Universal Tomonaga-Luttinger liquid phases in one-dimensional strongly attractive SU(N) fermionic cold atoms

A simple set of algebraic equations is derived for the exact low-temperature thermodynamics of one-dimensional multi-component strongly attractive fermionic atoms with enlarged SU(N) spin symmetry and Zeeman splitting. Universal multi-component Tomonaga-Luttinger liquid (TLL) phases are thus determined. For linear Zeeman splitting, the physics of the gapless phase at low temperatures belongs to the universality class of a two-component asymmetric TLL corresponding to spin-neutral N-atom composites and spin-(N-1)/2 single atoms. The equation of states is also obtained to open up the study of multi-component TLL phases in 1D systems of N-component Fermi gases with population imbalance.Comment: 12 pages, 3 figure

Scanning Tunneling Spectroscopy and Vortex Imaging in the Iron-Pnictide Superconductor BaFe1.8_{1.8}Co0.2_{0.2}As2_2

We present an atomic resolution scanning tunneling spectroscopy study of superconducting BaFe$_{1.8}$Co$_{0.2}$As$_2$ single crystals in magnetic fields up to $9 \text{Tesla}$. At zero field, a single gap with coherence peaks at $\overline{\Delta}=6.25 \text{meV}$ is observed in the density of states. At $9 \text{T}$ and $6 \text{T}$, we image a disordered vortex lattice, consistent with isotropic, single flux quantum vortices. Vortex locations are uncorrelated with strong scattering surface impurities, demonstrating bulk pinning. The vortex-induced sub-gap density of states fits an exponential decay from the vortex center, from which we extract a coherence length $\xi=27.6\pm 2.9 \text{\AA}$, corresponding to an upper critical field $H_{c2}=43 \text{T}$.Comment: 4 pages, 4 figure

Universal local pair correlations of Lieb-Liniger bosons at quantum criticality

The one-dimensional Lieb-Liniger Bose gas is a prototypical many-body system featuring universal Tomonaga-Luttinger liquid (TLL) physics and free fermion quantum criticality. We analytically calculate finite temperature local pair correlations for the strong coupling Bose gas at quantum criticality using the polylog function in the framework of the Yang-Yang thermodynamic equations. We show that the local pair correlation has the universal value $g^{(2)}(0)\approx 2 p/(n\varepsilon)$ in the quantum critical regime, the TLL phase and the quasi-classical region, where $p$ is the pressure per unit length rescaled by the interaction energy $\varepsilon=\frac{\hbar^2}{2m} c^2$ with interaction strength $c$ and linear density $n$. This suggests the possibility to test finite temperature local pair correlations for the TLL in the relativistic dispersion regime and to probe quantum criticality with the local correlations beyond the TLL phase. Furthermore, thermodynamic properties at high temperatures are obtained by both high temperature and virial expansion of the Yang-Yang thermodynamic equation.Comment: 8 pages, 6 figures, additional text and reference

Vision-based judgment of tomato maturity under growth conditions

To determine the picking time of tomato and design the control strategy for the harvesting robot, the judgment of tomato maturity under natural conditions is required. Tomato samples were collected based on the fruit growth conditions which were divided into five different stages in this article: breakers, turning, pink, light-red, and red stages. The visible CCD camera VS-880HC was adopted to shoot visible images, while the near-infrared images at a wavelength of 810 nm were screened by MS- 3100 multi-spectral camera. The variations of samples, about color features, were analyzed. The tests indicated that with the changes in maturity, the hue-mean of tomato decreased and the red-green colordifference image mean increased. The standard deviations of the hue-mean and red-green image mean were the largest values for tomato in the pink stage, but the intensity mean of the near-infrared image for tomato in the pink stage had the lowest value. Hue-mean and red-green color-difference image mean can be used as a criterion for the judgment of tomato maturity, and the tests indicated that the redgreen mean method was more satisfactory than that of the hue-mean in the maturity recognition methods of tomato fruit with an accuracy of over 96%. The intermediate divisions of five different maturity stages, which were divided by red-green color-difference image mean, were 0, 23.5, 42.5 and 70. The judgment errors of the two methods are mainly caused by the recognition of tomatoes at the pink stage.Key words: Tomato, maturity, image, judgment

Modulatory effects of the landscape sequences on pedestrians emotional states using EEG

This study aimed to investigate the impact of specific landscape elements on pedestrians’ emotional experiences during walking. During the study, footages were recorded by participants while walking to obtain real-time visual element data, including greenery, building and road visibility. And electroencephalogram (EEG) indicators of β/α, (α+θ)/β, θ/β and θ/α ratio were collected to represent levels of arousal, fatigue, attention and relaxation. Our findings suggested strong correlations between θ/α ratio with both greenery and road visibility. Conversely, other indicators were primarily influenced by greenery and building visibility. Regarding the combined impact of elements, the most positive emotions were observed when green visibility exceeded 51%. However, the peak alertness was achieved with building visibility between 5.2% and 31%. The lowest fatigue and the highest attention level were recorded under building visibility less than 5.2%, and the highest level of relaxation occurred with road visibility less than 10%. In terms of the influence of time, the entire walking process could be delineated by the five and 8 min marks, classified into novelty, adaptation and sustained phase based on the patterns of emotional changes observed in the participants. Consequently, the visual elements and their combinations, and duration play regulatory roles in pedestrians' emotional experiences

Tourism cloud management system: the impact of smart tourism

Abstract This study investigates the possibility of supporting tourists in a foreign land intelligently by using the Tourism Cloud Management System (TCMS) to enhance and better their tourism experience. Some technologies allow tourists to highlight popular tourist routes and circuits through the visualisation of data and sensor clustering approaches. With this, a tourist can access the shared data on a specific location to know the sites of famous local attractions, how other tourists feel about them, and how to participate in local festivities through a smart tourism model. This study surveyed the potential of smart tourism among tourists and how such technologies have developed over time while proposing a TCMS. Its goals were to make physical/paper tickets redundant via the introduction of a mobile app with eTickets that can be validated using camera and QR code technologies and to enhance the transport network using Bluetooth and GPS for real-time identification of tourists’ presence. The results show that a significant number of participants engage in tourist travels, hence the need for smart tourism and tourist management. It was concluded that smart tourism is very appealing to tourists and can improve the appeal of the destination if smart solutions are implemented. This study gives a first-hand review of the preference of tourists and the potential of smart tourism

Numerical Analysis of low voltage Arc Motion Process at Various Frequencies

A three-dimensional (3D) magneto-hydro-dynamic (MHD) model of air arc plasma is built to investigate the frequency effects on the arc motion process with different number of splitter plates. Based on this model, the arc voltage and current density are obtained. The arc motion time is normalized with the frequency and compared at different numbers of splitter plate. The result shows that the normalized time and the arc voltage peak increase with increases of the number of splitter plate

Sample-adaptive multiple kernel learning

Copyright © 2014, Association for the Advancement of Artificial Intelligence. Existing multiple kernel learning (MKL) algorithms indiscriminately apply a same set of kernel combination weights to all samples. However, the utility of base kernels could vary across samples and a base kernel useful for one sample could become noisy for another. In this case, rigidly applying a same set of kernel combination weights could adversely affect the learning performance. To improve this situation, we propose a sample-adaptive MKL algorithm, in which base kernels are allowed to be adaptively switched on/off with respect to each sample. We achieve this goal by assigning a latent binary variable to each base kernel when it is applied to a sample. The kernel combination weights and the iatent variables are jointly optimized via margin maximization principle. As demonstrated on five benchmark data sets, the proposed algorithm consistently outperforms the comparable ones in the literature