Mason Journals (George Mason Univ.)
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Measuring the Impact of Automation on Industry-Level Employment Growth
No full textThis study investigates the impact of automation on industry-level employment growth—a key dimension of AI-driven economic transformation since late 2022. Using employment data from 2021 and automation probability estimates from 2023, we examine the relationship between employment growth and automation likelihood across industries. On average, a 10-percentage-point increase in the probability of automation is associated with a 0.52% decline in employment. Further analysis, normalizing employment change by automation probability, reveals substantial heterogeneity across sectors. For instance, community and social service occupations exhibit limited sensitivity to automation (1.87%), while office and administrative support occupations experience a more pronounced decline (-0.04%). Additionally, a cross-industry comparison of robot adoption and job displacement from 2013 to 2023 shows a positive correlation between the two, particularly in manufacturing, logistics, and healthcare. These findings highlight the sector-specific nature of automation’s labor market effects and offer insights into workforce adjustment in an increasingly automated economy
Mathematical Modeling of Oxidative Phosphorylation in the Mitochondria
No full textOxygen plays an important role in energy production within the mitochondria by facilitating ATP synthesis through the process of oxidative phosphorylation. Thus, understanding oxygen consumption is important for studying both cellular physiology and disease. This project updates an existing mitochondrial model to include oxygen consumption using ordinary differential equations (ODEs) that simulate chemical concentrations and metabolic reactions. Through FORTRAN-based simulations, the model tracks oxygen levels within the cell and calculates fluxes that represent major mitochondrial processes, such as Complex IV activity and the detoxification of reactive oxygen species (ROS). These equations are then solved with the Fourth Order Runge-Kutta method, allowing the model to compute changes in chemical concentrations and simulate biological responses to varying oxygen levels. Preliminary results indicate variation in oxygen consumption rates with different initial ADP concentrations, which is being examined to ensure that oxygen uptake is accurately modeled. Future work will consequently focus on simulating hypoxic conditions to study their impact on mitochondrial behavior. Overall, incorporating oxygen consumption into the model allows for the analysis of how oxygen availability affects mitochondrial behavior, serving as a stepping stone to simulate disease-like conditions
Physics-based Simulation of RRAM for Reliable Memory Technology
No full textResistive Random Access Memory (RRAM) is an emerging non-volatile memory candidate that offers fast switching speed, low power consumption, and high scalability. RRAM functions by applying a positive voltage to an electrode to form a conductive filament through an insulating switching layer, resulting in high current flow (SET), and applying a negative voltage to rupture the filament and reduce current flow (RESET). However, its relatively large device-to-device and cycle-to-cycle variability remains a critical challenge, limiting RRAM’s reliability. While possible sources of variability have been identified, a comprehensive understanding of the impact of specific device parameters on RRAM’s variability is still missing. This work creates a novel, physics-based model of RRAM that simulates its switching behavior by tracking the evolution of both the conductive filament and the gap distance between the filament and top electrode. By updating these lengths at discrete time intervals while considering various parameters including temperature, electrode resistance, and activation energy, the model accurately calculates current as a ratio of the filament length to the total switching layer thickness and produces a current-voltage (I-V) characteristic of the simulated RRAM device. These I-V curves match experimental RRAM devices with the same parameters, tested using a probe station, validating the model. The study found that a local temperature rise inside a device may cause cycle-to-cycle variability. Activation energy and switching layer thickness were found to be important physical parameters in device-to-device variability, with activation energy decreasing and switching layer thickness increasing SET/RESET voltages and memory window. These findings provide quantitative guidelines for designing and experimentally developing RRAM devices with increased reliability in emerging computing hardware applications
Research papers’ supplemental materials authenticity analyzed with Confidential Computing utilizing Intel TDX
