9 resultados para Reduces Infarct Size

em Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco


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We conduct experiments to investigate the effects of different majority requirements on bargaining outcomes in small and large groups. In particular, we use a Baron-Ferejohn protocol and investigate the effects of decision rules on delay (number of bargaining rounds needed to reach agreement) and measures of "fairness" (inclusiveness of coalitions, equality of the distribution within a coalition). We find that larger groups and unanimity rule are associated with significantly larger decision making costs in the sense that first round proposals more often fail, leading to more costly delay. The higher rate of failure under unanimity rule and in large groups is a combination of three facts: (1) in these conditions, a larger number of individuals must agree, (2) an important fraction of individuals reject offers below the equal share, and (3) proposers demand more (relative to the equal share) in large groups.

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The learning of probability distributions from data is a ubiquitous problem in the fields of Statistics and Artificial Intelligence. During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models due to their advantageous theoretical properties. Some of these algorithms can be used to search for a maximum likelihood decomposable model with a given maximum clique size, k, which controls the complexity of the model. Unfortunately, the problem of learning a maximum likelihood decomposable model given a maximum clique size is NP-hard for k > 2. In this work, we propose a family of algorithms which approximates this problem with a computational complexity of O(k · n^2 log n) in the worst case, where n is the number of implied random variables. The structures of the decomposable models that solve the maximum likelihood problem are called maximal k-order decomposable graphs. Our proposals, called fractal trees, construct a sequence of maximal i-order decomposable graphs, for i = 2, ..., k, in k − 1 steps. At each step, the algorithms follow a divide-and-conquer strategy based on the particular features of this type of structures. Additionally, we propose a prune-and-graft procedure which transforms a maximal k-order decomposable graph into another one, increasing its likelihood. We have implemented two particular fractal tree algorithms called parallel fractal tree and sequential fractal tree. These algorithms can be considered a natural extension of Chow and Liu’s algorithm, from k = 2 to arbitrary values of k. Both algorithms have been compared against other efficient approaches in artificial and real domains, and they have shown a competitive behavior to deal with the maximum likelihood problem. Due to their low computational complexity they are especially recommended to deal with high dimensional domains.

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[EN] The objective of this study was to determine whether a short training program, using real foods, would decreased their portion-size estimation errors after training. 90 student volunteers (20.18±0.44 y old) of the University of the Basque Country (Spain) were trained in observational techniques and tested in food-weight estimation during and after a 3-hour training period. The program included 57 commonly consumed foods that represent a variety of forms (125 different shapes). Estimates of food weight were compared with actual weights. Effectiveness of training was determined by examining change in the absolute percentage error for all observers and over all foods over time. Data were analyzed using SPSS vs. 13.0. The portion-size errors decreased after training for most of the foods. Additionally, the accuracy of their estimates clearly varies by food group and forms. Amorphous was the food type estimated least accurately both before and after training. Our findings suggest that future dietitians can be trained to estimate quantities by direct observation across a wide range of foods. However this training may have been too brief for participants to fully assimilate the application.

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Póster presentado en The Energy and Materials Research Conference - EMR2015 celebrado en Madrid (España) entre el 25-27 de febrero de 2015

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[EN] This study analyzes the relationship between board size and economic-financial performance in a sample of European firms that constitute the EUROSTOXX50 Index. Based on previous literature, resource dependency and agency theories, and considering regulation developed by the OECD and European Union on the normative of corporate governance for each country in the sample, the authors propose the hypotheses of both positive linear and quadratic relationships between the researched parameters. Using ROA as a benchmark of financial performance and the number of members of the board as measurement of the board size, two OLS estimations are performed. To confirm the robustness of the results the empirical study is tested with two other similar financial ratios, ROE and Tobin s Q. Due to the absence of significant results, an additional factor, firm size, is employed in order to check if it affects firm performance. Delving further into the nature of this relationship, it is revealed that there exists a strong and negative relation between firm size and financial performance. Consequently, it can be asseverated that the generic recommendation one size fits all cannot be applied in this case; which conforms to the Recommendations of the European Union that dissuade using generic models for all countries.

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This paper analyses the economic inequality in the municipalities of the Basque Country during the period 1996 and 2010. We have used dates from the Udalmap database mainly the GDP per capita. We have drawn Lorenz Curves and also we have computed Gini indexes to analyse the evolution of inequality during this period. Therefore, we have concluded that there has been an increase of the economic inequality in the municipalities of the Basque Country during this period of time.

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In the present study we have investigated the population genetic structure of albacore (Thunnus alalunga, Bonnaterre 1788) and assessed the loss of genetic diversity, likely due to overfishing, of albacore population in the North Atlantic Ocean. For this purpose, 1,331 individuals from 26 worldwide locations were analyzed by genotyping 75 novel nuclear SNPs. Our results indicated the existence of four genetically homogeneous populations delimited within the Mediterranean Sea, the Atlantic Ocean, the Indian Ocean and the Pacific Ocean. Current definition of stocks allows the sustainable management of albacore since no stock includes more than one genetic entity. In addition, short-and long-term effective population sizes were estimated for the North Atlantic Ocean albacore population, and results showed no historical decline for this population. Therefore, the genetic diversity and, consequently, the adaptive potential of this population have not been significantly affected by overfishing.