Can Science Methods Really be Taught On-Line?

In this paper, the process in which both project-based curriculum strategies and Blackboard web-based technology were utilized to develop on-line methods courses to prepare science teachers is described. A discussion of the curriculum design process includes a rationale for using project-based instruction in methods courses, along with a rationale for using Blackboard, a web-based technology, as the environment for the course. The course‘s potential for creating highly qualified science teachers as defined by the No Child Left Behind legislation of 2001 and the National Science Teacher Association‘s Standards for Science Teacher Preparation will be explored [1]

Maximizing the Impact of Professional Development for Earth Science Teachers

This study examines the extent to which follow-up sessions can provide support for earth science teachers as they apply what they learn from professional development coursework during the academic year with their own students. Data include direct observation of follow-up sessions of courses for teachers; interviews with course co-instructors and teacher participants; and, document analysis of teacher products with a focus on the lesson plans, laboratory/activity sheets for students, and virtual field trips that teacher participants submitted and shared during follow-up sessions. Strategies are recommended to assist earth science content faculty in increasing the impact of their work with teachers and hence, student instruction

Method of manufacturing carbon nanotubes

A process for manufacturing carbon nanotubes, including a step of inducing electrical current through a carbon anode and a carbon cathode under conditions effective to produce the carbon nanotubes, wherein the carbon cathode is larger than the carbon anode. Preferably, a welder is used to induce the electrical current via an arc welding process. Preferably, an exhaust hood is placed on the anode, and the process does not require a closed or pressurized chamber. The process provides high-quality, single-walled carbon nanotubes, while eliminating the need for a metal catalyst

Continuous Wavelets on Compact Manifolds

Let $\bf M$ be a smooth compact oriented Riemannian manifold, and let $\Delta_{\bf M}$ be the Laplace-Beltrami operator on ${\bf M}$. Say 0 \neq f \in \mathcal{S}(\RR^+), and that $f(0) = 0$. For $t > 0$, let $K_t(x,y)$ denote the kernel of $f(t^2 \Delta_{\bf M})$. We show that $K_t$ is well-localized near the diagonal, in the sense that it satisfies estimates akin to those satisfied by the kernel of the convolution operator $f(t^2\Delta)$ on \RR^n. We define continuous ${\cal S}$-wavelets on ${\bf M}$, in such a manner that $K_t(x,y)$ satisfies this definition, because of its localization near the diagonal. Continuous ${\cal S}$-wavelets on ${\bf M}$ are analogous to continuous wavelets on \RR^n in \mathcal{S}(\RR^n). In particular, we are able to characterize the H$\ddot{o}$lder continuous functions on ${\bf M}$ by the size of their continuous ${\mathcal{S}}-$wavelet transforms, for H$\ddot{o}$lder exponents strictly between 0 and 1. If $\bf M$ is the torus \TT^2 or the sphere $S^2$, and $f(s)=se^{-s}$ (the ``Mexican hat'' situation), we obtain two explicit approximate formulas for $K_t$, one to be used when $t$ is large, and one to be used when $t$ is small

Bayesian optimization for materials design

We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as possible. We focus on the case when materials designs are parameterized by a low-dimensional vector. Bayesian optimization is built on a statistical technique called Gaussian process regression, which allows predicting the performance of a new design based on previously tested designs. After providing a detailed introduction to Gaussian process regression, we introduce two Bayesian optimization methods: expected improvement, for design problems with noise-free evaluations; and the knowledge-gradient method, which generalizes expected improvement and may be used in design problems with noisy evaluations. Both methods are derived using a value-of-information analysis, and enjoy one-step Bayes-optimality

Kinetic Studies on Photodeposition of Polydiacetylene Thin Film from Solution: Preliminary Determination of the Rate Law

Preliminary kinetic studies were undertaken on the photodeposition of thin films of a polydiacetylene derivative of 2-methyl-4-nitroaniline from monomer solutions onto quartz substrates. Solutions of the monomer, DAMNA, in 1,2-dichloroethane at various concentrations were irradiated at 364 nm using an argon-ion laser at several intensities. It was found that the rate of polydiacetylene (PDAMNA) film photodeposition varies linearly with UV light intensity and as the square root of monomer concentration

Bounded prefix-suffix duplication

We consider a restricted variant of the prefix-suffix duplication operation, called bounded prefix-suffix duplication. It consists in the iterative duplication of a prefix or suffix, whose length is bounded by a constant, of a given word. We give a sufficient condition for the closure under bounded prefix-suffix duplication of a class of languages. Consequently, the class of regular languages is closed under bounded prefix-suffix duplication; furthermore, we propose an algorithm deciding whether a regular language is a finite k-prefix-suffix duplication language. An efficient algorithm solving the membership problem for the k-prefix-suffix duplication of a language is also presented. Finally, we define the k-prefix-suffix duplication distance between two words, extend it to languages and show how it can be computed for regular languages