广西经济增长、金融发展与城乡收入差距的实证研究

探讨了经济增长及金融发展与城乡收入差距之间互动影响,刻画了三者间的逻辑关系,并基于广西1990-2017年的统计数据,运用状态空间模型及卡尔曼滤波算法对三者间动态关系进行了实证分析.结果显示:经济增长对城乡收入差距呈现倒U型曲线形态,而金融发展则显示出具有不断缩小城乡收入差距的趋势.     

In Situ Generated Gold Nanoparticles on Active Carbon as Reusable Highly Efficient Catalysts for a C −C Stille Coupling

Gold nanoparticle catalysts are important in many industrial production processes. Nevertheless, for traditional C ?C cross‐coupling reactions they have been rarely used and Pd catalysts usually give a superior performance. Herein we report that in situ formed gold metal nanoparticles are highly active catalysts for the cross coupling of allylstannanes and activated alkylbromides to form C ?C bonds. Turnover numbers up to 29 000 could be achieved in the presence of active carbon as solid support, which allowed for convenient catalyst recovery and reuse. The present study is a rare case where a gold metal catalyst is superior to Pd catalysts in a cross‐coupling reaction of an organic halide and an organometallic reagent.     

Information Network Modeling for U.S. Banking Systemic Risk

In this work we investigate whether information theory measures like mutual information and transfer entropy, extracted from a bank network, Granger cause financial stress indexes like LIBOR-OIS (London Interbank Offered Rate-Overnight Index Swap) spread, STLFSI (St. Louis Fed Financial Stress Index) and USD/CHF (USA Dollar/Swiss Franc) exchange rate. The information theory measures are extracted from a Gaussian Graphical Model constructed from daily stock time series of the top 74 listed US banks. The graphical model is calculated with a recently developed algorithm (LoGo) which provides very fast inference model that allows us to update the graphical model each market day. We therefore can generate daily time series of mutual information and transfer entropy for each bank of the network. The Granger causality between the bank related measures and the financial stress indexes is investigated with both standard Granger-causality and Partial Granger-causality conditioned on control measures representative of the general economy conditions.     

Hydrogenation of acetylene into ethane–ethene mixtures over modified Pd–alumina catalysts

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基于SVM的固化土无侧限抗压强度模型

为实现较少试验次数下固化土无侧限抗压强度(qu)的准确预测, 提出了基于支持向量机(SVM)的固化土qu的预测模型. 以固化剂各组分掺入比、龄期、初始含水量、固化剂掺量等因素为输入量, 固化土的qu作为输出量, 以径向基为核函数, 采用网格搜索法和交叉验证法进行参数优化, 建立了基于SVM的固化土qu的预测模型. 算例分析表明: 该模型适用于任意条件下固化土qu的精确预测, 且在较小试验成本下实现与响应面法相当的预测精度.     

One‐pot green synthesis of Cu/bone nanocomposite and its catalytic activity in the synthesis of 1‐substituted 1H‐1,2,3,4‐tetrazoles and reduction of hazardous pollutants

In this work, a simple and green method is reported for the biosynthesis of Cu/bone nanocomposite using Cordyline fruticosa extract as a stabilizer and reductant. Animal bone was used as a natural support to prevent the accumulation of Cu nanoparticles. The catalytic activity of Cu/bone nanocomposite was assessed in the synthesis of 1‐substituted 1H‐1,2,3,4‐tetrazoles and reduction of various organic dyes, including 4‐nitrophenol (4‐NP), nigrosin (NS), congo red (CR) and methylene blue (MB). The best catalytic performance in the synthesis of 1‐substituted tetrazoles was achieved using 0.05 g of Cu/bone nanocomposite at 120°C. In addition, under optimal conditions, the absorption bands corresponding to 4‐NP, CR, NS and MB completely disappeared after about 6 min, 3 min, 50 s and 7 s, respectively. The biosynthesis protocol used in the preparation of Cu/bone nanocomposite offers a very attractive area for further research.     

The support designs of the triply even binary codes of length 48

In this paper, we present examples of codes all of whose weight classes support 1‐designs, with duals whose classes include two that support 2‐designs. We can find these examples in the triply even binary codes of length 48, which have been classified by Betsumiya and Munemasa.     

Change points in heavy‐tailed multivariate time series: Methods using precision matrices

We propose two robust data‐driven techniques for detecting network structure change points between heavy‐tailed multivariate time series for situations where both the placement and number of change points are unknown. The first technique utilizes the graphical lasso method to estimate the change points, whereas the second technique utilizes the tlasso method. The techniques not only locate the change points but also estimate an undirected graph (or precision matrix) representing the relationship between the time series within each interval created by pairs of adjacent change points. An inference procedure on the edges is used in the graphs to effectively remove false‐positive edges, which are caused by the data deviating from normality. The techniques are compared using simulated multivariate t‐distributed (heavy‐tailed) time series data and the best method is applied to two financial returns data sets of stocks and indices. The results illustrate the method's ability to determine how the dependence structure of the returns changes over time. This information could potentially be used as a tool for portfolio optimization.