LOUIS University of Alabama in Huntsville
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Analysis of second-order decoupled time-stepping schemes for the Navier-Stokes-Debye-Hückel model of electroosmotic flow
Get PDFElectroosmotic (EO) flow has attracted much attention in recent years due to applications in microfluidics, such as in lab-on-a-chip type systems. Because of the inherent difficulties in measuring and monitoring microfluidic channels, analysis of EO flow is often performed by numerical simulation. The Navier-Stokes-Debye-Hückel model — a simplification of the Nernst-Planck model which couples conservation of the ionic concentrations to the Navier-Stokes equations — is the preferred model for studying such flows when the zeta potential is small. In this work, we study the well-posedness of the unsteady Debye-Huckël model, and propose decoupled time-stepping schemes for numerical simulation. We demon- strate the well-posedness of the evolutionary Debye-Hückel model by Galerkin approximation, a priori estimates, and the Aubin-Simon compactness theorem for Bochner spaces. In particular, we prove the existence of weak solutions in two and three spatial dimensions, as well as uniqueness of the solution in two dimensions. We also propose and study two second-order accurate decoupled time stepping schemes based on mixed finite element spatial discretization. The time stepping schemes employ an extrapolation in time of the nonlinear terms such that their skew-symmetry properties are preserved. We prove that the both of these decoupled time stepping schemes are unconditionally stable. We also establish optimal error bounds for the velocity, potentials, and pressure under a mild restriction on the time-step size for the first scheme, and without any restriction for the second. In addition we study the long-time stability of the discrete scheme and show that in two spatial dimensions it is stable for all time in both 2 and 1 norms. We conclude with numerical results that support our theoretical claims and demonstrate the practical value of our proposed schemes
Analyzing code generation by AI models: an ANOVA-based study of quality, consistency, and composite ranking
No full textAs generative Artificial Intelligence(AI) tools become increasingly common in software development, there is a growing need to understand how well these tools perform beyond just producing code that runs. This thesis examines the performance of four popular generative AI models, ChatGPT (GPT-4 mini), GitHub Copilot, Code LLaMA 3.3, and DeepSeek Web, in generating code that is not only functionally correct but also efficient and maintainable. To do this, we tested each model on six real-world-style coding problems sourced from LeetCode, covering a range of algorithmic challenges like dynamic programming, graph traversal, and array manipulation. Using a consistent prompting strategy, we collected Python code samples from each model and evaluated them using established software engineering metrics: Lines of Code, Cyclomatic Complexity, Halstead Complexity, and the Maintainability Index. We then applied a detailed statistical analysis, including ANOVA, post hoc testing, and nonparametric methods, to see which models consistently performed best. Our results show that the type of problem has the biggest impact on the complexity and length of the code, but when it comes to how maintainable the code is, the Artificial Intelligence(AI) model itself matters a lot. LLaMA produced the most maintainable code across the board, while GitHub Copilot often generated more complex, harder-to-maintain solutions. ChatGPT and DeepSeek showed similar and generally solid performance, landing somewhere in the middle. This research goes beyond simple pass/fail benchmarks and provides a clearer and more nuanced understanding of how generative AI tools behave in practical programming tasks. Developers, educators, and tool makers can use these findings to choose the right AI assistant for their needs and better understand where these models shine and where they still fall short
Marjorie Stephenson: Bacterial Biochemist & Fellow of the British Royal Society
No full textMarjory Stephenson (born January 24, 1885 – December 12, 1948) was one of the most influential scientists of her time, known for her pioneering work in the newly emerging fields of chemical microbiology and bacterial biochemistry. Being one of the first scientists to work with and recognize bacteria as a model organism in her laboratory in Cambridge, she laid the foundation for studying the processes of bacterial metabolism (particularly enzymes), forever changing how scientists study microorganisms. Because of her many contributions to microbiology research, she was one of the first two women ever (the other being Kathleen Lonsdale), to be inducted as a Fellow of the Royal Society in 1945.https://louis.uah.edu/honors-399/1030/thumbnail.jp
Peparing MUSE Data Cubes to Probe Cold Gas around Galaxies with Sodium
No full texthttps://louis.uah.edu/rceu-hcr/1525/thumbnail.jp
Assessing the selection and impact of completeness in technical measure sets in systems engineering
