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Anomaly Detection (AD) has recently received considerable attention in various applications such as biometrics, computer vision, and machine learning. To detect anomalies, modelling and encapsulating normal data is still an open problem, especially if only normal (non-anomalous) data is available for training time, making it a challenging problem. In this thesis, the challenge of a pure AD design is studied using non-anomalous samples only. For that, several pure AD models are developed so that each one deals with a specific domain. Presentation attacks (PA) are recognised as a considerable threat to biometric devices. To counteract PAs, the majority of approaches formulate the problem as a two-class classification. Nevertheless, the two-class formulation does not perform robustly due to its poor generalisation performance in the presence of novel PAs. To address this limitation, a pure AD model is trained where the real-access is considered as 'normal' and PAs are presumed to be 'anomalous' observations. An aspect of PAD design that has been overlooked is the use of client-specific information in the context of AD. As the first contribution, client-specific information is adopted to build the one-class classifiers (OCCs). The idea of constructing a fusion of OCCs has received increasing attention. Nevertheless, very few studies in the literature have been concerned with developing a general methodology of OCC fusion design. In the thesis, it is aimed to redress this limitation by proposing a generic OCC fusion method with three novel contributions: (a) A new score normalisation method is proposed as a preprocessing step to multiple classifier fusion that can cope well with heavy-tailed non-anomalous data distributions (b) A novel fitness function is defined which requires only normal observations to estimate the competency of OCCs (c) A new pruning method is proposed to discard OCCs having no/less informative data from the fusion to improve the AD results. Up to this point, Convolutional neural Networks (CNNs) pretrained on Imagenet have been used in the thesis to extract the features from image data. To train a CNN from scratch, a deep network is pretrained using self-supervised learning for an auxiliary geometric transformation (GT) classification task. The key contribution is a novel loss function that augments the standard cross-entropy by an additional term that plays a significant role in the later stages of self-supervised learning. The pretrained network is finetuned for the downstream task using non-anomalous data only, and a GT model for the data is constructed. Anomalies are detected by fusing the output of several decision functions defined using the learnt GT class model. Extensive experiments on publicly available AD datasets demonstrate the effectiveness of the proposed contributions and lead to significant performance gains compared to the state-of-the-art methods. This includes benchmarking datasets in PAD, conventional tabular datasets in the machine learning domain and common computer vision.

This thesis reports on the characterization of the state-of-the-art In(Ga)As/GaAs quantum dot (QD) material grown by molecular beam epitaxy for optical telecommunication applications. A wide variety of characterization methods are employed to investigate the material properties and characteristics of a number of QD-based devices enabling future device optimization. The motivation that prompted this study was predicated mainly upon two technological advantages. First, that the QDs gain spectra exhibits a symmetric gain shape and thus the change of refractive index with respect to gain is negligible at the lasing wavelength. This is therefore expected to result in a zero or a very small linewidth enhancement factor (LEF), which is desirable for instance, for high-speed modulation purposes where frequency chirp under modulation, which is directly proportional to the LEF, may be substantially reduced. Second, the fact that not only QDs exhibit a damped frequency response attributed to the carrier relaxation dynamics but also as the resilience of a laser to optical feedback is inversely proportional to the fourth power of the LEF, QD lasers are expected to demonstrate a relatively higher feedback insensitivity. This bodes well for operating these devices isolator free, which would be greatly cost-effective. The absorption and gain spectra of the QD active material are investigated in chapters 2 and 3, respectively. The LEF of QD lasers at a range of temperatures is studied in chapter 3, which confirms the expectation for the first time for In(Ga)As/GaAs QD lasers from -10 oC to 85 oC. Subsequently, the findings of chapters 2 and 3 are employed in chapter 4 with an electro absorption modulator device in mind which would be able to operate with chirp control. In chapter 5, the modulation response of QD lasers is investigated through examining the relative intensity noise (RIN) spectra in the electrical domain. The resilience of the devices to optical feedback is subsequently studied through the RIN characteristics at a range of temperatures. Chapter 6 provides a summary of the thesis findings and possible future works that may be carried out as continuation to this project, which fell outside of the remit of this work.

This study is focused on the concept of urban intensification, investigating the costs and benefits of higher density urban development. It attempts to define the exact physical patterns and spatial characteristic of the density of residential urban areas in order to establish the extent to which design qualities are delivered or compromised in high density urban form. In this way, the study attempts to identify any interrelationships between three factors: urban density, urban layout and urban design quality; and to see whether there is a consensus that higher density is linked to changes in urban layout, and has consequences in terms of urban quality. The study covers these three concepts at two spatial scales, namely the urban block and the neighbourhood. The work demonstrates that significant changes in morphological patterns such as plot size or control and ownership of open spaces, result from higher density design and, that there are subsequent impacts on particular design qualities such as overall adaptability or biodiversity of the residential neighbourhood. An analytical framework, elaborated from the theory, was developed taking into account appropriate measures of urban density (e.g. people or built form density measures), descriptors of urban morphology (e.g. average size of plots and open spaces), and indicators of urban design quality (e.g. mixed use ratios, levels of diversity in house types and sizes). The methods of analysis and testing of the theoretical proposition include, first, the use of computer simulation of urban tissue prototypes in three defined ranges of density; second, testing the acceptability and preferences of design typologies via focus group discussions with designers, developers and potential local users. Finally, this research develops a transferable method for defining and measuring design qualities which are important for specific localities, a method which can be used to evaluate possible emerging urban typologies in high density residential schemes.

Analytical, experimental and computational studies were carried out to investigate the mixing and dispersion of neutrally buoyant tracer in the nearshore region due to the effects of waves and currents. The main objective of this study was to quantify the mixing processes in the nearshore region. Theoretical approaches were developed to quantify the contribution of diffusive and dispersive mixing in the nearshore due to wave activity. An analytical model was developed to quantify the diffusive and dispersive mixing mechanisms based on mathematical solutions for the advection-diffusion equation. Mixing under the combined effects of waves and currents were studied through measurement of hydrodynamic and fluorometric tracing experiments from a largescale facility at the Danish Hydraulic Institute, Denmark. The experiments were conducted in a shallow water basin for a range of hydrodynamic conditions covering wave steepness between 2 – 5%. Data from detailed measurements were used to examine the spreading of a solute inside the surf zone and seawards of the breaker region. The overall depth-averaged on-offshore mixing coefficient obtained from the hydrodynamic experimental studies were compared to the mixing coefficients determined from the tracer measurements. It was shown that inside the surfzone, the shear dispersion is the dominant mixing factor, which is almost an order of magnitude greater than the diffusive mixing. The location of the breaker point and the wave height across the nearshore is shown to be important for determining the mixing coefficient. Further detailed spatial and temporal variations of flow hydrodynamics across the nearshore were investigated through a series of laboratory experiments with the use of Particle Image Velocimetry. The experiments were undertaken in a dedicate wave flume at the University of Warwick. Through analysis of the PIV data, new information on the spatial variation of diffusion and dispersion in the shallow water column of the nearshore region was obtained. Flow visualisation of the PIV results identified three distinct hydrodynamic processes during the bore, undertow and the bore/undertow interaction, which were the primary mixing mechanisms in the nearshore region. The temporal variation of dispersion coefficient shows that intense shearing mechanisms exist during wave bore/undertow interactions. The numerical capabilities of Smoothed Particle Hydrodynamics, a Lagrangian, meshless, particle-based method in modelling the nearshore hydrodynamics were explored in this study. The numerical data was used to quantify the mixing processes. By using suitable estimates of turbulent diffusion and cross-shore wave-induced velocity, a theoretical approximation of the overall mixing within the surfzone and seaward of the breaker region can be obtained. A comparison between the theoretical model and previous laboratory and field studies on the nearshore mixing suggests that the mixing is proportional to wave height (H1.5). It is demonstrated that inside the surfzone, the mixing is dominated by the vertical structure of the cross-shore velocity. The pioneering Royal society works of Svendsen & Putrevu (1994) identified the temporally-averaged dispersion, as a function of distance from the shore. Using a combination of experimental, mathematical and numerical formulations, this study has identified and quantified for the first time, the temporal and spatial dispersive mixing processes in the nearshore (over the full wave cycle), within the challenging and complex environment of the surf zone. It has been shown that the cross-shore circulation provides lateral mixing that exceeds that of the turbulence by an order of magnitude inside the surfzone. The turbulence still remains an essential part of the flow since it is the primary driver which through the vertical mixing is responsible for defining the vertical velocity profiles, which then determine strength of the dispersion.

