O2分子激发态a1Δg和b1Σ+g态的分析势能函数

以aug-cc-pVQZ,cc-pV5Z,6-311++g（d,p）和6-311++g（3df,3pd）等为基函数,采用多组态相互作用（MRCI）方法对O₂分子最低的两个激发态¹Δ_g和¹Σ⁺_g的平衡结构、谐振频率和势能曲线进行了计算.并选用Murrell-Sorbie势能函数对曲线进行拟合,利用拟合的参数值计算出了力常数和光谱数据.结果表明计算值与实验值符合较好. 关键词： MRCI 势能函数 力常数 光谱数据     

普朗克常量测定系统中实验数据的采集和处理

介绍了在光电效应实验系统改造中,关于实验数据的采集和处理方法。     

基于FPGA的超高速线阵CCD信号采集系统的设计与实现

线阵CCD已广泛应用于在线检测、图像识别等系统,目前高帧率采集系统多在200~500Hz之间。高速线阵CCD采集系统,如1K甚至10KHz以上的采集要求,设计难度大,电路实现复杂,需要专用处理器,产品成本高,提出了一种采用并行高速FPGA驱动线阵CCD,通过常规分立元件完成模拟信号处理,实现数字信号实时传输的方案。该方案不仅简化了硬件设计上的难度,在同等性能情况下,可实现每秒万帧的高速采样,大幅度降低了成本。方案选用Altera FPGA作为控制核心,实现高速信号采集的同时,在片上实现一定的图像算法,不仅加速了图像处理速度,同时降低了计算机的处理压力。最后,本电路通过USB2.0接口,完成数据的实时传输。设计具有高帧率、高灵敏度、性能稳定,便携使用等特点,同时还有一定的通用性,已应用于一些光学系统中。     

复数滤波器在数字全息图处理中的应用

数字全息图在微电路检测、粒度分析、透明场测量等小孔径、小视场对象测量和细胞观测等显微测量方面有着广泛的应用前景。根据离轴数字全息图的频谱特点,提出采用复数滤波器,利用计算机及相关算法对离轴数字全息图进行滤波,消除其零级及共轭像,同时可以得到全息图的相位分布。讨论了复数滤波器的设计方法,给出了实验结果。为数字全息图的压缩、再现和相息图制作提供了方便。     

Reverse fingerprinting, similarity searching by group fusion and fingerprint bit importance

Summary Recent research has shown that using data fusion rules in fingerprint-based similarity searching can improve results over traditional searches. Group fusion scores, which use multiple reference compounds, have in particular been shown to be quite effective in increasing enrichment rates over single reference structure based searches. In this paper, the effectiveness of using data fusion with multiple reference compounds to increase similarity search recall rates was investigated using 44 biological targets and four different 2D fingerprinting systems, including a new 2D typed triangle fingerprinting system introduced here. Scaffold-hopping abilities using data fusion rules were investigated using eight (8) different classes of scaffolds active against cGMP phosphodiesterase isoform 5 (PDE5). An approach to using the reference group for ranking and visualizing important fingerprints bits, or reverse fingerprinting, was presented, and used to score and visualize important pharmacophore features within sample active molecules. Finally, similarity statistics within the reference groups were investigated and compared to recall rates.     

考虑大数据营销和风险规避的绿色供应链决策与协调

通过建立考虑大数据营销及零售商风险规避的博弈模型,对绿色供应链定价、产品绿色度及利润进行比较分析。研究发现：无论集中决策、双方风险中性分散决策还是仅零售商风险规避分散决策,考虑大数据营销时的供应链整体期望利润和产品绿色度较高,且大数据营销效率因子对产品绿色度的增加有正向作用;双方风险中性分散决策下,一定条件下,两部定价契约能够有效协调供应链整体利润,实现帕累托改进;仅零售商风险规避分散决策下,零售商的风险规避行为会降低其对大数据营销的投入,一定条件下,两部定价契约也能够实现供应链整体期望利润的帕累托改进。     

