An Interactive Model to Teach Motor Skills

There has been a tremendous increase in our knowledge of hum motor performance over the last few decades. Our theoretical understanding of how an individual learns to move is sophisticated and complex. It is difficult however to relate much of this information in practical terms to physical educators, coaches, and therapists concerned with the learning of motor skills (Shumway-Cook & Woolcott, 1995). Much of our knowledge stems from lab testing which often appears to bear little relation to real-life situations. This lack of ecological validity has slowed the flow of information from the theorists and researchers to the practitioners. This paper is concerned with taking some small aspects of motor learning theory, unifying them, and presenting them in a usable fashion. The intention is not to present a recipe for teaching motor skills, but to present a framework from which solutions can be found. If motor performance research has taught us anything, it is that every individual and situation presents unique challenges. By increasing our ability to conceptualize the learning situation we should be able to develop more flexible and adaptive responses to the challege of teaching motor skills. The model presented here allows a teacher, coach, or therapist to use readily available observations and known characteristics about a motor task and to conceptualize them in a manner which allows them to make appropriate teaching/learning decisions.

Selfmapping : o mapa colaborativo como medium de visualização espácio-temporal

THE MAP AS A COLLABORATIVE MEDIUM FOR SPATIO-TEMPORAL VISUALIZATION This dissertation focuses on the relationship between maps and spatio-temporal data visualization. It is divided into two components: theoretical framework and practical approach. The study begins by questioning the role of the map in today’s digital society and particularly its role in visualization, and finishes with the conceptualization and development of an interactive dot map that visualizes data from Instagram and Twitter. Nowadays, geographic information is no longer produced just by experts, but also by ordinary people that are able to participate in data creation and exchange. The Web 2.0 lies in the heart of this change, where social media represent a significant tool for producing geotagged content, allowing its users to share their location and to spatially reference their publications. Furthermore, amateur mapmaking and neogeography have benefited from the emergence of several new devices that enable the creation of digital maps that are interactive, adaptable and easily shared on the Web. This study adopts a descriptive approach calling upon the diverse aspects of the map and its evolution as a medium for visualizing geotagged data, highlighting collaborative mapping as an emerging subject area that is of mandatory future research. Relevant projects are also analyzed in order to identify trends and different approaches for visualizing social media data in its spatial context, intended to support the project’s conceptualization, development and evaluation. The created map demonstrates how spatial knowledge and perception of place are now redefined by the contributions of individuals; it also shows how that activity produces new sources of geographic information, forcing the development of new techniques and approaches that allow an adequate exploration of content and visualization methods of the contemporary map

JAABA: interactive machine learning for automatic annotation of animal behavior

We present a machine learning-based system for automatically computing interpretable, quantitative measures of animal behavior. Through our interactive system, users encode their intuition about behavior by annotating a small set of video frames. These manual labels are converted into classifiers that can automatically annotate behaviors in screen-scale data sets. Our general-purpose system can create a variety of accurate individual and social behavior classifiers for different organisms, including mice and adult and larval Drosophila.

Aplicações baseadas em geolocalizações para gestão de instalações desportivas: exemplo de utilização da API da GOOGLE Maps no concelho de Guimarães

