The network design is a planning process of placing system components to provide service or meet certain needs in an economical way. It has strong links to real application areas, such as transportation network, communication network, supply chain, power grid, water distribution systems, etc. In practice, these infrastructures are very vulnerable to any failures of system components. Therefore, the design of such infrastructure networks should be robust and survivable to any failures caused by many factors, for example, natural disasters, intentional attacks, system limits, etc. In this dissertation, we first summarize the background and motivations of our research topic on network design problems. Different from literature on network design, we consider both uncertain node and link failures during the network design process. The first part of our research is to design a survivable network with mixed connectivity requirements, or the (k,l)-connectivity. The designed network can still be connected after failures of any k vertices and (l-1) edges or failures of any (k-1) vertices and l edges. After formally proving its relationships to edge and vertex disjoint paths, we present two integer programming (IP) formulations, valid inequalities to strengthen the IP formulations, and a cutting plane algorithm. Numerical experiments are performed on randomly generated graphs to compare these approaches. Special cases of this problem include: when k=0, l=1, this problem becomes the well-known minimum spanning tree problem; and when k=0, l ≥ 1, this problem is to find a minimum-cost l-edge-connected spanning subgraph, while when k ≥ 2, l=0, the problem is to find a minimum-cost k-vertex-connected spanning subgraph. As a generalization of k-minimum spanning tree and λ-edge-connected spanning subgraph problems for network design, we consider the minimum-cost λ-edge-connected k-subgraph problem, or the (k, λ)-subgraph problem, which is to find a minimum-cost λ-edge-connected subgraph of a graph with at least k vertices. This problem can be considered as designing k-minimum spanning tree with higher connectivity requirements. We also propose several IP formulations for exactly solving the (k, λ)-subgraph problem, based on some graph properties, for example, requirements of cutsets for a division of the graph and paths between any two vertices. In addition, we study the properties of (k,2)-subgraphs, such as connectivity, bridgeless, and strong orientation properties. Based on these properties, we propose several stronger and more compact IP formulations for solving the (k,2)-subgraph problem, which is a direct generalization of the k-minimum spanning tree problem. Serving as a virtual backbone for wireless ad hoc networks, the connected dominating set problem has been widely studied. We design a robust and survivable connected dominating set for a virtual backbone of a larger graph for ad hoc network. More specifically, we study the (k,l)-connected d-dominating set problem. Given a graph G=(V,E), a subset D ⊆ V is a (k,l)-connected d-dominating set if the subgraph induced by D has mixed connectivity at least (k,l) and every vertex outside of S has at least d neighbors from D. The type of virtual backbone is survivable and also robust for sending message under certain number of both node and link failures. We study the properties of such dominating set and also IP formulations. In addition, we design a cutting plane algorithm to solve it.

Evidence of networks in the resting-brain reflecting the spontaneous brain activity is perhaps the most significant discovery to understand intrinsic brain functionality. Moreover, subsequent detection of dynamics in these networks can be milestone in differentiating the normal and disordered brain functions. However, capturing the correct dynamics is a challenging task since no ground truths' are present for comparison of the results. The change points of these networks can be different for different subjects even during normal brain functions. Even for the same subject and session, dynamics can be different at the start and end of the session based on the fatigue level of the subject scanned. Despite the absence of ground truths, studies have analyzed these dynamics using the existing methods and some of them have developed new algorithms too. One of the most commonly used method for this purpose is sliding window correlation. However, the result of the sliding window correlation is dependent on many parameters and without the ground truth there is no way of validating the results. In addition, most of the new algorithms are complicated, computationally expensive, and/or focus on just one aspect on these dynamics. This study applies the algorithms and concepts from signal processing, image processing, video processing, information theory, and machine learning to analyze the results of the sliding window correlation and develops a novel algorithm to detect change points of these networks adaptively. The findings in this study are divided into three parts: 1) Analyzing the extent of variability in well-defined networks of rodents and humans with sliding window correlation applying concepts from information theory and machine learning domains. 2) Analyzing the performance of sliding window correlation using simulated networks as ground truths for best parameters’ selection, and exploring its dependence on multiple frequency components of the correlating signals by processing the signals in time and Fourier domains. 3) Development of a novel algorithm based on image similarity measures from image and video processing that maybe employed to identify change points of these networks adaptively.

