Synthesis and properties of peroxy derivatives of epoxy resins based on Bisphenol A: Effects of the presence of boron trifluoride ethereate

Oligomers containing peroxy groups have been synthesized on the basis of epoxides reacting with tert–butylhydroperoxide in the presence of boron trifluoride etherate. Dependence of reaction rates upon the quantity of catalyst, temperature and also nature of hydroperoxide has been investigated: A kinetic equation for the process has been formulated and also a kinetic scheme for the reaction taking into account the presence of boron trifluoride etherate is proposed. Structures of the synthesized peroxy oligomers have been confirmed by IR- and NMR-spectroscopy. The reaction initiating properties of the oligomers so synthesized were studied using as the example the styrene polymerization reaction. Electronic Publication     

Synthesis of some fumaric acid polyamides from active diester monomers

Summary The solution polycondensation of two active diesters, bis(2,4-dinitrophenyl) fumarate and 1,1-(fumaroyldioxy)bis(benzotriazole), with a series of alphatic and aromatic diamines in N-methyl pyrolidone at room temperature, was investigated. The polycondensation reactions yielded a series of fumaric acids polyamides. The polymers obtained from aliphatic diamines were soluble in trifluroacetic acid and exhibited well-defined melting points. The polymers obtained from aromatic diamines are infusible at temperatures below 300°C but were thermally stable and soluble in dimethylacetamide containing 4% LiC1.     

Simultaneous electrolytic production of xylitol and xylonic acid from xylose

A new electrocatalytic process for simultaneous electrolytic production of xylitol and xylonic acid or the corresponding xylonate salts directly from -d-xylose in alkaline solutions of neutral salt as supporting electrolyte has been developed and presented. An electrocatalytically active cathode coating with essentially hydridic features has been employed to enhance Faradaic yields in xylitol production at low current densities by heterogeneous reaction of hydrogenation with H-adatoms, and the entire mechanism has been proved by experimental evidence. An anionic selective anodic coating for titanium substrate was also used to suppress oxygen evolution and optimize the xylonic acid production by direct oxidation with anodically generated bromine, and to carry out the electrode process at almost equilibrium potential. The kinetic effect of specifically adsorbable anions for decreasing cathodic current yields in xylitol production and the resulting necessity for membrane separation of catholyte from anolyte, with proper optimization of the supporting neutral salt composition, has also been emphasized.Paper partly presented at 39th and 40th ISE-Meetings at Glasgow UK, 1988 and Kyoto, Japan, 1989.     

Triple-product optical processor for analysis of pulse-repetition and carrier frequencies of radar signals

A triple-product acousto-optic processor output was studied analytically and numerically. The processor is capable of detecting the carrier frequency and the modulation envelope of a signal.     

Serum from diabetic BB/W rats enhances calcium currents in primary sensory neurons

We examined the hypothesis that exposure of nondiabetic rat dorsal root ganglion (DRG) neurons to sera from diabetic BB/W rats would produce an increase in calcium currents associated with impaired regulation of the inhibitory G protein-calcium channel complex. Acutely dissociated rat DRGs were incubated for 18-24 h in medium supplemented with sera (10% vol/vol) from either diabetic rats with neuropathy or age-matched, nondiabetic controls. Exposure of DRG neurons to sera from diabetic BB/W rats resulted in a surface membrane immunofluorescence pattern when treated with an anti-rat light-chain antibody that was not observed in neurons exposed to control sera. Calcium current density (IDCa) was assessed with the use of the whole cell variation of the patch-clamp technique. IDCa in neurons exposed to diabetic sera was significantly increased compared with neurons exposed to control sera. Guanine nucleotide-binding (G) protein regulation of calcium channel function was examined with the use of a two-pulse "facilitation" or IDCa enhancement protocol in the presence of activators [guanosine 5'-O-(3-thiotriphosphate) (GTP gamma S)] or antagonists [guanosine 5'-O-(2-thiodiphosphate) (GDP beta S) and pertussis toxin (PTX)] of G protein function. Facilitation was significantly decreased in neurons exposed to diabetic sera. Intracellular diffusion of neurons with GDP beta s blocked facilitation, whereas dialysis with GTP gamma s increased facilitation to a similar magnitude in neurons exposed to either diabetic or control sera. Treatment with PTX resulted in a significant increase in IDCa and approximately 50% decrease in facilitation in neurons treated with control sera but no significant changes in neurons exposed to diabetic sera. We conclude that serum from diabetic BB/W rats with neuropathy contains an autoimmune immunoglobulin that impairs regulation of the inhibitory G protein-calcium channel complex, resulting in enhanced calcium influx. Regulation of the inhibitory G protein-calcium channel complex involves PTX-sensitive and -insensitive G proteins.     

