Sensor-based Nonlinear and Nonstationary Dynaimc Analysis of Online Structural Health Monitoring

This dissertation focuses on robust online Structural Health Monitoring (SHM) framework for civil engineering structures. The proposed framework improves the diagnostic and prognostic schemes for damage-state awareness and structural life prediction in civil engineering structures. The underlying research achieves three main objectives, namely, (1) sensor placement optimization using partial differential equation modeling and Fisher information matrix, (2) structural damage detection using quasi-recursive correlation dimension (QRCD), and (3) structural damage prediction using online empirical mode decomposition (EMD).The research methodology includes three research tasks: Firstly, to formulate the optimal criteria for the sensor placement optimization damage detection problem based upon a partial differential equation (PDE) analytical model. The PDE model is derived and then validated through experimental results using correlation analysis. Secondly, to develop a novel quasi-recursive correlation dimension method for structural damage detection. The QRCD algorithm is integrated with an attractor analysis and overlapping windowing technique. Thirdly, to design an online structural damage prediction method based on empirical mode decomposition. The proposed SHM prediction scheme consists of two steps: prediction based change point detection using Hilbert instantaneous phase, and damage severity prediction using the energy index of the most representative intrinsic mode function (IMF).Study results show that; (1) the proposed optimal sensor placement method leads to an optimal spatial location for a collection of sensors, which are sensitive to structural damage, (2) the proposed damage detection algorithm can significantly alleviate the complexity of computation for correlation dimension to approximate O(N), making the online monitoring of nonlinear/nonstationary processes more applicable and efficient; and (3) the proposed empirical mode decomposition method for online damage prediction overcomes the boundary effects of the sifting process, and it has significant prediction accuracy improvement (greater than 30%) over other commonly used prediction techniques.Industrial Engineering & Managemen

Predicting trajectories of symptom change during and following treatment in adolescents with Unipolar Major Depression.

Objective: Definitions of treatment response used in randomised controlled trials for unipolar major depression are non-standardised and arbitrary. Proportion of non-responders has been estimated as ranging from 20%-40% across such trials. I aimed to classify depressed adolescents recruited to the UK IMPACT trial into different trajectories of depression symptom response using a longitudinal data-driven approach: growth mixture modelling (GMM) and investigate potential predictors of trajectory classes in this cohort. Method: 465 depressed adolescents received manualised psychological therapies in the IMPACT trial. GMM was used to plot the trajectories of self-reported depressive symptoms measured at 6 nominal time points over 86 weeks from randomisation, and categorise patients into their most likely trajectory class. Chapters 2-4 investigated the prognostic value of a number of variables. Chapter 2 investigated a battery of demographic and clinical variables including subclinical psychotic symptoms. Chapter 3 focused on a subsample of patients: 109 of the 465 with structural magnetic resonance imaging (MRI) data. FreeSurfer was used to extract cortical thickness (CT) and surface area (SA) measures from 4 regions of interest (ROI; rostral anterior cingulate, dorsolateral prefrontal cortex, orbitofrontal cortex, and insular cortex). Chapter 4 focused on another subsample of patients: 166 of the 465 with salivary basal cortisol data at both waking and evening. Logistic regressions were used in Chapters 2-4 to investigate whether these variables were associated with increased likelihood of membership to a certain GMM class. Results: A piecewise GMM categorised patients into two classes with initially similar and subsequently distinct trajectories. Both groups had a significant decline in depressive symptoms over the first 18 weeks. Eighty-four per cent of patients were classed as “continued-improvers” through reporting an improvement in symptoms over the full duration of the study. A further class of 15.9% of patients were termed “halted-improvers” who had higher depression scores at baseline, faster recovery but no further improvement after 18 weeks. This data-driven method of classification showed only moderate agreement with a priori classification methods, and suggested misclassification rate could be as great as 31%. Co-morbid psychiatric disorders at baseline moderately increased the liability of being a member of the halted-improvers class (OR = 1.40, CI 1.00-1.96). No other clinical, neurological or cortisol variable reached statistical significance for predicting trajectory class. Conclusion: A fast reduction in depressive symptoms in the first few weeks of treatment may not indicate a good prognosis. Further, halted-improvement may not be apparent until after 18 weeks of treatment. Capitalizing on repeated symptom assessments with longitudinal data driven modelling may improve the precision of revealing patient groups with differential responses to treatment. Further work should seek to validate these trajectories in a separate sample of adolescents.The IMPACT study was funded by the National Institute for Health Research (NIHR) Health Technology Assessment (HTA) programme (project number 06/05/01). MR-IMPACT was funded by the Medical Research Council (grant: G0802226) and IMPACT-GH was funded by the Evelyn Trust. My doctoral studentship was awarded by the Neuroscience in Psychiatry (NSPN.Org) Consortium itself funded by a strategic award from the Wellcome Trust (095844/Z/11/Z) awarded to Professor Ian Goodyer and Professor Peter Fonagy

