Optimal blood glucose regulation of diabetic patients using single network adaptive critics

Diabetes is a long-term disease during which the body's production and use of insulin are impaired, causing glucose concentration level to increase in the bloodstream. Regulating blood glucose levels as close to normal as possible leads to a substantial decrease in long-term complications of diabetes. In this paper, an intelligent online feedback-treatment strategy is presented for the control of blood glucose levels in diabetic patients using single network adaptive critic (SNAC) neural networks (which is based on nonlinear optimal control theory). A recently developed mathematical model of the nonlinear dynamics of glucose and insulin interaction in the blood system has been revised and considered for synthesizing the neural network for feedback control. The idea is to replicate the function of pancreatic insulin, i.e. to have a fairly continuous measurement of blood glucose and a situation-dependent insulin injection to the body using an external device. Detailed studies are carried out to analyze the effectiveness of this adaptive critic-based feedback medication strategy. A comparison study with linear quadratic regulator (LQR) theory shows that the proposed nonlinear approach offers some important advantages such as quicker response, avoidance of hypoglycemia problems, etc. Robustness of the proposed approach is also demonstrated from a large number of simulations considering random initial conditions and parametric uncertainties. Copyright (C) 2009 John Wiley & Sons, Ltd.

Metal-insulator transitions in metal clusters: a high-energy spectroscopy study of palladium and silver clusters

Bremsstrahlung isochromat spectroscopy (BIS) along with ultraviolet and X-ray photoelectron spectroscopy (UPS and XPS) has been employed to investigate the electron states of Pd and Ag deposited on amorphous graphite at different coverages. The metal core level binding energies increase with decreasing cluster size while the UPS valence bands show a decrease in the 4d states at E(F) accompanied by a shift in the intensity maximum to higher binding energies. BIS measurements show the emergence of new states closer to E(F) with increase in the cluster size. It is pointed out that the observed spectral shifts cannot be accounted for by final-state effects alone and that initial-state effects have a significant role. It therefore appears that a decrease in cluster size is accompanied by a metal-insulator transition.

The stability of transmembrane helices: A molecular dynamics study on the isolated helices of bacteriorhodopsin

Bacteriorhodopsin (bR) continues to be a proven testing ground for the study of integral membrane proteins (IMPs). It is important to study the stability of the individual helices of bR, as they are postulated to exist as independently stable transmembmne helices (TMHs) and also for their utility as templates for modeling other IMPs with the postulated seven-helix bundle topology. Toward this purpose, the seven helices of bR have been studied by molecular dynamics simulation in this study. The suitability of using the backbone-dependent rotamer library of side-chain conformations arrived at from the data base of globular protein structures in the case TMHs has been tested by another set of ? helix simulations with the side-chain orientations taken from this library. The influence of the residue's net charge oil the helix stability was examined by simulating the helices III, IV, and VI (from both of the above sets of helices) with zero net charge on the side chains. The results of these 20 simulations demonstrate in general the stability of the isolated helices of bR in conformity with the two-stage hypothesis of IMP folding. However, the helices I, II, V, and VII are more stable than the other three helices. The helical nature of certain regions of III, IV, and VI are influenced by factors such as the net charge and orientation of several residues. It is seen that the residues Arg, Lys, Asp, and Glu (charged residues), and Ser, Thr, Gly, and Pro, play a crucial role in the stability of the helices of bR. The backbone-dependent rotamer library for the side chains is found to be suitable for the study of TMHs in IMP. (C) 1996 John Wiley & Sons, Inc.

Dynamics of Fractional Brownian Walks

We investigate the dynamics of polymers whose solution configurations are represented by fractional Brownian walks. The calculation of the two dynamical quantities considered here, the longest relaxation time tau(r) and the intrinsic viscosity [eta], is formulated in terms of Langevin equations and is carried out within the continuum approach developed in an earlier paper. Our results for tau(r) and [eta] reproduce known scaling relations and provide reasonable numerical estimates of scaling amplitudes. The possible relevance of the work to the study of globular proteins and other compact polymeric phases is discussed.

Inverse surface melting in confined clusters: Ar-13 in zeolite L

Monte Carlo and molecular dynamics simulations on an Ar-13 cluster in zeolite L have been carried out at a series of temperatures to understand the rigid-nonrigid transition corresponding to the solid-liquid transition exhibited by the free Ar-13 cluster. The icosahedral geometry of the free cluster is no longer preferred when the cluster is confined in the zeolite. The root-mean-squared pair distance fluctuation, delta, exhibits a sharp, well-defined rigid-nonrigid transition at 17 K as compared to 27 K for the free cluster. Multiple peaks in the distribution of short-time averages of the guest-host interaction energy indicate coexistence of two phases.; It is shown that this transition is associated with the inner atoms becoming mobile at 17 K even while the outer layer atoms, which are in close proximity to the zeolitic wall, continue to be comparatively immobile. This may be contrasted with the melting of large free clusters of 40 or more atoms which exhibit surface melting. Guest-host interactions seem to play a predominant role in determining the properties of confined clusters. We demonstrate that the volume of the cluster increases rather sharply at 17 and 27 K respectively for the confined and the free cluster. Power spectra suggest that the motion of the inner atoms is generally parallel to the atoms which form the cage wall.

