El medio ambiente urbano en Zaragoza. Observaciones sobre la «isla de calor».

Sin resume

Kondo lattice model: Unitary transformations, spin dynamics, strongly correlated charged modes, and vacuum instability

Using unitary transformations, we express the Kondo lattice Hamiltonian in terms of fermionic operators that annihilate the ground state of the interacting system and that represent the best possible approximations to the actual charged excitations. In this way, we obtain an effective Hamiltonian which, for small couplings, consists in a kinetic term for conduction electrons and holes, an RKKY-like term, and a renormalized Kondo interaction. The physical picture of the system implied by this formalism is that of a vacuum state consisting in a background of RKKY-induced spin correlations, where two kinds of elementary modes can be excited: Soft neutral modes associated with deformations of the spin liquid, which lead to very large low-temperature values of the heat capacity and magnetic susceptibility, and charged modes corresponding to the excitation of electrons and holes in the system. Using the translational and spin rotational symmetries, we construct a simple ansatz to determine the charged excitations neglecting the effects of the spin correlations. Apart from the `normal', uncorrelated states, we find strongly correlated charged modes involving soft electrons (or holes) and spin fluctuations, which strongly renormalize the low-energy charged spectrum, and whose energy becomes negative beyond a critical coupling, signaling a vacuum instability and a transition to a new phase.Comment: 35 pages, revtex 3.

Detection of visual defects in citrus fruits: multivariate image analysis vs graph image segmentation

¿The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40261-6_28This paper presents an application of visual quality control in orange post-harvesting comparing two different approaches. These approaches correspond to two very different methodologies released in the area of Computer Vision. The first approach is based on Multivariate Image Analysis (MIA) and was originally developed for the detection of defects in random color textures. It uses Principal Component Analysis and the T2 statistic to map the defective areas. The second approach is based on Graph Image Segmentation (GIS). It is an efficient segmentation algorithm that uses a graph-based representation of the image and a predicate to measure the evidence of boundaries between adjacent regions. While the MIA approach performs novelty detection on defects using a trained model of sound color textures, the GIS approach is strictly an unsupervised method with no training required on sound or defective areas. Both methods are compared through experimental work performed on a ground truth of 120 samples of citrus coming from four different cultivars. Although the GIS approach is faster and achieves better results in defect detection, the MIA method provides less false detections and does not need to use the hypothesis that the bigger area in samples always correspond to the non-damaged areaLópez García, F.; Andreu García, G.; Valiente González, JM.; Atienza Vanacloig, VL. (2013). Detection of visual defects in citrus fruits: multivariate image analysis vs graph image segmentation. En Computer Analysis of Images and Patterns. Springer Verlag (Germany). 8047:237-244. doi:10.1007/978-3-642-40261-6S237244804

Prostate Diffusion Weighted-Magnetic Resonance Image analysis using Multivariate Curve Resolution methods

[EN] Multivariate Curve Resolution (MCR) has been applied on prostate Diffusion Weighted-Magnetic Resonance Images (DW-MRI). Different physiological-based modeling approaches of the diffusion process have been submitted to validation by sequentially incorporating prior knowledge on the MCR constraints. Results validate the biexponential diffusion modeling approach and show the capability of the MCR models to find, characterize and locate the behaviors related to the presence of an early prostate tumor.The authors want to thank prof. Anna de Juan for her comments and help in using the software for this study. This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI 2011-28112-004-02.Aguado Sarrió, E.; Prats-Montalbán, JM.; Sanz Requena, R.; Marti Bonmati, L.; Alberich Bayarri, Á.; Ferrer Riquelme, AJ. (2015). Prostate Diffusion Weighted-Magnetic Resonance Image analysis using Multivariate Curve Resolution methods. Chemometrics and Intelligent Laboratory Systems. 140:43-48. https://doi.org/10.1016/j.chemolab.2014.11.002S434814

Climatic drivers of the historical variations in cereal prices in the northeast of the Iberian peninsula in the 17th century

The 17th century knew in Spain several periods of agricultural crisis producing shortages in staple foods and a rising in grain prices. The fluctuations of the prices of wheat, barley and rye are relatively well documented in several areas of the country, however, the knowledge about the influence of climatic and environmental factors on these variations is still limited. In this work, we present the historical records of grain prices of four cities of different geographical areas in northeastern Iberian Peninsula during period 1630-1660, and they are compared with drought indices, reconstructed from documentary and dendroclimatic proxies. We observed that prices variations coincide with regional anomalies in spring-summer drought. Direct correlations between them are low (0.435), however, if analysis is focused on extreme values, the climatic influence is higher: prices are high during dry periods and lower during wet periods. This correspondence is higher in previous and following years to the Guerra dels Segadors, showing that the exchange of goods and the coherence of data were controlled by sociopolitical and environmental factors, being the latter more influential in peacetime. ©2021 José M. Cuadrat, Francisco J. Alfaro Pérez, Ernesto Tejedor Vargas, Mariano Barriendos, Roberto Serrano-Notivoli, Miguel Á. Saz Sánche

MCR-ALS on metabolic networks: Obtaining more meaningful pathways

[EN] With the aim of understanding the flux distributions across a metabolic network, i.e. within living cells, Principal Component Analysis (PCA) has been proposed to obtain a set of orthogonal components (pathways) capturing most of the variance in the flux data. The problems with this method are (i) that no additional information can be included in the model, and (ii) that orthogonality imposes a hard constraint, not always reasonably. To overcome these drawbacks, here we propose to use a more flexible approach such as Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) to obtain this set of biological pathways through the network. By using this method, different constraints can be included in the model, and the same source of variability can be present in different pathways, which is reasonable from a biological standpoint. This work follows a methodology developed for Pichia pastoris cultures grown on different carbon sources, lately presented in González-Martínez et al. (2014). In this paper a different grey modelling approach, which aims to incorporate a priori knowledge through constraints on the modelling algorithms, is applied to the same case of study. The results of both models are compared to show their strengths and weaknesses.Research in this study was partially supported by the Spanish Ministry of Science and Innovation and FEDER funds from the European Union through grants DPI2011-28112-C04-01 and DPI2011-28112-C04-02. The authors are also grateful to Biopolis SL for supporting this research.Folch-Fortuny, A.; Tortajada Serra, M.; Prats-Montalbán, JM.; Llaneras Estrada, F.; Picó Marco, JA.; Ferrer Riquelme, AJ. (2015). MCR-ALS on metabolic networks: Obtaining more meaningful pathways. Chemometrics and Intelligent Laboratory Systems. 142:293-303. https://doi.org/10.1016/j.chemolab.2014.10.004S29330314