Horizontal and Vertical Interdependence in the Multinational Subsidiary Networks. ACES Working Paper No. 7, July 2008

Existing theoretical studies have predicted that a multinational firm's location choices are interdependent across countries. Little has, however, been done to test the hypothesis at individual subsidiary level. This paper uses a detailed French multinational subsidiary dataset and estimates how a firm's decision to invest in a foreign country is not only conditional on the characteristics of that country but also the firm's existing subsidiary network Using a structural spatial econometric model, the paper finds evidence of both horizontal and vertical interdependence in multinationals' location decisions. The results indicate that while multinational firms have little incentive to duplicate their production in countries with low bilateral trade costs, they are motivated to build a vertical subsidiary network in these countries ~ especially when the countries have complementary comparative advantages.

Tractable molecular theory of transport of Lennard-Jones fluids in nanopores

We present here a tractable theory of transport of simple fluids in cylindrical nanopores, which is applicable over a wide range of densities and pore sizes. In the Henry law low-density region the theory considers the trajectories of molecules oscillating between diffuse wall collisions, while at higher densities beyond this region the contribution from viscous flow becomes significant and is included through our recent approach utilizing a local average density model. The model is validated by means of equilibrium as well nonequilibrium molecular dynamics simulations of supercritical methane transport in cylindrical silica pores over a wide range of temperature, density, and pore size. The model for the Henry law region is exact and found to yield an excellent match with simulations at all conditions, including the single-file region of very small pore size where it is shown to provide the density-independent collective transport coefficient. It is also shown that in the absence of dispersive interactions the model reduces to the classical Knudsen result, but in the presence of such interactions the latter model drastically overpredicts the transport coefficient. For larger micropores beyond the single-file region the transport coefficient is reduced at high density because of intermolecular interactions and hindrance to particle crossings leading to a large decrease in surface slip that is not well represented by the model. However, for mesopores the transport coefficient increases monotonically with density, over the range studied, and is very well predicted by the theory, though at very high density the contribution from surface slip is slightly overpredicted. It is also seen that the concept of activated diffusion, commonly associated with diffusion in small pores, is fundamentally invalid for smooth pores, and the apparent activation energy is not simply related to the minimum pore potential or the adsorption energy as generally assumed. (C) 2004 American Institute of Physics.

Flow regime, hydrodynamics, floc size distribution and sludge properties in activated sludge bubble column, air-lift and aerated stirred reactors

This paper presents a comparative study how reactor configuration, sludge loading and air flowrate affect flow regimes, hydrodynamics, floc size distribution and sludge solids-liquid separation properties. Three reactor configurations were studied in bench scale activated sludge bubble column reactor (BCR), air-lift reactor (ALR) and aerated stirred reactor (ASR). The ASR demonstrated the highest capacity of gas holdup and resistance, and homogeneity in flow regimes and shearing forces, resulting in producing large numbers of small and compact floes. The fluid dynamics in the ALR created regularly directed recirculation forces to enhance the gas holdup and sludge flocculation. The BCR distributed a high turbulent flow regime and non-homogeneity in gas holdup and mixing, and generated large numbers of larger and looser floes. The sludge size distributions, compressibility and settleability were significantly influenced by the reactor configurations associated with the flow regimes and hydrodynamics.

The role of migration in urban transition : studies of the relationship between migration and modernisation for Brisbane and Stockholm

Wilbur Zelinsky formulated a Hypothesis of Mobility Transition in 1971,in which he tried to relate all aspects of mobility to the Demographic Transition and modernisation. This dissertation applies the theoretical framework, proposed by Zelinsky and extended to encompass a family of transitions, to understand migration patterns of city regions. The two city regions, Brisbane and Stockholm, are selected as case studies, representing important city regions of similar size, but drawn from contrasting historical settings. A comparison of the case studies with the theoretical framework aims to determine how the relative contributions of net migration, the source areas of migrants, and the migration intensity change with modernisation. In addition, the research also aims to identify aspects of modernisation affecting migration. These aspects of migration are analysed with a "historical approach" and a "multivariate approach". An extensive investigation into the city regions' historical background provides the source, from which evidence for a relationship between migration and modernisation is extracted. With this historical approach, similarities and differences in migration patterns are identified. The other research approach analyse multivariate data, from the last two decades, on migration flows and modernisation. Correlations between migration and key aspects of modernisation are tested with multivariate regression, based on an alternative version of a spatial interaction model. The project demonstrates that the changing functions of cities and the structural modernisation are influential on migration. Similar patterns are found, regarding the relative contributions of net migration and natural increase to population growth. The research finds links between these changes in the relative contribution of net migration and demographic modernisation. The findings on variations in urban and rural source areas of migrants to city regions do not contradict the expected pattern, but data limitations prevent definite conclusion to be drawn. The assessment of variations in migration intensity resulted in the expected pattern not being supported. Based on Swedish data, the hypothesised increase in migration intensity is rejected. Interactional migration data also show patterns different from those derived from the theoretical framework. The findings, from both research approaches, suggested that structural modernisation affected migration flows more than demographic modernisation. The findings lead to a formulation of hypothesised patterns for migration to city regions. The study provides an important research contribution by applying the two research approaches to city regions. It also combines the study of internal and international migration to address the research objectives within a framework of transitional change.

