Flocking behaviour: Agent-based simulation and hierarchical leadership.

We have studied how leaders emerge in a group as a consequence of interactions among its members. We propose that leaders can emerge as a consequence of a self-organized process based on local rules of dyadic interactions among individuals. Flocks are an example of self-organized behaviour in a group and properties similar to those observed in flocks might also explain some of the dynamics and organization of human groups. We developed an agent-based model that generated flocks in a virtual world and implemented it in a multi-agent simulation computer program that computed indices at each time step of the simulation to quantify the degree to which a group moved in a coordinated way (index of flocking behaviour) and the degree to which specific individuals led the group (index of hierarchical leadership). We ran several series of simulations in order to test our model and determine how these indices behaved under specific agent and world conditions. We identified the agent, world property, and model parameters that made stable, compact flocks emerge, and explored possible environmental properties that predicted the probability of becoming a leader.

Generative models of the human connectome.

The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.

Life-time growth patterns of pumpkinseed Lepomis gibbosus introduced to Europe, relative to North American populations

The pumpkinseed Lepomis gibbosus, an omnivorous, nest guarding North American sunfish, was introduced into European waters about 100 years ago. To assess growth performance following introduction, we reviewed the available data for North American and European populations of pumpkinseed and compared the back-calculated age-specific growth for juveniles (standard length, SL, at age two) and adults (age two to five increment) as well as adult body size (SL at age five), von Bertalanffy growth model parameters and the index of growth (in length) performance (φ′). For continental comparisons of growth trajectory, mean growth curves for North American and Europe were calculated with the von Bertalanffy model using pooled data sets for each continent. Juvenile growth rate did not differ between European and North American pumpkinseed, but mean adult body size and adult growth rate were both significantly greater in North American than European populations. Adult body size decreased with increasing latitude (ANOVA) in North American populations, but this was not observed with adult growth rate. In contrast, adult body size tended to increase with latitude in European populations. Adult body size correlated significantly with φ′. The von Bertalanffy model described the overall growth patterns of North American and European populations reasonably well, but on the individual population level, length asymptotes were unrealistic (estimates that were > 20 % of the mean back-calculated size for the oldest age class) for a third of European populations and 80% of the North American populations. In contrast to North American pumpkinseed populations, somatic growth in European populations appears to be compromised by limited, but adequate, food resources, probably due to strong intraspecific interactions. This appears to be especially acute in adults, having potential ramifications for life span and reproductive allocation

Uncertainties in 4pi-gamma coincidence counting

The 4πβ-γ coincidence counting method and its close relatives are widely used for the primary standardization of radioactivity. Both the general formalism and specific implementation of these methods have been well-documented. In particular, previous papers contain the extrapolation equations used for various decay schemes, methods for determining model parameters and, in some cases, tabulated uncertainty budgets. Two things often lacking from experimental reports are both the rationale for estimating uncertainties in a specific way and the details of exactly how a specific component of uncertainty was estimated. Furthermore, correlations among the components of uncertainty are rarely mentioned. To fill in these gaps, the present article shares the best-practices from a few practitioners of this craft. We explain and demonstrate with examples of how these approaches can be used to estimate the uncertainty of the reported massic activity. We describe uncertainties due to measurement variability, extrapolation functions, dead-time and resolving-time effects, gravimetric links, and nuclear and atomic data. Most importantly, a thorough understanding of the measurement system and its response to the decay under study can be used to derive a robust estimate of the measurement uncertainty.

Modeling of Gold Cyanidation

The chemistry of gold dissolution in alkaline cyanide solution has continually received attention and new rate equations expressing the gold leaching are still developed. The effect of leaching parameters on gold gold cyanidation is studied in this work in order to optimize the leaching process. A gold leaching model, based on the well-known shrinking-core model, is presented in this work. It is proposed that the reaction takes place at the reacting particle surface which is continuously reduced as the reaction proceeds. The model parameters are estimated by comparing experimental data and simulations. The experimental data used in this work was obtained from Ling et al. (1996) and de Andrade Lima and Hodouin (2005). Two different rate equations, where the unreacted amount of gold is considered in one equation, are investigated. In this work, it is presented that the reaction at the surface is the rate controlling step since there is no internal diffusion limitation. The model considering the effect of non-reacting gold shows that the reaction orders are consistent with the experimental observations reported by Ling et al. (1996) and de Andrade Lima and Hodouin (2005). However, it should be noted that the model obtained in this work is based on assumptions of no side reactions, no solid-liquid mass transfer resistances and no effect from temperature.

