New Set Of Models Research Articles

Modelling the effect of lactic acid bacteria from starter- and aroma culture on growth of Listeria monocytogenes in cottage cheese

Four mathematical models were developed and validated for simultaneous growth of mesophilic lactic acid bacteria from added cultures and Listeria monocytogenes, during chilled storage of cottage cheese with fresh- or cultured cream dressing. The mathematical models include the effect of temperature, pH, NaCl, lactic- and sorbic acid and the interaction between these environmental factors. Growth models were developed by combining new and existing cardinal parameter values. Subsequently, the reference growth rate parameters (μref at 25°C) were fitted to a total of 52 growth rates from cottage cheese to improve model performance. The inhibiting effect of mesophilic lactic acid bacteria from added cultures on growth of L. monocytogenes was efficiently modelled using the Jameson approach. The new models appropriately predicted the maximum population density of L. monocytogenes in cottage cheese. The developed models were successfully validated by using 25 growth rates for L. monocytogenes, 17 growth rates for lactic acid bacteria and a total of 26 growth curves for simultaneous growth of L. monocytogenes and lactic acid bacteria in cottage cheese. These data were used in combination with bias- and accuracy factors and with the concept of acceptable simulation zone. Evaluation of predicted growth rates of L. monocytogenes in cottage cheese with fresh- or cultured cream dressing resulted in bias-factors (Bf) of 1.07–1.10 with corresponding accuracy factor (Af) values of 1.11 to 1.22. Lactic acid bacteria from added starter culture were on average predicted to grow 16% faster than observed (Bf of 1.16 and Af of 1.32) and growth of the diacetyl producing aroma culture was on average predicted 9% slower than observed (Bf of 0.91 and Af of 1.17). The acceptable simulation zone method showed the new models to successfully predict maximum population density of L. monocytogenes when growing together with lactic acid bacteria in cottage cheese. 11 of 13 simulations of L. monocytogenes growth were within the acceptable simulation zone, which demonstrated good performance of the empirical inter-bacterial interaction model. The new set of models can be used to predict simultaneous growth of mesophilic lactic acid bacteria and L. monocytogenes in cottage cheese during chilled storage at constant and dynamic temperatures. The applied methodology is likely to be applicable for safety prediction of other types of fermented and unripened dairy products where inhibition by lactic acid bacteria is important for growth of pathogenic microorganisms.

AE9, AP9 and SPM: New Models for Specifying the Trapped Energetic Particle and Space Plasma Environment

The radiation belts and plasma in the Earth’s magnetosphere pose hazards to satellite systems which restrict design and orbit options with a resultant impact on mission performance and cost. For decades the standard space environment specification used for spacecraft design has been provided by the NASA AE8 and AP8 trapped radiation belt models. There are well-known limitations on their performance, however, and the need for a new trapped radiation and plasma model has been recognized by the engineering community for some time. To address this challenge a new set of models, denoted AE9/AP9/SPM, for energetic electrons, energetic protons and space plasma has been developed. The new models offer significant improvements including more detailed spatial resolution and the quantification of uncertainty due to both space weather and instrument errors. Fundamental to the model design, construction and operation are a number of new data sets and a novel statistical approach which captures first order temporal and spatial correlations allowing for the Monte-Carlo estimation of flux thresholds for user-specified percentile levels (e.g., 50th and 95th) over the course of the mission. An overview of the model architecture, data reduction methods, statistics algorithms, user application and initial validation is presented in this paper.

Mixed LW/ESL models for the analysis of sandwich plates with composite faces

In this paper, a new set of models in the framework of Carrera’s Unified Formulation (CUF) is presented for the static analysis of sandwich plates. Concerning the study of multilayered structures, it is important to fulfill the interlaminar continuity conditions for the transverse stresses. For this aim, the CUF has been conceived by allowing to handle in a unified manner many theories that can differ by the order of expansion and the variables description in the thickness direction. This last can be Equivalent-Single-Layer (ESL), if the variables are assumed for the whole laminate, or Layer-Wise (LW), if the variables are described independently for each layer. The models considered in this work are derived from the Reissner Mixed Variational Theorem (RMVT) in order to model a priori the transverse shear and normal stresses. If sandwich plates with composite faces are considered, the transverse stresses are described as LW while the displacements are assumed ESL in the faces and LW at sandwich level (faces+core). This procedure permits to save degrees of freedom, preserving the same accuracy of a full layer-wise description. The new formulation is implemented in the framework of finite element analysis. Comparisons are made with 3D results.

