Cellulose-polyamide 66 blends. Part II: Mechanical behavior

Blends of the natural polymer cellulose with a synthetic polymer, polyamide 66, are studied in order to determine if the expected strong interaction between them, due to hydrogen bonds, could improve their mechanical properties such as strength and elongation at break. In a previous work {Part I, J. Polym. Sci. Polym. Phys., 32 , 1437 (1994)}, the preparation technique and the characterization of cellulose-polyamide 66 (PA66) blends were described in detail. Several samples in the composition range between 0 to 70 wt % of PA66 were carefully dried and examined using dynamic mechanical and tensile tests. Based on previous work a new percolation model has been developed. It takes both linear and nonlinear mechanical behaviors into account and allows for the effect of adhesion between material domains. From comparison between experimental and predicted data, it is concluded that a partial miscibility between the amorphous phases of cellulose and PA66 exists and is responsible for a strong adhesion at their interface. Solid-state ¹³C nuclear magnetic resonance has also been used to study these samples and supports the existence of strong interactions between both homopolymers. © 1995 John Wiley & Sons, Inc.     

Medium-range structural order in liquid Ni20Al80 alloy: Experimental and molecular dynamics studies

In this Letter, structural properties of liquid Ni₂₀Al₈₀ have been studied through molecular dynamics (MD) simulations, and results agree well with X-ray experiments. The prepeak in the structure factor manifests the existence of medium-range structural order (MRSO) in liquid Ni₂₀Al₈₀. Analyses in MRSO reveal the dominance of ideal icosahedra and defective icosahedra which, furthermore, form clusters and share common atoms.     

基于任意形状颗粒集合二值图像的体积估算模型

基于任意形状颗粒集合的二值图像,提出了估计其体积(或质量)的方法.首先利用发光背景台面获取的颗粒灰度图像变换为相应的二值图像,得到颗粒的边界轮廓.然后再基于其边界信息,定义与颗粒形状特征相关的描述量,并将其无量纲化.将由此定义的无量纲参变量作为回归变量建立一个多元线性回归(multiple linear regression)模型用以估计颗粒集合的扁平度,进而估算颗粒体积.回归变量的系数由随机采样的501个样本颗粒(尺寸范围为4.75～25 mm)用误差最小平方和求得.将模型应用于由具有相似统计分布特征的其他颗粒集合,并将得到的体积估计值与其体积真实值相比较,实验结果显示模型的相对误差在±2%以内.     

Learning and transfer in dynamic decision environments

An important aspect of learning is the ability to transfer knowledge to new contexts. However, in dynamic decision tasks, such as bargaining, firefighting, and process control, where decision makers must make repeated decisions under time pressure and outcome feedback may relate to any of a number of decisions, such transfer has proven elusive. This paper proposes a two-stage connectionist model which hypothesizes that decision makers learn to identify categories of evidence requiring similar decisions as they perform in dynamic environments. The model suggests conditions under which decision makers will be able to use this ability to help them in novel situations. These predictions are compared against those of a one-stage decision model that does not learn evidence categories, as is common in many current theories of repeated decision making. Both models' predictions are then tested against the performance of decision makers in an Internet bargaining task. Both models correctly predict aspects of decision makers' learning under different interventions. The two-stage model provides closer fits to decision maker performance in a new, related bargaining task and accounts for important features of higher-performing decision makers' learning. Although frequently omitted in recent accounts of repeated decision making, the processes of evidence category formation described by the two-stage model appear critical in understanding the extent to which decision makers learn from feedback in dynamic tasks. Faison (Bud) Gibson is an Assistant Professor at College of Business, Eastern Michigan University. He has extensive experience developing and empirically testing models of decision behavior in dynamic decision environments.     

