Two classes of nonlocal Evolution Equations related by a shared Traveling Wave Problem

We consider reaction-diffusion equations and Korteweg-de Vries-Burgers (KdVB) equations, i.e. scalar conservation laws with diffusive-dispersive regularization. We review the existence of traveling wave solutions for these two classes of evolution equations. For classical equations the traveling wave problem (TWP) for a local KdVB equation can be identified with the TWP for a reaction-diffusion equation. In this article we study this relationship for these two classes of evolution equations with nonlocal diffusion/dispersion. This connection is especially useful, if the TW equation is not studied directly, but the existence of a TWS is proven using one of the evolution equations instead. Finally, we present three models from fluid dynamics and discuss the TWP via its link to associated reaction-diffusion equations

Terminological challenges in the translation of science documentaries: a case-study

This article aims to describe some of the main terminological problems audiovisual translators have to face when dealing with the translation of science documentaries, specifically in the English-Catalan combination. The first section of the article presents some theoretical concepts which underlie this research and which are taken, for the most part, from Cabré's Communicative Theory of Terminology. Then, specific terminological problems audiovisual translators have to solve are described using the data provided by a corpus of four science documentaries lasting approximately 50 minutes each. These challenges include identifying a term, understanding a term, finding the right equivalent, dealing with the absence of an adequate equivalent, solving denominative variations, choosing between in vivo and in vitro terminology, and overcoming mistranscriptions

The influence of fractional diffusion in Fisher-KPP equations

We study the Fisher-KPP equation where the Laplacian is replaced by the generator of a Feller semigroup with power decaying kernel, an important example being the fractional Laplacian. In contrast with the case of the stan- dard Laplacian where the stable state invades the unstable one at constant speed, we prove that with fractional diffusion, generated for instance by a stable L\'evy process, the front position is exponential in time. Our results provide a mathe- matically rigorous justification of numerous heuristics about this model

Datos no Normales en el ANOVA de Medidas Repetidas: Impacto en el Error Tipo I y Potencia

Background: Repeated measures designs are commonly used in health and social sciences research. Although there are other, more advanced, statistical analyses, the F-statistic of repeated measures analysis of variance (RM-ANOVA) remains the most widely used procedure for analyzing differences in means. The impact of the violation of normality has been extensively studied for between-subjects ANOVA, but this is not the case for RM-ANOVA. Therefore, studies that extensively and systematically analyze the robustness of RM-ANOVA under the violation of normality are needed. This paper reports the results of two simulation studies aimed at analyzing the Type I error and power of RM-ANOVA when the normality assumption is violated but sphericity is fulfilled. Method: Study 1 considered 20 distributions, both known and unknown, and we manipulated the number of repeated measures (3, 4, 6, and 8) and sample size (from 10 to 300). Study 2 involved unequal distributions in each repeated measure. The distributions analyzed represent slight, moderate, and severe deviation from normality. Results: Overall, the results show that the Type I error and power of the F-statistic are not altered by the violation of normality. Conclusions: RM-ANOVA is generally robust to non-normality when the sphericity assumption is met

Repeated Measures ANOVA and adjusted F-tests when sphericity is violated: Which procedure is best?

Introduction: One-way repeated measures ANOVA requires sphericity. Research indicates that violation of this assumption has an important impact on Type I error. Although more advanced alternative procedures exist, most classical texts recommend the use of adjusted F-tests, which are frequently employed because they are intuitive, easy to apply, and available in most statistical software. Adjusted F-tests differ in the procedure used to estimate the corrective factor ε, the most common being the Greenhouse-Geisser (F-GG) and Huynh-Feldt (F-HF) adjustments. Although numerous studies have analyzed the robustness of these procedures, the results are inconsistent, thus highlighting the need for further research. Methods: The aim of this simulation study was to analyze the performance of the Fstatistic, F-GG, and F-HF in terms of Type I error and power in one-way designs with normal data under a variety of conditions that may be encountered in real research practice. Values of ε were fixed according to the Greenhouse–Geisser procedure (ε). We manipulated the number of repeated measures (3, 4, and 6) and sample size (from 10 to 300), with ε values ranging from the lower to its upper limit. Results: Overall, the results showed that the F-statistic becomes more liberal as sphericity violation increases, whereas both F-HF and F-GG control Type I error; of the two, F-GG is more conservative, especially with large values of ε and small samples. Discussion: If different statistical conclusions follow from application of the two tests, we recommend using F-GG for ε values below 0.60, and F-HF for ε values equal to or above 0.60

Robustez de los Modelos Lineales Mixtos Generalizados para Diseños Split-Plot con Datos Binarios

This paper examined the robustness of the generalized linear mixed model (GLMM). The GLMM estimates fixed and random effects, and it is especially useful when the dependent variable is binary. It is also useful when the dependent variable involves repeated measures, since it can model correlation. The present study used Monte Carlo simulation to analyze the empirical Type I error rates of GLMMs in split-plot designs. The variables manipulated were sample size, group size, number of repeated measures, and correlation between repeated measures. Extreme conditions were also considered, including small samples, unbalanced groups, and different correlation in each group (pairing between group size and correlation between repeated measures). For balanced groups, the results showed that the group effect was robust under all conditions, while for unbalanced groups the effect tended to be conservative with positive pairing and liberal with negative pairing. Regarding time and interaction effects, the results showed, for both balanced and unbalanced groups, that: (a) The test was robust with low correlation (.2), but conservative for medium values of correlation (.4 and .6), and (b) the test tended to be conservative for positive and negative pairing, especially the latter