Recent developments in intelligent biomedical polymers

Intelligent polymers or stimuli-responsive polymers may exhibit distinct transitions in physical-chemical properties, including conformation, polarity, phase structure and chemical composition in response to changes in environmental stimuli. Due to their unique 'intelligent' characteristics, stimuli-sensitive polymers have found a wide variety of applications in biomedical and nanotechnological fields. This review focuses on the recent developments in biomedical application of intelligent polymer systems, such as intelligent hydrogel systems, intelligent drug delivery systems and intelligent molecular recognition systems. Also, the possible future directions for the application of these intelligent polymer systems in the biomedical field are presented.

Energy conversion in shape memory alloy heat engine - Part II: Simulation

In this paper, a theoretical model proposed in Part I (Zhu et al., 2001a) is used to simulate the behavior of a twin crank NiTi SMA spring based heat engine, which has been experimentally studied by Iwanaga et al. (1988). The simulation results are compared favorably with the measurements. It is found that (1) output torque and heat efficiency decrease as rotation speed increase; (2) both output torque and output power increase with the increase of hot water temperature; (3) at high rotation speed, higher water temperature improves the heat efficiency. On the contrary, at low rotation speed, lower water temperature is more efficient; (4) the effects of initial spring length may not be monotonic as reported. According to the simulation, output torque, output power and heat efficiency increase with the decrease of spring length only in the low rotation speed case. At high rotation speed, the result might be on the contrary.

Energy Conversion in Shape Memory Alloy Heat Engine Part I: Theory

Shape Memory Alloy (SMA) can be easily deformed to a new shape by applying a small external load at low temperature, and then recovers its original configuration upon heating. This unique shape memory phenomenon has inspired many novel designs. SMA based heat engine is one among them. SMA heat engine is an environment-friendly alternative to extract mechanical energy from low-grade energies, for instance, warm wastewater, geothermal energy, solar thermal energy, etc. The aim of this paper is to present an applicable theoretical model for simulation of SMA-based heat engines. First, a micro-mechanical constitutive model is derived for SMAs. The volume fractions of austenite and martensite variants are chosen as internal variables to describe the evolution of microstructure in SMA upon phase transition. Subsequently, the energy equation is derived based on the first thermodynamic law and the previous SMA model. From Fourier’s law of heat conduction and Newton’s law of cooling, both differential and integral forms of energy conversion equation are obtained.

Application of uniform design and genetic algorithm in optimization of reversed-phase chromatographic separation

An optimization method based on uniform design in conjunction with genetic algorithm is described. According to the proposed method, the uniform design technique was applied to the design of starting experiments, which can reduce the number of experiments compared with traditional simultaneous methods, such as simplex. And genetic algorithm was used in optimization procedure, which can improve the rapidity of optimal procedure. The hierarchical chromatographic response function was modified to evaluate the separation equality of a chromatogram. An iterative procedure was adopted to search for the optimal condition to improve the accuracy of predicted retention and the quality of the chromatogram. The optimization procedure was tested in optimization of the chromatographic separation of 11 alkaloids in reversed-phase ion pair chromatography and satisfactory optimal result was obtained. (C) 2003 Elsevier B.V. All rights reserved.

A novel invariant and sequence comparison for DNA

We consider numerical characterization of DNA primary sequence based on the positions of bases (a, t, c, g) and the pairs of bases X, Y in DNA (X, Y=a, t, c, g). This leads to a representation of DNA by a numerical sequence. Then, we extract a novel invariant (molecular connectivity index) from the derived numerical sequences. The suitable invariant can offer a characterization of DNA primary sequence. Finally, we provide an illustration of its utility by making a comparison between ten DNA sequences belonging to beta-globin gene in different species. The evolutionary relationships of ten species we have revealed in this contribution accord with phylogenetic tree properly.

Extended topological indices and prediction of activities of chiral compounds

The activities/properties of two molecules with identical formula but different configuration states of the asymmetric atoms are different. Thus, usually the common topological indices are not suitable. In this study, the chiral topological indices were obtained by extending A(mi) indices suggested by our laboratory and molecular connectivity indices. The modified topologial indices have been used for the studies on D2 for dopamine receptor and a receptor activities of fourteen N-alkylated 3-(3-hydroxypyenyl)-piperidines. It has been observed that selected variables possess low correlations. The results obtained by using multiple regression analysis and artificial neural networks are satisfactory.

A QSAR study of the antiallergic activities of substituted benzamides and their structures

Multiple regression analysis (MRA) and comparative molecular field analysis (CoMFA) have been used in studies of the correlation between the antiallergic activities of substituted benzamides and their structures. The results achieved using Coh IFA based on 3D factors are much better than those obtained using MRA based on mainly 2D structural information. The CoMFA results reveal that the steric effects are more important than the electrostatic effects for the activities of substituted benzamides. (C) 1999 Elsevier Science B.V. All rights reserved.

Classification of valence changes of trivalent rare earth ions in alkaline earth borates using artificial neural networks

The investigations of classification on the valence changes from RE3+ to RE2+ (RE = Eu, Sm, Yb, Tm) in host compounds of alkaline earth berate were performed using artificial neural networks (ANNs). For comparison, the common methods of pattern recognition, such as SIMCA, KNN, Fisher discriminant analysis and stepwise discriminant analysis were adopted. A learning set consisting of 24 host compounds and a test set consisting of 12 host compounds were characterized by eight crystal structure parameters. These parameters were reduced from 8 to 4 by leaps and bounds algorithm. The recognition rates from 87.5 to 95.8% and prediction capabilities from 75.0 to 91.7% were obtained. The results provided by ANN method were better than that achieved by the other four methods. (C) 1999 Elsevier Science B.V. All rights reserved.

Computer perception of topological symmetry by all-paths algorithm

During the development of our ESESOC system (Expert System for the Elucidation of the Structures of Organic Compounds), computer perception of topological symmetry is essential in searching for the canonical description of a molecular structure, removing the irredundant connections in the structure generation process, and specifying the number of peaks in C-13- and H-1-NMR spectra in the structure evaluation process. In the present paper, a new path identifier is introduced and an algorithm for detection of topological symmetry from a connection table is developed by the all-paths method. (C) 1999 Elsevier Science B.V. All rights reserved.

SEARCH FOR THE BASIC REGULARITIES OF THE TRANSITION EMISSION OF EUROPIUM(II) ION IN COMPLEX FLUORIDES USING PATTERN-RECOGNITION

The relationship between structures of complex fluorides and spectral structure of Eu(II) ion in complex fluorides (AB(m)F(n)) is investigated by means of pattern recognition methods, such as KNN, ALKNN, BAYES, LLM, SIMCA and PCA. A learning set consisting of 32 f-f transition emission host compounds and 31 d-f transition emission host compounds and a test set consisting of 27 host compounds were characterized by 12 crystal structural parameters. These parameters, i.e. features, were reduced from 12 to 6 by multiple criteria for the classification of these host compounds as f-f transition emission or d-f transition emission. A recognition rate from 79.4 to 96.8% and prediction capabilities from 85.2 to 92.6% were obtained. According to the above results, the spectral structures of Eu(II) ion in seven unknown host lattices were predicted.