Are quantization rules for horizon areas universal?

Doubts have been expressed on the universality of holographic/string-inspired quantization rules for the horizon areas of stationary black holes or the products of their radii, already in simple 4-dimensional general relativity. Realistic black holes are not stationary but time-dependent. Using two examples of 4D general-relativistic spacetimes containing dynamical black holes for at least part of the time, it is shown that the quantization rules (even counting virtual horizons) cannot hold, except possibly at isolated instants of time, and do not seem to be universal.Comment: One example and one figure added, two figures improved, bibliography expanded and updated. Matches the version accepted for publication in Phys. Rev.

Enfoque de la educación para el desarrollo sostenible en la formación docente en el Ecuador

La UNESCO tiene como prioridad ejercer un liderazgo mundial y regional en materia de educación y la contribución al logro de una educación de calidad para todos/as. Además, la Asamblea General de la ONU la designó como organismo rector del Decenio de las Naciones Unidas de la Educación para el Desarrollo Sostenible (2005-2014). En Ecuador, la UNESCO promovió la realización de dos estudios: i) “Incorporación del enfoque de la Educación para el Desarrollo Sostenible (EDS) en los currículos de instituciones formadoras de docentes”; ii) “Incorporación del enfoque de la Educación para el Desarrollo Sostenible (EDS) en los currículos de instituciones formadoras de docentes en la zona de la Reserva de la Biósfera Yasuní (RBY). El segundo estudio constituyó una derivación del primero, tratado de manera específica en una de las principales áreas naturales protegidas del país

Cluster Algorithm Renormalization Group Study of Universal Fluctuations in the 2D Ising Model

In this paper we propose a novel method to study critical systems numerically by a combined collective-mode algorithm and Renormalization Group on the lattice. This method is an improved version of MCRG in the sense that it has all the advantages of cluster algorithms. As an application we considered the 2D Ising model and studied wether scale invariance or universality are possible underlying mechanisms responsible for the approximate "universal fluctuations" close to a so-called bulk temperature $T^*(L)$. "Universal fluctuations" was first proposed in [1] and stated that the probability density function of a global quantity for very dissimilar systems, like a confined turbulent flow and a 2D magnetic system, properly normalized to the first two moments, becomes similar to the "universal distribution", originally obtained for the magnetization in the 2D XY model in the low temperature region. The results for the critical exponents and the renormalization group flow of the probability density function are very accurate and show no evidence to support that the approximate common shape of the PDF should be related to both scale invariance or universal behavior.Comment: 6 pages, 4 figures and 3 table

The Effect of Explicit Instruction on Learning Vocabulary and Text Comprehension

The present study examined whether vocabulary intervention improves text comprehension by explicitly teaching words. The participants were institutionalized students aged between 9 and 12 years (n = 23), who completed five sessions based on solid vocabulary instruction. The semantic understanding of the taught words was measured before and after the intervention. Subsequently, they read two matched texts, one containing taught words (i.e., explicit instruction) and one without (i.e., minimal guidance instruction). The data suggest that solid vocabulary instruction significantly improved word comprehension. Specifically, they performed better in terms of the number of words learned and the amount of responses to implicit and explicit questions. It is recommended for teachers to conduct direct and explicit vocabulary interventions to improve reading comprehension in institutionalized students. Keywords: explicit teaching, reading skills, institutionalized students. Resumen El presente estudio examinó si la intervención del vocabulario mejora el conocimiento de textos enseñando explícitamente las palabras. Los participantes fueron estudiantes de 9 a 12 años en condición de institucionalización (n = 23), quienes completaron 5 sesiones basadas en instrucción de vocabulario sólido. La comprensión semántica de las palabras enseñadas se midió antes y después de la intervención. Posteriormente, leyeron 2 textos emparejados, 1 que contiene palabras enseñadas (i.e., instrucción explicita) y 1 no (i.e., instrucción con guía mínima). Los datos sugieren que la instrucción del vocabulario sólido mejoró significativamente la comprensión de palabras. Específicamente, tuvieron mejor desempeño en el número de palabras aprendidas y en la cantidad de las respuestas a las preguntas implícitas y explícitas. Se recomienda a los docentes conducir intervenciones directas y explícitas de vocabulario para mejorar la comprensión lectora en estudiantes institucionalizados. Palabras Clave: enseñanza explícita, destreza lectora, estudiantes institucionalizados

Towards Portability of Models for Predicting Students’ Final Performance in University Courses Starting from Moodle Logs

Predicting students’ academic performance is one of the older challenges faced by the educational scientific community. However, most of the research carried out in this area has focused on obtaining the best accuracy models for their specific single courses and only a few works have tried to discover under which circumstances a prediction model built on a source course can be used in other different but similar courses. Our motivation in this work is to study the portability of models obtained directly from Moodle logs of 24 university courses. The proposed method intends to check if grouping similar courses by the degree or the similar level of usage of activities provided by the Moodle logs, and if the use of numerical or categorical attributes affect in the portability of the prediction models. We have carried out two experiments by executing the well-known classification algorithm over all the datasets of the courses in order to obtain decision tree models and to test their portability to the other courses by comparing the obtained accuracy and loss of accuracy evaluation measures. The results obtained show that it is only feasible to directly transfer predictive models or apply them to different courses with an acceptable accuracy and without losing portability under some circumstances