Observation of a valence transition in (Pr,Ca)CoO3 cobaltites: charge migration at the metal-insulator transition

X-ray absorption spectroscopy measurements in Pr0.5Ca0.5CoO3 and (Pr,Y)0.55Ca0.45CoO3 compositions reveal that the valence of praseodymium ions is stable and essentially +3 (Pr [4f 2]) in the metallic state, but abruptly changes when carriers localize approaching the oxidation state +4 (Pr [4f 1]). This mechanism appears to be the driving force of the metal-insulator transition. The ground insulating state of Pr0.5Ca0.5CoO3 is an homogeneous Co3.5-d state stabilized by a charge transfer from Pr to Co sites: 1/2Pr3+ + Co3.5 \to 1/2Pr3+2d + Co3.5-d, with 2d \approx 0.26 e-.Comment: Submitted. 14 pages, 4 Figure

Ga substitution as an effective variation of Mn-Tb coupling in multiferroic TbMnO3

Ga for Mn substitution in multiferroic TbMnO$_{3}$ has been performed in order to study the influence of Mn-magnetic ordering on the Tb-magnetic sublattice. Complete characterization of TbMn$_{1-x}$Ga$_x$O$_{3}$ ($x$ = 0, 0.04, 0.1) samples, including magnetization, impedance spectroscopy, and x-ray resonant scattering and neutron diffraction on powder and single crystals has been carried out. We found that keeping the same crystal structure for all compositions, Ga for Mn substitution leads to the linear decrease of $T_{\rm N}^{\rm Mn}$ and $\tau^{\rm Mn}$, reflecting the reduction of the exchange interactions strength $J_{\rm Mn-Mn}$ and the change of the Mn-O-Mn bond angles. At the same time, a strong suppression of both the induced and the separate Tb-magnetic ordering has been observed. This behavior unambiguously prove that the exchange fields $J_{\rm Mn-Tb}$ have a strong influence on the Tb-magnetic ordering in the full temperature range below $T_{\rm N}^{\rm Mn}$ and actually stabilize the Tb-magnetic ground state.Comment: 9 pages, 8 figure

Розвиток суспільно-політичного процесу на Волині в період незалежності

У статті розглянуто основні етапи розвитку суспільно-політичного процесу на Волині в період незалежності України. Трансформаційні процеси, що проходять в області, попри свою місцеву особливість, виходять із чергового етапу розвитку політичної системи України, а отже, розвиток суспільно-політичного процесу на Волині прямо залежний від всеукраїнського процесу трансформації.In the article the basic stages of the development of social and political process are considered in Volyn region during the period of independence of Ukraine. The transformation processes, which pass in this region, without regard to the local feature go out from the regular stage of the development of the political system of Ukraine, and consequently, the development of social and political process in Volyn region depend directly upon the all-Ukrainian process of transformation

Long-range structure of Cu(InxGa1-x)3Se5: A complementary neutron and anomalous x-ray diffraction study

The following article appeared in Journal of Applied Physics 109.1 (2011): 013518 and may be found at http://scitation.aip.org/content/aip/journal/jap/109/1/10.1063/1.3524183Distinguishing the scattering contributions of isoelectronic atomic species by means of conventional x-ray- and/or electron diffraction techniques is a difficult task. Such a problem occurs when determining the crystal structure of compounds containing different types of atoms with equal number of electrons. We propose a new structural model of Cu(InxGa1-x) 3Se5 which is valid for the entire compositional range of the CuIn3Se5-CuGa3Se5 solid solution. Our model is based on neutron and anomalous x-ray diffraction experiments. These complementary techniques allow the separation of scattering contributions of the isoelectronic species Cu+ and Ga3+, contributing nearly identically in monoenergetic x-ray diffraction experiments. We have found that CuIII3Se5 (III =In,Ga) in its room temperature near-equilibrium modification exhibits a modified stannite structure (space group I4̄2m). Different occupation factors of the species involved, Cu+ In3+, Ga3+, and vacancies have been found at three different cationic positions of the structure (Wyckoff sites 2a, 2b, and 4d) depending on the composition of the compound. Significantly, Cu+ does not occupy the 2b site for the In-free compound, but does for the In-containing case. Structural parameters, including lattice constants, tetragonal distortions, and occupation factors are given for samples covering the entire range of the CuIn 3Se5-CuGa3Se5 solid solution. At the light of the result, the denotation of Cu-poor 1:3:5 compounds as chalcopyrite-related materials is only valid in reference to their composition.This work was supported financially by the PPP-program Acciones Integradas Hispano-Alemanas of the DAAD under the Contract No. 314-Al-e-dr (HA2006-0025 spanish reference). Sylvio Haas is gratefully acknowledged for his support in anomalous XRD data acquisition and conversion

Dy-Fe and Dy-FeCo Magneto-optical Alloys Studied by X-Ray Magnetic Circular Dichroism

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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset