Effect of Ni-doping on magnetism and superconductivity in Eu0.5K0.5Fe2As2

The effect of Ni-doping on the magnetism and superconductivity in Eu0.5K0.5Fe2As2 has been studied through a systematic investigation of magnetic and superconducting properties of Eu0.5K0.5(Fe1-xNix)2As2 (x = 0, 0.03, 0.05, 0.08 and 0.12) compounds by means of dc and ac magnetic susceptibilities, electrical resistivity and specific heat measurements. Eu0.5K0.5Fe2As2 is known to exhibit superconductivity with superconducting transition temperature Tc as high as 33 K. The Ni-doping leads to a rapid decrease in Tc; Tc is reduced to 23 K with 3% Ni-doping, and 8% Ni-doping suppresses the superconductivity to below 1.8 K. In 3% Ni-doped sample Eu0.5K0.5(Fe0.97Ni0.03)2As2 superconductivity coexists with short range ordering of Eu2+ magnetic moments at Tm ~ 6 K. The suppression of superconductivity with Ni-doping is accompanied with the emergence of a long range antiferromagnetic ordering with TN = 8.5 K and 7 K for Eu0.5K0.5(Fe0.92Ni0.08)2As2 and Eu0.5K0.5(Fe0.88Ni0.12)2As2, respectively. The temperature and field dependent magnetic measurements for x = 0.08 and 0.12 samples reflect the possibility of a helical magnetic ordering of Eu2 moments. We suspect that the helimagnetism of Eu spins could be responsible for the destruction of superconductivity as has been observed in Co-doped EuFe2As2. The most striking feature seen in the resistivity data for x = 0.08 is the reappearance of the anomaly presumably due to spin density wave transition at around 60 K. This could be attributed to the compensation of holes (K-doping at Eu-site) by the electrons (Ni-doping at Fe site). The anomaly associated with spin density wave further shifts to 200 K for x = 0.12 for which the electron doping has almost compensated the holes in the system.Comment: 9 pages, 10 figure

Histograms of Points, Orientations, and Dynamics of Orientations Features for Hindi Online Handwritten Character Recognition

A set of features independent of character stroke direction and order variations is proposed for online handwritten character recognition. A method is developed that maps features like co-ordinates of points, orientations of strokes at points, and dynamics of orientations of strokes at points spatially as a function of co-ordinate values of the points and computes histograms of these features from different regions in the spatial map. Different features like spatio-temporal, discrete Fourier transform, discrete cosine transform, discrete wavelet transform, spatial, and histograms of oriented gradients used in other studies for training classifiers for character recognition are considered. The classifier chosen for classification performance comparison, when trained with different features, is support vector machines (SVM). The character datasets used for training and testing the classifiers consist of online handwritten samples of 96 different Hindi characters. There are 12832 and 2821 samples in training and testing datasets, respectively. SVM classifiers trained with the proposed features has the highest classification accuracy of 92.9\% when compared to the performances of SVM classifiers trained with the other features and tested on the same testing dataset. Therefore, the proposed features have better character discriminative capability than the other features considered for comparison.Comment: 21 pages, 12 jpg figure

A Classifier Using Global Character Level and Local Sub-unit Level Features for Hindi Online Handwritten Character Recognition

A classifier is developed that defines a joint distribution of global character features, number of sub-units and local sub-unit features to model Hindi online handwritten characters. The classifier uses latent variables to model the structure of sub-units. The classifier uses histograms of points, orientations, and dynamics of orientations (HPOD) features to represent characters at global character level and local sub-unit level and is independent of character stroke order and stroke direction variations. The parameters of the classifier is estimated using maximum likelihood method. Different classifiers and features used in other studies are considered in this study for classification performance comparison with the developed classifier. The classifiers considered are Second Order Statistics (SOS), Sub-space (SS), Fisher Discriminant (FD), Feedforward Neural Network (FFN) and Support Vector Machines (SVM) and the features considered are Spatio Temporal (ST), Discrete Fourier Transform (DFT), Discrete Cosine Transform (SCT), Discrete Wavelet Transform (DWT), Spatial (SP) and Histograms of Oriented Gradients (HOG). Hindi character datasets used for training and testing the developed classifier consist of samples of handwritten characters from 96 different character classes. There are 12832 samples with an average of 133 samples per character class in the training set and 2821 samples with an average of 29 samples per character class in the testing set. The developed classifier has the highest accuracy of 93.5\% on the testing set compared to that of the classifiers trained on different features extracted from the same training set and evaluated on the same testing set considered in this study.Comment: 23 pages, 8 jpg figures. arXiv admin note: text overlap with arXiv:2310.0822

Sr-Nd isotope geochemistry of the early Precambrian sub-alkaline mafic igneous rocks from the southern Bastar craton, Central India

Sr–Nd isotope data are reported for the early Precambrian sub-alkaline mafic igneous rocks of the southern Bastar craton, central India. These mafic rocks are mostly dykes but there are a few volcanic exposures. Field relationships together with the petrological and geochemical characteristics of these mafic dykes divide them into two groups; Meso-Neoarchaean sub-alkaline mafic dykes (BD1) and Paleoproterozoic (1.88 Ga) sub-alkaline mafic dykes (BD2). The mafic volcanics are Neoarchaean in age and have very close geochemical relationships with the BD1 type. The two groups have distinctly different concentrations of high-field strength (HFSE) and rare earth elements (REE). The BD2 dykes have higher concentrations of HFSE and REE than the BD1 dykes and associated volcanics and both groups have very distinctive petrogenetic histories. These rocks display a limited range of initial 143Nd/144Nd but a wide range of apparent initial 87Sr/86Sr. Initial 143Nd/144Nd values in the BD1 dykes and associated volcanics vary between 0.509149 and 0.509466 and in the BD2 dykes the variation is between 0.510303 and 0.510511. All samples have positive εNd values the BD1 dykes and associated volcanics have εNd values between +0.3 and +6.5 and the BD2 dykes between +1.9 to +6.0. Trace element and Nd isotope data do not suggest severe crustal contamination during the emplacement of the studied rocks. The positive εNd values suggest their derivation from a depleted mantle source. Overlapping positive εNd values suggest that a similar mantle source tapped by variable melt fractions at different times was responsible for the genesis of BD1 (and associated volcanics) and BD2 mafic dykes. The Rb–Sr system is susceptible to alteration and resetting during post-magmatic alteration and metamorphism. Many of the samples studied have anomalous apparent initial 87Sr/86Sr suggesting post-magmatic changes of the Rb–Sr system which severely restricts the use of Rb–Sr for petrogenetic interpretation

Dementia services today-tomorrow

© Indian Journal of Psychiatry This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: Dementia is the most common disorders of old age. It is predicted that between 2011 and 2020 it will increase 100% in western world and 300% in China, India, and other South Asian countries. Therefore, it is important to know how to diagnose and manage dementia and how to run dementia services that are medico-socioculturally competent to look after people with dementia in the 21st century including advances in person centered care. Objectives: Imparting skills in the management of dementia at present and in future. Materials and Methods: Various clinical, sociocultural, and environmental issues related to dementias will be discussed. Presently available dementia treatment modalities and management strategies will be outlined and strategies for future will be discussed. Results and Conclusions: The audience will be equipped and trained in dementia management strategies for present and future.http://www.indianjpsychiatry.org/article.asp?issn=0019-5545;year=2014;volume=56;issue=5;spage=16;epage=18;aulast