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Latent Profile Analysis of Posttraumatic Stress Disorder, Complex Posttraumatic Stress Disorder, and Borderline Personality Disorder: A Replication and Extension
No full textThe distinction between posttraumatic stress disorder (PTSD), complex PTSD (CPTSD), and borderline personality disorder (BPD) remains a subject of scientific debate, particularly in trauma-exposed populations where symptom overlap may be more frequent. This dissertation presents the findings of a latent profile analysis (LPA) that replicates and extends existing research on the discriminative validity of PTSD, CPTSD, and BPD symptoms. Participants were 509 undergraduate students with mixed-trauma exposure who completed measures of PTSD, CPTSD, BPD, and other trauma-related and clinical features. Results of the LPA indicated that a four-profile model revealed the best fit to the data and theoretical interpretability. The model included a “Comorbid” profile, characterized by high endorsement of all symptoms; a “High PTSD” profile, characterized by high levels of PTSD symptoms and comparatively lower levels of disturbances in self-organization (DSO) and BPD symptoms; a “High DSO and BPD” profile, characterized by low levels of PTSD symptoms and relatively higher levels of DSO and BPD symptoms; and a “Low Symptom” profile, characterized by low endorsement of all symptoms. Profiles were compared on multiple external correlates, including childhood maltreatment, trauma- and BPD-related features, clinical and personality pathology, and trauma history characteristics. Pairwise comparisons showed that the High PTSD and High DSO and BPD profiles are each related to external risk factors, but in distinct ways. The co-occurrence of PTSD and self-other dysfunction, as reflected in the Comorbid profile, appears to be associated with the highest levels of associated distress and impairment. Overall, PTSD symptoms appeared distinguishable from CPTSD and BPD symptoms; however, CPTSD and BPD symptoms exhibited a high degree of clinical and conceptual overlap. Findings highlight the need for continued refinement of CPTSD and BPD diagnostic frameworks
Optimization and Evaluation of Drone Operational Parameters for Enhanced Spray Efficiency in a Peach Tree Orchard
No full textThis study evaluated the effectiveness of drone-based pesticide application in fruit orchards using a DJI Agras T40. Two experiments were conducted: one in a simulated canopy and another in a peach orchard, assessing the effects of operation mode, application volume, flight height, and droplet size on spray coverage, droplet density, droplet diameter, and droplet size uniformity. Three drone operational modes (standard, fruit tree, and spinning) were tested. In the simulated canopy, the spinning mode achieved the highest coverage (20.81%) and droplet density (172.44 drops/cm²), while the standard mode offered the most uniform distribution. In the orchard, the fruit tree mode achieved the highest spray coverage (6.55%) and droplet density (90.09 drops/cm²). The conventional air-blast sprayer provided greater coverage (58.77%) and droplet density (193.11 drops/cm²), the drone treatments however, demonstrated efficient canopy penetration and reduced chemical use, especially in targeted applications. These findings support the potential of optimized drone spraying as a sustainable and efficient alternative for precision agriculture and informed future design improvements for orchard pest management
Examining the Impact of Voltage Unbalance on Power Converter Harmonic Emissions
Get PDFThe continuously evolving state of the power grid along with the increased integration of renewable energy sources have put an emphasis on power quality disturbances and the standards that adhere to them. There are multiple standards dedicated to these disturbances, such as harmonic emissions caused by power converters and voltage unbalance. While these standards partially address these disturbances, the exact effect they have on one another is either unclear or not fully addressed. To quantify the relationship between harmonics and voltage unbalance, an extensive set of studies was performed on rectifiers and inverters with the objective of potentially applying the results to power quality standards. The studies on rectifiers consisted of simulations on rectifier circuits with different load characteristics while manipulating the source voltage to different types of unbalance. This ultimately resulted in the development of equations representing the harmonics in terms of the source voltage unbalance percentage, as well as a method for testing the harmonic response to unbalance of similar systems. The studies on inverters consisted of simulations and analytical solutions performed on three-phase pulse-width modulation and square wave inverters while manipulating the AC-side voltage to different types of unbalance. This ultimately resulted in a clear connection between the low-order non-characteristic harmonics of square wave and pulse-width modulation inverters, a concern for standards regarding renewable energy sources. These results are contributions to power quality standards and the overall topic of power quality
The Impact of Principal Data Literacy Actions on Teacher Data-Based Decision Making in High-Poverty Schools: A Convergent Mixed Method with Thematic Analysis of Alabama High Flyer Schools
No full textThis mixed methods convergent case study explored the organizational structures, principal actions, and professional development practices that support teachers' use of data-based decision-making practices. The study filled a gap in the research focused on the impact of principal leadership in teaching practices. Due to the increasing emphasis on data-driven instruction in K–12 education, researchers needed to identify specific principal actions that influence teacher data literacy and instructional decision-making.
