Al-pillared clays supported rare earths and palladium catalysts for deep oxidation of low concentration of benzene

Al-pillared clays supported rare earths (RE/Al-PILC) are prepared and used as supports of palladium catalysts for deep oxidation of low concentrations of benzene (130-160 ppm). The supports and catalysts are characterized by X-ray powder diffraction (XRD), FT-IR, BET, transmission electron microscopy (TEM) and temperature-programmed reduction (H₂-TPR). The results show that Al-pillaring results in a strong increase in the basal spacing (d_0 0 1) from about 1.2 to 1.8 nm, and an increase in the BET surface area from 63.6 (±3.2) to 238.8 (±11.9) m²/g. Activity tests of deep oxidation of low concentration benzene show catalysts supported on Al-PILC and RE/Al-PILC are obviously more active than that on raw clay. Pd/6% Ce/Al-PILC, in particular, can catalyze the complete oxidation of low concentration benzene at a temperature as low as about 290 °C.     

A new family of proximity graphs: Class cover catch digraphs

Motivated by issues in machine learning and statistical pattern classification, we investigate a class cover problem (CCP) with an associated family of directed graphs—class cover catch digraphs (CCCDs). CCCDs are a special case of catch digraphs. Solving the underlying CCP is equivalent to finding a smallest cardinality dominating set for the associated CCCD, which in turn provides regularization for statistical pattern classification. Some relevant properties of CCCDs are studied and a characterization of a family of CCCDs is given.     

Action learning versus strategy learning

This article seeks to ascertain whether the strategy‐learning model of Hanaki, Sethi, Erev, and Peterhansl (2003) better accounts for observed behavior than do the various action‐learning models. It does so by measuring the goodness‐of‐fit of the models' predictions against published experimental results for such games as Coordination, Prisoner's Dilemma, and Chicken. The fit is measured via the mean squared deviation (MSD) between the observed behavior and the one predicted by the model. The results show that, for Chicken, the strategy‐learning model fits the observed data much better than do the action‐learning models. The best action‐learning model, on the other hand, fits the observed data well in Coordination. Overall, the strength of the strategy‐learning model is best shown in games where alternations between the two stage‐game Nash equilibria are often observed in the laboratory experiments. © 2004 Wiley Periodicals, Inc. Complexity 9: 41–50, 2004     

Decentralized iterative learning control for a class of large scale interconnected dynamical systems

The problem of decentralized iterative learning control for a class of large scale interconnected dynamical systems is considered. In this paper, it is assumed that the considered large scale dynamical systems are linear time-varying, and the interconnections between each subsystem are unknown. For such a class of uncertain large scale interconnected dynamical systems, a method is presented whereby a class of decentralized local iterative learning control schemes is constructed. It is also shown that under some given conditions, the constructed decentralized local iterative learning controllers can guarantee the asymptotic convergence of the local output error between the given desired local output and the actual local output of each subsystem through the iterative learning process. Finally, as a numerical example, the system coupled by two inverted pendulums is given to illustrate the application of the proposed decentralized iterative learning control schemes.     

Application of a Near-Optimal Reinforcement Learning Controller to a Robotics Problem in Manufacturing: A Hybrid Approach

Optimization theory provides a framework for determining the best decisions or actions with respect to some mathematical model of a process. This paper focuses on learning to act in a near-optimal manner through reinforcement learning for problems that either have no model or the model is too complex. One approach to solving this class of problems is via approximate dynamic programming. The application of these methods are established primarily for the case of discrete state and action spaces. In this paper we develop efficient methods of learning which act in complex systems with continuous state and action spaces. Monte-Carlo approaches are employed to estimate function values in an iterative, incremental procedure. Derivative-free line search methods are used to obtain a near-optimal action in the continuous action space for a discrete subset of the state space. This near-optimal control policy is then extended to the entire continuous state space via a fuzzy additive model. To compensate for approximation errors, a modified procedure for perturbing the generated control policy is developed. Convergence results under moderate assumptions and stopping criteria are established.     

Sum-fuzzy implementation of a choice function using artificial learning procedure with fixed fraction

In one if his paper Luo transformed the problem of sum-fuzzy rationality into artificial learning procedure and gave an algorithm which used the learning rule of perception. This paper extends the Luo method for finding a sum-fuzzy implementation of a choice function and offers an algorithm based on the artificial learning procedure with fixed fraction. We also present a concrete example which uses this algorithm.     

基于课程标准的教学——以常见阴、阳离子的检验为例

以常见的阴、阳离子的检验为例，研究了如何实施基于标准的教学。校本教材的开发为标准、教材、教学、评价的一致性提供保障；以学生应知的和能做的驱动课堂活动；根据达成标准应有怎样的质量表现，试卷编制先于教学设计。课堂上，“教”“学”双方都明确学习目标，教师提供多种策略来满足学生多样的学习需要，如提供工具，搭建脚手架，并以“微”研究性学习的方式展开教学，给学生提供了充分的进步空间。     

核心素养为本的单元教学设计与实施——以“探究水的组成”为例

以“探究水的组成”教学为例，通过课标、教材及学习者分析，从认识角度、探究水平、认识水平等3个维度整体规划“身边的化学物质”主题单元目标学习进阶，明确“探究水的组成”课时目标，通过温故建模、据模探究、探究推理等3个阶段的教学实施，建立具体物质的研究思路模型，运用模型研究陌生物质（氢气），初步形成定量研究物质组成的能力。     

构建网络学习资源提高民族高校有机化学课程教学质量

High-quality online course materials were reconstructed through feedback from undergraduate students and online data analysis regarding constructed learning materials on the Chao Xing Learning Platform. They were used for studying organic chemistry courses online and offline in university for nationalities. This model, based on student-centered teaching, can effectively transform students from passively accepting knowledge to actively learning and internalizing knowledge. This strategy can also enhance the students' learning initiative and the effects of learning, and hopefully be helpful to the universities in multi-ethnic areas in developing fundamental disciplined construction of organic chemistry and other courses.