Averaging, slaving and balance dynamics in a simple atmospheric model

We report numerical results from a study of balance dynamics using a simple model of atmospheric motion that is designed to help address the question of why balance dynamics is so stable. The non-autonomous Hamiltonian model has a chaotic slow degree of freedom (representing vortical modes) coupled to one or two linear fast oscillators (representing inertia-gravity waves). The system is said to be balanced when the fast and slow degrees of freedom are separated. We find adiabatic invariants that drift slowly in time. This drift is consistent with a random-walk behaviour at a speed which qualitatively scales, even for modest time scale separations, as the upper bound given by Neishtadt’s and Nekhoroshev’s theorems. Moreover, a similar type of scaling is observed for solutions obtained using a singular perturbation (‘slaving’) technique in resonant cases where Nekhoroshev’s theorem does not apply. We present evidence that the smaller Lyapunov exponents of the system scale exponentially as well. The results suggest that the observed stability of nearly-slow motion is a consequence of the approximate adiabatic invariance of the fast motion.

A universal agro-hydrological model for water and nitrogen cycles in the soil-crop system SMCR_N: critical update and further validation

Agro-hydrological models have widely been used for optimizing resources use and minimizing environmental consequences in agriculture. SMCRN is a recently developed sophisticated model which simulates crop response to nitrogen fertilizer for a wide range of crops, and the associated leaching of nitrate from arable soils. In this paper, we describe the improvements of this model by replacing the existing approximate hydrological cascade algorithm with a new simple and explicit algorithm for the basic soil water flow equation, which not only enhanced the model performance in hydrological simulation, but also was essential to extend the model application to the situations where the capillary flow is important. As a result, the updated SMCRN model could be used for more accurate study of water dynamics in the soil-crop system. The success of the model update was demonstrated by the simulated results that the updated model consistently out-performed the original model in drainage simulations and in predicting time course soil water content in different layers in the soil-wheat system. Tests of the updated SMCRN model against data from 4 field crop experiments showed that crop nitrogen offtakes and soil mineral nitrogen in the top 90 cm were in a good agreement with the measured values, indicating that the model could make more reliable predictions of nitrogen fate in the crop-soil system, and thus provides a useful platform to assess the impacts of nitrogen fertilizer on crop yield and nitrogen leaching from different production systems. (C) 2010 Elsevier B.V. All rights reserved.

Calibration and evaluation of individual-based models using Approximate Bayesian Computation

This paper investigates the feasibility of using approximate Bayesian computation (ABC) to calibrate and evaluate complex individual-based models (IBMs). As ABC evolves, various versions are emerging, but here we only explore the most accessible version, rejection-ABC. Rejection-ABC involves running models a large number of times, with parameters drawn randomly from their prior distributions, and then retaining the simulations closest to the observations. Although well-established in some fields, whether ABC will work with ecological IBMs is still uncertain. Rejection-ABC was applied to an existing 14-parameter earthworm energy budget IBM for which the available data consist of body mass growth and cocoon production in four experiments. ABC was able to narrow the posterior distributions of seven parameters, estimating credible intervals for each. ABC’s accepted values produced slightly better fits than literature values do. The accuracy of the analysis was assessed using cross-validation and coverage, currently the best available tests. Of the seven unnarrowed parameters, ABC revealed that three were correlated with other parameters, while the remaining four were found to be not estimable given the data available. It is often desirable to compare models to see whether all component modules are necessary. Here we used ABC model selection to compare the full model with a simplified version which removed the earthworm’s movement and much of the energy budget. We are able to show that inclusion of the energy budget is necessary for a good fit to the data. We show how our methodology can inform future modelling cycles, and briefly discuss how more advanced versions of ABC may be applicable to IBMs. We conclude that ABC has the potential to represent uncertainty in model structure, parameters and predictions, and to embed the often complex process of optimizing an IBM’s structure and parameters within an established statistical framework, thereby making the process more transparent and objective.

