Investigating the Effects of Various Crowd Characteristics on the Dynamic Properties of an Occupied Structure

One of the challenges for structural engineers during design is considering how the structure will respond to crowd-induced dynamic loading. It has been shown that human occupants of a structure do not simply add mass to the system when considering the overall dynamic response of the system, but interact with it and may induce changes of the dynamic properties from those of the empty structure. This study presents an investigation into the human-structure interaction based on several crowd characteristics and their effect on the dynamic properties of an empty structure. The dynamic properties including frequency, damping, and mode shapes were estimated for a single test structure by means of experimental modal analysis techniques. The same techniques were utilized to estimate the dynamic properties when the test structure was occupied by a crowd with different combinations of size, posture, and distribution. The goal of this study is to isolate the occupant characteristics in order to determine the significance of each to be considered when designing new structures to avoid crowd serviceability issues. The results are presented and summarized based on the level of influence of each characteristic. The posture that produces the most significant effects based on the scope of this research is standing with bent knees with a maximum decrease in frequency of the first mode of the empty structure by 32 percent atthe highest mass ratio. The associated damping also increased 36 times the damping of the empty structure. In addition to the analysis of the experimental data, finite element models and a two degree-of-freedom model were created. These models were used to gain an understanding of the test structure, model a crowd as an equivalent mass, and also to develop a single degree-of-freedom (SDOF) model to best represent a crowd of occupants based on the experimental results. The SDOF models created had an averagefrequency of 5.0 Hz, within the range presented in existing biomechanics research, and combined SDOF systems of the test structure and crowd were able to reproduce the frequency and damping ratios associated with experimental tests. Results of this study confirmed the existence of human-structure interaction andthe inability to simply model a crowd as only additional mass. The two degree-offreedom model determined was able to predict the change in natural frequency and damping ratio for a structure occupied by multiple group sizes in a single posture. These results and model are the preliminary steps in the development of an appropriate methodfor modeling a crowd in combination with a more complex FE model of the empty structure.

Spatial and temporal variation in population genetic structure of wild Nile tilapia (Oreochromis niloticus) across Africa

Background: Reconstructing the evolutionary history of a species is challenging. It often depends not only on the past biogeographic and climatic events but also the contemporary and ecological factors, such as current connectivity and habitat heterogeneity. In fact, these factors might interact with each other and shape the current species distribution. However, to what extent the current population genetic structure reflects the past and the contemporary factors is largely unknown. Here we investigated spatio-temporal genetic structures of Nile tilapia (Oreochromis niloticus) populations, across their natural distribution in Africa. While its large biogeographic distribution can cause genetic differentiation at the paleo-biogeographic scales, its restricted dispersal capacity might induce a strong genetic structure at micro-geographic scales. Results: Using nine microsatellite loci and 350 samples from ten natural populations, we found the highest genetic differentiation among the three ichthyofaunal provinces and regions (Ethiopian, Nilotic and Sudano-Sahelian) (R(ST) = 0.38 - 0.69). This result suggests the predominant effect of paleo-geographic events at macro-geographic scale. In addition, intermediate divergences were found between rivers and lakes within the regions, presumably reflecting relatively recent interruptions of gene flow between hydrographic basins (R(ST) = 0.24 - 0.32). The lowest differentiations were observed among connected populations within a basin (R(ST) = 0.015 in the Volta basin). Comparison of temporal sample series revealed subtle changes in the gene pools in a few generations (F = 0 - 0.053). The estimated effective population sizes were 23 - 143 and the estimated migration rate was moderate (m similar to 0.094 - 0.097) in the Volta populations. Conclusions: This study revealed clear hierarchical patterns of the population genetic structuring of O. niloticus in Africa. The effects of paleo-geographic and climatic events were predominant at macro-geographic scale, and the significant effect of geographic connectivity was detected at micro-geographic scale. The estimated effective population size, the moderate level of dispersal and the rapid temporal change in genetic composition might reflect a potential effect of life history strategy on population dynamics. This hypothesis deserves further investigation. The dynamic pattern revealed at micro-geographic and temporal scales appears important from a genetic resource management as well as from a biodiversity conservation point of view.

