The Accuracy and Reliability of Traditional Surface Flow Type Mapping: Is It Time for A New Method of Characterising Physical River Habitat?

Surface flow types (SFT) are advocated as ecologically relevant hydraulic units, often mapped visually from the bankside to characterise rapidly the physical habitat of rivers. SFT mapping is simple, non-invasive and cost-efficient. However, it is also qualitative, subjective and plagued by difficulties in recording accurately the spatial extent of SFT units. Quantitative validation of the underlying physical habitat parameters is often lacking, and does not consistently differentiate between SFTs. Here, we investigate explicitly the accuracy, reliability and statistical separability of traditionally mapped SFTs as indicators of physical habitat, using independent, hydraulic and topographic data collected during three surveys of a c. 50m reach of the River Arrow, Warwickshire, England. We also explore the potential of a novel remote sensing approach, comprising a small unmanned aerial system (sUAS) and Structure-from-Motion photogrammetry (SfM), as an alternative method of physical habitat characterisation. Our key findings indicate that SFT mapping accuracy is highly variable, with overall mapping accuracy not exceeding 74%. Results from analysis of similarity (ANOSIM) tests found that strong differences did not exist between all SFT pairs. This leads us to question the suitability of SFTs for characterising physical habitat for river science and management applications. In contrast, the sUAS-SfM approach provided high resolution, spatially continuous, spatially explicit, quantitative measurements of water depth and point cloud roughness at the microscale (spatial scales ≤1m). Such data are acquired rapidly, inexpensively, and provide new opportunities for examining the heterogeneity of physical habitat over a range of spatial and temporal scales. Whilst continued refinement of the sUAS-SfM approach is required, we propose that this method offers an opportunity to move away from broad, mesoscale classifications of physical habitat (spatial scales 10-100m), and towards continuous, quantitative measurements of the continuum of hydraulic and geomorphic conditions which actually exists at the microscale.

Discrete-event Simulation and Optimization to Improve the Performance of a Healthcare System

Thesis (Ph.D.)--University of Washington, 2016-08

Hindfoot Kinematics of Healthy Subjects Using Lateral Wedge Insoles in a Biplane Fluoroscopy System

Thesis (Master's)--University of Washington, 2016-08

On precise three-dimensional environment modeling via UAV-based photogrammetric systems