Sensor control for multi-object state-space estimation using random finite sets

The problem addressed in this paper is information theoretic sensor control for recursive Bayesian multi-object state-space estimation using random finite sets. The proposed algorithm is formulated in the framework of partially observed Markov decision processes where the reward function associated with different sensor actions is computed via the Rényi or alpha divergence between the multi-object prior and the multi-object posterior densities. The proposed algorithm in implemented via the sequential Monte Carlo method. The paper then presents a case study where the problem is to localise an unknown number of sources using a controllable moving sensor which provides range-only detections. Four sensor control reward functions are compared in the study and the proposed scheme is found to perform the best.     

CAD based shape optimization for gas turbine component design

In order to improve product characteristics, engineering design makes increasing use of Robust Design and Multidisciplinary Design Optimisation. Common to both methodologies is the need to vary the object’s shape and to assess the resulting change in performance, both executed within an automatic loop. This shape change can be realised by modifying the parameter values of a suitably parameterised Computer Aided Design (CAD) model. This paper presents the adopted methodology and the achieved results when performing optimisation of a gas turbine disk. Our approach to hierarchical modelling employing design tables is presented, with methods to ensure satisfactory geometry variation by commercial CAD systems. The conducted studies included stochastic and probabilistic design optimisation. To solve the multi-objective optimisation problem, a Pareto optimum criterion was used. The results demonstrate that CAD centric approach enables significant progress towards automating the entire process while achieving a higher quality product with the reduced susceptibility to manufacturing imperfections.     

Synchronization of diffusively-coupled limit cycle oscillators

We develop analytical and numerical conditions to determine whether limit cycle oscillations synchronize in diffusively coupled systems. We examine two classes of systems: reaction–diffusion PDEs with Neumann boundary conditions, and compartmental ODEs, where compartments are interconnected through diffusion terms with adjacent compartments. In both cases the uncoupled dynamics are governed by a nonlinear system that admits an asymptotically stable limit cycle. We provide two-time scale averaging methods for certifying stability of spatially homogeneous time-periodic trajectories in the presence of sufficiently small or large diffusion and develop methods using the structured singular value for the case of intermediate diffusion. We highlight cases where diffusion stabilizes or destabilizes such trajectories.     

Mobility Tracking in Cellular Networks Using Particle Filtering

Mobility tracking based on data from wireless cellular networks is a key challenge that has been recently investigated both from a theoretical and practical point of view. This paper proposes Monte Carlo techniques for mobility tracking in wireless communication networks by means of received signal strength indications. These techniques allow for accurate estimation of mobile station's (MS) position and speed. The command process of the MS is represented by a first-order Markov model which can take values from a finite set of acceleration levels. The wide range of acceleration changes is covered by a set of preliminary determined acceleration values. A particle filter and a Rao-Blackwellised particle filter are proposed and their performance is evaluated both over synthetic and real data. A comparison with an extended Kalman filter (EKF) is performed with respect to accuracy and computational complexity. With a small number of particles the RBPF gives more accurate results than the PF and the EKF. A posterior Cramer Rao lower bound (PCRLB) is calculated and it is compared with the filters' root- mean-square error performance.     

Neural network Reinforcement Learning for visual control of robot manipulators

It is known that most of the key problems in visual servo control of robots are related to the performance analysis of the system considering measurement and modeling errors. In this paper, the development and performance evaluation of a novel intelligent visual servo controller for a robot manipulator using neural network Reinforcement Learning is presented. By implementing machine learning techniques into the vision based control scheme, the robot is enabled to improve its performance online and to adapt to the changing conditions in the environment. Two different temporal difference algorithms (Q-learning and SARSA) coupled with neural networks are developed and tested through different visual control scenarios. A database of representative learning samples is employed so as to speed up the convergence of the neural network and real-time learning of robot behavior. Moreover, the visual servoing task is divided into two steps in order to ensure the visibility of the features: in the first step centering behavior of the robot is conducted using neural network Reinforcement Learning controller, while the second step involves switching control between the traditional Image Based Visual Servoing and the neural network Reinforcement Learning for enabling approaching behavior of the manipulator. The correction in robot motion is achieved with the definition of the areas of interest for the image features independently in both control steps. Various simulations are developed in order to present the robustness of the developed system regarding calibration error, modeling error, and image noise. In addition, a comparison with the traditional Image Based Visual Servoing is presented. Real world experiments on a robot manipulator with the low cost vision system demonstrate the effectiveness of the proposed approach.