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Transportation onto log-Lipschitz perturbations
No full textWe establish sufficient conditions for the existence of globally Lipschitz transport maps between probability measures and their log-Lipschitz perturbations, with dimension-free bounds. Our results include Gaussian measures on Euclidean spaces and uniform measures on spheres as source measures. More generally, we prove results for source measures on manifolds satisfying strong curvature assumptions. These seem to be the first examples of dimension-free Lipschitz transport maps in non-Euclidean settings, which are moreover sharp on the sphere. We also present some applications to functional inequalities, including a new dimension-free Gaussian isoperimetric inequality for log-Lipschitz perturbations of the standard Gaussian measure. Our proofs are based on the Langevin flow construction of transport maps of Kim and Milman
Predicting Flood Risks to City Infrastructure Systems Utilizing Scalable, Time Sensitive Modeling
No full textFlooding is emerging as the most expensive and frequent natural hazard around the world. Floods are highly dynamic in nature and cause physical damage to our built environment, loss of life, economic damage, and major impacts to society. An example of this is the at-ground road system, which comprises 30-60% of a city’s area in the US, is highly susceptible to flood damage, while still needs to act as evacuation routes for local residents. Similarly, the underground built system is extremely vulnerable to flooding damage as well as life risk to anyone within it. With urban landscapes constantly evolving, accurately predicting flood propagation and extent is imperative to mitigate these risks, especially as floods worsen due to climate change.
Historically, the focus of flood risk assessment through industry and academia has been on the coastal urban environment, assessing the impact of fluvial flooding. This resulted in many risk assessment tools that mostly caters to the infinite amount of flood water identified from a riverine or coastal fluvial flooding. As for the rain-driven impact, the common practice simply changed the flood modeling to pluvial oriented, keeping the rest of the risk tool components identical for the different flood mechanisms. For pluvial flooding, existing urban flood modeling tools such as SWMM and PC-SWMM are limited by their catchment-based approach, neglecting surface runoff dynamics and spatial-temporal flood impacts. Consequently, these tools fail to capture the full extent of rain-driven floods, underestimating their severity and impact on urban environments.
Addressing this gap requires sophisticated simulations that account for rain event characteristics and city morphology, yet such simulations are computationally demanding and require detailed urban data. Currently, flood impact analysis tools lack specificity for pluvial flood risks and do not address the risks to various city systems beyond building damage. As a result, the contribution of pluvial floods to overall flood risks is underestimated, compromising infrastructure resilience. As flood model results are a critical component in flood risk assessments, the accuracy of spatial temporal urban flood results will allow the pluvial flood impact assessment to be simplified and the flood damage to the different urban systems will be quantified.
This research aims to develop a scalable and streamlined method to accurately quantify the risks of rain-driven floods to urban infrastructure systems. It addresses three key questions: (1) To what extent does current practice underestimate pluvial flood impacts? (2) What are the impacts of pluvial flooding on pavement systems when incorporating spatial-temporal modeling? (3) What is the significance of modeling pluvial floods using urban underground spaces? Using advanced flood modeling and numerical soil-water infiltration techniques, this research will quantify damages and lifecycle impacts to pavement and underground spaces systems. The method will provide information on the spatial and temporal distribution of flood damage and will enable scaling up single-element assessments to system-wide impacts. This holistic approach will improve urban flood risk management, supporting informed decision-making and the development of resilient infrastructure systems.Ph.D
