Evaluation of the Plant–Craig stochastic convection scheme (v2.0) in the ensemble forecasting system MOGREPS-R (24 km) based on the Unified Model (v7.3)

The Plant–Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic scheme only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant–Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant–Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

Exact Search Directions for Optimization of Linear and Nonlinear Models Based on Generalized Orthonormal Functions

A novel technique for selecting the poles of orthonormal basis functions (OBF) in Volterra models of any order is presented. It is well-known that the usual large number of parameters required to describe the Volterra kernels can be significantly reduced by representing each kernel using an appropriate basis of orthonormal functions. Such a representation results in the so-called OBF Volterra model, which has a Wiener structure consisting of a linear dynamic generated by the orthonormal basis followed by a nonlinear static mapping given by the Volterra polynomial series. Aiming at optimizing the poles that fully parameterize the orthonormal bases, the exact gradients of the outputs of the orthonormal filters with respect to their poles are computed analytically by using a back-propagation-through-time technique. The expressions relative to the Kautz basis and to generalized orthonormal bases of functions (GOBF) are addressed; the ones related to the Laguerre basis follow straightforwardly as a particular case. The main innovation here is that the dynamic nature of the OBF filters is fully considered in the gradient computations. These gradients provide exact search directions for optimizing the poles of a given orthonormal basis. Such search directions can, in turn, be used as part of an optimization procedure to locate the minimum of a cost-function that takes into account the error of estimation of the system output. The Levenberg-Marquardt algorithm is adopted here as the optimization procedure. Unlike previous related work, the proposed approach relies solely on input-output data measured from the system to be modeled, i.e., no information about the Volterra kernels is required. Examples are presented to illustrate the application of this approach to the modeling of dynamic systems, including a real magnetic levitation system with nonlinear oscillatory behavior.

Chitosan as a removing agent of beta-lactoglobulin from membrane models

Many chitosan biological activities depend on the interaction with biomembranes, but so far it has not been possible to obtain molecular-level evidence of chitosan action. In this article, we employ Langmuir phospholipid monolayers as cell membrane models and show that chitosan is able to remove beta-lactoglobulin (BLG) from negatively charged dimyristoyl phosphatidic acid (DMPA) and dipalmitoyl phosphatidyl glycerol (DPPG). This was shown with surface pressure isotherms and elasticity and PM-IRRAS measurements in the Langmuir monolayers, in addition to quartz crystal microbalance and fluorescence spectroscopy measurements for Langmuir-Blodgett (LB) films transferred onto solid substrates. Some specificity was noted in the removal action because chitosan was unable to remove BLG incorporated into neutral dipalmitoyl phosphatidyl choline (DPPC) and cholesterol monolayers and had no effect on horseradish peroxidase and urease interacting with DMPA. An obvious biological implication of these findings is to offer reasons that chitosan can remove BLG from lipophilic environments, as reported in the recent literature.

Interaction of oligonucleotide-based amphiphilic block copolymers with cell membrane models

Oligonucleotides have unique molecular recognition properties, being involved in biological mechanisms such as cell-surface receptor recognition or gene silencing. For their use in human therapy for drug or gene delivery, the cell membrane remains a barrier, but this can be obviated by grafting a hydrophobic tail to the oligonucleotide. Here we demonstrate that two oligonucleotides, one consisting of 12 guanosine units (G(12)), and the other one consisting of five adenosine and seven guanosine (A(5)G(7)) units, when functionalized with poly(butadiene), namely PB-G(12) and PB-A(5)G(7), can be inserted into Langmuir monolayers of dipalmitoyl phosphatidyl choline (DPPC), which served as a cell membrane model. PB-G(12) and PB-A(5)G(7) were found to affect the DPPC monolayer even at high surface pressures. The effects from PB-G(12) were consistently stronger, particularly in reducing the elasticity of the DPPC monolayers, which may have important biological implications. Multilayers of DPPC and nucleotide-based copolymers could be adsorbed onto solid supports, in the form of Y-type LB films, in which the molecular-level interaction led to lower energies in the vibrational spectra of the nucleotide-based copolymers. This successful deposition of solid films opens the way for devices to be produced which exploit the molecular recognition properties of the nucleotides. (C) 2010 Elsevier Inc. All rights reserved.

Cyclocreatine, an anticancer and neuroprotective agent. Spectroscopic, structural and theoretical study

The structural, spectroscopic and theoretical study of cyclocreatine (1-carboxymethyl-2-iminoimidazolidine, CyCre) has been performed prompted by the biological relevance of the molecule and its potential role as a ligand in biometalic compounds. The crystal structure of CyCre has been determined by X-ray diffraction methods. The compound crystallizes as a zwitterion in the monoclinic system, space group P2(1)/c. The crystal is further stabilized by a network of N-H center dot center dot center dot O bonds. Infrared and Raman spectra of the solid, electronic spectra of aqueous solutions at different pH values and (1)H and (13)C NMR spectra have been recorded and analyzed. Band assignments were accomplished with the help of theoretical calculations. Optimized molecular geometries, harmonic vibrational frequencies and molecular electrostatic potentials were calculated using methods based on the density functional theory. (C) 2010 Elsevier B.V. All rights reserved.

A logic-based agent that plans for extended reachability goals

Planning to reach a goal is an essential capability for rational agents. In general, a goal specifies a condition to be achieved at the end of the plan execution. In this article, we introduce nondeterministic planning for extended reachability goals (i.e., goals that also specify a condition to be preserved during the plan execution). We show that, when this kind of goal is considered, the temporal logic CTL turns out to be inadequate to formalize plan synthesis and plan validation algorithms. This is mainly due to the fact that the CTL`s semantics cannot discern among the various actions that produce state transitions. To overcome this limitation, we propose a new temporal logic called alpha-CTL. Then, based on this new logic, we implement a planner capable of synthesizing reliable plans for extended reachability goals, as a side effect of model checking.