Quantitative data were collected through surveys measuring teachers' self-reported data use practices and perceptions of principal support. The principal's perspective on both their and teachers' practices and perceptions of data use practices were also collected through the surveys. Qualitative data were gathered through semi-structured interviews with principals and teachers, focusing on the beliefs about data use, how data is used, and the influences of principals in data-based decision-making practices. A thematic analysis was conducted to identify key themes and relationships between principal actions and changes in teacher practices.
Findings indicated that consistent principal involvement in data discussions, the establishment of structured collaboration routines, and targeted professional learning opportunities were associated with higher levels of teacher data use and instructional responsiveness. Additionally, schools with clear, supportive data-use policies demonstrated powerful student outcomes through student ownership and self-advocacy. The results suggested that specific leadership behaviors and organizational supports are critical in fostering effective, sustainable, data-driven instruction.
This study contributed to the existing literature by identifying practical actions principals can implement to enhance teacher data literacy practices. A product of the study is a professional development framework to guide principals in deepening their data literacy skills while fostering higher levels of data-based decision-making in teachers
Material Property Evolution of Underfills Encapsulants, Electronic Molding Compounds, Thermal Interface Materials and Magnetic ACAs
No full textThe evolution of material properties in electronic packaging plays a critical role in the reliability of semiconductor devices operating in harsh environments. This dissertation investigates the thermo-mechanical behaviors of underfill encapsulants, epoxy molding compounds (EMCs), thermal interface materials (TIMs), and magnetically oriented anisotropic conductive adhesives (ACAs), emphasizing their viscoelasticity, thermal stability, and interfacial integrity under high-temperature aging.
Underfill materials, critical in flip-chip technology, were characterized for their ability to mitigate coefficient of thermal expansion (CTE) mismatches between silicon dies and organic substrates. Accelerated aging tests revealed the degradation mechanisms affecting their mechanical properties and thermal reliability. DMA analyses identified changes in storage modulus, loss modulus, and glass transition temperature (Tg), demonstrating their temperature-dependent viscoelastic behavior.
For EMCs, long-term aging studies highlighted the influence of filler content and microstructure on thermal and mechanical properties. High silica filler concentrations, exceeding 80%, contributed to superior thermal stability but introduced challenges in fatigue performance. Finite element modeling elucidated stress distributions at the wire interface, correlating material aging with increased stress accumulation.
TIMs were evaluated for their efficiency in reducing thermal resistance between microelectronic components. Conventional greases and phase-change materials demonstrated limitations in high-power applications due to viscosity-induced reliability issues. Novel TIM formulations incorporating carbon-based fillers, such as carbon nanotubes and graphene, exhibited superior thermal conductivities, though their widespread adoption remains constrained by cost considerations.
Magnetically oriented ACAs were explored for their potential in flexible and stretchable electronics. By aligning conductive particles under a magnetic field, these materials achieved directional conductivity and robust mechanical performance. Experimental and finite element studies demonstrated enhanced interconnect reliability under mechanical and thermal cycling.
This work provides valuable insights into material design and selection strategies for electronic packaging, addressing challenges posed by elevated temperatures and mechanical stresses. The findings contribute to the advancement of packaging technologies, enabling reliable performance in applications ranging from consumer electronics to automotive and aerospace systems
Modeling in Complex Large-Scale Topologies Applied to Urban Water Systems
Get PDFThe increasing frequency of extreme rainfall events and the growing reliance on underground conveyance infrastructure have heightened the need for reliable simulation tools that can capture rapid pressurization and mixed‐flow dynamics in urban water networks. This dissertation advances the state of the art in one‐dimensional hydraulic modeling by addressing two complementary problems: (1) accurately representing air-phase interactions during transient filling in complex, large‐scale stormwater tunnels and intermittent drinking‐water supply systems; and (2) integrating air‐phase compressibility into the existing solver SWMM without prohibitive computational cost and version constraints.