Alzheimer's disease (AD) is developing into the single greatest healthcare challenge in the coming decades. The development of early and effective treatments that can prevent the pathological damage responsible for AD-related dementia is of utmost priority for healthcare authorities. The role of the APOE-ε4 genotype, which has been shown to increase an individual's risk of developing AD, is of central interest to this goal. Understanding the mechanism by which possession of this gene modulates brain function, leading to a predisposition towards AD is an active area of research. Functional connectivity (FC) is an excellent candidate for linking APOE-related differences in brain function to sites of AD pathology. Magnetoencephalography (MEG) is a neuroimaging tool that can provide a unique insight into the electrophysiology underpinning resting-state networks (RSNs) - whose dysfunction is postulated to lead to a predisposition to AD. This thesis presents a range of methods for measuring functional connectivity in MEG data. We first develop a set of novel adaptations for preprocessing MEG data and performing source reconstruction using a beamformer (chapter 3). We then develop a range of analyses for measuring FC through correlations in the slow envelope oscillations of band-limited source-space MEG data (chapter 4). We investigate the optimum time scales for detecting FC. We then develop methods for extracting single networks (using seed-based correlation) and multiple networks (using ICA). We proceed to develop a group-statistical framework for detecting spatial differences in RSNs and present a preliminary finding for APOE-genotype-dependent differences in RSNs (chapter 5). We also develop a statistical framework for quantifying task-locked temporal differences in functional networks during task-positive experiments (chapter 6). Finally, we demonstrate a data-driven parcellation and network analysis pipeline that includes a novel correction for signal leakage between parcels. We use this framework to show evidence of stationary cross-frequency FC (chapter 7).

Associative plasticity, which involves modification of synaptic strength by coactivation of two synaptic inputs, has been demonstrated in many species. Here I explore whether it is possible to induce associative plasticity within a corticocortical pathway in the human brain using a novel protocol that activates two brain areas repeatedly with double-site transcranial magnetic stimulation (TMS). The pathway between ventral premotor cortex (PMv) and primary motor cortex (M1) which computes hand movements for precision grasp was manipulated. First, I selectively potentiated physiological connectivity between the stimulated brain areas. The effects as assessed with paired-pulse TMS were in accordance with principles of spike timing-dependent plasticity (STDP), pathwayspecific and showed a different pattern of expression during rest and during performance of a naturalistic prehension task. Furthermore, I demonstrated that effects evolved rapidly, lasted for up to three hours and were reversible. In a follow-up study, the protocol‘s effects on network interactions were investigated using functional magnetic resonance imaging (fMRI), specifically focussing on functional connectivity of network nodes within the wider parietofrontal circuit controlling reaching-and-grasping. The study demonstrated that functional connectivity was causally modified between stimulated nodes and that those changes in coupling also affected parallel, functionally-related pathways. Comparison of neurophysiological (paired-pulse TMS) and functional (fMRI) connectivity between individuals revealed a linear relationship of these connectivity indices; the first can assess the physiological nature of the interaction, whereas the latter can elucidate global network effects, making the techniques complementary. Neurophysiological interactions of ipsilesional and contralesional PMv-M1 were tested in chronic subcortical stroke patients during grasping. Patients showed a diminished facilitatory influence of ipsilesional PMv on M1 compared to healthy controls which might contribute to their motor disability. Application of paired-associative TMS “normalised“ the reduced effective influence of ipsilesional PMv on M1 and this effect correlated with the patient‘s potential to improve their dexterity.

