Pure Nash equilibria in finite two-person non-zero-sum games

In this paper we study bimatrix games. The payoff matrices have properties closely related to concavity of functions. For such games we find sufficient conditions for the existence of pure Nash equilibria.     

Convergence of the unitary algorithm with a unimodular Wilkinson shift

In applying the algorithm to compute the eigenvalues of a unitary Hessenberg matrix, a projected Wilkinson shift of unit modulus is proposed and proved to give global convergence with (at least) a quadratic asymptotic rate for the iteration. Experimental testing demonstrates that the unimodular shift produces more efficient numerical convergence.

     

Multi-step-prediction of chaotic time series based on co-evolutionary recurrent neural network

This paper proposes a co-evolutionary recurrent neural network （CERNN） for the multi-step-prediction of chaotic time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent neural networks by coevolutionary strategy. The searching space was separated into two subspaces and the individuals are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability of recurrent neural network to incorporate past experience due to internal recurrence. The effectiveness of CERNN is evaluated by using three benchmark chaotic time series data sets： the Lorenz series, Mackey-Glass series and real-world sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic time series.     

A spatial model of antipredator vigilance

The authors are also members of the Centre for the Study of Evolution at the University of Sussex (Email: C.J.Proctor{at}sussex.ac.uk and M.Broom{at}sussex.ac.uk) Many species of animals have to perform two contradictozy tasks:feeding, and avoiding becoming food for others. A large numberof theoretical and empirical studies have investigated the trade-offbetween feeding and antipredator vigilance, especially in birds.An important factor which has been neglected in these studiesis that of the area occupied by the flock. If individuals feedclose together, competition increases and feeding rates decrease.However, if individuals space themselves widely, then vigilanceefficiency goes down and there is an increased predation risk.We develop a vigilance model which allows birds to control thearea the flock occupies as well as their vigilance rate. Theoptimal strategy is found for the birds under a variety of environmentalconditions. In particular the effect of each environmental parameteron this optimum is considered in turn. How the model can beadapted for different bird species is also investigated.     

增强大学生学习自我效能感的化学实验教学策略*——以“药物制剂工程实验”为例

以提高大学生化学实验积极性和实验学习效率为目标,在“药物制剂工程实验”课程中实施能增强大学生学习自我效能感的化学实验教学策略。以“乳剂的制备”实验为研究背景,设计并实施了三阶段的实验教学流程。同时,通过构建过程性评价量化指标、开展三方评价和问卷调查等形式,对该化学实验教学策略的实施效果进行了评价。通过对比实验组和对照组学生的过程性评价成绩和问卷调查结果,发现实验组平均得分大多高于对照组,说明采用了本实验教学策略的实验组学生的自我效能感高于对照组,因此,本实验中采取的提高大学生自我效能感的化学实验教学措施确实有效且具有实际意义。     

TiO2-supported Single-atom Catalysts: Synthesis,Structure, and Application

In recent years, single-atom catalysts(SACs) have attracted increasing attention in catalysis. However, their stability is considerably challenging. As a result, fine-tuning the interaction of metal single atoms(SA) with different types of supports has emerged as an effective strategy for improving their thermal and chemical stabilities. Owing to its non-toxicity, cost-effectiveness, high abundance, and excellent stability, as well as presence of rich, tunable, and reliable anchor sites for metal SA, TiO₂ has been extensively explored as a superior support for SACs. In this review, recent advances of TiO₂-supported SACs(M₁/TiO₂) are discussed, and synthetic strategies, structure elucidation, and catalytic applications are summarized. First, the recently developed synthetic strategies for M₁/TiO₂arehighlighted and summarized, identifying the major challenges for the precise fabrication of M₁/TiO₂. Subsequently, key characterization techniques for the structure identification of M₁/TiO₂are discussed. Next, catalytic applications of M₁/TiO₂ are highlighted, viz. photocatalysis, electrocatalysis, and thermocatalysis. In addition, the mechanism via geometric structures and electronic states of metal centers facilitate catalytic reactions is outlined. Finally, opportunities and challenges of M₁/TiO₂ in catalysis are discussed, which may inspire the future development of M₁/TiO₂ for multifunctional catalytic applications.     

Protein mapping of peanut extract with capillary electrophoresis

Protein separation can be achieved with different modes of capillary electrophoresis, such as with capillary gel electroporesis (CGE) or with capillary zone electrophoresis (CZE). CZE protein mapping of peanut extract was approached in four different ways, combining neutral-coated or multilayer-coated capillaries with pHs well over or under the isoelectric point range of the proteins of interest. At acidic pHs, the mobility ranges of the major peanut allergens Ara h1, Ara h2, Ara h3, and Ara h6 were identified. Although the pH is a major factor in CZE separation, buffers with different compositions but with the same pH and ionic strength showed significantly different resolutions. Different components of the electrolyte were studied in a multifactorial design of experiment. CE-SDS and CZE proved to be suitable for protein mapping and we were able to distinguish different batches of peanut extract and burned peanut extract.     

单原子催化的关键进展与未来挑战

单原子催化剂(SACs)兼具均相与多相催化剂的双重优势, 表现出最大化的原子利用率、 超高的本征活性与选择性以及易与产物分离的特点, 受到人们的广泛关注. 然而, 由于单个原子较高的表面能以及不稳定性, 设计与制备单原子催化剂仍是一大挑战. 本文综合评述了近年来单原子催化剂的稳定化策略、 高载量催化剂的制备方法以及批量制备技术等方面的关键研究进展, 并简要分析了单原子催化剂未来发展所面临的问题与挑战, 最后对单原子催化的发展方向进行了展望.     

Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit nonidentifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almost no extra computational cost; nevertheless, they can boost estimation efficiency by several orders of magnitude as is shown in extensive simulation studies. To conclude, the application of our algorithm to a 26-dimensional exchange rate dataset illustrates the superior performance of the new approach for real-world data. Supplementary materials for this article are available online.