X-ray photoelectron spectroscopic investigations of Cu-Ni, Au-Ag, Ni-Pd, and Cu-Pd bimetallic clusters

Core-level binding energies of the component metals in bimetallic clusters of various compositions in the Ni-Cu, Au-Ag, Ni-Pd, and Cu-Pd systems have been measured as functions of coverage or cluster size, after having characterized the clusters with respect to sizes and compositions. The core-level binding energy shifts, relative to the bulk metals, at large coverages or cluster size, Delta E(a), are found to be identical to those of bulk alloys. By substracting the Delta E(a) values from the observed binding energy shifts, Delta E, we obtain the shifts, Delta E(c), due to cluster size. The Delta E(c) values in all the alloy systems increase with the decrease in cluster size. These results establish the additivity of the binding energy shifts due to alloying and cluster size effects in bimetallic clusters.

Mapping adaptive resonance theory onto ring and mesh architectures

In recent years, parallel computers have been attracting attention for simulating artificial neural networks (ANN). This is due to the inherent parallelism in ANN. This work is aimed at studying ways of parallelizing adaptive resonance theory (ART), a popular neural network algorithm. The core computations of ART are separated and different strategies of parallelizing ART are discussed. We present mapping strategies for ART 2-A neural network onto ring and mesh architectures. The required parallel architecture is simulated using a parallel architectural simulator, PROTEUS and parallel programs are written using a superset of C for the algorithms presented. A simulation-based scalability study of the algorithm-architecture match is carried out. The various overheads are identified in order to suggest ways of improving the performance. Our main objective is to find out the performance of the ART2-A network on different parallel architectures. (C) 1999 Elsevier Science B.V. All rights reserved.

Essential dynamics and sidechain hydrogen bond cluster studies on eosinophil cationic protein

Eosinophil Cationic Protein (ECP) is a member of RNase A superfamily which carries out the obligatory catalytic role of cleaving RNA. It is involved in a variety of biological functions. Molecular dynamics simulations followed by essential dynamics analysis on this protein are carried out with the goal of gaining insights into the dynamical properties at atomic level. The top essential modes contribute to subspaces and to the transition phase. Further, the sidechain-sidechain/sidechain-mainchain hydrogen bond clusters are analyzed in the top modes, and compared with those of crystal structure. The role of residues identified by these methods is discussed in the context of concerted motion, structure and stability of the protein.

Nonlinear dynamics of eucaryotic pyruvate dehydrogenase multienzyme complex: Decarboxylation rate, oscillations, and multiplicity

Pyruvate conversion to acetyl-CoA by the pyruvate dehydrogenase (PDH) multienzyme complex is known as a key node in affecting the metabolic fluxes of animal cell culture. However, its possible role in causing possible nonlinear dynamic behavior such as oscillations and multiplicity of animal cells has received little attention. In this work, the kinetic and dynamic behavior of PDH of eucaryotic cells has been analyzed by using both in vitro and simplified in vivo models. With the in vitro model the overall reaction rate (v(1)) of PDH is shown to be a nonlinear function of pyruvate concentration, leading to oscillations under certain conditions. All enzyme components affect v, and the nonlinearity of PDH significantly, the protein X and the core enzyme dihydrolipoamide acyltransferase (E2) being mostly predominant. By considering the synthesis rates of pyruvate and PDH components the in vitro model is expanded to emulate in vivo conditions. Analysis using the in vivo model reveals another interesting kinetic feature of the PDH system, namely, multiple steady states. Depending on the pyruvate and enzyme levels or the operation mode, either a steady state with high pyruvate decarboxylation rate or a steady state with significantly lower decarboxylation rate can be achieved under otherwise identical conditions. In general, the more efficient steady state is associated with a lower pyruvate concentration. A possible time delay in the substrate supply and enzyme synthesis can also affect the steady state to be achieved and lead's to oscillations under certain conditions. Overall, the predictions of multiplicity for the PDH system agree qualitatively well with recent experimental observations in animal cell cultures. The model analysis gives some hints for improving pyruavte metabolism in animal cell culture.