AN ENCOMPASSING APPROACH TO MODELING FISHERY DYNAMICS: MODELING DYNAMIC NONLINEAR SYSTEMS

Uncertainty about the nature and significance of nonlinearities and the manner in which dynamics affect future realizations makes model specification the most difficult aspect of modeling dynamic systems. By interpreting several popular fishery models as subcases of a nesting dynamic Taylor series approximation, we isolate the specification differences between these models in a way that accounts for commonalities. On the argument that the differences due to alternative nonlinear forms are likely to be small compared to more mundane considerations such as delay difference and general dynamic lag specification, we propose an alternative model that uses the terms from the first order approximation common to all models combined with a data-based determination of the appropriate lags using the methods of state space time series analysis. Finally, the success of the alternative models is judged in an application to Pacific halibut data.     

Long-range predictive adaptive fuzzy relational control

A class of long-range predictive adaptive fuzzy relational controllers is presented. The plant behavior is described over an extended time horizon by a fuzzy relational model which is identified based on input-output closed-loop observations of the plant variables. In this class of adaptive controllers the control law attempts to minimize a quadratic cost over an extended control horizon. When used with linear models, this approach has revealed a significant potential for overcoming the limitations of one-step ahead schemes, such as the stabilization of non-minimum phase plants. Here, a uniform framework is adopted for implementing both the fuzzy model and the fuzzy controller, namely distributed fuzzy relational structures gaining from their massive parallel processing features and from the learning capabilities typical of the connectivist approaches. Issues such as maintenance during the adaptation process of the meaning of linguistic terms used at both fuzzy systems interfaces are addressed, namely by introducing a new design methodology for on-line fuzzy systems interface adaptation. The examples presented reinforce the claim of the usefulness of this new approach.     

A density functional theory computational study of the role of ligand on the stability of CuI and CuII species associated with ATRP and SET‐LRP

Atom transfer radical polymerization (ATRP) and single electron‐transfer living radical polymerization (SET‐LRP) both utilize copper complexes of various oxidation states with N‐ligands to perform their respective activation and deactivation steps. Herein, we utilize DFT (B3YLP) methods to determine the preferred ligand‐binding geometries for Cu/N‐ligand complexes related to ATRP and SET‐LRP. We find that those ligands capable of achieving tetrahedral complexes with Cu^I and trigonal bipyramidal with axial halide complexes with [Cu^IIX]⁺ have higher energies of stabilization. We were able to correlate calculated preferential stabilization of [Cu^IIX]⁺ with those ligands that perform best in SET‐LRP. A crude calculation of energy of disproportionation revealed that the same preferential binding of [Cu^IIX]⁺ results in increased propensity for disproportionation. Finally, by examining the relative energies of the basic steps of ATRP and SET‐LRP, we were able to rationalize the transition from the ATRP mechanism to the SET‐LRP mechanism as we transition from typical nonpolar ATRP solvents to polar SET‐LRP solvents. © 2007 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 45: 4950–4964, 2007     

从结构电算谈设计概念

使用计算机进行结构设计虽然能大大减轻计算工作量，但不能取代人的设计概念，只有在设计人员具备较全面的设计概念；有一定设计经验；同时深入了解程度的情况下，才能确保工程设计的质量。     

Global Optimal Image Reconstruction from Blurred Noisy Data by a Bayesian Approach

In this paper, a procedure is presented which allows the optimal reconstruction of images from blurred noisy data. The procedure relies on a general Bayesian approach, which makes proper use of all the available information. Special attention is devoted to the informative content of the edges; thus, a preprocessing phase is included, with the aim of estimating the jump sizes in the gray level. The optimization phase follows; existence and uniqueness of the solution is secured. The procedure is tested against simple simulated data and real data.     

Monte Carlo simulation of grain growth in polycrystalline materials

Understanding the kinetics of grain growth, under the influence of second phase (such as impurities, voids and bubbles) is fundamental to advances in the control of microstructural evolution. As a precursor to this objective, we have investigated the grain growth kinetics in a polycrystalline material using a standard Q-state Potts’ model under Monte Carlo settings. Based on physical reasoning, new modifications are suggested to circumvent some of the disadvantages in the basic Potts model. The efficacy of these modifications vis-à-vis the basic model is verified. The influence of second phase particles on the impurity loaded grain boundaries is investigated for the study of grain growth kinetics.