Synthesis of novel poly[(2,5-dimethoxy-p-phenylene)vinylene] precursors having two eliminatable groups: An approach for the control of conjugation length

Poly[(2,5-dimethoxy-p-phenylene)vinylene] (DMPPV) of varying conjugation length was synthesized by selective elimination of organic soluble precursor polymers that contained two eliminatable groups, namely, methoxy and acetate groups. These precursor copolymers were in turn synthesized by competitive nucleophilic substitution of the sulfonium polyelectrolyte precursor (generated by the standard Wessling route) using methanol and sodium acetate in acetic acid. The composition of the precursor copolymer, in terms of the relative amounts of methoxy and acetate groups, was controlled by varying the composition of the reaction mixture during nucleophilic substitution. Thermal elimination of these precursor copolymers at 250 degrees C, yielded partially conjugated polymers, whose color varied from light yellow to deep red. FT-IR studies confirmed that, while essentially all the acetate groups were eliminated, the methoxy groups were intact and caused the interruption in conjugation. Preliminary photoluminescence studies of the partially eliminated DMPPV samples showed a gradual shift in the emission maximum from 498 to 598 nm with increasing conjugation lengths, suggesting that the color of LED devices fabricated from such polymers can, in principle, be fine-tuned.

Implementation of Art1 and Art2 Artificial Neural Networks on Ring and Mesh Architectures

The Artificial Neural Networks (ANNs) are being used to solve a variety of problems in pattern recognition, robotic control, VLSI CAD and other areas. In most of these applications, a speedy response from the ANNs is imperative. However, ANNs comprise a large number of artificial neurons, and a massive interconnection network among them. Hence, implementation of these ANNs involves execution of computer-intensive operations. The usage of multiprocessor systems therefore becomes necessary. In this article, we have presented the implementation of ART1 and ART2 ANNs on ring and mesh architectures. The overall system design and implementation aspects are presented. The performance of the algorithm on ring, 2-dimensional mesh and n-dimensional mesh topologies is presented. The parallel algorithm presented for implementation of ART1 is not specific to any particular architecture. The parallel algorithm for ARTE is more suitable for a ring architecture.

A direct adaptive neural command controller design for an unstable helicopter

This paper presents an off-line (finite time interval) and on-line learning direct adaptive neural controller for an unstable helicopter. The neural controller is designed to track pitch rate command signal generated using the reference model. A helicopter having a soft inplane four-bladed hingeless main rotor and a four-bladed tail rotor with conventional mechanical controls is used for the simulation studies. For the simulation study, a linearized helicopter model at different straight and level flight conditions is considered. A neural network with a linear filter architecture trained using backpropagation through time is used to approximate the control law. The controller network parameters are adapted using updated rules Lyapunov synthesis. The off-line trained (for finite time interval) network provides the necessary stability and tracking performance. The on-line learning is used to adapt the network under varying flight conditions. The on-line learning ability is demonstrated through parameter uncertainties. The performance of the proposed direct adaptive neural controller (DANC) is compared with feedback error learning neural controller (FENC).

Denoising neural data with state-space smoothing: Method and application

Neural data are inevitably contaminated by noise. When such noisy data are subjected to statistical analysis, misleading conclusions can be reached. Here we attempt to address this problem by applying a state-space smoothing method, based on the combined use of the Kalman filter theory and the Expectation–Maximization algorithm, to denoise two datasets of local field potentials recorded from monkeys performing a visuomotor task. For the first dataset, it was found that the analysis of the high gamma band (60–90 Hz) neural activity in the prefrontal cortex is highly susceptible to the effect of noise, and denoising leads to markedly improved results that were physiologically interpretable. For the second dataset, Granger causality between primary motor and primary somatosensory cortices was not consistent across two monkeys and the effect of noise was suspected. After denoising, the discrepancy between the two subjects was significantly reduced.

Synthesis and biological evaluation of novel 1-(4-methoxyphenethyl)-1H-benzimidazole-5-carboxylic acid derivatives and their precursors as antileukemic agents

We report here the synthesis and preliminary evaluation of novel 1-(4-methoxyphenethyl)-1H-benzimidazole-5-carboxylic acid derivatives 6(a–k) and their precursors 5(a–k) as potential chemotherapeutic agents. In each case, the structures of the compounds were determined by FTIR, 1H NMR and mass spectroscopy. Among the synthesized molecules, methyl 1-(4-methoxyphenethyl)-2-(4-fluoro-3-nitrophenyl)-1H-benzimidazole-5-carboxylate (5a) induced maximum cell death in leukemic cells with an IC50 value of 3 μM. Using FACS analysis we show that the compound 5a induces S/G2 cell cycle arrest, which was further supported by the observed down regulation of CDK2, Cyclin B1 and PCNA. The observed downregulation of proapoptotic proteins, upregulation of antiapoptotic proteins, cleavage of PARP and elevated levels of DNA strand breaks indicated the activation of apoptosis by 5a. These results suggest that 5a could be a potent anti-leukemic agent.