Star formation history: secular processes in “main sequence“ galaxies versus merger-driven starbursts

AbstractSome recent works indicate that most star-forming galaxies follow a main sequence in the SFR-stellar mass plane with a surprisingly low scatter of ≈0.2 dex, suggesting that the star formation in these objects is driven by secular processes. Nevertheless, Herschel identified a population of starbursting galaxies, probably triggered by mergers, which display a large excess of specific star formation rate (sSFR=SFR/Mstar) compared to the main sequence. We will present a new set of models for the contribution of these two populations to the IR/sub-mm luminosity function, but also to source counts selected at various wavelengths.Our model is based on the stellar mass function of star-forming galaxies, the distribution of sSFR measured at z=2 and its double-Gaussian decomposition, and the observed evolution of the main sequence in the sSFR-Mass plane as a function of redshift. We found that the non-Schechter bright-end of the LF is due to the starbursting galaxies, which represent only 4% in number density and 15% in luminosity density. This fraction of starbursts is remarkably constant with the redshift at 0<z<2, contrary to naive expectation from hierarchical merging. It thus suggests that the majority of stars in the Universe were formed through secular processes. We will then discuss the contribution of starbursting and main sequence galaxies to the number counts and the selection effects towards starbursts sources for various flux-limited IR/sub-mm samples.We will also present studies of the clustering properties of the main sequence and starburst galaxies at z 2. These measurements suggest strong links between star formation rate, stellar mass and halo mass in the main sequence galaxies. In addition, we will present some clues suggesting that main sequence and starbursting galaxies follows the same M*-Mhalo relation.”

Causes of variation in malaria infection dynamics: insights from theory and data.

Parasite strategies for exploiting host resources are key determinants of disease severity (i.e., virulence) and infectiousness (i.e., transmission between hosts). By iterating the development of theory and empirical tests, we investigated whether variation in parasite traits across two genetically distinct clones of the rodent malaria parasite, Plasmodium chabaudi, explains differences in within-host infection dynamics and virulence. First, we experimentally tested key predictions of our earlier modeling work. As predicted, the more virulent genotype produced more progeny parasites per infected cell (burst size), but in contrast to predictions, invasion rates of red blood cells (RBCs) did not differ between the genotypes studied. Second, we further developed theory by confronting our earlier model with these new data, testing a new set of models that incorporate more biological realism, and developing novel theoretical tools for identifying differences between parasite genotypes. Overall, we found robust evidence that differences in burst sizes contribute to variation in dynamics and that differential interactions between parasites and host immune responses also play a role. In contrast to previous work, our model predicts that RBC age structure is not important for explaining dynamics. Integrating theory and empirical tests is a potentially powerful way of progressing understanding of disease biology.

On long-term credit risk assessment and rating: towards a new set of models

Institutional investors are supposed to assess credit risk by using a combination of quantitative information such as option models and qualitative assessments. Although option models can be easily constructed, they are not so suitable for the assessment of long-term credit risk that is required by institutional investors. This is mainly because the probability of bankruptcy varies so widely depending on the timing of assessment. We propose a new set of assessment models for long-term credit risk which does not necessarily use stock prices and may incorporate business cycles. The new grand model consists of the two pillars: a long-term cash flow prediction model and a credit risk spread assessment model. The calculated values derived from these models are effectively usable for reasonable calculation of risk spreads. It is quite interesting to see that our investigation indicates that rating bias may exist in the credit risk assessment of the market.

