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We proposed a gradient multi-Helmholtz cavities muffler for low-frequency broad bandgaps. The simulation and experiment results of our analysis claimed that this structure can reduce noise in resonance frequency, and the range of absorption frequency is enlarged with the number of cavities increase, almost 24.75 times that of one cavity. The transmission loss around the center frequency also increases from 45dB to 100dB. In addition, the relationship between noise attenuation and the distance of the cavities is also studied. Results show a deeper valley appears in the transmission loss curve with an increase of the distance, which greatly affects the sound absorption performance.

To address the issues of low accuracy and significant noise impact in edge continuity detection of art and design images, this paper proposes a visual attention-based edge continuity detection method for art and design images. Determine the edge position points of art and design images using the Laplace operator, and obtain the horizontal and vertical gradients of the image edges by calculating the first-order difference method to determine the amplitude of the image edge gradient; By using the maximum and minimum operations in binary morphology instead of intersection and union operations, the image grayscale is determined, and the HSI color space features are determined to complete the edge feature extraction of art and design images. Using the SUSAN operator to clarify the kernel similarity zone of the edges of art and design images, removing similar edge image pixels, using spatial domain denoising and frequency domain denoising to reduce the edge noise of art and design images, and achieving edge preprocessing of art and design images. Introducing visual attention mechanism to transform the pixel space of edge features in art and design images, performing threshold segmentation on the edges of art and design images, and continuously annotating the segmented edge pixels. Introducing loss function to improve the convergence speed of detection, constructing an art and design image edge continuity detection model based on visual attention mechanism, outputting detection results, and implementing research. The experimental results show that the proposed method has better continuity due to the successful avoidance of noise interference by introducing visual attention mechanisms and other operations. As the number of detected pixels increases, the detection deviation rate of the proposed method remains between 0.02% and 0.03%. The proposed method has a lower detection bias rate and can effectively improve the performance of edge continuity detection in art and design images.

We present evidence against the well-established education–health gradient by relating education to measured hypertension status in 5,873 men and 6,152 women aged 40$+$ in Indonesia. Once a basic set of covariates was controlled for, the two variables were not statistically significantly related. We argue that this lack was due to neglect of chronic diseases. It appears that the assumption of full information in theories on the education–health gradient is too strong to be applied to the developing world. Therefore, more information needs to be provided to the public regarding the seriousness of chronic diseases and preventive and curative methods.

In order to solve the problems of poor adaptability when setting threshold and the high probability of detecting pseudo-edges in the existing methods of edge detection, the paper proposes an adaptive edge-detection method based on histogram. Multi-scale wavelet transform is used to preprocess the image, the image details are highlighted obviously, and it also can avoid the effect of manual setting filter coefficients. Difference of gray values between the pixels of local area are used to calculate the gradients comprehensively, it extends the gradient direction to four directions. When calculating the gradient of edge pixel, the four directions make the expression of the gradients of edge points more perfect and avoid the edge points missing. The adaptive method is used to compute the threshold of edge-detection, the image is represented by histogram. Then use the ratio of the number of pixels in the bar and the total numbers of pixels to set the initial threshold. The regions on both sides of the initial threshold are used to calculate the high threshold and low threshold until the reasonable error between the current threshold and the previous threshold is very small iteratively. The acquired threshold makes the self-adaptability more reasonable and stronger, it also avoids the detection errors, the connection errors and the pseudo-edges which are caused by setting threshold artificially. The experimental results show that the proposed algorithm of edge detection has a good effect of preserving edge detail and filtering noise of image.

