The Role of the Delay Time in the Modeling of Biological Range Expansions

The time interval between successive migrations of biological species causes a delay time in the reaction-diffusion equations describing their space-time dynamics. This lowers the predicted speed of the waves of advance, as compared to classical models. It has been shown that this delay-time effect improves the modeling of human range expansions. Here, we demonstrate that it can also be important for other species. We present two new examples where the predictions of the time-delayed and the classical (Fisher) approaches are compared to experimental data. No free or adjustable parameters are used. We show that the importance of the delay effect depends on the dimensionless product of the initial growth rate and the delay time. We argue that the delay effect should be taken into account in the modeling of range expansions for biological species

The Market Behavior of Emerging Stock Markets in Europe

This study investigates the over and underreaction effects in nine emerging stock markets of Europe. Especially, the possible behavioral aspects behind them are an area of interest. These aspects would link them strongly to behavioral finance. Second, our aim is to provide more evidence of the similar or dissimilar behavior in general among these countries. Third, the possibility to gain abnormal returns from these markets is also under investigation. Data from nine emerging stock market indexes in Europe is gathered from January 1, 1998 to January 1, 2008 to find answers to the stated questions. Studies for the over and underreaction effects are done using a variant of the event study methodology which in this case includes two different calculation methods for the expected returns. Studies are performed using 60 day time intervals. The results between the two different methods used are relatively similar concerning the over and underreaction effects. Another of the methods, however, suggests there to be behavioral aspects behind the effects interpreted. On the other hand, the another method does not support this suggestion. However, a conclusion can be made that the factors driving these countries' behavior are related to their geographical location and to the fact that they are emerging countries.

A panel data analysis of FDI and informal labor markets

The objective of this paper is to examine whether informal labor markets affect the flows of Foreign Direct Investment (FDI), and also whether this effect is similar in developed and developing countries. With this aim, different public data sources, such as the World Bank (WB), and the United Nations Conference on Trade and Development (UNCTAD) are used, and panel econometric models are estimated for a sample of 65 countries over a 14 year period (1996-2009). In addition, this paper uses a dynamic model as an extension of the analysis to establish whether such an effect exists and what its indicators and significance may be.

Testing the Random Walk Hypothesis on the Baltic Stock Markets

The purpose of the thesis is to analyze whether the returns of general stock market indices of Estonia, Latvia and Lithuania follow the random walk hypothesis (RWH), and in addition, whether they are consistent with the weak-form efficiency criterion. Also the existence of the day-of-the-week anomaly is examined in the same regional markets. The data consists of daily closing quotes of the OMX Tallinn, Riga and Vilnius total return indices for the sample period from January 3, 2000 to August 28, 2009. Moreover, the full sample period is also divided into two sub-periods. The RWH is tested by applying three quantitative methods (i.e. the Augmented Dickey-Fuller unit root test, serial correlation test and non-parametric runs test). Ordinary Least Squares (OLS) regression with dummy variables is employed to detect the day-of-the-week anomalies. The random walk hypothesis (RWH) is rejected in the Estonian and Lithuanian stock markets. The Latvian stock market exhibits more efficient behaviour, although some evidence of inefficiency is also found, mostly during the first sub-period from 2000 to 2004. Day-of-the-week anomalies are detected on every stock market examined, though no longer during the later sub-period.

Supercritical CO2 recovery of caffeine from green coffee oil: new experimental solubility data and modeling

The caffeine solubility in supercritical CO2 was studied by assessing the effects of pressure and temperature on the extraction of green coffee oil (GCO). The Peng-Robinson¹ equation of state was used to correlate the solubility of caffeine with a thermodynamic model and two mixing rules were evaluated: the classical mixing rule of van der Waals with two adjustable parameters (PR-VDW) and a density dependent one, proposed by Mohamed and Holder² with two (PR-MH, two parameters adjusted to the attractive term) and three (PR-MH3 two parameters adjusted to the attractive and one to the repulsive term) adjustable parameters. The best results were obtained with the mixing rule of Mohamed and Holder² with three parameters.

Temperature measurements by oh lif and chemiluminescence kinetic modeling for ethanol flames

OH LIF-thermometry was applied to premixed ethanol flames at atmospheric pressure in a burner for three flame conditions. Flame temperatures were simulated from energy equation with PREMIX code of CHEMKIN software package for comparison. A kinetic modeling based on a model validated through chemiluminescence measurements and on a set of reactions for nitrogen chemistry was evaluated. Marinov's mechanism was also tested. Sensitivity analysis was performed for fuel-rich flame condition with Φ = 1.34. Simulated temperatures from both reaction mechanisms evaluated were higher than experimental values. However, the proposed kinetic modeling resulted in temperature profiles qualitatively very close to the experimental.