An approximate dynamic programming approach for improving accuracy of lossy data compression by Bloom filters

Bloom filters are a data structure for storing data in a compressed form. They offer excellent space and time efficiency at the cost of some loss of accuracy (so-called lossy compression). This work presents a yes-no Bloom filter, which as a data structure consisting of two parts: the yes-filter which is a standard Bloom filter and the no-filter which is another Bloom filter whose purpose is to represent those objects that were recognised incorrectly by the yes-filter (that is, to recognise the false positives of the yes-filter). By querying the no-filter after an object has been recognised by the yes-filter, we get a chance of rejecting it, which improves the accuracy of data recognition in comparison with the standard Bloom filter of the same total length. A further increase in accuracy is possible if one chooses objects to include in the no-filter so that the no-filter recognises as many as possible false positives but no true positives, thus producing the most accurate yes-no Bloom filter among all yes-no Bloom filters. This paper studies how optimization techniques can be used to maximize the number of false positives recognised by the no-filter, with the constraint being that it should recognise no true positives. To achieve this aim, an Integer Linear Program (ILP) is proposed for the optimal selection of false positives. In practice the problem size is normally large leading to intractable optimal solution. Considering the similarity of the ILP with the Multidimensional Knapsack Problem, an Approximate Dynamic Programming (ADP) model is developed making use of a reduced ILP for the value function approximation. Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver (B&B), and this is what can be recommended for use in yes-no Bloom filters. In a wider context of the study of lossy compression algorithms, our researchis an example showing how the arsenal of optimization methods can be applied to improving the accuracy of compressed data.

abctools: an R package for tuning approximate Bayesian computation analyses

Approximate Bayesian computation (ABC) is a popular family of algorithms which perform approximate parameter inference when numerical evaluation of the likelihood function is not possible but data can be simulated from the model. They return a sample of parameter values which produce simulations close to the observed dataset. A standard approach is to reduce the simulated and observed datasets to vectors of summary statistics and accept when the difference between these is below a specified threshold. ABC can also be adapted to perform model choice. In this article, we present a new software package for R, abctools which provides methods for tuning ABC algorithms. This includes recent dimension reduction algorithms to tune the choice of summary statistics, and coverage methods to tune the choice of threshold. We provide several illustrations of these routines on applications taken from the ABC literature.

A modified rat model of neonatal anoxia: Development and evaluation by pulseoximetry, arterial gasometry and Fos immunoreactivity

Neonatal anoxia is a worldwide clinical problem that has serious and lasting consequences. The diversity of models does not allow complete reproducibility, so a standardized model is needed. In this study, we developed a rat model of neonatal anoxia that utilizes a semi-hermetic system suitable for oxygen deprivation. The validity of this model was confirmed using pulse oximetry, arterial gasometry, observation of skin color and behavior and analysis of Fos immunoreactivity in brain regions that function in respiratory control. For these experiments, 87 male albino neonate rats (Rattus norvegicus, lineage Wistar) aged approximate 30 postnatal hours were divided into anoxia and control groups. The pups were kept in an euthanasia polycarbonate chamber at 36 +/- 1 degrees C, with continuous 100% nitrogen gas flow at 3 L/min and 101.7 kPa for 25 min. The peripheral arterial oxygen saturation of the anoxia group decreased 75% from its initial value. Decreased pH and partial pressure of oxygen and increased partial pressure of carbon dioxide were observed in this group, indicating metabolic acidosis, hypoxia and hypercapnia. respectively. Analysis of neuronal activation showed Fos immunoreactivity in the solitary tract nucleus, the lateral reticular nucleus and the area postrema, confirming that those conditions activated areas related to respiratory control in the nervous system. Therefore, the proposed model of neonatal anoxia allows standardization and precise control of the anoxic condition, which should be of great value in indentifying both the mechanisms underlying neonatal anoxia and novel therapeutic strategies to combat or prevent this widespread public health problem. (C) 2011 Elsevier B.V. All rights reserved.

