention (p<0.05) than KC and FC, irrespective of the water storage time. Water storage increased significantly the push-out bond strength in T7 and T90, regardless of the cement type (p<0.05). The results showed that fiber posts luted to post spaces with the self-cured resin cement exhibited the best bonding performance throughout the 180-day water storage period. All cements exhibited a tendency to increase the bond strength after 7 and 90 days of water storage, decreasing thereafter.

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The aim of this study was to investigate the fracture strength of endodontically treated teeth restored with different posts and variable ferrule heights. Sixty freshly extracted human canines were treated endodontically and randomly assigned to 6 groups (n=10), being restored with custom-made cast post-and-core (CP0 and CP3 groups), prefabricated post and composite resin core (PF0 and PF3 groups), and composite resin (CR0 and CR3 groups). The CP0, PF0 and CR0 groups presented no ferrule and the CP3, PF3 and CR3 presented 3 mm of coronal structure. All teeth were restored with full metal crowns. The fracture strength was measured in a universal testing machine at 45o to the long axis of the tooth until failure. Data were analyzed statistically by 2-way ANOVA and Tukey's test (?=0.05). When the mean fracture strength values were compared (CP0 group - 820.20 N, CP3 group - 1179.12 N; PF0 group - 561.05 N; PF3 group - 906.79 N; CR0 group - 297.84 N; and CR3 group - 1135.15 N) there was statistically significant among the groups (p<0.05), except for the three groups with 3 mm of coronal remaining, which were similar to each other. The results of this study showed that the ferrule in crowns promoted significantly higher fracture strength in the endodontically treated teeth.