GTM: the generative topographic mapping

This thesis describes the Generative Topographic Mapping (GTM) --- a non-linear latent variable model, intended for modelling continuous, intrinsically low-dimensional probability distributions, embedded in high-dimensional spaces. It can be seen as a non-linear form of principal component analysis or factor analysis. It also provides a principled alternative to the self-organizing map --- a widely established neural network model for unsupervised learning --- resolving many of its associated theoretical problems. An important, potential application of the GTM is visualization of high-dimensional data. Since the GTM is non-linear, the relationship between data and its visual representation may be far from trivial, but a better understanding of this relationship can be gained by computing the so-called magnification factor. In essence, the magnification factor relates the distances between data points, as they appear when visualized, to the actual distances between those data points. There are two principal limitations of the basic GTM model. The computational effort required will grow exponentially with the intrinsic dimensionality of the density model. However, if the intended application is visualization, this will typically not be a problem. The other limitation is the inherent structure of the GTM, which makes it most suitable for modelling moderately curved probability distributions of approximately rectangular shape. When the target distribution is very different to that, theaim of maintaining an `interpretable' structure, suitable for visualizing data, may come in conflict with the aim of providing a good density model. The fact that the GTM is a probabilistic model means that results from probability theory and statistics can be used to address problems such as model complexity. Furthermore, this framework provides solid ground for extending the GTM to wider contexts than that of this thesis.

Data assimilation for precipitation nowcasting using Bayesian inference

This work introduces a new variational Bayes data assimilation method for the stochastic estimation of precipitation dynamics using radar observations for short term probabilistic forecasting (nowcasting). A previously developed spatial rainfall model based on the decomposition of the observed precipitation field using a basis function expansion captures the precipitation intensity from radar images as a set of ‘rain cells’. The prior distributions for the basis function parameters are carefully chosen to have a conjugate structure for the precipitation field model to allow a novel variational Bayes method to be applied to estimate the posterior distributions in closed form, based on solving an optimisation problem, in a spirit similar to 3D VAR analysis, but seeking approximations to the posterior distribution rather than simply the most probable state. A hierarchical Kalman filter is used to estimate the advection field based on the assimilated precipitation fields at two times. The model is applied to tracking precipitation dynamics in a realistic setting, using UK Met Office radar data from both a summer convective event and a winter frontal event. The performance of the model is assessed both traditionally and using probabilistic measures of fit based on ROC curves. The model is shown to provide very good assimilation characteristics, and promising forecast skill. Improvements to the forecasting scheme are discussed

Outlier detection in scatterometer data:Neural network approaches

Satellite-borne scatterometers are used to measure backscattered micro-wave radiation from the ocean surface. This data may be used to infer surface wind vectors where no direct measurements exist. Inherent in this data are outliers owing to aberrations on the water surface and measurement errors within the equipment. We present two techniques for identifying outliers using neural networks; the outliers may then be removed to improve models derived from the data. Firstly the generative topographic mapping (GTM) is used to create a probability density model; data with low probability under the model may be classed as outliers. In the second part of the paper, a sensor model with input-dependent noise is used and outliers are identified based on their probability under this model. GTM was successfully modified to incorporate prior knowledge of the shape of the observation manifold; however, GTM could not learn the double skinned nature of the observation manifold. To learn this double skinned manifold necessitated the use of a sensor model which imposes strong constraints on the mapping. The results using GTM with a fixed noise level suggested the noise level may vary as a function of wind speed. This was confirmed by experiments using a sensor model with input-dependent noise, where the variation in noise is most sensitive to the wind speed input. Both models successfully identified gross outliers with the largest differences between models occurring at low wind speeds. © 2003 Elsevier Science Ltd. All rights reserved.