Dynamics of Spatial Game Theory Networks with Novel Modifications

Introduction: Advances in biotechnology have shed light on many biological processes. In biological networks, nodes are used to represent the function of individual entities within a system and have historically been studied in isolation. Network structure adds edges that enable communication between nodes. An emerging fieldis to combine node function and network structure to yield network function. One of the most complex networks known in biology is the neural network within the brain. Modeling neural function will require an understanding of networks, dynamics, andneurophysiology. It is with this work that modeling techniques will be developed to work at this complex intersection. Methods: Spatial game theory was developed by Nowak in the context of modeling evolutionary dynamics, or the way in which species evolve over time. Spatial game theory offers a two dimensional view of analyzingthe state of neighbors and updating based on the surroundings. Our work builds upon this foundation by studying evolutionary game theory networks with respect to neural networks. This novel concept is that neurons may adopt a particular strategy that will allow propagation of information. The strategy may therefore act as the mechanism for gating. Furthermore, the strategy of a neuron, as in a real brain, isimpacted by the strategy of its neighbors. The techniques of spatial game theory already established by Nowak are repeated to explain two basic cases and validate the implementation of code. Two novel modifications are introduced in Chapters 3 and 4 that build on this network and may reflect neural networks. Results: The introduction of two novel modifications, mutation and rewiring, in large parametricstudies resulted in dynamics that had an intermediate amount of nodes firing at any given time. Further, even small mutation rates result in different dynamics more representative of the ideal state hypothesized. Conclusions: In both modificationsto Nowak's model, the results demonstrate the network does not become locked into a particular global state of passing all information or blocking all information. It is hypothesized that normal brain function occurs within this intermediate range and that a number of diseases are the result of moving outside of this range.

Spatial Learning and Stress Response of Male Rats Prenatally Exposed to Dexamethasone

Dexamethasone is routinely administered to women at risk for a preterm birth in order to enhance fetal lung development and reduce uterine contractions. Research has demonstrated possible behavioral abnormalities in adulthood as a result of dexamethasone treatment. Using nonlinear mixed effects modeling, this study found thatprenatal dexamethasone treatment impaired spatial learning and memory of adult male Sprague-Dawley rats. Prenatal dexamethasone treatment also led to more anxiety related behaviors on Elevated Plus Maze testing 1.5 hours after a stress challenge. Because theassumptions underlying the independent samples t-test were violated, the randomization test was used to compare groups on the Elevated Plus Maze.

Analysis and Prediction of Transient Opacity Spikes Using Dimensional Modeling

Dimensional modeling, GT-Power in particular, has been used for two related purposes-to quantify and understand the inaccuracies of transient engine flow estimates that cause transient smoke spikes and to improve empirical models of opacity or particulate matter used for engine calibration. It has been proposed by dimensional modeling that exhaust gas recirculation flow rate was significantly underestimated and volumetric efficiency was overestimated by the electronic control module during the turbocharger lag period of an electronically controlled heavy duty diesel engine. Factoring in cylinder-to-cylinder variation, it has been shown that the electronic control module estimated fuel-Oxygen ratio was lower than actual by up to 35% during the turbocharger lag period but within 2% of actual elsewhere, thus hindering fuel-Oxygen ratio limit-based smoke control. The dimensional modeling of transient flow was enabled with a new method of simulating transient data in which the manifold pressures and exhaust gas recirculation system flow resistance, characterized as a function of exhaust gas recirculation valve position at each measured transient data point, were replicated by quasi-static or transient simulation to predict engine flows. Dimensional modeling was also used to transform the engine operating parameter model input space to a more fundamental lower dimensional space so that a nearest neighbor approach could be used to predict smoke emissions. This new approach, intended for engine calibration and control modeling, was termed the "nonparametric reduced dimensionality" approach. It was used to predict federal test procedure cumulative particulate matter within 7% of measured value, based solely on steady-state training data. Very little correlation between the model inputs in the transformed space was observed as compared to the engine operating parameter space. This more uniform, smaller, shrunken model input space might explain how the nonparametric reduced dimensionality approach model could successfully predict federal test procedure emissions when roughly 40% of all transient points were classified as outliers as per the steady-state training data.