This thesis considers the problem of supporting traffic with elastic bandwidth requirements and hard end-to-end delay constraints in multi-hop wireless networks, with focus on source transmission rates and link data rates as the key resource allocation decisions. Specifically, the research objective is to develop a source rate control and scheduling strategy that guarantees bounded average end-to-end queueing delay and maximises the overall utility of all incoming traffic, using network utility maximisation framework. The network utility maximisation based approaches to support delay-sensitive traffic have been predominantly based on either reducing link utilisation, or approximation of links as M/D/1 queues. Both approaches lead to unpredictable transient behaviour of packet delays, and inefficient link utilisation under optimal resource allocation. On the contrary, in this thesis an approach is proposed where instead of hard delay constraints based on inaccurate M/D/1 delay estimates, traffic end-to-end delay requirements are guaranteed by proper forms of concave and increasing utility functions of their transmission rates. Specifically, an alternative formulation is presented where the delay constraint is omitted and sources’ utility functions are multiplied by a weight factor. The alternative optimisation problem is solved by a distributed scheduling algorithm incorporating a duality-based rate control algorithm at its inner layer, where optimal link prices correlate with their average queueing delays. The proposed approach is then realised by a scheduling algorithm that runs jointly with an integral controller whereby each source regulates the queueing delay on its paths at the desired level, using its utility weight coefficient as the control variable. Since the proposed algorithms are based on solving the alternative concave optimisation problem, they are simple, distributed and lead to maximal link utilisation. Hence, they avoid the limitations of the previous approaches. The proposed algorithms are shown, using both theoretical analysis and simulation, to achieve asymptotic regulation of end-to-end delay given the step size of the proposed integral controller is within a specified range.

The complex domain provides a natural processing framework for a large class of signals encountered in communications, radar, biomedical engineering and renewable energy. Statistical signal processing in C has traditionally been viewed as a straightforward extension of the corresponding algorithms in the real domain R, however, recent developments in augmented complex statistics show that, in general, this leads to under-modelling. This direct treatment of complex-valued signals has led to advances in so called widely linear modelling and the introduction of a generalised framework for the differentiability of both analytic and non-analytic complex and quaternion functions. In this thesis, supervised and blind complex adaptive algorithms capable of processing the generality of complex and quaternion signals (both circular and noncircular) in both noise-free and noisy environments are developed; their usefulness in real-world applications is demonstrated through case studies. The focus of this thesis is on the use of augmented statistics and widely linear modelling. The standard complex least mean square (CLMS) algorithm is extended to perform optimally for the generality of complex-valued signals, and is shown to outperform the CLMS algorithm. Next, extraction of latent complex-valued signals from large mixtures is addressed. This is achieved by developing several classes of complex blind source extraction algorithms based on fundamental signal properties such as smoothness, predictability and degree of Gaussianity, with the analysis of the existence and uniqueness of the solutions also provided. These algorithms are shown to facilitate real-time applications, such as those in brain computer interfacing (BCI). Due to their modified cost functions and the widely linear mixing model, this class of algorithms perform well in both noise-free and noisy environments. Next, based on a widely linear quaternion model, the FastICA algorithm is extended to the quaternion domain to provide separation of the generality of quaternion signals. The enhanced performances of the widely linear algorithms are illustrated in renewable energy and biomedical applications, in particular, for the prediction of wind profiles and extraction of artifacts from EEG recordings.

In this thesis we will investigate inference processes for predicate languages. The main question we are concerned with in this thesis is how to choose a probability function amongst those that satisfy a certain knowledge base. This question has been extensively studied for propositional logic and we shall investigate it for first order languages. We will first study the generalisation of Minimum Distance. MD. and Centre of Mass. CMco inference processes to unary predicate languages and then we will investigate the generalisations of itie Maximum Entropy inference process to general polyadic languages. For the case of the Maximum Entropy inference process we will study and compare two generalisations. the BP-method and the W-method. We will show that the two methods agree for the unary and :E, knowledge bases and we conjecture that the result holds for the II, knowledge bases too. We shall show that neither of these generalisations for the Maximum Entropy inference process is universally well defined for a first order language and we shall study some of the problems associated with generalising this inference process to polyadic languages.

The concurrent deployment of high-quality wireless networks and large-scale cloud services offers the promise of secure ubiquitous access to seemingly limitless amount of content. However, as users' expectations have grown more demanding, the performance and connectivity failures endemic to the existing networking infrastructure have become more apparent. These problems are in general exacerbated by user mobility. The work presented in this dissertation demonstrates that performance of services for mobile users is significantly affected by environmental factors that are hard to characterize ahead of deployment. This work includes development and evaluation of large-scale mobile experimentation infrastructures (DOME, GENI) that facilitate longitudinal studies of today's technologically diverse mobile environment over a period of four years. Based on the insights gained from these studies, a mechanism called Spider is presented that provides for efficiently utilizing Wi-Fi deployments in highly mobile scenarios to achieve higher throughput and improved connectivity. This work presents the first in-depth analysis of the performance of attempting concurrent AP connections from highly mobile clients. Spider provides a 400% improvement in throughput and 54% improvement in connectivity over stock Wi-Fi implementations. The last part of this dissertation demonstrates there are predictable differences among performances of a cellular network in different geographical locations of a town. Consequently, patterns of data transmission between a server on the Internet and a moving cell phone can reveal the geographic travel path of that phone. While the GPS and location-awareness features on phones explicitly share this information, phone users will likely be surprised to learn that disabling these features does not suffice to prevent a remote server from determining their travel path. We showed that a simple HMM-based classifier can discover and exploit features of the geography surrounding possible travel paths to determine the path a phone took, using only data visible at the remote server on the Internet. Having gathered hundreds of traces over a large geographic area, we showed that the HMM-based technique is able to distinguish mobile phones from stationary phones with up to 94.7% accuracy. Routes taken by each mobile phone could be distinguished with up to 75.9% accuracy using the same technique. This dissertation proposes new tools and techniques for characterization of the impact of the environment on the performance of mobile networks. The concrete set of results and insights gained from this work demonstrates mechanisms for improving connectivity and throughput in highly mobile scenarios while at the same time raising new challenges for maintaining the privacy of mobile users. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

