Self-consistent microscopic treatment of the effects of self-motion of the probe on ionic and dipolar solvation dynamics

A simple but self-consistent microscopic theory for the time dependent solvation energy of both ions and dipoles is presented which includes, for the first time, the details of the self-motion of the probe on its own solvation dynamics. The theory leads to several interesting predictions. The most important of them is that, for dipolar solvation, both the rotational and the translational motions of the dipolar solute probe can significantly accelerate the rate of solvation. In addition, the rotational self-motion of the solute can also give rise to an additional mechanism of nonexponentiality in solvation time correlation functions in otherwise slow liquids. A comparison between the present theoretical predictions and the recent experimental studies of Maroncelli et al. on solvation dynamics of aniline in l-propanol seems to indicate that the said experiments have missed the initial solvent response up to about 45 ps. After mapping the experimental results on the redefined time scale, the theoretical results can explain the experimental results for solvation of aniline in 1-propanol very well. For ionic solvation, the translational motion is significant for light solutes only. For example, for Li+ in water, translational motion speeds up the solvation by about 20%. The present theory demonstrates that in dipolar solvation the partial quenching of the self-motion due to the presence of specific solute-solvent interactions (such as H-bonding) may lead to a much slower solvation than that when the self-motion is present. This point has been discussed. In addition, we present the theoretical results for solvation of aniline in propylene carbonate, Here, the solvation is predicted to be complete within 15-20 ps.

Treatment of electroplating effluent through emulsion-free liquid embrane

Separation of dissolved heavy metals such-as Cr(VI) and Cu(II) from electroplating effluents using a new technique of emulsion-free liquid membrane (EFLM) has been studied. Experimental results show that nearly 95% extraction is obtained resulting in stripping phase enrichment up to 50 times relative to feed. It is also found that emulsion-free liquid membranes are highly efficient and superior to other types of liquid membranes.

An Efficient Robust Algorithm for the Surface-Potential Calculation of Independent DG MOSFET

Although the recently proposed single-implicit-equation-based input voltage equations (IVEs) for the independent double-gate (IDG) MOSFET promise faster computation time than the earlier proposed coupled-equations-based IVEs, it is not clear how those equations could be solved inside a circuit simulator as the conventional Newton-Raphson (NR)-based root finding method will not always converge due to the presence of discontinuity at the G-zero point (GZP) and nonremovable singularities in the trigonometric IVE. In this paper, we propose a unique algorithm to solve those IVEs, which combines the Ridders algorithm with the NR-based technique in order to provide assured convergence for any bias conditions. Studying the IDG MOSFET operation carefully, we apply an optimized initial guess to the NR component and a minimized solution space to the Ridders component in order to achieve rapid convergence, which is very important for circuit simulation. To reduce the computation budget further, we propose a new closed-form solution of the IVEs in the near vicinity of the GZP. The proposed algorithm is tested with different device parameters in the extended range of bias conditions and successfully implemented in a commercial circuit simulator through its Verilog-A interface.

The actor-critic algorithm as multi-time-scale stochastic approximation

The actor-critic algorithm of Barto and others for simulation-based optimization of Markov decision processes is cast as a two time Scale stochastic approximation. Convergence analysis, approximation issues and an example are studied.

Regarding the effect of aging heat treatment on the failure behaviour of a SiC reinforced Al based metal matrix composite

In the present study, 6061 Al metallic matrix was reinforced by 12.2 wt% df SiC particulates using liquid metallurgy route. The composite material thus obtained was extruded and characterized in the as-solutionized and peak aged conditions in order to delineate the effect of aging associated precipitation of secondary phases on the tensile fracture behavior of the composite samples. The results' of microstructural characterization studies carried out using scanning electron microscope revealed the increased presence of precipitated secondary phases in the metallic matrix and a more pronounced interfacial segregation of alloying elements in case of peak aged samples when compared to the as-solutionized samples. The results of the fractographic studies conducted on the as-solutionized samples revealed that the failure was dominated by the SiC particulates cracking while for the peak aged samples the fracture surface revealed a comparatively more pronounced SiC/6061 Al debonding and reduced SiC particulates cracking. This change in the failure behavior was rationalized in terms of embrittlement of the interfacial region brought about by the aging heat treatment and is correlated, in addition, with the mechanical properties of the composite samples in as-solutionized and peak aged conditions.

