Search results for "69"

Unsupervised quantitative methods to analyze student reasoning lines: Theoretical aspects and examples

2019

[This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] A relevant aim of research in education is to find and study the reasoning lines that students deploy when dealing with problematic situations. This can be done through an analysis of the answers students give to a questionnaire. In this paper, we discuss some methodological aspects involved in the quantitative analysis of a questionnaire by means of two different clustering methods, a hierarchical one and a nonhierarchical one. We start from the coding procedures needed to obtain analyzable data from the questionnaire and from a definition of a correlation coefficient suitable for measuri…

Investigating the quality of mental models deployed by undergraduate engineering students in creating explanations: The case of thermally activated p…

2013

This paper describes a method aimed at pointing out the quality of the mental models undergraduate engineering students deploy when asked to create explanations for phenomena or processes and/or use a given model in the same context. Student responses to a specially designed written questionnaire are quantitatively analyzed using researcher-generated categories of reasoning, based on the physics education research literature on student understanding of the relevant physics content. The use of statistical implicative analysis tools allows us to successfully identify clusters of students with respect to the similarity to the reasoning categories, defined as ``practical or everyday,'' ``descri…

Using deep learning to generate synthetic B-mode musculoskeletal ultrasound images

2020

Background and objective\ud Deep learning approaches are common in image processing, but often rely on supervised learning, which requires a large volume of training images, usually accompanied by hand-crafted labels. As labelled data are often not available, it would be desirable to develop methods that allow such data to be compiled automatically. In this study, we used a Generative Adversarial Network (GAN) to generate realistic B-mode musculoskeletal ultrasound images, and tested the suitability of two automated labelling approaches.\ud \ud Methods\ud We used a model including two GANs each trained to transfer an image from one domain to another. The two inputs were a set of 100 longitu…

Timing and patterns of the ENSO signal in Africa over the last 30 years: insights from normalized difference vegetation index data.

2014

Abstract A more complete picture of the timing and patterns of the ENSO signal during the seasonal cycle for the whole of Africa over the three last decades is provided using the normalized difference vegetation index (NDVI). Indeed, NDVI has a higher spatial resolution and is more frequently updated than in situ climate databases, and highlights the impact of ENSO on vegetation dynamics as a combined result of ENSO on rainfall, solar radiation, and temperature. The month-by-month NDVI–Niño-3.4 correlation patterns evolve as follows. From July to September, negative correlations are observed over the Sahel, the Gulf of Guinea coast, and regions from the northern Democratic Republic of Congo…

The Rank of Trifocal Grassmann Tensors

2019

Grassmann tensors arise from classical problems of scene reconstruction in computer vision. Trifocal Grassmann tensors, related to three projections from a projective space of dimension k onto view-spaces of varying dimensions are studied in this work. A canonical form for the combined projection matrices is obtained. When the centers of projections satisfy a natural generality assumption, such canonical form gives a closed formula for the rank of the trifocal Grassmann tensors. The same approach is also applied to the case of two projections, confirming a previous result obtained with different methods in [6]. The rank of sequences of tensors converging to tensors associated with degenerat…

Xot, mussol, Mochuelo europeo (VER0000122)

Altres noms vulgars: Little Owl (Anglès), Chevêche d'Athéna (Francès), Steinkauz (Alemany) Gabinet de Vertebrats (Departament de Zoologia), Facultat de Ciències Biològiques (Campus de Burjassot), C/ Doctor Moliner, s/n, Bloque B. 5é plant, Burjassot (Valencia). Armari: 2-2

Òliba, Lechuza común (VER0000210)

Barn Owl (Anglès), Effraie des clochers (Francès), Europäische Schleiereule (Alemany) Gabinet de Vertebrats (Departament de Zoologia), Facultat de Ciències Biològiques (Campus de Burjassot), C/ Doctor Moliner, s/n, Bloque B. 5é plant, Burjassot (Valencia). Armari: 2-2 Valencia 26

Òliba, Lechuza común (VER0000211)

Barn Owl (Anglès), Effraie des clochers (Francès), Europäische Schleiereule (Alemany) Gabinet de Vertebrats (Departament de Zoologia), Facultat de Ciències Biològiques (Campus de Burjassot), C/ Doctor Moliner, s/n, Bloque B. 5é plant, Burjassot (Valencia). Armari: 2-2 Valencia

Òliba, Lechuza común (VER0000119)

Altres noms vulgars: Barn Owl (Anglès), Effraie des clochers (Francès), Europäische Schleiereule (Alemany) Gabinet de Vertebrats (Departament de Zoologia), Facultat de Ciències Biològiques (Campus de Burjassot), C/ Doctor Moliner, s/n, Bloque B. 5é plant, Burjassot (Valencia). Armari: 2-2 Valencia

Òliba, Lechuza común (VER0000120)

Altres noms vulgars: Barn owl guttata (Anglès), Effraie des clochers guttata (Francès), Schleiereule-guttata (Alemany) Gabinet de Vertebrats (Departament de Zoologia), Facultat de Ciències Biològiques (Campus de Burjassot), C/ Doctor Moliner, s/n, Bloque B. 5é plant, Burjassot (Valencia). Armari: 2-2 Valencia