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Veils of the Unseen
No full textThis thesis explores the dynamic interplay between consciousness and unconsciousness and how this relationship influences visual language in my art practice. My research investigates how unconscious impulses and conscious choices intersect in the creative process, shaping artistic expression and viewer interpretation. I examine how visual art can serve as a medium through which hidden emotional and psychological states, such as memory, desire, and internal conflict, emerge and take form. Rooted in the practice of automatism and influenced by Surrealist strategies, my work engages with theories by Jacques Lacan, particularly his notion of the gaze, and Gilles Deleuze’s concepts of extraction and isolation. These theoretical frameworks have guided my response to visual repetition and unconscious copying. My art seeks to make visible what often remains hidden and to disrupt habitual ways of seeing, encouraging audiences to reconsider what they perceive as familiar. Ultimately, this thesis offers a deeper inquiry into how visual art can be a powerful tool for accessing unconscious content and opening new possibilities for self-reflection and psychological understanding
Roaring Skies: The Law of Supersonic Commercial Flight and Arguments for Its Return
Full text linkThe prospect of supersonic commercial flight is no longer confined to history; it is reemerging as a viable transportation model in the 21st century. With major airlines placing orders for next-generation supersonic aircraft and regulatory agencies, including the Federal Aviation Administration (FAA) and the International Civil Aviation Organization (ICAO), reconsidering longstanding restrictions, the return of supersonic flight is imminent. This article examines the legal, regulatory, and policy considerations that have shaped supersonic aviation’s trajectory and explores the challenges that remain for its full reintroduction into global airspace.
First, this article surveys the historical rise and fall of supersonic passenger aviation, analyzing the regulatory frameworks that led to the demise of Concorde and the stagnation of further supersonic development. Second, it assesses recent technological advances and their implications for regulatory reform, focusing on noise abatement, emissions reduction, and the potential for sustainable aviation fuels. Third, it evaluates current legal obstacles to supersonic flight, including national and international restrictions on overland operations, and proposes solutions to reconcile safety, environmental, and economic concerns.
Ultimately, this article argues that the regulatory landscape must evolve to accommodate modern supersonic commercial aviation. As new aircraft manufacturers seek to revolutionize air travel, policymakers must balance innovation with environmental and legal considerations. The 2020’s have the potential to fundamentally transform travel, potentially marking a new era of faster, more efficient global air transportation
Quixotes, Quacks, and Laughing Philosophers: Humor and Intellectual Authority in the Long Eighteenth Century
Full text linkQuixotes, Quacks, and Laughing Philosophers: Humor and Intellectual Authority in the Long Eighteenth Century recovers the fascinating and forgotten story of John Elliot (1747–1787), a novelist, physician, mad scientist, pioneering optical theorist, criminal lunatic, and Gothic antihero whose life and work illuminate a strange new history of science, medicine, law, and literature in the eighteenth and nineteenth centuries
Improving Compliance with International Humanitarian Law in an Era of Maneuver War and Mission Command
Full text linkThis Article proposes a new paradigm in international humanitarian law (IHL) to help junior military leaders make ethical combat decisions that are both legally and tactically sound. Driven by the realities of modern maneuver warfare and inspired by the spirit of mission command—a concept that emphasizes quick, decentralized decisions—we propose a new philosophical framework for ethical decision making in ground combat. Specifically, we argue that the traditional balance between the IHL principles of military necessity and humanity is better suited to the detached targeting processes associated with indirect fires and air power than to the split-second decisions required in direct ground combat. By replacing the amorphous principle of humanity with an expanded version of Additional Protocol I’s constant care principle, junior combat leaders can be equipped with a more wieldable IHL framework that will enable them to accomplish their missions and consistently mitigate civilian risk even in the chaos of combat.
