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Unfolding Morphologies: The Dynamics of Folding for Adaptive Spatial Design
No full textThis thesis investigates folding as a transformative design strategy for creating responsive architectural elements. While folding techniques are often used in architectural design, their potential for adaptive responsiveness is, in most cases, untapped due to a limited understanding of their underlying behavior. Focusing on small-scale prototypes, particularly architectural textiles and wearable designs, this research examines the Miura-Ori folding pattern as a fundamental system for generating dynamic, reconfigurable forms. Using component studies and physical prototypes, a bi-stable wearable is created to demonstrate folding’s ability to produce flexible configurations that respond to environmental and user input. This wearable illustrates folding’s capacity to contribute both functional adaptability and aesthetic versatility. Through this work, folding is positioned as a powerful tool for adaptive, user-oriented design, fostering innovation in responsive architectural spaces
Optimizing Steel Connections in Spatial Structures: Prototyping Joinery with a Focus on Wire Arc Additive Manufacturing for Structural Efficiency and Fabrication
No full textStructural steel joints are a critical component in design that can determine the success of any architectural project. Steel connections are fundamental to architectural design, impacting structural integrity, safety, and aesthetic quality. While typical joints at a smaller scale have been mastered in the construction industry, developing unique structures in a larger context with custom joinery often creates challenges with accuracy, efficiency, and manufacturing cost. An alternative to conventional techniques is Additive Manufacturing (3D printing), which can efficiently develop optimized and intricate structural components while adhering to time and budget constraints. In particular, additively manufactured steel nodes are the prime focus of this research, which looks into developing joint designs derived from topology optimization to answer the question of whether the structural integrity can be maintained while focusing on the aesthetic appeal of the connection design. Currently, some experimentation is underway utilizing 3D-printed steel structures on a large scale. However, this stream is still relatively undeveloped and requires further exploration for efficiency and sustainability in the construction industry. The research focuses on developing small-scale prototypes of design iterations to eliminate ambiguities, selecting the more efficient design in terms of aesthetics, structure, and fabrication ability with the available technology. For this, ten experiments were done, which included six topology optimized joints and two simplified final joints, in addition to two sectional parts. The selected joint design was further tested for its key features using the Wire Arc Additive Manufacturing (WAAM) with a six-axis robotic arm in two experiments that involved approximately twelve hours of total weld time. These tests specifically explored the impact of process parameters and the main features of the joint, including achieving curvature and examining the impact of gravity on the weld pool during the deposition process. The presented research provides critical insights into how curvature can be effectively achieved, while ensuring adequate integrity and structural performance. The findings from the presented experiments contribute to new possibilities of structural design to understand how different typologies of joints impact structural performance while not limiting design possibilities
Quickest Change Detection in Nonlinear Hidden Markov Models Using a Generalized CUSUM Procedure
No full textFault diagnosis in modern aircraft engines is crucial for monitoring due to the rising need for high performance and safety. Early detection of system changes within a controllable range can prevent significant breakdowns. It's essential to track jet engine states and detect real-time dynamic mode shifts. This thesis explores change detection theories and procedures to handle dynamic instabilities in jet engines. We use the Moore-Greitzer equation to model flow and pressure changes in axial-flow compressors, specifically focusing on a reduced planar system. The research addresses QCD problems in nonlinear hidden Markov models, using pressure rise coefficients as observational data. The standard QCD scheme doesn't apply, so we employ the generalized CUSUM procedure, where the post-change distribution depends on an unknown change point, despite its non-recursive nature. We adopt standard filtering theory to approximate log-likelihood ratios with particle methods. To manage computational costs, we adjust the CUSUM-like procedure with an assumption of immediate change to enable recursion. This research focuses on how changes in the system excite shifts from a steady state to a new equilibrium or periodic oscillations. We assess the performance of generalized CUSUMs with particle filters through random simulations in surge modes of the Moore–Greitzer model with external forcing. Observations reveal similarities and differences between generalized CUSUMs and a special CUSUM that assumes immediate change, influenced by phase errors and the relationship of the steady state to the limit cycle. Signal noise mitigates the phase effects of the limit cycle.