Chapter 1 presents the introduction and objectives of the dissertation. In Chapter 2, an idealized stormwater collection networks (SCNs) with dendritic and looped topologies are subjected to uniform rainfall in EPA SWMM 5.1 to characterize pressurization onset and propagation. Eight nondimensional flow indices (NDFIs) are introduced to quantify system responses across conduit slopes, roughness, and rainfall rates, providing a framework for system‐wide vulnerability assessment. Looped networks are recommended over dendritic, as the results indicate more resiliency of looped topologies to systemwide pressurization due to flow spread out.
In Chapter 3, SWMM’s algorithms (EXTRAN, Preissmann slot, and custom high‐celerity slots) are benchmarked using the Richmond Transport Tunnel, to incorporate the effects of complex geometries with an abrupt discontinuity of 4.27 m to 1.07 m in diameter. Three spatial discretizations and eight Courant‐based time steps are systematically varied and compared against a finite‐volume HAST solver. Continuity errors and Nash–Sutcliffe efficiencies reveal the limitations of standard SWMM implementations at abrupt geometry changes. The DxD10 spatial algorithm coupled with a time step that yields a Courant condition close to unity are recommended, along with the SWMM’s standard Preissmann slot algorithm. Results also indicate that the Sjöberg’s transition reduced the numerical instabilities of the slot algorithms, agreeing with the literature.
The study presented in Chapter 4 introduces a methodology to estimate systemwide air pressure in complex topologies using EPANET. Controlled experiments on a looped laboratory network simulate water‐supply priming were conducted. An adapted emitter formulation is proposed to simulate air pressure behavior under filling conditions using EPANET up to the slamming of water against the ventilation points. Results indicate that limiting the inflow rates into the system are recommended to prevent excessive pressure pulses from water hammering regardless of ventilation. Results also indicated a reasonable agreement between measured and modeled air pressure; however, caution must be taken when extrapolating these results to a real scenario.
Finally, in Chapter 5, Surge-SWMM model is introduced, a model under development that couples SWMM’s dynamic‐wave hydraulics with a polytropic air‐phase model proposed by Zhou et al. (2002). By coupling these two models, Surge-SWMM retains SWMM’s computational efficiency while capturing air compressibility effects. Benchmarks on single pipe filling scenarios demonstrate the feasibility of the approach. Collectively, this work potentially delivers a new methodology and a software framework for pipe filling modeling in urban drainage and distribution networks
An Integrated Methodology for Flight Vehicle Sizing Subject to Stability and Control Constraints
No full textThis dissertation focuses on incorporating stability and control into the conceptual sizing of novel flight vehicle concepts based on flying qualities guidelines defined for both rotor-borne and wing-borne flight conditions. The nonlinear model of the bare airframe is
numerically linearized at discretized segment points during sizing iterations and the dynamic modes are evaluated using eigenvalues of the linear time-invariant system. Aircraft dynamic stability and flying qualities guidelines for both fixed-wing and rotary-wing vehicles are included as constraints for respective flight modes and the vehicle sizing rules are defined to size respective control effectors while satisfying the constraints at all the trim points throughout the mission profile, including possible off-nominal flight conditions. Control requirements are posed for nominal and off-nominal flight conditions, and a control effector sizing method is developed to meet those requirements in the conceptual sizing. Two different methods are presented in this study: (i) nonlinear time domain simulation is used to evaluate flight dynamic performance and control effectors are sized to meet the enforced constraints at each sizing iteration, and (ii) maneuverability requirements are defined in terms of desired moment requirements and the attainable moment subset approach is used to size the respective control effectors to meet desired moments. For an overactuated system with multiple control effectors, this dissertation presents an approach to determine the control allocation to meet desired controllability requirements for both nominal and off-nominal flight conditions, including possible failure scenarios. In addition to incorporating flight dynamics constraints into the sizing process, this dissertation also demonstrates a robust design approach to account for aleatory and epistemic uncertainties in the conceptual design stage by using Monte-Carlo simulations to assess the probability of achieving certain performance targets in the presence of such uncertainties. An electrified general aviation aircraft is proposed, and a comparative study is presented against the conventional baseline aircraft, where block fuel and range performances are compared against the baseline, subject to the uncertainties in the assumed technology and operational variables
Spotting Inventory: Autonomous Item Localization on Active Construction Sites Using a Boston Dynamics Spot® with Bayesian Filtering and Object Detection
No full textAccurate and complete location information is crucial on construction sites for effectively tracking resources and ensuring the successful completion of a project. However, the construction sector has numerous inventory management challenges, including dependence on outdated technology and the prevalence of manual processes. Moreover, the absence of real-time data, coupled with the dynamic nature of material movement, exacerbates the challenges of effective inventory management. Human-based tracking requires considerable manual labor and time, and when resources are managed over prolonged periods, it is also susceptible to human error.