Multiplexed angiogenic biomarker quantification on single cells

Clinical and biomedical research seeks single-cell quantification to better understand their roles in a complex, multi-cell environment. Recently, quantification of vascular endothelial growth factor receptors (VEGFRs) provided important insights into endothelial cell (EC) characteristics and response in tumor microenvironments. However, data on other angiogenic receptors, such as platelet derived growth factor receptors (PDGFRs), Tie receptors, are also necessary for the development of an accurate angiogenesis model. To gain insights on the involvement of these angiogenic receptors in angiogenesis, I develop a method to quantify receptor concentrations as well as the cell-by-cell heterogeneity. I establish protocols to measure cell membrane VEGFR, NRP1, Tie2, and PDGFR concentration on several cell and tissue models including human dermal fibroblasts (HDFs) in vitro, a 2D endothelial/fibroblast co-culture model in vitro, and a patient-derived xenograft (PDX) model of glioblastoma (GBM). I demonstrate VEGF-A165-mediated downregulation of membrane PDGFRα (~25%) and PDGFRβ (~30%) on HDFs, following a 24-hour treatment. This supports the idea that VEGF-A165 acts independently of VEGFRs to signal through PDGFRα and PDGFRβ. I uncover high intratumoral heterogeneity within the GBM PDX model, with tumor EC-like subpopulations having high concentrations of membrane VEGFR1, VEGFR2, EGFR, IGFR, and PDGFRs. To gain greater insights into cell heterogeneity and examine angiogenic signaling pathways as a whole, I utilize the unique spectral properties of quantum dots (Qdots), and combines Qdots with qFlow cytometry, to dually quantify VEGFR1 and VEGFR2 on human umbilical vein endothelial cells (HUVECs). To enable this quantification, I reduce nonspecific binding between Qdot-conjugated antibodies and cells, identify optimal labeling conditions, and establish that 800 – 20,000 is the dynamic range where accurate Qdot-enabled quantification can be achieved. Through these optimizations we demonstrate measurement of 1,100 VEGFR1 and 6,900 VEGFR2 per HUVEC. 24 h VEGF-A165 treatment induce ~90% upregulation of VEGFR1 and ~30% downregulation of VEGFR2 concentration. We further analyze HUVEC heterogeneity and observe that 24 h VEGF-A165 treatment induces ~15% decrease in VEGFR2 heterogeneity. Overall, we demonstrate experimental and analysis strategies for quantifying two or more RTKs at single-level using Qdots, which will provide new insights into biological systems

From Ecological Epitome to Medical Model: An investigation into Applications for the use of Daphnia in Heart Science.

The primary aim of this research was to determine whether Daphnia might become a model for cardiovascular concentration-response trials. This would provide a high throughput means of testing cardiac therapeutics without resort to small mammal trials. We found Daphnia are inappropriate in this context due to high population variance and sensitivity to small, subtle, environmental changes. A new aim was developed to determine whether beat-to-beat variation could be correlated with an individual’s response to toxic insult. Further, to develop more accurate and efficient means of gathering heart rhythm data by recording heart movement from whole live Daphnia. This opens the way to individualising cardio therapeutics; by correlating the stability of individual hearts with response to cardiac insult, regression analysis provides a means of finding a prediction tool. Daphnia are a convenient example here, but successful scoring systems might also be applied to the human heart via analysis of ECG readouts. Collecting signals from whole live Daphnia did not fulfil the goal of gathering heart data as this instead recorded limb movement. However, this provides a means of improving toxicology testing in aquatic ecology. This thesis offers three contributions to knowledge: 1. Daphnia are an inappropriate model for cardiovascular therapeutic dose-response trials due to extreme environmental sensitivities. 2. Baseline heart rhythm can be correlated with paired response to cardiac insult, with significance at the 0.01 alpha level, using an adjusted version of the Lyapnov equation; Finite Time Growth (Wessel, 2010). However, this is only if population variation is adequate. It is better applied to a natural in situ population than a homegenic lab population. 3. A novel technique for measuring Daphnia electromechanical movement records feeding limbs rather than the heart. This offers a novel and more efficient technique for aquatic ecotoxicology, where visual observation or films of the same are currently used

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018

Energy: A continuing bibliography with indexes

This bibliography lists 1546 reports, articles, and other documents introduced into the NASA scientific and technical information system from April 1, 1981 through June 30, 1981