The movement of the trunk and breast during front crawl and breaststroke swimming

Breast displacement has been investigated in various activities to inform bra design, with the goal of minimising movement; however, breast motion during swimming has yet to be considered. The aim was to investigate trunk and breast kinematics whilst wearing varying levels of breast support during two swimming strokes. Six larger-breasted females swam front crawl and breaststroke (in a swimming flume), in three breast support conditions while three video cameras recorded the motion of the trunk and right breast. Trunk and relative breast kinematics were calculated. Greater breast displacement occurred mediolaterally in the swimsuit condition (7.8, s = 1.5 cm) during front crawl and superioinferiorly in the bare-breasted condition (3.7, s = 1.6 cm) during breaststroke, with the sports bra significantly reducing breast displacements. During front crawl, the greatest trunk roll occurred in the sports bra condition (43.1, s = 8.3°) and during breaststroke greater trunk extension occurred in the swimsuit condition (55.4, s = 5.0°); however, no differences were found in trunk kinematics between the three breast support conditions. Results suggest that the swimsuit was ineffective as a means of additional support for larger-breasted women during swimming; incorporating design features of sports bras into swimsuits may improve the breast support provided.     

Outlier detection by means of robust regression estimators for use in engineering science

This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates （MSR） were computed and used as comparison criteria. The results showed that the least median of squares （LMS） and least trimmed squares （LTS） were the most successful methods for data that included leverage points, masking and swamping effects or critical and concentrated outliers. We recommend using LMS and LTS as diagnostic tools to classify outliers, because they remain robust even when applied to models that are heavily contaminated or that have a complicated structure of outliers.     

Applying Neuroscientific Findings to Education: The Good, the Tough, and the Hopeful

ABSTRACT— Advances in neuroscience during the past century have yielded important insights into mental functioning, but their implications for the field of education have remained largely unexplored. In a bold attempt to bridge this gap, Immordino-Yang presents findings from 2 boys, Nico and Brooke, each of whom lost half of his brain. The remarkable recovery of functions in the 2 boys highlights the degree to which children's emotional and social experiences shape brain development, as well as the importance of plasticity. Immordino-Yang places emphasis on cognitive plasticity—the ability to use different strategies in solving a task—which is clearly evident in the boys' performance. It is possible, however, that neural plasticity may have occurred as well, either prior to or after surgery. Although it may not be possible to distinguish between cognitive and neural plasticity at this point, Immordino-Yang makes a crucial contribution. By placing these findings in an educational context and presenting their implications in a clear and compelling fashion, she successfully brings neuroscience and education a notch closer.     

Development at the Youth Level of a Conception of the Causes of Behavior and the Effectiveness of a Learning Program in This Area

Differential effects of variety versus repetition in the development of categorization skills were studied with fifty-four 5-year-old Head Start children, using a sequence of programmed booklets to teach a social studies unit on workers.

It was hypothesized that children receiving Intermediate Variety (two categories, twelve instances, presented once) would demonstrate superior performance on transfer to new instances of the trained categories when compared to those trained with Low Variety (two categories, six instances, presented twice). Secondary hypotheses were that children trained with Low Variety would demonstrate superior performance on a mastery test when compared to children receiving Intermediate Variety, and that High Variety (four categories, six instances) would be superior to both Low and Intermediate Variety in transfer to new categories.

By analysis of covariance, scores of the Intermediate Variety treatment were significantly higher (p<.05) on Near Transfer, supporting the hypothesis that children receiving an Intermediate Variety will demonstrate superior performance on the transfer to new instances when compared with those trained on Low Variety. The secondary hypotheses could be neither supported nor rejected on the basis of the data.     

Mathematical Communication in the Classroom: A Teacher Makes a Difference

The National Council of Teachers of Mathematics (NCTM) states that “Communication is an essential part of mathematics and mathematics education” (2000, p. 60). In fact, communication is one of the five process standards emphasized by NCTM. The communication standard highlights the importance of young children communicating their mathematical thinking coherently to peers and teachers. This standard also states that young children should use math language to express mathematical ideas (Baroody, 2000; Ginsburg, Inoue, & Seo, 1999; NCTM, 2000; Rubenstein & Thompson, 2002; Whitin & Whitin, 2003). Teachers must create a link between mathematics and language (Rubenstein & Thompson, 2002; Stigler & Hiebert, 2004). This article focuses on the informal strategies used by Melissa (a kindergarten teacher) that promoted the use of math language. The strategies were identified during a 3-month observational period in her classroom at Clinton Elementary (pseudonym). Clinton Elementary is located in a low-income neighborhood of a southern city that has a population of approximately 450,000. The neighborhood population is predominantly African-American (Davis, 1994).     