Bayesian Estimation of Noise

In mathematical modeling the estimation of the model parameters is one of the most common problems. The goal is to seek parameters that fit to the measurements as well as possible. There is always error in the measurements which implies uncertainty to the model estimates. In Bayesian statistics all the unknown quantities are presented as probability distributions. If there is knowledge about parameters beforehand, it can be formulated as a prior distribution. The Bays’ rule combines the prior and the measurements to posterior distribution. Mathematical models are typically nonlinear, to produce statistics for them requires efficient sampling algorithms. In this thesis both Metropolis-Hastings (MH), Adaptive Metropolis (AM) algorithms and Gibbs sampling are introduced. In the thesis different ways to present prior distributions are introduced. The main issue is in the measurement error estimation and how to obtain prior knowledge for variance or covariance. Variance and covariance sampling is combined with the algorithms above. The examples of the hyperprior models are applied to estimation of model parameters and error in an outlier case.

Modeling reaction kinetics and mass transfer in ozonation in water solutions

This dissertation is based on four articles dealing with modeling of ozonation. The literature part of this considers some models for hydrodynamics in bubble column simulation. A literature review of methods for obtaining mass transfer coefficients is presented. The methods presented to obtain mass transfer are general models and can be applied to any gas-liquid system. Ozonation reaction models and methods for obtaining stoichiometric coefficients and reaction rate coefficients for ozonation reactions are discussed in the final section of the literature part. In the first article, ozone gas-liquid mass transfer into water in a bubble column was investigated for different pH values. A more general method for estimation of mass transfer and Henry’s coefficient was developed from the Beltrán method. The ozone volumetric mass transfer coefficient and the Henry’s coefficient were determined simultaneously by parameter estimation using a nonlinear optimization method. A minor dependence of the Henry’s law constant on pH was detected at the pH range 4 - 9. In the second article, a new method using the axial dispersion model for estimation of ozone self-decomposition kinetics in a semi-batch bubble column reactor was developed. The reaction rate coefficients for literature equations of ozone decomposition and the gas phase dispersion coefficient were estimated and compared with the literature data. The reaction order in the pH range 7-10 with respect to ozone 1.12 and 0.51 the hydroxyl ion were obtained, which is in good agreement with literature. The model parameters were determined by parameter estimation using a nonlinear optimization method. Sensitivity analysis was conducted using object function method to obtain information about the reliability and identifiability of the estimated parameters. In the third article, the reaction rate coefficients and the stoichiometric coefficients in the reaction of ozone with the model component p-nitrophenol were estimated at low pH of water using nonlinear optimization. A novel method for estimation of multireaction model parameters in ozonation was developed. In this method the concentration of unknown intermediate compounds is presented as a residual COD (chemical oxygen demand) calculated from the measured COD and the theoretical COD for the known species. The decomposition rate of p-nitrophenol on the pathway producing hydroquinone was found to be about two times faster than the p-nitrophenol decomposition rate on the pathway producing 4- nitrocatechol. In the fourth article, the reaction kinetics of p-nitrophenol ozonation was studied in a bubble column at pH 2. Using the new reaction kinetic model presented in the previous article, the reaction kinetic parameters, rate coefficients, and stoichiometric coefficients as well as the mass transfer coefficient were estimated with nonlinear estimation. The decomposition rate of pnitrophenol was found to be equal both on the pathway producing hydroquinone and on the path way producing 4-nitrocathecol. Comparison of the rate coefficients with the case at initial pH 5 indicates that the p-nitrophenol degradation producing 4- nitrocathecol is more selective towards molecular ozone than the reaction producing hydroquinone. The identifiability and reliability of the estimated parameters were analyzed with the Marcov chain Monte Carlo (MCMC) method. @All rights reserved. No part of the publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of the author.