Design of Lewis Acidic Pnictenium Ions Using Carbone and Capping Arene Ligands for Bond Functionalization
No full textInterest in the chemistry of antimony and bismuth is rapidly growing due to isolation of low coordinate, subvalent or Lewis acidic compounds that can mediate reactivity traditionally reserved for their d block counterparts. Ligand strategies play a key role in the isolation of such species. Anionic ligands with large steric profiles, as well as carbenes, have been widely implemented to stabilize subvalent heavy group 15 element compounds. However, synthetic strategies to prepare Lewis acidic antimony and bismuth complexes remain underexplored. Cationization is one of the most common methods used to enhance the Lewis acidity of heavy group 15 elements by creating a vacant p orbital on the pnictogen atom. Lewis acids are also employed in frustrated Lewis pair (FLP) chemistry to enable intra- and intermolecular reactivity. Carbone ligands, which are neutral, 4 electron donor ligands, offer a unique ability to support highly electrophilic main-group elements. This dissertation investigates the stabilization of heavy pnictenium ions using neutral donor ligands, such as carbodicarbenes and capping arene ligands, and explores their potential in Lewis acid-mediated chemistry. In Chapter Two, the synthesis and characterization of a series carbodicarbene-pnictenium ions is described. The utilization of strongly donating carbodicarbene ligands enables the isolation of mono-, di- and tri-cationic antimony and bismuth cations. These ions have multiple bond character between carbon and antimony/bismuth, representing some of the first examples of stibaalkene and bismaalkene cationic compounds. The Lewis acidity of these ions was assessed using the Gutmann-Beckett method and computationally derived fluoride ion affinities, the latter of which indicates Lewis superacidity for the bis(pyridyl)carbodicarbene-pnictenium trications. In Chapter Three, the reactivity of the bis(pyridyl)-carbodicarbene stibenium trication toward C(sp³)–H and C(sp)–H bonds is demonstrated. The Lewis superacidic antimony cation mimics the chemistry of frustrated Lewis pairs in the presence of the sterically encumbered base 2,6-di-tert-butylpyridine to enable C–H bond breaking of acetonitrile and a set of terminal alkynes. Kinetic analyses, in conjunction with density functional theory, support a mechanism by which acetonitrile coordinates to antimony, acidifying the C–H bonds, which can be subsequently deprotonated by the base in solution. The resulting stiba-methylene nitrile and stiba-alkynyl adducts undergo reactivity with elemental iodine to generate iodoacetonitrile and 1-iodoalkynes while reforming a stibenium trication. In Chapter Four, capping arene ligands are coordinated to antimony and bismuth tribromide to afford a series of κ²-bound complexes. Bromide abstraction from these neutral adducts affords ionic compounds. Both the neutral and ionic species have distinctive Menschutkin interactions, whereby the lone pair on the pnictogen atom is oriented toward the π system of the pendant arene. Shortening of the distances between the pyridyl nitrogen atoms and pnictogen atom are observed upon cationization from the neutral adducts. The Lewis acidity of these complexes was assessed using the Gutmann-Beckett method. Notably, acceptor numbers as high as 111 are observed for these ions.Ph.D
Exciton Dynamics and Optical Properties of Lead Halide Perovskite Nanocrystals: From Nanorods to Nanocubes
No full textLead halide perovskites, particularly CsPbBr3, have emerged as leading light emitters for their spectral purity, brightness, and facile synthesis. Their soft, ionic lattice makes them unusually defect tolerant but introduces problems with stability. Additionally, dephasing mechanisms and coupling to phonons are not yet well understood in these semiconductors.
In the first part of the thesis, I investigate highly confined, anisotropic CsPbBr3 nanorods, elucidating the photophysics governing their broad single-particle linewidths. I utilize ensemble and single particle photoluminescence techniques across a wide temperature range in order to pinpoint exciton-phonon coupling mechanisms, structural and surface effects, and spin mixing in these novel materials.
