Os efeitos do Game Design no processo de criação de jogos digitais utilizados no ensino de Química e Ciências: o que devemos considerar?

Pós-graduação em Educação para a Ciência - FC

Comparação do desenvolvimento neurológico entre os gêneros em alunos ingressantes no ensino fundamental em uma escola municipal em Bauru

Human beings' motor capacity development is essential, because it facilitates movement, enabling the performance of motor activities and others. In pre-school, boys and girls acquire the motor capacity development considered basic that, through several factors such as neurological maturation, which is in charge of providing more complete movements, and body growth, which has the goal of a better body domain, makes them improve individually the performance of motor abilities, but it can minimize them if laterality does not develop spontaneously. There by, this study focused on assessing the neurological development between genders of students engaging in fundamental school. Forty-three children of both genders at age six took part in this study. The evaluation occurred through the LEFÉVRE protocol (1976), which aims at assessing the maturity and development of the Nervous System through tests of the Evolutionary Neurological Exam (ENE), which comprises a series of tests involving specific tasks, regarding age in the static balance items, dynamic balance, appendicular coordination, trunk-member coordination, motor persistency, sensibility and synkinesis. Neurological development comparisons were performed between boys and girls for each item of the battery of tests. The results of the motor persistency and sensitiveness and sensorial activity tests were below the average for six-year-old children, but there was not any difference in gender comparison for each item of the battery of tests, only a small tendency for one of the genders, but meaningless, having a variance in only two tests of the synkinesis exam (Foot-hand and paper ball with the dominant hand), in which boys had a bigger percentage of synkinesis presence than girls did. The results also showed that the majority of the children assessed, both boys and girls have a right manual and pedal preference and the ocular laterality is approximately the same for left and right-handers. It was..

How to progress from discourse to practice? a new agenda for change in medical schools into the next decade

In the context of medical school instruction, the segmented approach of a focus on specialties and excessive use of technology seem to hamper the development of the professional-patient relationship and an understanding of the ethics of this relationship. The real world presents complexities that require multiple approaches. Engagement in the community where health competence is developed allows extending the usefulness of what is learned. Health services are spaces where the relationship between theory and practice in health care are real and where the social role of the university can be revealed. Yet some competencies are still lacking and may require an explicit agenda to enact. Ten topics are presented for focus here: environmental awareness, involvement of students in medical school, social networks, interprofessional learning, new technologies for the management of care, virtual reality, working with errors, training in management for results, concept of leadership, and internationalization of schools. Potential barriers to this agenda are an underinvestment in ambulatory care infrastructure and community-based health care facilities, as well as in information technology offered at these facilities; an inflexible departmental culture; and an environment centered on a discipline-based medical curriculum.

Petri nets and autonomous systems: A case study

This work presents the Petri net-based modeling of an autonomous robot's navigation system used for the application of supplies in agriculture. The model was developed theoretically and implemented through the CPNTools software. It simulates the behavior of the robot, capturing environmental characteristics by means of sensors, making appropriate decisions, and forwarding them to the corresponding actuators. By exciting the model using CPNTools it is possible to simulate situations that the robot might undergo, without the need to expose it to real potentially dangerous situations. ©2009 IEEE.

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.

A new implementation of population based incremental learning method for optimizations in electromagnetics

To enhance the global search ability of population based incremental learning (PBIL) methods, it is proposed that multiple probability vectors are to be included on available PBIL algorithms. The strategy for updating those probability vectors and the negative learning and mutation operators are thus re-defined correspondingly. Moreover, to strike the best tradeoff between exploration and exploitation searches, an adaptive updating strategy for the learning rate is designed. Numerical examples are reported to demonstrate the pros and cons of the newly implemented algorithm.

A new implementation of Population Based Incremental Learning method for optimization studies in electromagnetics

To enhance the global search ability of Population Based Incremental Learning (PBIL) methods, It Is proposed that multiple probability vectors are to be Included on available PBIL algorithms. As a result, the strategy for updating those probability vectors and the negative learning and mutation operators are redefined as reported. Numerical examples are reported to demonstrate the pros and cons of the newly Implemented algorithm. ©2006 IEEE.

A video-based biometric authentication for e-learning web applications

In the last years there was an exponential growth in the offering of Web-enabled distance courses and in the number of enrolments in corporate and higher education using this modality. However, the lack of efficient mechanisms that assures user authentication in this sort of environment, in the system login as well as throughout his session, has been pointed out as a serious deficiency. Some studies have been led about possible biometric applications for web authentication. However, password based authentication still prevails. With the popularization of biometric enabled devices and resultant fall of prices for the collection of biometric traits, biometrics is reconsidered as a secure remote authentication form for web applications. In this work, the face recognition accuracy, captured on-line by a webcam in Internet environment, is investigated, simulating the natural interaction of a person in the context of a distance course environment. Partial results show that this technique can be successfully applied to confirm the presence of users throughout the course attendance in an educational distance course. An efficient client/server architecture is also proposed. © 2009 Springer Berlin Heidelberg.

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.

Teacher-learning process based on an immersive and interactive environment

This paper explores the benefits of using immersive and interactive multiprojection environments (CAVE) to visualize molecules, and how it improves users’ understanding. We have proposed and implemented a tool for teachers to manipulate molecules and another to edit molecules and assist students at home. The contribution of the present research project are these tool that allows investigating structures, properties and dynamics of a molecular system which are extremely complex and comprises millions of atoms. The experience is enriched through multimedia information associated with parts of the model; for example, videos and text can be linked to specific molecule, demonstrating some detail. This solution is based on a teaching-learning process.