This research addresses change detection in the Moore-Greitzer PDE model, where disturbances in axial flow at the compressor inlet are combined with a modified ODE system. We simulate the PDE system by obtaining time series from a finite-dimensional Moore-Greitzer system using the Fourier spectral method. Employing proper orthogonal decomposition (POD), we reduce model dimensions while maintaining fidelity with fewer basis functions. The reduced model is validated by reconstructing the PDE system with POD modes capturing about 95% energy of the full model in inner product and Sobolev spaces. We examine generalized CUSUM statistics to detect dynamic changes, using POD bases and particle filters, with simulations showing the effectiveness of CUSUM statistics using {\em in situ} and {\em a priori} POD modes. Validated reduced models implement the generalized CUSUM for stall detection in stochastic systems, showing that the generalized CUSUM statistics are more effective and robust than recursive CUSUM-like procedures
Low-Power CMOS-like Flexible Circuits With Unipolar TFTs
No full textThe increasing demand for Thin Film Transistor (TFT) electronics is driven by their cost effectiveness and suitability for high-volume production, making them ideal for applications in flexible electronics, wearable devices, on-body sensors, and Internet-of things (IoT) devices. TFT technologies, including organic, transition metal oxide (TMO), and amorphous silicon (a-Si:H) TFTs, have demonstrated significant potential for large-area, low-cost fabrication. Their integration into flexible electronics has enabled innovations such as bendable displays, electronic skin, and smart patches. However, unlike Complementary Metal-Oxide-Semiconductor (CMOS) technology, TFTs lack complementary transistors. Therefore, most complex circuits on flexible substrates remain reliant on rigid CMOS integrated circuits (IC)s, connected via soft ribbon cables. This dependency introduces challenges in signal interfacing, cost, and reliability, which constrain the scalability and complexity of flexible electronics. In display technology, TFTs are predominantly used for pixel circuits, while off-panel CMOS ICs handle control and driver circuits. Particularly, for high resolution displays, the need for numerous bonding pads to interface with off-panel ICs creates bottlenecks due to mechanical pitch constraints, and limited scalability, and exacerbates power dissipation caused by high-capacitance bonding pads. Addressing these challenges requires integrating control logic and driver circuits directly onto the TFT backplane to minimize reliance on off-panel CMOS circuits.
Unipolar TFT logic circuits face challenges such as limited output swing and significant direct path current, further complicating their use in low-power applications. To overcome these limitations, various fabrication techniques have been explored to create complementary transistors, but their higher production costs and complexities have limited practical adoption. While several circuit designs have been proposed to achieve full output swing using unipolar TFTs, the persistent issue of substantial direct path current remains a critical obstacle. Moreover, most existing studies fail to consider the impact of bending on device power consumption and performance. This thesis presents a methodology for designing low-power, full-swing TFT digital circuits, including logic gates, decoders, D Flip-Flops (DFF), and Static Random Access Memory (SRAM) memory cells. The proposed circuits are fabricated on both glass and flexible polyethylene naphthalate (PEN) substrates, with an in-depth analysis of substrate bending effects on device performance. This study paves the way for enhancing existing TFT models by incorporating bending effects. The designs achieve significant advancements, including over 46.2% power reduction compared to state-of-the-art 3-to-8 address decoder design. These contributions lay the groundwork for low-power “system-on-flex” applications, providing a path toward scalable, efficient, and integrated flexible electronics
Comparative Analysis of MPC and Integrated Skyhook-LQR Controllers for a CDC Damper Suspension System in Passenger Cars
No full textThe performance of vehicle suspension systems is critical for ride comfort, handling stability, and safety. Passive suspensions, while simple and reliable, lack adaptability to road conditions, whereas fully active suspensions provide superior performance but are impractical for passenger vehicles due to high energy consumption. Semi-active suspensions offer a balance by dynamically adjusting damping forces with minimal power requirements, but their effectiveness depends on the chosen control strategy.
