: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

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Nonlinear Time Series Analysis.

The widespread use of molecular machines in biology has long suggested that great rewards could come from bridging the gap between synthetic molecular systems and the machines of the macroscopic world. In the last two decades, it has proved possible to design synthetic molecular systems with architectures where triggered large amplitude positional changes of submolecular components occur. Perhaps the best way to appreciate the technological potential of controlled molecular-level motion is to recognize that nanomotors and molecular-level machines lie at the heart of every significant biological process. Over billions of years of evolution, nature has not repeatedly chosen this solution for performing complex tasks without good reason. When mankind learns how to build artificial structures that can control and exploit molecular level motion and interface their effects directly with other molecular-level substructures and the outside world, it will potentially impact on every aspect of functional molecule and materials design. An improved understanding of physics and biology will surely follow.

The first steps on the long path to the invention of artificial molecular machines were arguably taken in 1827 when the Scottish botanist Robert Brown observed the haphazard motion of tiny particles under his microscope.1,2 The explanation for Brownian motion, that it is caused by bombardment of the particles by molecules as a consequence of the kinetic theory of matter, was later provided by Einstein, followed by experimental verification by Perrin.3,4 The random thermal motion of molecules and its implications for the laws of thermodynamics in turn inspired Gedankenexperiments (“thought experiments”) that explored the interplay (and apparent paradoxes) of Brownian motion and the Second Law of Thermodynamics. Richard Feynman’s famous 1959 lecture “There’s plenty of room at the bottom” outlined some of the promise that manmade molecular machines might hold.5,6 However, Feynman’s talk came at a time before chemists had the necessary synthetic and analytical tools to make molecular machines. While interest among synthetic chemists began to grow in the 1970s and 1980s, progress accelerated in the 1990s, particularly with the invention of methods to make mechanically interlocked molecular systems (catenanes and rotaxanes) and control and switch the relative positions of their components.7−24

Here, we review triggered large-amplitude motions in molecular structures and the changes in properties these can produce. We concentrate on conformational and configurational changes in wholly covalently bonded molecules and on catenanes and rotaxanes in which switching is brought about by various stimuli (light, electrochemistry, pH, heat, solvent polarity, cation or anion binding, allosteric effects, temperature, reversible covalent bond formation, etc.). Finally, we discuss the latest generations of sophisticated synthetic molecular machine systems in which the controlled motion of subcomponents is used to perform complex tasks, paving the way to applications and the realization of a new era of “molecular nanotechnology”.

1.1. The Language Used To Describe Molecular Machines
Terminology needs to be properly and appropriately defined and these meanings used consistently to effectively convey scientific concepts. Nowhere is the need for accurate scientific language more apparent than in the field of molecular machines. Much of the terminology used to describe molecular-level machines has its origins in observations made by biologists and physicists, and their findings and descriptions have often been misinterpreted and misunderstood by chemists. In 2007 we formalized definitions of some common terms used in the field (e.g., “machine”, “switch”, “motor”, “ratchet”, etc.) so that chemists could use them in a manner consistent with the meanings understood by biologists and physicists who study molecular-level machines.14

The word “machine” implies a mechanical movement that accomplishes a useful task. This Review concentrates on systems where a stimulus triggers the controlled, relatively large amplitude (or directional) motion of one molecular or submolecular component relative to another that can potentially result in a net task being performed. Molecular machines can be further categorized into various classes such as “motors” and “switches” whose behavior differs significantly.14 For example, in a rotaxane-based “switch”, the change in position of a macrocycle on the thread of the rotaxane influences the system only as a function of state. Returning the components of a molecular switch to their original position undoes any work done, and so a switch cannot be used repetitively and progressively to do work. A “motor”, on the other hand, influences a system as a function of trajectory, meaning that when the components of a molecular motor return to their original positions, for example, after a 360° directional rotation, any work that has been done is not undone unless the motor is subsequently rotated by 360° in the reverse direction. This difference in behavior is significant; no “switch-based” molecular machine can be used to progressively perform work in the way that biological motors can, such as those from the kinesin, myosin, and dynein superfamilies, unless the switch is part of a larger ratchet mechanism.14

Artificial Molecular Machines

Solid supported lipid bilayers: From biophysical studies to sensor design.

Micro total analysis systems. Latest advancements and trends.

Biofabrication with chitosan.

