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METAL FILMS FOR HIGH-PERFORMANCE OPTOELECTRONIC DEVICES: UNDERSTANDING BAND HYBRIDIZATION EFFECTS
No full textThe development of alloyed metal films for high-performance optoelectronic devices, like ultrafast Schottky barrier photodetectors, is key to advancing technologies in ultrafast detection and communication. However, exploring these materials experimentally becomes increasingly difficult as the number of alloy components grows. For example, moving from binary to ternary or quaternary systems creates a combinatorial explosion of possible compositions, making it nearly impossible to test them all in reasonable timeframe. Additionally, there’s a lack of frameworks that not only predict the properties of these alloys but also explain why certain composition perform better- particularly in terms of band hybridization and its impact on hot carrier generation and lifetime.
To address this, we propose a framework that combines transfer learning (TL) with physics-based modeling. TL allows us to leverage knowledge from simpler or well-studied systems to guide the exploration of more complex alloys, reducing the need for extensive experimentation. But more than just predicting properties, this approach helps us understand the underlying physics -such as how band hybridization influences electronic structure and hot carrier dynamics. This insight is critical for identifying which alloy compositions are most promising and why, rather than relying on trial and error.
In this work, we develop a physics-informed model validated by spectroscopic ellipsometry, X-ray diffraction, and ultrafast pump-probe spectroscopy. This model generates synthetic data to train machine learning surrogate models, which are then refined using TL to adapt to new alloy systems. The goal is to optimize key properties like hot carrier lifetime and optical properties while providing clear physical explanations for why certain compositions excel. By bridging prediction with understanding, this approach accelerates the discovery of high-performance materials for next-generation optoelectronic devices
Designing Cybersecurity Measurement Systems for Global and Organizational Intelligence
No full textThe growing interdependence of digital infrastructures has expanded organizational
attack surfaces beyond traditional perimeters. This thesis tackles two complementary
problems with distinct methods: (i) generating Cyber Threat Intelligence (CTI) from DNS
cache snooping, where non-recursive queries to public resolvers reveal privacy-preserving
lower bounds on domain interest at global scale; and (ii) maintaining an always-current
view of external exposure by continuously discovering, contextualizing, and prioritizing
Internet-facing assets.
The first contribution, MudHunter, presents a distributed domain name system
(DNS) measurement framework that leverages cache-snooping to infer lower bounds on
domain access activity. By issuing non-recursive queries from 130 globally distributed van-
tage points, MudHunter estimates population-level domain interest without compromising
privacy or requiring authoritative visibility. The resulting empirical results reveal global
access behaviors, regional exposure trends, and malicious ecosystem signals, demonstrating
how passive DNS observation can inform CTI at scale.
The second contribution, the Continuous Threat Exposure Management (CTEM)
framework, operationalizes continuous external risk monitoring. It automates asset discov-
ery, vulnerability enrichment, and risk prioritization into a unified, data-driven pipeline.
The framework integrates large-scale scanning, correlation with structured vulnerability
sources (NVD, CISA KEV, EPSS), and dynamic exposure scoring to provide an always-
current view of organizational risk. A modular architecture, built around event buses, a
database, and RESTful APIs, supports continuous ingestion, enrichment, and visualization
through dashboards and automated interfaces.
viBoth systems share a unifying philosophy: meaningful security insight emerges
from continuous, measurement-based CTI. MudHunter embodies this principle by trans-
forming large-scale DNS cache observations into reproducible empirical evidence about
how global resolvers operate and how malicious infrastructure propagates through them.
