A perturbative approach to J mixing in f-electron systems: Application to actinide dioxides

We present a perturbative model for crystal-field calculations, which keeps into account the possible mixing of states labelled by different quantum number J. Analytical J-mixing results are obtained for a Hamiltonian of cubic symmetry and used to interpret published experimental data for actinide dioxides. A unified picture for all the considered compounds is proposed by taking into account the scaling properties of the crystal-field potential.Comment: 16 pages + 4 figures; will appear http://prb.aps.or

S-mixing and quantum tunneling of the magnetization in molecular nanomagnets

The role of $S$-mixing in the quantum tunneling of the magnetization in nanomagnets has been investigated. We show that the effect on the tunneling frequency is huge and that the discrepancy (more than 3 orders of magnitude in the tunneling frequency) between spectroscopic and relaxation measurements in Fe$_8$ can be resolved if $S$-mixing is taken into account.Comment: REVTEX, 10 pages, 3 jpg figures, to appear in PR

Predicting population size at large scale: The case of two large felids

Approaches that allow capitalizing on local population estimates to derive global population estimates with associated uncertainty are urgently needed, especially for naturally rare species of conservation concern. Here we used published population density estimates to predict large-scale density patterns and derive global population estimates for two species of large felids, the leopard and the tiger. We modelled population density for the leopard (n = 392) and the tiger (n = 547) as a function of environmental and anthropogenic variables, while controlling for differences in sampling method and sampling area, time of data collection, spatial autocorrelation, subspecies and political protection. We used Bayesian inference to generate a distribution of plausible population sizes. Both species showed higher densities in high productivity areas, the leopard being more abundant in high precipitation, high level of terrain roughness and agricultural areas, and the tiger in areas with low croplands and low roughness. Primary roads density showed a negative effect on both species. Secondary roads density was associated to higher densities for the leopard but lower densities for the tiger. Livestock biomass showed a humped relationship with tigers’ densities. Temporal trends in average density were negative for the tiger, experiencing an average decline of 34% (IQR: 11% − 53%). In contrast, the trend for leopards showed a marginal, yet uncertain, increase in recent years 21% (IQR: − 5% − 57%). We predicted a global population estimate of 261,636 (IQR = 146,768 − 461,512) and 5201 tigers (IQR = 2596 − 10,460). Large-scale models of population density that rely on unstructured data can contribute to our understanding of species ecology, produce robust population size estimates for conservation assessment and inform large-scale conservation planning. At the same time, the uncertainty around these estimates highlights the limited knowledge available for these species which should be accounted for in conservation assessments

The accelerated expansion of the Universe as a quantum cosmological effect

We study the quantized Friedmann-Lema\^{\i}tre-Robertson-Walker (FLRW) model minimally coupled to a free massless scalar field. In a previous paper, \cite{fab2}, solutions of this model were constructed as gaussian superpositions of negative and positive modes solutions of the Wheeler-DeWitt equation, and quantum bohmian trajectories were obtained in the framework of the Bohm-de Broglie (BdB) interpretation of quantum cosmology. In the present work, we analyze the quantum bohmian trajectories of a different class of gaussian packets. We are able to show that this new class generates bohmian trajectories which begin classical (with decelerated expansion), undergo an accelerated expansion in the middle of its evolution due to the presence of quantum cosmological effects in this period, and return to its classical decelerated expansion in the far future. We also show that the relation between luminosity distance and redshift in the quantum cosmological model can be made close to the corresponding relation coming from the classical model suplemented by a cosmological constant, for $z<1$. These results suggest the posibility of interpreting the present observations of high redshift supernovae as the manifestation of a quantum cosmological effect

The evolution of the AGN content in groups up to z~1

Determining the AGN content in structures of different mass/velocity dispersion and comparing them to higher mass/lower redshift analogs is important to understand how the AGN formation process is related to environmental properties. We use our well-tested cluster finding algorithm to identify structures in the GOODS North and South fields, exploiting the available spectroscopic redshifts and accurate photometric redshifts. We identify 9 structures in GOODS-south (presented in a previous paper) and 8 new structures in GOODS-north. We only consider structures where at least 2/3 of the members brighter than M_R=-20 have a spectroscopic redshift. For those group members that coincide with X-ray sources in the 4 and 2 Msec Chandra source catalogs respectively, we determine if the X-ray emission originates from AGN activity or it is related to the galaxies' star-formation activity. We find that the fraction of AGN with Log L_H > 42 erg/s in galaxies with M_R < -20 is on average 6.3+-1.3%, much higher than in lower redshift groups of similar mass and more than double the fraction found in massive clusters at a similarly high redshift. We then explore the spatial distribution of AGN in the structures and find that they preferentially populate the outer regions. The colors of AGN host galaxies in structures tend to be confined to the green valley, thus avoiding the blue cloud and, partially, also the red-sequence, contrary to what happens in the field. We finally compare our results to the predictions of two sets of semi analytic models to investigate the evolution of AGN and evaluate potential triggering and fueling mechanisms. The outcome of this comparison attests the importance of galaxy encounters, not necessarily leading to mergers, as an efficient AGN triggering mechanism. (abridged)Comment: 11 pages, 8 figures, Accepted accepted for publication in A&

Comparison of energy consumption and costs of different HEVs and PHEVs in European and American context

This paper will analyse on the one hand the potential of Plug in Hybrid electric Vehicles to significantly reduce fuel consumption and displace it torward various primary energies thanks to the electricity sector. On the other hand the total cost of ownership of two different PHEV architectures will be compared to a conventional cehicle and a HEV without external charging

Comparison of VLBI, TV and traveling clock techniques for time transfer

A three part experiment was conducted to develop and compare time transfer techniques. The experiment consisted of (1) a very long baseline interferometer (VLBI), (2) a high precision portable clock time transfer system between the two sites, and (3) a television time transfer. A comparison of the VLBI and traveling clock shows each technique can perform satisfactorily at the five nsec level. There was a systematic offset of 59 nsec between the two methods, which we attributed to a difference in epochs between VLBI formatter and station clock. The VLBI method had an internal random error of one nsec at the three sigma level for a two day period. Thus, the Mark II system performed well, and VLBI shows promise of being an accurate method of time transfer. The TV system, which had technical problems during the experiment, transferred time with a random error of about 50 nsec

An automatic deep learning approach for coronary artery calcium segmentation

Coronary artery calcium (CAC) is a significant marker of atherosclerosis and cardiovascular events. In this work we present a system for the automatic quantification of calcium score in ECG-triggered non-contrast enhanced cardiac computed tomography (CT) images. The proposed system uses a supervised deep learning algorithm, i.e. convolutional neural network (CNN) for the segmentation and classification of candidate lesions as coronary or not, previously extracted in the region of the heart using a cardiac atlas. We trained our network with 45 CT volumes; 18 volumes were used to validate the model and 56 to test it. Individual lesions were detected with a sensitivity of 91.24%, a specificity of 95.37% and a positive predicted value (PPV) of 90.5%; comparing calcium score obtained by the system and calcium score manually evaluated by an expert operator, a Pearson coefficient of 0.983 was obtained. A high agreement (Cohen's k = 0.879) between manual and automatic risk prediction was also observed. These results demonstrated that convolutional neural networks can be effectively applied for the automatic segmentation and classification of coronary calcifications