Propuesta de medida del desempeño innovador: aplicación en las empresas innovadoras españolas

En este artículo se aporta una medida del grado de desempeño innovador de la empresa. Este indicador resume los efectos de las actividades de innovación tecnológica en los productos, en los procesos y en la sostenibilidad de la empresa. Los datos proceden de las empresas españolas, representadas en el Panel de Innovación Tecnológica (PITEC) del 2006, que han desarrollado actividades de innovación tecnológica en el período 2004-2006. Además, se realiza una caracterización de las empresas innovadoras españolas empleando el indicador propuesto.

Carta enviada por Miguel Arutza a Justo Garate en 1960

1 carta (mecanografiada) ; 137x214mm. Ubicación: Caja 1 - Carpeta 68

Anisotropic expansion and SNIa: An open issue

We review the appropriateness of using SNIa observations to detect potential signatures of anisotropic expansion in the Universe. We focus on Union2 and SNLS3 SNIa datasets and use the hemispherical comparison method to detect possible anisotropic features. Unlike some previous works where nondiagonal elements of the covariance matrix were neglected, we use the full covariance matrix of the SNIa data, thus obtaining more realistic and not underestimated errors. As a matter of fact, the significance of previously claimed detections of a preferred direction in the Union2 dataset completely disappears once we include the effects of using the full covariance matrix. Moreover, we also find that such apreferred direction is aligned with the orthogonal direction of the SDSS observational plane and this suggests a clear indication that the SDSS subsample of the Union2 dataset introduces a significant bias, making the detected preferred direction unphysical. We thus find that current SNIa surveys are inappropriate to test anisotropic features due to their highly non-homogeneous angular distribution in the sky. In addition, after removal of the highest in homogeneous sub-samples, the number of SNIa is too low. Finally, we take advantage of the particular distribution of SNLS SNIa sub- sample in the SNLS3 data set, in which the observations were taken along four different directions. We fit each direction independently and find consistent results at the 1 sigma level. Although the likelihoods peak at relatively different values of Omega(m), the low number of data along each direction gives rise to large errors so that the likelihoods are sufficiently broad as to overlap within 1 sigma. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons. org/licenses/by/4.0/).