Precise orbit determination and analysis from satellite altimetry and laser ranging

For optimum utilization of satellite-borne instrumentation, it is necessary to know precisely the orbital position of the spacecraft. The aim of this thesis is therefore two-fold - firstly to derive precise orbits with particular emphasis placed on the altimetric satellite SEASAT and secondly, to utilize the precise orbits, to improve upon atmospheric density determinations for satellite drag modelling purposes. Part one of the thesis, on precise orbit determinations, is particularly concerned with the tracking data - satellite laser ranging, altimetry and crossover height differences - and how this data can be used to analyse errors in the orbit, the geoid and sea-surface topography. The outcome of this analysis is the determination of a low degree and order model for sea surface topography. Part two, on the other hand, mainly concentrates on using the laser data to analyse and improve upon current atmospheric density models. In particular, the modelling of density changes associated with geomagnetic disturbances comes under scrutiny in this section. By introducing persistence modelling of a geomagnetic event and solving for certain geomagnetic parameters, a new density model is derived which performs significantly better than the state-of-the-art models over periods of severe geomagnetic storms at SEASAT heights. This is independently verified by application of the derived model to STARLETTE orbit determinations.

Orientation masking and cross-orientation suppression (XOS):implications for estimates of channel bandwidth

Most contemporary models of spatial vision include a cross-oriented route to suppression (masking from a broadly tuned inhibitory pool), which is most potent at low spatial and high temporal frequencies (T. S. Meese & D. J. Holmes, 2007). The influence of this pathway can elevate orientation-masking functions without exciting the target mechanism, and because early psychophysical estimates of filter bandwidth did not accommodate this, it is likely that they have been overestimated for this corner of stimulus space. Here we show that a transient 40% contrast mask causes substantial binocular threshold elevation for a transient vertical target, and this declines from a mask orientation of 0° to about 40° (indicating tuning), and then more gently to 90°, where it remains at a factor of ∼4. We also confirm that cross-orientation masking is diminished or abolished at high spatial frequencies and for sustained temporal modulation. We fitted a simple model of pedestal masking and cross-orientation suppression (XOS) to our data and those of G. C. Phillips and H. R. Wilson (1984) and found the dependency of orientation bandwidth on spatial frequency to be much less than previously supposed. An extension of our linear spatial pooling model of contrast gain control and dilution masking (T. S. Meese & R. J. Summers, 2007) is also shown to be consistent with our results using filter bandwidths of ±20°. Both models include tightly and broadly tuned components of divisive suppression. More generally, because XOS and/or dilution masking can affect the shape of orientation-masking curves, we caution that variations in bandwidth estimates might reflect variations in processes that have nothing to do with filter bandwidth.

Inference of helicopter airframe condition

The goal of this paper is to model normal airframe conditions for helicopters in order to detect changes. This is done by inferring the flying state using a selection of sensors and frequency bands that are best for discriminating between different states. We used non-linear state-space models (NLSSM) for modelling flight conditions based on short-time frequency analysis of the vibration data and embedded the models in a switching framework to detect transitions between states. We then created a density model (using a Gaussian mixture model) for the NLSSM innovations: this provides a model for normal operation. To validate our approach, we used data with added synthetic abnormalities which was detected as low-probability periods. The model of normality gave good indications of faults during the flight, in the form of low probabilities under the model, with high accuracy (>92 %). © 2013 IEEE.

Binocular summation, binocular fusion and the transition to diplopia

The visual system combines spatial signals from the two eyes to achieve single vision. But if binocular disparity is too large, this perceptual fusion gives way to diplopia. We studied and modelled the processes underlying fusion and the transition to diplopia. The likely basis for fusion is linear summation of inputs onto binocular cortical cells. Previous studies of perceived position, contrast matching and contrast discrimination imply the computation of a dynamicallyweighted sum, where the weights vary with relative contrast. For gratings, perceived contrast was almost constant across all disparities, and this can be modelled by allowing the ocular weights to increase with disparity (Zhou, Georgeson & Hess, 2014). However, when a single Gaussian-blurred edge was shown to each eye perceived blur was invariant with disparity (Georgeson & Wallis, ECVP 2012) – not consistent with linear summation (which predicts that perceived blur increases with disparity). This blur constancy is consistent with a multiplicative form of combination (the contrast-weighted geometric mean) but that is hard to reconcile with the evidence favouring linear combination. We describe a 2-stage spatial filtering model with linear binocular combination and suggest that nonlinear output transduction (eg. ‘half-squaring’) at each stage may account for the blur constancy.

