Optimal multicommodity flow through the complete graph with random edge capacities

We consider a multicommodity flow problem on a complete graph whose edges have random, independent, and identically distributed capacities. We show that, as the number of nodes tends to infinity, the maximumutility, given by the average of a concave function of each commodity How, has an almost-sure limit. Furthermore, the asymptotically optimal flow uses only direct and two-hop paths, and can be obtained in a distributed manner.

Stability of scheduled multi-access communication over quasi-static flat fading channels with random coding and independent decoding

The stability of scheduled multiaccess communication with random coding and independent decoding of messages is investigated. The number of messages that may be scheduled for simultaneous transmission is limited to a given maximum value, and the channels from transmitters to receiver are quasistatic, flat, and have independent fades. Requests for message transmissions are assumed to arrive according to an i.i.d. arrival process. Then, we show the following: (1) in the limit of large message alphabet size, the stability region has an interference limited information-theoretic capacity interpretation, (2) state-independent scheduling policies achieve this asymptotic stability region, and (3) in the asymptotic limit corresponding to immediate access, the stability region for non-idling scheduling policies is shown to be identical irrespective of received signal powers.

Effect of associating anions on phosphorus retention in soil II. Under variable anion concentration

The results of the present investigation reveal that the presence of anions in the reacting medium greatly modify the reactions between soil and solution P. Associating anions reduce considerably the retention of phosphate in soils. Citrate, tartrate, and silicate are found to be superior to arsenate, oxalate, and fluoride in reducing phosphate retention in soil. The performance of associating anions depends on the pH and P concentration of the reacting medium. The nature and properties of soil also play a highly significant role on the effectiveness of associating anions.

Detection of a periodic structure hidden in random background: the role of signal amplitude in the matched filter detection method

The matched filter method for detecting a periodic structure on a surface hidden behind randomness is known to detect up to (r(0)/Lambda) gt;= 0.11, where r(0) is the coherence length of light on scattering from the rough part and 3 is the wavelength of the periodic part of the surface-the above limit being much lower than what is allowed by conventional detection methods. The primary goal of this technique is the detection and characterization of the periodic structure hidden behind randomness without the use of any complicated experimental or computational procedures. This paper examines this detection procedure for various values of the amplitude a of the periodic part beginning from a = 0 to small finite values of a. We thus address the importance of the following quantities: `(a)lambda) `, which scales the amplitude of the periodic part with the wavelength of light, and (r(0))Lambda),in determining the detectability of the intensity peaks.

An instantaneous pulse frequency meter for random pulses

Counting-rate meters normally used for finding pulse frequencies are sluggish in their response to any rapid change in the pulse repetition frequency (P.R.F.). An instrument is described which measures each pulse interval and provides immediately afterwards an output voltage proportional to the reciprocal of interval duration. A response to a change in the P.R.F. as rapidly as is physically possible is obtained. The instrument has wide application in low level radiation detection and in several other fields especially for rapidly varying counting-rates.

First passage probability during random vibration

The probability that a random process crosses an arbitrary level for the first time is expressed as a Gram—Charlier series, the leading term of which is the Poisson approximation. The coefficients of this series are related to the moments of the number of level crossings. The results are applicable to both stationary and non-stationary processes. Some numerical results are presented for the response process of a linear single-degree-of-freedom oscillator under Gaussian white noise excitation.

Nonlinear vibration analysis of composite laminated and sandwich plates with random material properties

Nonlinear vibration analysis is performed using a C-0 assumed strain interpolated finite element plate model based on Reddy's third order theory. An earlier model is modified to include the effect of transverse shear variation along the plate thickness and Von-Karman nonlinear strain terms. Monte Carlo Simulation with Latin Hypercube Sampling technique is used to obtain the variance of linear and nonlinear natural frequencies of the plate due to randomness in its material properties. Numerical results are obtained for composite plates with different aspect ratio, stacking sequence and oscillation amplitude ratio. The numerical results are validated with the available literature. It is found that the nonlinear frequencies show increasing non-Gaussian probability density function with increasing amplitude of vibration and show dual peaks at high amplitude ratios. This chaotic nature of the dispersion of nonlinear eigenvalues is also r

