Analysis of Some Queueing Models Related To N-Policy: Operations Research

This study is about the analysis of some queueing models related to N-policy.The optimal value the queue size has to attain in order to turn on a single server, assuming that the policy is to turn on a single server when the queue size reaches a certain number, N, and turn him off when the system is empty.The operating policy is the usual N-policy, but with random N and in model 2, a system similar to the one described here.This study analyses “ Tandem queue with two servers”.Here assume that the first server is a specialized one.In a queueing system,under N-policy ,the server will be on vacation until N units accumulate for the first time after becoming idle.A modified version of the N-policy for an M│M│1 queueing system is considered here.The novel feature of this model is that a busy service unit prevents the access of new customers to servers further down the line.It is deals with a queueing model consisting of two servers connected in series with a finite intermediate waiting room of capacity k.Here assume that server I is a specialized server.For this model ,the steady state probability vector and the stability condition are obtained using matrix – geometric method.

Salting coefficient of hydroxybenzoic acids

The Setschenow parameter and thermodynamic parameters of transfer of 2- and 4- hydroxybenzoic acids from water to salt solutions have been reported. The data have been rationalised by considering the structure breaking effects of the ions of the salts, the localized hydrolysis model, the internal pressure theory and the theory of water structure due to Symons.

Onset And Characterisation Of Chaos In A Two Dimensional Nonlinear Deterministic Model

Nature is full of phenomena which we call "chaotic", the weather being a prime example. What we mean by this is that we cannot predict it to any significant accuracy, either because the system is inherently complex, or because some of the governing factors are not deterministic. However, during recent years it has become clear that random behaviour can occur even in very simple systems with very few number of degrees of freedom, without any need for complexity or indeterminacy. The discovery that chaos can be generated even with the help of systems having completely deterministic rules - often models of natural phenomena - has stimulated a lo; of research interest recently. Not that this chaos has no underlying order, but it is of a subtle kind, that has taken a great deal of ingenuity to unravel. In the present thesis, the author introduce a new nonlinear model, a ‘modulated’ logistic map, and analyse it from the view point of ‘deterministic chaos‘.

Properties of Anisotropic Superconductors in SR Model

The present thesis deals with the theoretical investigations on the effect of anisotropy on various properties of magnetically doped superconductors described by fihiba — Rusinov model.Chapter 1 is introductory. It contains a brief account of the current status of theory of superconductivity. In’ chapter 2 we give the formulation of the problem. Chapter 2.1 gives the BCS theory. The effect of magnetic impurities in superconductors as described by A8 theory is given in chapter 2.2A and that described by SR model is discussed in chapter 2.28. Chapter 2.2c deals with Kondo effect. In chapter 2.3 the anisotropy problem is reviewed. Our calculations, results and discussions are given in chapter 3. Chapter 3.1 deals with Josephson tunnel effect. In chapter 3.2 the thermodynamic critical field H62 is described. Chtpter 3.3 deals with the density of states. The ultrasonic attenuation coefficient and ufitlear spin relaxation are given in chapter 3.4 and 3.5 respectively. In chapter 3.6 we give the upper critical field calculations and chapter 3.7 deals with the response function. The Kondo effect is given in chapter 3.8. In chapter 4 we give the sumary of our results

A Comparative Study of Convective Parameterization Schemes in WRF-NMM Model

Severe local storms, including tornadoes, damaging hail and wind gusts, frequently occur over the eastern and northeastern states of India during the pre-monsoon season (March-May). Forecasting thunderstorms is one of the most difficult tasks in weather prediction, due to their rather small spatial and temporal extension and the inherent non-linearity of their dynamics and physics. In this paper, sensitivity experiments are conducted with the WRF-NMM model to test the impact of convective parameterization schemes on simulating severe thunderstorms that occurred over Kolkata on 20 May 2006 and 21 May 2007 and validated the model results with observation. In addition, a simulation without convective parameterization scheme was performed for each case to determine if the model could simulate the convection explicitly. A statistical analysis based on mean absolute error, root mean square error and correlation coefficient is performed for comparisons between the simulated and observed data with different convective schemes. This study shows that the prediction of thunderstorm affected parameters is sensitive to convective schemes. The Grell-Devenyi cloud ensemble convective scheme is well simulated the thunderstorm activities in terms of time, intensity and the region of occurrence of the events as compared to other convective schemes and also explicit scheme

