Most bacteria live in surface-attached colonies known as biofilms, which contain distinct cell types embedded in a self-produced extracellular network of polymers. Differentiation into functionally-distinct sub-populations of cells, or phenotypes, is primarily a result of nutrient availability and extracellular signals. These inputs change over time, leading to spatial and temporal patterns in the relative populations of phenotypes. Although transitions between phenotypes have been investigated in single cells, the triggers for this process within an intact biofilm are not well understood. / Engineering and Applied Sciences

South Florida has transformed from a natural to a managed ecosystem upon channelization of Kissimmee River and the wetlands in the 1960’s. The drainage has resulted in fast transport of water and nutrient, and subsequently eutrophication of the downstream water bodies. The intervention required: intensive management of the shallow groundwater to balance ecological water requirement; and nutrient removal, namely phosphorus, to minimize eutrophication. The study was set to examine and develop an operational prediction method for groundwater-phosphorus interactions to support the wetlands management. Accordingly, a point scale and a spatio-temporal groundwater level was simulated using sequence based Markovian stochastic analysis and dynamic factor analysis methods respectively. A root mean square error of 0.12m and 0.15m was observed for a point and spatio-temporal groundwater prediction. Soluble and sequestered phosphorus were also simulated at 13% error using a watershed based model called ArcWAM. A spatial analysis on simulated soluble phosphorus and groundwater level indicated similarity of patterns (spatial correlation) 99% of the time. A geographically weighted multivariate analysis of soluble phosphorus using predictors of groundwater level, total phosphorus of surficial water, and distance from Kissimmee River showed a goodness of fit (R2 ) of 0.2 – 0.7. Amongst the factors, the groundwater explained 70% of the soluble phosphorus variability. In summary, an increase in soluble phosphorus was observed with groundwater rise and a decrease during groundwater recession. A reversed relationship was identified for the total phosphorus. Presumably, organic matter in the root zone has contributed to increased soluble phosphorus with the rise in groundwater. On the other hand, solubility of calcium carbonate from the karst aquifers seems to fix and precipitate phosphorus during recession of groundwater. The least sequestration of phosphorus, observed in oversaturated wetlands also suggested that nutrient removal on karst hydrogeology could be risky unless a check is made using vegetation strip to enhance phosphorus uptake. The study concluded that phosphorus could be operationally predicted associated with forecasting of groundwater fluctuation. Further research is recommended to explore factors that could be derived either empirically or from satellite data for prediction of soluble phosphorus at minimum cost.

Modern GIS applications for transportation and defense often require the ability to store the evolving positions of a large number of objects as they are observed in motion, and to support queries on this spatiotemporal data in real time. Because the M-Tree has been proven as an index for spatial network databases, we have selected it to be enhanced as a spatiotemporal index. We present modifications to the tree which allow trajectory reconstruction with fast insert performance and modifications which allow the tree to be built with awareness of the spatial locality of reference in spatiotemporal data.

Thesis advisor: John E. Ebel / The seismicity of the mid Atlantic Ridge (MAR) was compared in space and time with the seismicity along the Atlantic continental margins of Europe, Africa, North America, the Carribean and South America in a bid to appraise the level of influence of the ridge push force at the MAR on the Atlantic coastal seismicity. By analyzing the spatial and temporal patterns of many earthquakes (along with the patterns in their stress directions) in diverse places with similar tectonic settings, it is hoped that patterns that might be found indicate some of the average properties of the forces that are causing the earthquakes. The spatial analysis of the dataset set used shows that areas with higher seismic moment release along the north MAR spatially correlate with areas with relatively lower seismic moment release along the north Atlantic continental margins (ACM) and vice versa. This inverse spatial correlation observed between MAR seismicity and ACM seismicity might be due to the time (likely a long time) it takes stress changes from segments of the MAR currently experiencing high seismic activity to propagate to the associated passive margin areas presently experiencing relatively low seismic activity. Furthermore, the number of Atlantic basin and Atlantic coast earthquakes occurring away from the MAR is observed to be independent of the proximity of earthquake’s epicenters from the MAR axis. The effect of local stress as noted by Wysession et al. (1995) might have contributed to the independence of Atlantic basin and Atlantic coast earthquake proximity from the MAR. The Latchman (2011) observation of strong earthquakes on a specific section of the MAR being followed by earthquakes on Trinidad and Tobago was tested on other areas of the MAR and ACM. It was found that that the temporal delay observed by Latchman does not exist for the seismicity along other areas along the MAR and ACM. Within the time window used for this study, it appears that seismicity is occurring randomly in space away from the MAR. The weak anticorrelations between ACM and MAR seismicity show that the ridge push force probably has some level of influence on the ACM seismicity. However, as revealed from previous research on the study area, the forces resulting from lateral density contrasts related to topographic features and lateral density variations between oceanic and continental crust also appear to significantly influence the seismicity of the Atlantic coastal margins. / Thesis (MS) — Boston College, 2015. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Geology and Geophysics.