At the tactical level of war, this proposal will significantly reduce the unnecessary suffering and destruction too often associated with modern ground combat, especially in extremely kinetic environments like Ukraine. At the strategic level, it will also enhance perceived legitimacy and foster greater respect for the law of war. This Article surveys modern conflict environments, discusses mission command philosophy, and then ties both to the need for a new IHL paradigm if we are to expect soldiers to both win wars and reduce unnecessary death and destruction while doing so. Ultimately, this Article explains why—for legal, tactical, and ethical reasons—IHL education for junior military leaders should be reoriented around a renewed understanding of military necessity and constant care
A Democratic Participation Model for Corporate Governance
Full text linkCorporate law is in the grip of a fundamental conundrum: whether corporations should seek only to serve shareholders or instead attend to the interests of all stakeholders. The doctrine of shareholder primacy, which focuses the corporation’s attention on the goal of maximizing shareholder wealth, has been startingly successful, capturing the theory and practice of corporate governance for roughly fifty years. But recently the costs of this monomaniacal focus on the financial interests of one set of corporate participants have become clearer. At a time when the original reasons for restricting the corporate franchise to shareholders have been shown to rest on faulty assumptions and the misapplication of standard economic theory, shareholder primacy’s fingerprints have been discovered all over potentially catastrophic problems such as dramatically rising income and economic inequality, accelerating climate change, and the unleashing of vast sums of money upon politics. Stakeholderism promises a better way—a focus on meeting the needs of all stakeholders in the corporation through a balanced approach to governance. But stakeholder theorists have largely offered only hortatory suggestions for corporate boards, unable to develop concrete reforms to implement their concepts. So we now find ourselves at a stalemate: Shareholder primacy improperly orients the purpose of the corporation around maximizing shareholder wealth and power, while stakeholder theory has failed to develop a workable model of governance that would put its ideas into practice.
This Article breaks the corporate governance stalemate by presenting a new model of corporate governance based on the theory of democratic participation. The model supports the extension of the corporate franchise beyond shareholders to other stakeholders, but only when governance rights can accurately capture the preferences of those with sufficiently strong interests in a manageable way. We explain the principles of the model and apply it to a variety of stakeholders with potential governance claims: shareholders, employees, creditors, consumers, and communities, among others. Assessing their interests, the accuracy of markers for those interests, and the manageability of those markers, we show how a variety of firm participants could be integrated into the governing structure of the corporation. This new model would allow appropriate stakeholders to define and effectuate their own interests through governance and would help ensure that the corporate purpose debate results in something more than empty rhetoric
The FDA\u27s Crisis is Everyone\u27s Crisis
No full textOn April 1, 2025, the US Department of Health and Human Services (HHS) fired about 3500 US Food and Drug Administration (FDA) employees, nearly 20\% of agency staff. HHS also forced out career senior leaders at the FDA, including the director of the Center for Biologics Evaluation and Research, the director of the Center for Tobacco Products, and the chief medical officer, and has suggested a dramatic reorganization of the FDA into “five shared services offices,” potentially replacing the FDA’s centers. This followed the February 2025 firing of roughly 700 FDA employees, including staff responsible for overseeing food and device safety (before some were called back). And these changes at the FDA come amid a long list of controversial government-wide staffing directives from the Trump administration, including instructions to implement return-to-office policies, consult the US Department of Government Efficiency (DOGE) about career appointments, and hire no more than 1 employee for every 4 that depart
Sounds of the World: Guiding and Motivating Players Through Sound Valences
Full text linkThe purpose of this research is to integrate sound design techniques and practices into the larger level and game design process to provide greater support and motivations for players. The researcher created a custom single-player level in The Elder Scrolls V: Skyrim that tasks the player with navigating an enchanted castle, relying on sound valences to guide their path. The researcher gathered data on the implemented sound valences by observing testers’ playtests, the navigational choices the testers made throughout the level, and the connotations of the sound valences themselves
Quantum Chemical Methods And Multiscale Modeling In Computer-Aided Drug Design
Full text linkComputer-Aided Drug Design (CADD) leverages a diverse toolkit of computational methods to accelerate the discovery and development of novel therapeutics. Among these, quantum chemical calculations provide unparalleled accuracy in understanding molecular interactions, albeit at a higher computational cost. This accuracy is crucial for identifying and quantifying fundamental interactions that dictate drug efficacy and selectivity, as exemplified by our investigation of ruthenium polypyridyl complexes. These metal-based compounds are model systems for studying covalent coordination bonds between ruthenium and its ligands and noncovalent interactions with DNA and protein targets. Local Mode Vibrational Theory emerges as a powerful lens within this framework, enabling the assessment of drug candidates by probing their intrinsic stability and interaction strengths with intended targets. This analysis utilizes local mode force constants and their correlations with other chemical properties to provide a detailed picture of molecular behavior. Deep learning models significantly enhance this framework by accelerating conformational sampling in protein dynamics simulations, enabling more efficient exploration of the vast conformational landscape.