CTEM, in turn, applies the same philosophy within organizational environments, continu-
ously measuring, enriching, and prioritizing security exposures through data-driven anal-
ysis. Together, these works advance the state of empirical cyber threat intelligence by
demonstrating that rigorous, measurement-based methodologies can yield deeper under-
standing and more transparent reasoning about the evolving threat landscape
Evaluation of Host Resistance to Rotylenchulus reniformis in Sweetpotato Germplasm
No full textThe reniform nematode (Rotylenchulus reniformis) is an emerging plant-parasitic threat to sweetpotato production. Dissimilar from the galling caused by root-knot nematode infections, infections from the reniform nematode occur without visible damage to the root system. In the southern United States, heavily infested sweetpotato fields can lose up to 40% of their total yield. Growers rely heavily on fumigants and nematicides which provide inconsistent control and are not sustainable in the long-term. Past research has been more focused on the efficacy of chemicals than in the identification of host resistance or tolerance in sweetpotato germplasm. This study evaluated past screening projects and streamlined a protocol that considers both nematode reproduction and sweetpotato development. Using this optimized protocol, this project screened 52 sweetpotato entries from a pool of commercial cultivars, advanced breeding lines from the LSU Sweetpotato Breeding Program, and genetically diverse germplasm from the USDA Genetic Resource Information Network (GRIN). Although no entry completely stopped reproduction of the reniform nematode, two commercial cultivars (\u27Vermillion\u27 and \u27Heartogold\u27) and three GRIN plant introductions (\u27Regal\u27, \u27Saing-Mi\u27, and \u27Morada Sombica\u27) supported 70-80% less reproduction than the susceptible cultivar \u27Beauregard\u27. By creating a reproducible framework and applying it to a broad range of germplasm, this study provides a foundation for future sweetpotato breeding efforts aimed at reducing reliance on chemical nematicides and improving yield in reniform nematode infested fields
Valorizing Seafood Waste Streams Using Black Soldier Fly Larvae: Implications for Sustainable Agriculture
No full textThe progressive culture of environmental sustainability demands manufacturers to reduce their negative social and environmental impact but provides an inadequate framework for innovative solutions to waste issues. Processing waste from shrimp and crab operations often exceeds 40-70% of raw material weight. Current trends of waste disposal to landfills not only pollute coastal ecosystems but also cost processors significant amounts of money. This study evaluated the use of Black Soldier Fly larvae (BSFL) to valorize shrimp and crab shells into protein-rich feed ingredient and nutrient-dense frass through laboratory and commercial-scale trials. In a controlled micro trial, shrimp and crab shells were incorporated into diets at 25%, 40%, 50% and 60% inclusion and compared to a 100% grain-based feed (positive control). Larval survival remained high across treatments, with optimal growth observed at 25% shell inclusion. Larval crude protein (~45% DM) was stable, while crude fat declined and ash increased at higher inclusions. Frass from shell diets were enriched in phosphorus and calcium, highlighting its potential as a fertilizer. A macro trial conducted under near-commercial conditions confirmed feasibility at scale which reflected similar trends in production metrics. Mineral enrichment was notable in both larvae and frass, while heavy metals remained within safety limits. These findings demonstrate that BSFL can efficiently convert seafood waste into marketable feed ingredient and fertilizer products. Moderate inclusion optimizes larval performance, while higher inclusions enhance frass nutrient value. This strategy may offer Louisiana’s seafood industry a sustainable pathway to valorize the waste into valuable commodities. Comprehensive economic analysis and further research on the efficacy and safety of the resulting byproducts are recommended before commercial implementation of this approach
Colonial Legacy and the Variation in UN Peacekeeping Operations and Peacekeeping Mandate
No full textWhile much of the existing literature on the determinants of UN peacekeeping deployment and political mandates focuses on factors such as civilian casualties, conflict duration, and external influences like third-party interests, alliances, and bilateral agreements, there is limited research on the role of colonial legacies in predicting peacekeeping outcomes. This study investigates how colonial legacies—specifically, countries that gained independence through violence and those with an indirect rule legacy—affect peacekeeping and political mandates. I argue that peacekeeping is more likely in countries with a violent independence history than those with an indirect rule legacy. Conversely, political mandates are more common in countries with an indirect rule legacy than those with violent independence. I analyzed variations in peacekeeping and political mandates using logistic regression based on these colonial legacies. The results support the hypothesis that peacekeeping is more frequent in post-violent independence countries, while political mandates are evenly pursued across both colonial legacies
