Approaching literature review for academic purposes: The Literature Review Checklist

A sophisticated literature review (LR) can result in a robust dissertation/thesis by scrutinizing the main problem examined by the academic study; anticipating research hypotheses, methods and results; and maintaining the interest of the audience in how the dissertation/thesis will provide solutions for the current gaps in a particular field. Unfortunately, little guidance is available on elaborating LRs, and writing an LR chapter is not a linear process. An LR translates students’ abilities in information literacy, the language domain, and critical writing. Students in postgraduate programs should be systematically trained in these skills. Therefore, this paper discusses the purposes of LRs in dissertations and theses. Second, the paper considers five steps for developing a review: defining the main topic, searching the literature, analyzing the results, writing the review and reflecting on the writing. Ultimately, this study proposes a twelve-item LR checklist. By clearly stating the desired achievements, this checklist allows Masters and Ph.D. students to continuously assess their own progress in elaborating an LR. Institutions aiming to strengthen students’ necessary skills in critical academic writing should also use this tool

An evaluation of the somatosensory profile of hemiparetic individuals

The purpose of this study was to evaluate the somatosensory profile of 18 hemiparetic spastic victims of stroke with and without blocking vision. Maximal isometric contraction test was used for flexor and extensor muscles of the hip and knee, and flexor plantar muscles. The number of cycles per minute on stationary bike was also measured with eyes opened and closed. Significant differences were found suggesting the existence of miscommunication between sensory-motor neural mechanisms responsible for voluntary motor actions in these individuals

Uma revisão conceitual de metais como suporte para seu ensino

Neste trabalho discutimos uma proposta que vai mais além das propriedades que constituem o objeto da assimilação propriamente dito. Acreditamos que para que um aprendiz se aproprie de um conceito químico, este deve ser apresentado não como um conhecimento isolado, mas como elemento estrutural da ciência. Desta maneira, esta proposta foi elaborada visando atender ao critério de apresentar o conceito de metais num nível de detalhe apropriado, de modo a facilitar o trabalho de análise e planejamento de seu ensin

Transfer learning for galaxy morphology from one survey to another

© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society.Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.Peer reviewedFinal Accepted Versio