This study evaluates two advanced control strategies: Model Predictive Control (MPC) and an integrated Skyhook-Linear Quadratic Regulator (LQR) controller. A seven-degree-of-freedom (7-DOF) full-vehicle model is developed using Lagrange’s equations to analyze vehicle dynamics. MPC is formulated as a predictive controller that anticipates road disturbances, while the integrated Skyhook-LQR controller combines classical Skyhook damping with optimal state feedback for improved stability.
The controllers are implemented in a CarSim-Simulink simulation environment and tested under various road conditions. To assess real-world feasibility, experimental testing is conducted on a passenger vehicle equipped with semi-active CDC dampers using the Skyhook-LQR controller.
The comparative analysis highlights key trade-offs. MPC provides superior predictive control and optimizes suspension performance across multiple axes but requires high computational power, limiting real-time implementation. The integrated Skyhook-LQR controller, while computationally efficient and practical for embedded systems, has limitations in handling complex disturbances.
These findings underscore the importance of selecting control strategies based on application requirements. While MPC enhances ride comfort and stability, Skyhook-LQR remains a more feasible real-time solution. This study contributes to the advancement of semi-active suspension technology, offering insights for future vehicle suspension designs
Quality of antenatal care and its potential impacts on delivery services and postnatal care compliance among reproductive women in Bangladesh: A situation analysis from the Bangladesh Demographic and Health Survey 2017
No full text© 2025 Nurunnahar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background
Ensuring quality antenatal care (ANC) and postnatal care (PNC) is crucial for reducing maternal and neonatal mortality rates. However, there are gaps in assessing the quality of ANC, leading to the proposal of standards by the World Health Organization. The study aims to examine the impact of quality ANC on delivery services and PNC compliance in Bangladesh using data from the Bangladesh Demographic and Health Survey (BDHS), providing insights for policymakers to improve maternal and neonatal health outcomes.
Methods
This study used data from 2017 Bangladesh Demographic and Health Survey (BDHS) to investigate the impact of quality antenatal care (qANC) on delivery services and PNC in Bangladesh. The study population included ever-married women aged 15-49 years who had experienced a recent pregnancy. The analysis assessed the relationship between qANC and facility delivery, skilled birth attendant (SBA)-assisted delivery, and PNC services within 48 hours of delivery. The study employed a two-stage stratified cluster sampling design, and data analysis was conducted using generalized linear models and considered various demographic and socioeconomic factors.
Results
Key findings include a low rate of qANC services (18%), with pregnancy-related counseling being the lowest component. About 82% received at least one ANC visit, but only 18.3% received a quality visit. Higher compliance with facility delivery (ARR: 1.3; 95% CI: 1.27-1.41), SBA-conducted delivery (ARR: 1.3; 95% CI: 1.24-1.35), and PNC services for both mother (ARR: 1.3; 95% CL: 1.24-1.35) and child (ARR: 1.3; 95% CL: 1.23-1.35) within 48 hours were observed when quality ANC was received. Factors such as completing secondary education, engaging in skilled/unskilled manual labor and higher wealth quintile were associated with better delivery and post-delivery outcomes.
Conclusion
Ensuring qANC and expanding PNC service use remain challenging in Bangladesh. Increasing the provision of qANC is crucial, as it is associated with higher adherence to PNC
Closest Point Geometry Processing: Extensions and Applications of the Closest Point Method for Geometric Problems in Computer Graphics
No full textThis thesis develops theoretical aspects and numerical methods for solving partial differential equations (PDEs) posed on any object for which closest point queries can be evaluated. In geometry processing (and computer graphics in general), objects are represented on a computer in many different ways. Requiring only closest point queries allows the methods we develop to be used with nearly any representation. Objects can be manifold or nonmanifold, open or closed, orientable or not, and of any codimension or even mixed codimension. Our work focuses on solving PDE on manifolds using the closest point method (CPM), although some nonmanifold examples are also included.
