Absence of a True Vortex-Glass Phase above the Bragg Glass Transition Line in Bi-2212

In magnetic measurements on Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ (Bi-2212) single crystals, a general peak with a dynamical feature on both $S-H$ and $S-T$ curves was found with S the magnetic relaxation rate. At higher fields, the characteristic exponent $\mu$ becomes negative, together with the positive curvature of $logE$ vs. $logj$ and the scaling based on the 2D vortex glass theory or plastic creep theory, we conclude that the vortex motion above the second peak is plastic when $j\to 0$ and there is no vortex glass phase at finite temperatures in Bi-2212. The peak of S is then explained as the crossover between different meta-stable vortex states.Comment: 10 pages, 5 figures, To appear in Physica

Dimensional Crossover of Vortex Dynamics Induced by Gd Substitution on Bi2212 Single Crystals

The vortex dynamics of Bi$_2$Sr$_2$Ca$_{1-x}$Gd$_x$Cu$_2$O$_{8+\delta}$ single crystals is investigated by magnetic relaxation and hysteresis measurements. By substituting $Ca$ with $Gd$, it is found that the interlayer Josephson coupling is weakened and the anisotropy is increased, which leads to the change of vortex dynamics from 3D elastic to 2D plastic vortex creep. Moreover, the second magnetization peak, which can be observed in samples near the optimal doping, is absent in the strongly underdoped (with 2D vortex) region.Comment: 16 Pages, 6 Figures, To appear in Physica

AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden

The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, their deployment is hindered by the high cost of labelling the crop images to provide data for model training. This study examines the capabilities of semi-supervised and active learning to minimise effort when labelling cotton lint samples while maintaining high classification accuracy. Random forest classification models were developed using supervised learning, semi-supervised learning, and active learning to determine Egyptian cotton grade. Compared to supervised learning (80.20-82.66%) and semi-supervised learning (81.39-85.26%), active learning models were able to achieve higher accuracy (82.85-85.33%) with up to 46.4% reduction in the volume of labelled data required. The primary obstacle when using machine learning for Egyptian cotton grading is the time required for labelling cotton lint samples. However, by applying active learning, this study successfully decreased the time needed from 422.5 to 177.5 min. The findings of this study demonstrate that active learning is a promising approach for developing accurate and efficient machine learning models for grading food and industrial crops

An image processing and machine learning solution to automate Egyptian cotton lint grading

Egyptian cotton is one of the most important commodities for the Egyptian economy and is renowned globally for its quality, which is largely assessed and graded by manual inspection. This grading has several drawbacks, including significant labor requirements, low inspection efficiency, and influence from inspection conditions such as light and human subjectivity. This work proposes a low-cost solution to replace manual inspection with classification models to grade Egyptian cotton lint using images captured by a charge-coupled device camera. While this method has been evaluated for classifying US and Chinese upland cotton staples, it has not been tested on Egyptian cotton, which has unique characteristics and grading requirements. Furthermore, the methodology to develop these classification models has been expanded to include image processing techniques that remove the influence of trash on color measurements and extract features that capture the intra-sample variance of the cotton samples. Three different supervised machine learning algorithms were evaluated: artificial neural networks; random forest; and support vector machines. The highest accuracy models (82.13–90.21%) used a random forest algorithm. The models’ accuracy was limited by the human error associated with labeling the cotton samples used to develop the classification models. Unsupervised machine learning methods, including k-means clustering, hierarchical clustering, and Gaussian mixture models, were used to indicate where labeling errors occurred

Flux-lattice melting in two-dimensional disordered superconductors

The flux line lattice melting transition in two-dimensional pure and disordered superconductors is studied by a Monte Carlo simulation using the lowest Landau level approximation and quasi-periodic boundary condition on a plane. The position of the melting line was determined from the diffraction pattern of the superconducting order parameter. In the clean case we confirmed the results from earlier studies which show the existence of a quasi-long range ordered vortex lattice at low temperatures. Adding frozen disorder to the system the melting transition line is shifted to slightly lower fields. The correlations of the order parameter for translational long range order of the vortex positions seem to decay slightly faster than a power law (in agreement with the theory of Carpentier and Le Doussal) although a simple power law decay cannot be excluded. The corresponding positional glass correlation function decays as a power law establishing the existence of a quasi-long range ordered positional glass formed by the vortices. The correlation function characterizing a phase coherent vortex glass decays however exponentially ruling out the possible existence of a phase coherent vortex glass phase.Comment: 12 pages, 21 figures, final version to appear in Phys. Rev.

Existence of the Abrikosov vortex state in two-dimensional type-II superconductors without pinning

Theory alternative to the vortex lattice melting theories is advertised. The vortex lattice melting theories are science fiction cond-mat/9811051 because the Abrikosov state is not the vortex lattice with crystalline long-range order. Since the fluctuation correction to the Abrikosov solution is infinite in the thermodynamic limit (K.Maki and H.Takayama, 1972) any fluctuation theory of the mixed state should consider a superconductor with finite sizes. Such nonperturbative theory for the easiest case of two-dimensional superconductor in the lowest Landau level approximation is presented in this work. The thermodynamic averages of the spatial average order parameter and of the Abrikosov parameter $\beta_{a}$ are calculated. It is shown that the position H_{c4} of the transition into the Abrikosov state (i.e. in the mixed state with long-range phase coherence) depends strongly on sizes of two-dimensional superconductor. Fluctuations eliminate the Abrikosov vortex state in a wide region of the mixed state of thin films with real sizes and without pinning disorders, i.e. H_{c4} << H_{c2}. The latter has experimental corroboration in Phys.Rev.Lett. 75, 2586 (1995).Comment: 4 pages, 0 figure

A new approach for the limit to tree height using a liquid nanolayer model

Liquids in contact with solids are submitted to intermolecular forces inferring density gradients at the walls. The van der Waals forces make liquid heterogeneous, the stress tensor is not any more spherical as in homogeneous bulks and it is possible to obtain stable thin liquid films wetting vertical walls up to altitudes that incompressible fluid models are not forecasting. Application to micro tubes of xylem enables to understand why the ascent of sap is possible for very high trees like sequoias or giant eucalyptus.Comment: In the conclusion is a complementary comment to the Continuum Mechanics and Thermodynamics paper. 21 pages, 4 figures. Continuum Mechanics and Thermodynamics 20, 5 (2008) to appea

Critical Behavior of the Supersolid transition in Bose-Hubbard Models

We study the phase transitions of interacting bosons at zero temperature between superfluid (SF) and supersolid (SS) states. The latter are characterized by simultaneous off-diagonal long-range order and broken translational symmetry. The critical phenomena is described by a long-wavelength effective action, derived on symmetry grounds and verified by explicit calculation. We consider two types of supersolid ordering: checkerboard (X) and collinear (C), which are the simplest cases arising in two dimensions on a square lattice. We find that the SF--CSS transition is in the three-dimensional XY universality class. The SF--XSS transition exhibits non-trivial new critical behavior, and appears, within a $d=3-\epsilon$ expansion to be driven generically first order by fluctuations. However, within a one--loop calculation directly in $d=2$ a strong coupling fixed point with striking ``non-Bose liquid'' behavior is found. At special isolated multi-critical points of particle-hole symmetry, the system falls into the 3d Ising universality class.Comment: RevTeX, 24 pages, 16 figures. Also available at http://www.cip.physik.tu-muenchen.de/tumphy/d/T34/Mitarbeiter/frey.htm