Soon after the advent of signal-recording techniques in the brain, functional connectivity (FC), a measure of interregional neural interactions, became an important tool to assess brain function and its relation to structure. It was discovered that certain groups of regions in the brain corresponding to behavioural domains are organized into intrinsic networks of connectivity (ICNs). These networks were shown to exhibit high FC during rest, and also during task. ICNs are not only delineated by areas which correspond to various behaviours, but can be modulated in the long and short-term in their connectivity by disease conditions, learning, and task performance. The significance of changes in FC, permanent and transient, is poorly understood with respect to even the simplest ICNs corresponding to motor and visual regions. A better grasp on how to interpret these changes could elucidate the mechanisms and implications of patterns in FC changes during therapy and basic tasks. The aim of this work is to examine long-term changes in the connectivity of several ICNs as a result of modulation by stroke and rehabilitation, and to assess short term changes due to simple, continuous task performance in healthy volunteers. To explore long-term changes in ICN connectivity, fifteen hemiparetic stroke patients underwent resting state scanning and behavioural testing before and after a two-week session of Constraint Induced Movement Therapy (CIMT). It was found that therapy led to localized increases in FC within the sensorimotor ICN. To assess transient changes in FC with task, sixteen healthy volunteers underwent a series of scans during rest, continuous performance of a non-demanding finger-tapping task, viewing of a continuous visual stimulus, and a combined (but uncoupled) visual and motor task. Group Independent Component Analysis (ICA) revealed that canonical ICNs remained robustly connected during task conditions as well as during rest, and dual regression/seed analyses showed that visual and sensorimotor ICNs showed divergent patterns of changes in FC, with the former showing increased intra-network connectivity and the latter decreased intra-network connectivity. Additionally, it was found that task activation within ICNs has a relationship to these changes in FC. Overall, these results suggest that modulation of functional connectivity is a valuable and informative tool in the study of disease recovery and task performance.

Although traditional researchers exploring perfectionism frequently cast the construct in a negative light, a steady stream of recent studies have demonstrated that perfectionistic beliefs can yield both positive and negative outcomes. Despite this progression in the research, perfectionism remains an understudied phenomenon among youth, especially as it relates to the ways in which these individuals are perceived by others. The current study builds on the previous literature by exploring adolescent perfectionism across a variety of psychological and psychoeducational dimensions. Moreover, a unique addition to the literature offered by this study was the inclusion of peer-reports along with self-reported measures in hopes of gaining a fuller understanding of the psychosocial characteristics of perfectionistic youth. The incorporation of peer reports also allowed a novel approach to the study of perfectionism by exploring this construct through the lens of their adolescent colleagues. Self and peer reported data was drawn from a sample of 816 ninth grade students representing three separate high schools. MANOVA results revealed a number of differences between perfectionistic subtypes across both self and peer-reported data. More specifically, adaptive perfectionists rated themselves as having less anxiety and depression as compared to their maladaptive and non-perfectionistic counterparts. Adaptive perfectionists were also reported to have stronger interpersonal relationships and greater social connectivity than their peers. Moreover, both adaptive and maladaptive perfectionists reported significantly higher GPAs than non-perfectionists. Peer informant data indicated that adaptive perfectionists were rated as having the highest academic expectations followed by maladaptive perfectionists and then non-perfectionists. Contrary to expectations, no significant differences were found between cluster groupings on peer reported social withdrawnness. Findings suggest that adaptive perfectionism is associated with a range of positive psychological, psychoeducational and psychosocial outcomes. Conversely, maladaptive perfectionism appears to be related to several behaviors which may impede healthy adolescent functioning. Implications regarding the improved assessment of perfectionism and intervention strategies aimed at both students and professionals working within the school domain are discussed.