A brief review of the problems involved in hydrostatic extrusion equipment is given and the need for a proper study of the hydrostatic extrusion dies is discussed. The literature is reviewed to find the best method of evaluating the pressure distribution and the average coefficient of friction at the tool-metal interface. The method used here for evaluating the pressure distribution and the average coefficient of friction in hydrostatic extrusion dies is explained and the apparatus is described. The pressure distributions and the coefficients of friction at the die-billet interface for the axisymmetric hydrostatic extrusion of annealed copper through 20° and 40° dies are obtained and the results are discussed. An upper bound solution for evaluating the pressure distribution in the dies for the case of axi-symmetric hydrostatic extrusion is explained and the pressure distributions obtained by this method for the 20° and 40° dies are compared with the experimental ones. Stress distributions in the 20° and 40° dies are also obtained by the finite element method using both the experimental and theoretical pressure distributions. An elastic-plastic finite element method for evaluating the stress distributions in the billet and hence the pressure distribution in the die for steady state axisymmetric extrusion is suggested. The method is applied to the extrusion of annealed copper billets through the 20° and 40° dies and the pressure distributions obtained in this way are compared with the previous ones. A method for obtaining a die lard geometry which could possibly overcome the surface cracking which occurs in the hydrostatic extrusion of brittle materials is also suggested. Finally a die with a short cylindrical portion at the entry, which can promote hydrodynamic lubrication at slow speeds is made and successfully tested.

Abstract: In this work, a novel visual navigation method is proposed to estimate the state of mobile and fixed cold-spray material deposition systems using a stereocamera sensor installed in the workspace. Unlike other visual localization algorithms that exploit costly onboard sensors such as LiDARs or fully rely on distinct visual cues on the robot and grid markers in the environment, our method significantly reduces the cost and complexity of the sensory setup by utilizing a cost-effective remote stereo vision system. This allows for the localization of the target system regardless of its appearance or the environment and enables scalability for tracking and operation of multiple mobile material deposition systems at the same time. To achieve this aim, deep neural networks, kinematic constraints, and learning-aided state observers are employed to detect and estimate the location and orientation of the deposition system. A physical model of the system is fused with a remote visual sensing module and is proposed. This accounts for frames in which depth estimation accuracy is reduced due to perceptually degraded conditions in the cold spraying context. The visual state estimation algorithm is evaluated on a fixed and mobile setup that demonstrates the accuracy and reliability of the proposed method. Moreover, a model predictive controller is formulated, designed, and implemented to enable the task of autonomous mobile robot trajectory tracking. The architecture of the model predictive controller incorporates essential kinematic constraints, input bounds, and control input smoothness, thereby ensuring the generation of a feasible input for the autonomous mobile material deposition system to seamlessly trace a predefined trajectory. A comprehensive comparative analysis is conducted between this model predictive controller and its PID counterpart, encompassing a rigorous evaluation through a series of simulated and real-world tests, aiming to elucidate their respective performances and characteristics.

Deep learning has revolutionized the way humans interact with technology, enabling complex tasks that were once thought to be impossible. Its ability to process vast amounts of data and learn from patterns has led to significant advancements in various areas, such as image and speech recognition, natural language processing, and robotics. These advancements have had a significant impact on humans daily lives, from virtual assistants to self-driving cars, personalized recommendations on social media platforms, and fraud detection in banking and finance. While deep learning has enabled remarkable advancements across various industries, its wide adoption has led to alarming repercussions in terms of carbon emissions and energy consumption. This is due to the high computational and storage requirements of deep learning models, which result in the use of large data centers and computing infrastructures that consume a vast amount of energy and have become a significant contributor to carbon emissions. As deep learning models become increasingly complex, the energy consumption required for training andinference is expected to rise, exacerbating the problem. This becomes more crucial because the current computing infrastructures used for training and serving inference for these models are significantly underutilized. This PhD dissertation sets out to take on this imperative challenge and rethink the design of custom neural accelerators and their adoption in both cloud and edge infrastructures by devising solutions across the whole compute stack ranging from circuits to systems and algorithms. To that end, the contributions of this dissertation are as follows: • Devising BIHIWE, a programmable mixed-signal DNN accelerator that leverages the innate energy-efficiency of analog computing. To address the challenges associated with analog computing, I leverage the mathematical properties of deep learning operations and define a new computing model for dot-product operations along with its mixed-signal computing circuitry. I further design a programmable hierarchical clustered architecture to integrate the mixed-signal compute units and propose solutions to further mitigate the non-ideality in analog computing. • Designing ultra-energy efficient acceleration solution for deeply quantized neural networks. The proposed design intersperses bit parallelism within data-level parallelism and dynamically interweaves the two together. This design paradigm enables dynamic composition of narrow-bitwidth vector engines at the bit granularity based on the required bitwidth of the DNN layers. This new composition mode amortizes the cost of aggregation and operand-delivery across a vector of elements and brings forth significant energy savings and performance improvements. • Proposing a novel microarchitecture and Instruction Set Architecture for a companion processor in neural accelerators that tackles the challenges associated with executing emerging and novel operations in DNNs. The design strikes a balance between customization and programmability to keep up with the volatility of deep learning research while offering significant performance and energy gains compared to prior work. • Proposing Planaria, the very first neural accelerator design that offers simultaneous multi-tenant acceleration of DNNs. The design introduces and leverages the novel concept of runtime architecture fission, which breaks a monolithic accelerator into smaller yet full-fledged accelerators to enable spatial co-location of multiple DNN inference requests. To best utilize this microarchitecture capability, I also propose a task scheduling algorithm that breaks up the accelerator with respect to the current server load, DNN topology, and task priorities, all while considering the latency bounds of the tasks. This work opens a new dimension in the design of neural accelerators that considers utilization, cost-effectiveness, and responsiveness in datacenters. • Devising a mathematical formulation for pruning the inconsequential operations in self-attention layers of transformer models. This formulation piggy backs on the back-propagation training to analytically co-optimize the threshold and the weights simultaneously, striking a formally optimal balance between accuracy and computation pruning. Additionally, I propose a bit-serial architecture, dubbed LEOPARD, to maximize the benefits by terminating computation even before pruning the following calculation without any approximation.

