Mediação do processo ensino-aprendizagem com o jogo de empresas mercado virtual: uma pesquisa de opinião

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aquatic weed automatic classification using machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Time-place learning in food-restricted Nile tilapia

Time-place learning based on food association was investigated in eight food-restricted Nile tilapias. Each fish was individually housed for 10 days in an experimental tank for adjustments to laboratory conditions, and fed daily in excess. Feeding was then interrupted for 17 days. Training was then started, based on a food-restricted regime in a tank divided into three interconnected compartments. Daily food was offered in one compartment (left or right side) of the tank in the morning and on the opposite side in the afternoon, for a continuous 30-day period. Frequency of choices on the right side was measured on days 10, 20 and 30 (during these test days, fish were not fed). Following this 30-day conditioning period, the Nile tilapias were able to switch sides at the correct period of the day to get food, suggesting that food restriction facilitates time-place learning discrimination. (C) 2007 Elsevier B.V. All rights reserved.

Time-place learning in individually reared angelfish, but not in pearl cichlid

Time-place learning based on food association was investigated in the cichlids angelfish (Pterophyllum scalare) and pearl cichlid (Geophagus brasiliensis) reared in isolation, therefore eliminating social influence on foraging. During a 30-day period, food was placed in one side of the aquarium (containing three compartments) in the morning and in the opposite side in the afternoon. Learning was inferred by the number of correct side choices of all fish in each day of test (15th and 30th). During the test day fish were not fed. The angelfish learned to switch sides at the correct day period in order to get food, suggesting this species has time-place learning ability when individually reared. on the other hand, the same was not observed for pearl cichlid. (c) 2006 Elsevier B.V. All rights reserved.

High frequencies of plexiform neurofibromas, mental retardation, learning difficulties, and scoliosis in Brazilian patients with neurofibromatosis type 1

A clinical study of Brazilian patients with neurofibromatosis type 1 (NF1) was performed in a multidisciplinary Neurofibromatosis Program called CEPAN (Center of Research and Service in Neurofibromatosis). Among 55 patients (60% females, 40% males) who met the NIH criteria for the diagnosis of NF1, 98% had more than six café-au-lait patches, 94.5% had axillary freckling, 45% had inguinal freckling, and 87.5% had Lisch nodules. Cutaneous neurofibromas were observed in 96%, and 40% presented plexiform neurofibromas. A positive family history of NF1 was found in 60%, and mental retardation occurred in 35%. Some degree of scoliosis was noted in 49%, 51% had macrocephaly, 40% had short stature, 76% had learning difficulties, and 2% had optic gliomas. Unexpectedly high frequencies of plexiform neurofibromas, mental retardation, learning difficulties, and scoliosis were observed, probably reflecting the detailed clinical analysis methods adopted by the Neurofibromatosis Program. These same patients were screened for mutations in the GAP-related domain/GRD (exons 20-27a) by single-strand conformation polymorphism. Four different mutations (Q1189X, 3525-3526delAA, E1356G, c.4111-1G>A) and four polymorphisms (c.3315-27G>A, V1146I, V1317A, c.4514+11C>G) were identified. These data were recently published.

Power-law distribution in a learning process: competition, learning and natural selection

In the present work, we propose a model for the statistical distribution of people versus number of steps acquired by them in a learning process, based on competition, learning and natural selection. We consider that learning ability is normally distributed. We found that the number of people versus step acquired by them in a learning process is given through a power law. As competition, learning and selection is also at the core of all economical and social systems, we consider that power-law scaling is a quantitative description of this process in social systems. This gives an alternative thinking in holistic properties of complex systems. (C) 2004 Elsevier B.V. All rights reserved.

Auditory processing disorder in patients with language-learning impairment and correlation with malformation of cortical development

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Proposal of convergence of e-Learning systems for t-Learning

This article presents considerations about viability on reutilize existing web based e-Learning systems on Interactive Digital TV environment according to Digital TV standard adopted in Brazil. Considering the popularity of Moodle system in academic and corporative area, such system was chosen as a foundation for a survey into its properties to create a specification of an Application Programming Interface (API) for convergence to t-Learning characteristics that demands efforts in interface design area due the fact that computer and TV concepts are totally different. This work aims to present studies concerning user interface design during two stages: survey and detail of functionalities from an e-Learning system and how to adapt them for the Interactive TV regarding usability context and Information Architecture concepts.

Influence of the ICDAS e-learning program for occlusal caries detection on dental students

The aim of this study was to evaluate the influence of ICDAS training in a group of dental students for occlusal caries detection in permanent teeth. Premolars and molars (N=104) with occlusal surfaces varying from ICDAS scores 0 to 6 were cleaned, one occlusal site per tooth was selected, and a photograph taken to identify the site. Eight senior dental students examined the teeth twice with a one-week interval between examinations during each of two phases: before and after the ICDAS e-learning program. Teeth were histologically assessed for caries extension. Intraclass correlation coefficients for intra- and interexaminer repeatability were high, both before (0.75 and 0.72, respectively) and after e-learning (0.82 and 0.78, respectively). The ICDAS scores decreased significantly from before to after e-learning (p=0.0001). Correlation between ICDAS scores and histology scores was moderate (0.57 before e-learning and 0.61 after). Although the ROC curve shows an improvement in the use of the ICDAS scoring after e-learning, the difference was not significant (p=0.10). Specificity of the ICDAS scores significantly improved after e-learning (77 percent vs. 36 percent), and sensitivity was reduced slightly after e-learning (87 percent vs. 92 percent). The ICDAS e-learning program improved the performance of the diagnostic skills of the investigated students for the detection of occlusal caries lesions.

On-line collaborative learning for in-service teacher education

This article brings some of the results of a study that analyzes a hybrid course for in-service teachers in the Project Teletandem Brazil: foreign languages for all. In this project, Brazilian teachers of Spanish as a foreign language took part in a blended tandem learning course, communicating via videoconferencing with Uruguayan teachers of Portuguese as a foreign language. The aim of the study was to verify Brazilian teachers' concepts and beliefs concerning language and culture and how the teletandem interactions affected them. After the interactions, teachers' views of culture seemed to also incorporate aspects of culture as an interpersonal process, instead of the factual and static view which was previously predominant. Therefore teacher education programs must consider the possibility of conjugating theory and reflective practice through the use of videoconference tools in order to allow teachers to experience culture rather learn facts about it. © 2011 ACADEMY PUBLISHER.

Gestational protein restriction induces CA3 dendritic atrophy in dorsal hippocampal neurons but does not alter learning and memory performance in adult offspring

Studies have demonstrated that nutrient deficiency during pregnancy or in early postnatal life results in structural abnormalities in the offspring hippocampus and in cognitive impairment. In an attempt to analyze whether gestational protein restriction might induce learning and memory impairments associated with structural changes in the hippocampus, we carried out a detailed morphometric analysis of the hippocampus of male adult rats together with the behavioral characterization of these animals in the Morris water maze (MWM). Our results demonstrate that gestational protein restriction leads to a decrease in total basal dendritic length and in the number of intersections of CA3 pyramidal neurons whereas the cytoarchitecture of CA1 and dentate gyrus remained unchanged. Despite presenting significant structural rearrangements, we did not observe impairments in the MWM test. Considering the clear dissociation between the behavioral profile and the hippocampus neuronal changes, the functional significance of dendritic remodeling in fetal processing remains undisclosed. © 2012 ISDN.

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks. © 2013 Elsevier B.V. All rights reserved.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

Unsupervised manifold learning using Reciprocal kNN Graphs in image re-ranking and rank aggregation tasks

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)