Effects of Hydrological Connectivity on Created Marsh Sustainability
No full textWhile efforts are being made to curb the high rate of coastal land loss in Louisiana, and even to restore ecosystem services and functions, it is generally accepted that restoration of these areas and functions is not a one-to-one value. It may take decadal time scales to reach efficient functional processing of nutrients and carbon storage rates, which may never reach true capacity comparable to similar naturally-organized systems. Although it is imperative to preserve these areas, restoration of ecosystem services and biogeochemical processes of coastal wetlands are preferable to future without action scenarios. Effectively directing the use of the limited time and resources available is paramount, especially considering these efforts are taking place on such a large, coast-wide scale covering hundreds of square kilometers. Most notably, the strategic consideration and placement of marsh creation (MC) projects in relation to the hydrodynamic influences of sediment diversion operations can increase sediment trapping efficiencies in the receiving basin and can not only actively build coastal land in a sediment-poor area previously cut off from the Mississippi River (MR), but may also create marshes that can be sustained much longer than without the introduction of these sediments allowing coastal marshlands to survive pressures of relative sea level rise (RSLR) (Meselhe et al., 2017). While highly sophisticated models focus on longer time scales and larger areas, their elongated spin up time makes judging the progress of restoration projects on shorter time scales (\u3c 25 years) more difficult. Numerical modeling programs, such as NUMAR, are valuable tools that can be utilized to determine potential outcomes of specified future environmental changes in parameters and calculated effects of restoration efforts, including the sustainability of MC projects. Here we were able to determine that, when coupled with real-world organic and inorganic sediment data from coastal wetlands experiencing the natural deltaic fluctuations of the MR, NUMAR predicted that five MC projects near the proposed Mid-Barataria Sediment Diversion (MBSD) within Barataria Basin would be able to net nearly 11 cm of vertical accretion and therefore survive the effects of RSLR, subsisting well beyond their projected 20-year life expectancy
Theory of Two-Level Tunneling Systems in Superconductors
No full textWe develop a field theory formulation for the interaction of an ensemble of two-level tunneling systems (TLSs) with the electronic states of a superconductor. Predictions for the impact of two-level tunneling systems on superconductivity are presented, including Tc and the spectrum of quasiparticle states for conventional BCS superconductors. We show that nonmagnetic TLS impurities in conventional s-wave superconductors can act as pair-breaking or pair-enhancing defects depending on the level population of the distribution of TLS impurities. We present calculations of the enhancement of superconductivity, both Tc and the order parameter, for TLS defects in thermal equilibrium with the electrons and lattice. The scattering of quasiparticles by TLS impurities leads to subgap states below the bulk excitation gap, ∆, as well as resonances in the continuum above ∆. The energies and spectral weights of these states depend on the distribution of tunnel splittings, while the spectral weights are particularly sensitive to the level occupation of the TLS impurities. Under microwave excitation, or decoupling from the thermal bath, a nonequilibrium level population of the TLS distribution generates subgap quasiparticle states near the Fermi level that contribute to dissipation and thus degrade the performance of superconducting devices at low temperatures
Regular black holes and their relationship to polymerized models and mimetic gravity