In the second part of the thesis, I focus on the opposite size regime, where collective behaviour dominates the optical properties. I develop a novel spectroscopy to pinpoint dephasing mechanisms that could reduce superradiant and coherent emission in order to promote rational design and future integration of these nanocrystals into quantum information devices.Ph.D
Performance Analysis for High-Dimensional Bell-State Quantum Illumination
No full textQuantum illumination (QI) is an entanglement-based protocol for improving LiDAR/radar detection of unresolved targets beyond what a classical LiDAR/radar of the same average transmitted energy can do. Originally proposed by Seth Lloyd as a discrete-variable quantum LiDAR, it was soon shown that his proposal offered no quantum advantage over its best classical competitor. Continuous-variable, specifically Gaussian-state, QI has been shown to offer a true quantum advantage, both in theory and in table-top experiments. Moreover, despite its considerable drawbacks, the microwave version of Gaussian-state QI continues to attract research attention. A recent QI study by Armanpreet Pannu, Amr Helmy, and Hesham El Gamal (PHE), however, has: (i) combined the entangled state from Lloyd’s QI with the channel models from Gaussian-state QI; (ii) proposed a new positive operator-valued measurement for that composite setup; and (iii) claimed that, unlike Gaussian-state QI, PHE QI achieves the Nair–Gu lower bound on QI target-detection error probability at all noise brightnesses. PHE’s analysis was asymptotic, i.e., it presumed infinite-dimensional entanglement. The current paper works out the finite-dimensional performance of PHE QI. It shows that there is a threshold value for the entangled-state dimensionality below which there is no quantum advantage, and above which the Nair–Gu bound is approached asymptotically. Moreover, with both systems operating with error-probability exponents 1 dB lower than the Nair–Gu bound, PHE QI requires enormously higher entangled-state dimensionality than does Gaussian-state QI to achieve useful error probabilities in both high-brightness (100 photons/mode) and moderate-brightness (1 photon/mode) noise. Furthermore, neither system has an appreciable quantum advantage in low-brightness (much less than 1 photon/mode) noise
The Design and Deployment of a Self-Powered, LoRaWAN-Based IoT Environment Sensor Ensemble for Integrated Air Quality Sensing and Simulation
No full textThe goal of this study is to describe a design architecture for a self-powered IoT (Internet of Things) sensor network that is currently being deployed at various locations throughout the Dallas-Fort Worth metroplex to measure and report on Particulate Matter (PM) concentrations. This system leverages diverse low-cost PM sensors, enhanced by machine learning for sensor calibration, with LoRaWAN connectivity for long-range data transmission. Sensors are GPS-enabled, allowing precise geospatial mapping of collected data, which can be integrated with urban air quality forecasting models and operational forecasting systems. To achieve energy self-sufficiency, the system uses a small-scale solar-powered solution, allowing it to operate independently from the grid, making it both cost-effective and suitable for remote locations. This novel approach leverages multiple operational modes based on power availability to optimize energy efficiency and prevent downtime. By dynamically adjusting system behavior according to power conditions, it ensures continuous operation while conserving energy during periods of reduced supply. This innovative strategy significantly enhances performance and resource management, improving system reliability and sustainability. This IoT network provides localized real-time air quality data, which has significant public health benefits, especially for vulnerable populations in densely populated urban environments. The project demonstrates the synergy between IoT sensor data, machine learning-enhanced calibration, and forecasting methods, contributing to scientific understanding of microenvironments, human exposure, and public health impacts of urban air quality. In addition, this study emphasizes open source design principles, promoting transparency, data quality, and reproducibility by exploring cost-effective sensor calibration techniques and adhering to open data standards. The next iteration of the sensors will include edge processing for short-term air quality forecasts. This work underscores the transformative role of low-cost sensor networks in urban air quality monitoring, advancing equitable policy development and empowering communities to address pollution challenges
Planning Beyond Crisis: The Promise of Insurgent Planning in Post-Disaster Mocoa
No full textDuring the evening of March 31, 2017, a catastrophic landslide engulfed the Colombian city of Mocoa, killing at least 335 people in roughly thirty minutes. Seventy others disappeared, and over one hundred people were reported injured across 48 neighborhoods, and roughly 1,500 housing units were destroyed. With a total of 22,000 people impacted, this catastrophe was the deadliest disaster affecting Colombia in recent decades. Yet, despite an alignment of major national political commitments, international cooperation, and a multi-million-dollar humanitarian budget, reconstruction plans have not been completed seven years later. Why? As the first comprehensive analysis of the landslide and its aftermath, this dissertation is a novel investigation into the competing forces that ultimately canceled the central reconstruction plan, demonstrating that the kind of disruption caused by the disaster mobilized new actors and new forms of agency. In contrast to the popular perception that this kind of lack of remediation suggests the failure of urban governance, the dissertation speaks to the success of activists who have neutralized the government’s reconstruction plan, which activists perceived as worsening the circumstances leading up to both the catastrophe and recovery. Distinguishing between the “landslide” and the “larger disaster,” the dissertation further explains the government’s proposed reconstruction plan within a history of violent extraction, dispossession and displacement. Framing an original case consisting of fifteen planning vignettes to trace actions, reactions and counteractions, I expose the reduction of the planning process as crisis urbanism. My research contributes to our understanding of variability among insurgent planning actors and their invented spaces for engagement in the context of disaster, by defining technocratic resistance as a valid form of dissent inside the government, and by proposing a new device for the study of insurgent planning called transformative spaces enabling local community’s right to plan. Drawing on contemporary debates on anti-crisis, risk and decolonial thought, the dissertation imagines an alternative paradigm for planning beyond crisis that enables radical community action through dissenting grassroots leadership.
