湍流大气中聚焦平台光束焦移及光斑尺度

 通过理论分析和数值模拟,对聚焦平台光束大气传输的焦移问题进行了研究。聚焦平台光束在湍流大气中传输时,湍流将导致光束的束腰向发射点移动,且湍流越强移动的幅度越大;大气湍流强度相同时,聚焦平台光束发射口径越小,其束腰移动的幅度越大。另外,通过调节发射系统的焦距,使聚焦平台光束的束腰正好位于接收探测器处时,接收探测器上的激光功率密度并非最大,只有当发射系统的焦距等于激光的传输距离时,接收探测器上的激光功率密度才会最大。     

平顶高斯光束在梯度折射率介质中的传输特性

 利用广义惠更斯-菲涅耳衍射积分法,推导出平顶高斯光束在梯度折射率介质中传输时的解析表达式,对平顶高斯光束在梯度折射率介质中的传输特性进行了分析,讨论了介质梯度折射率系数和光束阶数对传输特性的影响。研究表明,平顶高斯光束在梯度折射率介质中传输时轴上光强分布呈现周期性变化,其周期决定于介质梯度折射率系数,而与光束的阶数无关;轴上峰值处的横向光强分布受梯度折射率系数和光束阶数的影响较大,横向光强的最大值随着梯度折射率系数的增大而增大。     

电光有机聚合物多模干涉分束器

分束器是光纤通信系统中光无源网络和光子集成回路的重要器件.1×2 MMI(multimode interferences)电光有机聚合物分束器是利用自成像效应设计的.分析了1×2 MMI 的工作原理.利用有效折射率方法(effective index method,EIM)计算出1×2 MMI 三维多模脊形波导的有效折射率分布.利用自成像效应的成像规律计算出成像位置并用导模传输分析法(guided-mode propagation analysis,MPA)对输出波导的光强进行模拟.分析表明:在波长为 1.55μm 的情况下,1×2 MMI 的输出波导的光强在未加电极时可以实现预定分束比输出;加上电极之后,由于电光效应可调控有机聚合物波导折射率的改变,利用有限差分光束传播法模拟输出波导的分束比可在7.6 dB 范围内可调.     

Numerical optimization of In-mole fractions and layer thicknesses in AlxGa1−xN/AlN/GaN high electron mobility transistors with InGaN back barriers

The effects of the In-mole fraction (x) of an In_xGa_1−xN back barrier layer and the thicknesses of different layers in pseudomorphic Al_yGa_1−yN/AlN/GaN/In_xGa_1−xN/GaN heterostructures on band structures and carrier densities were investigated with the help of one-dimensional self-consistent solutions of non-linear Schrödinger-Poisson equations. Strain relaxation limits were also calculated for the investigated Al_yGa_1−yN barrier layer and In_xGa_1−xN back barriers. From an experimental point of view, two different optimized structures are suggested, and the possible effects on carrier density and mobility are discussed.     

拉盖尔高斯涡旋光束的传输

针对拉盖尔高斯涡旋光束,推导了其传输后目标平面上光电场的解析表达式,理论研究表明,传输一段距离后, 对于拉盖尔高斯光束的光斑大小的描述,高斯光斑尺寸已经不再适用.如果采用光强最亮处的半径来表示目标平面上的光斑大小则比较方便. 除了传输中的衍射导致光束展宽以外, 横截面上光束的相位分布也发生了独特的变化. 等相位线由原来的射线转化为弧线,拓扑电荷数为正时,弧线朝顺时针方向弯曲,拓扑电荷数为负时,弧线朝逆时针方向弯曲. 关键词： 涡旋光束 传输 光斑尺寸 相位分布     

Dynamics of organizational rumor communication on connecting multi-small-world networks

We study the dynamics of an epidemic-like model for the spread of a rumor on a connecting multi-small-world-network (CM-SWN) model, which represents organizational communication in the real world. It has been shown that this model exhibits a transition between regimes of localization and propagation at a finite value of network randomness. Here, by numerical means, we perform a quantitative characterization of the evolution in the three groups under two evolution rules, namely the conformity and obeying principles. The variant of a dynamic CM-SWN, where the quenched disorder of small-world networks is replaced by randomly changing connections between individuals in a single network and stable connection by star nodes between networks, is also analysed in detail and compared with a mean-field approximation.     