The collective dynamics of self-propelled particles

We propose a method for the dynamic simulation of a collection of self-propelled particles in a viscous Newtonian fluid. We restrict attention to particles whose size and velocity are small enough that the fluid motion is in the creeping flow regime. We propose a simple model for a self-propelled particle, and extended the Stokesian Dynamics method to conduct dynamic simulations of a collection of such particles. In our description, each particle is treated as a sphere with an orientation vector p, whose locomotion is driven by the action of a force dipole Sp of constant magnitude S0 at a point slightly displaced from its centre. To simplify the calculation, we place the dipole at the centre of the particle, and introduce a virtual propulsion force Fp to effect propulsion. The magnitude F0 of this force is proportional to S0. The directions of Sp and Fp are determined by p. In isolation, a self-propelled particle moves at a constant velocity u0 p, with the speed u0 determined by S0. When it coexists with many such particles, its hydrodynamic interaction with the other particles alters its velocity and, more importantly, its orientation. As a result, the motion of the particle is chaotic. Our simulations are not restricted to low particle concentration, as we implement the full hydrodynamic interactions between the particles, but we restrict the motion of particles to two dimensions to reduce computation. We have studied the statistical properties of a suspension of self-propelled particles for a range of the particle concentration, quantified by the area fraction φa. We find several interesting features in the microstructure and statistics. We find that particles tend to swim in clusters wherein they are in close proximity. Consequently, incorporating the finite size of the particles and the near-field hydrodynamic interactions is of the essence. There is a continuous process of breakage and formation of the clusters. We find that the distributions of particle velocity at low and high φa are qualitatively different; it is close to the normal distribution at high φa, in agreement with experimental measurements. The motion of the particles is diffusive at long time, and the self-diffusivity decreases with increasing φa. The pair correlation function shows a large anisotropic build-up near contact, which decays rapidly with separation. There is also an anisotropic orientation correlation near contact, which decays more slowly with separation. Movies are available with the online version of the paper.

A Neuro-Adaptive Approach for Robust Control of High Performance Aircrafts with Parameter Inaccuracies

For high performance aircrafts, the flight control system needs to be quite effective in both assuring accurate tracking of pilot commands, while simultaneously assuring overall stability of the aircraft. In addition, the control system must also be sufficiently robust to cater to possible parameter variations. The primary aim of this paper is to enhance the robustness of the controller for a HPA using neuro-adaptive control design. Here the architecture employs a network of Gaussian Radial basis functions to adaptively compensate for the ignored system dynamics. A stable weight mechanism is determined using Lyapunov theory. The network construction and performance of the resulting controller are illustrated through simulations with a low-fidelity six –DOF model of F16 that is available in open literature.

An Efficient MPI_Allgather for Grids

Allgather is an important MPI collective communication. Most of the algorithms for allgather have been designed for homogeneous and tightly coupled systems. The existing algorithms for allgather on Gridsystems do not efficiently utilize the bandwidths available on slow wide-area links of the grid. In this paper, we present an algorithm for allgather on grids that efficiently utilizes wide-area bandwidths and is also wide-area optimal. Our algorithm is also adaptive to gridload dynamics since it considers transient network characteristics for dividing the nodes into clusters. Our experiments on a real-grid setup consisting of 3 sites show that our algorithm gives an average performance improvement of 52% over existing strategies.

Model-following Neuro-adaptive approach for robust control of high performance aircrafts

Based on dynamic inversion, a relatively straightforward approach is presented in this paper for nonlinear flight control design of high performance aircrafts, which does not require the normal and lateral acceleration commands to be first transferred to body rates before computing the required control inputs. This leads to substantial improvement of the tracking response. Promising results are obtained from six degree-offreedom simulation studies of F-16 aircraft, which are found to be superior as compared to an existing approach (which is also based on dynamic inversion). The new approach has two potential benefits, namely reduced oscillatory response (including elimination of non-minimum phase behavior) and reduced control magnitude. Next, a model-following neuron-adaptive design is augmented the nominal design in order to assure robust performance in the presence of parameter inaccuracies in the model. Note that in the approach the model update takes place adaptively online and hence it is philosophically similar to indirect adaptive control. However, unlike a typical indirect adaptive control approach, there is no need to update the individual parameters explicitly. Instead the inaccuracy in the system output dynamics is captured directly and then used in modifying the control. This leads to faster adaptation, which helps in stabilizing the unstable plant quicker. The robustness study from a large number of simulations shows that the adaptive design has good amount of robustness with respect to the expected parameter inaccuracies in the model.

Adaptive control of vibration transmission in a strut system

In this article, theoretical and the experimental studies are reported on the adaptive control of vibration transmission in a strut system subjected to a longitudinal pulse train excitation. In the control scheme, a magneto-strictive actuator is employed at the downstream transmission point in the secondary path. The actuator dynamics is taken into account. The system boundary parameters are first estimated off-line, and later employed to simulate the system dynamics. A Delayed-X Filtered-E spectral algorithm is proposed and implemented in real time. The underlying mechanics based filter construction allows for the time varying system dynamics to be taken into account. This work should be of interest for active control of vibration and noise transmission in helicopter gearbox support struts and other systems.