Preparation of glasses and glass ceramics of heavy metal oxides containing silver: optical, structural and electrochemical properties

Silver containing heavy metal oxide glasses and glass ceramics of the system WO3-SbPO4-PbO-AgCl with different AgCl contents have been prepared and their thermal, structural and optical properties characterized. Glass ceramics containing metallic silver nanoparticles have been prepared by annealing glass samples at temperatures above the glass transition and analyzed by transmission electron microscopy and energy dispersive X-ray microanalysis. The presence of the metallic clusters has been also confirmed by the observation of a surface plasmon resonance band in the visible range. Cyclic voltammetric measurements indicated the presence of metallic silver into the glasses, even before to perform the thermal treatment.

QSAR modeling: um novo pacote computacional open source para gerar e validar modelos QSAR

QSAR modeling is a novel computer program developed to generate and validate QSAR or QSPR (quantitative structure- activity or property relationships) models. With QSAR modeling, users can build partial least squares (PLS) regression models, perform variable selection with the ordered predictors selection (OPS) algorithm, and validate models by using y-randomization and leave-N-out cross validation. An additional new feature is outlier detection carried out by simultaneous comparison of sample leverage with the respective Studentized residuals. The program was developed using Java version 6, and runs on any operating system that supports Java Runtime Environment version 6. The use of the program is illustrated. This program is available for download at lqta.iqm.unicamp.br.

Kinetic modeling of the biosorption of Cd2+ ions from aqueous solutions onto Eichhornia crassipes roots using potentiometry: low-cost alternative to conventional methods

This work presents the use of potentiometric measurements for kinetic studies of biosorption of Cd2+ ions from aqueous solutions on Eichhornia crassipes roots. The open circuit potential of the Cd/Cd2+ electrode of the first kind was measured during the bioadsorption process. The amount of Cd2+ ions accumulated was determined in real time. The data were fit to different models, with the pseudo-second-order model proving to be the best in describing the data. The advantages and limitations of the methodology proposed relative to the traditional method are discussed.

CARBOXYLATION OF SILVER NANOPARTICLES FOR THE IMMOBILIZATION OF β-GALACTOSIDASE AND ITS EFFICACY IN GALACTO-OLIGOSACCHARIDES PRODUCTION

The present study investigated the carboxylation of silver nanoparticles (AgNPs) by 1:3 nitric acid-sulfuric acid mixtures for immobilizing Aspergillus oryzae β-galactosidase. Carboxylated AgNPs retained 93% enzyme upon immobilization and the enzyme did not leach out appreciably from the modified nanosupport in the presence of 100 mmol L-1 NaCl. Atomic force micrograph revealed the binding of β-galactosidase on the modified AgNPs. The optimal pH for soluble and carboxylated AgNPs adsorbed β-galactosidase (IβG) was observed at pH 4.5 while the optimal operating temperature was broadened from 50 ºC to 60 ºC for IβG. Michaelis constant, Km was increased two and a half fold for IβG while Vmax decreases slightly as compared to soluble enzyme. β-galactosidase immobilized on surface functionalized AgNPs retained 70% biocatalytic activity even at 4% galactose concentration as compared to enzyme in solution. Our study showed that IβG produces greater amount of galacto-oligosaccharides at higher temperatures (50 ºC and 60 ºC) from 0.1 mol L-1 lactose solution at pH 4.5 as compared to previous reports.

SIMULTANEOUS DETERMINATION OF PROTOCATECHUIC ALDEHYDE AND PROTOCATECHUIC ACID USING THE LOCALIZED SURFACE PLASMON RESONANCE PEAK OF SILVER NANOPARTICLES AND CHEMOMETRIC METHODS

A simple and sensitive spectrophotometric method is proposed for the simultaneous determination of protocatechuic acid and protocatechuic aldehyde. The method is based on the difference in the kinetic rates of the reactions of analytes with [Ag(NH3)2]+ in the presence of polyvinylpyrrolidone to produce silver nanoparticles. The data obtained were processed by chemometric methods using principal component analysis artificial neural network and partial least squares. Excellent linearity was obtained in the concentration ranges of 1.23-58.56 µg mL-1 and 0.08-30.39 µg mL-1 for PAC and PAH, respectively. The limits of detection for PAC and PAH were 0.039 and 0.025 µg mL-1, respectively.