Using near-infrared process analysis to study gas-solid adsorption process as well as its data treatment based on artificial neural network and partial least squares

This paper demonstrates the application of near-infrared (NIR) process analysis to study gas-solid adsorption process non-invasively: its experimental setup, data treatment, and potentials as a convenient tool to investigate the gas-solid adsorption process. The experimental setup includes a differential adsorption bed (DAB) monitored by a NIR spectrometer via an optical fiber probe, which makes it convenient and reliable to construct adsorption mass-transfer models. A chemometrics strategy based on back propagation-artificial neural network (BP-ANN) and partial least squares (PLS) has been developed to treat NIR spectra collected during the adsorption process because of the obvious nonlinearity in concentration prediction. This nonlinear problem results from the great concentration variation of the adsorbate adsorbed by the adsorbent during the whole adsorption process, the extraordinarily low concentration of the adsorbed adsorbate at the beginning of the process, and probably NIR distinction between the adsorbate on the first adsorption layer at the beginning of the process and that on the other layers afterward. With the strategy, NIR spectra are pretreated with PLS for data compression and noise reduction, and then a BP-ANN is built as the nonlinear calibration model. As compared with linear calibration algorithm, our strategy has the higher predication ability for the whole adsorption process, even with less calibration samples. Finally, as an example the kinetics of aniline-silica gel adsorption process has been studied through the experimental setup and chemometrics strategy.     

Measurement of(n,α)and(n,2n)reaction cross sections at a neutron energy of 14.92±0.02 MeV for potassium and copper with uncertainty propagation

Experimentally measured neutron activation cross sections are presented for the ⁶⁵Cu(n,0)^62mCu,⁴¹ K(n,a)^38Cl,and ⁶⁵Cu(n.2n)⁶⁴Cu reactions with detailed uncertainty propagation.The neutron cross secions were measured at an incident energy of 14.92±0.02 MeV,and the neutrons were based on the(d,n)a fusion reaction.The ²⁷ Al(n,a)²⁴Na reaction was used as a reference reaction for the normalization of the neutron flux.The pre-calib-rated lead-shielded HPGe detector was used to detect the residues'γ-ray spetra.The data from the measured cross sections are compared to the previously measured cross sections from the EXFOR database,theoretically calculated cross sections using the TALYS and EMPIRE codes,and evaluated nuclear data.     

Ergodic stationary distribution of a stochastic rumor propagation model with general incidence function

In daily lives, when emergencies occur, rumors will spread widely on the internet. However, it is quite difficult for the netizens to distinguish the truth of the information. The main reasons are the uncertainty of netizens' behavior and attitude, which make the transmission rates of these information among social network groups be not fixed. In this paper, we propose a stochastic rumor propagation model with general incidence function. The model can be described by a stochastic differential equation. Applying the Khasminskii method via a suitable construction of Lyapunov function, we first prove the existence of a unique solution for the stochastic model with probability one. Then we show the existence of a unique ergodic stationary distribution of the rumor model, which exhibits the ergodicity. We also provide some numerical simulations to support our theoretical results. The numerical results give us some possible methods to control rumor propagation. Firstly, increasing noise intensity can effectively reduce rumor propagation when $\widehat{\mathcal{R}}$₀>1. That is, after rumors spread widely on social network platforms, government intervention and authoritative media coverage will interfere with netizens' opinions, thus reducing the degree of rumor propagation. Secondly, speed up the rumor refutation, intensify efforts to refute rumors, and improve the scientific quality of netizen (i.e., increase the value of β and decrease the value of α and γ), which can effectively curb the rumor propagation.     

Quantitative Analysis of Formaldehyde Using UV‐VIS Spectrophotometer Pattern Recognition and Artificial Neural Networks

Abstract

This study is focused on the quantitative analysis of formaldehyde in aqueous solution using a Fluoral‐P reagent and describes the characterization of the reaction, including the effect of reagent concentrations, pH, response time, dynamic range, reproducibility, photostability, and selectivity by using an ultraviolet‐visible (UV‐VIS) spectrophotometer. The relative standard deviation value was 1.79 to 2.12%. The dynamic range of the complex gives a linear stimulation of 0.00 to 3.60 ppm for the concentration of formaldehyde. The reproducibility of this study is high, with 1.79 and 2.12% for 20 and 40 ppm of formaldehyde, respectively. The interference from acetaldehyde (formaldehyde: acetaldehyde=1:100) was lower than 2.10%. In addition, the application of artificial neural networks to quantitative analysis for formaldehyde has also been done in this study to optimize the dynamic range of formaldehyde involved in the formation of Fluoral‐P–formaldehyde complex. A three‐layer feed‐forward network and the back propagation algorithm‐operated training process were used in this study. For quantitative analysis of formaldehyde, artificial neural networks, networking with 23 hidden neurons and 40,000 cycle numbers with 0.001% learning rate, produce the best training results, with sum‐squared error value 0.5847.