Saudi Arabia has been trying to change its long tradition of relying on fossil fuels and seek renewable energy sources such as wind power. In this thesis, I firstly provide a comprehensive assessment of wind energy resources and associated spatio-temporal patterns over Saudi Arabia in both current and future climate conditions, based on a Regional Climate Model output. A high wind energy potential exists and is likely to persist at least until 2050 over a vast area ofWestern Saudi Arabia, particularly in the region between Medina and the Red Sea coast and during Summer months. Since an accurate assessment of wind extremes is crucial for risk management purposes, I then present the first high-resolution risk assessment of wind extremes over Saudi Arabia. Under the Bayesian framework, I measure the uncertainty of return levels and produce risk maps of wind extremes, which show that locations in the South of Saudi Arabia and near the Red Sea and the Persian Gulf are at very high risk of disruption of wind turbine operations. In order to perform spatial predictions of the bivariate wind random field for efficient turbine control, I propose parametric variogram matrix (function) models for cokriging, which have the advantage of allowing for a smooth transition between a joint second-order and intrinsically stationary vector random field. Under Gaussianity, the covariance function is central to spatio-temporal modeling, which is useful to understand the dynamics of winds in space and time. I review the various space-time covariance structures and models, some of which are visualized with animations, and associated tests. I also discuss inference issues and a case study based on a high-resolution wind-speed dataset. The Gaussian assumption commonly made in statistics needs to be validated, and I show that tests for independently and identically distributed data cannot be used directly for spatial data. I then propose a new multivariate test for spatial data by accounting for the spatial dependence. The new test is easy to compute, has a chi-square null distribution, and has a good control of the type I error and a high empirical power.

Land degradation is one of the most important drivers of landscape change around the globe. This dissertation examines land use-land cover change within a mosaic landscape in Eastern Terai, India, and shows evidence of anthropogenic factors contributing to landscape change. Land use and land cover change were examined within the Alipurduar Subdivision, a representative of the Eastern Terai landscape and the Jaldapara Wildlife Sanctuary, a protected area nested within Alipurduar through the use of multi-temporal satellite data over the past 28 years (1978 – 2006). This study establishes the potential of remote sensing technology to identify the drivers of landscape change; it provides an assessment of how regional drivers of landscape change influence the change within smaller local study extents and provides a methodology to map different types of grassland and monitor their loss within the region. The Normalized Difference Vegetation Index (NDVI) and a Normalized Difference Dry Index (NDDI) were found instrumental in change detection and the classification of different grasslands found inside the park based on their location, structure, and composition. Successful spectral segregation of different types of grasslands and their direct association with different grassland specialist species (e.g., hispid hare, hog deer, Bengal florican) clearly showed the potential of remote sensing technology to efficiently monitor these grasslands and assist in species conservation. Temporal analysis provided evidence of the loss of dense forest and grasslands within both study areas with a considerably higher rate of loss outside the protected area than inside. Results show a decline of forest from 40% in 1978 to 25% in 2006 across Alipurduar. Future trends project forest cover and grassland within Alipurduar to reduce to 15% and 5%, respectively. Within the Alipurduar, deforestation due to growth of tea industry was the primary driver of change. Flooding changed the landscape, but more intensely inside the wildlife preserve. Change of the river course inside Jaldapara during the flood of 1968 significantly altered the distribution of grassland inside the park. Unless, the direction of landscape change is altered, future trends predict growth of the tea industry within the region, increased forest loss, and homogenization of the landscape.

