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Bulus-Rossini L.A.,CONICET | Costanzo-Caso P.A.,CONICET | Duchowicz R.,CONICET | Sicre E.E.,Argentine Business University, Buenos Aires
Optics Communications | Year: 2010

The temporal Radon-Wigner transform (RWT), which is the squared modulus of the fractional Fourier transform (FRT) for a varying fractional order p, is here employed as a tool for pulse compression applications. To synthesize the compressed pulse, a selected FRT irradiance is optically produced employing a photonic device that combines phase modulation and dispersive transmission. For analysis purposes, the complete numerical generation of the RWT with 0 < p < 1 is proposed to select the value of p required for pulse compression. To this end, the amplitude and phase of the signal to be processed should be known. In order to obtain this information we use a method based on the recording of two different FRT irradiances of the pulse. The amplitude and phase errors of the recovered signal, which are inherent to the recording process, are discussed in connection with the RWT production. Numerical simulations were performed to illustrate the implementation of the proposed method. The technique is applied to compress signals commonly found in fiber optic transmission systems, such as chirped gaussian pulses, pulses distorted by second and third-order dispersion and nonlinear self-modulated pulses. © 2010 Elsevier B.V. All rights reserved.

Wehbe R.,Argentine Business University, Buenos Aires
IEEE Latin America Transactions | Year: 2013

During the requirements elicitation phase a software engineer tries to determine what the customer really wants (and needs.) This task requires, on the one hand, strong communication skills and, on the other hand, a good engineering background to construct a coherent set of requirements. There is a thus a conflict informal and formal techniques. Scenarios are a powerful communication tool and may be given a formal semantics so that they may be amenable to formal verification. We present a method to verifying properties of scenarios based on fixed points of predicate transformers. This work is adapted from an approach that had been originally proposed by Sifakis for transition systems. Scenarios are modelled in a similar way to Diijkstras guarded commands. © 2003-2012 IEEE.

Corradini M.G.,Argentine Business University, Buenos Aires | Normand M.D.,University of Massachusetts Amherst | Eisenberg M.,University of Massachusetts Amherst | Peleg M.,University of Massachusetts Amherst
Applied and Environmental Microbiology | Year: 2010

Heat activates the dormant spores of certain Bacillus spp., which is reflected in the "activation shoulder" in their survival curves. At the same time, heat also inactivates the already active and just activated spores, as well as those still dormant. A stochastic model based on progressively changing probabilities of activation and inactivation can describe this phenomenon. The model is presented in a fully probabilistic discrete form for individual and small groups of spores and as a semicontinuous deterministic model for large spore populations. The same underlying algorithm applies to both isothermal and dynamic heat treatments. Its construction does not require the assumption of the activation and inactivation kinetics or knowledge of their biophysical and biochemical mechanisms. A simplified version of the semicontinuous model was used to simulate survival curves with the activation shoulder that are reminiscent of experimental curves reported in the literature. The model is not intended to replace current models to predict dynamic inactivation but only to offer a conceptual alternative to their interpretation. Nevertheless, by linking the survival curve's shape to probabilities of events at the individual spore level, the model explains, and can be used to simulate, the irregular activation and survival patterns of individual and small groups of spores, which might be involved in food poisoning and spoilage. Copyright © 2010, American Society for Microbiology. All Rights Reserved.

Horowitz J.,University of Massachusetts Amherst | Normand M.D.,University of Massachusetts Amherst | Corradini M.G.,Argentine Business University, Buenos Aires | Peleg M.,University of Massachusetts Amherst
Applied and Environmental Microbiology | Year: 2010

After a short time interval of length δt during microbial growth, an individual cell can be found to be divided with probability P d(t)δt dead with probability Pm(t)δt, or alive but undivided with the probability 1 - [Pd(t) + P m(t)]δt, where t is time, Pd(t) expresses the probability of division for an individual cell per unit of time, and P m(t) expresses the probability of mortality per unit of time. These probabilities may change with the state of the population and the habitat's properties and are therefore functions of time. This scenario translates into a model that is presented in stochastic and deterministic versions. The first, a stochastic process model, monitors the fates of individual cells and determines cell numbers. It is particularly suitable for small populations such as those that may exist in the case of casual contamination of a food by a pathogen. The second, which can be regarded as a large-population limit of the stochastic model, is a continuous mathematical expression that describes the population's size as a function of time. It is suitable for large microbial populations such as those present in unprocessed foods. Exponential or logistic growth with or without lag, inactivation with or without a "shoulder," and transitions between growth and inactivation are all manifestations of the underlying probability structure of the model. With temperature-dependent parameters, the model can be used to simulate nonisothermal growth and inactivation patterns. The same concept applies to other factors that promote or inhibit microorganisms, such as pH and the presence of antimicrobials, etc. With Pd(t) and Pm(t) in the form of logistic functions, the model can simulate all commonly observed growth/mortality patterns. Estimates of the changing probability parameters can be obtained with both the stochastic and deterministic versions of the model, as demonstrated with simulated data. Copyright © 2010, American Society for Microbiology. All Rights Reserved.