No full textConfidential computing is an emerging technology that enables secure computation by ensuring data confidentiality, data integrity, and code integrity—unauthorized entities cannot view, alter, or manipulate data or code during processing. To assess the reliability of published research, our team used confidential computing, specifically Intel Trust Domain Extensions (TDX), to analyze the accuracy of supplemental materials in research papers from Management Science. The motivation was to ensure that published code remained unaltered and secure from external interference during replication, and to check for its accuracy in the paper. Using a virtual machine deployed on Microsoft Azure with free credits, we executed code in a confidential environment. This approach not only assured computational integrity but also demonstrated improved performance. Notably, the confidential computing environment ran approximately 10% faster than a non-secure setup, though this may reflect efficiencies from the virtual machine rather than TDX itself. Our findings show that confidential computing enhances the trustworthiness of research reproducibility efforts by shielding code from tampering and ensuring faithful execution. However, one limitation is that TDX only secures locally executed applications—web-based platforms like SAS OnDemand or free versions of MATLAB are excluded. We conclude that confidential computing is a promising tool for auditing and verifying research, offering both performance gains and security, strengthening reproducibility across scientific disciplines
Tokenized Stocks and the Liquidity Mirage: Structural Risks and Regulatory Arbitrage in RWA Markets
No full textThe emergence of tokenized Real-World Assets (RWAs) promises to revolutionize financial markets by offering on-chain representations of traditional equities with enhanced liquidity and accessibility. However, the nascent market is characterized by opaque structures and significant risks, with a critical gap between its theoretical potential and observed reality. This study conducts a rigorous investigation of 106 tokenized stock products, synthesizing legal prospectuses, issuer disclosures, and on-chain transaction data to construct a definitive taxonomy of the ecosystem. Our analysis reveals a market dominated by Swiss-based Special Purpose Vehicles (SPVs), a strategy driven by regulatory arbitrage. Crucially, while these products successfully replicate the price of their off-chain counterparts, on-chain analysis exposes a fundamental failure to deliver on liquidity promises, revealing negligible trading volumes, concentrated ownership, and a near-total absence of secondary market activity. We further identify systemic risks stemming from reliance on centralized infrastructure and opaque custodial arrangements. Ultimately, this research demonstrates that tokenized stocks currently serve to democratize equity access for a niche retail audience but have failed to attract institutional capital or create liquid on-chain markets, exposing the critical frictions that must be resolved for this market to mature
Governance at the Edge: How Structure, Jurisdiction, and Regulation Shape Risk in Cryptocurrency Exchanges
No full textThe proliferation of cryptocurrency exchanges is central to the digital asset economy, yet the sector is plagued by frequent operational failures and regulatory enforcement actions. Existing risk assessments often rely on simplistic metrics like trading volume, lacking a systematic analysis of the qualitative factors that drive an exchange's risk profile. This study addresses this gap by examining the relationship between an exchange's structural characteristics and its market behavior. A comprehensive dataset of over 800 active and defunct exchanges was analyzed, mapping their governance structures, regulatory frameworks, product offerings, and geographic exposures. The analysis reveals that specific governance models and the permissiveness of an exchange's primary jurisdiction are strongly correlated with its product complexity and exposure to illicit finance. Notably, exchanges operating in jurisdictions with weak regulatory oversight and opaque ownership structures are significantly more likely to offer high-leverage derivative products and be implicated in enforcement actions. These findings provide a foundational framework for regulators and investors to better assess the inherent risks associated with an exchange's operational and legal structure, moving beyond surface-level metrics to a more nuanced understanding of counterparty risk
Correlates of Risk: Unpacking the Relationship Between Governance, Regulation, and Cryptocurrency Exchange Behavior
No full textThe proliferation of cryptocurrency exchanges is central to the digital asset economy, yet the sector is plagued by frequent operational failures and regulatory enforcement actions. Existing risk assessments often rely on simplistic metrics like trading volume, lacking a systematic analysis of the qualitative factors that drive an exchange's risk profile. This study addresses this gap by examining the relationship between an exchange's structural characteristics and its market behavior. A comprehensive dataset of over 800 active and defunct exchanges was analyzed, mapping their governance structures, regulatory frameworks, product offerings, and geographic exposures. The analysis reveals that specific governance models and the permissiveness of an exchange's primary jurisdiction are strongly correlated with its product complexity and exposure to illicit finance. Notably, exchanges operating in jurisdictions with weak regulatory oversight and opaque ownership structures are significantly more likely to offer high-leverage derivative products and be implicated in enforcement actions. These findings provide a foundational framework for regulators and investors to better assess the inherent risks associated with an exchange's operational and legal structure, moving beyond surface-level metrics to a more nuanced understanding of counterparty risk