Statistical Optimum Design of Heat Exchangers

The optimal design of a heat exchanger system is based on given model parameters together with given standard ranges for machine design variables. The goals set for minimizing the Life Cycle Cost (LCC) function which represents the price of the saved energy, for maximizing the momentary heat recovery output with given constraints satisfied and taking into account the uncertainty in the models were successfully done. Nondominated Sorting Genetic Algorithm II (NSGA-II) for the design optimization of a system is presented and implemented inMatlab environment. Markov ChainMonte Carlo (MCMC) methods are also used to take into account the uncertainty in themodels. Results show that the price of saved energy can be optimized. A wet heat exchanger is found to be more efficient and beneficial than a dry heat exchanger even though its construction is expensive (160 EUR/m2) compared to the construction of a dry heat exchanger (50 EUR/m2). It has been found that the longer lifetime weights higher CAPEX and lower OPEX and vice versa, and the effect of the uncertainty in the models has been identified in a simplified case of minimizing the area of a dry heat exchanger.

Cinética e caracterização de ramnolipídeos produzidos por Pseudomonas aeruginosa MSIC02 utilizando glicerol como fonte de carbono

Glycerol, a co-product of biodiesel production, was used as a carbon source for the kinetics studies and production of biosurfactants by P. aeruginosa MSIC02. The highest fermentative parameters (Y PX = 3.04 g g-1; Y PS = 0.189 g g-1, P B = 31.94 mg L-1 h-1 and P X = 10.5 mg L-1 h-1) were obtained at concentrations of 0.4% (w/v) NaNO3 and 2% (w/v) glycerol. The rhamnolipid exhibited 80% of emulsification on kerosene, surface tension of 32.5 mN m-1, CMC = 28.2 mg L-1, C20 (concentration of surfactant in the bulk phase that produces a reduction of 20 dyn/cm in the surface tension of the solvent) = 0.99 mg L-1, Γm (surface concentration excess) = 2.4 x 10-26 mol Å-2 and S (surface area) = 70.4 Ų molecule-1 with solutions containing 10% NaCl. A mathematical model based on logistic equation was considered to representing the process. Model parameters were estimated by non-linear regression method. This approach was able to give a good description of the process.

Metaanin muodostumisen mallintaminen kaatopaikalla

Methane-rich landfill gas is generated when biodegradable organic wastes disposed of in landfills decompose under anaerobic conditions. Methane is a significant greenhouse gas, and landfills are its major source in Finland. Methane production in landfill depends on many factors such as the composition of waste and landfill conditions, and it can vary a lot temporally and spatially. Methane generation from waste can be estimated with various models. In this thesis three spreadsheet applications, a reaction equation and a triangular model for estimating the gas generation were introduced. The spreadsheet models introduced are IPCC Waste Model (2006), Metaanilaskentamalli by Jouko Petäjä of Finnish Environment Institute and LandGEM (3.02) of U.S. Environmental Protection Agency. All these are based on the first order decay (FOD) method. Gas recovery methods and gas emission measurements were also examined. Vertical wells and horizontal trenches are the most commonly used gas collection systems. Emission measurements chamber method, tracer method, soil core and isotope measurements, micrometeorological mass-balance and eddy covariance methods and gas measuring FID-technology were discussed. Methane production at Ämmässuo landfill of HSY Helsinki Region Environmental Services Authority was estimated with methane generation models and the results were compared with the volumes of collected gas. All spreadsheet models underestimated the methane generation at some point. LandGEM with default parameters and Metaanilaskentamalli with modified parameters corresponded best with the gas recovery numbers. Reason for the differences between evaluated and collected volumes could be e.g. that the parameter values of the degradable organic carbon (DOC) and the fraction of decomposable degradable organic carbon (DOCf) do not represent the real values well enough. Notable uncertainty is associated with the modelling results and model parameters. However, no simple explanation for the discovered differences can be given within this thesis.