Speed prediction models for sustainable road safety management

This paper illustrates an experimental analysis conducted in 2010 on statistically significant number of roadway sections belonging to two - lane rural roads in Northern Italy. The aim of this research is to develop operating speed prediction models on tangents and circular curves to perform roadway alignment consistency analysis for travel safety in context with current operating speeds. Acquired relationships were particularly interesting and different explanatory variables were introduced in the predictive models which are dependent on examined geometric roads features. These relationships constitute a new set of models about the operating speeds to design and verify roads geometric alignments adding to those already available in the scientific literature and, then, to plot speed profiles to illustrate complete driver speed behaviour on two-lane rural roads individualizing critical roadway sections where the speed differences, between road geometric components, are inappropriate.

SEMIEMPIRICAL MODELS OF THE SOLAR ATMOSPHERE. III. SET OF NON-LTE MODELS FOR FAR-ULTRAVIOLET/EXTREME-ULTRAVIOLET IRRADIANCE COMPUTATION

Semiempirical atmospheric models of solar surface features as observed at moderate resolution are useful tools for understanding the observed solar spectral irradiance variations. Paper I described a set of models constructed to reproduce the observed radiance spectrum for solar surface features at ~2 arcsec resolution that constitute an average over small-scale features such as granulation. Paper II showed that a revision of previous models of low-chromospheric inter-network regions explains the observed infrared CO lines in addition to the UV and radio continuum from submillimeter to centimetric wavelengths. The present paper (1) shows that the Ca II H and K line wing observations are also explained by the new quiet-Sun-composite model, (2) introduces new low-chromospheric models of magnetic features that follow the ideas in Paper II, (3) introduces new upper chromospheric structures for all quiet-Sun and active-region models, and (4) shows how the new set of models explains EUV/FUV observations of spectral radiance and irradiance. This paper also discusses the chromospheric radiative-loss estimates in each of the magnetic features. The new set of models provides a basis for the spectral irradiance synthesis at EUV/FUV wavelengths based on the features observed on the solar surface.

Stellar populations of early-type galaxies in different environments

This is the second paper of a series devoted to the study of the stellar content of early-type galaxies. The goal of the series is to set constraints on the evolutionary status of these objects Methods. We use a new set of models that include an improved stellar library (MILES) to derive simple stellar population (SSP)-equivalent parameters in a sample of 98 early-type galaxies. The sample contains galaxies in the field, poor groups, and galaxies in the Virgo and Coma clusters. Results. We find that low-density environment galaxies span a larger range in SSP age and metallicity than their counterparts in high density environments, with a tendency for lower sigma galaxies to be younger. Early-type galaxies in low-density environments appear on average similar to 1.5 Gyr younger and more metal rich than their counterparts in high density environments. The sample of low-density environment galaxies shows an age-metallicity relation in which younger galaxies are found to be more metal rich, but only when metallicity is measured with a Fe-sensitive index. Conversely, there is no age-metallicity relation when the metallicity is measured with a Mg sensitive index. The mass-metallicity relation is only appreciable for the low-density environment galaxies when the metallicity is measured with a Mg-sensitive index, and not when the metallicity is measured with other indicators. On the contrary, this relation exists for the high-density environment galaxies independent of the indicator used to measure the metallicity. Conclusions. This suggests a dependence of the mass-metallicity relation on the environment of the galaxies. Our data favour a scenario in which galaxies in low density environments have suffered a more extended star formation history than the galaxies in the Coma cluster, which appear to host more homogenous stellar populations.

Classification using Hierarchical Naïve Bayes models

Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well-performing set of classifiers is the Naive Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe an instance are conditionally independent given the class of that instance. When this assumption is violated (which is often the case in practice) it can reduce classification accuracy due to information double-counting and interaction omission. In this paper we focus on a relatively new set of models, termed Hierarchical Naive Bayes models. Hierarchical Naive Bayes models extend the modeling flexibility of Naive Bayes models by introducing latent variables to relax some of the independence statements in these models. We propose a simple algorithm for learning Hierarchical Naive Bayes models in the context of classification. Experimental results show that the learned models can significantly improve classification accuracy as compared to other frameworks.