Malayalam Isolated Digit Recognition using HMM and PLP cepstral coefficient

Development of Malayalam speech recognition system is in its infancy stage; although many works have been done in other Indian languages. In this paper we present the first work on speaker independent Malayalam isolated speech recognizer based on PLP (Perceptual Linear Predictive) Cepstral Coefficient and Hidden Markov Model (HMM). The performance of the developed system has been evaluated with different number of states of HMM (Hidden Markov Model). The system is trained with 21 male and female speakers in the age group ranging from 19 to 41 years. The system obtained an accuracy of 99.5% with the unseen data

Microstructure and random magnetic anisotropy in Fe–Ni based nanocrystalline thin films

Nanocrystalline Fe–Ni thin films were prepared by partial crystallization of vapour deposited amorphous precursors. The microstructure was controlled by annealing the films at different temperatures. X-ray diffraction, transmission electron microscopy and energy dispersive x-ray spectroscopy investigations showed that the nanocrystalline phase was that of Fe–Ni. Grain growth was observed with an increase in the annealing temperature. X-ray photoelectron spectroscopy observations showed the presence of a native oxide layer on the surface of the films. Scanning tunnelling microscopy investigations support the biphasic nature of the nanocrystalline microstructure that consists of a crystalline phase along with an amorphous phase. Magnetic studies using a vibrating sample magnetometer show that coercivity has a strong dependence on grain size. This is attributed to the random magnetic anisotropy characteristic of the system. The observed coercivity dependence on the grain size is explained using a modified random anisotropy model

Effect of thermal annealing on Fe40Ni38B18Mo4 thin films: modified Herzer model for magnetic evolution

Magnetic properties of nano-crystalline soft magnetic alloys have usually been correlated to structural evolution with heat treatment. However, literature reports pertaining to the study of nano-crystalline thin films are less abundant. Thin films of Fe40Ni38B18Mo4 were deposited on glass substrates under a high vacuum of ≈ 10−6 Torr by employing resistive heating. They were annealed at various temperatures ranging from 373 to 773K based on differential scanning calorimetric studies carried out on the ribbons. The magnetic characteristics were investigated using vibrating sample magnetometry. Morphological characterizations were carried out using atomic force microscopy (AFM), and magnetic force microscopy (MFM) imaging is used to study the domain characteristics. The variation of magnetic properties with thermal annealing is also investigated. From AFM and MFM images it can be inferred that the crystallization temperature of the as-prepared films are lower than their bulk counterparts. Also there is a progressive evolution of coercivity up to 573 K, which is an indication of the lowering of nano-crystallization temperature in thin films. The variation of coercivity with the structural evolution of the thin films with annealing is discussed and a plausible explanation is provided using the modified random anisotropy model

Model Diagnostics in the presence of measurement error

The problem of using information available from one variable X to make inferenceabout another Y is classical in many physical and social sciences. In statistics this isoften done via regression analysis where mean response is used to model the data. Onestipulates the model Y = µ(X) +ɛ. Here µ(X) is the mean response at the predictor variable value X = x, and ɛ = Y - µ(X) is the error. In classical regression analysis, both (X; Y ) are observable and one then proceeds to make inference about the mean response function µ(X). In practice there are numerous examples where X is not available, but a variable Z is observed which provides an estimate of X. As an example, consider the herbicidestudy of Rudemo, et al. [3] in which a nominal measured amount Z of herbicide was applied to a plant but the actual amount absorbed by the plant X is unobservable. As another example, from Wang [5], an epidemiologist studies the severity of a lung disease, Y , among the residents in a city in relation to the amount of certain air pollutants. The amount of the air pollutants Z can be measured at certain observation stations in the city, but the actual exposure of the residents to the pollutants, X, is unobservable and may vary randomly from the Z-values. In both cases X = Z+error: This is the so called Berkson measurement error model.In more classical measurement error model one observes an unbiased estimator W of X and stipulates the relation W = X + error: An example of this model occurs when assessing effect of nutrition X on a disease. Measuring nutrition intake precisely within 24 hours is almost impossible. There are many similar examples in agricultural or medical studies, see e.g., Carroll, Ruppert and Stefanski [1] and Fuller [2], , among others. In this talk we shall address the question of fitting a parametric model to the re-gression function µ(X) in the Berkson measurement error model: Y = µ(X) + ɛ; X = Z + η; where η and ɛ are random errors with E(ɛ) = 0, X and η are d-dimensional, and Z is the observable d-dimensional r.v.