Single-walled carbon nanotube bundle under hydrostatic pressure studied by the first-principles calculations

The structural, electronic, optical and vibrational properties of the collapsed (10,10) single-walled carbon nanotube bundle under hydrostatic pressure have been studied by the first-principles calculations. Some features are observed in the present study: First, a collapsed structure is found, which is distinct from both of the herringbone and parallel structures obtained previously. Secondly, a pseudo-gap induced by the collapse appears along the symmetry axis \textit{$\Gamma$X}. Thirdly, the relative orientation between the collapsed tubes has an important effect on their electronic, optical and vibrational properties, which provides an efficient experimental method to distinguish unambiguously three different collapsed structures.Comment: 14 pages, 6 figure

Image Denoising via Nonlinear Hybrid Diffusion

A nonlinear anisotropic hybrid diffusion equation is discussed for image denoising, which is a combination of mean curvature smoothing and Gaussian heat diffusion. First, we propose a new edge detection indicator, that is, the diffusivity function. Based on this diffusivity function, the new diffusion is nonlinear anisotropic and forward-backward. Unlike the Perona-Malik (PM) diffusion, the new forward-backward diffusion is adjustable and under control. Then, the existence, uniqueness, and long-time behavior of the new regularization equation of the model are established. Finally, using the explicit difference scheme (PM scheme) and implicit difference scheme (AOS scheme), we do numerical experiments for different images, respectively. Experimental results illustrate the effectiveness of the new model with respect to other known models

Hyperspectral Remote Sensing Estimation Models of Aboveground Biomass in Gannan Rangelands

AbstractAccurate estimates of grass biomass can provide valuable information about the productivity and functioning of rangelands and grassland ecological resource utilizing more reasonable. In order to improve the biomass quantitative analysis with hyperspectral remote sensing data, a field experiment was carried out in Gannan rangelands, Gansu province. To achieve this objective, fresh grass aboveground biomass and hyperspectral canopy reflectance were collected at four types pasture in august 2007. On the base of the analysis of spectral characteristic of four grasslands and correlation between original spectral, hyperspectral feature variables and aboveground biomass of four rest grazing grasslands, the experiment data were classified two groups. One group was used as the training sample to build the regression of models with the one-sample linear method, the nonlinear method and stepwise analysis method, another group was used to the testing sample to predict the precision of regression models. Results show that the regression of quadratic model using RVI provide a better univariate regression involving hyperspectral indices for grass aboveground fresh biomass estimation compared other models in Gannan rangelands, the estimation standard deviation was 0.178 (kg/m2), In conclusion, the results of this paper indicate that the grassland biomass can be estimated at the canopy level using the hyperspectral reflectance

Effect of variable heat treatment modes on microstructures in Fe-Cr-B cast iron alloy

The effect of heat treatment mode on the microstructure of Fe-Cr-B cast iron alloys was investigated in this paper by comparing the difference of precipitation patterns of secondary particles after thermal cycling treatment (TCT) with those after normal heat treatment (NHT). No obvious differences were found in precipitation patterns of secondary particles between TCT and NHT when experimental temperature was below Ar. However, when temperature was over Ar, there were significant differences, with secondary particles prominently segregated at the grain boundaries under TCT, while the particles evenly distributed in the matrix under NHT. The reason for the microstructure differences could be associated with the development of non-equilibrium segregation of boron during TCT

Hybrid resource provisioning for cloud workflows with malleable and rigid tasks

[EN] In cloud computing, reserved and on-demand instances are generally provided by service providers. Hybridization of the two alternatives can considerably save costs when renting resources from the cloud. However, it is a big challenge to determine the appropriate amount of reserved and on-demand resources in terms of users' requirements. In this paper, the workflow scheduling problem with both reserved and on-demand instances is considered. The objective is to minimize the total rental cost under deadline constrains. The considered problem is mathematically modeled. A multiple sequence-based earliest finish time method is proposed to construct schedules for the workflows. Four different rules are used to generate initial task allocation sequences. Types and quantities of resources are determined by a free time block-based schedule construction mechanism. New sequences are generated by a variable neighborhood search method. Experimental and statistical analyses and results demonstrate that the proposed algorithm algorithm generates considerable cost savings when compared to the algorithms with only on-demand or reserved instances.l This work is supported by the National Key Research and Development Program of China (No. 2017YFB1400801), the National Natural Science Foundation of China (Nos. 61572127, 61872077, 61832004) and Collaborative Innovation Center of Wireless Communications Technology. Rub~en Ruiz is supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD-Optimization of scheduling problems in container yards" (No. DPI2015-65895-R) partly financed with FEDER funds.Chen, L.; Li, X.; Guo, Y.; Ruiz García, R. (2021). Hybrid resource provisioning for cloud workflows with malleable and rigid tasks. IEEE Transactions on Cloud Computing. 9(3):1089-1102. https://doi.org/10.1109/TCC.2019.2894836S108911029

Parameterized hemodynamic response function data of healthy individuals obtained from resting-state functional MRI in a 7T MRI scanner

Functional magnetic resonance imaging (fMRI), being an indirect measure of brain activity, is mathematically defined as a convolution of the unmeasured latent neural signal and the hemodynamic response function (HRF). The HRF is known to vary across the brain and across individuals, and it is modulated by neural as well as non-neural factors. Three parameters characterize the shape of the HRF, which is obtained by performing deconvolution on resting-state fMRI data: response height, time-to-peak and full-width at half-max. The data provided here, obtained from 47 healthy adults, contains these three HRF parameters at every voxel in the brain, as well as HRF parameters from the default-mode network (DMN). In addition, we have provided functional connectivity (FC) data from the same DMN regions, obtained for two cases: data with deconvolution (HRF variability minimized) and data with no deconvolution (HRF variability corrupted). This would enable researchers to compare regional changes in HRF with corresponding FC differences, to assess the impact of HRF variability on FC. Importantly, the data was obtained in a 7T MRI scanner. While most fMRI studies are conducted at lower field strengths, like 3T, ours is the first study to report HRF data obtained at 7T. FMRI data at ultra-high fields contains larger contributions from small vessels, consequently HRF variability is lower for small vessels at higher field strengths. This implies that findings made from this data would be more conservative than from data acquired at lower fields, such as 3T. Results obtained with this data and further interpretations are available in our recent research study (Rangaprakash et al., in press) [1]. This is a valuable dataset for studying HRF variability in conjunction with FC, and for developing the HRF profile in healthy individuals, which would have direct implications for fMRI data analysis, especially resting-state connectivity modeling. This is the first public HRF data at 7T