Isotope effect in impure high T_c superconductors

The influence of various kinds of impurities on the isotope shift exponent \alpha of high temperature superconductors has been studied. In these materials the dopant impurities, like Sr in La_{2-x}Sr_xCuO_4, play different role and usually occupy different sites than impurities like Zn, Fe, Ni {\it etc} intentionally introduced into the system to study its superconducting properties. In the paper the in-plane and out-of-plane impurities present in layered superconductors have been considered. They differently affect the superconducting transition temperature T_c. The relative change of isotope shift coefficient, however, is an universal function of T_c/T_{c0} (T_{c0} reffers to impurity free system) {\it i.e.} for angle independent scattering rate and density of states function it does not depend whether the change of T_c is due to in- or out-of-plane impurities. The role of the anisotropic impurity scattering in changing oxygen isotope coefficient of superconductors with various symmetries of the order parameter is elucidated. The comparison of the calculated and experimental dependence of \alpha/\alpha_0, where \alpha_0 is the clean system isotope shift coefficient, on T_c/T_{c0} is presented for a number of cases studied. The changes of \alpha calculated within stripe model of superconductivity in copper oxides resonably well describe the data on La_{1.8}Sr_{0.2}Cu_{1-x}(Fe,Ni)_xO_4, without any fitting parameters.Comment: 8 pages, 6 figures, Phys. Rev. B67 (2003) accepte

Deep Topology Classification: A New Approach for Massive Graph Classification

The classification of graphs is a key challenge within many scientific fields using graphs to represent data and is an active area of research. Graph classification can be critical in identifying and labelling unknown graphs within a dataset and has seen application across many scientific fields. Graph classification poses two distinct problems: the classification of elements within a graph and the classification of the entire graph. Whilst there is considerable work on the first problem, the efficient and accurate classification of massive graphs into one or more classes has, thus far, received less attention. In this paper we propose the Deep Topology Classification (DTC) approach for global graph classification. DTC extracts both global and vertex level topological features from a graph to create a highly discriminate representation in feature space. A deep feed-forward neural network is designed and trained to classify these graph feature vectors. This approach is shown to be over 99% accurate at discerning graph classes over two datasets. Additionally, it is shown to be more accurate than current state of the art approaches both in binary and multi-class graph classification tasks

Second-to-Fourth Digit Ratio Has a Non-Monotonic Impact on Altruism

Gene-culture co-evolution emphasizes the joint role of culture and genes for the emergence of altruistic and cooperative behaviors and behavioral genetics provides estimates of their relative importance. However, these approaches cannot assess which biological traits determine altruism or how. We analyze the association between altruism in adults and the exposure to prenatal sex hormones, using the second-to-fourth digit ratio. We find an inverted U-shaped relation for left and right hands, which is very consistent for men and less systematic for women. Subjects with both high and low digit ratios give less than individuals with intermediate digit ratios. We repeat the exercise with the same subjects seven months later and find a similar association, even though subjects' behavior differs the second time they play the game. We then construct proxies of the median digit ratio in the population (using more than 1000 different subjects), show that subjects' altruism decreases with the distance of their ratio to these proxies. These results provide direct evidence that prenatal events contribute to the variation of altruistic behavior and that the exposure to fetal hormones is one of the relevant biological factors. In addition, the findings suggest that there might be an optimal level of exposure to these hormones from social perspective.Financial support from the Spanish Ministry of Science and Innovation (ECO2010{17049; ECO2009-09120), the Government of Andalusia Project for Excellence in Research (P07.SEJ.02547), the Government of the Basque Country (IT-223–07) and Fundacion Ramon Areces (I+D-2011)is gratefully acknowledged

Hyperspectral Reflectance and Indices for Characterizing the Dynamics of Crop–Weed Competition for Water

Understanding the spectral characteristics of crops in response to stress caused by weeds is a basic step in improving the precision of agricultural technologies that manage weeds in the field. This research focused on the competition between corn (Zea mays) and redroot pigweed (Amaranthus retroflexus), a common weed that strongly reduces corn yield. The aim of this research was to characterize the physiological changes that occur in corn during early growth because of crop–weed competition and to examine the ability to detect the effect of competition through hyperspectral measurements. A greenhouse experiment was conducted, and corn plants were examined during early growth, with and without weed competition. Hyperspectral measurements were combined with physiological measurements to examine the reflectance and photosynthetic activity of corn. Changes were expected to appear mainly in the short-wave infrared region (SWIR) due to competition for water. Relative water content (RWC), chlorophyll content, photosynthetic rate, and stomatal conductance were reduced in the presence of weeds, and intercellular CO2 levels increased. Deeper SWIR light absorption occurred in the weed treatment as expected, accompanied by spectral changes in the visible (VIS) and near infrared (NIR) ranges. The results highlight the potential of using spectral measurements as an indicator of competition for water