In this paper, the gradient anechoic coating whose density changes exponentially along direction of thickness is investigated. A numerical model is established by finite element method (FEM) to analyze the underwater sound absorption performance under different density distribution. The calculation results show that the exponential anechoic coating has better sound absorption performance compared with the homogeneous medium and linear anechoic coating. In addition, a discrete layered method is proposed to achieve gradient characteristics. In order to change the equivalent density of each layer, periodically distributed semi-cylindrical steel scatterers with different diameters are embedded in each layer. Therefore, the density function of the whole coating changes in exponential gradient with stepped function. Based on the sound absorption mechanism of multiple scattering and waveform conversion, the sound absorption is improved in low-frequency band from 0 Hz to 1000 Hz. The exponential gradient anechoic coating has potential applications in underwater sound absorption and vibration control.

In the neural network literature, many preprocessing techniques, such as feature de-correlation, input unbiasing and normalization, are suggested to accelerate multilayer perceptron training. In this paper, we show that a network trained with an original data set and one trained with a linear transformation of the original data will go through the same training dynamics, as long as they start from equivalent states. Thus preprocessing techniques may not be helpful and are merely equivalent to using a different weight set to initialize the network. Theoretical analyses of such preprocessing approaches are given for conjugate gradient, back propagation and the Newton method. In addition, an efficient Newton-like training algorithm is proposed for hidden layer training. Experiments on various data sets confirm the theoretical analyses and verify the improvement of the new algorithm.

This paper aims to minimize power consumption and sustain customer satisfaction for a datacenter. In some distributed computing environments, especially in a cloud computing environment, the most concerned issue for a datacenter designer is power consumption instead of its speed. This is because the storage devices always consume much electricity. To save power, we can assign to each device proper workload and turn off some spare devices. Therefore, partitioning data and allocating them among the storage devices becomes the first priority. However, due to some complex constraints, the data partition problem is very time-consuming or even NP-hard. Therefore, we propose a gradient-based method to deal with the problem. The novelty of the proposed method lies in that the discretized combinatorial problem is solved by a calculus-based technique. Experimental results show that significant energy is saved by shutting down as many uncalled-for devices as possible. The results also suggest that other similar optimization problems can be solved by the linearly-convergent method.

Recently many techniques, e.g., Google or AltaVista, are available for classifying well-organized, hierarchical crisp categories from human constructed web pages such as that in Yahoo. However, given the current rate of web-page production, there is an urgent need of classifiers that are able to autonomously classify web-page categories that have overlaps. In this paper, we present a competitive learning method for this problem, which based on a new objective function and gradient descent scheme. Experimental results on real-world data show that the approach proposed in this paper gives a better performance in classifying randomly-generated, knowledge-overlapped categories as well as hierarchical crisp categories.

The aim of this paper is to establish an approach to quantitatively determine the elasto-plastic parameters of the Mo-modified Ti obtained by the plasma surface alloying technique. A micro-indentation test is conducted on the surface under 10N. Considering size effects, nanoindentation tests are conducted on the cross-section with two loads of 6 and 8mN. Assuming nanoindentation testing sublayers are homogeneous, finite element reverse analysis is adopted to determine their plastic parameters. According to the gradient distributions of the elasto-plastic parameters with depth in the Mo-modified Ti, two types of mathematical expressions are proposed. Compared with the polynomial expression, the linear simplified expression does not need the graded material to be sectioned and has practical utility in the surface treatment industry. The validation of the linear simplified expression is verified by the micro-indentation test and corresponding finite element forward analysis. This approach can assist in improving the surface treatment process of the Mo-modified Ti and further enhancing its load capacity and wear resistance.

Living on a Gradient: A Glimpse on How Genetic Evolution shapes High-Altitude Tibetans’ Survival

Ascletis as the First Chinese Company to File Clinical Applications for Interferon-Free HCV Treatment

Anti-Cancer formulation derived from Traditional Chinese Medicine receives FDA’s approval for Phase III Clinical Trial

Kunming Institute of Zoology unveils the most diverse form of HIV-1 viral strain transmission at Burma Border

Chinese Biologists Find Duckweed to Tackle Water Pollution

A new publication in Science on the parvalbumin-positive excitatory visual pathway

