National Experimental University of Guayana, Puerto Ordaz

www.uneg.edu.ve
Ciudad Guayana, Venezuela
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Guevara C.,National Experimental University of Guayana, Puerto Ordaz | Aguilar J.,Technical University of Loja
Proceedings of the 2016 42nd Latin American Computing Conference, CLEI 2016 | Year: 2016

This article presents a Model of an Adaptive Learning Object (MOAA) for virtual environments from a definition of Adaptive Learning Object, and a proposition of extension of the LOM standard to specify the adaptation metadata. The MASINA methodology and the UML diagrams are used to describe it. The model specifies modularly and independently two categories of rules, of adaptation and conversion, giving it versatility and flexibility to perform different types of adaptation to the learning objects, incorporating or removing rules in each category. © 2016 IEEE.


Mohali S.R.,University of Los Andes, Venezuela | Castro-Medina F.,National Experimental University of Guayana, Puerto Ordaz | Urbez-Torres J.R.,Agriculture and Agri Food Canada | Gubler W.D.,University of California at Davis
Forest Pathology | Year: 2017

Although several Botryosphaeriaceae species have been relatively well-studied on economically important crops and forest plantations, little is known regarding their presence and ecology on native tree species in the natural tropical forests in South America. Therefore, the aim of this study was to determine the fungi associated with blue stain symptoms of the wood on Ficus insipida in lumber yards from the Imataca natural forest in Eastern Venezuela using morphological descriptions and DNA sequences of the internal transcribed spacer region (ITS1-5.8S-ITS2) and part of the translation elongation factor 1-α (TEF) gene. Results of this study showed the botryosphaeriaceous taxa Lasiodiplodia theobromae and L. venezuelensis to be the main fungi associated with blue stain symptoms. This study represents the first report of these fungi on F. insipida lumber in the Natural Forest of Venezuela. © 2017 Blackwell Verlag GmbH.


Mosquera D.,National Experimental University of Guayana, Puerto Ordaz | Aguilar J.,University of Los Andes, Venezuela | Aguilar J.,National Polytechnic School of Ecuador
Proceedings - International Conference of the Chilean Computer Science Society, SCCC | Year: 2017

This article presents a Generic Autonomic Model based on Multi-Agent Systems, to characterize the reflection capabilities of ARMAGAeco-c, which is an autonomic reflective architecture for the management of eco-connectivist learning environments. In the eco-Connectivism is planed the configuration, the stabilization and the unification of an ecology of knowledge, composed of emerging clusters of personal learning environments. For this, it uses association rules, which are dynamically adapted for the analysis of the connections that occur between apprentices, in a socialized context of learning. The multi-agent system provides an intelligent model of dynamic configuration of roles, with the objective of providing configuration, stabilization and unification of the ecology of eco-connectivist knowledge, using concepts inspired from the ecology, the data mining and the collaborative filtering. Adaptive capacities of personal learning environments are provided by recommendation agents, which reason about emerging clusters based on a model of ecological survival. This enables to characterize the ecological unification of knowledge between individuals. © 2016 IEEE.


PubMed | Des Moines University, National University of Costa Rica, National University of Colombia, Brock University and 328 more.
Type: Journal Article | Journal: Ecology and evolution | Year: 2017

The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.

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