Limitations of Remotely-sensed Aerosol as a Spatial Proxy for Fine Particulate Matter

Recent research highlights the promise of remotely-sensed aerosol optical depth (AOD) as a proxy for ground-level PM2.5. Particular interest lies in the information on spatial heterogeneity potentially provided by AOD, with important application to estimating and monitoring pollution exposure for public health purposes. Given the temporal and spatio-temporal correlations reported between AOD and PM2.5 , it is tempting to interpret the spatial patterns in AOD as reflecting patterns in PM2.5 . Here we find only limited spatial associations of AOD from three satellite retrievals with PM2.5 over the eastern U.S. at the daily and yearly levels in 2004. We then use statistical modeling to show that the patterns in monthly average AOD poorly reflect patterns in PM2.5 because of systematic, spatially-correlated error in AOD as a proxy for PM2.5 . Furthermore, when we include AOD as a predictor of monthly PM2.5 in a statistical prediction model, AOD provides little additional information to improve predictions of PM2.5 when included in a model that already accounts for land use, emission sources, meteorology and regional variability. These results suggest caution in using spatial variation in AOD to stand in for spatial variation in ground-level PM2.5 in epidemiological analyses and indicate that when PM2.5 monitoring is available, careful statistical modeling outperforms the use of AOD.

Semiparametric Latent Variable Regression Models for Spatio-temporal Modeling of Mobile Source Particles in the Greater Boston Area

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon, and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalised spline formation of the model that relates to generalised kringing of the latent traffic pollution variable and leads to a natural Bayesian Markov Chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degress of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separately

Practical Large-Scale Spatio-Temporal Modeling of Particulate Matter Concentrations

The last two decades have seen intense scientific and regulatory interest in the health effects of particulate matter (PM). Influential epidemiological studies that characterize chronic exposure of individuals rely on monitoring data that are sparse in space and time, so they often assign the same exposure to participants in large geographic areas and across time. We estimate monthly PM during 1988-2002 in a large spatial domain for use in studying health effects in the Nurses' Health Study. We develop a conceptually simple spatio-temporal model that uses a rich set of covariates. The model is used to estimate concentrations of PM10 for the full time period and PM2.5 for a subset of the period. For the earlier part of the period, 1988-1998, few PM2.5 monitors were operating, so we develop a simple extension to the model that represents PM2.5 conditionally on PM10 model predictions. In the epidemiological analysis, model predictions of PM10 are more strongly associated with health effects than when using simpler approaches to estimate exposure. Our modeling approach supports the application in estimating both fine-scale and large-scale spatial heterogeneity and capturing space-time interaction through the use of monthly-varying spatial surfaces. At the same time, the model is computationally feasible, implementable with standard software, and readily understandable to the scientific audience. Despite simplifying assumptions, the model has good predictive performance and uncertainty characterization.

MODELING FUNCTIONAL DATA WITH SPATIALLY HETEROGENEOUS SHAPE CHARACTERISTICS

We propose a novel class of models for functional data exhibiting skewness or other shape characteristics that vary with spatial or temporal location. We use copulas so that the marginal distributions and the dependence structure can be modeled independently. Dependence is modeled with a Gaussian or t-copula, so that there is an underlying latent Gaussian process. We model the marginal distributions using the skew t family. The mean, variance, and shape parameters are modeled nonparametrically as functions of location. A computationally tractable inferential framework for estimating heterogeneous asymmetric or heavy-tailed marginal distributions is introduced. This framework provides a new set of tools for increasingly complex data collected in medical and public health studies. Our methods were motivated by and are illustrated with a state-of-the-art study of neuronal tracts in multiple sclerosis patients and healthy controls. Using the tools we have developed, we were able to find those locations along the tract most affected by the disease. However, our methods are general and highly relevant to many functional data sets. In addition to the application to one-dimensional tract profiles illustrated here, higher-dimensional extensions of the methodology could have direct applications to other biological data including functional and structural MRI.

BIVARIATE BINOMIAL SPATIAL MODELLING LOA loa PREVALENCE IN TROPICAL AFRICA

We present a state-of-the-art application of smoothing for dependent bivariate binomial spatial data to Loa loa prevalence mapping in West Africa. This application is special because it starts with the non-spatial calibration of survey instruments, continues with the spatial model building and assessment and ends with robust, tested software that will be used by the field scientists of the World Health Organization for online prevalence map updating. From a statistical perspective several important methodological issues were addressed: (a) building spatial models that are complex enough to capture the structure of the data but remain computationally usable; (b)reducing the computational burden in the handling of very large covariate data sets; (c) devising methods for comparing spatial prediction methods for a given exceedance policy threshold.

