From lattice-gas models to nonlinear diffusion models: A derivation of macroscopic equations and fluctuations

We derive nonlinear diffusion equations and equations containing corrections due to fluctuations for a coarse-grained concentration field. To deal with diffusion coefficients with an explicit dependence on the concentration values, we generalize the Van Kampen method of expansion of the master equation to field variables. We apply these results to the derivation of equations of phase-separation dynamics and interfacial growth instabilities.

Metabolic effects of a mixed and a high-carbohydrate low-fat diet in man, measured over 24 h in a respiration chamber.

1. The relation between dietary carbohydrate: lipid ratio and the fuel mixture oxidized during 24 h was investigated in eleven healthy volunteers (six females, and five males) in a respiration chamber. Values of the fuel mixture oxidized were estimated by continuous indirect calorimetry and urinary nitrogen measurements. 2. The subjects, were first given a mixed diet for 7 d and spent the last 24 h of the 7 d period in a respiration chamber for continuous gas-exchange measurement. The fuels oxidized during 2.5 h or moderate exercise were also measured in the respiration chamber. After an interval of 2 weeks from the end of the mixed-diet period, the same subjects were given an isoenergetic high-carbohydrate low-fat diet for 7 d, and the same experimental regimen was repeated. 3. Dietary composition markedly influenced the fuel mixture oxidized during 24 h and this effect was still present 12 h after the last meal in the postabsorptive state. However, the diets had no influence on the substrates oxidized above resting levels during exercise. With both diets, the 24 h energy balance was slightly negative and the energy deficit was covered by lipid oxidation. 4. With the high-carbohydrate low-fat diet, the energy expenditure during sleep was found to be higher than that with the mixed diet. 5. It is concluded that: (a) the composition of the diet did not influence the fuel mixture utilized for moderate exercise, (b) the energy deficit calculated for a 24 h period was compensated by lipid oxidation irrespective of the carbohydrate content of the diet, (c) energy expenditure during sleep was found to be higher with the high-carbohydrate low-fat diet than with the mixed diet.

Nonlinear effects in the dynamics of transient pattern formation in nematics

A nonlinear calculation of the dynamics of transient pattern formation in the Fréedericksz transition is presented. A Gaussian decoupling is used to calculate the time dependence of the structure factor. The calculation confirms the range of validity of linear calculations argued in earlier work. In addition, it describes the decay of the transient pattern.

Evaluating effects of spectral training data distribution on continuous field mapping performance

Continuous field mapping has to address two conflicting remote sensing requirements when collecting training data. On one hand, continuous field mapping trains fractional land cover and thus favours mixed training pixels. On the other hand, the spectral signature has to be preferably distinct and thus favours pure training pixels. The aim of this study was to evaluate the sensitivity of training data distribution along fractional and spectral gradients on the resulting mapping performance. We derived four continuous fields (tree, shrubherb, bare, water) from aerial photographs as response variables and processed corresponding spectral signatures from multitemporal Landsat 5 TM data as explanatory variables. Subsequent controlled experiments along fractional cover gradients were then based on generalised linear models. Resulting fractional and spectral distribution differed between single continuous fields, but could be satisfactorily trained and mapped. Pixels with fractional or without respective cover were much more critical than pure full cover pixels. Error distribution of continuous field models was non-uniform with respect to horizontal and vertical spatial distribution of target fields. We conclude that a sampling for continuous field training data should be based on extent and densities in the fractional and spectral, rather than the real spatial space. Consequently, adequate training plots are most probably not systematically distributed in the real spatial space, but cover the gradient and covariate structure of the fractional and spectral space well. (C) 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

Effects of a school-based physical activity program on physical and psychosocial quality of life in elementary school children: a cluster-randomized trial.