The global water crisis is a critical issue that the world is facing, and the lack of access to clean water not only leads to health-related problems, but also causes social injustice. Groundwater is an important source for high quality water but is highly prone to contamination. Developing new remediation methods and improving traditional ones are critical to restoring groundwater resources. Iron oxide particles are promising materials for the remediation of different classes of groundwater contaminants, such as nitroaromatic compounds and heavy metals, because the minerals can facilitate contaminant destruction or immobilization. Because of the complexity of subsurface environments in terms of mineralogy, geochemistry, and hydrology, understanding the interaction of iron oxide particles and their reactivity in presence different subsurface constituents is necessary. In this thesis, iron oxide nanoparticle reactivity was investigated under more realistic, environmentally relevant conditions including continuous exposure to reactants, attachment to other minerals, and the presence of natural organic matter. In this work, kinetic studies in combination with solid-state characterization were used to elucidate the evolving reactivity and mineralogy of iron oxide particles (goethite and hematite) during reaction with a nitroaromatic probe compound and chromate. The kinetics of model pollutants were quantified and used to evaluate the effect of evolving mineralogy and the presence of natural organic matter on reactivity. Materials characterization techniques, including X-ray diffraction and electron microscopy, were used to quantify changes in the minerals present and mineral growth. Spectroscopy and chromatography techniques provided information about which fractions of natural organic matter associated with minerals and how strong these associations were. The findings of this research demonstrated that the reactivity of iron oxide particles evolved during exposure to contaminants and depends on the iron oxide phase, hydrodynamics of water flow, and the presence of other environmentally relevant components like natural organic matter. By advancing our understanding of iron oxide reactivity in the subsurface environments, better prediction of the efficacy and optimization of remediation methods are possible.

Wastewater collection systems are among the most critical components of modern society as any disturbance in their services can incur economic loss, cause irreversible damage to environment, and significantly impact public health. With the rise in the intensity and frequency of extreme weather events as well as continuous aging and deterioration of wastewater collection systems, the structural integrity of deteriorated sewer pipes is exacerbated, and the degradation of this critical infrastructure is accelerated. Due to the buried nature of wastewater collection systems and the high cost associated with inspection of these assets, the condition assessment of sewer pipes is often delayed under limited budget. The high vulnerability of wastewater collection systems highlights the need for reliable risk assessment frameworks in order to proactively manage the risk of short- and long-term consequences induced by the failure of these assets. Considering that wastewater collection systems are commonly designed for a long service life and the costly condition assessment associated with this infrastructure, there is a high demand to accurately identify the vulnerable components and subsequently, facilitate the decision-making under shortfall of funding. Reliability and risk assessment can support proactive management of sewer systems by identifying and prioritizing the most vulnerable yet critical sewer pipes. Current studies on reliability and risk assessment of wastewater collection systems have a few limitations that might result in inaccurate estimation of the performance of the system which may potentially misinform the decision makers.For example, the existing computational models to predict the future performance of sewer pipes are simplified in several aspects, which means that they cannot be used for reliability analysis and decision making. In addition, the influential factors relate to the performance of underground wastewater collection systems are not well identified. Moreover, Fragility models are key components for reliability analysis, risk management, and resilience assessment of critical infrastructure systems have not been developed for wastewater collection systems.To address the aforementioned limitations, in this study:-A high-fidelity three-dimensional Finite Element (FE) model of underground concrete sewer pipes, surrounding soil, and pavement layers is developed in ANSYS platform. The FE model is capable of capturing nonlinear behaviors of concrete, soil, and pipe-soil interactions, in addition to simulating crack formation and crushing of concrete pipes. Corrosion impacts are simulated by sequentially removing concrete layers inside the pipe so that corrosion effects from previous years are taken into account.-The impacts of a number of design and uncertain variables on the serviceability performance of concrete wastewater collection systems throughout their service life are investigated using the developed FE model.-A comprehensive temporal global sensitivity analysis (GSA) is performed to analyze uncertainties contributing to the service life performance of sewer pipes under compounding effects of corrosion deterioration and traffic loads. For GSA, a comprehensive set of environmental, structural, and geotechnical variables including environmental factors that contribute to the sulfide corrosion of concrete pipes, material properties of concrete, surrounding soil layers, and pavement layers, as well as uncertainties in truckloads are considered.-The first set of temporal fragility models for underground concrete sewer pipes under the compounding effects of corrosion and truck loads are developed. Subsequently, age-dependent reliability analysis is performed for serviceability and collapse of pipes conditional on truck loads.-A framework for risk assessment of underground concrete wastewater collection systems is proposed which relies on fragility models for estimating the failure probability of sewer pipes. To evaluate the economic, social, and environmental consequences of failure, an enhanced weight scoring summation method is adopted in which a new weight which accounts for the types of failure is considered.

Functional brain mapping correlates the brain anatomy and physiology to our perceptions and behaviors and allows for development of neurotherapeutic technologies. Therapeutic direct stimulation of the cortex and subcortical areas, despite its high invasiveness, remains a crucial technique for studying the neurophysiological correlates of brain networks. For example stimulation of visual cortex for restoring vision and deep brain stimulation for treatment of movement disorders provide unique opportunities to study the neural correlates of visual perception and motor behaviors. While these techniques have been around for several decades, the functional role of targeted areas and their interaction with electrical stimulation is yet to be fully understood. This work takes advantage of invasive neurosurgical techniques to refine the functional mapping of visual and motor systems. Through the application of a cortical visual prosthesis we created subject-specific retinotopic maps of the visual cortex. The functional efficacy of these maps were tested in blind subjects and was shown to restore some degree of vision allowing them to localize a static object and identify the direction of a moving target. Moreover we used intra-operative recordings from ventrolateral thalamus and sensorimotor cortex of patients with essential tremor to investigate the functional role of thalamo-cortical network in voluntary movement. By analyzing different measures of oscillatory power and connectivity, we depicted a casual mechanism in which thalamic low frequency oscillations modulate the movement-related cortical broad-band gamma activity. This understanding of visual and motor maps can advance the efficacy of visual prosthesis and deep brain stimulation therapy by highlighting the functional organization of the visual cortex and oscillatory mechanisms of thalamo-cortical interactions.