Detecting Earnings Management Using Neural Networks

Detecting Earnings Management Using Neural Networks. Trying to balance between relevant and reliable accounting data, generally accepted accounting principles (GAAP) allow, to some extent, the company management to use their judgment and to make subjective assessments when preparing financial statements. The opportunistic use of the discretion in financial reporting is called earnings management. There have been a considerable number of suggestions of methods for detecting accrual based earnings management. A majority of these methods are based on linear regression. The problem with using linear regression is that a linear relationship between the dependent variable and the independent variables must be assumed. However, previous research has shown that the relationship between accruals and some of the explanatory variables, such as company performance, is non-linear. An alternative to linear regression, which can handle non-linear relationships, is neural networks. The type of neural network used in this study is the feed-forward back-propagation neural network. Three neural network-based models are compared with four commonly used linear regression-based earnings management detection models. All seven models are based on the earnings management detection model presented by Jones (1991). The performance of the models is assessed in three steps. First, a random data set of companies is used. Second, the discretionary accruals from the random data set are ranked according to six different variables. The discretionary accruals in the highest and lowest quartiles for these six variables are then compared. Third, a data set containing simulated earnings management is used. Both expense and revenue manipulation ranging between -5% and 5% of lagged total assets is simulated. Furthermore, two neural network-based models and two linear regression-based models are used with a data set containing financial statement data from 110 failed companies. Overall, the results show that the linear regression-based models, except for the model using a piecewise linear approach, produce biased estimates of discretionary accruals. The neural network-based model with the original Jones model variables and the neural network-based model augmented with ROA as an independent variable, however, perform well in all three steps. Especially in the second step, where the highest and lowest quartiles of ranked discretionary accruals are examined, the neural network-based model augmented with ROA as an independent variable outperforms the other models.

Switching frequency variation control in hysteresis PWM controller for IM drives using variable parabolic bands for current error space phasor

Variation of switching frequency over the entire operating speed range of an induction motor (M drive is the major problem associated with conventional two-level three-phase hysteresis controller as well as the space phasor based PWM hysteresis controller. This paper describes a simple hysteresis current controller for controlling the switching frequency variation in the two-level PWM inverter fed IM drives for various operating speeds. A novel concept of continuously variable hysteresis boundary of current error space phasor with the varying speed of the IM drive is proposed in the present work. The variable parabolic boundary for the current error space phasor is suggested for the first time in this paper for getting the switching frequency pattern with the hysteresis controller, similar to that of the constant switching frequency voltage-controlled space vector PWM (VC-SVPWM) based inverter fed IM drive. A generalized algorithm is also developed to determine parabolic boundary for controlling the switching frequency variation, for any IM load. Only the adjacent inverter voltage vectors forming a triangular sector, in which tip of the machine voltage vector ties, are switched to keep current error space vector within the parabolic boundary. The controller uses a self-adaptive sector identification logic, which provides smooth transition between the sectors and is capable of taldng the inverter up to six-step mode of operation, if demanded by drive system. The proposed scheme is simulated and experimentally verified on a 3.7 kW IM drive.

Theoretical foundations for multiple rendezvous of glowworm-inspired mobile agents with variable local-decision domains

We present the theoretical foundations for the multiple rendezvous problem involving design of local control strategies that enable groups of visibility-limited mobile agents to split into subgroups, exhibit simultaneous taxis behavior towards, and eventually rendezvous at, multiple unknown locations of interest. The theoretical results are proved under certain restricted set of assumptions. The algorithm used to solve the above problem is based on a glowworm swarm optimization (GSO) technique, developed earlier, that finds multiple optima of multimodal objective functions. The significant difference between our work and most earlier approaches to agreement problems is the use of a virtual local-decision domain by the agents in order to compute their movements. The range of the virtual domain is adaptive in nature and is bounded above by the maximum sensor/visibility range of the agent. We introduce a new decision domain update rule that enhances the rate of convergence by a factor of approximately two. We use some illustrative simulations to support the algorithmic correctness and theoretical findings of the paper.

Popular Matchings with Variable Job Capacities

We consider the problem of matching people to jobs, where each person ranks a subset of jobs in an order of preference, possibly involving ties. There are several notions of optimality about how to best match each person to a job; in particular, popularity is a natural and appealing notion of optimality. However, popular matchings do not always provide an answer to the problem of determining an optimal matching since there are simple instances that do not adroit popular matchings. This motivates the following extension of the popular rnatchings problem:Given a graph G; = (A boolean OR J, E) where A is the set of people and J is the set of jobs, and a list < c(1), c(vertical bar J vertical bar)) denoting upper bounds on the capacities of each job, does there exist (x(1), ... , x(vertical bar J vertical bar)) such that setting the capacity of i-th, job to x(i) where 1 <= x(i) <= c(i), for each i, enables the resulting graph to admit a popular matching. In this paper we show that the above problem is NP-hard. We show that the problem is NP-hard even when each c is 1 or 2.

Phase diagram of a hard-sphere system in a quenched random potential: A numerical study

We report numerical results for the phase diagram in the density-disorder plane of a hard-sphere system in the presence of quenched, random, pinning disorder. Local minima of a discretized version of the Ramakrishnan-Yussouff free energy functional are located numerically and their relative stability is studied as a function of the density and the strength of disorder. Regions in the phase diagram corresponding to liquid, glassy, and nearly crystalline states are mapped out, and the nature of the transitions is determined. The liquid to glass transition changes from first to second order as the strength of the disorder is increased. For weak disorder, the system undergoes a first-order crystallization transition as the density is increased. Beyond a critical value of the disorder strength, this transition is replaced by a continuous glass transition. Our numerical results are compared with those of analytical work on the same system. Implications of our results for the field-temperature phase diagram of type-II superconductors are discussed.