Solvent assisted decomposition of the tetrahedral intermediate of the transesterification reaction to biodiesel production. A density functional study

B3LYP/6-31 + G(d) calculations were employed to investigate the mechanism of the transesterification reaction between a model monoglyceride and the methoxide and ethoxide anions. The gas-phase results reveal that both reactions have essentially the same activation energy (5.9 kcal mol(-1)) for decomposition of the key tetrahedral intermediate. Solvent effects were included by means of both microsolvation and the polarizable continuum solvation model CPCM. Both solvent approaches reduce the activation energy, however, only the microsolvation model is able to introduce some differentiation between methanol and ethanol, yielding a lower activation energy for decomposition of the tetrahedral intermediate in the reaction with methanol (1.1 kcal mol(-1)) than for the corresponding reaction with ethanol (2.8 kcal mol(-1)), in line with experimental evidences. Analysis of the individual energy components within the CPCM approach reveals that electrostatic interactions are the main contribution to stabilization of the transition state. (C) 2009 Elsevier Ltd. All rights reserved.

MODELLING AND FORECASTING INFLATION RATES IN GHANA: AN APPLICATION OF SARIMA MODELS

Ghana faces a macroeconomic problem of inflation for a long period of time. The problem in somehow slows the economic growth in this country. As we all know, inflation is one of the major economic challenges facing most countries in the world especially those in African including Ghana. Therefore, forecasting inflation rates in Ghana becomes very important for its government to design economic strategies or effective monetary policies to combat any unexpected high inflation in this country. This paper studies seasonal autoregressive integrated moving average model to forecast inflation rates in Ghana. Using monthly inflation data from July 1991 to December 2009, we find that ARIMA (1,1,1)(0,0,1)12 can represent the data behavior of inflation rate in Ghana well. Based on the selected model, we forecast seven (7) months inflation rates of Ghana outside the sample period (i.e. from January 2010 to July 2010). The observed inflation rate from January to April which was published by Ghana Statistical Service Department fall within the 95% confidence interval obtained from the designed model. The forecasted results show a decreasing pattern and a turning point of Ghana inflation in the month of July.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.

Parameterization Of Turbulence Models Using 3DVAR Data Assimilation

In this research the 3DVAR data assimilation scheme is implemented in the numerical model DIVAST in order to optimize the performance of the numerical model by selecting an appropriate turbulence scheme and tuning its parameters. Two turbulence closure schemes: the Prandtl mixing length model and the two-equation k-ε model were incorporated into DIVAST and examined with respect to their universality of application, complexity of solutions, computational efficiency and numerical stability. A square harbour with one symmetrical entrance subject to tide-induced flows was selected to investigate the structure of turbulent flows. The experimental part of the research was conducted in a tidal basin. A significant advantage of such laboratory experiment is a fully controlled environment where domain setup and forcing are user-defined. The research shows that the Prandtl mixing length model and the two-equation k-ε model, with default parameterization predefined according to literature recommendations, overestimate eddy viscosity which in turn results in a significant underestimation of velocity magnitudes in the harbour. The data assimilation of the model-predicted velocity and laboratory observations significantly improves model predictions for both turbulence models by adjusting modelled flows in the harbour to match de-errored observations. 3DVAR allows also to identify and quantify shortcomings of the numerical model. Such comprehensive analysis gives an optimal solution based on which numerical model parameters can be estimated. The process of turbulence model optimization by reparameterization and tuning towards optimal state led to new constants that may be potentially applied to complex turbulent flows, such as rapidly developing flows or recirculating flows.

Managing Dual Enrollment Programs: Review of Program Models, Operations, and Outcomes

Universities that offer dual enrollment programs for high school students must manage increasing student demand in the face of changing state legislation. This brief examines how institutions finance their dual enrollment programs, and how they ensure academic rigor as their programs continue to grow. The report also considers how dual enrollment programs ensure that students can transfer credits, and what strategies they use to encourage DE students to matriculate as undergraduates.

Predictability of equity models

In this study, we verify the existence of predictability in the Brazilian equity market. Unlike other studies in the same sense, which evaluate original series for each stock, we evaluate synthetic series created on the basis of linear models of stocks. Following Burgess (1999), we use the “stepwise regression” model for the formation of models of each stock. We then use the variance ratio profile together with a Monte Carlo simulation for the selection of models with potential predictability. Unlike Burgess (1999), we carry out White’s Reality Check (2000) in order to verify the existence of positive returns for the period outside the sample. We use the strategies proposed by Sullivan, Timmermann & White (1999) and Hsu & Kuan (2005) amounting to 26,410 simulated strategies. Finally, using the bootstrap methodology, with 1,000 simulations, we find strong evidence of predictability in the models, including transaction costs.

The historic religious buildings of Ribeira Grande: implementation of christian models in the early colonies, 15th till 17th century, on the example of Cape Verde Islands

Rui Carita

Acceleration of the decomposition of Sicilian lemon leaves as an auxiliary measure in the control of citrus black spot

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Effect of cationic micelles on the decomposition of alpha-aminophenyl cephalosporins

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Thermal stability and thermal decomposition of sucralose

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Evaluation of hybrid models for static and multistage transmission system planning

Neste trabalho é analisada a aplicação de algoritmos heurísticos para o Modelo Híbrido Linear - Hybrid Linear Model (HLM) - no problema de planejamento da expansão de sistemas de transmissão. O HLM é um modelo relaxado que ainda não foi suficientemente explorado. Assim, é realizada uma análise das características do modelo matemático e das técnicas de solução que podem ser usadas para resolver este tipo de modelo. O trabalho analisa em detalhes um algoritmo heurístico construtivo para o HLM e faz uma extensão da modelagem e da técnica de solução para o planejamento multi-estágio da expansão de sistemas de transmissão. Dentro deste contexto, também é realizada uma avaliação da qualidade das soluções encontradas pelo HLM e as possibilidades de aplicação deste modelo em planejamento de sistemas de transmissão. Finalmente, são apresentados testes com sistemas conhecidos na literatura especializada.

Straw decomposition of nitrogen-fertilized grasses intercropped with irrigated maize in an integrated crop-livestock system

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Signaling pathways associated with the expression of inflammatory mediators activated during the course of two models of experimental periodontitis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)