This thesis contains simplified simulations of polymers in the entangled melt/solution state and in the glassy state in fast extensional flows where chains become locally stretched at modest strains. Comparisons to experiments show that the “Entangled Kink Dynamics” simulations for entangled chains and the “Hybrid Brownian Dynamics” simulations for glassy polymers improve on existing models. We also present atomistic Molecular Dynamics (MD) simulations of the interactions of for Poly(N-isopropylacrylamide)” (pNIPAAm) excipient with a hydrophobic drug, phenytoin, to improve drug release in the GI tract. In the first part of the thesis, the conventional “tube-model” constitutive equation is found to predict overly rapid approach to a steady-state extensional viscosity of entangled polystyrene solutions in fast extensional flow. Based on an analysis of the conformation of polymer chains at high strains, a new and simple simulation technique, which we call the “Entangled Kink Dynamics” (EKD), is developed to study the late stage dynamics of polymer chains. In this approach, a highly strained polymer chain in extensional flow is modeled as a quasi-one-dimensional chain with kinks at which the polymer folds back on itself. Polymer strands between the kinks are considered fully stretched and Brownian force is neglected in comparison with drag and tension. The behavior of entangled chains in the kinked state is studied for the cases of dilute chains, affinely moving kinks, and kinks entangled with kinks on other chains. To compare the kink dynamics predictions with experimental results, input is needed from more detailed “single-chain slip-spring” simulations. With this input, EKD simulations show a much better prediction of experimental extensional viscosity of entangled long polystyrene solutions, compared to conventional tube models. Validating our model, we explain the potential downfalls of the tube theory and provide insight into achieving a better constitutive equation for entangled polymeric melts and solutions. Secondly, we present a modified Hybrid Brownian dynamics model to study the rheology of polymeric glasses. Using a Coarse-Grained Brownian Dynamics (CG-BD) model for polymer chains suspended in a glassy solvent that represents the segmental model of the glassy polymer, governed by a simple fluidity model, we study the stress and conformation evolution of glassy polymers under fast uniaxial extensional flows and compare our results with fine-grained molecular dynamics simulations of polymeric glass. Although our CG-BD simulations do not have the effect of entanglements explicitly, the conformation evolution of polymer chains in our hybrid technique is found to be similar to that found in MD results, where the entanglements are taken into account explicitly. This proves that in fast flows of glassy polymers, chain conformation is governed by sub-entanglement dynamics and formation of folds, or kinks. The importance of sub-entanglement scales in generating stress becomes hugely important at high strains where polymeric glass show strain hardening. By calculating the number of entangled folds on the chain in the strain hardening regime, we prove that the huge rise in the stress is not due to the effect of entanglements. Instead, we show that strain hardening behavior is caused because of the highly stretched strands between nascent fold points, below the length scales of entanglement spacing.

Three combinatorial problems are studied and efficient algorithms are presented for each of them. The first problem is concerned with lot-sizing, the second one arises in exam-scheduling, and the third lies on the intersection of the k-median and k-center clustering problems.

Abnormal alterations in cerebrospinal fluid (CSF) flow and intrinsic cardiac-induced deformations of brain tissue are thought to play an important role in the pathophysiology of various neurological disorders. Using magnetic resonance imaging (MRI), numerical phantoms, and experimental platforms, we studied the feasibility and performance of multiple MRI techniques and numerical models to quantify CSF and neural tissues biomechanics. First, we evaluated the accuracy of time-resolved three directional phase contrast MRI (4D PCMRI) in quantifying CSF velocity and relative pressure using numerical and experimental phantoms. 4D PCMRI was shown to be accurate in measuring peak through-plane velocities. 4D PCMRI-derived CSF pressure gradients were markedly smaller than the reference solutions. However, these underestimations were consistent across different geometries and flow boundary conditions. Our results supported the use of 4D PCMRI to assess CSF velocities and showed the possible utility of this technique for comparison of pressure-based parameters between patients. Next, we assessed the impact of neural tissue motion on CSF dynamics using a moving boundary computational fluid dynamics simulation. A relatively small neural tissue motion (~150 to 300 µm) was found to increase peak CSF velocity and pressure dissociation across the craniovertebral junction by up to 60 and 120%, respectively.Finally, we examined the utility of displacement-encoding with stimulated echoes (DENSE) MRI in regional quantification of brain tissue motion and strain. Quantification of small neural tissue motion and strain and their regional variations was shown to be feasible using DENSE MRI. Medial and inferior brain structures had significantly larger motion and strain compared to those located more peripherally. Our results showed that DENSE MRI has the potential to be utilized as a tool to evaluate the changes in brain tissue dynamics resulting from alterations in biomechanical stresses and tissue properties.This dissertation research showed that the approaches combining engineering tools and MRI sequences could be helpful to develop MRI-derived parameters and biomarkers for improved assessment of neurological disorders. Based on these findings, future research should focus on the possible clinical utilities of the parameters described herein to quantify CSF hydrodynamics and brain tissue dynamics.

A new type of MacCormack scheme, using a modified Low Dissipation and DispersionRunge-Kutta time marching method, is presented. This scheme is using two stages in every stepwhich implements biased spatial differencing stencils and for the remaining stages uses non-dissipative centraldifferencing stencils.Because of using the MacCormack-type scheme in this method, the new scheme carries an inherentartificial dissipation which uses the ease of implementing boundary condition specificationsof a two-stage MacCormack scheme.

In the past decade, growing number of commercialized “lab on a chip” systems have been addressing needs in biology for patients and researchers. The ability to miniaturize conventional systems used in lab while increasing sensitivity in addition to newly developed platforms, have enabled new applications such as point-of-care diagnostics and single-cell sequencing. This dissertation focuses on new droplet microfluidic platforms developed for addressing the current challenges (controlling encapsulation beyond Poisson distribution, removing bulky and expensive pumps,…) in commercialization of single-cell and single-molecule assays. In chapter 1, current state of the art, recent commercialized products and their limitations are discussed.In chapter 2, new findings about particle interactions in single-phase microfluidics leading to “preferred interparticle spacings” and trajectory attractors in inertial microflow regimes are presented. Furthermore, these findings have been combined with new modifications made to conventional flow focusing droplet generators in order to avoid jetting regime in inertial regimes, thus allowing making use of preferred spacing between cells/particles in inertial flows for controlling number of cells/particles encapsulated in droplets (one cell/particle per drop). Our findings could be used for many applications such as drug discovery. The other application could be for droplet sequencing (drop-seq) where for example RNA of individual cells are sequenced and it is important that only one cell is present in each droplet to generate unbiased data. In chapter 3 and 4 of the dissertation, new patented technology for generation of droplets, replacing bulky and expensive pumps, with body force exerted on the ferrofluid by magnetic field acting on magnetic nanoparticles mixed with the fluid. Thus the cost and footprint of the device is reduced significantly, allowing for use in limited-resource settings (global health) for quantification of biomolecules by controlled magnetic dosing of nanoliter droplets.

We present new analysis of the low-mass stellar initial mass function ($\sim0.4-0.8M_\odot$) in the Local Group dwarf spheroidal galaxy, Draco. Using archival HST/ACS and WFC3 optical imaging, we construct deep color-magnitude (CMD) diagrams in 3 different felds at 3 different galactocentric radii and measure the IMF by modeling the resolved lower main sequence. We model the optical color-magnitude diagrams of each feld assuming two different IMF models (power-law, log-normal), five different stellar evolution libraries (Padova, BaSTI, Dartmouth, Victoria, PARSEC), and a binary star model. For a single-sloped power-law IMF model, we find that the IMF slope steepens by up to 0.7 dex for radii between 150 and 300pc, while the binary fraction remains approximately constant. The absolute values of the IMF slopes at any radius depend strongly on the adopted stellar models, suggesting that current knowledge of the lower-main sequence stars is uncertain. In fact, utilizing different stellar models has resulted in up to 0.67 dex difference in the IMF slope. All fields show more consistent log-normal parameters, which are also in reasonable agreement with values for a standard Chabrier IMF. However, there are large degeneracies between the characteristic mass and dispersion of the log-normal, that can only be reduced with data that extends to lower stellar masses.