The present study tested the effect of a school-based physical activity (PA) program on quality of life (QoL) in 540 elementary school children. First and fifth graders were randomly assigned to a PA program or a no-PA control condition during one academic year. QoL was assessed by the Child Health Questionnaire at baseline and postintervention. Based on mixed linear model analyses, physical QoL in first graders and physical and psychosocial QoL in fifth graders were not affected by the intervention. In first graders, the PA intervention had a positive impact on psychosocial QoL (effect size [d], 0.32; p < .05). Subpopulation analyses revealed that this effect was caused by an effect in urban (effect size [d], 0.38; p < .05) and overweight first graders (effect size [d], 0.45; p < .05). In conclusion, a school-based PA intervention had little effect on QoL in elementary school children.

Particulate matter exposure in cars is associated with cardiovascular effects in healthy young men

Exposure to fine airborne particulate matter (PM(2.5)) is associated with cardiovascular events and mortality in older and cardiac patients. Potential physiologic effects of in-vehicle, roadside, and ambient PM(2.5) were investigated in young, healthy, nonsmoking, male North Carolina Highway Patrol troopers. Nine troopers (age 23 to 30) were monitored on 4 successive days while working a 3 P.M. to midnight shift. Each patrol car was equipped with air-quality monitors. Blood was drawn 14 hours after each shift, and ambulatory monitors recorded the electrocardiogram throughout the shift and until the next morning. Data were analyzed using mixed models. In-vehicle PM(2.5) (average of 24 microg/m(3)) was associated with decreased lymphocytes (-11% per 10 microg/m(3)) and increased red blood cell indices (1% mean corpuscular volume), neutrophils (6%), C-reactive protein (32%), von Willebrand factor (12%), next-morning heart beat cycle length (6%), next-morning heart rate variability parameters, and ectopic beats throughout the recording (20%). Controlling for potential confounders had little impact on the effect estimates. The associations of these health endpoints with ambient and roadside PM(2.5) were smaller and less significant. The observations in these healthy young men suggest that in-vehicle exposure to PM(2.5) may cause pathophysiologic changes that involve inflammation, coagulation, and cardiac rhythm.

Selection via simulated individual BLUP based on family genotypic effects in sugarcane

The objective of this work was to propose a new selection strategy for the initial stages of sugarcane improvement, based on the methodology 'simulated individual BLUP (BLUPIS)', which promotes a dynamic allocation of individuals selected in each full-sib family, using BLUP as a base for both the genotypic effects of the referred families and plot effects. The method proposed applies to single full-sib families or those obtained from unbalanced or balanced diallel crosses, half-sib families and self-pollinated families. BLUPIS indicates the number of individuals to be selected within each family, the total number of clones to be advanced, and the number of families to contribute with selected individuals. Correlation between BLUPIS and true BLUP was 0.96, by method validation. Additionally, BLUPIS allows the identification of which replication contains the best individuals of each family.

Effects of sample size on the performance of species distribution models

A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence-absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size (n < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.

Facilitatory and interfering effects of neighbourhood density on speech production: Evidence from aphasic errors.

In a system where tens of thousands of words are made up of a limited number of phonemes, many words are bound to sound alike. This similarity of the words in the lexicon as characterized by phonological neighbourhood density (PhND) has been shown to affect speed and accuracy of word comprehension and production. Whereas there is a consensus about the interfering nature of neighbourhood effects in comprehension, the language production literature offers a more contradictory picture with mainly facilitatory but also interfering effects reported on word production. Here we report both of these two types of effects in the same study. Multiple regression mixed models analyses were conducted on PhND effects on errors produced in a naming task by a group of 21 participants with aphasia. These participants produced more formal errors (interfering effect) for words in dense phonological neighbourhoods, but produced fewer nonwords and semantic errors (a facilitatory effect) with increasing density. In order to investigate the nature of these opposite effects of PhND, we further analysed a subset of formal errors and nonword errors by distinguishing errors differing on a single phoneme from the target (corresponding to the definition of phonological neighbours) from those differing on two or more phonemes. This analysis confirmed that only formal errors that were phonological neighbours of the target increased in dense neighbourhoods, while all other errors decreased. Based on additional observations favouring a lexical origin of these formal errors (they exceeded the probability of producing a real-word error by chance, were of a higher frequency, and preserved the grammatical category of the targets), we suggest that the interfering effect of PhND is due to competition between lexical neighbours and target words in dense neighbourhoods.