Latent Classification Models

One of the simplest, and yet most consistently well-performing set of classifiers is the Naïve Bayes models. These models rely on two assumptions: (i) All the attributes used to describe an instance are conditionally independent given the class of that instance, and (ii) all attributes follow a specific parametric family of distributions. In this paper we propose a new set of models for classification in continuous domains, termed latent classification models. The latent classification model can roughly be seen as combining the Naïve Bayes model with a mixture of factor analyzers, thereby relaxing the assumptions of the Naïve Bayes classifier. In the proposed model the continuous attributes are described by a mixture of multivariate Gaussians, where the conditional dependencies among the attributes are encoded using latent variables. We present algorithms for learning both the parameters and the structure of a latent classification model, and we demonstrate empirically that the accuracy of the proposed model is significantly higher than the accuracy of other probabilistic classifiers.

Generalized Reverse Discrete Choice Models

Marketing practitioners and academics have shown a keen interest in the processes that drive consumers’ choices since the early work of Guadagni and Little (1982). Over the past decade or so, a number of alternative models have been proposed, implemented and analyzed. The common behavioral assumption that underlines these models of discrete choice is random utility maximization (RUM). The RUM assumption, in its simplest form, posits that a consumer with a finite set of brands to choose from chooses the brand that gives her the maximum amount of utility. An alternative approach would be to assume that consumers choose the alternative that offers them the least disutility. Our paper proposes and tests a broad class of generalized extreme value models based on this hypothesis. We model the decision process of the consumer the assumption random disutility minimization (RDM) and derive a new class of discrete choice models based on this assumption. Our findings reveal that there are significant theoretical and econometric differences between the discrete choice models derived from a RUM framework and the RDM framework proposed in this paper. On the theoretical front we find that the class of discrete choice models based on the assumption of disutility minimization is structurally different from the models in the literature. Further, the models in this class are available in closed form and exhibit the same flexibility as the GEV models proposed by McFadden (1978). In fact, the number of parameters are identical to and have the same interpretation as those obtained via RUM based GEV models. In addition to the theoretical differences we also uncover significant empirical insights. With the computing effort and time for both models being roughly the same this new set of models offers marketing academics and researchers a viable new tool with which to investigate discrete choice behavior.

A model grid for the spectral analysis of X-ray emission in young Type Ia supernova remnants

We address a new set of models for the spectral analysis of the X-ray emission from young, ejecta-dominated Type Ia supernova remnants. These models are based on hydrodynamic simulations of the interaction between Type Ia supernova explosion models and the surrounding ambient medium, coupled to self-consistent ionization and electron heating calculations in the shocked supernova ejecta, and the generation of synthetic spectra with an appropriate spectral code. The details are provided elsewhere, but in this paper we concentrate on a specific class of Type Ia explosion models (delayed detonations), commenting on the differences that arise between their synthetic X-ray spectra under a variety of conditions.

Speciation and inversions: chimps and humans.

A new set of models has resurrected a role for chromosomal inversions in the formation of new species. Traditional models, which are generally considered to be unlikely in most cases, had imagined that inversions might aid speciation by directly causing low hybrid fitness. In contrast, the newer models focus on the effect that inversions have on local recombination rates. A test of these models found a strikingly high rate of amino-acid substitution within regions where humans and chimpanzees differ by inversions, suggesting perhaps that our ancestral species underwent a divergence process in which gene flow and inversions played a key role. However, it remains uncertain whether this interesting finding is actually consistent with the proposed model.

Reliability of the variables in a new set of models that predict outcome after stroke

Objectives: To provide valid predictions of outcome, the variables included in a prognostic model must be capable of reliable collection. The authors have recently reported a set of simple but...