Deterioration in wastewater infrastructure systems is recognized as a serious, worldwide concern. Deterioration due to sulfate attack is one of the primary reasons of functional and structural failures in concrete sewer pipes which is a significant component in wastewater infrastructure systems. Any type of failure may lead to serious health, environmental, and economical issues, lack of the wastewater services, incidents, and traffic delays. The deterioration model presented by Environmental Protection Agency (EPA) is used to obtain the corrosion rate. Subsequently, a time-dependent model is utilized to determine the loss of wall thickness in concrete pipes. In this study, ANSYS is used to construct a nonlinear three-dimensional Finite Element model, which is capable of simulating the behavior of buried concrete sewer pipes under gravity and truck loads. Deterioration of concrete pipe is modeled in ANSYS in such a way that a continuous degradation of the system is considered during the service life of the pipes. A Monte Carlo-based reliability assessment method that uses Latin Hypercube Sampling technique is employed to capture the uncertainties associated with concrete and soil characteristics. The crack width which is the primary reason for leakage in the concrete pipes, is defined as the limit state function, and can be obtained from the tensile stresses on the pipe wall. Hence, the descending portion of stress-strain curve of concrete on the tension side is studied through post-processing of ANSYS results. The results indicate the significant impact of moderate deterioration on the performance and probability of failure of concrete sewer pipes for the serviceability level. In addition, elastic modulus of sidefill and bedding soil are found to have considerable effect on the stress distribution along the deteriorated concrete pipe.

The goal of this research was to develop a site-targeting nanotherapeutic drug delivery platform for suppression of chronic vascular inflammation and regression associated with debilitating conditions such as pulmonary arterial hypertension (PAH) and diabetic retinopathy (DR). Importantly, loss of endothelium-derived nitric oxide (NO), an endogenous anti-inflammatory and pro-vasculogenic factor that prevents leukocyte-endothelial cell (EC) adhesion and capillary regression, is strongly implicated in chronic inflammation associated with these conditions. Thus, restoring NO levels represents a viable approach for anti-inflammatory therapies. Nitroglycerin (NTG) markedly enhances nitric oxide (NO) bioavailability. However, its ability to mimic the anti-inflammatory and pro-vasculogenic properties of NO remains unknown. Here, the overarching goal was to examine whether (1) NTG can suppress vascular inflammation and regression, (2) a nanotechnological drug delivery approach can be leveraged to simultaneously amplify its anti-inflammatory effects and ameliorate adverse effects associated with conventional high-dose NTG administration, and finally (3) NTG nanoformulation can be modified to selectively deliver NTG to inflamed ICAM-1-expressing vessels. My findings reveal that NTG significantly inhibits monocyte adhesion to inflamed ECs and prevents EC capillary regression in vitro through an increase in endothelial NO and decrease in endothelial ICAM-1 clustering. More importantly, nanoliposomal NTG (NTG-NL) produced an approximately 70-fold increase in NTG drug efficacy when compared with free NTG while preventing excessive mitochondrial superoxide production and loss of arterial vasorelaxation associated with high NTG doses. Finally, to facilitate targeting of NTG-NL to inflamed ICAM-1-expressing vessels, whole ICAM-1 IgG and non-immunogenic anti-ICAM-1 scFv fragment were tethered to the surface of NTG-NL. As a proof-of-concept study whole ICAM-1 IgG-modified NLs demonstrated preferential targeting to inflamed vessel, in vivo. Importantly, however, the translational potential of these NLs lies with the non-immunogenic scFv fragment. The following in vitro studies reveal that NTG-NL modified with anti-ICAM-1 scFv exhibits 6-fold greater binding to inflamed (ICAM-1-expressing) ECs than to normal ECs and achieves superior anti-inflammatory effects. Thus, these findings provide the rationale to examine this novel site-targeting NTG nanotherapeutic as a potentially superior therapy for various vascular inflammation-mediated conditions. Addressing these critical issues related to potential NTG-based therapy forms the central theme of the following dissertation.

The energy harvesting sources has been introduced as a promising alternative for battery power. However, harvested energy is inherently sporadic, unstable, and unreliable. For this reason, a non-volatile processor has been previously proposed to bridge the intermittent executions in frequent power losses. Nonetheless, recurrent power failures reduce overall system performance which has forced researchers to look into multi-input energy harvesting systems. The purpose of this study is to investigate the possible solutions to improve the reliability and functionality of battery-less devices. This study has two major objectives: (1) implementing periodic checkpointing on WISP5, and (2) proposing optimized multi-input single-output energy harvesting system. The WISP5 was acquired from the Sensor Systems Laboratory, University of Washington, as a viable RFID energy harvesting system to implement software checkpointing techniques. We performed the periodic checkpointing every 50ms based on the RFID power fluctuation style. Then, we explored a number of possible maximum power point tracking techniques to extract maximum power from harvesters. As a result, we verified that the open circuit voltage control is the most cost effective and efficient technique for both thermoelectric (TEG) and photovoltaic (PV) . Also, we revealed that in low-level input voltages, following the fact that the maximum power extraction can be achieved at half of open circuit voltage does not result in maximum possible efficiency. Therefore, by adjusting the converter input voltage at about 66% of open circuit voltage, we improved power efficiency by about 18%.

Image-based techniques are a promising non-destructive approach for road pavement condition evaluation. The main objective of this study is to extract, quantify and evaluate important surface defects, such as cracks, using an automated computer vision-based system to provide a better understanding of the pavement deterioration process. To achieve this objective, an automated crack-recognition software was developed, employing a series of image processing algorithms of crack extraction, crack grouping, and crack detection. Bottom-hat morphological technique was used to remove the random background of pavement images and extract cracks, selectively based on their shapes, sizes, and intensities using a relatively small number of user-defined parameters. A technical challenge with crack extraction algorithms, including the Bottom-hat transform, is that extracted crack pixels are usually fragmented along crack paths. For de-fragmenting those crack pixels, a novel crack-grouping algorithm is proposed as an image segmentation method, so called MorphLink-C. Statistical validation of this method using flexible pavement images indicated that MorphLink-C not only improves crack-detection accuracy but also reduces crack detection time. Crack characterization was performed by analysing imagerial features of the extracted crack image components. A comprehensive statistical analysis was conducted using filter feature subset selection (FSS) methods, including Fischer score, Gini index, information gain, ReliefF, mRmR, and FCBF to understand the statistical characteristics of cracks in different deterioration stages. Statistical significance of crack features was ranked based on their relevancy and redundancy. The statistical method used in this study can be employed to avoid subjective crack rating based on human visual inspection. Moreover, the statistical information can be used as fundamental data to justify rehabilitation policies in pavement maintenance. Finally, the application of four classification algorithms, including Artificial Neural Network (ANN), Decision Tree (DT), k-Nearest Neighbours (kNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is investigated for the crack detection framework. The classifiers were evaluated in the following five criteria: 1) prediction performance, 2) computation time, 3) stability of results for highly imbalanced datasets in which, the number of crack objects are significantly smaller than the number of non-crack objects, 4) stability of the classifiers performance for pavements in different deterioration stages, and 5) interpretability of results and clarity of the procedure. Comparison results indicate the advantages of white-box classification methods for computer vision based pavement evaluation. Although black-box methods, such as ANN provide superior classification performance, white-box methods, such as ANFIS, provide useful information about the logic of classification and the effect of feature values on detection results. Such information can provide further insight for the image-based pavement crack detection application.