Colorization is a color manipulation mechanism employing user-assisted color hints for changing grayscale images into colored ones. Several colorization algorithms have been constructed, and many of these methods are able to produce appropriately colorized images given a surprisingly sparse set of hints supplied by the user. However, these color images may not in fact look realistic. Moreover, the contrast in the colorized image may not match the gradient perceived in the original grayscale image. We argue that it is this departure from the original gradient that contributes to the unreal appearance in some colorizations. To correct this, we make use of the Di Zenzo gradient of a color image derived from the structure tensor, and adjust the colorized image such that the Di Zenzo definition of the maximum-contrast gradient agrees with the gradient in the original gray image. We present a heuristic method to this end and guided by this approach devise an optimization-based method. Our gradient projection tends to result in more natural-looking images in the resulting adjusted colorization. To explore the proposed method we utilize minimalist sets of color hints and find in particular that "hotspots" of unrealistic color are subdued into regions of more realistic color. This paper is not aimed at introducing a new basic colorization but instead our method is meant to make any colorization look more realistic; we demonstrate that this is the case for several different basic methods. In fact, we even find that a very simplistic colorization algorithm can be used provided the projection proposed here is then used to make the colorization more realistic looking.

The aim of this paper is to study the existence of compact multiply Einstein warped products $M=B{\times }_{f_1}{F}_1{\times }_{f_2}{F}_2\times \cdots {\times }_{f_n}{F}_n$, $f_i:B\to (0,\infty )$, $f_i\in {{𝒞}}^{\infty }(B)$ for every $i\in \{1,\dots ,n\}$, $n\ge 1$. We prove the following :

• (a)$\lambda >0$ where $\lambda$ is the Einstein constant of $M$,
• (b)${\mu }_i>0$ where ${\mu }_i$ is the Einstein constant of $F_i$ for every $i\in \{1,\dots ,n\}$,
• (c)$dimB\ge 2$ and $dim{F}_i\ge 2$ for every $i\in \{1,\dots ,n\}$,
• (d)$dimB\ge 3$ for equal warping functions.

The propagation of compaction waves in layered cellular material subjected to air-blast is analyzed to examine the mechanism of compaction wave and reveal the phenomena that develop at the interface between the cellular layers. Similar to the previous studies of cellular materials under dynamic loading, the topology of cellular materials is neglected and homogeneous properties are assumed. The rigid-perfectly plastic-locking (R-PP-L) material idealization and the simple shock theory are employed to analyze the compaction situations. Analytical solutions for compaction wave propagation of double-layer cellular materials with two gradient-arrangements under air-blast loading have been worked out. The densification wave occurs at the blast end and then gradually propagates to the distal end for layers’ densities increase in the propagation direction (positive gradient). While compaction waves simultaneously form in both layers and propagate to the distal end in the same direction for the negative gradient. The finite element (FE) models using the Voronoi technique are carried out with practical aluminum foam to verify the predictions of the theoretical analysis. The potential of layered cellular materials to design efficient structural components under air-blast load is discussed, which would outperform their corresponding single counterpart with equal mass.

The influence of a multi-layer core on the blast response of composite sandwich cylinders under internal explosive loading was investigated. Experiments were conducted first to obtain the fundamental deformation and failure patterns of composite cylinders with uniform, double-layer, and triple-layer profile cores. They were compared with the finite element model for prediction with good agreement. The mechanisms of energy absorption and deformation of a composite sandwich were explored by parametric analysis. Experimental results indicated that compaction wave in a double-layer core was initiated from the inner face sheet and then propagated to the outer face sheet when the gradient was positive; however, the core densification started at the inner surfaces of both layers and propagated to the outer face sheet together. The maximum radial deflection decreased with increasing face sheet thickness or decreasing blast loading. The percentage of energy absorbed by the core increased with decreasing face sheet thickness or increasing blast loading. This study revealed a possibility to reduce the maximum deflection (same structural mass and same energy absorption) for the sandwich cylinders by using a proper core distribution.