Exposure modeling of high-frequency electromagnetic fields

We developed a geospatial model that calculates ambient high-frequency electromagnetic field (HF-EMF) strengths of stationary transmission installations such as mobile phone base stations and broadcast transmitters with high spatial resolution in the order of 1 m. The model considers the location and transmission patterns of the transmitters, the three-dimensional topography, and shielding effects by buildings. The aim of the present study was to assess the suitability of the model for exposure monitoring and for epidemiological research. We modeled time-averaged HF-EMF strengths for an urban area in the city of Basel as well as for a rural area (Bubendorf). To compare modeling with measurements, we selected 20 outdoor measurement sites in Basel and 18 sites in Bubendorf. We calculated Pearson's correlation coefficients between modeling and measurements. Chance-corrected agreement was evaluated by weighted Cohen's kappa statistics for three exposure categories. Correlation between measurements and modeling of the total HF-EMF strength was 0.67 (95% confidence interval (CI): 0.33-0.86) in the city of Basel and 0.77 (95% CI: 0.46-0.91) in the rural area. In both regions, kappa coefficients between measurements and modeling were 0.63 and 0.77 for the total HF-EMF strengths and for all mobile phone frequency bands. First evaluation of our geospatial model yielded substantial agreement between modeling and measurements. However, before the model can be applied for future epidemiologic research, additional validation studies focusing on indoor values are needed to improve model validity.Journal of Exposure Science and Environmental Epidemiology (2008) 18, 183-191; doi:10.1038/sj.jes.7500575; published online 4 April 2007.

Modeling of electroosmotic and electrophoretic mobilization in capillary and microchip isoelectric focusing

Our dynamic capillary electrophoresis model which uses material specific input data for estimation of electroosmosis was applied to investigate fundamental aspects of isoelectric focusing (IEF) in capillaries or microchannels made from bare fused-silica (FS), FS coated with a sulfonated polymer, polymethylmethacrylate (PMMA) and poly(dimethylsiloxane) (PDMS). Input data were generated via determination of the electroosmotic flow (EOF) using buffers with varying pH and ionic strength. Two models are distinguished, one that neglects changes of ionic strength and one that includes the dependence between electroosmotic mobility and ionic strength. For each configuration, the models provide insight into the magnitude and dynamics of electroosmosis. The contribution of each electrophoretic zone to the net EOF is thereby visualized and the amount of EOF required for the detection of the zone structures at a particular location along the capillary, including at its end for MS detection, is predicted. For bare FS, PDMS and PMMA, simulations reveal that EOF is decreasing with time and that the entire IEF process is characterized by the asymptotic formation of a stationary steady-state zone configuration in which electrophoretic transport and electroosmotic zone displacement are opposite and of equal magnitude. The location of immobilization of the boundary between anolyte and most acidic carrier ampholyte is dependent on EOF, i.e. capillary material and anolyte. Overall time intervals for reaching this state in microchannels produced by PDMS and PMMA are predicted to be similar and about twice as long compared to uncoated FS. Additional mobilization for the detection of the entire pH gradient at the capillary end is required. Using concomitant electrophoretic mobilization with an acid as coanion in the catholyte is shown to provide sufficient additional cathodic transport for that purpose. FS capillaries dynamically double coated with polybrene and poly(vinylsulfonate) are predicted to provide sufficient electroosmotic pumping for detection of the entire IEF gradient at the cathodic column end.