These challenges highlight the essential requirement for automation to enhance efficiency and productivity. Previous studies have demonstrated the benefits of employing auto-identification and localization technologies to automate asset tracking on construction sites. Numerous case studies and field testing have proven the benefits of technology-enabled onsite materials monitoring compared to human onsite materials management. To this end, integrating computer vision with RFID can improve the precision of material identification and location, offering a more thorough solution for inventory management.
This dissertation has employed a sensor fusion of complementary technologies to automate inventory management on construction sites. The hybrid system consisted of a quadruped robot enhanced with RFID-based recursive probabilistic localization and custom object detection. The objective was to identify and locate inventory elements on construction sites using an autonomous quadruped robot. This integration can facilitate the advancement of more complex systems for physical asset management. Integrating RFID, computer vision, and robotics for inventory management on construction sites represents a significant advancement in the construction industry, addressing traditional challenges such as inefficiency, labor-intensive processes, and
poor inventory management.
This dissertation predominantly utilized exploratory experimental quantitative research as its core methodology, supplemented by qualitative approaches to establish the research need and to validate the conceptual framework as the tangible outcome of the dissertation. Interview findings revealed that asset tracking decisions on construction sites are primarily driven by cost, visibility, and perceived risk. Large, high-value equipment is prioritized, while smaller tools are often excluded due to the high relative cost of tracking technologies. A predominantly reactive approach persists, relying on manual checks and ad hoc processes influenced by site-specific factors. This research successfully deployed a localization algorithm on an autonomous mobile platform in the
context of RFID-based probabilistic localization. The performance assessment showed that the system could read all installed tags with a median localization error of 1.569 meters/ 5 feet 2 inches for the four power levels tested. The 1.569-meter localization error may suffice for coarse-grained
tracking, e.g., identifying pallets of bulk materials in open laydown yards. For custom object detection-based identification and localization, the experimental findings indicate that the combination of low asset height, higher Spot® height, and a 0° incidence angle between Spot® and the asset markedly improves the system's ability to detect assets from extended ranges. Of all evaluated configurations, the combination of Medium Spot® speed, High Spot® height, and Low asset height, especially at a 0° incidence angle, yielded the greatest median distance and the most concentrated observation distribution, rendering it the most suitable configuration overall.
The dissertation's tangible outcome is a conceptual framework that optimally localizes physical inventory in an unmapped space by synchronizing two mutually exclusive localizations coming from RFID based probabilistic and custom object detection-based approaches. A weighted fusion attains the finalized localization of a typical physical asset; this approach will ensure that the more reliable sensor at any given moment contributes more heavily to the final localization estimation (i.e., optimum localization), while the less reliable sensor has a diminished influence.
For future work, the proposed conceptual framework can be enhanced to incorporate safety-critical features, including human proximity detection, real-time worker monitoring, and emergency override capabilities, to ensure practical applicability on actual construction sites. As construction sites progressively integrate mobile platforms, such as drones and terrestrial robots, future developments must facilitate localizations facilitated by multi-robotic systems, their reliable coordination by incorporating communication protocols, collision avoidance mechanisms, and collective spatial awareness. Additionally, the growing complexity of construction projects necessitates the concurrent tracking of diverse physical assets. This introduces technical challenges related to sensor data alignment, information overload management, signal interference in dense environments, and system scalability. Addressing these challenges will be critical to advancing the framework toward deployment-ready, scalable solutions for collaborative, automated construction sites
Manipulate polaritonic nano-light with environmental permittivity
No full textPolaritons, hybrid quasiparticles formed by coupling electromagnetic waves with dipolar excitations in materials, offer deep subwavelength confinement of light. This unique feature allows access to nanoscale optical fields and energy transfer, far beyond the diffraction limit of conventional optics. To visualize and analyze such nanoscale light-matter interactions, we employ scattering-type scanning near-field optical microscopy (s-SNOM), which achieves spatial resolution down to ~10 nm.