Keywords: crisis urbanism, technocratic resistance, insurgent planning, regenerative planning, anti-crisis, risk, decolonial thoughtPh.D
Coalesce: An Accessible Mixed-Initiative System for Designing Community-Centric Questionnaires
No full textIUI ’25, Cagliari, ItalyEffectively incorporating community input into civic decision-making processes is crucial for fostering inclusive governance. However, public officials often face challenges in formulating effective questions to gather meaningful insights due to constraints such as time, resources, and limited experience in questionnaire design. This paper explores the potential of leveraging large language models (LLMs) to address this challenge. We present Coalesce, a novel mixed-initiative system that utilizes LLMs to assist civic leaders in crafting tailored and impactful questions for surveys, interviews, and conversation guides. Guided by best practices in questionnaire design, Coalesce improves question readability, enhances specificity, and reduces bias. To inform our design, we conducted a formative interview study with 30 civic leaders and implemented an iterative human-centered design process involving 14 feedback sessions. We built a fully-functional system before evaluating it through a real-world user study with 16 participants who applied the platform to their own community engagement projects. Our findings show that Coalesce improved participants’ confidence in questionnaire design, supported diverse workflows, and fostered learning while raising important questions about human agency and over-reliance on AI. These insights highlight the potential for intelligent user interfaces to reshape how civic leaders engage with their communities, fostering more informed and inclusive decision-making processes
Future phytoplankton diversity in a changing climate
No full textThe future response of marine ecosystem diversity to continued anthropogenic forcing is poorly constrained. Phytoplankton are a diverse set of organisms that form the base of the marine ecosystem. Currently, ocean biogeochemistry and ecosystem models used for climate change projections typically include only 2−3 phytoplankton types and are, therefore, too simple to adequately assess the potential for changes in plankton community structure. Here, we analyse a complex ecosystem model with 35 phytoplankton types to evaluate the changes in phytoplankton community composition, turnover and size structure over the 21st century. We find that the rate of turnover in the phytoplankton community becomes faster during this century, that is, the community structure becomes increasingly unstable in response to climate change. Combined with alterations to phytoplankton diversity, our results imply a loss of ecological resilience with likely knock-on effects on the productivity and functioning of the marine environment.</jats:p
Determining optimal inventory positions in an urban network
No full textSupply chain networks are becoming increasingly complex due to the aggressive growth of multiple digital trends, like the rise of e-commerce and the increased customer expectations, which have been enhanced through the pandemic over the last few years. Therefore, this study proposes a model to develop an inventory optimization strategy for a multi-tier supply chain case study in the US market, considering the supply and demand variability for local and international distribution. First, different approaches from the theoretical perspective are analyzed, from traditional inventory management to the new end-to-end perspectives. After that, details of the methodology will be explained, considering the statistical benefits of demand pooling. Finally, real numbers from a case study are applied to the methodology to measure the solution's impact, followed by the conclusions found from the study