Photo- and Thermo-Chromic Study of the Ethylenediamine Trimolybdate

The ethylenediamine trimolybdate (ENTMo) can show unusually photochromic and thermochromic properties and there exists in the difference of chromic mechanisms, which has been proved in our previous work [I]. In this paper, X-ray powder diffraction (XRD), Fourier transform infrared (FTIR) and laser Raman spectroscopy (LRS) of the colored samples are characterized and analyzed in detail. The crystal structure, the inorganic skeleton and the microenvironment of center ions of the colored samples do not substantively change except distortion. The color difference of the photochromic and the thermochromic samples is discussed and that the difference of reduction sites result in their different chromic mechanisms is suggested.

Thermo- and pH-sensitive poly(vinylmethyl ether)/carboxymethylchitosan hydrogels crosslinked using electron beam irradiation or using glutaraldehyde as a crosslinker

BACKGROUND: Stimuli-sensitive or intelligent hydrogels have been investigated for many biomedical and pharmaceutical applications. Those hydrogels with dual sensitivity will have more extensive potential applications. The aim of the work presented was to prepare a series of thermo- and pH-sensitive hydrogels based on poly(vinylmethyl ether) (PVME) and carboxymethylchitosan (CMCS). The hydrogels were crosslinked using electron beam irradiation (EB) or using glutaraldehyde (GA) as a crosslinker at room temperature.

Synthesis of biodegradable thermo- and pH-responsive hydrogels for controlled drug release

Novel intelligent hydrogels composed of biodegradable and pH-sensitive poly(L-glutamic acid) (PGA) and temperature sensitive poly(N-isopropylacrylamide-co-2-hydroxyethyl methacrylate) (PNH) were synthesized and characterized for controlled release of hydrophilic drug. The influence of pH on the equilibrium swelling ratios of the hydrogels was investigated. A higher PNH content resulted in lower equilibrium swelling ratios. Although temperature had little influence on the swelling behaviors of the hydrogels, the changes of optical transmittance of hydrogels as a function of temperature were marked, which showed that the PNH part of hydrogel exhibited hydrophobic property at temperature above the lower critical solution temperature (LCST). The biodegradation rate of the stimuli-sensitive hydrogels in the presence of enzyme was directly proportional to the PGA content. Lysozyme was chosen as a model drug and loaded into the hydrogels.

Structural analysis of PEKEKK(T/I) using WAXD, imaging plates and SAXS

Structures and crystal form transition of the novel aryl ether ketone polymer containing meta-phenylene linkage: PEKEKK(T/I) were investigated by wide angle X-ray diffraction (WAXD), imaging plates (IPs) and small angle X-ray scattering (SAXS). The energy of activation of the decomposition reaction and degree of crystallinity of PEKEKK(T/I) were determined by WAXD and thermo-gravimetric analysis (TGA), respectively. Results obtained from WAXD and IPs show that crystal forms I and II coexist in the PEKEKK(T/I) samples isothermally cold crystallized in the temperature range from 180degreesC to 240degreesC and only form I occurs in PEKEKK(T/I) samples isothermally cold crystallized at 270degreesC. The radius of gyration (Rg), thickness of microregions with electron-density fluctuations (E) and distribution of particle sizes were investigated by SAXS.

Synthesis and thermo-decomposition mechanism of rare earth complexes with phenylacetic acid

Rare earth complexes with phenylacetic acid (LnL(3) . nH(2)O, Ln is Ce, Nd, Pr, Ho, Er, Yb and Y, L is phenylacetate, n = 1-2) were prepared and characterized by elemental analysis, IR spectroscopy, chemical analysis, and X-ray crystal structure. The mechanism of thermal decomposition of the complexes was studied by means of TG-DTG, DTA and DSC. The activation energy and enthalpy change for the dehydration and melting processes were determined.

The Bifurcation Interpreter: A Step Towards the Automatic Analysis of Dynamical Systems

The Bifurcation Interpreter is a computer program that autonomously explores the steady-state orbits of one-parameter families of periodically- driven oscillators. To report its findings, the Interpreter generates schematic diagrams and English text descriptions similar to those appearing in the science and engineering research literature. Given a system of equations as input, the Interpreter uses symbolic algebra to automatically generate numerical procedures that simulate the system. The Interpreter incorporates knowledge about dynamical systems theory, which it uses to guide the simulations, to interpret the results, and to minimize the effects of numerical error.

Application of dynamical Lie algebraic method to atom-diatomic molecule scattering

The dynamical Lie algebraic approach developed by Alhassid and Levine combined with intermediate picture is applied to the study of translational-vibrational energy transfer in the collinear collision between an atom and an anharmonic oscillator. We find that the presence of the anharmonic terms indeed has an effect on the vibrational probabilities of the oscillator. The computed probabilities are in good agreement with those obtained using exact quantum method. It is shown that the approach of dynamical Lie algebra combining with intermediate picture is reasonable in the treating of atom-anharmonic oscillator scattering.

Continuous configuration time-dependent self-consistent field method for polyatomic quantum dynamical problems

A new continuous configuration time-dependent self-consistent field method has been developed to study polyatomic dynamical problems by using the discrete variable representation for the reaction system, and applied to a reaction system coupled to a bath. The method is very efficient because the equations involved are as simple as those in the traditional single configuration approach, and can account for the correlations between the reaction system and bath modes rather well. (C) American Institute of Physics.

Dynamical Systems in the Sensorimotor Loop: On the Interrelation Between Internal and External Mechanisms of Evolved Robot Behavior

Huelse, M., Wischmann, S., Manoonpong, P., Twickel, A.v., Pasemann, F.: Dynamical Systems in the Sensorimotor Loop: On the Interrelation Between Internal and External Mechanisms of Evolved Robot Behavior. In: M. Lungarella, F. Iida, J. Bongard, R. Pfeifer (Eds.) 50 Years of Artificial Intelligence, LNCS 4850, Springer, 186 - 195, 2007.

The acquisition of phonology in a dynamical model of human information processing: a preliminary account

The problem of the acquisition of first language phonology is dealt with within the general information-processing perspective. In this sense, language acquisition is viewed as a process of biologically founded pattern formation due to information exchanges between an adult and a child. Moreover, the process is cognitive in that the child, as a goal-seeking and error correcting individual, undertakes an intricate task of compressing a huge variety of linguistic stimuli in order to build an effective information code. It is further assumed that the basic mechanism which leads to the establishment of fully articulate linguistic ability is that of simulation. The mechanism works through a compression of a set of initial variables (i.e. initial conditions) into a minimum length algorithm and a subsequent construction of an integrated system of language-specific attractors. It is only then that the language user is capable of participating in an information transaction in a fully developed manner.

Dynamical Instabilities and Deterministic Chaos in Ballistic Electron Motion in Semiconductor Superlattices

We consider the motion of ballistic electrons within a superlattice miniband under the influence of an alternating electric field. We show that the interaction of electrons with the self-consistent electromagnetic field generated by the electron current may lead to the transition from regular to chaotic dynamics. We estimate the conditions for the experimental observation of this deterministic chaos and discuss the similarities of the superlattice system with the other condensed matter and quantum optical systems.

Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Simultaneous Learning of Non-Linear Manifold and Dynamical Models for High-Dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.