Natural disasters such as floods and earthquakes can cause multiple deaths, injuries, and severe damage to properties. In order to minimize the impact of such disasters, emergency response plans should be developed well in advance of such events. Moreover, because different organizations such as non-governmental organizations (NGOs), governments, and militaries are involved in emergency response, the development of a coordination scheme is necessary to efficiently organize all the activities and minimize the impact of disasters. The logistics network design component of emergency management includes determining where to store emergency relief materials, the corresponding quantities and distribution to the affected areas in a cost effective and timely manner. In a two-echelon humanitarian relief chain, relief materials are pre-positioned first in regional rescue centers (RRCs), supply sources, or they are donated to centers. These materials are then shipped to local rescue centers (LRCs) that distribute these materials locally. Finally, different relief materials will be delivered to demand points (also called affected areas or AAs). Before the occurrence of a disaster, exact data pertaining to the origin of demand, amount of demand at these points, availability of routes, availability of LRCs, percentage of usable pre-positioned material, and others are not available. Hence, in order to make a location-allocation model for pre-positioning relief material, we can estimate data based on prior events and consequently develop a stochastic model. The outputs of this model are the location and the amount of pre-positioned material at each RRC as well as the distribution of relief materials through LRCs to demand points. Once the disaster occurs, actual values of the parameters we seek (e.g., demand) will be available. Also, other supply sources such as donation centers and vendors can be taken into account. Hence, using updated data, a new location-allocation plan should be developed and used. It should be mentioned that in the aftermath of the disaster, new parameters such as reliability of routes, ransack probability of routes and priority of singular demand points will be accessible. Therefore, the related model will have multiple objectives. In this dissertation, we first develop a comprehensive pre-positioning model that minimizes the total cost while considering a time limit for deliveries. The model incorporates shortage, transportation, and holding costs. It also considers limited capacities for each RRC and LRC. Moreover, it has the availability of direct shipments (i.e., shipments can be done from RRCs directly to AAs) and also has service quality. Because this model is in the class of two-stage stochastic facility location problems, it is NP-hard and should be solved heuristically. In order to solve this model, we propose using Lagrangian Heuristic that is based on Lagrangian Relaxation. Results from the first model are amounts and locations of pre-positioned relief materials as well as their allocation plan for each possible scenario. This information is then used as a part of the input for the second model, where the facility location problem will be formulated using real data. In fact, with pre-positioned items in hand, other supplies sources can be considered as necessary. The resulting multi-objective problem is formulated based on a widely used method called lexicography goal programming. The real-time facility location model of this dissertation is multi-product. It also considers the location problem for LRCs using real-time data. Moreover, it considers the minimization of the total cost as one of the objectives in the model and it has the availability of direct shipments. This model is also NP-hard and is solved using the Lagrangian Heuristic. One of the contributions of this dissertation is the development of Lagrangian Heuristic method for solving the pre-positioning and the real- time models. Based on the results of Lagrangian Heuristic for the pre-positioning model, almost all the deviations from optimal values are below 5%, which shows that the Heuristics works acceptably for the problem. Also, the execution times are no more than 780 seconds for the largest test instances. Moreover, for the real-time model, though not directly comparable, the solutions are fairly close to optimal and the execution time for the largest test instance is below 660 seconds. Hence, the efficiency of the heuristic for real-time model is satisfactory.

The aim of this research is to develop and build a low cost portable integrated frequency-domain and continuous wave (CW) system for real-time spectroscopic imaging of human tissue. This system measures four tissue chromophore concentrations (water, lipid, deoxygenated, and oxygenated hemoglobin) using eight near-infrared wavelengths ranging from 660nm to 980nm, in real-time. The frequency domain (FD) module measures the phase and amplitude of photon density waves from 50-500 MHz with an operating speed of 0.5Hz, while the CW module uses frequency multiplexing to achieve sampling rates up to 250Hz. The FD component provides quantitative information about optical scattering and absorption using the acquired phase and amplitude data at four wavelengths (660, 690, 785, and 830nm). The CW component expands spectral bandwidth and improves acquisition speed by measuring only low frequency (11-19 KHz) intensity changes at four wavelengths (880, 904, 915, and 975nm). The CW system has a 50 dB dynamic range, enabling measurements in tissue with a source-detector spacing up to 4 cm and it is immune to background noise from ambient light by utilizing low-frequency modulation and bandpass filtering. The performance of this hybrid system and its equivalency to previous diffuse optical spectroscopy systems are tested and validated using a tissue-simulating phantom with an embedded inclusion. The system enables continuous scanning of surfaces which can replace discrete measurements on a grid pattern used in previous systems. The instrument outperforms the current system's data acquisition speed by 2 orders of magnitude while reducing the overall cost by $9000. We demonstrate in-vivo applications of this combined instrument by measuring abdomen, muscle and brain tissues. The extremely fast data acquisition enables high resolution characterization of the pulsatile waveform. Finally, vasculature reactivity and hemodynamics are measured and characterized by using vascular occlusion and paced breathing models.

The development of optical sources and components suitable for the mid-infrared is crucial for applications in this spectral range to reach the maturity level of their counterparts in the visible and near-infrared spectral regimes. The recent commercialization of quantum cascade lasers is leading to further interest in this spectral range. Wideband mid-infrared coherent sources, such as supercontinuum generation, have yet to be fully developed. A mid-infrared supercontinuum source would allow for unique applications in spectroscopy and sensing. Over the last decade, it has been shown that high-index confinement in highly nonlinear fibers pumped with high-peak-power pulses is an excellent approach to supercontinuum generation in the visible and near-infrared. Nonlinear waveguides such as fibers offer an obvious advantage in increasing the nonlinear interaction length maintained with a small cross section. In addition, fiber systems do not require optical alignment and are mechanically stable and robust with respect to the environmental changes. These properties have made fiber systems unique in applications where they are implemented in a harsh and unstable environment. In extending this approach into the mid-infrared, I have used chalcogenide glass fibers. Chalcogenide glasses have several attractive features for this application: they have high refractive indices for high optical-confinement, have a wide transparency window in the mid-infrared, and have a few orders-of-magnitude higher nonlinearity than silica glass and other mid-IR glasses. Producing chalcogenide glass fiber tapers offer, furthermore, the possibility of dispersion control and stronger field confinement and hence higher nonlinearity, desired for supercontinuum generation.