Furthermore, introducing mechanical forces through mechanochemistry offers a unique avenue for influencing chemical reactions, potentially leading to more efficient and controlled synthesis of drug molecules. By integrating quantum chemical calculations, Local Mode Vibrational Theory, mechanochemical simulations, and data-driven approaches like machine learning, we aim to establish a comprehensive and multi-scale theoretical framework for drug discovery. This approach, encompassing everything from the quantum interactions of drug molecules to the dynamics of protein targets, paves the way for designing more effective and targeted therapies
A Framework for Vehicle Bridge Strike Detection, Strike Characterization, and Damage Estimation for Railroad Bridges
No full textRail bridges provide vital crossings for freight and passenger trains over natural and man made obstructions in terrain. Over time these structures develop damage due to aging or unexpected external loading events. Specifically, low clearance rail bridges are susceptible to frequent strikes from overheight vehicles or equipment. It is critical to detect those strikes once they occur to ensure the bridge and the public safety and to also meet FRA regulations of mandatory post-strike bridge inspection. Early bridge damage detection also reduces bridge closure times and prevents further deterioration. Not every bridge strike represents an immediate risk to safe bridge operation, thus this dissertation presents a comprehensive methodology that detects vehicle bridge strikes in real-time, characterizes strike severity post detection, and detects and quantifies damage if present in the bridge. First, the system leverages the improved accessibility and scalability of bridge instrumentation technology and interrogates bridge data using mechanics and Machine Learning (ML) algorithms to rapidly detect strikes and determine whether an immediate inspection is necessary or can be safely deferred. Specifically, this dissertation develops parallel heterogeneous data-fusion convolutional neural networks (PHD-CNN) operating on data collected from in service rail bridges to improve detection and classification of vehicle-bridge strikes. The method provides a mechanism to homogenize and fuse disparate data streams for use as inputs to a classifier that distinguishes bridge strikes from passing trains. Optimum PHD-CNN networks detect, on average, 95% of bridge strikes with false positive rates less than 2%. Next, operating on identified strike data the framework utilizes principal components analysis, an unsupervised machine learning technique, to characterize strike severity. The system analyzes extracted v severity-related features to group strikes with similar characteristics together and then compares them to user defined thresholds to determine strike severity. Finally, for an observed change in the system’s fundamental frequency this dissertation presents an energy-based mechanics relationship to provide a feasible domain of potential damage scenarios to detect, localize, and characterize damage. The final output of the system comprises practical guidance to inspectors by (1) indicating the presence of damage, (2) locating the damage, and (3) quantitatively estimating the severity of the damage; thus, the method attains a Rytter level 3. Rytter levels comprise four stages of damage evaluation: detection, localization, quantification, and prediction of remaining structure life. They are a widely used framework in structural health monitoring to rate the capabilities of damage assessment systems
Load Forecasting And Modeling For Power System
Full text linkAccurate load forecasting and modeling play a pivotal role in ensuring the stability, reliability, and economic efficiency of modern power systems. With the increasing integration of renewable energy sources, distributed energy resources, and demand-side management strategies, power systems are becoming more dynamic and complex, making traditional load forecasting methods inadequate. This dissertation introduces two novel approaches to address the challenges associated with day-ahead load forecasting and load modeling.
First, a Diffusion Model-Based Probabilistic Day-Ahead Load Forecasting (PDALF) Framework is proposed to enhance the accuracy and robustness of load forecasting. By employing a conditional denoising diffusion probabilistic model (DDPM), the framework, termed DALNet (Diffusion-Augmented Load Network), generates load curves by progressively adding and removing Gaussian noise in a Markov chain. This approach effectively models the complex distribution of load data, avoiding the error accumulation inherent in rolling forecasts and capturing intra-day correlations. Additionally, a Temporal Multi-Scale Attention Block (TMSAB) is integrated into DALNet to extract both positional and temporal information, further improving prediction accuracy. Comparative experiments on real-world datasets, including GEFCom2014 and Arizona State University\u27s load data, demonstrate that DALNet significantly outperforms traditional benchmarks such as LSTM, Transformer, and Bayesian Neural Networks (BNNs), offering superior reliability, sharpness, and overall forecasting performance.
Second, this dissertation presents a Reinforcement Learning-Based Symbolic Regression (SR) Framework for load modeling to address the limitations of fixed-form parametric models and complex machine learning models that lack interpretability. Leveraging the Actor-Critic reinforcement learning architecture, a trainable expression tree structure is designed to discover mathematical expressions that describe the relationship between load characteristics and system variables. To balance model complexity and interpretability, a candidate pool is employed to refine the best-performing expressions using policy gradient optimization and gradient-based fine-tuning. Case studies demonstrate that the proposed symbolic regression framework effectively captures the nonlinear dynamics of load responses under various grid disturbances, outperforming conventional parametric models and artificial neural networks in terms of accuracy, interpretability, and computational efficiency.
The combination of these two innovative approaches provides a comprehensive solution to the challenges of probabilistic load forecasting and dynamic load modeling in modern power systems. By bridging the gap between accuracy, interpretability, and scalability, this work contributes to advancing the state-of-the-art in power system analysis and operation