Approximate solution of the autocatalytic hydrolysis of cellulose

Depolymerization of cellulose in homogeneous acidic medium is analyzed on the basis of autocatalytic model of hydrolysis with a positive feedback of acid production from the degraded biopolymer. The normalized number of scissions per cellulose chain, S(t)/nA degrees A = 1 - C(t)/C(0), follows a sigmoid behavior with reaction time t, and the cellulose concentration C(t) decreases exponentially with a linear and cubic time dependence, C(t) = C(0)exp[-at - bt (3)], where a and b are model parameters easier determined from data analysis.

Classification of Parkinson’s Disease using MultiPass Lvq,Logistic Model Tree,K-Star for Audio Data set : Classification of Parkinson Disease using Audio Dataset

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

Vector and axial-vector correlators in a non-local chiral quark model

The behavior of the non-perturbative parts of the isovector-vector and isovector and isosinglet axial-vector correlators at Euclidean momenta is studied in the framework of a covariant chiral quark model with non-local quark-quark interactions. The gauge covariance is ensured with the help of the P-exponents, with the corresponding modification of the quark-current interaction vertices taken into account. The low- and high-momentum behavior of the correlators is compared with the chiral perturbation theory and with the QCD operator product expansion, respectively. The V-A combination of the correlators obtained in the model reproduces quantitatively the ALEPH and OPAL data on hadronic tau decays, transformed into the Euclidean domain via dispersion relations. The predictions for the electromagnetic pi(+/-) - pi(0) mass difference and for the pion electric polarizability are also in agreement with the experimental values. The topological susceptibility of the vacuum is evaluated as a function of the momentum, and its first moment is predicted to be chi'(0) approximate to (50 MeV)(2). In addition, the fulfillment of the Crewther theorem is demonstrated.

Custodial symmetry and extensions of the standard model

We show that the extension of the approximate custodial SU(2)(L+R) global symmetry to all the Yukawa interactions of the standard model Lagrangian implies the introduction of sterile right-handed neutrinos and the seesaw mechanism in this sector. In this framework, the observed quark and lepton masses may be interpreted as an effect of physics beyond the standard model. The mechanism used for breaking this symmetry in the Yukawa sector could be different from the one at work in the vector boson sector. We give three model independent examples of these mechanisms.

A Numerical Study of the FENE-CR Model Applied to a Jet Flow Problem

The FENE-CR model is investigated through a numerical algorithm to simulate the time-dependent moving free surface flow produced by a jet impinging on a flat surface. The objective is to demonstrate that by increasing the extensibility parameter L, the numerical solutions converge to the solutions obtained with the Oldroyd-B model. The governing equations are solved by an established free surface flow solver based on the finite difference and marker-and-cell methods. Numerical predictions of the extensional viscosity obtained with several values of the parameter L are presented. The results show that if the extensibility parameter L is sufficiently large then the extensional viscosities obtained with the FENE-CR model approximate the corresponding Oldroyd-B viscosity. Moreover, the flow from a jet impinging on a flat surface is simulated with various values of the extensibility parameter L and the fluid flow visualizations display convergence to the Oldroyd-B jet flow results.

QCD chiral symmetry restoration with a large number of quarks in a model with a confining propagator and dynamically massive gluons

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for a standard model-like Higgs boson in the mu(+)mu(-) and e(+)e(-) decay channels at the LHC

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for standard model production of four top quarks in the lepton plus jets channel in pp collisions at root S=8 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

An introductory informatics theoretical framework, based on an integrable lattice model using quantized function algebras, for studying DNA-ionized gas interaction system

Usually we observe that Bio-physical systems or Bio-chemical systems are many a time based on nanoscale phenomenon in different host environments, which involve many particles can often not be solved explicitly. Instead a physicist, biologist or a chemist has to rely either on approximate or numerical methods. For a certain type of systems, called integrable in nature, there exist particular mathematical structures and symmetries which facilitate the exact and explicit description. Most integrable systems, we come across are low-dimensional, for instance, a one-dimensional chain of coupled atoms in DNA molecular system with a particular direction or exist as a vector in the environment. This theoretical research paper aims at bringing one of the pioneering ‘Reaction-Diffusion’ aspects of the DNA-plasma material system based on an integrable lattice model approach utilizing quantized functional algebras, to disseminate the new developments, initiate novel computational and design paradigms.