Essays on city size distribution and real estate bubbles

This is a dissertation about urban systems; within this broad subject I tackle three issues, one that focuses on an observed inter-city relationship and two that focus on an intra-city phenomenon. In Chapter II I adapt a model of random emergence of economic opportunities from the firm growth literature to the urban dynamics situation and present several predictions for urban system dynamics. One of these predictions is that the older the city the larger and more diversified it is going to be on average, which I proceed to verify empirically using two distinct datasets. In Chapter III I analyze the Residential Real Estate Bubble that took place in Miami-Dade County from 1999 to 2006. I adopt a Spatial-Economic model developed for the Paris Bubble episode of 1984–1993 and formulate an innovative test of the results in terms of speculative intensity on the basis of proxies of investor activity available in my dataset. My results support the idea that the best or more expensive areas are also where the greatest speculative activity takes place and where the rapid increase in prices begins. The most significant departure from previous studies that emerges in my results is the absence of a wider gap between high priced areas and low priced areas in the peak year. I develop a measure of dispersion in value among areas and contrast the Miami-Dade and Paris episodes. In Chapter IV I analyze the impact on tax equity of a Florida tax-limiting legislation known as Save Our Homes. I first compare homesteaded and non-homesteaded properties, and second, look within the subset of homesteaded properties. I find that non-homesteaded properties increase their share of taxes paid relative to homesteaded properties during an up market, but that this is reversed during a down market. For the subset of homesteaded properties I find that the impact on tax equity of SOH will depend on differential growth rates among higher and lower valued homes, but during times of rapid home price appreciation, in a scenario of no differential growth rates in property values, SOH increases progressivity relative to the prior system.

Quantifying and Modeling the Influence of Forest on the Magnitude and Duration of Mountain Snow Storage in the Pacific Northwest, USA

Thesis (Ph.D.)--University of Washington, 2016-08

Using electroencephalography signals to control acoustical processing

Current hearing-assistive technology performs poorly in noisy multi-talker conditions. The goal of this thesis was to establish the feasibility of using EEG to guide acoustic processing in such conditions. To attain this goal, this research developed a model via the constructive research method, relying on literature review. Several approaches have revealed improvements in the performance of hearing-assistive devices under multi-talker conditions, namely beamforming spatial filtering, model-based sparse coding shrinkage, and onset enhancement of the speech signal. Prior research has shown that electroencephalography (EEG) signals contain information that concerns whether the person is actively listening, what the listener is listening to, and where the attended sound source is. This thesis constructed a model for using EEG information to control beamforming, model-based sparse coding shrinkage, and onset enhancement of the speech signal. The purpose of this model is to propose a framework for using EEG signals to control sound processing to select a single talker in a noisy environment containing multiple talkers speaking simultaneously. On a theoretical level, the model showed that EEG can control acoustical processing. An analysis of the model identified a requirement for real-time processing and that the model inherits the computationally intensive properties of acoustical processing, although the model itself is low complexity placing a relatively small load on computational resources. A research priority is to develop a prototype that controls hearing-assistive devices with EEG. This thesis concludes highlighting challenges for future research.

Application of GIS in potential beekeeping assessment: Case study of Montesinho Natural Park (Portugal)

Planners require solutions that address routine work needs and seems essential to improving efficiency and productivity. There are a great number of different factors related to beekeeper activity as well the quality and productivity of different bee products. The spatial analysis is a powerful tool for overlap and relates various levels of information on a map, and consequently a very useful for beekeeping activity planning. This work proposes and applies a methodology to potential beekeeping assessment in Montesinho Natural Park, a region in the northwest of Portugal. The beekeeping potential maps were developed with the following data sources: legal standards, vegetation, land use, topography, water resources, roads, electromagnetic fields, and some honey physico-chemical analysis. The design and implementation of spatial analysis model based on Geographic Information System (GIS) to beekeeping planning activities has already been described by Anjos et al (2014). Spatial analysis techniques allows to define the potential beekeeper map supporting the beekeeper management in this region. Anjos O, Silva G, Roque N, Fernandez P, 2014. GIS based analysis to support the beekeeping planning. Book of abstracts of the International Symposium on Bee Products 3rd edition – Annual meeting of the International Honey Commission (IHC), Faculty of medicine, University of Rijeka, p:61