Get PDFTechnical measurement is foundational in large-scale complex engineered systems (LSCES) design and contract awards due to the state of practice of systems engineering. Although technical measurement is ubiquitous in systems engineering practice, the selection and assessment of the impact of a set of technical measures is often heuristics-based, rather than based on empirical evidence or theory. Current technical measure set selection has been observed to be subject to many challenges which, in turn, can degrade system outcomes. Two main thrusts comprise the research: selection and impact. Motivated by the potential impacts of a poor set of technical measures, a significant body of literature has been published containing guidance on selecting technical measure sets. This dissertation seeks to foster improved selection practices for technical measure sets, establish that omissions of technical measures can impact systems, and provide evidence-based assessments of technical measure set completeness operationalizations. The dissertation first analyzes the identified guidance for technical measure selection in systems engineering literature for inconsistencies and areas of need. While it is generally understood that omissions of technical measures from a set can impact the resulting system alternative, little prior research has given evidence for the impacts of omissions. The impacts of omission are modeled in a LSCES case study using the NASA Human Landing System (HLS), comparing two systems engineering decision-making frameworks. It is shown that regardless of framework, omitting technical measures can change the system alternative chosen and the system acceptability. From the identified guidance, four operationalizations of completeness for technical measures sets are established. Rather than attempting to define completeness, the research demonstrates how the operationalization of completeness can be assessed. The assessments demonstrate how a set of technical measures that is complete for one operationalization is not necessarily complete for the remaining. This dissertation moves forward the body of knowledge on the topics of technical measure set selection and assessment. The findings move the foundational practice of technical measure selection towards one based on empirical evidence. The findings enable both the directive for “completeness” and the consequences of not achieving completeness in technical measure sets to be understood and assessed
Signal-to-interference degradation due to ADC dynamic range for a pulsed Doppler radar digital signal processor
Get PDFThe objective of this study was to develop an expression for signal processor signal-to-interference ratio (SIR) improvement that combines factors that have historically been treated separately into a simple closed form expression, specifically factors such as analog-to-digital converter (ADC) dynamic range, phase noise, sub-quanta signal levels, signal-to-noise ratio (SNR), gain, automatic gain control (AGC), receiver noise, and signal and clutter characteristics. The SIR expression developed in this study quantifies how much a signal processor’s performance is degraded by the inclusion of an analog-to-digital converter (ADC) with respect to an equivalent well designed all-analog design. Using this unified SIR metric, expressions for signal-to-clutter ratio improvement (ISCR) are also derived. The overall results of this study are new and unique expressions for SIR and SCR improvement that combine all the aforementioned factors. The unifying theories and expressions developed during this research, and documented herein, give simple closed form results, which give a practical approximation of observed performance, and allow for vetting of various design choices prior to costly hardware construction. The results of this analysis also prompted a study of how to design a receiver such that receiver noise would provide the ADC toggling necessary to overcome ADC dynamic range limitations and maximize the possible SIR improvement. Additionally, when measuring system performance directly, these expressions allow a means of better understanding the impact of various parameters by matching this analysis to measurement results. The application which initiated this analysis was testing of a fielded radar, where various system parameters are measured, rather than stipulated. The initial case considered is one where clutter power is assumed to control the AGC, wideband dither was provided by injecting noise from a calibrated source, and the dither noise power spectral density was white. The impact of ADC dynamic range on digital signal processor (DSP) performance is shown to be a degradation of SNR rather than a limitation of the SCR improvement or clutter attenuation capability of the DSP. A subsequent case that was considered was one in which the signal power drives the AGC. The unified SIR and SCR improvement expressions presented herein include theoretical results applicable to a broad range of radars and signal processors and can be used to aid in quantifying the effects of using an ADC in a receiver design
The impact of hip fracture multimodal pain management on mobilization and length of stay for hospitalized older adults