Corradini M.G.,Argentine Business University, Buenos Aires | Normand M.D.,University of Massachusetts Amherst | Peleg M.,University of Massachusetts Amherst
Journal of Food Science | Year: 2010

Microbial inactivation is described by a model based on the changing survival probabilities of individual cells or spores. It is presented in a stochastic and discrete form for small groups, and as a continuous deterministic model for larger populations. If the underlying mortality probability function remains constant throughout the treatment, the model generates first-order (" log-linear" ) inactivation kinetics. Otherwise, it produces survival patterns that include Weibullian (" power-law" ) with upward or downward concavity, tailing with a residual survival level, complete elimination, flat " shoulder" with linear or curvilinear continuation, and sigmoid curves. In both forms, the same algorithm or model equation applies to isothermal and dynamic heat treatments alike. Constructing the model does not require assuming a kinetic order or knowledge of the inactivation mechanism. The general features of its underlying mortality probability function can be deduced from the experimental survival curve's shape. Once identified, the function's coefficients, the survival parameters, can be estimated directly from the experimental survival ratios by regression. The model is testable in principle but matching the estimated mortality or inactivation probabilities with those of the actual cells or spores can be a technical challenge. The model is not intended to replace current models to calculate sterility. Its main value, apart from connecting the various inactivation patterns to underlying probabilities at the cellular level, might be in simulating the irregular survival patterns of small groups of cells and spores. In principle, it can also be used for nonthermal methods of microbial inactivation and their combination with heat. © 2010 Institute of Food Technologists®.

Bermudez-Aguirre D.,Washington State University | Corradini M.G.,Argentine Business University, Buenos Aires
Food Research International | Year: 2012

Salmonella spp. has recently been involved in a number of food-borne outbreaks with a high impact on pharmaceuticals, food safety, and the economy. These outbreaks have increased the need to understand the behavior of this microorganism under conventional and new technologies applied to reduce its presence in food products. In the last twenty years, a number of emerging food processing technologies have been proposed as alternatives to thermal food processing. Studies have proven that these technologies ensure microbial inactivation while producing foods with better nutritional and sensory characteristics. Salmonella is one of the target microorganisms under study for these novel technologies showing encouraging results. Salmonella inactivation using conventional and novel technologies often does not follow first order kinetics, posing the need for models that adequately describe its survival curves and have predictive ability. This manuscript presents a summary of some of the emerging technologies used to inactivate Salmonella species in different food products and model systems, along with their inactivation patterns. It also reviews the models currently proposed to describe and estimate Salmonella inactivation under conventional thermal treatments and their applicability and limitations to characterize the survival curves obtained during exposure to novel technologies. © 2011 Elsevier Ltd.

Peleg M.,University of Massachusetts Amherst | Normand M.D.,University of Massachusetts Amherst | Horowitz J.,University of Massachusetts Amherst | Corradini M.G.,Argentine Business University, Buenos Aires
Applied and Environmental Microbiology | Year: 2011

The expanded Fermi solution was originally developed for estimating the number of food-poisoning victims when information concerning the circumstances of exposure is scarce. The method has been modified for estimating the initial number of pathogenic or probiotic cells or spores so that enough of them will survive the food preparation and digestive tract's obstacles to reach or colonize the gut in sufficient numbers to have an effect. The method is based on identifying the relevant obstacles and assigning each a survival probability range. The assumed number of needed survivors is also specified as a range. The initial number is then estimated to be the ratio of the number of survivors to the product of the survival probabilities. Assuming that the values of the number of survivors and the survival probabilities are uniformly distributed over their respective ranges, the sought initial number is construed as a random variable with a probability distribution whose parameters are explicitly determined by the individual factors' ranges. The distribution of the initial number is often approximately lognormal, and its mode is taken to be the best estimate of the initial number. The distribution also provides a credible interval for this estimated initial number. The best estimate and credible interval are shown to be robust against small perturbations of the ranges and therefore can help assessors achieve consensus where hard knowledge is scant. The calculation procedure has been automated and made freely downloadable as a Wolfram Demonstration. Copyright © 2011, American Society for Microbiology. All Rights Reserved.