Kinetic modeling of mechanisms of industrially important organic reactions in gas and liquid phase

This dissertation is based on 5 articles which deal with reaction mechanisms of the following selected industrially important organic reactions: 1. dehydrocyclization of n-butylbenzene to produce naphthalene 2. dehydrocyclization of 1-(p-tolyl)-2-methylbutane (MB) to produce 2,6-dimethylnaphthalene 3. esterification of neopentyl glycol (NPG) with different carboxylic acids to produce monoesters 4. skeletal isomerization of 1-pentene to produce 2-methyl-1-butene and 2-methyl-2-butene The results of initial- and integral-rate experiments of n-butylbenzene dehydrocyclization over selfmade chromia/alumina catalyst were applied when investigating reaction 2. Reaction 2 was performed using commercial chromia/alumina of different acidity, platina on silica and vanadium/calcium/alumina as catalysts. On all catalysts used for the dehydrocyclization, major reactions were fragmentation of MB and 1-(p-tolyl)-2-methylbutenes (MBes), dehydrogenation of MB, double bond transfer, hydrogenation and 1,6-cyclization of MBes. Minor reactions were 1,5-cyclization of MBes and methyl group fragmentation of 1,6- cyclization products. Esterification reactions of NPG were performed using three different carboxylic acids: propionic, isobutyric and 2-ethylhexanoic acid. Commercial heterogeneous gellular (Dowex 50WX2), macroreticular (Amberlyst 15) type resins and homogeneous para-toluene sulfonic acid were used as catalysts. At first NPG reacted with carboxylic acids to form corresponding monoester and water. Then monoester esterified with carboxylic acid to form corresponding diester. In disproportionation reaction two monoester molecules formed NPG and corresponding diester. All these three reactions can attain equilibrium. Concerning esterification, water was removed from the reactor in order to prevent backward reaction. Skeletal isomerization experiments of 1-pentene were performed over HZSM-22 catalyst. Isomerization reactions of three different kind were detected: double bond, cis-trans and skeletal isomerization. Minor side reaction were dimerization and fragmentation. Monomolecular and bimolecular reaction mechanisms for skeletal isomerization explained experimental results almost equally well. Pseudohomogeneous kinetic parameters of reactions 1 and 2 were estimated by usual least squares fitting. Concerning reactions 3 and 4 kinetic parameters were estimated by the leastsquares method, but also the possible cross-correlation and identifiability of parameters were determined using Markov chain Monte Carlo (MCMC) method. Finally using MCMC method, the estimation of model parameters and predictions were performed according to the Bayesian paradigm. According to the fitting results suggested reaction mechanisms explained experimental results rather well. When the possible cross-correlation and identifiability of parameters (Reactions 3 and 4) were determined using MCMC method, the parameters identified well, and no pathological cross-correlation could be seen between any parameter pair.

Centralized Motion Control of a Linear Tooth Belt Drive: Analysis of the Performance and Limitations

A centralized robust position control for an electrical driven tooth belt drive is designed in this doctoral thesis. Both a cascaded control structure and a PID based position controller are discussed. The performance and the limitations of the system are analyzed and design principles for the mechanical structure and the control design are given. These design principles are also suitable for most of the motion control applications, where mechanical resonance frequencies and control loop delays are present. One of the major challenges in the design of a controller for machinery applications is that the values of the parameters in the system model (parameter uncertainty) or the system model it self (non-parametric uncertainty) are seldom known accurately in advance. In this thesis a systematic analysis of the parameter uncertainty of the linear tooth beltdrive model is presented and the effect of the variation of a single parameter on the performance of the total system is shown. The total variation of the model parameters is taken into account in the control design phase using a Quantitative Feedback Theory (QFT). The thesis also introduces a new method to analyze reference feedforward controllers applying the QFT. The performance of the designed controllers is verified by experimentalmeasurements. The measurements confirm the control design principles that are given in this thesis.