A Convenient Route for the Synthesis of Complex Metal Oxides Employing Solid-Solution Precursors

Synthesis of complex metal oxides by the thermal decomposition of solid-solution precursors (formed by isomorphous compounds of component metals) has been investigated since the method enables mixing of cations on an atomic scale and drastically reduces diffusion distances to a few angstroms. Several interesting oxides such as Ca2Fe03,5C, aCoz04,C a2C0205a, nd Ca,FeCo05 have been prepared by this technique starting from carbonate solid solutions of the type Ca,-,Fe,C03, Cal-,Co,C03, and Ca,-,,M,M'yC03 (M, M' = Mn, Fe, Co). The method has been extended to oxalate solid-solution precursors, and the possibility of making use of other kinds of precursor solid solutions is indicated.

Formation of Pb(Ti,Zr)O3, from thermal decomposition of oxalate precursors

Lead zir conyl oxalate hexahydrate (LZO) and lead titanyl zirconyl oxalate hydrate (LTZO) are prepared and characterized. Their thermal decompositions have been investigated by thermoanalytical and gas analysis techniques. The decomposition in air or oxygen has three steps — dehydration, decomposition of the oxalate to a carbonate and the decomposition of carbonate to PbZrO3. In non oxidising atmosphere, partial reduction of Pb(II) to Pb(0) takes place at the oxalate decomposition step. The formation of free metallic lead affects the stoichiometry of the intermediate carbonate and yields a mixture of Pb(Ti,Zr)O3 and ZrO2 as the final products. By maintaining oxidising atmosphere and low heating rate, direct preparation of stoichiometric, crystalline Pb(Ti,Zr)O3 at 550°C is possible from the corresponding oxalate precursor.

Immunospecific binding of tRNA precursors containing N6-(delta 2-isopentenyl) adenosine

Antibodies specific for N6-(delta 2-isopentenyl) adenosine (i6A) were immobilized on Sepharose and this adsorbent (Sepharose-anti-i6A) was used to selectively isolate bacteriophage T4 tRNA precursors containing i6A/ms2i6A from an unfractionated population of 32P-labeled T4 RNAs. The results showed that antibodies to i6A selectively bound only those tRNA precursors containing i6A/ms2i6A. Binding of tRNA precursors by antibody and specificity of the binding was assessed by membrane binding using 32P-labeled tRNA precursor. Binding was highly specific for i6A/ms2i6A residues in the tRNA precursors. This binding can be used to separate modified from unmodified precursor RNAs and to study the biosynthetic pathways of tRNA precursors.

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are considered as the classification criteria for grouping. The optimal number of groups is determined as 14 based on the Davies-Bouldin index approach. It is observed that the FCA approach performed better than the other two methodologies for the present study.

Rotorcraft parameter estimation using radial basis function neural network

Increased emphasis on rotorcraft performance and perational capabilities has resulted in accurate computation of aerodynamic stability and control parameters. System identification is one such tool in which the model structure and parameters such as aerodynamic stability and control derivatives are derived. In the present work, the rotorcraft aerodynamic parameters are computed using radial basis function neural networks (RBFN) in the presence of both state and measurement noise. The effect of presence of outliers in the data is also considered. RBFN is found to give superior results compared to finite difference derivatives for noisy data. (C) 2010 Elsevier Inc. All rights reserved.

Deposition of ZnO Films by Combustion Flame Pyrolysis of Solution Precursors

The structural features,including preferred orientation and surface morphology of zinc oxide (ZnO) films deposited by combustion flame pyrolysis were investigated as a function of process parameters, which include precursor solution concentration, substrate-nozzle (S-N) distance, gas flow rate, and duration of deposition. In this technique, the precursor droplets react within the flame and form a coating on an amorphous silica substrate held in or near the flame. Depending on the process parameters, the state of decomposition at which the precursor arrives on the substrate varies substantially and this in turn dictates the orientation and microstructure of the films.

Neural network approach for fault location in unbalanced distribution networks with limited measurements

This paper presents an Artificial Neural Network (ANN) approach for locating faults in distribution systems. Different from the traditional Fault Section Estimation methods, the proposed approach uses only limited measurements. Faults are located according to the impedances of their path using a Feed Forward Neural Networks (FFNN). Various practical situations in distribution systems, such as protective devices placed only at the substation, limited measurements available, various types of faults viz., three-phase, line (a, b, c) to ground, line to line (a-b, b-c, c-a) and line to line to ground (a-b-g, b-c-g, c-a-g) faults and a wide range of varying short circuit levels at substation, are considered for studies. A typical IEEE 34 bus practical distribution system with unbalanced loads and with three- and single- phase laterals and a 69 node test feeder with different configurations are considered for studies. The results presented show that the proposed approach of fault location gives close to accurate results in terms of the estimated fault location.