Experimental modulation bandwidth beyond the relaxation oscillation frequency in a monolithic twin-ridge laterally coupled diode laser based on lateral mode locking

Monolithic twin-ridge laterally coupled diode lasers emitting at 1.3microm are presented that have a small-signal modulation bandwidth beyond the relaxation oscillation frequency of a single ridge. Spectra and spectrally resolved far fields are presented for three bias conditions: only one ridge lasing, both ridges lasing just above threshold, and both ridges lasing at biases well above threshold. In the first two cases the spectrum has single-peaked longitudinal modes, whereas the third cases shows splitting to in-phase and out-of-phase modes. The splitting frequency of the optical spectrum is measured to be 7.7 GHz. Small-signal modulation measurements reveal a strong resonance at 7.7 GHz, demonstrating an effect of lateral mode locking. As a result of this effect, the twin-ridge laser can be made to have a -3-dB bandwidth beyond that associated with its relaxation oscillation frequency.     

Simultaneous Differential Pulse Adsorptive Stripping Determination of Imipramine and Its Metabolite Desipramine by the PLS‐1 Multivariate Method

A method, using stripping differential pulse voltammetry, for the simultaneous determination of Imipramine and its metabolite Desipramine is reported. Both compounds produce, at glassy carbon electrode, an electrochemical signal due to an adsorptive‐oxidative process. The voltammograms show a very high overlap degree between IM and DE peaks. The multivariate calibration method PLS‐1 was employed for the simultaneous determination of both compounds. An experimental design together with the response surface methodology has been used to find the optimum experimental conditions. The developed procedure has been utilized in the analysis of fortified human serum samples with good recovery values for each analyte.     

Modal series solution for an epidemic model

In this article, we generalize a recently proposed method to obtain an exact general solution for the classical Susceptible, Infected, Recovered and Susceptible (SIRS) epidemic mathematical model. This generalization is based upon the nonlinear coupling of two frequencies in an infinite modal series solution. It is shown that these series provide a nonstandard approach in order to obtain an accurate analytical solution for the classical SIRS epidemic model. Numerical results of the SIRS epidemic model for real and complex frequencies are included in order to test the validity and reliability of the method. This method could be applied to a wide class of models in physics, chemistry or engineering.     

Modeling the social obesity epidemic with stochastic networks

In this paper we extend a compartmental model to the case of a homogenous network epidemic model for a study of the dynamics of obese populations. The social epidemic network-based approach developed here uses different algorithms and points of views regarding the simulation of the dynamics of the network. First, Monte Carlo simulations for homogeneous networks using a traditional constant probability transition rates and a mean-field-like approach are presented. We show that these networks evolve towards an approximately stationary state, which coincides with the one obtained by the underlying classical compartmental continuous model. A mean-field-like approach is applied in order to reduce the large computation time required when dealing with large contact networks. We also investigate, using homogenous contact networks, the effect of the realistic assumption that the waiting times between subpopulations follow a gamma distribution instead of the traditional exponential distribution. It is concluded that careful attention must be paid to the distributions assumed for the state periods.     

Optimization and validation of a capillary electrophoresis methodology for inorganic anions in atmospheric aerosol samples

Optimization and validation of a method for the determination of inorganic anions, such as chloride, nitrate, sulphate, fluoride and phosphate, in atmospheric particulate matter is described. Ultrasonic extraction followed by CE, using CTAB as EOF modifier, has been established for this separation using chromate as visualization reagent for the indirect UV/vis detection. Optimization of the experimental chemical conditions (pH, surfactant concentration, chromate concentration and acetonitrile concentration) and instrumental parameters (temperature, applied voltage and injection time) is described. Linear calibrations plots are obtained for the five ions, with detection limits in the high ng/mL range. A standard reference material, SRM 1648 Urban Particulate Matter, has been used to validate the proposed method. Satisfactory reproducibility and acceptable agreement to the matter atmospheric samples has been found (recoveries ranging from 86 to 96%). Application to real particulate matter atmospheric samples, collected on high volume samplers from the air quality surveillance network of Extremadura, southwest Spain, is shown.     

A cellular automaton model for collective neural dynamics

A stochastic epidemic model for the collective behaviour of a large set of Boolean automata placed upon the sites of a complete graph is revisited. In this paper we study the generalisation of the model to take into account inhibitory neurons. The resulting stochastic cellular automata are completely defined by five parameters: the number of excitatory neurons, N, the number of inhibitory neurons, M, the probabilities of excitation, α, and inhibition, γ, among neurons and the spontaneous transition rate from the firing to the quiescent state, β.We propose that the background of the electroencephalographic signals could be mimicked by the fluctuations in the total number of firing neurons in the excitatory subnetwork. These fluctuations are Gaussian and the mean-square displacement from an initial state displays a strongly subdiffusive behaviour approximately given by , where NA=β/(β+Mγ), τ=2(Nα−β). Comparison with real EEG records exhibits good agreement with these predictions.     

Random fixed points of set-valued operators

Some random fixed point theorems for set-valued operators are obtained. The measurability of certain marginal maps is also studied. The underlying measurable space is not assumed to be a Suslin family.

     

A novel approach to obtain analytical-numerical solutions of nonlinear Lorenz system

In this research we apply an analytic approach to solve the well-known Lorenz system in the non-chaotic regime. The proposed approach is based on modal expansion by infinite series. The analytical-numerical results show that for real initial conditions and under the non-convective regime the modal expansion series reproduce correctly the dynamical behavior of the solution of the Lorenz system. The validity and reliability of the proposed analytical approach with few terms is tested by its application to the convective and non-convective regime with various parameter values. The main advantage is that the obtained solution is global and is presented in analytical form.     

Erratum to: “The penetrable-sphere fluid in the high-temperature,high-density limit” [Phys. Lett. A 323 (2004) 427]

A few misprints appearing in the paper [L. Acedo, A. Santos, Phys. Lett. A 323 (2004) 427] are corrected.     

A second-order phase transition in the complete graph stochastic epidemic model

A stochastic model for epidemic spread in a set of individuals placed upon the sites of a complete graph of relations is investigated. The model is defined by three parameters: the number of individuals or sites, N, the probability that an infected site transmits the disease to a susceptible site, α, and the probability of recovery of infected sites, β, both referred to the unit of time.We show that this system evolves towards a, approximately Gaussian, stationary distribution of infected sites whose mean and variance can be analytically estimated. Also, we find that the average fraction of infected sites, x, is zero for transmission probabilities below the critical value α_c=1-e^-β/N and grows linearly with α for 0<α-α_c1. A sharp peak observed in Monte Carlo simulations of the variance of the number of infected sites as a function of α allows us to classify this dynamical phase transition as second order with x playing the role of an order parameter. Some consequences of this model to the dynamics of highly connected complex systems, such as the brain cortex, are also discussed.