Les cellules détectent et intègrent une multitude de signaux d'instruction provenant de leur microenvironnement via un ensemble de récepteurs transmembranaires. Ces informations sont ensuite collectées au niveau des nœuds de signalisation intracellulaires pour être ensuite dispersées en cascades de signalisation afin de déterminer la destinée cellulaire. La manière dont un nœud de signalisation peut interpréter plusieurs stimuli et transmettre de manière spatio-temporelle les informations appropriées restent incomprises. Le proto-oncogène c-Src est une tyrosine kinase pléiotrope, un nœud signalisation essentiel au pilotage de nombreux processus cellulaires, tels que la migration, l'invasion, la dégradation et la division cellulaire. Nous avons développé une approche synthétique pour explorer la relation entre la structure de la SRC et la multiplicité des processus cellulaires qu’elle régule. Notre approche a abouti au découplage des différents modules composant la protéine SRC afin de comprendre l’impact de chacun d’eux sur son activité dans l’espace et dans le temps. Notre approche pour contrôler plusieurs états de la conformation SRC était la conception d’un OptoSrc capable à la fois de former des oligomères et d’être recruté à la membrane plasmique. Pour ce faire, nous avons modifié la structure de la SRC afin qu'elle soit potentiellement active dans le noir et nous l'avons fusionnée avec le CRY2 sensible à la lumière. La stimulation lumineuse induit l'hétérodimérisation CRY2 avec un CIBN ancré à la membrane plasmique et son homo-oligomérisation et déclenche une relocalisation de l’OptoSrc à la membrane plasmique ou son oligomérisation. Ce double système a permis de générer deux types de mobilitéz différentes au sein des adhérences focales à deux destins différents, la formation de lamellipodes dans un cas et la formation d’invadosomes dans l’autre. / Cells sense and integrate a multitude of instructional signals from their microenvironment through a diverse set of transmembrane receptors. This information is then collected at intracellular signaling nodes to later disperse down signaling cascades to drive cell fate. How one signaling node can interpret multiple stimuli and spatio-temporally transmit the appropriate information remains poorly understood. The proto-oncogene c-Src is a pleiotropic tyrosine kinase, is one such node essential for driving many cellular processes, such as migration, invasion, degradation, and cell division. We developed a synthetic approach to explore the relationship between SRC structure and the multiplicity of cellular processes it regulates. Our approach resulted in the decoupling of the different modules composing SRC protein to understand how each of them impacts its activity in space and time. Our approach to control multiple state of SRC conformation was the design of an OptoSrc both capable of forming oligomers and to be recruited at the plasma membrane. To do so, we modified SRC structure to be potentially active in the dark and fused it with light sensitive CRY2. Light stimulation induces CRY2 hetero-dimerization with a CIBN anchored at the plasma membrane and its homo-oligomerisation triggering relocalization of OptoSrc at the plasma and/or its oligomerization. This system generated two different type of mobility of OptoSrc inside focal adhesion inducing two different adhesion fates, the formation of invadosome in one case and the formation of lamellipodia on the other.

This thesis describes a robust and efficient design of Student-t based Robust Spatio-Temporal Prediction, namely, St-RSTP, to provide estimation based on observations over spatio-temporal neighbors. It is crucial to many applications in geographical information systems, medical imaging, urban planning, economy study, and climate forecasting. The proposed St-RSTP is more resilient to outliers or other small departures from model assumptions than its ancestor, the Spatio-Temporal Random Effects (STRE) model. STRE is a statistical model with linear order complexity for processing large scale spatiotemporal data. However, STRE has been shown sensitive to outliers or anomaly observations. In our design, the St-RSTP model assumes that the measurement error follows Student's t-distribution, instead of a traditional Gaussian distribution. To handle the analytical intractable inference of Student's t model, we propose an approximate inference algorithm in the framework of Expectation Propagation (EP). Extensive experimental evaluations, based on both simulation and real-life data sets, demonstrated the robustness and the efficiency of our Student-t prediction model compared with the STRE model. / Master of Science

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Intelligent transportation systems (ITS) have garnered considerable attention for providing efficient traffic management solutions. Traffic flow forecasting is a crucial component of it which serves as the foundation for various state-of-the-art deep learning approaches. Initially, researchers recognized that significant temporal changes from traffic flow data for modelling. However, as researchers delved deeper into the underlying correlations within traffic flow data, they discovered that spatial information from the road network also plays a crucial role in accurate forecasting. Consequently, deep learning methods that incorporate Spatio-temporal representation have been employed to address traffic flow forecasting. Although recent solutions to this problem are impressive, it is essential to discuss the reasoning behind the architecture of the model. The expression of each feature relies on selecting appropriate models for feature extraction and designing architectures that minimize information loss during modeling. In this thesis, the work focuses on graph-based Spatio-temporal feature engineering. The experiments are divided into two parts: 1). explores the efficient architecture for expressing spatial-temporal information by considering both different sequential modelling approaches. 2). Based on the result obtained, the second experiment focuses on multi- scale modelling to capture informative Spatio-temporal feature. We propose a model that incorporates sequential modeling and captures multi-scale Spatiotemporal semantics by employing residual connections in different hierarchy. We validate our model using three datasets, each containing varying information for extraction. Taking into account the dataset characteristics and the model structure, our model outperforms the baselines and state-of-the-art models. The experimental results indicate that the performance of sequential modeling and multi-scale semantics, combined with thoughtful model design, significantly contribute to the overall forecasting performance. Furthermore, our work serves as inspiration for expressive data mining methods that rely on appropriate feature extraction models and architecture design, taking into consideration the information content within the dataset.

