Analysis of the transient calibration of heat flux sensors: One dimensional case

The effect of transient heat flux on heat flux sensor response and calibration is analyzed. A one dimensional case was studied in order to elucidate the key parameters and trends for the problem. It has the added advantage that the solutions to the governing equations can be obtained by analytic means. The analytical results obtained to date indicate that the transient response of a heat flux sensor depends on the thermal boundary conditions, the geometry and the thermal properties of the sensor. In particular it was shown that if the thermal diffusivity of the sensor is small, then the transient behavior must be taken into account

Fate of the Peak Effect in a Type-II Superconductor: Multicriticality in the Bragg-Glass Transition

We have used small-angle-neutron-scattering (SANS) and ac magnetic susceptibility to investigate the global magnetic field H vs temperature T phase diagram of a single crystal Nb in which a first-order transition of Bragg-glass melting (disordering), a peak effect, and surface superconductivity are all observable. It was found that the disappearance of the peak effect is directly related to a multicritical behavior in the Bragg-glass transition. Four characteristic phase boundary lines have been identified on the H-T plane: a first-order line at high fields, a mean-field-like continuous transition line at low fields, and two continuous transition line associated with the onset of surface and bulk superconductivity. All four lines are found to meet at a multicritical point.Comment: 4 figure

Entanglement reciprocation between atomic qubits and entangled coherent state

Introducing classical fields, we can transfer entanglement completely from discrete qubits into entangled coherent state. The entanglement also can be retrieved from the continuous-variable state of the cavities to the atomic qubits. Via postselection measure, atomic entangled state and entangled coherent state can be mutual transformed fully.Comment: 5 pages, 3 fighres. accepted by J Phys

Fe-doping induced superconductivity in charge-density-wave system 1T-TaS2

We report the interplay between charge-density-wave (CDW) and superconductivity of 1$T$-Fe$_{x}$Ta$_{1-x}$S$_{2}$ ($0\leq x \leq 0.05$) single crystals. The CDW order is gradually suppressed by Fe-doping, accompanied by the disappearance of pseudogap/Mott-gap as shown by the density functional theory (DFT) calculations. The superconducting state develops at low temperatures within the CDW state for the samples with the moderate doping levels. The superconductivity strongly depends on $x$ within a narrow range, and the maximum superconducting transition temperature is 2.8 K as $x=0.02$. We propose that the induced superconductivity and CDW phases are separated in real space. For high doping level ($x>0.04$), the Anderson localization (AL) state appears, resulting in a large increase of resistivity. We present a complete electronic phase diagram of 1$T$-Fe$_{x}$Ta$_{1-x}$S$_{2}$ system that shows a dome-like $T_{c}(x)$

Multi-Level Cross Residual Network for Lung Nodule Classification.

Computer-aided algorithm plays an important role in disease diagnosis through medical images. As one of the major cancers, lung cancer is commonly detected by computer tomography. To increase the survival rate of lung cancer patients, an early-stage diagnosis is necessary. In this paper, we propose a new structure, multi-level cross residual convolutional neural network (ML-xResNet), to classify the different types of lung nodule malignancies. ML-xResNet is constructed by three-level parallel ResNets with different convolution kernel sizes to extract multi-scale features of the inputs. Moreover, the residuals are connected not only with the current level but also with other levels in a crossover manner. To illustrate the performance of ML-xResNet, we apply the model to process ternary classification (benign, indeterminate, and malignant lung nodules) and binary classification (benign and malignant lung nodules) of lung nodules, respectively. Based on the experiment results, the proposed ML-xResNet achieves the best results of 85.88% accuracy for ternary classification and 92.19% accuracy for binary classification, without any additional handcrafted preprocessing algorithm