Fir (Abies Mill.) forests of Mexico are relicts of the boreal forests that advanced southwards during glaciation periods. Mexico is a center of diversification of the Abies genus, as there are eight species in its territory, six of which are endemic. The forests of Abies religiosa (Kunth) Schltdl. & Cham. near Mexico City are subject to a process of deterioration. We analyzed the fragmentation dynamics of the A. religiosa forest in the northern region of the Sierra Nevada, Mexico. Land cover change detection was done by means of high-resolution images acquired by the SPOT satellite in 2005, 2010, 2015, and 2018. Habitat fragmentation was observed, with a decrease in the size of dense Abies masses. The area covered by Abies decreased by 22.9%. The area occupied by forest openings increased 3% from 2005 to 2010 and then decreased by 1.8% and 1.6% in the following periods. The land patch type Other Forest Cover increased in both frequency and size, with the area increasing by 23.3%, which warns of a change process towards this patch type. The formation of increasingly smaller and isolated remnants of A. religiosa forest in the Sierra Nevada can lead to the loss of this vegetation relict and its replacement by other types of cover in the short term.
This paper analyses the capabilities of different techniques to build a semantic representation of educational digital resources. Educational digital resources are modeled using the Learning Object Metadata (LOM) standard, and these semantic representations can be obtained from different LOM fields, like the title, description, among others, in order to extract the features/characteristics from the digital resources. The feature extraction methods used in this paper are the Best Matching 25 (BM25), the Latent Semantic Analysis (LSA), Doc2Vec, and the Latent Dirichlet allocation (LDA). The utilization of the features/descriptors generated by them are tested in three types of educational digital resources (scientific publications, learning objects, patents), a paraphrase corpus and two use cases: in an information retrieval context and in an educational recommendation system. For this analysis are used unsupervised metrics to determine the feature quality proposed by each one, which are two similarity functions and the entropy. In addition, the paper presents tests of the techniques for the classification of paraphrases. The experiments show that according to the type of content and metric, the performance of the feature extraction methods is very different; in some cases are better than the others, and in other cases is the inverse.
This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.
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