Abstract : Images acquired from unmanned aerial vehicles (UAVs) can provide data with unprecedented spatial and temporal resolution for three-dimensional (3D) modeling. Solutions developed for this purpose are mainly operating based on photogrammetry concepts, namely UAV-Photogrammetry Systems (UAV-PS). Such systems are used in applications where both geospatial and visual information of the environment is required. These applications include, but are not limited to, natural resource management such as precision agriculture, military and police-related services such as traffic-law enforcement, precision engineering such as infrastructure inspection, and health services such as epidemic emergency management. UAV-photogrammetry systems can be differentiated based on their spatial characteristics in terms of accuracy and resolution. That is some applications, such as precision engineering, require high-resolution and high-accuracy information of the environment (e.g. 3D modeling with less than one centimeter accuracy and resolution). In other applications, lower levels of accuracy might be sufficient, (e.g. wildlife management needing few decimeters of resolution). However, even in those applications, the specific characteristics of UAV-PSs should be well considered in the steps of both system development and application in order to yield satisfying results. In this regard, this thesis presents a comprehensive review of the applications of unmanned aerial imagery, where the objective was to determine the challenges that remote-sensing applications of UAV systems currently face. This review also allowed recognizing the specific characteristics and requirements of UAV-PSs, which are mostly ignored or not thoroughly assessed in recent studies. Accordingly, the focus of the first part of this thesis is on exploring the methodological and experimental aspects of implementing a UAV-PS. The developed system was extensively evaluated for precise modeling of an open-pit gravel mine and performing volumetric-change measurements. This application was selected for two main reasons. Firstly, this case study provided a challenging environment for 3D modeling, in terms of scale changes, terrain relief variations as well as structure and texture diversities. Secondly, open-pit-mine monitoring demands high levels of accuracy, which justifies our efforts to improve the developed UAV-PS to its maximum capacities. The hardware of the system consisted of an electric-powered helicopter, a high-resolution digital camera, and an inertial navigation system. The software of the system included the in-house programs specifically designed for camera calibration, platform calibration, system integration, onboard data acquisition, flight planning and ground control point (GCP) detection. The detailed features of the system are discussed in the thesis, and solutions are proposed in order to enhance the system and its photogrammetric outputs. The accuracy of the results was evaluated under various mapping conditions, including direct georeferencing and indirect georeferencing with different numbers, distributions and types of ground control points. Additionally, the effects of imaging configuration and network stability on modeling accuracy were assessed. The second part of this thesis concentrates on improving the techniques of sparse and dense reconstruction. The proposed solutions are alternatives to traditional aerial photogrammetry techniques, properly adapted to specific characteristics of unmanned, low-altitude imagery. Firstly, a method was developed for robust sparse matching and epipolar-geometry estimation. The main achievement of this method was its capacity to handle a very high percentage of outliers (errors among corresponding points) with remarkable computational efficiency (compared to the state-of-the-art techniques). Secondly, a block bundle adjustment (BBA) strategy was proposed based on the integration of intrinsic camera calibration parameters as pseudo-observations to Gauss-Helmert model. The principal advantage of this strategy was controlling the adverse effect of unstable imaging networks and noisy image observations on the accuracy of self-calibration. The sparse implementation of this strategy was also performed, which allowed its application to data sets containing a lot of tie points. Finally, the concepts of intrinsic curves were revisited for dense stereo matching. The proposed technique could achieve a high level of accuracy and efficiency by searching only through a small fraction of the whole disparity search space as well as internally handling occlusions and matching ambiguities. These photogrammetric solutions were extensively tested using synthetic data, close-range images and the images acquired from the gravel-pit mine. Achieving absolute 3D mapping accuracy of 11±7 mm illustrated the success of this system for high-precision modeling of the environment.

Existence and weak-strong uniqueness for the Navier-Stokes-Smoluchowski system over moving domains

This dissertation concerns the well-posedness of the Navier-Stokes-Smoluchowski system. The system models a mixture of fluid and particles in the so-called bubbling regime. The compressible Navier-Stokes equations governing the evolution of the fluid are coupled to the Smoluchowski equation for the particle density at a continuum level. First, working on fixed domains, the existence of weak solutions is established using a three-level approximation scheme and based largely on the Lions-Feireisl theory of compressible fluids. The system is then posed over a moving domain. By utilizing a Brinkman-type penalization as well as penalization of the viscosity, the existence of weak solutions of the Navier-Stokes-Smoluchowski system is proved over moving domains. As a corollary the convergence of the Brinkman penalization is proved. Finally, a suitable relative entropy is defined. This relative entropy is used to establish a weak-strong uniqueness result for the Navier-Stokes-Smoluchowski system over moving domains, ensuring that strong solutions are unique in the class of weak solutions.

Impact of radiofrequency ablation for patients with varicose veins on the budget of the German statutory health insurance system

Objectives: In contrast to other countries, surgery still represents the common invasive treatment for varicose veins in Germany. However, radiofrequency ablation, e.g. ClosureFast, becomes more and more popular in other countries due to potential better results and reduced side effects. This treatment option may cause less follow-up costs and is a more convenient procedure for patients, which could justify an introduction in the statutory benefits catalogue. Therefore, we aim at calculating the budget impact of a general reimbursement of ClosureFast in Germany. Methods: To assess the budget impact of including ClosureFast in the German statutory benefits catalogue, we developed a multi-cohort Markov model and compared the costs of a “World with ClosureFast” with a “World without ClosureFast” over a time horizon of five years. To address the uncertainty of input parameters, we conducted three different types of sensitivity analysis (one-way, scenario, probabilistic). Results: In the Base Case scenario, the introduction of the ClosureFast system for the treatment of varicose veins saves costs of about 19.1 Mio. € over a time horizon of five years in Germany. However, the results scatter in the sensitivity analyses due to limited evidence of some key input parameters. Conclusions: Results of the budget impact analysis indicate that a general reimbursement of ClosureFast has the potential to be cost-saving in the German Statutory Health Insurance.

Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting

Extreme learning machine (ELM) is originally proposed for single- hidden layer feed-forward neural networks (SLFN). From the functional equivalence of fuzzy logic systems and SLFN, the fuzzy logic systems can be interpreted as a special case of SLFN under some mild conditions. Hence the fuzzy logic systems can be trained using SLFN's learning algorithms. Considering the same equivalence, ELM is utilized here to train interval type-2 fuzzy logic systems (IT2FLSs). Based on the working principle of the ELM, the parameters of the antecedent of IT2FLSs are randomly generated while the consequent part of IT2FLSs is optimized using Moore-Penrose generalized inverse of ELM. Application of the developed model to electricity load forecasting is another novelty of the research work. Experimental results shows better forecasting performance of the proposed model over the two frequently used forecasting models.

Modelling and analysis of type 4 wind turbine generator system for utilization in frequency regulation studies

Green energy targets for coming decades advocates high penetration of wind energy in main energy matrix which also pose incendiary threat to stability and reliability of modern electric grid if their dynamic performance aspects are not assessed beforehand. Considering increasing interest in dynamic performance along with ancillary service assessment related to frequency regulation, development of suitable generic modeling has gained high priority. This paper presents modeling of type 4 full converter wind turbine generator system suitable for frequency regulation focusing on active power control. Complete model is a modification of WECC generic model with additional aerodynamic and pitch control model. Descriptions of individual sub models are presented and performance results are compared manufacturer specific GE type 4 WTG generic model by means of simulations in the MATLAB ® Power System Block set.

Hybrid model for the training of interval type-2 fuzzy logic system

In this paper, a hybrid training model for interval type-2 fuzzy logic system is proposed. The hybrid training model uses extreme learning machine to tune the consequent part parameters and genetic algorithm to optimize the antecedent part parameters. The proposed hybrid learning model of interval type-2 fuzzy logic system is tested on the prediction of Mackey-Glass time series data sets with different levels of noise. The results are compared with the existing models in literature; extreme learning machine and Kalman filter based learning of consequent part parameters with randomly generated antecedent part parameters. It is observed that the interval type-2 fuzzy logic system provides improved performance with the proposed hybrid learning model.

Characteristics evaluation of a μ-Synthesis H∞ controller for a grid-connected three-phase photovoltaic system

This paper presents a μ-Synthesis H∞ Controller for regulating the switching signal of the inverter connected with a three-phase photovoltaic (PV) system. To facilitate the control design, the system is represented in terms of state space realization with uncertainties. The control design involves selecting proper weighting functions and performing synthesis. The controller order is reduced by Henkel-norm method. Simulations are carried out to evaluate the characteristics of the controller under parametric uncertainties. It is found out that the proposed controller is inherently stable, possesses significantly small tracking error, and preserves nominal performance, robust stability and robust performance for the grid-connected three-phase PV system.

Geosynthetics in Antarctica: performance of a composite barrier system to contain hydrocarbon-contaminated soil after three years in the field

An overview of the design and performance of geosynthetics in composite barrier systems for biopiles used to remediate hydrocarbon-contaminated soil at Casey Station, Antarctica, is presented. Seven instrumented biopiles were constructed over three field seasons. To minimize the risk of hydrocarbon migration to groundwater, composite barrier systems were used (each using different combinations of geosynthetic clay liners (GCLs), high density polyethylene (HDPE) geomembranes (GMB), and geotextiles (GTXs)). One biopile used a co-extruded geomembrane (HDPE with an ethylene vinyl alcohol (EVOH) core). The liner system was subject to a combination of coupled phenomena that could interact and affect the GMB-GCL composite barrier performance. The exposure conditions involved potential freeze-thaw cycling, hydration-desiccation cycles, cation exchange, direct and diffusive exposure to hydrocarbons. The effect of these phenomena was investigated by monitoring GCL and GMB sacrificial coupons. GCL coupons were placed between the main GCL component and the main geomembrane component of the composite liner and GMB coupons placed between the main GMB sheet and the GTX protection layer. Coupons were exhumed from the biopiles each year. The exhumed GCL field moisture content values ranged from 162% to 22%. After three (3) years in the field, GCL coupons that had undergone at least one hydration/desiccation cycle showed no significant change in swell index values or fluid loss values. The measured hydraulic conductivity of exhumed GCL coupons from Biopiles 1 and 2 (3 × 10-11 m s-1) was within the expected range and not significantly different from the values for virgin GCL. GMB coupons exhumed after three years from Biopiles 1 and 2 showed no significant change in oxidative induction time (OIT), melt flow index or tensile properties. Diffusion tests were performed as an index test for establishing the performance of the GMBs as a diffusive barrier to hydrocarbons, with permeation parameters for BTEX contaminants ranging from P g = 0.9-9.2 × 10-13 m2 s-1 for the exhumed GMB (with values depending on the contaminant and GMB). These values were similar to the parameters obtained for virgin GMBs and there was no significant change with field exposure, with GMBs appearing to be performing well.