A fault-tolerant distributed deadlock detection algorithm

In this paper, we propose a new fault-tolerant distributed deadlock detection algorithm which can handle loss of any resource release message. It is based on a token-based distributed mutual exclusion algorithm. We have evaluated and compared the performance of the proposed algorithm with two other algorithms which belong to two different classes, using simulation studies. The proposed algorithm is found to be efficient in terms of average number of messages per wait and average deadlock duration compared to the other two algorithms in all situations, and has comparable or better performance in terms of other parameters.

A token-based distributed algorithm for total order atomic broadcast

In this paper, we propose a new token-based distributed algorithm for total order atomic broadcast. We have shown that the proposed algorithm requires lesser number of messages compared to the algorithm where broadcast servers use unicasting to send messages to other broadcast servers. The traditional method of broadcasting requires 3(N - 1) messages to broadcast an application message, where N is the number of broadcast servers present in the system. In this algorithm, the maximum number of token messages required to broadcast an application message is 2N. For a heavily loaded system, the average number of token messages required to broadcast an application message reduces to 2, which is a substantial improvement over the traditional broadcasting approach.

Constant pressure laminar, transitional and turbulent flows - An approximate unified treatment

A nondimensional number that is constant in two-dimensional, incompressible and constant pressure laminar and fully turbulent boundary, layer flows has been proposed. An extension of this to constant pressure transitional flow is discussed.

Identification of delamination in composite beams using spectral estimation and a genetic algorithm

An efficient strategy for identification of delamination in composite beams and connected structures is presented. A spectral finite-element model consisting of a damaged spectral element is used for model-based prediction of the damaged structural response in the frequency domain. A genetic algorithm (GA) specially tailored for damage identification is derived and is integrated with finite-element code for automation. For best application of the GA, sensitivities of various objective functions with respect to delamination parameters are studied and important conclusions are presented. Model-based simulations of increasing complexity illustrate some of the attractive features of the strategy in terms of accuracy as well as computational cost. This shows the possibility of using such strategies for the development of smart structural health monitoring softwares and systems.

Machine-tool selection and operation allocation in FMS: solving a fuzzy goal-programming model using a genetic algorithm

The decision-making process for machine-tool selection and operation allocation in a flexible manufacturing system (FMS) usually involves multiple conflicting objectives. Thus, a fuzzy goal-programming model can be effectively applied to this decision problem. The paper addresses application of a fuzzy goal-programming concept to model the problem of machine-tool selection and operation allocation with explicit considerations given to objectives of minimizing the total cost of machining operation, material handling and set-up. The constraints pertaining to the capacity of machines, tool magazine and tool life are included in the model. A genetic algorithm (GA)-based approach is adopted to optimize this fuzzy goal-programming model. An illustrative example is provided and some results of computational experiments are reported.

Neural network based optimal routing algorithm for communication networks

This paper presents the capability of the neural networks as a computational tool for solving constrained optimization problem, arising in routing algorithms for the present day communication networks. The application of neural networks in the optimum routing problem, in case of packet switched computer networks, where the goal is to minimize the average delays in the communication have been addressed. The effectiveness of neural network is shown by the results of simulation of a neural design to solve the shortest path problem. Simulation model of neural network is shown to be utilized in an optimum routing algorithm known as flow deviation algorithm. It is also shown that the model will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology. (C) 2002 Elsevier Science Ltd. All rights reserved.

Performance analysis of the random early detection algorithm

In this article we consider a finite queue with its arrivals controlled by the random early detection algorithm. This is one of the most prominent congestion avoidance schemes in the Internet routers. The aggregate arrival stream from the population of transmission control protocol sources is locally considered stationary renewal or Markov modulated Poisson process with general packet length distribution. We study the exact dynamics of this queue and provide the stability and the rates of convergence to the stationary distribution and obtain the packet loss probability and the waiting time distribution. Then we extend these results to a two traffic class case with each arrival stream renewal. However, computing the performance indices for this system becomes computationally prohibitive. Thus, in the latter half of the article, we approximate the dynamics of the average queue length process asymptotically via an ordinary differential equation. We estimate the error term via a diffusion approximation. We use these results to obtain approximate transient and stationary performance of the system. Finally, we provide some computational examples to show the accuracy of these approximations.