Avoiding Pitfalls in Comparison of Activity and Selectivity of Solid Catalysts for Electrochemical HMF Oxidation

Electrocatalytic oxidation of 5-hydroxymethylfurfural (HMF) offers a renewable approach to produce the value-added platform chemical 2,5-furandicarboxylic acid (FDCA). The key for the economic viability of this approach is to develop active and selective electrocatalysts. Nevertheless, a reliable catalyst evaluation protocol is still missing, leading to elusive conclusions on criteria for a high-performing catalyst. Herein, we demonstrate that besides the catalyst identity, secondary parameters such as materials of conductive substrates for the working electrode, concentration of the supporting electrolyte, and electrolyzer configurations have profound impact on the catalyst performance and thus need to be optimized before assessing the true activity of a catalyst. Moreover, we highlight the importance of those secondary parameters in suppressing side reactions, which has long been overlooked. The protocol is validated by evaluating the performance of free-standing Cu-foam, and CuCoO modified with NaPO₂H₂ and Ni, which were immobilized on boron-doped diamond (BDD) electrodes. Recommended practices and figure of merits in carefully evaluating the catalyst performance are proposed.     

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.     

Effect of atmospheric pressure pulsed discharge spatial inhomogeneity on Ar I and H I lines: Electron number density study

The spatial inhomogeneity of pulsed atmospheric pressure discharge in argon is investigated using the electron number density N_e diagnostics procedure applied to asymmetrically broadened Ar I lines. A dedicated fitting procedure is used for describing Ar I 703.0 nm line shape recorded from argon gas discharge and H I (at 486.13 and 656.28 nm) lines recorded from Ar-H₂ gas mixture discharge. The results revealed the change in N_e in both axial and radial directions. The additional Ar I lines at 614.5, 710.7, 731.2, and 731.6 nm, recorded from integral spatial radiation, are analysed as well to confirm the results from the plasma column region. The possibility of using AlO (B²∑⁺–X²∑⁺) and CN (B²∑⁺–X²∑⁺) molecular bands for gas temperature T_g measurements in this type of gas discharge source is demonstrated and T_g used as an input parameter for the N_e diagnostics procedure. For the proper identification of molecular band spectral lines, the Fortrat parabolas are constructed. The results obtained from Ar I 703.0 nm line indicate three different N_e values, with N_e1 ≈ 0.6 × 10¹⁶ cm⁻³, N_e2 ≈ 3.6 × 10¹⁶ cm⁻³, and N_e3 ≈ 19 × 10¹⁶ cm⁻³ measured from the plasma column. These N_e values increase in the cathode and anode region.     

超声全矩阵数据联合稀疏重构的多测量向量模型应用

针对单测量向量模型(Single Measurement Vector,SMV)等传统压缩感知方法处理超声全矩阵数据时,存在重构精度低和重构耗时长等问题,本文研究了多测量向量模型(Multiple Measurement Vectors,MMV)应用的可行性。针对铝合金试块中不同深度的φ2 mm横通孔,分别使用MMV模型中的多测量稀疏贝叶斯(Multiple Sparse Bayesian Learning,MSBL)算法和SMV模型中的稀疏贝叶斯(Sparse Bayesian Learning,SBL)算法进行超声全矩阵数据重构,并实施全聚焦成像。随后,引入归一化均方误差和阵列性能因子评价图像和信号的重构效果。实验结果表明,SBL算法在25%采样率时的归一化均方误差为1.9%,而MSBL算法仅需15%采样率即可达到相似效果且耗时更少。