This study was based on samples taken from 7 cruises between February 2004 and November 2004 and intended to investigate the effects of hydrographic conditions on the spatiotemporal variations of fish larvae assemblage in the waters surrounding Taiwan. The cold, low salinity, and nutrient-rich waters were usually found in the seas northwest of Taiwan in winter, probably due to the invasion of China Coastal Current (CCC). While the dominance of warm and saline waters in the Taiwan Strait in summer and in the waters south and east of Taiwan throughout the year implied that the waters of these areas might be controlled by Kuroshio Current (KC) and South China Sea Waters. In total, 622 fish larvae taxa belonging to 295 genera and 138 families were identified by this study with an overall mean abundance of 760.52 ¡Ó 75.63 ind./1000m3. No significant difference in abundance was found among seasons. The 10 predominant taxa, which constituted 32% of the total number of fish larvae, were Engraulis japonicus, Benthosema pterotum, Scomber japonicus, Vinciguerria nimbaria, Encrasicholina punctifer, Sigmops gracilis, Auxis sp., Trichiurus lepturus, Bleekeria mitsukurii, and Maurolicus sp.. The distribution patterns of fish larvae showed a close relationship with the water masses, higher abundance and lower species richness of fish larvae were always found in the northwestern and northern waters of Taiwan where the CCC prevails, and lower abundance and higher species diversity were present in the eastern waters of Taiwan where the KC dominates. Results of cluster analysis distinguished three station groups, the western, transitional, and eastern groups, and the distribution patterns of these station groups were also corresponded with hydrographic conditions. Furthermore, two seasonal groups of fish larvae assemblages were also recognized, spring-summer group and autumn-winter group. Results of correlation coefficients showed that total abundance of fish larvae and the most dominant taxa were positively related to zooplankton abundance but negatively related to phytoplankton abundance, and suggested that food source might be a key factor to determine the abundance and distribution of fish larvae in the waters surrounding Taiwan. Results of indicator species analysis designated Engraulis japonicus the indicator species of the CCC, and Vinciguerria nimbaria, Maurolicus sp., and Sigmops gracilis the indicator species of the KC.

Neuroscientists have uncovered much about the dorsal and ventral visual object processing pathways. However, little is understood about the functional development of these pathways in human infants. Behavioral data has shown that as early as 2.5 months, infants are sensitive to spatiotemporal information for object individuation in occlusion events. This study used Near Infrared Spectroscopy to assess neural activation (as evidenced by an increase in HbO2) in four areas of the pathways: primary visual cortex (O1), posterior parietal cortex (P3), lateral occipital (T5), and inferior temporal (T3) in awake human infants aged 5.5 months while they view either a spatiotemporaldiscontinuity event or a control event. Three major predictions were made: 1) since the events contain visually distinct objects, there should be significant neural activation in O1 to both events, 2) if the dorsal route mediates the processing of spatiotemporal discontinuities, then there should also be a significant increase in P3 in response to the spatiotemporal-discontinuity event but not to the control event, and 3) activation present in T3 and T5 should not vary by condition if the ventral pathway is not responsible for the processing of spatiotemporal discontinuities. Results supported all three predictions.

In the analysis of spatio-temporal data, statistical inference based on the Gaussian assumption is ubiquitous due to its many attractive properties. However, data collected from different fields of science rarely meet the assumption of Gaussianity. One option is to apply a monotonic transformation to the data such that the transformed data have a distribution that is close to Gaussian. In this thesis, we focus on a flexible two-parameter family of transformations, the Tukey g-and-h (TGH) transformation. This family has the desirable properties that the two parameters g ∈ R and h ≥ 0 involved control skewness and tail-heaviness of the distribution, respectively. Applying the TGH transformation to a standard normal distribution results in the univariate TGH distribution. Extensions to the multivariate case and to a spatial process were developed recently. In this thesis, motivated by the need to exploit wind as renewable energy, we tackle the challenges of modeling big spatio-temporal data that are non-Gaussian by applying the TGH transformation to different types of Gaussian processes: spatial (random field), temporal (time series), spatio-temporal, and their multivariate extensions. We explore various aspects of spatio-temporal data modeling techniques using transformed Gaussian processes with the TGH transformation. First, we use the TGH transformation to generate non-Gaussian spatial data with the Matérn covariance function, and study the effect of non-Gaussianity on Gaussian likelihood inference for the parameters in the Matérn covariance via a sophisticatedly designed simulation study. Second, we build two autoregressive time series models using the TGH transformation. One model is applied to a dataset of observational wind speeds and shows advantaged in accurate forecasting; the other model is used to fit wind speed data from a climate model on gridded locations covering Saudi Arabia and to Gaussianize the data for each location. Third, we develop a parsimonious spatio-temporal model for time series data on a spatial grid and utilize the aforementioned Gaussianized climate model wind speed data to fit the latent Gaussian spatio-temporal process. Finally, we discuss issues under a unified framework of modeling multivariate trans-Gaussian processes and adopt one of the TGH autoregressive models to build a stochastic generator for global wind speed.