Prediction of spatial orientation and morphology of calcaneonavicular coalitions

BACKGROUND: Calcaneonavicular coalitions (CNC) have been reported to be associated with anatomical aberrations of either the calcaneus and/or navicular bones. These morphological abnormalities may complicate accurate surgical resection. Three-dimensional analysis of spatial orientation and morphological characteristics may help in preoperative planning of resection. MATERIALS AND METHODS: Sixteen feet with a diagnosis of CNC were evaluated by means of 3-D CT modeling. Three angles were defined that were expressed in relation to one reproducible landmark (lateral border of the calcaneus): the dorsoplantar inclination, anteroposterior inclination, and socket angle. The depth and width of the coalitions were measured and calculated to obtain the estimated contact surface. Three-dimensional reconstructions of the calcanei served to evaluate the presence, distortion or absence of the anterior calcaneal facet and presence of a navicular beak. The interrater correlations were assessed in order to obtain values for the accuracy of the measurement methods. Sixteen normal feet were used as controls for comparison of the socket angle; anatomy of the anterior calcaneal facet and navicular beak as well. RESULTS: The dorsoplantar inclination angle averaged 50 degrees (+/-17), the anteroposterior inclination angle 64 degrees (+/-15), and the pathologic socket angle 98 degrees (+/-11). The average contact area was 156 mm(2). Ninety-four percent of all patients in the CNC group revealed a plantar navicular beak. In 50% of those patients the anterior calcaneal facet was replaced by the navicular portion and in 44% the facet was totally missing. In contrast, the socket angle in the control group averaged 77 degrees (+/-18), which was found to be statistically different than the CNC group (p = 0.0004). Only 25% of the patients in the control group had a plantar navicular beak. High, statistically significant interrater correlations were found for all measured angles. CONCLUSION: Computer-aided CT analysis and reconstructions help to determine the spatial orientations of CNC in space and provide useful information in order to anticipate morphological abnormalities of the calcaneus and navicular.

Studies of spatial clustering of inertial particles in turbulence

We present studies of the spatial clustering of inertial particles embedded in turbulent flow. A major part of the thesis is experimental, involving the technique of Phase Doppler Interferometry (PDI). The thesis also includes significant amount of simulation studies and some theoretical considerations. We describe the details of PDI and explain why it is suitable for study of particle clustering in turbulent flow with a strong mean velocity. We introduce the concept of the radial distribution function (RDF) as our chosen way of quantifying inertial particle clustering and present some original works on foundational and practical considerations related to it. These include methods of treating finite sampling size, interpretation of the magnitude of RDF and the possibility of isolating RDF signature of inertial clustering from that of large scale mixing. In experimental work, we used the PDI to observe clustering of water droplets in a turbulent wind tunnel. From that we present, in the form of a published paper, evidence of dynamical similarity (Stokes number similarity) of inertial particle clustering together with other results in qualitative agreement with available theoretical prediction and simulation results. We next show detailed quantitative comparisons of results from our experiments, direct-numerical-simulation (DNS) and theory. Very promising agreement was found for like-sized particles (mono-disperse). Theory is found to be incorrect regarding clustering of different-sized particles and we propose a empirical correction based on the DNS and experimental results. Besides this, we also discovered a few interesting characteristics of inertial clustering. Firstly, through observations, we found an intriguing possibility for modeling the RDF arising from inertial clustering that has only one (sensitive) parameter. We also found that clustering becomes saturated at high Reynolds number.

Proteins in silico-modeling and sampling

Proteins are linear chain molecules made out of amino acids. Only when they fold to their native states, they become functional. This dissertation aims to model the solvent (environment) effect and to develop & implement enhanced sampling methods that enable a reliable study of the protein folding problem in silico. We have developed an enhanced solvation model based on the solution to the Poisson-Boltzmann equation in order to describe the solvent effect. Following the quantum mechanical Polarizable Continuum Model (PCM), we decomposed net solvation free energy into three physical terms– Polarization, Dispersion and Cavitation. All the terms were implemented, analyzed and parametrized individually to obtain a high level of accuracy. In order to describe the thermodynamics of proteins, their conformational space needs to be sampled thoroughly. Simulations of proteins are hampered by slow relaxation due to their rugged free-energy landscape, with the barriers between minima being higher than the thermal energy at physiological temperatures. In order to overcome this problem a number of approaches have been proposed of which replica exchange method (REM) is the most popular. In this dissertation we describe a new variant of canonical replica exchange method in the context of molecular dynamic simulation. The advantage of this new method is the easily tunable high acceptance rate for the replica exchange. We call our method Microcanonical Replica Exchange Molecular Dynamic (MREMD). We have described the theoretical frame work, comment on its actual implementation, and its application to Trp-cage mini-protein in implicit solvent. We have been able to correctly predict the folding thermodynamics of this protein using our approach.