No full textOver 95% of hip fractures are related to falling, which equates to approximately 300,000 hip fractures annually in the United States. Poorly managed pain and immobility are risk factors predisposing patients to prolonged hospitalization, permanent disability, and mortality. At an academic medical center, low adherence to the use of a multimodal pain management order set contributes to the sequelae of unfavorable outcomes. Through stakeholder education, this quality improvement project sought to improve adherence to an MPM order set to promote effective pain management, early mobilization, and decreased length of stay. On a surgical unit, surgeons (n=6; 100%) and nurses (n=46; 88%) received education on the surgical stress response, mitigating the response, and the impact of multimodal pain management (MPM) and mobilization. The physical therapists received a summary of hip fracture care. Order set adherence was monitored with ongoing feedback provided to the surgeons. Nurses and physical therapists received support and feedback through daily patient rounds, unit huddles, and an education board. Patient outcomes were monitored; pain scores, daily morphine milligram equivalents, early mobilization, functional level, and length of stay (LOS). Provider order set adherence increased by 52%. Early mobilization was 75% (n=30), which was an improvement. The mean LOS was 5.87 and decreased to 5.19. By promoting the use of MPM, thereby enhancing pain control, patients can progress more readily to functional restoration, reducing their complication risk and LOS
Fault tolerance in Krylov subspace methods
Get PDFThe increasing complexity of modern high-performance computing (HPC) systems, with their vast number of processing units and extensive memory hierarchies, introduces significant challenges to system resilience, particularly in the face of soft errors. Krylov subspace methods, which play a pivotal role in solving large-scale sparse linear systems and eigenvalue problems, are iterative in nature and thus susceptible to the propagation of soft errors. In this paper, we examine the fault tolerance properties of two widely-used Krylov subspace methods: the Lanczos method and the Bi- Conjugate Gradient (BiCG) method. Specifically, we analyze the impact of soft errors introduced during the critical Sparse Matrix-Vector Multiplication (SpMV) operation on the accuracy of eigenvalue computations in the Lanczos method and the convergence behavior in the BiCG method. Our empirical results reveal that while both methods demonstrate an intrinsic resilience to errors, the BiCG method exhibits superior self-correcting characteristics. Moreover, we observe a strong correlation between the row-2-norm of the sparse matrix and the slowdowns, suggesting that targeted fault protection can enhance overall algorithmic robustness. Additionally, we present a comparative analysis of the fault tolerance between the BiCG and Preconditioned Conjugate Gradient (PCG) method, emphasizing the importance of BiCG in Krylov Subspace Methods
Aviation weather forecasting utilizing an artificial neural network
Get PDFWeather forecasting is critical to minimize risk and maximize efficiency for flying operations and is challenging due to the uncertainty involved in atmospheric changes. Operational area weather is becoming more critical with the growing reliance on air domain for transportation. This study investigates whether the forecasting accuracy using a Hybrid Long-Short Term Memory (LSTM) Artificial Neural Network (ANN) outperforms the San Antonio International Airport (KSAT) Terminal Aerodrome Forecast (TAF) by integrating diverse data sources – regional Meteorological Aerodrome Report (METAR), TAF, Avian Advisory System (AHAS) Data and geomagnetic activity K Index Data. When tested on one year of historical data, the Hybrid LSTM model exceeded the KSAT TAF’s average mean absolute error (MAE) for eleven continuous weather parameters by 64% and exceeded the accuracy for nine binary weather parameters by an average of 4%, highlighting the performance of the ANN compared to the legal forecast for KSAT
A deep learning multi-channel framework for indoor image retrieval
No full textProcessing and analyzing multi-source image data is vital across a wide range of fields, including artificial intelligence (AI), deep learning, computer vision, geolocation, autonomous navigation, remote sensing, and forensic investigations. Although conventional image matching methods such as Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF) have proven effective in controlled setting, they often face challenges when applied to diverse datasets that exhibit substantial variations in viewpoint, scale, lighting conditions, and modality. Recent developments in deep learning, including convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), and self-supervised learning, have led to the creation of more efficient and precise methods for image matching and retrieval. This thesis proposes a novel AI-driven deep-learning-based framework designed to enhance the robustness and scalability of image matching. The framework is tailored for indoor scene-based image matching to support image localization, addressing challenges such as lighting variations, occlusions, lack of sensor calibration and sensor discrepancies. It employs a channel-aware autoencoder that captures meaningful features by leveraging spectral differences in RGB channels, thereby improving the model’s ability to distinguish between visually similar indoor environments. By incorporating latent feature representations, the framework enhances scene classification and retrieval accuracy, potentially contributing to advancements in deep learning-based indoor positioning systems, robotics, and forensic analysis. The experimental results highlight that the proposed AI-driven frameworks outperform traditional image matching methods by a significant margin. Together, these contributions advance the field of deep learning for image-based search and localization, providing scalable and intelligent solutions that can be applied across various real-world contexts