Performance of various correlation measures in quantum renormalization-group method: A case study of quantum phase transition

We have investigated quantum phase transition employing the quantum renormalization group (QRG) method while in most previous literature barely entanglement (concurrence) has been demonstrated. However, it is now well known that entanglement is not the only signature of quantum correlations and a variety of computable measures have been developed to characterize quantum correlations in the composite systems. As an illustration, two cases are elaborated: one dimensional anisotropic (i) XXZ model and (ii) XY model, with various measures of quantum correlations, including quantum discord (QD), geometric discord (GD), measure-induced disturbance (MID), measure-induced nonlocality (MIN) and violation of Bell inequalities (eg. CHSH inequality). We have proved that all these correlation measures can effectively detect the quantum critical points associated with quantum phase transitions (QPT) after several iterations of the renormalization in both cases. Nonetheless, it is shown that some of their dynamical behaviors are not totally similar with entanglement and even when concurrence vanishes there still exists some kind of quantum correlations which is not captured by entanglement. Intriguingly, CHSH inequality can never be violated in the whole iteration procedure, which indicates block-block entanglement can not revealed by the CHSH inequality. Moreover, the nonanalytic and scaling behaviors of Bell violation have also been discussed in detail. As a byproduct, we verify that measure-induced disturbance is exactly equal to the quantum discord measured by \sigma_z for general X-structured states.Comment: Published version. 10 pages, 8 figure

Simultaneous determination of captopril and hydrochlorothiazide by time-resolved chemiluminescence with artificial neural network calibration

AbstractThe combined use of chemometrics and chemiluminescence (CL) measurements, with the aid of the stopped-flow mixing technique, developed a simple time-resolved CL method for the simultaneous determination of captopril (CPL) and hydrochlorothiazide (HCT). The stopped-flow technique in a continuous-flow system was employed in this work in order to emphasize the kinetic differences between the two analytes in cerium (IV)-rhodamine 6G CL system. After the flow was stopped, an initial rise of CL signal was observed for HCT standards, while a direct decay of CL signal was obtained for CPL standards. The mixed CL signal was monitored and recorded on the whole process of continuousflow/stopped-flow, and the obtained data were processed by the chemometric approach of artificial neural network. The relative prediction error (RPE) of CPL and HCT was 5.9% and 8.7%, respectively. The recoveries of CPL and HCT in tablets were found to fall in the range between 95% and 106%. The proposed method was successfully applied to the simultaneous determination of CPL and HCT in a compound pharmaceutical formulation

Kovacs Effect Studied Using The Distinguishable Particles Lattice Model Of Glass

Kovacs effect is a characteristic feature of glassy relaxation. It consists in a non-monotonic evolution of the volume (or enthalpy) of a glass after a succession of two abrupt temperatures changes. The second change is performed when the instantaneous value of the volume coincides with the equilibrium one at the final temperature. While this protocol might be expected to yield equilibrium dynamics right after the second temperature change, the volume instead rises and reaches a maximum, the so-called Kovacs hump, before dropping again to the final equilibrium value. Kovacs effect constitutes one of the hallmarks of aging in glasses. In this paper we reproduce all features of the Kovacs hump by means of the Distinguishable Particles Lattice Model (DPLM) which is a particle model of structural glasses.Comment: 4 pages, 2 figure

Hi-LASSIE: High-Fidelity Articulated Shape and Skeleton Discovery from Sparse Image Ensemble

Automatically estimating 3D skeleton, shape, camera viewpoints, and part articulation from sparse in-the-wild image ensembles is a severely under-constrained and challenging problem. Most prior methods rely on large-scale image datasets, dense temporal correspondence, or human annotations like camera pose, 2D keypoints, and shape templates. We propose Hi-LASSIE, which performs 3D articulated reconstruction from only 20-30 online images in the wild without any user-defined shape or skeleton templates. We follow the recent work of LASSIE that tackles a similar problem setting and make two significant advances. First, instead of relying on a manually annotated 3D skeleton, we automatically estimate a class-specific skeleton from the selected reference image. Second, we improve the shape reconstructions with novel instance-specific optimization strategies that allow reconstructions to faithful fit on each instance while preserving the class-specific priors learned across all images. Experiments on in-the-wild image ensembles show that Hi-LASSIE obtains higher fidelity state-of-the-art 3D reconstructions despite requiring minimum user input

Enhancing performance of ZnO dye-sensitized solar cells by incorporation of multiwalled carbon nanotubes

A low-temperature, direct blending procedure was used to prepare composite films consisting of zinc oxide [ZnO] nanoparticles and multiwalled carbon nanotubes [MWNTs]. The mesoporous ZnO/MWNT films were fabricated into the working electrodes of dye-sensitized solar cells [DSSCs]. The pristine MWNTs were modified by an air oxidation or a mixed acid oxidation treatment before use. The mixed acid treatment resulted in the disentanglement of MWNTs and facilitated the dispersion of MWNTs in the ZnO matrix. The effects of surface property and loading of MWNTs on DSSC performance were investigated. The performance of DSSCs was found to depend greatly on the type and the amount of MWNTs incorporated. At a loading of 0.01 wt%, the acid-treated MWNTs were able to increase the power conversion efficiency of fabricated cells from 2.11% (without MWNTs) to 2.70%