Transportation planning and urban development in the United States have synchronously emerged over the past few decades to encompass goals associated with sustainability, improved connectivity, complete streets and mitigation of environmental impacts. These goals have evolved in tandem with some of the relatively more traditional objectives of supply-side improvements such as infrastructure and capacity expansion. Apart from the numerous federal regulations in the US transportation sector that reassert sustainability motivations, metropolitan planning organizations and civic societies face similar concerns in their decision-making and policy implementation. However, overall transportation planning to incorporate these wide-ranging objectives requires characterization of large-scale transportation systems and traffic flow through them, which is dynamic in nature, computationally intense and a non-trivial problem. Thus, these contemporary questions lie at the interface of transportation planning, urban development and sustainability planning. They have the potential of being effectively addressed through state-of-the-art transportation modeling tools, which is the main motivation and philosophy of this thesis. From the research standpoint, some of these issues have been addressed in the past typically from the urban design, built-environment, public health and vehicle technology and mostly qualitative perspectives, but not as much from the traffic engineering and transportation systems perspective---a gap in literature which the thesis aims to fill. Specifically, it makes use of simulation-based dynamic traffic assignment (DTA) to develop modeling paradigms and integrated frameworks to seamlessly incorporate these in the transportation planning process. In addition to just incorporating them in the planning process, DTA-based paradigms are able to accommodate numerous spatial and temporal dynamics associated with system traffic, which more traditional static models are not able to. Besides, these features are critical in the context of the planning questions of this study. Specifically, systemic impacts of suburban and urban street pattern developments typically found in US cities in past decades of the 20th century have been investigated. While street connectivity and design evolution is mostly regulated through local codes and subdivision ordinances, its impacts on traffic and system congestion requires modeling and quantitative evidence which are explored in this thesis. On the environmental impact mitigation side, regional emission inventories from the traffic sector have also been quantified. Novel modeling approaches for the street connectivity-accessibility problem are proposed. An integrated framework using the Environmental Protection Agency's regulatory MOVES model has been developed, combining it with mesoscopic-level DTA simulation. Model demonstrations and applications on real and large-sized study areas reveal that different levels of connectivity and accessibility have substantial impacts on system-wide traffic---as connectivity levels reduce, traffic and congestion metrics show a gradually increasing trend. As regards emissions, incorporation of dynamic features leads to more realistic emissions inventory generation compared to default databases and modules, owing to consideration of the added dynamic features of system traffic and region-specific conditions. Inter-dependencies among these sustainability planning questions through the common linkage of traffic dynamics are also highlighted. In summary, the modeling frameworks, analyses and findings in the thesis contribute to some ongoing debates in planning studies and practice regarding ideal urban designs, provisions of sustainability and complete streets. Furthermore, the integrated emissions modeling framework, in addition to sustainability-related contributions, provides important tools to aid MPOs and state agencies in preparation of state implementation plans for demonstrating conformity to national ambient air-quality standards in their regions and counties. This is a critical condition for them to receive federal transportation funding. / text

A new method is presented here for petrophysical interpretation of heterogeneous carbonates using well logs and core data. Developing this new method was necessary because conventional evaluation methods tend to yield inaccurate predictions of pore connectivity and permeability in the studied field. Difficulties in the petrophysical evaluation of this field are related to shoulder-bed effects, presence of non-connected porosity, rock layers that are thinner than the vertical resolution of well-logging tools, and the effect of oil-base mud (OBM) invasion in the measurements. These problems give rise to uncommon measurements and rock properties, such as: (a) reservoir units contained within thinly bedded and laminated sequences, (b) very high apparent resistivity readings in the oil-bearing zone, (c) separation of apparent resistivity logs with different depths of investigation, (d) complex unimodal and bimodal transverse relaxation distributions of nuclear magnetic resonance (NMR) measurements, (e) reservoir units having total porosity of 0.02 to 0.26 and permeability between 0.001mD to 4.2D, (f) significant differences between total and sonic porosity, and (g) low and constant gamma-ray values. The interpretation method introduced in this thesis is based on the detection of layer boundaries and rock types from high-resolution well logs and on the estimation of layer-by-layer properties using numerical simulation of resistivity, nuclear, and NMR logs. Layer properties were iteratively adjusted until the available well logs were reproduced by numerical simulations. This method honors the reservoir geology and physics of the measurements while adjusting the layer properties; it reduces shoulder-bed effects on well logs, especially across thinly bedded and laminated sequences, thereby yielding improved estimates of interconnected porosity and permeability in rocks that have null mobile water saturation and that were invaded with OBM. Additionally, dynamic simulations of OBM invasion in free-water depth intervals were necessary to estimate permeability. It is found that NMR transverse relaxation measurements are effective for determining rock and fluid properties but are unreliable in the accurate calculation of porosity and permeability in thinly bedded and highly laminated depth sections. In addition, this thesis shows that low resistivity values are associated with the presence of microporosity, and high resistivity values are associated with the presence of interconnected and vuggy porosity. In some layers, a fraction of the vuggy porosity is associated with isolated pores, which does not contribute to fluid flow. An integrated evaluation using multiple measurements, including sonic logs, is therefore necessary to detect isolated porosity. After the correction and simulation, results show, on average, a 34% improvement between estimated and core-measured permeability. Closer agreement was not possible because of limitations in tool resolution and difficulty in obtaining a precise depth match between core and well-log measurements. / text