A new way of analyzing measured or calculated vibrational spectra in terms of internal vibrational modes associated with the internal parameters used to describe geometry and conformation of a molecule is described. The internal modes are determined by solving the Euler–Lagrange equations for molecular fragments ϕn described by internal parameters ζn. An internal mode is localized in a molecular fragment by describing the rest of the molecule as a collection of massless points that just define molecular geometry. Alternatively, one can consider the new fragment motions as motions that are obtained after relaxing all parts of the vibrating molecule but the fragment under consideration. Because of this property, the internal modes are called adiabatic internal modes, and the associated force constants ka, adiabatic force constants. Minimization of the kinetic energy of the vibrating fragment ϕn yields the adiabatic mass ma (corresponding to 1/Gnn of Wilson's G matrix) and, by this, adiabatic frequencies ωa. Adiabatic modes are perfectly suited to analyze and understand the vibrational spectra of a molecule in terms of internal parameter modes in the same way as one understands molecular geometry in terms of internal coordinates. © 1998 John Wiley & Sons, Inc. Int J Quant Chem 67: 1–9, 1998

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A new way of analyzing vibrational spectra. I. Derivation of adiabatic internal modes

Methods based on self-assembly, self-organization, and forced shape transformations to form synthetic or semisynthetic enclosed lipid bilayer structures with several properties similar to biological nanocompartments are reviewed. The procedures offer unconventional micro- and nanofabrication routes to yield complex soft-matter devices for a variety of applications for example, in physical chemistry and nanotechnology. In particular, we describe novel micromanipulation methods for producing fluid-state lipid bilayer networks of nanotubes and surface-immobilized vesicles with controlled geometry, topology, membrane composition, and interior contents. Mass transport in nanotubes and materials exchange, for example, between conjugated containers, can be controlled by creating a surface tension gradient that gives rise to a moving boundary or by induced shape transformations. The network devices can operate with extremely small volume elements and low mass, to the limit of single molecules and particles at a length scale where a continuum mechanics approximation may break down. Thus, we also describe some concepts of anomalous fluctuation-dominated kinetics and anomalous diffusive behaviours, including hindered transport, as they might become important in studying chemistry and transport phenomena in these confined systems. The networks are suitable for initiating and controlling chemical reactions in confined biomimetic compartments for rationalizing, for example, enzyme behaviors, as well as for applications in nanofluidics, bioanalytical devices, and to construct computational and complex sensor systems with operations building on chemical kinetics, coupled reactions and controlled mass transport.

Biomimetic nanoscale reactors and networks.

We study the process of charged polymer translocation, driven by an external electric potential, through a narrow pore in a membrane We assume that the number of polymer segments, m, having passed the entrance pore mouth, is a slow variable governing the translocation process Outside the pore the probability that there is an end segment at the entrance pore mouth, is taken as the relevant parameter In particular we derive an expression for the free energy as a function of m, F(m) F(m) is used in the Smoluchowski equation in order to obtain the flux of polymers through the pore In the low voltage regime we find a thresholdlike behavior and exponential dependence on voltage Above this regime the flux depends linearly on the applied voltage At very high voltages the process is diffusion limited and the flux saturates to a constant value The model accounts for all features of the recent experiments by Henrickson et al [Phys Rev Lett 85, 3057 (2000)] for the flux of DNA molecules through an α-hemolysin pore as a function of applied voltage

Charged polymer membrane translocation

The concept of characterizing normal vibrational modes l in terms of m internal vibrational modes v typical of molecular fragments or structural subunits is n developed. Essential for this concept is the amplitude A that provides the basis for a nm quantitative comparison of modes l and v and, by this, facilitates the extraction of m n chemical information out of vibrational spectra. Twelve possibilities of defining amplitude . .

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A New Way of Analyzing Vibrational Spectra. III. Characterization of Normal Vibrational Modes in Terms of Internal Vibrational Modes

Adiabatic internal frequencies are compared with c-vector frequencies and intrinsic frequencies. It is shown that c-vector modes are not suitable to characterize molecular fragments f since they are not localized in f and their definition leads to nn unreasonable frequency values. Intrinsic frequencies suffer from a strong dependence on the set of internal parameters chosen to describe the geometry of the molecule. Apart from this, they represent averaged frequencies, for which mass effects and electronic effects are not properly separated. Adiabatic frequencies are based on a dynamic principle, separate properly mass effects and electronic effects and do not depend in any

https://sites.smu.edu/dedman/catco/publications/pdf/IJQC_67_11_1998.pdf

Zoran Konkoli

Papers

A new way of analyzing vibrational spectra. I. Derivation of adiabatic internal modes

Biomimetic nanoscale reactors and networks.

Charged polymer membrane translocation

A New Way of Analyzing Vibrational Spectra. III. Characterization of Normal Vibrational Modes in Terms of Internal Vibrational Modes

A new way of analyzing vibrational spectra : II. Comparison of internal mode frequencies