Extracellular Vesicles Derived from Borrelia burgdorferi Leads to Prolonged Neuroinflammation in Lyme Disease
No full textIt is estimated that 10-20% of patients treated using antibiotics for Lyme disease develop post-treatment Lyme disease syndrome (PTLDS), characterized by decline in cognitive testing, processing speed, verbal recall, and working memory. The cause and pathology of PTLDS is still an area of active research. Production of reactive oxygen species (ROS) along with mitochondrial interaction with immune proteins such as toll-like receptors (TLRs) and interferons are critical in regulating inflammation. There is currently little research regarding mitochondrial ties to inflammation in Lyme disease. Bacterial extracellular vesicles (BEVs) from cultured Borrelia burgdorferi B31 were found to contain immunogenic molecules. HMC3 human microglial cells were treated with Borrelia BEVs overnight. Total RNA was then collected and PCR was used to amplify expressed genes. The HMC3 cells showed upregulated expression of interferon-alpha (IFN-α), aconitate decarboxylase 1 (Acod1), and toll-like receptor 2, 4, and 9 (TLR2, TLR4, TLR9). These findings suggest mitochondrial metabolic functions along with pathways such as ROS production or energy metabolism modulated by Acod1 may be disrupted, activating proinflammatory genes and contributing to persistent neuroinflammation. Thus, mitochondrial dysfunction from exposure to Borrelia BEVs can promote a proinflammatory state. This supports Borrelia BEVs as antigenic reservoirs driving chronic symptoms, with potential as diagnostic markers and therapeutic targets
Deep Intrinsic Surprise-Regularized Control: Scaling Temporal-Difference Updates for Stability in Deep Q-Networks
No full textDeep reinforcement learning (DRL) has driven major advances in autonomous control, but standard Deep Q-Network (DQN) agents, while already scaling updates by temporal-difference (TD) error and gradients, typically use fixed learning rates without an explicit mechanism to modulate overall update magnitudes. Though prioritized experience replays and adaptive optimizers indirectly shape learning dynamics, few methods explicitly adjust each update’s scale through an intrinsic signal. We introduce Deep Intrinsic Surprise-Regularized Control (DISRC), a biologically inspired augmentation to DQN that computes a deviation-based surprise score via a moving latent setpoint in a LayerNorm-based encoder, scaling each Q-update in proportion to both TD error and surprise intensity. This design promotes higher plasticity during early exploration and more conservative updates as learning stabilizes. We evaluated DISRC on CartPole-v1 under identical settings to a vanilla DQN across multiple runs. The vanilla DQN achieved a mean reward of 419.68 in the final 100 episodes, reached the 200-reward threshold in 147 episodes, and produced an area under the reward curve (AUC) of 150,605.00. DISRC achieved a mean reward of 159.84, required 556 episodes to reach the threshold, and achieved an AUC of 46,234.50. DISRC’s lower reward standard deviation (92.96 vs. 149.22) reflects more uniform, though consistently suboptimal, episode returns. Although DISRC underperforms on this dense-reward benchmark, we hypothesize that strong external rewards may diminish or override the benefits of intrinsic surprise modulation. We are looking to explore DISRC in more sparse-reward or complex environments, where intrinsic regulation could more meaningfully guide exploration and learning. This work introduces a novel mechanism for regulating update magnitudes in off-policy agents, positioning DISRC as a promising direction for stability-enhanced, biologically grounded DRL
Evaluating Maneuvering Capabilities of the Enpulsion Nano Lark for the NASA Landholt Mission
No full textThe NASA Landholt Mission will be an "artificial star" satellite in a one-year geosynchronous (GEO) or near-GEO orbit to improve stellar flux calculations and analysis. The satellite will need to perform two types of propulsion maneuvers throughout the mission: station-keeping in GEO to stay in the provided orbital slot, and a low-thrust spiral transfer from GEO to a graveyard orbit roughly 300 km above GEO following the primary operations. The Enpulsion Nano Lark (EN), a Field Emission Electric Propulsion system, is planned to perform the maneuvers. Indium propellant with the EN is limited, so we perform preliminary calculations to test of the EN would be able to meet the mission requirements. Using typical Delta-V (the units used to characterize velocity changes required for orbital energy adjustments) levels for satellites in GEO (45-55 m/s), and using the Tsiolkovsky Rocket Equation to relate Delta-V to propellant mass, we estimate a worst-case scenario of roughly 160 g of propellant will be consumed throughout all maneouvers and potential orbital anomalies. Given the 220 g provided by the EN, we calculate a margin for propellant of roughly 27% for the primary mission. This preliminary evidence suggests that the EN will be able to accomplish the necessary maneuvers for the Landholt Mission.