No full textWe present further applications of the formalism introduced by K. H. Akinori Tanaka and Akio Tomiya in [Deep Learning and Phyiscs (Springer, Switzerland, 2021)], which allows the embedding of a broad class of generalized Lemaître-Tolmann-Bondi (LTB) models into effective spherically symmetric spacetimes. We focus on regular black hole models, where a broad class of models can be considered, including for example loop quantum gravity (LQG)-inspired models as well as models with a regular center, e.g., of Bardeen and Hayward. For a certain class of regular black hole models, we can formulate a Birkhoff-like theorem in LTB coordinates. We further show that, depending on the properties of the polymerization functions characterizing such regular black hole models in this formalism, the uniqueness of the effective spherically symmetric vacuum solutions might not be given in general in Schwarzschild-like coordinates. Furthermore, we introduce a reconstruction algorithm that allows for a subclass of these models to construct from a given metric in Schwarzschild-like coordinates the corresponding effective spherically symmetric model, its dynamics as a 1+1-dimensional field theory, as well as a corresponding covariant Lagrangian of extended mimetic gravity in four dimensions. Such a reconstruction allows us to obtain Lagrangians of extended mimetic gravity models for black holes with a regular center, e.g., the Bardeen and Hayward metric, as well as for effective LQG inspired models. Moreover, the reconstruction enables us to extend regular black hole models to general inhomogeneous dust collapse models. For the latter, within this formalism, we can investigate and explore various physical properties of the models such as the existence of weak shell-crossing singularities from a novel perspective
Search for light sterile neutrinos with two neutrino beams at MicroBooNE
No full textThe existence of three distinct neutrino flavours, νe, νμ and ντ, is a central tenet of the Standard Model of particle physics1,2. Quantum-mechanical interference can allow a neutrino of one initial flavour to be detected sometime later as a different flavour, a process called neutrino oscillation. Several anomalous observations inconsistent with this three-flavour picture have motivated the hypothesis that an additional neutrino state exists, which does not interact directly with matter, termed as ‘sterile’ neutrino, νs (refs. 3, 4, 5, 6, 7, 8–9). This includes anomalous observations from the Liquid Scintillator Neutrino Detector (LSND)3 experiment and Mini-Booster Neutrino Experiment (MiniBooNE)4,5, consistent with νμ → νe transitions at a distance inconsistent with the three-neutrino picture. Here we use data obtained from the MicroBooNE liquid-argon time projection chamber10 in two accelerator neutrino beams to exclude the single light sterile neutrino interpretation of the LSND and MiniBooNE anomalies at the 95% confidence level (CL). Moreover, we rule out a notable portion of the parameter space that could explain the gallium anomaly6, 7–8. This is one of the first measurements to use two accelerator neutrino beams to break a degeneracy between νe appearance and disappearance, which would otherwise weaken the sensitivity to the sterile neutrino hypothesis. We find no evidence for either νμ → νe flavour transitions or νe disappearance that would indicate non-standard flavour oscillations. Our results indicate that previous anomalous observations consistent with νμ → νe transitions cannot be explained by introducing a single sterile neutrino state
Quantum Classical Algorithm for the Study of Phase Transitions in the Hubbard Model via Dynamical Mean-Field Theory
No full textModeling many-body quantum systems is widely regarded as one of the most promising applications for near-term noisy quantum computers. However, in the near term, system size limitation will remain a severe barrier for applications in materials science or strongly correlated systems. A promising avenue of research is to combine many-body physics with machine learning for the classification of distinct phases. We present a workflow that synergizes quantum computing, many-body theory, and quantum machine learning (QML) for studying strongly correlated systems. In particular, it can capture a putative quantum phase transition of the stereotypical strongly correlated system, the Hubbard model. Following the recent proposal of the hybrid quantum-classical algorithm for the two-site dynamical mean-field theory (DMFT), we present a modification that allows the self-consistent solution of the single bath site DMFT. The modified algorithm can be generalized for multiple bath sites. This approach is used to generate a database of zero-temperature wavefunctions of the Hubbard model within the DMFT approximation. We then use a QML algorithm to distinguish between the metallic phase and the Mott insulator phase to capture the metal-to-Mott insulator phase transition. We train a recently proposed quantum convolutional neural network (QCNN) and then utilize the QCNN as a quantum classifier to capture the phase transition region. This work provides a recipe for application to other phase transitions in strongly correlated systems and represents an exciting application of small-scale quantum devices realizable with near-term technology