Tag Archives: glibenclamide

br Results br Conclusion br Acknowledgement This work was supported



This work was supported in part by the Natural Science Foundation of China under Grant No.61471216 and in part by the Special Foundation for the Development of Strategic Emerging Industries of Shenzhen under Grant No.JCYJ20150831192224146 and No.JCYJ20150601165744635.

Image classification is a significant branch of computer vision. In this branch, the representation based classification methods have attracted considerable attention. A good representation for target images is greatly beneficial to improve the performance of image classification [1,2]. An object can be distinguished from the others when its image is well represented by the other images from this object. The combination of multiple representations of images is an effective method to improve the performance of representation based methods [3,4]. Therefore, it glibenclamide is an important and meaningful topic to find a proper representation for representation based image classification methods.
At present, face recognition has been studied widely and many useful methods have been presented [5–9]. However, we still face with some great challenges. Different poses and expressions, various intensities of illuminations and insufficient training samples seriously influence the recognition effects. In order to address these challenges, people have made many efforts. For various illuminations, by handling the original images to enhance pixels with moderate intensities of the original images and reduce the importance of other pixels, Xu et al. [10] obtained the complementary images to improve the accuracy of image classification. Producing the mirror image of the face and integrating the original face image and its mirror image are also useful to improve the recognition accuracy of representation-based face recognition [11]. For the problem of insufficient training samples, Huang et al. [12] proposed a robust kernel collaborative representation classification method based on virtual samples for face recognition to reduce the influence of insufficient training samples. The use of symmetrical face images generated from original face images is very useful to overcome the problem of varying appearances of faces [13,14]. Until now, many works focus on generating virtual or synthesized face images to enhance the recognition accuracy [15–19]. The simultaneous use of original face images and their virtual face images can improve the accuracy of face recognition. What is more, several works have shown that virtual image obtained by exploiting the adjacent rows of original image are also useful for image classification [20–24].
Wright et al. [25] proposed the sparse representation classification (SRC) algorithm which can reach satisfactory result. There are many SRC algorithms [26–30]. However, the original SRC algorithm with the constraint of l minimization is time consuming. Zhang et al. [31] proved that the essence to obtain the satisfactory performance of the SRC algorithm is the collaborative representation but not the sparsity, and proposed a collaborative representation classification (CRC) method with the constraint of l minimization. CRC methods can obtain comparative performance to SRC algorithm, but is much faster than SRC algorithm. Various representation methods with the constraints of l minimization are also proposed, such as linear regression classification (LRC) [32], and two phase sparse representation [33–35]. They not only used simple constraint conditions but also achieved satisfactory recognition accuracy.
The remainder of Helper virus paper is organized as follows. Section 2 presents the proposed novel representation method of images. Section 3 describes the underlying rationale of the proposed method. Section 4 shows the experimental results. Section 5 provides the conclusions of this paper.

The proposed method

In parallel to this singular and proper to

In parallel to this singular and proper to ZnO multi-functionality, it has the additional specificity of being able to be synthesized in various shapes within its nano-scaled form such as nano-spheres, nano-tetrapods, nano-platelets, nano-discs, nano-rings, nano-belts, nano-wires, nano-rods, nanotubes … [70]. Relatively to oxides family, it is effortless to grow oriented ZnO nano-rods on several type of crystalline or amorphous substrates by various physical or chemical processes. This later exceptional quality of ZnO to be grown in the attractive form of oriented nano-rods combined with its intrinsic UV sensitivity related to its surface oxygen photo-activity, could be used to design photo-induced reversible hydrophilic/hydrophobic surfaces. As it will be shown below in this section, this exceptional tunable hydrophilicity/hydrophobicity of ZnO nano-rods deposited onto a substrate is related to the surface roughness of the deposit itself as depicted in Fig. 9. Indeed, the trick to reach super-hydrophobic states consists of achieving rough (or textured) hydrophobic solids (Fig. 10). Because the liquid doesn\’t enter inside the roughness, a drop sits on a patchwork of solid and air, which leads to contact angles typically between 165° and 175°. More interestingly, this effect is also characterized by a very low contact angle hysteresis (Δθ < 10°), because the air trapped below the drop “homogenizes” the solid. Hence, the surface roughness of such ZnO nano-rods has a considerable influence on both the contact angle itself and its hysteresis. An important aspect is the length scale involved. For not too rough surfaces (s significantly below the wavelength of light λ), the effect of surface roughness can be ascribed by the so-called Wenzel equation [72]. The equation predicts that if a molecularly hydrophobic surface is rough, the appearance is that of an even more hydrophobic surface. If a glibenclamide surface is roughened it becomes more hydrophilic. Most solid surface are also chemically inhomogeneous and in that regard Cassie considered such ###http://www.apexbt.com//media/diy/images/structpng/A1005.png####a case of a smooth but chemically patch wise heterogeneous surface [73]. Consequentially, if there are two different kinds of region with contact angle θ1 and θ2 as depicted in Fig. 10c, which occupy the surface ratios f1 and f2 the apparent average contact angle is cosθapparent = f1cosθ1 + f2cosθ2. θ1 and θ2 are the Young contact angles on large domains of 1 and 2, respectively [74]. For drop coexisting with an impregnating the solid texture, f1 = fS, θ1 = θ, f2 = 1 − fS and θ2 = 0, fS is the surface area of the second stage (i.e. the top of the spikes, or the top of the ridges) normalized by the total surface area of the sample (fS < 1) so that the effective contact angle to be given by the relation [75] ;  [76]: cosθapparent = −1 + fS (cosθ + 1). As a direct application in this section, acetyl CoA shown that Cassie–Baxter type oriented ZnO nano-rods structures exhibit unique photo-induced and tunable wettability of water droplets under UV radiations. It demonstrates that the wettability of milli and micro-droplets of water onto nano-structured ZnO surfaces can be optically controlled in a reversible way. More accurately, this section reports on controllable wettability of water droplets onto aligned W-doped ZnO nanorods films fabricated by pulsed laser deposition. These 1-D inorganic oxide films exhibit hydrophobicity and hydrophilicity at different conditions, while the wettability can be reversibly switched by UV irradiation. Such a special wettability would further the applications of ZnO films to additional important fields such as biology and photonics. While the water wettability is related to the texture of doped ZnO nano-rods, the UV photo-activity seems to be caused by the polarity of the ZnO basal surfaces. If this argument is valid, the wettability would be crystallographic orientation dependent not only in the case of ZnO but common to other oxides such as nano-scaled TiO2.