Modeling of electrolyte sorption – from phase equilibria to dynamic separation systems

In this thesis, general approach is devised to model electrolyte sorption from aqueous solutions on solid materials. Electrolyte sorption is often considered as unwanted phenomenon in ion exchange and its potential as an independent separation method has not been fully explored. The solid sorbents studied here are porous and non-porous organic or inorganic materials with or without specific functional groups attached on the solid matrix. Accordingly, the sorption mechanisms include physical adsorption, chemisorption on the functional groups and partition restricted by electrostatic or steric factors. The model is tested in four Cases Studies dealing with chelating adsorption of transition metal mixtures, physical adsorption of metal and metalloid complexes from chloride solutions, size exclusion of electrolytes in nano-porous materials and electrolyte exclusion of electrolyte/non-electrolyte mixtures. The model parameters are estimated using experimental data from equilibrium and batch kinetic measurements, and they are used to simulate actual single-column fixed-bed separations. Phase equilibrium between the solution and solid phases is described using thermodynamic Gibbs-Donnan model and various adsorption models depending on the properties of the sorbent. The 3-dimensional thermodynamic approach is used for volume sorption in gel-type ion exchangers and in nano-porous adsorbents, and satisfactory correlation is obtained provided that both mixing and exclusion effects are adequately taken into account. 2-Dimensional surface adsorption models are successfully applied to physical adsorption of complex species and to chelating adsorption of transition metal salts. In the latter case, comparison is also made with complex formation models. Results of the mass transport studies show that uptake rates even in a competitive high-affinity system can be described by constant diffusion coefficients, when the adsorbent structure and the phase equilibrium conditions are adequately included in the model. Furthermore, a simplified solution based on the linear driving force approximation and the shrinking-core model is developed for very non-linear adsorption systems. In each Case Study, the actual separation is carried out batch-wise in fixed-beds and the experimental data are simulated/correlated using the parameters derived from equilibrium and kinetic data. Good agreement between the calculated and experimental break-through curves is usually obtained indicating that the proposed approach is useful in systems, which at first sight are very different. For example, the important improvement in copper separation from concentrated zinc sulfate solution at elevated temperatures can be correctly predicted by the model. In some cases, however, re-adjustment of model parameters is needed due to e.g. high solution viscosity.

Spatio-temporal modelling of the duration of the cotton cycle in the State Of Goiás, Brazil

The objective of this work was to develop and validate a mathematical model to estimate the duration of cotton (Gossypium hirsutum L. r. latifolium hutch) cycle in the State of Goiás, Brazil, by applying the method of growing degree-days (GD), and considering, simultaneously, its time-space variation. The model was developed as a linear combination of elevation, latitude, longitude, and Fourier series of time variation. The model parameters were adjusted by using multiple-linear regression to the observed GD accumulated with air temperature in the range of 15°C to 40°C. The minimum and maximum temperature records used to calculate the GD were obtained from 21 meteorological stations, considering data varying from 8 to 20 years of observation. The coefficient of determination, resulting from the comparison between the estimated and calculated GD along the year was 0.84. Model validation was done by comparing estimated and measured crop cycle in the period from cotton germination to the stage when 90 percent of bolls were opened in commercial crop fields. Comparative results showed that the model performed very well, as indicated by the Pearson correlation coefficient of 0.90 and Willmott agreement index of 0.94, resulting in a performance index of 0.85.

Multidata inverse problems and Bayesian solution methods in astronomy

Statistical analyses of measurements that can be described by statistical models are of essence in astronomy and in scientific inquiry in general. The sensitivity of such analyses, modelling approaches, and the consequent predictions, is sometimes highly dependent on the exact techniques applied, and improvements therein can result in significantly better understanding of the observed system of interest. Particularly, optimising the sensitivity of statistical techniques in detecting the faint signatures of low-mass planets orbiting the nearby stars is, together with improvements in instrumentation, essential in estimating the properties of the population of such planets, and in the race to detect Earth-analogs, i.e. planets that could support liquid water and, perhaps, life on their surfaces. We review the developments in Bayesian statistical techniques applicable to detections planets orbiting nearby stars and astronomical data analysis problems in general. We also discuss these techniques and demonstrate their usefulness by using various examples and detailed descriptions of the respective mathematics involved. We demonstrate the practical aspects of Bayesian statistical techniques by describing several algorithms and numerical techniques, as well as theoretical constructions, in the estimation of model parameters and in hypothesis testing. We also apply these algorithms to Doppler measurements of nearby stars to show how they can be used in practice to obtain as much information from the noisy data as possible. Bayesian statistical techniques are powerful tools in analysing and interpreting noisy data and should be preferred in practice whenever computational limitations are not too restrictive.