Prediction of an outcome using NETwork Clusters (NET-C)

Birth weight is a key consequence of environmental exposures and metabolic alterations and can influence lifelong health. While a number of methods have been used to examine associations of trace element (including essential nutrients and toxic metals) concentrations or metabolite concentrations with a health outcome, birth weight, studies evaluating how the coexistence of these factors impacts birth weight are extremely limited. Here, we present a novel algorithm NETwork Clusters (NET-C), to improve the prediction of outcome by considering the interactions of features in the network and then apply this method to predict birth weight by jointly modelling trace element and cord blood metabolite data. Specifically, by using trace element and/or metabolite subnetworks as groups, we apply group lasso to estimate birth weight. We conducted statistical simulation studies to examine how both sample size and correlations between grouped features and the outcome affect prediction performance. We showed that in terms of prediction error, our proposed method outperformed other methods such as (a) group lasso with groups defined by hierarchical clustering, (b) random forest regression and (c) neural networks. We applied our method to data ascertained as part of the New Hampshire Birth Cohort Study on trace elements, metabolites and birth outcomes, adjusting for other covariates such as maternal body mass index (BMI) and enrollment age. Our proposed method can be applied to a variety of similarly structured high-dimensional datasets to predict health outcomes.     

Quantum Bayesian computation

Quantum Bayesian computation is an emerging field that levers the computational gains available from quantum computers. They promise to provide an exponential speed-up in Bayesian computation. Our article adds to the literature in three ways. First, we describe how quantum von Neumann measurement provides quantum versions of popular machine learning algorithms such as Markov chain Monte Carlo and deep learning that are fundamental to Bayesian learning. Second, we describe quantum data encoding methods needed to implement quantum machine learning including the counterparts to traditional feature extraction and kernel embeddings methods. Third, we show how quantum algorithms naturally calculate Bayesian quantities of interest such as posterior distributions and marginal likelihoods. Our goal then is to show how quantum algorithms solve statistical machine learning problems. On the theoretical side, we provide quantum versions of high dimensional regression, Gaussian processes and stochastic gradient descent. On the empirical side, we apply a quantum FFT algorithm to Chicago house price data. Finally, we conclude with directions for future research.     

带有相依重尾冲击的Poisson噪音过程尾的一致渐近性质

本文考虑了带有某种相依重尾冲击的Poisson噪音过程尾的一致渐近性质.当冲击是二元上尾渐近独立的非负随机变量具有长尾和控制变化尾分布且噪音函数具有正的上下界时,得到了过程尾概率的一致渐近公式.进而,当冲击具有连续的一致变化尾分布时,去除了噪音函数具有正的下界的限制.对于噪音函数不一定具有正的上界的情形,当冲击具有两两负象限相依结构时,也得到了一致渐近性结果.     

Dynamic Optical Vortex Trajectory Guided by the Symmetric Pearcey Gaussian Vortex Beam in the Uniformly Moving Parabolic Potential

This paper analytically and numerically proposes the propagation dynamics of the symmetric Pearcey Gaussian vortex beam (SPGVB) in the uniformly moving parabolic potential. The optical vortex located in the initial plane produces a vortex channel in the presence of the uniformly moving parabolic potential, called the vortex trajectory. The vortex trajectory can be manipulated dynamically by configuring different combinations of the parameters, and the optical intensity and the focal position can also be affected. Moreover, the spatial dynamic vortex trajectory is derived analytically, and the 2D on-axis and off-axis vortex scenarios are also presented. Our work expands the methods of the vortex trajectory manipulation and may broaden more practical potential applications in the particle manipulation.     

Corrosion prevention of mild steel in acidic medium by 2-Pyrrolidin-1-yl-1,3-thiazole-5-carboxylic acid: Theoretical and experimental approach

The inhibition efficiency of 2-Pyrrolidin-1-yl-1,3-thiazole-5-carboxylic acid (PTCA) against mild steel (MS) corrosion was investigated in acidic solution by using quantum chemical calculations based on Density Functional Theory (DFT) method and electrochemical measurements. The electrochemical impedance spectroscopy (EIS), potentiodynamic, potential zero charge (pzc) analysis and electrochemical noise (EN) measurements at various concentrations (from 0.1 to 10 mM) and immersion times were utilized in experimental part. The surface analysis was achieved scanning electron microscope (SEM) and contact angle measurements in the absence and presence of 10 mM PTCA. According to DFT results, PTCA exhibited 3.737 eV band gap and 8.130 Debye dipole moment which were a signal of potentially convenient corrosion inhibitor properties. PTCA has a remarkable corrosion inhibition capability to mild steel, which inhibited both anodic and cathodic corrosion rates, relying on it's physically adsorption on the metal solution interface and protection ability was increased with increasing PTCA concentration. The obtained adsorption equilibrium constant was 11.11 × 10³ M^-1 and calculated standard free energy of adsorption was ?33.03 kJ mol^?1. The determined activation energy values were 55.58 kJ mol^?1 and 96.86 kJ mol^?1 in 0.5 M HCl in the absence and presence of 10 mM PTCA, respectively. PTCA demonstrated a strong inhibition efficiency of 98.3%, after 168 h immersion, according to the EIS results. As a consequently, we recommend that PTCA is a convenient inhibitor in 0.1 M HCl for mild steel protection against corrosion.     

PATH INTEGRAL SOLUTION OF NONLINEAR DYNAMIC BEHAVIOR OF STRUCTURE UNDER WIND EXCITATION

IntroductionThe dynamic behavior of the nonlinear structure under wind excitation has beenobserved very complicated.Taking guyed masts as an example,only a few collapsingaccidents occurred under extreme atmospheric conditions[1],many took place under mild…     

Noise Reduction in Non-Premixed Lifted-Jet Flames

The characteristic changes in non-premixed lifted flames when excited by hole tones from a cavity, placed in the flow path of the fuel gas, were studied. A significant reduction of the sound pressure level was observed in the low-frequency noise at the flame base of the lifted flame when the hole tones were induced in the jet. The liftoff height and the mean diameter of the flame base decreased for a given jet Reynolds number. The blow-off velocities also increased suggesting improved flame stability in the presence of the hole tones induced by the cavity. Incorporation of the cavity upstream of a burner nozzle is demonstrated to give a quieter lifted flame with improved stability characteristics. This revised version was published online in July 2006 with corrections to the Cover Date.     

随机扰动的同宿分岔系统研究

利用一维扩展过程的奇点理论并结合能量包络的随机平均法，考查“隐藏在余维２分岔点之后”的同宿分岔系统受参激白噪声影响的分岔行为。     

等截面梁有限变形的传递函数增量算法

本文介绍了一种计算等截面梁有限变形的新方法－传递函数增量算法，它是一种半解析数值计算方法。此算法充分利用增量失空法Ｇａｕｓｓ求积公式计算非线性有限变形的特点，并将这些特点与传递函数方法，有效地结合起来，既避免了数值方法计算量大的困难，又使得求解高阶非线性微分方程的解析解成为可能，算例分析表明，这是一种易编程，计算量小，收敛快，求解精度高的行之有效的计算方法。     

结构声振研究的功率流方法

功率流方法是近年来研究柔性结构动态特性的主要手段, 其计算可分为低频段、中频段和高 频段. 本文讨论了有限元法、导纳功率流法、统计能量分析法、能量有限元法、参数化统计 能量分析法、子结构法、Lagrange乘子混合法、有限元/统计能量分析混合法、有限元/功 率流有限元混合法等方法, 各种方法适用频段各不相同, 综合应用这些方法, 可以解决绝大 多数声振预测问题.