Modified AHP for gene selection and cancer classification using type-2 fuzzy logic

This paper proposes a modification to the analytic hierarchy process (AHP) to select the most informative genes that serve as inputs to an interval type-2 fuzzy logic system (IT2FLS) for cancer classification. Unlike the conventional AHP, the modified AHP allows us to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test, and signal-to-noise ratio. The IT2FLS is introduced for the classification task due to its great ability for handling nonlinear, noisy, and outlier data, which are common problems in cancer microarray gene expression profiles. An unsupervised learning strategy using the fuzzy c-means clustering is employed to initialize parameters of the IT2FLS. Other classifiers such as multilayer perceptron network, support vector machine, and fuzzy ARTMAP are also implemented for comparisons. Experiments are carried out on three well-known microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, and prostate. Rather than the traditional cross validation, leave-one-out cross-validation strategy is applied for the experiments. Results demonstrate the performance dominance of the IT2FLS against the competing classifiers. More noticeably, the modified AHP improves the classification performance not only of the IT2FLS but of all other classifiers as well. Accordingly, the proposed combination between the modified AHP and IT2FLS is a powerful tool for cancer classification and can be implemented as a real clinical decision support system that is useful for medical practitioners.

c-Type cytochrome biogenesis can occur via a natural Ccm system lacking CcmH, CcmG, and the heme-binding histidine of CcmE

The Ccm cytochrome c maturation System I catalyzes covalent attachment of heme to apocytochromes c in many bacterial species and some mitochondria. A covalent, but transient, bond between heme and a conserved histidine in CcmE along with an interaction between CcmH and the apocytochrome have been previously indicated as core aspects of the Ccm system. Here, we show that in the Ccm system from Desulfovibrio desulfuricans, no CcmH is required, and the holo-CcmE covalent bond occurs via a cysteine residue. These observations call for reconsideration of the accepted models of System I-mediated c-type cytochrome biogenesis. © 2010 by The American Society for Biochemistry and Molecular Biology, Inc.

Chronic inflammation inhibits GH secretion and alters the serum insulin-like growth factor system in rats

Adjuvant-induced arthritis in rats is associated with growth failure, hypermetabolism and accelerated protein breakdown. The aim of this work was to study the effects of adjuvant-induced arthritis on GH and insulin-like growth factor-I (IGF-I). Arthritis was induced by an intradermal injection of complete Freund's adjuvant and rats were killed 18 and 22 days later. IGF-I and GH levels were measured by radioimmunoassay. Pituitary GH mRNA was analyzed by northern blot and IGF binding proteins (IGFBPs) by western blot. Arthritic rats showed a decrease in both serum and hepatic concentrations of IGF-I. On the contrary, arthritis increased the circulating IGFBPs. The serum concentration of IGF-I in the arthritic rats was negatively correlated with the body weight loss observed in these animals. Arthritis decreased the serum concentration of GH and this decrease seems to be due to an inhibition of GH synthesis, since pituitary GH mRNA content was decreased in arthritic rats (p<0.01). These data suggest that the decrease in body weight gain in arthritic rats may be, at least in part, secondary to the decrease in GH and IGF-I secretion. Furthermore, the increased serum IGFBPs may also be involved in the disease process.