Micha P.,University of Massachusetts Amherst | Corradini M.G.,Argentine Business University, Buenos Aires
Critical Reviews in Food Science and Nutrition | Year: 2011

Most of the models of microbial growth in food are Empirical algebraic, of which the Gompertz model is the most notable, Rate equations, mostly variants of the Verhulst's logistic model, or Population Dynamics models, which can be deterministic and continuous or stochastic and discrete. The models of the first two kinds only address net growth and hence cannot account for cell mortality that can occur at any phase of the growth. Almost invariably, several alternative models of all three types can describe the same set of experimental growth data. This lack of uniqueness is by itself a reason to question any mechanistic interpretation of growth parameters obtained by curve fitting alone. As argued, all the variants of the Verhulst's model, including the Baranyi-Roberts model, are empirical phenomenological models in a rate equation form. None provides any mechanistic insight or has inherent advantage over the others. In principle, models of all three kinds can predict non-isothermal growth patterns from isothermal data. Thus a modeler should choose the simplest and most convenient model for this purpose. There is no reason to assume that the dependence of the "maximum specific growth rate" on temperature, pH, water activity, or other factors follows the original or modified versions of the Arrhenius model, as the success of Ratkowsky's square root model testifies. Most sigmoid isothermal growth curves require three adjustable parameters for their mathematical description and growth curves showing a peak at least four. Although frequently observed, there is no theoretical reason that these growth parameters should always rise and fall in unison in response to changes in external conditions. Thus quantifying the effect of an environmental factor on microbial growth require that all the growth parameters are addressed, not just the "maximum specific growth rate." Different methods to determine the "lag time" often yield different values, demonstrating that it is a poorly defined growth parameter. The combined effect of several factors, such as temperature and pH or a w, need not be "multiplicative" and therefore ought to be revealed experimentally. This might not be always feasible, but keeping the notion in mind will eliminate theoretical assumptions that are hard to confirm. Modern mathematical software allows to model growing or dying microbial populations where cell division and mortality occur simultaneously and can be used to explain how different growth patterns emerge. But at least in the near future, practical problems, like translating a varying temperature into a corresponding microbial growth curve, will be solved with empirical rate models, which despite not being "mechanistic" are perfectly suitable for this purpose. © Taylor and Francis Group, LLC.

Timilsina G.R.,The World Bank | Chisari O.O.,Argentine Business University, Buenos Aires | Romero C.A.,Argentine Business University, Buenos Aires
Energy Policy | Year: 2013

Argentina is one of the world's largest biodiesel producers and the largest exporter, using soybeans as feedstock. Using a computable general equilibrium model that explicitly represents the biofuel industry, this study carries out several simulations on two sets of issues: (i) international markets for biofuel and feedstock, such as an increase in prices of soybean, soybean oil, and biodiesel, and (ii) domestic policies related to biofuels, such as an introduction of biofuel mandates. Both sets of issues can have important consequences to the Argentinean economy. The simulations indicate that increases in international prices of biofuels and feedstocks would increase Argentina's gross domestic product and social welfare. Increases in international prices of ethanol and corn also can benefit Argentina, but to a lesser extent. The domestic mandates for biofuels, however, would cause small losses in economic output and social welfare because they divert part of biodiesel and feedstock from exports to lower-return domestic consumption. An increase in the export tax on either feedstock or biodiesel also would lead to a reduction in gross domestic product and social welfare, although government revenue would rise. © 2012 Elsevier Ltd.

Peleg M.,University of Massachusetts Amherst | Normand M.D.,University of Massachusetts Amherst | Corradini M.G.,Argentine Business University, Buenos Aires
Powder Technology | Year: 2010

The Unconfined Yield Stress (σc) and Major Consolidation Stress (σ1) of a cohesive powder′s compact are found by constructing two Mohr semicircles that are tangential to the Yield Loci Curve (YLC); the first passing through the origin (0,0) and the second at the consolidation conditions (σ0,τ0). When the YLC can be described by the Warren-Spring equation (τ/C)n = (σ + Τ)/Τ or an alternative algebraic expression, this translates into finding the solution of two pairs of simultaneous equations that set the conditions for the tangential YLC and corresponding Mohr semicircles to have the same value and slope at their respective contact points. Once the Mohr semicircle′s equation that corresponds to the consolidation conditions has been found, the Effective Angle of Internal Friction (δ) is calculated in a similar manner. The numerical calculation procedure has been automated in a freely downloadable program posted on the web as a Wolfram Project Demonstration. It allows the user to choose and adjust the values of C, T, n and σ0, and the plot′s scales, by moving sliders on the computer screen. The program calculates and displays the corresponding values of σc, σ1 and δ, and plots the YLC, two Mohr semicircles and the line that defines δ. Since a linear YLC is just a special case of the model where n = 1, the program can be used with input parameters originally obtained by linear regression. But although the program can render reasonable estimates of the principal stresses σc, σ1 and δ in this case too, the physical meaning of C, and especially T, is unclear when calculated by extrapolation instead of being determined experimentally. © 2009 Elsevier B.V. All rights reserved.

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