This paper has the objective of studying the propagation of surface waves in a transversely isotropic medium based on nonlocal strain gradient theory (NSGT). A characteristics equation for the existence of surface waves is discussed. This equation could be easily reduced to the ones of the gradient strain theory, nonlocal theory and classical theory. It has also been concluded that there exist escape frequency and cut-off frequency for the wave propagating in size-dependent materials based on the NSGT. Dispersion equation for the propagation of Rayleigh-type waves at the free surface has been derived. The effect of the nonlocal parameter, the strain gradient parameter on the Rayleigh wave propagation is considered through some numerical examples.

Constructivism was first popularised by Bruner (1960). The underlying theme in Bruner’s theoretical framework is that learning is an active process in which learners construct new ideas or concepts based upon their prior knowledge. This chapter describes how constructivism can be realised in instruction through a lesson design involving a carefully crafted task on the topic of Gradient of Function Curves at a point. The task affords opportunities to activate and differentiate students’ prior knowledge to generate, explore, critique and refine methods for problem solving. The lesson design allows teachers to first understand what students know about a new concept based on students’ representation and solution methods (RSMs) collected from the group work before the teacher teaches the canonical concept during lesson consolidation. The task, coupled with skillful facilitation and lesson consolidation built upon students’ RSMs, can help students develop a deep understanding of the targeted concept. Implications of such constructivist learning design on teachers’ classroom practice are also discussed.

A method of image enhancement based on the wavelet transformation is presented. In this method, a wavelet transformation is applied to the number plate image firstly, then the gradient is computed with the detail coefficients at each level, which is used to enhance the image. As the experimental results show, this method can improve the number plate image's contrast. Further, it builds a good base for future segmentation or recognition.

We consider Markov chains of order d that, satisfy a conditional constraint of the form E(a_ϑ (X_i-1, X_i) | X_i-1) = 0, where X_i-1 = (X_i-1, …, X_i-d). These comprise quasi-likelihood models and nonlinear and conditionally heteroscedastic autoregressive models with martingale innovations. Estimators for ϑ can be obtained from estimating equations . We review different criteria for choosing good weights W_ϑ(X_i-1). They usually lead to weights that depend on unknown features of the transition distribution and must be estimated. We compare the approach via estimating functions with other ways of constructing estimators for ϑ, and discuss efficiency of the estimators in the sense of Hájek and LeCam. Analogous comparisons may be made for regression models.

Band filter gradient (BFG) rugate porous silicon (PSi) was generated by an electrochemical etching of silicon wafer through an asymmetric electrode configuration in aqueous ethanolic HF solution. BFG rugate PSi prepared from anisotropic etching conditions displayed the reflection band whose reflection maximum varied spatially across the PSi. BFG rugate PSi displayed a prismatic reflection due to the disproportion of current from the position of cathode across the silicon wafer. In this work, we have developed a method to create planar gradient refractive index in Si substrate. Reflection peak of BFG rugate PSi was shifted to shorter wavelength and its pore sizes and film thickness were decreased across the silicon wafer.

Good adhesion between the reinforcement particle and the metal matrix is a prerequisite for the wider acceptance and use of metal matrix composites. In this study, composites containing up to 12.6 volume percentage of SiC particulate reinforcement, in an LM 10 matrix, were cast using the vortex method. After solidification of the composite and during its cooling to room temperature, tensile and compressive stresses would be setup in the matrix and reinforcement particle respectively, due to differences in their coefficient of thermal expansion. In a well bonded composite, this causes plastic deformation to occur in the matrix near the interface in order to accommodate the stresses. This plastic deformation would result in an increase in the hardness near the particle-matrix interface. In a poorly bonded cast composite, such stress fields would be absent. In this study, the stress field around a reinforcement particle was used to calculate the plastic zone radius around the particle. The hardness gradient near the particle-matrix interface was measured with a microhardness tester under 10 gf load. Preliminary results indicate that the presence of a significant hardness gradient within the plastic zone radius indicates good bonding between the particle and the matrix.