Electronic subband of single Siδ-doped GaAs structures

We have theoretically investigated the subband structure of single Si δ -doped GaAs inserted into a quantum well at T =  0 K. We will discuss the influence of the δ -doping concentration, the δ -layer thickness and diffusion of donor impurities. The spread of the impurities are taken into account in two different models: (i) a uniform distribution and (ii) a nonuniform distribution. In this paper, the nonuniform distribution is different from the Gaussian distribution use of other authors. The electronic structures have been calculated by solving the Schrödinger and Poisson equations self-consistently. We thus find the confining potential, the subband energies and their eigen envelope functions, the subband occupations and Fermi energy.     

Nanofiltration in non-aqueous solutions by porous silica–zirconia membranes

Porous membranes having various average pore sizes, ranging from 1 to 4 nm, were prepared from silica–zirconia composite colloidal sols by sol–gel processes, and were used for nanofiltration (NF) experiments in non-aqueous solutions of ethanol and methanol. Silica–zirconia membranes, which were tested in pure alcohol solutions for the first time after the preparation of the membrane, showed a gradual decrease in flux for approximately 100 h and then reached a steady flux. When the feed, after reaching the steady flux with ethanol, was changed to another alcohol, steady flux was attained after only several hours. Ethylene glycol, diethylene glycol, triethylene glycol, polyethylene glycol (PEG) of various molecular weights (PEG400, 600, 1000, and 2000) were nanofiltrated in methanol and ethanol solutions at 50°C. Rejections in non-aqueous solutions increased with applied pressure, which is similar to aqueous solutions. Control of pore size of silica–zirconia membranes showing molecular weight cut-offs in methanol solutions at 300, 600, 1000, and >1000, respectively, was possible by the appropriate choice of colloidal particle sizes. Rejection in methanol solution showed a tendency similar to that in ethanol solution, while rejection in methanol was slightly larger than in ethanol solutions. In addition, rejection in water was much smaller than in methanol solution. For example, the rejection of PEG600 in water and methanol was 0.03 and 0.74, respectively. These results suggest that solvent type plays an important role in determining rejection, as a result of the interaction with solvents and/or membrane surface.     

Assessment of changing interdependencies between human electroencephalograms using nonlinear methods

We investigate the problems that might arise when two recently developed methods for detecting interdependencies between time series using state space embedding are applied to signals of different complexity. With this aim, these methods were used to assess the interdependencies between two electroencephalographic channels from 10 adult human subjects during different vigilance states. The significance and nature of the measured interdependencies were checked by comparing the results of the original data with those of different types of surrogates. We found that even with proper reconstructions of the dynamics of the time series, both methods may give wrong statistical evidence of decreasing interdependencies during deep sleep due to changes in the complexity of each individual channel. The main factor responsible for this result was the use of an insufficient number of neighbors in the calculations. Once this problem was surmounted, both methods showed the existence of a significant relationship between the channels which was mostly of linear type and increased from awake to slow wave sleep. We conclude that the significance of the qualitative results provided for both methods must be carefully tested before drawing any conclusion about the implications of such results.     

High rate production of antihydrogen

We show that antihydrogen production is the dominant process when mixing antiprotons and positrons in the ATHENA apparatus, and that the initial production rate exceeds 300 Hz, decaying to 30 Hz within 10 s. A fraction of 65% of all observed annihilations is due to antihydrogen.     

基于深度学习和细观力学的颗粒材料本构关系研究

颗粒材料的本构关系对岩土工程等众多领域至关重要. 不同于传统的唯象本构理论, 本文基于机器学习模型探索了一种细观力学理论指导下的数据驱动型颗粒材料本构关系预测方法. 根据Vogit均质化假设, 建立了小应变条件下颗粒材料应力?应变解析关系, 此关系唯一地确定了一组与颗粒材料本构行为相关的细观组构变量. 这些变量与反应颗粒材料宏观性质的主应力和主应变信息通过一系列离散元三轴压缩数值试验获得. 考虑到细观组构变量为内变量, 不能直接作为本构模型的输入. 本文基于有向图方法将颗粒材料微观结构信息隐式地包含在应力?应变的预测当中, 并采用门控循环单元(GRU)循环神经网络作为基础深度学习模型描述有向图中结点之间的映射关系. 通过将有向图从目标节点沿源节点展开, 整个应力?应变预测模型可由两个神经网络分别训练并组装而成. 将训练后的深度学习模型在全新的数据集上进行测试, 结果表明该训练策略能有效捕捉到颗粒材料在常规三轴任意加卸载, 等中主应力系数b的真三轴加载, 和等平均有效应力p的真三轴加卸载等复杂多轴加载工况下的应力?应变响应关系, 模型具有良好的内插和外推预测能力. 考虑到深度学习模型捕捉颗粒材料力学响应的能力及其开放式学习的特点, 充分结合数据驱动方法和理论本构模型可能是颗粒材料本构研究的一个重要方向.      

Adaptive Lasso estimators for ultrahigh dimensional generalized linear models

Generalized linear models have been more widely used than linear models which exclude categorical variables. The penalized method becomes an effective tool to study ultrahigh dimensional generalized linear models. In this paper, we study theoretical results of the adaptive Lasso for generalized linear models in terms of diverging number of parameters and ultrahigh dimensionality. The asymptotic results are examined by several simulation studies.     

Singular solutions for second-order non-divergence type elliptic inequalities in punctured balls

We study the existence and non-existence of positive singular solutions of second-order non-divergence type elliptic inequalities of the form $\sum\limits_{i,j = 1}^N {a_{ij} (x)\frac{{\partial ^2 u}} {{\partial x_i \partial x_j }}} + \sum\limits_{i = 1}^N {b_i (x)\frac{{\partial u}} {{\partial x_i }} \geqslant K(x)u^p ,} - \infty < p - \infty , $ with measurable coefficients in a punctured ball B _R \{0} of ? ^N , N ≥ 1. We prove the existence of a critical value p* which separates the existence region from the non-existence region. We show that in the critical case p = p*, the existence of a singular solution depends on the rate at which the coefficients (a _{i j} ) and (b _i ) stabilize at zero, and we provide some optimal conditions in this setting.     

Uncertainty identification by the maximum likelihood method

To incorporate uncertainty in structural analysis, a knowledge of the uncertainty in the model parameters is required. This paper describes efficient techniques to identify and quantify variability in the parameters from experimental data by maximising the likelihood of the measurements, using the well-established Monte Carlo or perturbation methods for the likelihood computation. These techniques are validated numerically and experimentally on a cantilever beam with a point mass at an uncertain location. Results show that sufficient accuracy is attainable without a prohibitive computational effort. The perturbation approach requires less computation but is less accurate when the response is a highly nonlinear function of the parameters.