This study suggests a two-step approach to identifying and interpreting regional convergence clubs in Europe. The first step involves identifying the number and composition of clubs using a space-time panel data model for annual income growth rates in conjunction with Bayesian model comparison methods. A second step uses a Bayesian space-time panel data model to assess how changes in the initial endowments of variables (that explain growth) impact regional income levels over time. These dynamic trajectories of changes in regional income levels over time allow us to draw inferences regarding the timing and magnitude of regional income responses to changes in the initial conditions for the clubs that have been identified in the first step. This is in contrast to conventional practice that involves setting the number of clubs ex ante, selecting the composition of the potential convergence clubs according to some a priori criterion (such as initial per capita income thresholds for example), and using cross-sectional growth regressions for estimation and interpretation purposes. (authors' abstract)

Model-driven engineering promotes the migration from code-centric to model-based software development. Systems consist of model collections integrating different concerns and perspectives, while semi-automated model transformations generate executable code combining the information from these. Increasing the abstraction level to models required appropriate management technologies supporting the various software development activities. Among these, model comparison represents one of the most challenging tasks and plays an essential role in various modelling activities. Its hardness led researchers to propose a multitude of approaches adopting different approximation strategies and exploiting specific knowledge of the involved models. However, almost no support is provided for their evaluation against specific scenarios and modelling practices. This thesis presents Benji, a framework for the automated generation of model comparison benchmarks. Given a set of differences and an initial model, users generate models resulting from the application of the first on the latter. Differences consist of preconditions, actions and postconditions expressed using a dedicated specification language. The generator converts benchmark specifications to design-space exploration problems and produces the final solutions along with a model-based description of their differences with respect to the initial model. A set of representative use cases is used to evaluate the framework against its design principles, which resemble the essential properties expected from model comparison benchmark generators.

Atmospheric ammonia contributes to a number of environmental problems, but many questions regarding the behaviour of ammonia in the atmosphere remain. Field studies were performed to investigate the gas-particle partitioning of ammonia, the surface-atmosphere exchange, and to compare measurements with an online chemical transport model and offline thermodynamic models. A state-of-the-art instrument, Quantum Cascade Tunable Infrared Laser Differential Absorption Spectrometer (QC-TILDAS), with a novel sampling technique was used to measure ammonia. The detection limit of the instrument was found to be 690 ppt at 1 Hz and 42 ppt when averaged to 5 minutes. The uncertainty in the measurement is 10 % based on calibration from a permeation tube source. Laboratory and field tests show the ammonia time response to be slower at lower mixing ratios, and when the ambient relative humidity is high. Observations from the first field campaign discussed, the Border Air Quality and Meteorology Study (BAQS-Met), were compared to a chemical transport model AURAMS (A Unified Regional Air quality Modeling System). The model was often biased low in ammonia and ammonium and predicted an incorrect diurnal profile. Observations suggest a coupling between gas-particle and surface-atmosphere equilibria whereby a large atmospheric condensation sink induces emission of ammonia from the surface. A simple approach at representing the ammonia bi-direction flux more closely matched the observations, indicating that a fully coupled bi-directional flux parameterization in chemical transport models is necessary to accurately predict atmospheric ammonia. A suite of instrumentation during the CalNex 2010 field campaign allowed for in-depth analysis of gas-particle partitioning and estimation of aerosol pH. Observations were compared to predictions from the thermodynamic equilibrium models ISORROPIA and E-AIM. Deviations form equilibrium were found during periods of high levels of aerosol nitrate and positive net charge. The gas-particle partitioning was found to be very sensitive to aerosol pH.

Atmospheric ammonia contributes to a number of environmental problems, but many questions regarding the behaviour of ammonia in the atmosphere remain. Field studies were performed to investigate the gas-particle partitioning of ammonia, the surface-atmosphere exchange, and to compare measurements with an online chemical transport model and offline thermodynamic models. A state-of-the-art instrument, Quantum Cascade Tunable Infrared Laser Differential Absorption Spectrometer (QC-TILDAS), with a novel sampling technique was used to measure ammonia. The detection limit of the instrument was found to be 690 ppt at 1 Hz and 42 ppt when averaged to 5 minutes. The uncertainty in the measurement is 10 % based on calibration from a permeation tube source. Laboratory and field tests show the ammonia time response to be slower at lower mixing ratios, and when the ambient relative humidity is high. Observations from the first field campaign discussed, the Border Air Quality and Meteorology Study (BAQS-Met), were compared to a chemical transport model AURAMS (A Unified Regional Air quality Modeling System). The model was often biased low in ammonia and ammonium and predicted an incorrect diurnal profile. Observations suggest a coupling between gas-particle and surface-atmosphere equilibria whereby a large atmospheric condensation sink induces emission of ammonia from the surface. A simple approach at representing the ammonia bi-direction flux more closely matched the observations, indicating that a fully coupled bi-directional flux parameterization in chemical transport models is necessary to accurately predict atmospheric ammonia. A suite of instrumentation during the CalNex 2010 field campaign allowed for in-depth analysis of gas-particle partitioning and estimation of aerosol pH. Observations were compared to predictions from the thermodynamic equilibrium models ISORROPIA and E-AIM. Deviations form equilibrium were found during periods of high levels of aerosol nitrate and positive net charge. The gas-particle partitioning was found to be very sensitive to aerosol pH.