O contexto tecnológico em que vivemos é uma realidade. E a tendência é para ser assim também no futuro. Cada vez mais. É o caso das representações de locais e entidades em mapas digitais na web. Na visão de Crocker (2014), esta tendência é ainda mais acentuada, no âmbito das aplicações móveis, como mostram as mais diversas location-based applications. No setor do desporto e da respetiva gestão nem sempre foi fácil desenvolver aplicações, recorrendo a este tipo de representações espaciais. A tecnologia não era fácil e o know-how não era adequadamente qualificado. Mas, as empresas fornecedoras de tecnologia geoespacial simplificaram o desenvolvimento de aplicações web nesta área, através da utilização de application programming interfaces (API). Como refere Svennerberg (2010), estas API’s servem de interface entre um serviço proporcionado por uma empresa, caso da Google Maps (2013) e uma aplicação web ou móvel que utiliza esses serviços. Foi com este objetivo que desenvolvemos uma aplicação web, utilizando as metodologias próprias neste domínio, como a framework de Zachman (2009), tal como foi originalmente adaptada por Whitten e Bentley (2005), onde um dos módulos é precisamente a representação de espaços desportivos, recorrendo à utilização dos serviços da Google Maps. Para além disso, toda a aplicação é suportada numa abordagem Model-View-Control (MVC). Para conseguir representar as instalações desportivas num mapa, criámos uma base de dados MySQL, com dados de longitude e latitude, de cada instalação desportiva. Através de JavaScript criou-se o mapa propriamente dito, indicando o tipo (mapa de estradas, satélite ou street view) e as respetivas opções (nível de zoom, alinhamento, controlo de interface e posicionamente, entre muitas outras opções). O passo seguinte consistiu em passar os dados para o frontend da aplicação web. Para isso, recorreu-se à integração do PHP com as livrarias externas de código JavaSrcipt, criadas especificamente para o efeito (caso da MarkerManager). A implementação destas funcionalidades permite georeferenciar todos os tipos e géneros de espaços desportivos de um concelho, região ou País. Obteve-se ainda know-how, background e massa crítica, para o desenvolvimento de novas funcionalidades. A sua utilização em dispositivos móveis é outra das possibilidades atualmente já em desenvolvimento.

Maps (pdf) and raster images (tif) of predicted rural land cover suitability under current (2010) conditions and future climate scenarios

This study projects land cover probabilities under climate change for corn (maize), soybeans, spring and winter wheat, winter wheat-soybean double cropping, cotton, grassland and forest across 16 central U.S. states at a high spatial resolution, while also taking into account the influence of soil characteristics and topography. The scenarios span three oceanic-atmospheric global circulation models, three Representative Concentration Pathways, and three time periods (2040, 2070, 2100). As climate change intensifies, the suitable area for all six crops display large northward shifts. Total suitable area for spring wheat, followed by corn and soybeans, diminish. Suitable area for winter wheat and for winter wheat-soybean double-cropping expand northward, while cotton suitability migrates to new, more northerly, locations. Suitability for forest intensifies in the south while yielding to crops in the north; grassland intensifies in the western Great Plains as crop suitability diminishes. To maintain current broad geographic patterns of land use, large changes in the thermal response of crops such as corn would be required. A transition from corn-soybean to winter wheat-soybean doubling cropping is an alternative adaptation.

Ensemble prediction distribution maps of macroalgae for current conditions and four climate change scenarios and high resultion bathymetry for Potter Cove, WAP, Antarctica

Species distribution models (SDM) predict species occurrence based on statistical relationships with environmental conditions. The R-package biomod2 which includes 10 different SDM techniques and 10 different evaluation methods was used in this study. Macroalgae are the main biomass producers in Potter Cove, King George Island (Isla 25 de Mayo), Antarctica, and they are sensitive to climate change factors such as suspended particulate matter (SPM). Macroalgae presence and absence data were used to test SDMs suitability and, simultaneously, to assess the environmental response of macroalgae as well as to model four scenarios of distribution shifts by varying SPM conditions due to climate change. According to the averaged evaluation scores of Relative Operating Characteristics (ROC) and True scale statistics (TSS) by models, those methods based on a multitude of decision trees such as Random Forest and Classification Tree Analysis, reached the highest predictive power followed by generalized boosted models (GBM) and maximum-entropy approaches (Maxent). The final ensemble model used 135 of 200 calculated models (TSS > 0.7) and identified hard substrate and SPM as the most influencing parameters followed by distance to glacier, total organic carbon (TOC), bathymetry and slope. The climate change scenarios show an invasive reaction of the macroalgae in case of less SPM and a retreat of the macroalgae in case of higher assumed SPM values.

Census of Queensland, 1886. Report by the Registrar-General, with summary and detailed tables, illustrative maps, diagrams, and appendices.

[Vol. 1]: Report, tables, diagrams, appendices.--[Vol. 2]: Maps.