Development and Molecular Understanding of a Pd-Catalyzed Cyanation of Aryl Boronic Acids Enabled by High-Throughput Experimentation and Data Analysis

A synthetic method for the palladium-catalyzed cyanation of aryl boronic acids using bench stable and non-toxic N-cyanosuccinimide has been developed. High-throughput experimentation facilitated the screen of 90 different ligands and the resultant statistical data analysis identified that ligand σ-donation, π-acidity and sterics are key drivers that govern yield. Categorization into three ligand groups – monophosphines, bisphosphines and miscellaneous – was performed before the analysis. For the monophosphines, the yield of the reaction increases for strong σ-donating, weak π-accepting ligands, with flexible pendant substituents. For the bisphosphines, the yield predominantly correlates with ligand lability. The applicability of the designed reaction to a wider substrate scope was investigated, showing good functional group tolerance in particular with boronic acids bearing electron-withdrawing substituents. This work outlines the development of a novel reaction, coupled with a fast and efficient workflow to gain understanding of the optimal ligand properties for the design of improved palladium cross-coupling catalysts.     

最大泡压法测表面张力等温线的数据处理方法比较*

对乙酸、丙酸、正丁酸、2-甲基丁酸、3-甲基丁酸、正戊酸、正己酸、乙醇、丙醇、异丙醇、正丁醇、异丁醇、1,2-丙二醇等13种常用试剂在25 ℃下的表面张力数据拟合方法进行了比较,分析了图解法处理数据及多项式、一阶指数函数、幂函数、Shishkovsky经验公式拟合数据的优缺点。指出Shishkovsky经验公式拟合结果准确,受数据样本量影响小,是理想的表面张力数据拟合方法。     

Data-driven preventive maintenance for a heterogeneous machine portfolio

We describe a data-driven approach to optimize periodic maintenance policies for a heterogeneous portfolio with different machine profiles. When insufficient data are available per profile to assess failure intensities and costs accurately, we pool the data of all machine profiles and evaluate the effect of (observable) machine characteristics by calibrating appropriate statistical models. This reduces maintenance costs compared to a stratified approach that splits the data into subsets per profile and a uniform approach that treats all profiles the same.     

Gompertz zero-inflated cure rate regression models applied to credit risk data

In the financial market, it is important to consider that there is a proportion of customers that have settled their debt in time zero, immediately recovering their ability to pay. In this context, in this paper, we propose a survival analysis methodology that allows the insertion of times equal to zero in scenarios where credit risk is observed. The proposed model addresses the survival analysis model of the zero-inflated cure rate which incorporates the heterogeneity of three subgroups (individuals having events in the initial time, and individuals not susceptible and susceptible to the event). In our proposal, all available survival data of customers are modeled considering that the number of competitive causes follows a Poisson distribution and the baseline risk function follows a Gompertz distribution. The model parameter estimation is obtained by the maximum likelihood estimation procedure and simulation studies are conducted to evaluate the estimators' performance. The studied methodology will be applied to a credit database provided by a financial institution in Brazil.     

A Bayesian record linkage model incorporating relational data

In this article, we introduce a novel Bayesian approach for linking multiple social networks in order to discover the same real world person having different accounts across networks. In particular, we develop a latent model that allows us to jointly characterize the network and linkage structures relying on both relational and profile data. In contrast to other existing approaches in the machine learning literature, our Bayesian implementation naturally provides uncertainty quantification via posterior probabilities for the linkage structure itself or any function of it. Our findings clearly suggest that our methodology can produce accurate point estimates of the linkage structure even in the absence of profile information, and also, in an identity resolution setting, our results confirm that including relational data into the matching process improves the linkage accuracy. We illustrate our methodology using real data from popular social networks such as Twitter , Facebook , and YouTube .     

A review of Bayesian dynamic forecasting models: Applications in marketing

We briefly review the main developments of Bayesian dynamic models. The emphasis is on marketing applications. Typical examples in this area are discussed. The concepts of monitoring and intervention are carefully explained with illustrative examples and open source computational routines. We avoid algebraic developments and instead use graphical examples to illustrate theoretical aspects. Two real-world problems using Bayesian dynamic models are discussed. Finally, we describe recent developments and alternative proposals to formally address the dependence when dealing with the modeling of multiple time series.     

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.     

热力学数据对1, 3-丁二烯燃烧特性的影响

基于G4方法, 计算了1,^{3-丁二烯框架燃烧反应机理中102个物种的热力学数据, 并考察了振动非谐性、 频率校正因子以及受阻内转动对结果的影响. 结果表明, 考虑振动非谐性或采用不同的频率校正因子, 对热力学数据的影响不大; 考虑内转动后, 对热力学数据有较大影响. 而且考虑内转动后, 得到的热力学数据与实验热力学数据吻合得更好. 用所得热力学数据模拟了1,3-丁二烯的绝热燃烧温度以及点火延迟时间, 结果显示, 要得到可靠的绝热火焰温度, 对小分子(如CO和CO₂等)的热力学数据需要采用实验结果. 将用所得热力学数据模拟得到的点火延迟时间, 与机理本身的热力学数据所得点火延迟时间进行对比, 二者差别显著, 表明所得热力学数据主要通过改变一些反应的逆反应速率常数来影响点火延迟时间. 进一步确定了用所得热力学数据对点火延迟时间有显著影响的一些物种.}     

应用增量理论考虑应变硬化效应包辛格效应和温度对机械性质影响的自增强厚壁圆筒分析

本文提出的采用增量理论的分析模型和方法可以定量分析应变硬化、包辛格效应和材料机械性质随温度变化等因素对自增强厚壁圆筒中残余应力和操作应力分布的影响.厚壁圆筒看成由N个同轴薄壁圆筒套在一起组成的构件;采用包括弹性、塑性和温度应变的增量型本构关系和相容条件导出了自增强圆筒的基本方程;设计了计算机程序并给出了分析实例。分析结果表明,应变硬化会减小塑性区并降低残余应力;包辛格效应使反向屈服容易出现;温度升高将使残余应力和热应力松驰.