Analysis of Industrial Granular Flow Applications by Using Advanced Collision Models

Granular flow phenomena are frequently encountered in the design of process and industrial plants in the traditional fields of the chemical, nuclear and oil industries as well as in other activities such as food and materials handling. Multi-phase flow is one important branch of the granular flow. Granular materials have unusual kinds of behavior compared to normal materials, either solids or fluids. Although some of the characteristics are still not well-known yet, one thing is confirmed: the particle-particle interaction plays a key role in the dynamics of granular materials, especially for dense granular materials. At the beginning of this thesis, detailed illustration of developing two models for describing the interaction based on the results of finite-element simulation, dimension analysis and numerical simulation is presented. The first model is used to describing the normal collision of viscoelastic particles. Based on some existent models, more parameters are added to this model, which make the model predict the experimental results more accurately. The second model is used for oblique collision, which include the effects from tangential velocity, angular velocity and surface friction based on Coulomb's law. The theoretical predictions of this model are in agreement with those by finite-element simulation. I n the latter chapters of this thesis, the models are used to predict industrial granular flow and the agreement between the simulations and experiments also shows the validation of the new model. The first case presents the simulation of granular flow passing over a circular obstacle. The simulations successfully predict the existence of a parabolic steady layer and show how the characteristics of the particles, such as coefficients of restitution and surface friction affect the separation results. The second case is a spinning container filled with granular material. Employing the previous models, the simulation could also reproduce experimentally observed phenomena, such as a depression in the center of a high frequency rotation. The third application is about gas-solid mixed flow in a vertically vibrated device. Gas phase motion is added to coherence with the particle motion. The governing equations of the gas phase are solved by using the Large eddy simulation (LES) and particle motion is predicted by using the Lagrangian method. The simulation predicted some pattern formation reported by experiment.

Monte Carlo Methods in Parameter Estimation of Nonlinear Models

Yksi keskeisimmistä tehtävistä matemaattisten mallien tilastollisessa analyysissä on mallien tuntemattomien parametrien estimointi. Tässä diplomityössä ollaan kiinnostuneita tuntemattomien parametrien jakaumista ja niiden muodostamiseen sopivista numeerisista menetelmistä, etenkin tapauksissa, joissa malli on epälineaarinen parametrien suhteen. Erilaisten numeeristen menetelmien osalta pääpaino on Markovin ketju Monte Carlo -menetelmissä (MCMC). Nämä laskentaintensiiviset menetelmät ovat viime aikoina kasvattaneet suosiotaan lähinnä kasvaneen laskentatehon vuoksi. Sekä Markovin ketjujen että Monte Carlo -simuloinnin teoriaa on esitelty työssä siinä määrin, että menetelmien toimivuus saadaan perusteltua. Viime aikoina kehitetyistä menetelmistä tarkastellaan etenkin adaptiivisia MCMC menetelmiä. Työn lähestymistapa on käytännönläheinen ja erilaisia MCMC -menetelmien toteutukseen liittyviä asioita korostetaan. Työn empiirisessä osuudessa tarkastellaan viiden esimerkkimallin tuntemattomien parametrien jakaumaa käyttäen hyväksi teoriaosassa esitettyjä menetelmiä. Mallit kuvaavat kemiallisia reaktioita ja kuvataan tavallisina differentiaaliyhtälöryhminä. Mallit on kerätty kemisteiltä Lappeenrannan teknillisestä yliopistosta ja Åbo Akademista, Turusta.