基于前景理论和熵权法的交叉效率集结方法

传统的交叉效率集结过程通常采用算术平均方法,不仅会低估自评的重要性,而且未考虑决策者的风险偏好。针对上述问题,提出一种基于前景理论和熵权法的交叉效率集结方法。首先,求解交叉效率矩阵,运用熵权法确定他评过程中评价单元的指标权重。然后,引入前景理论以考虑决策者在交叉效率集结过程中的风险偏好,利用TOPSIS方法识别正负参考点,进而构造总体效用函数,得到前景交叉效率矩阵。随后,构建最大化前景价值模型,求解集结权重。该方法既考虑到交叉效率集结的相对重要性权重,又将决策者的风险偏好纳入到效率评价中,从而实现决策单元的全排序。最后,结合实例验证方法的有效性。     

Maximum likelihood estimation for second level fMRI data analysis with expectation trust region algorithm

The trust region method which originated from the Levenberg–Marquardt (LM) algorithm for mixed effect model estimation are considered in the context of second level functional magnetic resonance imaging (fMRI) data analysis. We first present the mathematical and optimization details of the method for the mixed effect model analysis, then we compare the proposed methods with the conventional expectation-maximization (EM) algorithm based on a series of datasets (synthetic and real human fMRI datasets). From simulation studies, we found a higher damping factor for the LM algorithm is better than lower damping factor for the fMRI data analysis. More importantly, in most cases, the expectation trust region algorithm is superior to the EM algorithm in terms of accuracy if the random effect variance is large. We also compare these algorithms on real human datasets which comprise repeated measures of fMRI in phased-encoded and random block experiment designs. We observed that the proposed method is faster in computation and robust to Gaussian noise for the fMRI analysis. The advantages and limitations of the suggested methods are discussed.     

Efficient computation of operator‐type response sensitivities for uncertainty quantification and predictive modeling: illustrative application to a spent nuclear fuel dissolver model

This work honors the 75th birthday of Professor Ionel Michael Navon by presenting original results highlighting the computational efficiency of the adjoint sensitivity analysis methodology for function‐valued operator responses by means of an illustrative paradigm dissolver model. The dissolver model analyzed in this work has been selected because of its applicability to material separations and its potential role in diversion activities associated with proliferation and international safeguards. This dissolver model comprises eight active compartments in which the 16 time‐dependent nonlinear differential equations modeling the physical and chemical processes comprise 619 scalar and time‐dependent model parameters, related to the model's equation of state and inflow conditions. The most important response for the dissolver model is the time‐dependent nitric acid in the compartment furthest away from the inlet, where measurements are available at 307 time instances over the transient's duration of 10.5 h. The sensitivities to all model parameters of the acid concentrations at each of these instances in time are computed efficiently by applying the adjoint sensitivity analysis methodology for operator‐valued responses. The uncertainties in the model parameters are propagated using the above‐mentioned sensitivities to compute the uncertainties in the computed responses. A predictive modeling formalism is subsequently used to combine the computational results with the experimental information measured in the compartment furthest from the inlet and then predict optimal values and uncertainties throughout the dissolver. This predictive modeling methodology uses the maximum entropy principle to construct an optimal approximation of the unknown a priori distribution for the a priori known mean values and uncertainties characterizing the model parameters and the computed and experimentally measured model responses. This approximate a priori distribution is subsequently combined using Bayes' theorem with the “likelihood” provided by the multi‐physics computational models. Finally, the posterior distribution is evaluated using the saddle‐point method to obtain analytical expressions for the optimally predicted values for the parameters and responses of both multi‐physics models, along with corresponding reduced uncertainties. This work shows that even though the experimental data pertains solely to the compartment furthest from the inlet (where the data were measured), the predictive modeling procedure used herein actually improves the predictions and reduces the predicted uncertainties for the entire dissolver, including the compartment furthest from the measurements, because this predictive modeling methodology combines and transmits information simultaneously over the entire phase‐space, comprising all time steps and spatial locations. Copyright © 2016 John Wiley & Sons, Ltd.