Using the SPI relative to 67 years data sets, a Markov chains approach has been utilized for several locations in Alentejo, southern Portugal, to characterize the stochasticity of droughts, which allowed predicting the transition from a class of severity to another up to 3 months ahead. Markov...

The purpose of this paper is to formally describe new optimization models for distributed telecommunication networks.Modern distributed networks put more focus on the processing of information and less on the actual transportation of datathan we are traditionally used to in telecommunications....

This paper reviews recent progress in modeling collective behaviors in myxobacteria using lattice gas cellular automata approach (LGCA). Myxobacteria are social bacteria that swarm, glide on surfaces and feed cooperatively. When starved, tens of thousands of cells change their movement pattern...

The aim of this paper is to obtain a stochastic model for an underlying asset pricing. Several stochastic models using time series are presented, such as stationary stochastic processes AR and MA or ARMA, and ARCH processes with conditional volatility as a stochastic process. Numerical data were...

A stochastic goal programming (GP) model is developed in orderto determine the daily production of desalination plants to meet the requirements of water blending stations (WBS) for major cities in the Eastern Province of the Kingdom of SaudiArabia. The WBS is assumed to be a control point in the...

We introduce the stochastic multiplicative point process modeling trading activity of financial markets. Such a model system exhibits power-law spectral density S(f)∝1/fβ, scaled as power of frequency for various values of β between 0.5 and 2. Furthermore, we analyze the relation between the...

A primary tool that consumers have for comparative shopping is the shopbot, which is short for shopping robot. These shopbots automatically search a large number of vendors for price and availability. Typically a shopbot searches a predefined set of vendors and reports all results, which can...

Stochastic neural network is a hierarchical network of stochastic neurons which emit 0 or 1 with the probability determined by the values of inputs. We have developed an efficient training algorithm so as to maximize the likelihood of such a neural network. This algorithm enables us to apply the...

We study the daily river flow fluctuations of 30 international rivers. Using the detrended fluctuation analysis, we study the correlations in the magnitudes of river flow increments (volatilities), and find power-law correlations in volatilities for time scales less than 1 year; these...