We develop fundamental extensions of CPM to enable its use for the first time with many applications in geometry processing. Two major impediments stood in our way: the complexity of manifolds commonly found in geometry processing and the inability to impose interior boundary conditions (IBCs) with CPM. We first develop a runtime and memory-efficient implementation of the grid-based CPM that allows the treatment of highly complex manifolds (involving tens of millions of degrees of freedom) and avoids the need for GPU or distributed memory hardware. We develop a linear system solver that can improve both memory and runtime efficiency by up to 2x and 41x, respectively. We further improve runtime by up to 17x with a novel spatial adaptivity framework. We then develop a general framework for IBC enforcement that also only requires closest point queries, which finally allows for many geometry processing applications to be performed with CPM. We implicitly partition the embedding space across (extended) interior boundaries. CPM's finite difference and interpolation stencils are adapted to respect this partition while preserving up to second-order accuracy. We show that our IBC treatment provides superior accuracy and handles more general BCs than the only existing method.
We deviate from the common grid-based CPM and further develop a discretization-free CPM by extending a Monte Carlo method to surface PDE. This enables CPM to enjoy common benefits of Monte Carlo methods, e.g., localized solutions, which are useful for view-dependent applications. Finally, we introduce an algorithm to compute geodesic paths that does not even require a manifold PDE; only heat flow on a 1D line and closest point queries are required. Our method is more general, robust, and always faster (up to 1000x) than the state-of-the-art for general representations. Our method can be up to 100,000x faster (with high-resolution meshes) or slower (with low-resolution meshes) than the state-of-the-art in terms of runtime. Convergence studies on example PDEs with analytical solutions are given throughout. We further demonstrate the effectiveness of our work for applications from geometry processing, including diffusion curves, vector field design, geodesic distance and paths, harmonic maps, and reaction-diffusion textures
The Impact of Waiting Time and Treatment Modality: An Empirical Analysis of a Pediatric Speech-Language Therapy Program
No full textDemand for multi-appointment, outpatient care services is high and growing, pressuring overburdened healthcare systems and highlighting the need for operational improvements. In this study, we investigate operational and scheduling-related factors, and their impact on the number of appointments patients require to be discharged, as well as their appointment attendance behaviour. We first utilize data from over 1,700 pediatric speech-language therapy patients to study the associations between waiting time for service, treatment modality, and the number of appointments patients require. Using a continuation ratio model, we estimate that, on average, each additional year a patient waits to begin treatment is associated with a 26.3\% increase in the total number of appointments needed. Furthermore, we show that offering all appointments in-person, versus via telemedicine, patients require 8\% fewer total visits to achieve their desired outcomes. While these are the average effects across the patient population, we also find significant differences between patients of different severities and the relationships between waiting time, modality, and the number of appointments needed. Secondly, we use a mixed-effects multinomial logistic regression to investigate the relationship between appointment modality and appointment attendance. Although in-person treatment is associated with patients requiring fewer total appointments to achieve desired outcomes, in-person appointments are also 4.42\% less likely to be attended. This highlights the need for management to consider the effects of scheduling decisions on multiple drivers of system throughput
Synchronous and quantum games: Graphical and algebraic methods
No full textThis is a mathematics thesis that contributes to an understanding of nonlocal games as formal objects. With that said, it does have connections to quantum physics and information theory.
Nonlocal games are interactive protocols modelling two players attempting to win a game, by answering a pair of questions posed by the referee, who then checks whether their answers are correct. The players may have access to a shared quantum resource state and may use a pre-arranged strategy. Upon receiving their questions, they can measure this state, subject to some separation constraints, in order to select their answers. A famous example is the CHSH game of [Cla+69], where making use of shared quantum entanglement gives the players an advantage over using classical strategies.