<p>Semi-natural grasslands, which are a declining and fragmented habitat in Europe, contain a high biodiversity, and are therefore of interest to conservation. This thesis examines how plant diversity is influenced by the landscape context, and how plant and fungal diversity can be targeted by practical conservation using indicator species and congruence between species groups. Reproduction and recruitment of the dioecious herb <i>Antennaria dioica </i>was also investigated, providing a case study on how fragmentation and habitat degradation may affect grassland plants.</p><p>Grassland size and heterogeneity were of greater importance for plant diversity in semi-natural grassland, than present or historical connectivity to other grasslands, or landscape characteristics. Larger grasslands were more heterogeneous than smaller grasslands, being the likely reason for the species-area relationship.</p><p>A detailed study on <i>A. dioica </i>discovered that sexual reproduction and recruitment may be hampered due to skewed sex-ratios. Sex-ratios were more skewed in small populations, suggesting that dioecious plants are likely to be particularly sensitive to reduced grassland size and fragmentation.</p><p>A study on indicators of plant species richness, used in a recent survey of remaining semi-natural grasslands in Sweden, revealed several problems. A high percentage of all indicator species were missed by the survey, removing an otherwise significant correlation between indicator species and plant species richness. Also, a null model showed that the chosen indicator species did not perform significantly better than species chosen at random from the available species pool, questioning the selection of the indicators in the survey. Diversity patterns of the threatened fungal genus <i>Hygrocybe</i> were not congruent with plant species richness or composition. Plants are thus a poor surrogate group for Hygrocybe fungi, and probably also for other grassland fungi. Implications from this thesis are that conservation of semi-natural grasslands should target several species groups, and that an appropriate scale for plant conservation may be local rather than regional.</p>

Connectivity is a fundamental tool for matroid theorists, which has become increasingly important in the eventual solution of many problems in matroid theory. Loosely speaking, connectivity can be used to help describe a matroid's structure. In this thesis, we prove a series of results that further the knowledge and understanding in the field of matroid connectivity. These results fall into two parts. First, we focus on 3-connected matroids. A chain theorem is a result that proves the existence of an element, or elements, whose deletion or contraction preserves a predetermined connectivity property. We prove a series of chain theorems for 3-connected matroids where, after fixing a basis B, the elements in B are only eligible for contraction, while the elements not in B are only eligible for deletion. Moreover, we prove a splitter theorem, where a 3-connected minor is also preserved, resolving a conjecture posed by Whittle and Williams in 2013. Second, we consider k-connected matroids, where k >= 3. A certain tree, known as a k-tree, can be used to describe the structure of a k-connected matroid. We present an algorithm for constructing a k-tree for a k-connected matroid M. Provided that the rank of a subset of E(M) can be found in unit time, the algorithm runs in time polynomial in |E(M)|. This generalises Oxley and Semple's (2013) polynomial-time algorithm for constructing a 3-tree for a 3-connected matroid.