Horizontal Directional Drilling (HDD) is used around the world when traditional open cut methods are not practical or impossible for installing pipelines. Maxi-sized drill rigs are the largest and most powerful directional drills and are more common in the field than ever before with over 5,000 rigs in operation world wide. The complexity of installations and the design associated with them continues to increase. This research has two main objectives. 1. Develop a real time data acquisition system for monitoring pullback forces on the product pipe; and, 2. Compare data gathered using maxi-sized drill rigs with current modelling methods using BoreAid. The first portion of the research, as listed above, required attaching multiple pressure transducers to the drilling display panel in an American Auger DD-1100 drill rig and recording, in real time, the carriage, rotation, and mud pressure as seen by the operator. This research also describes the various challenges and issues associated with developing real time in-the-bore data acquisition processes. Finally, future recommendations for further development of the in-the-bore data acquisition are discussed. The second portion of this research describes how the gathered data was processed into a workable data set. The field data was then compared to theoretical models by using the drill assistant tool BoreAid. The results of this comparison show that these models are appropriate for all size drill rigs, although some limitations are present.

Horizontal Directional Drilling (HDD) is used around the world when traditional open cut methods are not practical or impossible for installing pipelines. Maxi-sized drill rigs are the largest and most powerful directional drills and are more common in the field than ever before with over 5,000 rigs in operation world wide. The complexity of installations and the design associated with them continues to increase. This research has two main objectives. 1. Develop a real time data acquisition system for monitoring pullback forces on the product pipe; and, 2. Compare data gathered using maxi-sized drill rigs with current modelling methods using BoreAid. The first portion of the research, as listed above, required attaching multiple pressure transducers to the drilling display panel in an American Auger DD-1100 drill rig and recording, in real time, the carriage, rotation, and mud pressure as seen by the operator. This research also describes the various challenges and issues associated with developing real time in-the-bore data acquisition processes. Finally, future recommendations for further development of the in-the-bore data acquisition are discussed. The second portion of this research describes how the gathered data was processed into a workable data set. The field data was then compared to theoretical models by using the drill assistant tool BoreAid. The results of this comparison show that these models are appropriate for all size drill rigs, although some limitations are present.

By visual attention process biological and machine vision systems are able to select the most relevant regions from a scene. The relevancy process is achieved either by top-down factors, driven by task, or bottom-up factors, the visual saliency, which distinguish a scene region that are different from its surrounding. During the past 20 years numerous research efforts have aimed to model bottom-up visual saliency with many successful applications in computer vision and robotics.In this thesis we have performed a comparison between a state-of-the-art saliency model and subjective test (human eye tracking) using different evaluation methods over three generated dataset of synthetic patterns and natural images. Our results showed that the objective model is partially valid and highly center-biased.By using empirical data obtained from subjective experiments we propose a special function, the Probability of Characteristic Radially Dependency Function, to model the lateral distribution of visual attention process.

In this reviewwe discuss methodology to ascertain the amount of information in given data sets with respect to determination of model parameters with desired levels of uncertainty.We do this in the context of least squares (ordinary,weighted, iterative reweightedweighted or "generalized", etc.) based inverse problem formulations. The ideas are illustrated with several examples of interest in the biological and environmental sciences.

We consider two distinct techniques for estimating random parameters in random differential equation (RDE) models. In one approach, the solution to a RDE is represented by a collection of solution trajectories in the form of sample deterministic equations. In a second approach we employ pointwise equivalent stochastic differential equation (SDE) representations for certain RDEs. Each of the approaches is tested using deterministic model comparison techniques for a logistic growth model which is viewed as a special case of a more general Bernoulli growth model. We demonstrate efficacy of the preferred method with experimental data using algae growth model comparisons.

abstract: The main objective of mathematical modeling is to connect mathematics with other scientific fields. Developing predictable models help to understand the behavior of biological systems. By testing models, one can relate mathematics and real-world experiments. To validate predictions numerically, one has to compare them with experimental data sets. Mathematical modeling can be split into two groups: microscopic and macroscopic models. Microscopic models described the motion of so-called agents (e.g. cells, ants) that interact with their surrounding neighbors. The interactions among these agents form at a large scale some special structures such as flocking and swarming. One of the key questions is to relate the particular interactions among agents with the overall emerging structures. Macroscopic models are precisely designed to describe the evolution of such large structures. They are usually given as partial differential equations describing the time evolution of a density distribution (instead of tracking each individual agent). For instance, reaction-diffusion equations are used to model glioma cells and are being used to predict tumor growth. This dissertation aims at developing such a framework to better understand the complex behavior of foraging ants and glioma cells. / Dissertation/Thesis / Doctoral Dissertation Applied Mathematics 2019