Plume-driven upwelling under central Iceland

A suite of 70 basaltic samples from the Herdubreid region of the Northern Volcanic Zone (NVZ) in central Iceland has been analysed for major and trace element compositions. The average light rare earth element concentration of these basalts is more than a factor of 2 higher than that of basalts from the Theistareykir volcanic system near the northern end of the NVZ. Seismic surveys of the NVZ have shown that the crustal thickness increases from ∼20 km near Theistareykir to 32–40 km in central Iceland. The observed REE composition and crustal thickness of the Theistareykir area can be reproduced by a melting model where mantle with a potential temperature of ∼1480°C upwells under the spreading ridge and the mantle upwelling is driven by plate separation alone. However, plate-driven upwelling models cannot simultaneously reproduce the composition of the Herdubreid region lava and the observed crustal thickness. Forward and inverse techniques show that plate-driven models that match the crustal thickness underestimate the La concentration by more than a factor of 2, and models that reproduce the compositions underestimate the crustal thickness by a factor of 4–5. Therefore one of the assumptions involved in the plate-driven upwelling models is not appropriate for central Iceland. A new set of models was developed in which mantle upwelling rates are allowed to differ from those of plate-driven upwelling in order to investigate the role of plume-driven mantle upwelling. The lava composition and crustal thickness of the Herdubreid region can be reproduced by models where the upwelling rates near the base of the melting region (>100 km depth) are ∼10 times higher than those expected from plate-driven upwelling alone and the mantle potential temperature is 1480–1520°C. About half of the melt generation under central Iceland results from plume-driven upwelling, with the remainder caused by plate-driven upwelling of hot material. This result is in agreement with numerical models of ridge-centred plumes.

Interval models for target tracking algorithms

This paper presents a new set of models which utilize the target information implied by more than two scans of measurements. The new models are called interval models. Simulations prove that interval models have significant advantages over existing models in certain common scenarios.

O+, O, and O2 densities derived from measurements made by the High Resolution Airglow/Aurora Spectrograph (HIRAAS) sounding rocket experiment

We present the results of an analysis of the O II 834 Å and O I 1356 Å altitude profiles measured during a sounding rocket flight on March 19, 1992. The profiles were analyzed using a new set of models that used discrete inverse theory to seek a maximum likelihood fit to the data. Both profiles were fit simultaneously to ensure consistency of the retrieved ionosphere and thermospheric neutral density. During the analysis the thermospheric neutral density and temperature were modeled using the Mass Spectrometer Incoherent Scatter (MSIS‐86) model [Hedin, 1987]. Two parameters were used to scale the absolute MSIS O and O2 densities; the exospheric temperature was altered by varying the 10.7 cm solar flux (an MSIS‐86 input). The ionospheric O+ density was modeled by a three‐parameter Chapman layer. The retrieved MSIS scalars for the O and O2 densities were 0.47 ± 0.09 and 0.58 ± 0.14, respectively. These scalars indicate that the MSIS‐86 model predicted significantly higher O and O2 densities. The inferred exospheric temperature was 1125 K in good agreement with the MSIS‐86 prediction. The derived O density is in good agreement with the O density inferred from midultraviolet spectra observed during the same rocket flight [Bucsela et al., 1998]. The retrieved F region peak density, 1.98 ± 0.63 × 106 cm−3, peak height, 291 ± 22 km, and plasma scale height, 138 ± 24 km, all agreed with coincident digisonde measurements. Thus we have demonstrated that the ionospheric state can be accurately determined by inversion of observed O II 834 Å limb radiance profiles.

The evolution of macro models at the Federal Reserve Board

Large-scale macroeconomic models have been used at the Federal Reserve Board for nearly 30 years. After briefly reviewing the first generation of Fed models, which were based on the IS/LM/Phillips curve paradigm, the paper describes the structure and properties of a new set of models. The new models are more explicit in their treatment of expectations formation and household and firm intertemporal decision-making. The incorporation of more rigorous theoretical microfoundations is accomplished while maintaining a high standard of goodness of fit. Simulations illustrate the effects of alternative assumptions about the formation of expectations and policy credibility on system properties.

The Evolution of Macro Models at the Federal Reserve Board

Large-scale macroeconomic models have been used at the Federal Reserve Board for nearly thirty years. After briefly reviewing the first generation of Fed models, which were based on the IS/LM/Phillips curve paradigm, the paper describes the structure and properties of a new set of models. The new models are more explicit in their treatment of expectations formation and household and firm intertemporal decisionmaking. The incorporation of more rigorous theoretical microfoundations is accomplished while maintaining a high standard of goodness of fit. Simulations illustrate the effects of alternative assumptions about the formation of expectations and policy credibility on system properties.