A Maxwell-vector p-wave holographic superconductor in a particular background AdS black hole metric

We study the p-wave holographic superconductor for AdS black holes with planar event horizon topology for a particular Lovelock gravity, in which the action is characterized by a self-interacting scalar field nonminimally coupled to the gravity theory which is labeled by an integer $k$. As the Lovelock theory of gravity is the most general metric theory of gravity based on the fundamental assumptions of general relativity, it is a desirable theory to describe the higher dimensional spacetime geometry. The present work is devoted to studying the properties of the p-wave holographic superconductor by including a Maxwell field which nonminimally couples to a complex vector field in a higher dimensional background metric. In the probe limit, we find that the critical temperature decreases with the increase of the index $k$ of the background black hole metric, which shows that a larger $k$ makes it harder for the condensation to form. We also observe that the index $k$ affects the conductivity and the gap frequency of the holographic superconductors.Comment: 14 pages, 6 figure

Fully Probabilistic Analysis of FRP-to-Concrete Bonded Joints Considering Model Uncertainty

This work presents a full reliability-based analysis framework for fiber-reinforced polymer(FRP)-to-concrete bonded joints considering model uncertainty. Eight frequently used bond strength models for FRP-to-concrete bonded joints were calibrated by defining a model factor. A total of 641 well-documented tests were considered. Four of the eight models had model factors that correlated with input design parameters and the systematic part of the model factor was removed by a regression equation f. By doing this type of characterization, all eight model factors could be comparatively uniform and described by lognormally distributed random variables. The merit of the uniform model uncertainties after calibration for the eight models was established by the reliability analysis. This study improves the predictability of concrete strengthened with fiber composites and provides useful suggestions on their model uncertainties in engineering practice

Semiparametric efficient estimation of genetic relatedness with machine learning methods

In this paper, we propose semiparametric efficient estimators of genetic relatedness between two traits in a model-free framework. Most existing methods require specifying certain parametric models involving the traits and genetic variants. However, the bias due to model misspecification may yield misleading statistical results. Moreover, the semiparametric efficient bounds for estimators of genetic relatedness are still lacking. In this paper, we develop semiparametric efficient estimators with machine learning methods and construct valid confidence intervals for two important measures of genetic relatedness: genetic covariance and genetic correlation, allowing both continuous and discrete responses. Based on the derived efficient influence functions of genetic relatedness, we propose a consistent estimator of the genetic covariance as long as one of genetic values is consistently estimated. The data of two traits may be collected from the same group or different groups of individuals. Various numerical studies are performed to illustrate our introduced procedures. We also apply proposed procedures to analyze Carworth Farms White mice genome-wide association study data.Comment: 46pages,9 tables, 1 figur

Variational Denoising Network: Toward Blind Noise Modeling and Removal

Blind image denoising is an important yet very challenging problem in computer vision due to the complicated acquisition process of real images. In this work we propose a new variational inference method, which integrates both noise estimation and image denoising into a unique Bayesian framework, for blind image denoising. Specifically, an approximate posterior, parameterized by deep neural networks, is presented by taking the intrinsic clean image and noise variances as latent variables conditioned on the input noisy image. This posterior provides explicit parametric forms for all its involved hyper-parameters, and thus can be easily implemented for blind image denoising with automatic noise estimation for the test noisy image. On one hand, as other data-driven deep learning methods, our method, namely variational denoising network (VDN), can perform denoising efficiently due to its explicit form of posterior expression. On the other hand, VDN inherits the advantages of traditional model-driven approaches, especially the good generalization capability of generative models. VDN has good interpretability and can be flexibly utilized to estimate and remove complicated non-i.i.d. noise collected in real scenarios. Comprehensive experiments are performed to substantiate the superiority of our method in blind image denoising.Comment: 11 pages, 4 figure

Rockfall monitoring based on multichannel synthetic aperture radar

Rockfall influences the safety of infrastructure and transportation lines normal operation, application of SAR technology to monitor the rockfall could predict rockfall disaster. One of the most important factors in rockfall target detection is the signal-to-clutter-plus noise ratio (SCNR) after clutter suppression, which should be maximized before rockfall target detection. Through analyzing remainder rockfall target characteristic after clutter suppression, the method of removing quadratic FM component introduced by platform velocity and along track velocity of rockfall target is proposed. After removing quadratic FM component by Dechirp technology, remainder rockfall target is focused in Doppler region image and the SCNR of remainder rockfall target is maximized. So, it has a preferable result in rockfall target detection. To resolve the contradiction between calculated amount and the accuracy of along track velocity, this paper adopts the technology of gradual approach. The effectiveness of the presented method is demonstrated by both theoretic analysis and simulated data