Multi-Criteria Decision Making (MCDM) problems are often associated with tradeoffs between performances of the available alternative solutions under decision making criteria. These problems become more complex when performances are associated with uncertainty. This study proposes a stochastic MCDM procedure that can handle uncertainty in MCDM problems. The proposed method coverts a stochastic MCDM problem into many deterministic ones through a Monte-Carlo (MC) selection. Each deterministic problem is then solved using a range of MCDM methods and the ranking order of the alternatives is established for each deterministic MCDM. The final ranking of the alternatives can be determined based on winning probabilities and ranking distribution of the alternatives. Ranking probability distributions can help the decision-maker understand the risk associated with the overall ranking of the options. Therefore, the final selection of the best alternative can be affected by the risk tolerance of the decisionmakers. A Group Decision Support System (GDSS) is developed here with a user-friendly interface to facilitate the application of the proposed MC-MCDM approach in real-world multiparticipant decision making for an average user. The GDSS uses a range of decision making methods to increase the robustness of the decision analysis outputs and to help understand the sensitivity of the results to level of cooperation among the decision-makers. The decision analysis methods included in the GDSS are: 1) conventional MCDM methods (Maximin, Lexicographic, TOPSIS, SAW and Dominance), appropriate when there is a high cooperation level among the decision-makers; 2) social choice rules or voting methods (Condorcet Choice, Borda scoring, Plurality, Anti-Plurality, Median Voting, Hare System of voting, Majoritarian iii Compromise ,and Condorcet Practical), appropriate for cases with medium cooperation level among the decision-makers; and 3) Fallback Bargaining methods (Unanimity, Q-Approval and Fallback Bargaining with Impasse), appropriate for cases with non-cooperative decision-makers. To underline the utility of the proposed method and the developed GDSS in providing valuable insights into real-world hydro-environmental group decision making, the GDSS is applied to a benchmark example, namely the California‘s Sacramento-San Joaquin Delta decision making problem. The implications of GDSS‘ outputs (winning probabilities and ranking distributions) are discussed. Findings are compared with those of previous studies, which used other methods to solve this problem, to highlight the sensitivity of the results to the choice of decision analysis methods and/or different cooperation levels among the decision-makers

Abstract: Pipeline condition assessment is a primary task in running an efficient urban asset management program because in the case of system failure, the consequences can be significant to both municipalities and users. Selecting a suitable assessment protocol is crucial for municipalities, since the rehabilitation plans and network prioritization are set based on the structural rating system introduced by the selected protocol. Currently, North American municipalities use different condition assessment protocols, some of which are internationally-accepted, while others are developed based on local needs. This thesis compares structural condition grading systems using four condition assessment protocols; National Association of Sewer Service Companies’ Pipeline Assessment Certification Program, the Fourth Edition of Water Research Centre’s Sewerage Rating Manual, and early and modified editions of the City of Edmonton’s condition assessment standard. The differences and similarities among the four protocols are identified by performing surveys on more than 20,000 of sample pipelines. As a result, accuracy of each protocol is also established and presented.

Many systems in both the service and manufacturing sectors can be modeled and analyzed as queueing networks. In such systems, control and design is often an important issue that may significantly affect the performance. This dissertation focuses on the development of innovative techniques for the design and control of such systems. Special attention is given to real-world applications in (a) the design and control of patient flow in the hospital emergency departments, (b) design and control of service/call centers, and (c) the design and control of supply chains under disruption risks. With respect to application (a), using hospital data, analytical models, and simulation analyses we show how (1) better patient prioritization, (2) enhanced triage systems, and (3) improved patient flow designs allow emergency departments to significantly improve their performance with respect to both operational efficiency and patient safety. Regarding application (b), we give specific attention to a two-server and three-demand class network in the shape of a ``W'' with random server disruption and repair times. Studying this network, we show how effective control and design strategies that efficiently make use of (partial) flexibility of servers can be implemented to achieve high performance and resilience to server disruptions. In addition to establishing stability properties of different known control mechanisms, a new heuristic policy, termed Largest Expected Workload Cost (LEWC), is proposed and its performance is extensively benchmarked with respect to other widely used polices. Regarding application (c), we demonstrate how supply chains can boost their performance using better control and design strategies that efficiently take into account supply disruption risks. Motivated by several real-world examples of disruptions, production flexibility, and supply contracts within supply chains, we model the informational and operational flexibility approaches to designing a resilient supply chain. By analyzing optimal ordering policies, sourcing strategies, and the optimal levels of back-up capacity reservation contracts, various disruption risk mitigation strategies are considered and compared, and new insights into the design of resilient supply chains are provided.

Advancements in web technology such as the integration of operating platforms, the application of mobile applets and connectivity with databases into an encompassing web environment has promoted the development of complex web-based systems for enterprise computing. In this integration the inherent security flaws and associated problems of these technological advancements are also brought together under this web environment. These flaws, when exploited, could lead to system compromises of various kinds including stealing of security sensitive information, system take-over fabrication of fake electronic documents and illegal alteration to web sites contents. Therefore, the successful, prompt and appropriate responses of these complex web-based systems to security threats and breaches, such as in the cases of document tampering and misrepresentation from illegal users, are imperative in promoting the user's willingness and confidence when interacting with these systems. Accordingly, this has become one of the major concerns in development, management and operation of web-based systems. This thesis also studies the authentication of dynamic web contents through the application of a one-time digital signature. To this effect, relevant concepts and possible approaches have been discussed. Authentication and verification of dynamic contents, efficiently in the web environment, is suggested as a separate research area and as a direction for future work.

A general method for the synthesis of continuous- and discrete-time feedback control systems for single-input/single-output and multi-input/multi-output nonlinear processes was developed. The nonlinear controller synthesis formulas were derived in the presence of input constraints and time-delays under the assumption that the delay-free part of the process is minimum phase. Two conceptually different approaches were used for the derivation of the controllers: the model predictive approach and the geometric approach. Concrete theoretical connections between the two approaches were established. In particular, it was shown that under certain conditions, they lead to identical control laws. A theoretical framework for integrated design and operation of single-stage batch or semibatch reactors was developed. This approach includes systematic decoupling of optimization and design through conceptual decomposition of the reactor dynamics into two subsystems with distinct characteristics. In this framework, notions of feasibility, flexibility, controllability and safety of the design of batch processes were introduced and some criteria for their assessment were presented. As a case study, the approach was employed to design a batch polymerization reactor. The developed nonlinear control method was applied experimentally to one batch and two continuous stirred tank polymerization reactors. In the batch reactor study the nonlinear control method was implemented to start-up the reactor and then track a precalculated optimal temperature profile by manipulating two coordinated heat inputs. In the CSTR studies, reactor temperature and conversion were controlled by manipulating the two coordinated heat inputs and an initiator inlet flow rate of the CSTR residence time. Conversion was inferred from on-line measurement of the reacting mixture density. A key feature of one of the CSTRs was that its characteristic matrix is singular. Systematic tuning guidelines were proposed for the nonlinear control method. The experimental studies showed the sufficient robustness of the controller to modeling and observer initialization errors and the excellent servo and regulatory performance of the controller in the presence of active input and state constraints.