STABILITY ANALYSIS WITH APPLICATIONS OF A TWO-DIMENSIONAL DYNAMICAL SYSTEM ARISING FROM A STOCHASTIC MODEL FOR AN ASSET MARKET

We analyze the stability properties of equilibrium solutions and periodicity of orbits in a two-dimensional dynamical system whose orbits mimic the evolution of the price of an asset and the excess demand for that asset. The construction of the system is grounded upon a heterogeneous interacting agent model for a single risky asset market. An advantage of this construction procedure is that the resulting dynamical system becomes a macroscopic market model which mirrors the market quantities and qualities that would typically be taken into account solely at the microscopic level of modeling. The system`s parameters correspond to: (a) the proportion of speculators in a market; (b) the traders` speculative trend; (c) the degree of heterogeneity of idiosyncratic evaluations of the market agents with respect to the asset`s fundamental value; and (d) the strength of the feedback of the population excess demand on the asset price update increment. This correspondence allows us to employ our results in order to infer plausible causes for the emergence of price and demand fluctuations in a real asset market. The employment of dynamical systems for studying evolution of stochastic models of socio-economic phenomena is quite usual in the area of heterogeneous interacting agent models. However, in the vast majority of the cases present in the literature, these dynamical systems are one-dimensional. Our work is among the few in the area that construct and study analytically a two-dimensional dynamical system and apply it for explanation of socio-economic phenomena.

Multivariate measurement error models based on scale mixtures of the skew-normal distribution

Scale mixtures of the skew-normal (SMSN) distribution is a class of asymmetric thick-tailed distributions that includes the skew-normal (SN) distribution as a special case. The main advantage of these classes of distributions is that they are easy to simulate and have a nice hierarchical representation facilitating easy implementation of the expectation-maximization algorithm for the maximum-likelihood estimation. In this paper, we assume an SMSN distribution for the unobserved value of the covariates and a symmetric scale mixtures of the normal distribution for the error term of the model. This provides a robust alternative to parameter estimation in multivariate measurement error models. Specific distributions examined include univariate and multivariate versions of the SN, skew-t, skew-slash and skew-contaminated normal distributions. The results and methods are applied to a real data set.

Effects of Micelles and Vesicles on the Oximolysis of p-Nitrophenyl Diphenyl Phosphate: A Model System for Surfactant-Based Skin-defensive Formulations against Organophosphates

The rates of oximolysis of p-nitrophenyl diphenyl phosphate (PNPDPP) by Acetophenoxime; 10-phenyl-10-hydi-oxyiminodecanoic acid; 4-(9-carboxynonanyl)-1-(9-carboxy-1-hydroyiminononanyl) benzene; 1-dodecyl-2-[(hydroxyimino)methyl]-pyridinium chloride (IV) and N-methylpyridinium-2-aldoxime chloride were determined in micelles of N-hexadecyl-N,N,N-trimethylammonium chloride (CTAC), N-hexadecyl-N,N-dimethylammonium propanesulfonate and dioctadecyldimethylammonium chloride (DODAC) vesicles. The effects of CTAC micelles and DODAC vesicles on the rates of oxymolysis of O,O-Diethyl O-(4-nitrophenyl) phosphate (paraoxon) by oxime IV were also determined. Analysis of micellar and vesicular effects on oximolysis of PNPDPP, using pseudophase or pseudophase with explicit consideration of ion exchange models, required the determination of the aggregate`s effects on the pK(a), of oximes and on the rates of PNPDPP hydrolysis. All aggregates increased the rate of oximolysis of PNPDPP and the results were analyzed quantitatively. In particular, DODAC vesicles catalyzed the reaction and increased the rate of oximolysis of PNPDPP by IV several million fold at pH`s compatible with pharmaceutical formulations. The rate increase produced by DODAC vesicles on the rate of oximolysis paraoxon by IV demonstrates the pharmaceutical potential of this system, since the substrate is used as an agricultural defensive agent and the surfactant is extensively employed in cosmetic formulations. (C) 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:1040-1052, 2009

Residual-based test for Nonlinear Cointegration with application in PPPs

Nested by linear cointegration first provided in Granger (1981), the definition of nonlinear cointegration is presented in this paper. Sequentially, a nonlinear cointegrated economic system is introduced. What we mainly study is testing no nonlinear cointegration against nonlinear cointegration by residual-based test, which is ready for detecting stochastic trend in nonlinear autoregression models. We construct cointegrating regression along with smooth transition components from smooth transition autoregression model. Some properties are analyzed and discussed during the estimation procedure for cointegrating regression, including description of transition variable. Autoregression of order one is considered as the model of estimated residuals for residual-based test, from which the teststatistic is obtained. Critical values and asymptotic distribution of the test statistic that we request for different cointegrating regressions with different sample sizes are derived based on Monte Carlo simulation. The proposed theoretical methods and models are illustrated by an empirical example, comparing the results with linear cointegration application in Hamilton (1994). It is concluded that there exists nonlinear cointegration in our system in the final results.

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Parameter estimation of Poisson generalized linear mixed models based on three different statistical principles : a simulation study

Generalized linear mixed models are flexible tools for modeling non-normal data and are useful for accommodating overdispersion in Poisson regression models with random effects. Their main difficulty resides in the parameter estimation because there is no analytic solution for the maximization of the marginal likelihood. Many methods have been proposed for this purpose and many of them are implemented in software packages. The purpose of this study is to compare the performance of three different statistical principles - marginal likelihood, extended likelihood, Bayesian analysis-via simulation studies. Real data on contact wrestling are used for illustration.