This dissertation investigates phonon and exciton polaritons in van der Waals (vdW) materials and heterostructures, focusing on how environmental and structural modifications enable control over polaritonic behavior. Chapters 2, 3, and 5 explore mid-infrared phonon polaritons in anisotropic materials such as α-MoO3 and hexagonal boron nitride (hBN). First, we study suspended α-MoO3 flakes and demonstrate that removing the substrate results in a ~100% increase in polariton wavelength due to altered dielectric screening and negative dispersion. Simulations confirm strong anisotropic and mode-specific elongation effects.
Next, we investigate α-MoO3/graphene heterostructures and reveal a new class of charge transfer–induced hyperbolic polaritons. Direct charge transfer increases the polariton wavelength by nearly 100% and induces a Fermi level shift in graphene to ~0.6 eV. Lastly, we explore hBN/graphene stacks and demonstrate tunable hybrid plasmon-phonon polaritons. Using electrostatic gating, we achieve dynamic and reversible control over polariton reflectivity and propagation length at the graphene edge.
In Chapter 4, we turn to exciton-polariton waveguides in transition metal dichalcogenide (TMDC) heterostructures. We visualize guided optical modes in MoSe₂/MoS₂ bilayers using s-SNOM and observe stacking-induced wavelength modulation. Fourier analysis and geometric corrections reveal energy-dependent multimodal propagation. Dispersion modeling and finite element simulations confirm strong exciton-polariton coupling and validate our experimental findings.
Chapter 6 reviews recent advances in scanning probe IR imaging and spectroscopy for natural and biological materials, highlighting the broader relevance of these techniques.
Overall, this work provides fundamental insights into the manipulation of polaritons through environmental and structural engineering of vdW materials. The findings open up new opportunities for designing tunable nanophotonic devices, including reconfigurable optical circuits, mid-IR sources, quantum sensors, and nanoscale transistors based on light-matter interactions at the atomic scale
Effective Characterization, Mitigation, and Modeling of Cycle Life and Thermal Degradation in High-Performance Lithium-Ion Battery Systems
No full textAs lithium-ion (Li-ion) battery technologies continue to evolve, delivering higher energy and power densities, the importance of design optimization for achieving safer, high-performance battery systems continues to grow. This dissertation analyzes the dynamic interplay between three phenomena: heat generation, heat dissipation, and degradation kinetics. These three interdependent phenomena form a feedback loop that critically influences the operational safety and longevity of Li-ion battery systems. When heat generated within the cell exceeds heat dissipated from the cell, degradation accelerates. This in turn increases internal resistance within the cell and further intensifies heat generation. Understanding the dynamics of these phenomena is essential for optimizing battery pack design.
Through experimentally validated heat transfer models, this work evaluates the thermal response of two battery block structures under various amounts of thermal stress produced by both Joule heating and exothermic reaction energies. Notably, battery blocks with high thermal conductance and integrated latent heat capacity, such as the microfibrous mesh/phase change material (MFM/PCM) block, demonstrate superior thermal accommodation and operational stability under aggressive cycling conditions. While aluminum blocks offer effective thermal isolation between cells, their limited dissipation capacity leads to accelerated degradation in high C-rate applications, especially in aged cells. This highlights the need for thermal isolation within thermally conductive blocks to prevent cascading failure within the pack while also limiting degradation kinetics within the cell.
Due to the inherent failure risk, a novel methodology is developed in this work to conservatively estimate worst-case scenario failure energy using only the mass and composition of cell components, eliminating the need for destructive testing. This approach enables scalable, geometry-independent safety analysis for various battery chemistries, while incorporating atmospheric conditions that significantly influence failure severity.
Cascade-resistant battery pack designs are also explored due to the severity of a single-cell failure event. This work shows that while high thermal conductance aids in dissipating heat from failed cells, it can inadvertently expose neighboring cells to elevated temperatures. The MFM/PCM block mitigates this risk by combining latent heat absorption with effective thermal conductance, thereby preventing thermal runway propagation.
Cascading failure prevention is further explored by implementing degradation reaction kinetics within the battery block while it is under load leading to an organically triggered failure event in an end-of-life (EOL) cell. The MFM/PCM block effectively dissipates and absorbs the heat generated by both Joule heating and exothermic degradation reactions, preventing cascading failure in an active system operated at elevated C-rates.
The key contributions of this work include methodological advances in battery pack design, integration of aging and thermal degradation kinetics into operational battery block simulations, the development of simplified failure energy estimation methods, and the modeling of multi-chemistry thermal responses. These developments collectively define a design trade space for Li-ion battery systems that balances performance, safety, and longevity