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.     

液态金属流的表面张力问题中的两点边值问题解的存在性

本文讨论了在区域提纯过程中，研究液态金属流的表面张力时提出的一个带有非负参数Ｑ的两点边值问题．利用上、下解方法和Ｓｃｈａｕｄｅｒ不动点定理，证明了当０Ｑ８５１时，该问题至少有一个解，对已有结果０Ｑ＜１进行了重要的改进．     

高功率可调谐连续NaCl：（F2^＋）H色心激光器

报道了掺硫和掺氧ＮａＣｌ：（Ｆ２＾＋）Ｈ色心激光器最新实验结果。最高输出功率分别达３Ｗ和４．８Ｗ。实验比较了掺硫及掺氧（Ｆ２＾＋）Ｈ心的热光稳定性。     

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     

A laser micro-chopper approach for molecular beam probe

A laser micro-chopper has been designed to respond fast to a molecular beam. A method of spectral structure measurement is established to analyse the responding signals. In experiments, the minimum detectable signals correspond to an air molecular-beam intensity of 5 × 10⁸/s