This thesis contributes to two separate questions arising in the study of synchronous nonlocal games: their algebraic properties, and their generalization to the quantum question-and-answer setting. Synchronous games are those in which players must respond with the same answer, given the same question. First, we study a synchronous version of the linear constraint game, where the players must attempt to convince the referee that they share a solution to a system of linear equations over a finite field. We give a correspondence between two different algebraic objects modelling perfect strategies for this game, showing one is isomorphic to a quotient of the other. These objects are the game algebra of [OP16] and the solution group algebra of [CLS17]. We also demonstrate an equivalence of these linear system games to graph isomorphism games on graphs parameterized by the linear system.
Second, we extend nonlocal games to quantum games, in the sense that we allow the questions and answers to be quantum states of a bipartite system. We do this by quantizing the rule function, games, strategies, and correlations using a graphical calculus for symmetric monoidal categories applied to the category of finite dimensional Hilbert spaces. This approach follows the overall program of categorical quantum mechanics. To this generalized setting of quantum games, we extend definitions and results around synchronicity. We also introduce quantum versions of the classical graph homomorphism [MR16] and isomorphism [Ats+16] games, where the question and answer spaces are the algebras representing the “vertices” of quantum graphs, and we show that quantum tensor strategies realizing perfect correlations for these games correspond to morphisms between the underlying quantum graphs
Real-Time Short-Term Intersection Turning Movement Flows Forecasting Using Deep Learning Models for Advanced Traffic Management and Information Systems
No full textTraffic congestion remains a persistent challenge in urban transportation systems, causing excessive travel delays, increased fuel consumption, and severe environmental pollution. To address these issues, Advanced Traffic Management and Information Systems (ATMIS) have been developed, integrating real-time traffic monitoring, adaptive control strategies, and data-driven decision-making to enhance overall traffic efficiency. A crucial component of ATMIS is the real-time forecasting of intersection Turning Movement Flows (TMFs), which provides essential data for optimizing signal timings, improving vehicle routing, and implementing proactive congestion mitigation strategies. By leveraging accurate TMFs predictions, transportation agencies can dynamically adjust traffic signals, enhance intersection operations, and reduce delays, ultimately improving urban mobility and minimizing environmental impacts.
While numerous traffic forecasting models exist, they face significant limitations in capturing the complex spatial and temporal patterns inherent in intersection-level TMFs, as they primarily rely on historical traffic data without adequately modeling these dependencies. Moreover, most existing approaches fail to incorporate exogenous factors, such as weather conditions, road characteristics, and other time-dependent variables, which significantly influence traffic flow but are often ignored. These shortcomings lead to poor generalization performance when applied to hold-out intersections (few-shot) and unseen regions (zero-shot), making them less effective in real-world dynamic traffic environments.
To overcome these challenges, this study systematically develops and evaluates a deep learning-based TMFs forecasting framework designed for improved generalization and interpretability. First, we employ a Parallel Bidirectional LSTM (PB-LSTM) with multilayer perceptron (MLP) to capture both long-term seasonality and spatial dependencies, thereby enhancing the model's transferability across different locations, improving performance across hold-out intersections. Second, we integrate an encoder-decoder architecture using Deep Autoregressive (DeepAR) model, which enables probabilistic forecasting and quantifies uncertainty, ensuring robust predictions under varying traffic conditions. Third, we leverage the Temporal Fusion Transformer (TFT) to assess the relative importance of external covariates, such as weather conditions and road characteristics, improving interpretability and model reliability by identifying speed zone, road category, hour of the day, and temperature as key influential factors. Finally, we explore the potential of TimesFM, a decoder-only model, to enhance zero-shot learning capabilities, demonstrating strong performance in previously unseen intersections and new city datasets, particularly when enhanced with EMD and RF.
To evaluate model performance, we conduct a series of experiments, including hold-out intersection tests, cross-city generalization assessments, and evaluations under extreme weather conditions, to assess robustness and adaptability. Experimental results highlight the effectiveness of integrating exogenous factors and hybrid modeling approaches in improving real-time TMFs forecasting accuracy, generalizability, and robustness under dynamic conditions. These insights provide valuable contributions to the development of scalable and interpretable deep learning models for intersection-level traffic flow prediction, supporting more adaptive and data-efficient traffic management strategies