Translational progress on tumor biomarkers

There is an urgent need to apply basic research achievements to the clinic. In particular, mechanistic studies should be developed by bench researchers, depending upon clinical demands, in order to improve the survival and quality of life of cancer patients. To date, translational medicine has been addressed in cancer biology, particularly in the identification and characterization of novel tumor biomarkers. This review focuses on the recent achievements and clinical application prospects in tumor biomarkers based on translational medicine.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/115962/1/tca12294.pd

Simultaneous utilization of glucose and xylose for lipid production by Trichosporon cutaneum

<p>Abstract</p> <p>Background</p> <p>Biochemical conversion of lignocellulose hydrolysates remains challenging, largely because most microbial processes have markedly reduced efficiency in the presence of both hexoses and pentoses. Thus, identification of microorganisms capable of efficient and simultaneous utilization of both glucose and xylose is pivotal to improving this process.</p> <p>Results</p> <p>In this study, we found that the oleaginous yeast strain <it>Trichosporon cutaneum </it>AS 2.571 assimilated glucose and xylose simultaneously, and accumulated intracellular lipid up to 59 wt% with a lipid coefficient up to 0.17 g/g sugar, upon cultivation on a 2:1 glucose/xylose mixture in a 3-liter stirred-tank bioreactor. In addition, no classic pattern of diauxic growth behavior was seen; the microbial cell mass increased during the whole culture process without any lag periods. In shake-flask cultures with different initial glucose:xylose ratios, glucose and xylose were consumed simultaneously at rates roughly proportional to their individual concentrations in the medium, leading to complete utilization of both sugars at the same time. Simultaneous utilization of glucose and xylose was also seen during fermentation of corn-stover hydrolysate with a lipid content and coefficient of 39.2% and 0.15 g/g sugar, respectively. The lipid produced had a fatty-acid compositional profile similar to those of conventional vegetable oil, indicating that it could have potential as a raw material for biodiesel production.</p> <p>Conclusion</p> <p>Efficient lipid production with simultaneous consumption of glucose and xylose was achieved in this study. This process provides an exciting opportunity to transform lignocellulosic materials into biofuel molecules, and should also encourage further study to elucidate this unique sugar-assimilation mechanism.</p

Electrochemical Reducation of TiO2/Al2O3/C to Ti3AlC2 and Its Derived Two-Dimensional (2D) Carbides

Ti3AlC2 has been directly synthesized from TiO2/Al2O3/C mixture precursors (3TiO2/0.5Al2O3/1.5C and 2TiO2/0.5Al2O3/C) by a molten salt electrolysis process at 900?C and 3.2 V in molten CaCl2. The influence of initial carbon content on the electrosynthesized products has been investigated. The result shows that the main phase of the electrosynthesized products changes from Ti3AlC to Ti2AlC and then to Ti3AlC2 with the increasing carbon content, and the electrosynthesized Ti3AlC2 is carbon deficient. The morphology observation shows that the electrosynthesized Ti3AlC2 particles possess smooth surfaces and dense flake-like microstructure. The reaction mechanism of the electroreduction of TiO2/Al2O3/C mixture precursor has been discussed based on the time- and position-dependent phase constitution analysis. In addition, two-dimensional (2D) Ti3AlC2-derived carbides, i.e., Ti3C2Tx and TiCx have been successfully prepared from the electrosynthesized Ti3AlC2 by a chemical etching process and an electrochemical etching process, respectively. Both derived carbides exhibit the similar layered structure, in which single layer carbides are composed of plentiful nanometer carbides. It is suggested that the molten salt electrolysis process has a great potential to be used for the facile synthesis of Mn+1AXn phases (such as Ti3AlC2) from their oxides precursors, and the synthesized Mn+1AXn phases can be further converted into 2D carbidesauthorsversionPeer reviewe