Polytechnic University of Valencia

About: Polytechnic University of Valencia is a education organization based out in Valencia, Spain. It is known for research contribution in the topics: Catalysis & Population. The organization has 16282 authors who have published 40162 publications receiving 850234 citations.

Mobile learning vs. traditional classroom lessons: a comparative study

Abstract: Different methods can be used for learning, and they can be compared in several aspects, especially those related to learning outcomes. In this paper, we present a study in order to compare the learning effectiveness and satisfaction of children using an iPhone game for learning the water cycle vs. the traditional classroom lesson. The iPhone game includes multiple interaction forms and combined augmented reality AR mini-games with non-AR mini-games. The traditional classroom lesson had the same learning content as the iPhone game. Thirty-eight children participated in the study. The analyses showed that the children made significant learning gains about the water cycle, regardless of the method used. Even though the results showed that the iPhone method achieved higher knowledge results than the traditional classroom lesson, no statistically significant differences were found between the iPhone and the classroom lesson. When analysing the motivational outcomes, the results showed that the children found the iPhone game to be more satisfying than the classroom lessons. Since the iPhone game achieved similar learning results and a higher motivational effect than the classroom lesson, this suggests that games of this kind could be used as a tool in primary schools to reinforce students' lessons.

A novel mechanism of ion homeostasis and salt tolerance in yeast: the Hal4 and Hal5 protein kinases modulate the Trk1-Trk2 potassium transporter.

Abstract: The regulation of intracellular ion concentrations is a fundamental property of living cells. Although many ion transporters have been identified, the systems that modulate their activity remain largely unknown. We have characterized two partially redundant genes from Saccharomyces cerevisiae, HAL4/SAT4 and HAL5, that encode homologous protein kinases implicated in the regulation of cation uptake. Overexpression of these genes increases the tolerance of yeast cells to sodium and lithium, whereas gene disruptions result in greater cation sensitivity. These phenotypic effects of the mutations correlate with changes in cation uptake and are dependent on a functional Trk1-Trk2 potassium transport system. In addition, hal4 hal5 and trk1 trk2 mutants exhibit similar phenotypes: (i) they are deficient in potassium uptake; (ii) their growth is sensitive to a variety of toxic cations, including lithium, sodium, calcium, tetramethylammonium, hygromycin B, and low pH; and (iii) they exhibit increased uptake of methylammonium, an indicator of membrane potential. These results suggest that the Hal4 and Hal5 protein kinases activate the Trk1-Trk2 potassium transporter, increasing the influx of potassium and decreasing the membrane potential. The resulting loss in electrical driving force reduces the uptake of toxic cations and improves salt tolerance. Our data support a role for regulation of membrane potential in adaptation to salt stress that is mediated by the Hal4 and Hal5 kinases.

Competencies for young European higher education graduates: labor market mismatches and their payoffs

Abstract: Labor market rewards based on competencies are analyzed using a sample of young European higher education (HE) graduates. Estimates of monetary rewards are obtained from conventional earnings regressions, while estimates total rewards are based on job satisfaction and derived through ordered probit regressions. Results for income show that jobs with higher participative and methodological competency requirements are better paid. The results also show that higher requirements in terms of competencies increase graduates’ job satisfaction.

CNNs for automatic glaucoma assessment using fundus images: an extensive validation

Abstract: Most current algorithms for automatic glaucoma assessment using fundus images rely on handcrafted features based on segmentation, which are affected by the performance of the chosen segmentation method and the extracted features. Among other characteristics, convolutional neural networks (CNNs) are known because of their ability to learn highly discriminative features from raw pixel intensities. In this paper, we employed five different ImageNet-trained models (VGG16, VGG19, InceptionV3, ResNet50 and Xception) for automatic glaucoma assessment using fundus images. Results from an extensive validation using cross-validation and cross-testing strategies were compared with previous works in the literature. Using five public databases (1707 images), an average AUC of 0.9605 with a 95% confidence interval of 95.92–97.07%, an average specificity of 0.8580 and an average sensitivity of 0.9346 were obtained after using the Xception architecture, significantly improving the performance of other state-of-the-art works. Moreover, a new clinical database, ACRIMA, has been made publicly available, containing 705 labelled images. It is composed of 396 glaucomatous images and 309 normal images, which means, the largest public database for glaucoma diagnosis. The high specificity and sensitivity obtained from the proposed approach are supported by an extensive validation using not only the cross-validation strategy but also the cross-testing validation on, to the best of the authors’ knowledge, all publicly available glaucoma-labelled databases. These results suggest that using ImageNet-trained models is a robust alternative for automatic glaucoma screening system. All images, CNN weights and software used to fine-tune and test the five CNNs are publicly available, which could be used as a testbed for further comparisons.