Synthesis of amphiphilic magnetic microspheres by dispersion copolymerization of styrene and poly(ethylene oxide) macromonomer

Amphiphilic magnetic microspheres ranging in diameter from 5 to 100 µm were prepared by dispersion copolymerization of styrene and poly(ethylene oxide) vinylbenzyl (PEO‐VB) macromonomer (MPEO) in the presence of Fe₃O₄ magnetic fluid. The effects of various polymerization parameters on the average particle size were systematically investigated. The average particle size was found to increase with increasing styrene concentration and initiator concentration. It also increased with decreasing stabilizer concentration and molecular weight of MPEO. The content of the hydroxyl groups localized in the microspheres ranged from 0.01 to 0.2 mmol g^?1. © 2003 Society of Chemical Industry     

Impact of power control and lognormal shadowing on the mean transmit power of Bluetooth devices

This letter analyzes Bluetooth's power-control algorithm with a goal to study the mean transmit power required in the presence of lognormal shadowing. The following results are found. 1) A smaller power-control step size yields a lower mean transmit power. 2) When the standard deviations of lognormal shadowing are 3 and 6 dB, respectively, a Bluetooth device needs to consume 11.1 dB and 15.0 dB more in the transmit energy than the minimum one required in the absence of shadowing. 3) The transmit energy consumption varies by around 6 dB among Bluetooth devices as a result of the /spl plusmn/6 dB tolerance in the golden receive power range, which has a nominal size of 20 dB.     

CMV colitis masquerading as colon cancer--an unusual presentation of acquired immunodeficiency syndrome

To determine whether there is any correlation between sudden decrease in barometric pressure and onset of labor, a non-experimental, retrospective study at a 948-bed tertiary care hospital was done. Pregnant patients of 36 weeks gestation or more who presented with spontaneous onset of labor during the 48 hours surrounding the 12 occurrences of significant drop in barometric pressure in 1992 were included in the study. Significantly more occurrences of onset of labor were identified in the 24 hours after a drop in barometric pressure than were identified in the 24 hours prior to the drop in barometric pressure (P < 0.05). Therefore, the overall number of labor onsets increased in the 24 hours following a significant drop in barometric pressure.     

Selection of network and learning parameters for an adaptive neural robotic control scheme

This paper presents the results of a study in the design of a neural network based adaptive robotic control scheme. The neural network used here is a two hidden layer feedforward network and the learning scheme is the well-known backpropagation algorithm. The neural network essentially provides the inverse of the plant and acts in conjunction with a standard PD controller in the feedback loop. The objective of the controller is to accurately control the end position of a single link manipulator in the presence of large payload variations, variations in the link length and also variations in the damping constant. Based on results of this study, guidelines are presented in selecting the number of neurons in the hidden layers and also the parameters for the learning scheme used for training the network. Results also indicate that increasing the number of neurons in the hidden layer will improve the convergence speed of learning scheme up to a certain limit beyond which the addition of neurons will cause oscillations and instability. Guidelines for selecting the proper learning rate, momentum and fast backpropagation constant that ensure stability and convergence are presented. Also, a relationship between the r.m.s. error and the number of iterations used in training the neural network is established.     

A new method to estimate parameters of linear compartmental models using artificial neural networks

At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models.