abstract: Traditionally, visualization is one of the most important and commonly used methods of generating insight into large scale data. Particularly for spatiotemporal data, the translation of such data into a visual form allows users to quickly see patterns, explore summaries and relate domain knowledge about underlying geographical phenomena that would not be apparent in tabular form. However, several critical challenges arise when visualizing and exploring these large spatiotemporal datasets. While, the underlying geographical component of the data lends itself well to univariate visualization in the form of traditional cartographic representations (e.g., choropleth, isopleth, dasymetric maps), as the data becomes multivariate, cartographic representations become more complex. To simplify the visual representations, analytical methods such as clustering and feature extraction are often applied as part of the classification phase. The automatic classification can then be rendered onto a map; however, one common issue in data classification is that items near a classification boundary are often mislabeled. This thesis explores methods to augment the automated spatial classification by utilizing interactive machine learning as part of the cluster creation step. First, this thesis explores the design space for spatiotemporal analysis through the development of a comprehensive data wrangling and exploratory data analysis platform. Second, this system is augmented with a novel method for evaluating the visual impact of edge cases for multivariate geographic projections. Finally, system features and functionality are demonstrated through a series of case studies, with key features including similarity analysis, multivariate clustering, and novel visual support for cluster comparison. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016

The current study examined the change in articulatory patterns when speakers were asked to increase and decrease their speaking rate by matching metered and rhythmic audio recordings and by matching metered and rhythmic audiovisual recordings. There were 10 participants, five male and five female, ranging in age from 20 to 36 with a mean age of 25. Participants spoke the sentence "Don't fight or pout over a toy car"under rhythmic, metered, fast and slow conditions and in response to audio only or audiovisual models, resulting in eight speaking conditions: audio metered fast, audiovisual metered fast, audio metered slow, audiovisual metered slow, audio rhythmic fast, audiovisual rhythmic fast, audio rhythmic slow, and audiovisual rhythmic slow. Each participant had five sensors glued to their tongue, teeth and lips and articulatory movements were recorded with an NDI Wave electromagnetic articulograph. 10 tokens of the target utterance were analyzed for duration and Spatiotemporal Index (STI). STI was then computed for the vertical movements of the tongue, jaw and lower lip, as well as lip aperture in order to measure variability of speech movements over multiple sentence repetitions. Stroke metrics based on the speed history of the articulators were also computed in order to reveal average kinematic features of articulatory gestures, or the individual. movement strokes that occurred between successive speed minima in running speech. Statistical analysis revealed that STI measures did not change significantly in response to the different rate conditions. This study demonstrated that in neurotypical individuals, articulatory patterns including stroke count, onset speed, peak speed and hull area changed significantly in faster or slower speech. Additionally, the results revealed that both metered and rhythmic cues and both audio and audiovisual cues are effective in decreasing and increasing speaking rate without significantly impacting the STI (i.e., consistency) of articulatory movements. Therefore, it may be that a speaker's efforts to match the audio and audiovisual cues in real-time more significantly affected articulation patterns than whether cues were rhythmic, metered, audio or audiovisual.

Urban energy systems are often studied in a very similar way in the sense that the characteristics of the underlying physical infrastructure are weighted as the main determinants of energy use predictions, while the behavior of the human population in relation to this systemthe so-called ``energy consumers''in time and urban spaces is effectively neglected. The spatial and temporal variations in infrastructure-population interactivity greatly complicate urban energy systems; the unremitting growth in population and advances in technology mean that the dynamic interrelationship between the population and urban environment will continue to grow exponentially, resulting in increasing uncertainties, unreliable predictions and poor management decisions given the inadequacy of existing approaches. In this dissertation, I explore the interdependencies of spatiotemporal fluctuations of human mobility as an indicator for human activities and energy use in urban areas in three main studies. First, I show that the fluctuations of intra-urban human mobility and energy use have an underlying structure across both time and space, and that human mobility can indeed be used as a predictor for energy use in both dimensions. Second, I examine how one of the dominant drivers of this structure, namely individuals' location-based activities, influence patterns in energy supply and demand across building types (i.e. residential and commercial buildings) and show how variations in the human mobility networks of two distinct urban populations (the so-called returners and explorers) can explain fluctuations in energy use. Third, I introduce an integrated approach for predicting urban energy use across time and space by incorporating these interdependencies. Generating predictive models that capture the spatiotemporal variations in these determinants in urban settings, as suggested in this research, will contribute to our understanding of how variations in urban population activities for particular times and locations influence can be applied to estimate energy use patterns in surrounding areas. / PHD