To design a pedestrian crossing area reasonably can be a demanding task for traffic planners. There are several challenges, including determining the appropriate dimensions, and ensuring that pedestrians are exposed to the least risks. Pedestrian safety is especially obscure to analyze, given that many people in Stockholm cross the street illegally by running against the red light. To cope with these challenges, computational approaches of trajectory data visual analytics can be used to support the analytical reasoning process. However, it remains an unexplored field regarding how to visualize and communicate the street-crossing spatio-temporal data effectively. Moreover, the rendering also needs to deal with a growing data size for a more massive number of people. This thesis proposes a web-based interactive visual analytics tool for pedestrians' street-crossing behavior under various flow rates. The visualization methodology is also presented, which is then evaluated to have achieved satisfying communication and rendering effectiveness for maximal 180 agents over 100 seconds. In terms of the visualization scenario, pedestrians either wait for the red light or cross the street illegally; all people can choose to stop by a buffer island before they finish crossing. The visualization enables the analysis under multiple flow rates for 1) pedestrian movement, 2) space utilization, 3) crossing frequency in time-series, and 4) illegal frequency. Additionally, to acquire the initial trajectory data, Optimal Reciprocal Collision Avoidance (ORCA) algorithm is engaged in the crowd simulation. Then different visualization techniques are utilized to comply with user demands, including map animation, data aggregation, and time-series graph. / Att konstruera ett gångvägsområde kan rimligen vara en krävande uppgift för trafikplanerare. Det finns flera utmaningar, bland annat att bestämma lämpliga dimensioner och se till att fotgängare utsätts för minst risker. Fotgängarnas säkerhet är särskilt obskyrlig att analysera, eftersom många människor i Stockholm korsar gatan olagligt genom att springa mot det röda ljuset. För att klara av dessa utmaningar kan beräkningsmetoder för bana data visuell analys användas för att stödja den analytiska resonemangsprocessen. Det är emellertid ett oexplorerat fält om hur man visualiserar och kommunicerar gataövergången spatio-temporal data effektivt. Dessutom måste rendering också hantera en växande datastorlek för ett mer massivt antal människor. Denna avhandling föreslår ett webbaserat interaktivt visuellt analysverktyg för fotgängares gatöverföring under olika flödeshastigheter. Visualiseringsmetoden presenteras också, som sedan utvärderas för att ha uppnått tillfredsställande kommunikation och effektivitet för maximal 180 agenter över 100 sekunder. Vad beträffar visualiseringsscenariot, väntar fotgängare antingen på det röda ljuset eller tvärs över gatan; alla människor kan välja att stanna vid en buffertö innan de slutar korsa. Visualiseringen möjliggör analysen under flera flödeshastigheter för 1) fotgängarrörelse, 2) rymdutnyttjande, 3) korsfrekvens i tidsserier och 4) olaglig frekvens. För att förvärva den ursprungliga bana-data är Optimal Reciprocal Collision Avoidance (ORCA) algoritmen förknippad med folkmassimuleringen. Därefter utnyttjas olika visualiseringstekniker för att uppfylla användarnas krav, inklusive kartanimering, dataaggregering och tidsserier.

Finite mixture models have had a profound impact on the history of statistics, contributing to modelling heterogeneous populations, generalizing distributional assumptions, and lately, presenting a convenient framework for classification and clustering. A novel approach, via Gaussian mixture distribution, is introduced for modelling receiver operating characteristic curves. The absence of a closed-form for a functional form leads to employing the Monte Carlo method. This approach performs excellently compared to the existing methods when applied to real data. In practice, the data are often non-normal, atypical, or skewed. It is apparent that non-Gaussian distributions be introduced in order to better fit these data. Two non-Gaussian mixtures, i.e., t distribution and skew t distribution, are proposed and applied to real data. A novel mixture is presented to cluster spatial and temporal data. The proposed model defines each mixture component as a mixture of autoregressive polynomial with logistic links. The new model performs significantly better compared to the most well known model-based clustering techniques when applied to real data. / Thesis / Doctor of Philosophy (PhD)

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