Online multiplayer games for the social interactions of children with autism spectrum disorder: a resource for inclusive education

ABSTRACT

This article describes the support for social interactions received by three students with autism spectrum disorders (ASD) through their multimodal engagements with Minecraft®. The data were collected through at-screen observations and semi-structured interviews. Multimodal analysis of the data demonstrated that online multiplayer games supported social interactions through modes, such as speech, writing and gesture, and within physical and virtual spaces. The analysis revealed that online multiplayer games provided platforms for the students to use speech to engage in reciprocal conversations, to share information, to make requests, to give commands and to direct others. Additionally, screen-based written texts were used to attract the attention of others, send messages, communicate rules and maintain engagements with others within the students’ physical and virtual worlds. Furthermore, the findings showed that online multiplayer games supported the students’ uses, interpretations and mirroring of gestures for social interactions. The findings have implications for providing opportunities to support social interactions in multimodal ways that social spaces in face-to-face and offline contexts do not allow. The findings offer implications for targeting the students’ interests in online multiplayer games to support their capacity to initiate and sustain social interactions in inclusive educational settings.     

Long-Term Usage of a Consortial PDA Collection: If They Choose It,Will We Use It?*

Abstract

This paper examines the materials acquired during a consortial PDA pilot; which of the books are still being used, by which institutions, whether the size of a participating institution or the subject of a book have impact on the continued use of the books, and if there is a relationship between the schools that triggered a PDA purchase and the schools that have gone on to use a given title. Findings suggest that models involving some library mediation may be more effective in meeting the various needs of participating institutions.     

Analysis of named entity recognition and linking for tweets

Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area. Unlike carefully authored news text and other longer content, tweets pose a number of new challenges, due to their short, noisy, context-dependent, and dynamic nature. Information extraction from tweets is typically performed in a pipeline, comprising consecutive stages of language identification, tokenisation, part-of-speech tagging, named entity recognition and entity disambiguation (e.g. with respect to DBpedia). In this work, we describe a new Twitter entity disambiguation dataset, and conduct an empirical analysis of named entity recognition and disambiguation, investigating how robust a number of state-of-the-art systems are on such noisy texts, what the main sources of error are, and which problems should be further investigated to improve the state of the art.     

Discourse-aware rumour stance classification in social media using sequential classifiers

Rumour stance classification, defined as classifying the stance of specific social media posts into one of supporting, denying, querying or commenting on an earlier post, is becoming of increasing interest to researchers. While most previous work has focused on using individual tweets as classifier inputs, here we report on the performance of sequential classifiers that exploit the discourse features inherent in social media interactions or ‘conversational threads’. Testing the effectiveness of four sequential classifiers – Hawkes Processes, Linear-Chain Conditional Random Fields (Linear CRF), Tree-Structured Conditional Random Fields (Tree CRF) and Long Short Term Memory networks (LSTM) – on eight datasets associated with breaking news stories, and looking at different types of local and contextual features, our work sheds new light on the development of accurate stance classifiers. We show that sequential classifiers that exploit the use of discourse properties in social media conversations while using only local features, outperform non-sequential classifiers. Furthermore, we show that LSTM using a reduced set of features can outperform the other sequential classifiers; this performance is consistent across datasets and across types of stances. To conclude, our work also analyses the different features under study, identifying those that best help characterise and distinguish between stances, such as supporting tweets being more likely to be accompanied by evidence than denying tweets. We also set forth a number of directions for future research.     

A new approach to collection bias in academic libraries: The extent of corporate control in journal holdings

Questions of collection bias in library journal collections usually turn on a left-right political contrast. This article presents another approach that distinguishes between corporate and non-corporate controlled publications. Using this method, the catalog holdings of all Canadian academic libraries were searched to determine the penetration of socio-political titles of mainstream “corporate publishers” and those of “small publishers.” Canadian academic libraries privilege, by a wide margin, mainstream titles published by corporate entities, although some libraries have impressive “small publisher” collections. This article also analyzed the availability of mainstream and small publisher journals provided by ten popular electronic vendors. These vendors provide very low access to non-corporate “small publisher” titles while covering 100% of surveyed mainstream titles. The implications of such provision rates are examined from the perspective of the scholarly browsing process and the role of the university.