Superconducting Mechanism through direct and redox layer doping in Pnictides

The mechanism of superconductivity in pnictides is discussed through direct doping in superconducting FeAs and also in charge reservoir REO layers. The un-doped SmFeAsO is charge neutral SDW (Spin Density Wave) compound with magnetic ordering below 150 K. The Superconducting FeAs layers are doped with Co and Ni at Fe site, whereas REO layers are doped with F at O site. The electron doping in SmFeAsO through Co results in superconductivity with transition temperature (Tc) maximum up to 15 K, whereas F doping results in Tc upto 47 K in SmFeAsO. All these REFe/Co/NiAsO/F compounds are iso-structural to ZrCuSiAs structure. The samples are crystallized in a tetragonal structure with space group P4/nmm. Variation of Tc with different doping routes shows the versatility of the structure and mechanism of occurrence of superconductivity. It seems doping in redox layer is more effective than direct doping in superconducting FeAs layer.Comment: 4 Pages text + Figs: ([email protected]

A Survey on Emulation Testbeds for Mobile Ad-hoc Networks

AbstractMobile Ad hoc Network (MANET) can be said as a collection of mobile nodes, which builds a dynamic topology and a A resource constrained network. In this paper, we present a survey of various testbeds for Mobile Ad hoc Networks. Emulator provides environment without modifications to the software and validates software solutions for ad hoc network. A field test will show rather the simulation work is going on right track or not and going from the simulator to the real thing directly to analyze the performance and compare the results of routing protocols and mobility models. Analyzing and choosing an appropriate emulator according to the given environment is a time-consuming process. We contribute a survey of emulation testbeds for the choice of appropriate research tools in the mobile ad hoc networks

Structural, Magnetic and Magneto-caloric studies of Ni50Mn30Sn20Shape Memory Alloy

We have synthesized a nominal composition of Ni50Mn30Sn20 alloy using arc melting technique. Rietveld refinement confirms the austenite L21 structure in Fm-3m space group. Electrical resistivity has been found to clearly exhibiting two different phenomena viz. a magnetic transition from paramagnetic to ferromagnetic and a structural transition from austenite to martensitic phase. Thermo-magnetization measurements M(T) confirms ferromagnetic transition temperature TC at 222 K and martensitic transition starting at 127 K(MS). Magnetization measurement M(H) at 10 K confirms the ferromagnetic state. Frequency dependence of ac susceptibility \c{hi}' at low temperature suggests spin glass behavior in the system. The isothermal magnetic entropy change values have been found to be 1.14 J/Kg.K, 2.69 J/Kg.K and 3.9 J/Kg.K, with refrigeration capacities of 19.6 J/kg, 37.8 J/kg and 54.6 J/kg for the field change of 1, 2 and 3 Tesla respectively at 227 K.Comment: 16 pages text + Figs. Ni50Mn30Sn20 alloy: reasonable refrigeration capacity tunable to Room

RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification

Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various factors for cancer treatment. The classification of histological cell nuclei is a challenging task due to the cellular heterogeneity. This paper proposes an efficient Convolutional Neural Network (CNN) based architecture for classification of histological routine colon cancer nuclei named as RCCNet. The main objective of this network is to keep the CNN model as simple as possible. The proposed RCCNet model consists of only 1,512,868 learnable parameters which are significantly less compared to the popular CNN models such as AlexNet, CIFARVGG, GoogLeNet, and WRN. The experiments are conducted over publicly available routine colon cancer histological dataset "CRCHistoPhenotypes". The results of the proposed RCCNet model are compared with five state-of-the-art CNN models in terms of the accuracy, weighted average F1 score and training time. The proposed method has achieved a classification accuracy of 80.61% and 0.7887 weighted average F1 score. The proposed RCCNet is more efficient and generalized terms of the training time and data over-fitting, respectively.Comment: Published in ICARCV 201