Digital Commons Network^™

Decoding The Allosteric Grammar Of Protein Kinases: A Dual-Stream Framework Integrating Protein Language Models And Energy Landscape Frustration Analysis, Will Gatlin, Max Ludwick, Lucas Turano, Brandon Foley, Kamila Riedlova, Vít Škrhák, Marian Novotný, David Hoksza, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

The spatial and energetic encoding of allosteric regulatory sites remains a major challenge in structural biology, frequently representing a “blind spot” for sequence-based artificial intelligence (AI) models. We present a protein language model (PLM)-guided approach complemented by the energy landscape frustration analysis as a dual-stream framework to investigate the relationship between AI prediction of binding sites and biophysical organization of regulatory pockets across the human kinome. By probing a fine-tuned residue-level PLM classifier across 453 kinase structures, a clear performance gap is discovered between highly predictable orthosteric pockets (Types I, I.5, and II) and poorly resolved distal allosteric sites (Type …

Compound Multi-Hazard Assessment Under Cmip6 Climate Scenarios: Tracking Seasonal Flood-Landslide And Drought-Fire Interactions Across Nepal, Kripa Shrestha, Karthik Reddy Duddukunta, Pankaj Lal, Md Mehedi Hasan, Md Mahfuzur Rahman, Neera Shrestha Pradhan

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Climate-induced hazards in mountain systems rarely occur in isolation, instead they interact through cascading and compounding pathways that single-hazard assessments fail to capture. In Nepal, extreme monsoon rainfall can trigger landslides that dam rivers and generate secondary flooding, while prolonged dry spells create drought, desiccate vegetation fuels, and elevate fire risk. Yet, most regional studies map hazards independently, providing limited insight into how compound zones emerge under climate change. We therefore, develop a seasonal multi-hazard framework that model drought, forest fire, flood, and landslide susceptibility for historical baseline (1991-2020) and future (2021-2100). Three machine learning algorithms (Explainable Boosting Machine, Random …

Expert Interview: "The Mirror Of Ai" In The Domain Of Scientifc Information Research, Taitian Mao, Yulai Bao, Jianxiang Wei, Peng Wu, Chuanming Yu, Gan Tang, Dongyan Wei, Yifei Ma, Wei Wang, Yu Ma

Journal of Scientific Information Research

Professor Mao Taitian and colleagues argues elucidates the adaptation logic, practical pathways, and prerequisites of intelligent agents to empower the high-quality development of scientific information. Professor Bao Yulai and colleagues advocate that integrating the perceptual elasticity of domain-specific large models with the cognitive rigidity of ontology can establish a new paradigm for intelligence services in complex scenarios. The synergy between the two can not only expand the theoretical boundaries of information science and serve national strategies, but also advance intelligence services from assisted analysis to intelligent decision-making. Professor Wei Jianxiang and colleagues point out that generative artificial intelligence has triggered …

The Sigma Ring And Other Distinctive Features Of Surface Potentials Of Group 1 Systems, Kelling J. Donald, Victoria Rankin, Brice Di Carlo, Jonathan E. Findley, Ethan B. Leonard

Chemistry Faculty Publications

For a sigma hole induced on a terminal atom, such as a halogen atom (X) in a molecule R-X, it is commonly expected that the maximum potential in that sigma hole will arise along the extension of the R-X bond, opposite the overlap region. We find, however, that for the majority of group 1 halides (MX) that expectation fails. Conventional sigma holes appear on Na in its halides and hydrides, but not so for M = K, Rb, or Cs. Instead, a sigma ring─a latitudinal ring maximum─is observed in the surface potentials of those molecules. That anomaly is examined, and …

Detecting And Correcting Systematic Image Distortion In Digital Atomic-Resolution Images From Crystals: Projected Symmetry Of Graphite Calibration Samples For Scanning Prove Microscopy And Subperiodic Crystals For Aberration-Corrected Transmission Electron Microscopy, Tyler Michael Bortel

Dissertations and Theses

Though it is an impressive technology, Scanning Tunneling Microscopy (STM) produces images commonly plagued by small systematic errors, often called "drift". These effects have a variety of causes, most notably nonlinear behavior of piezoelectric actuator tubes responsible for the positioning of the scanning probe or sample. One result of these distortions is a potential incorrect classification of the symmetry groups and projected Laue classes of recorded images from crystals—made possible by using recently developed objective methods. An implementation of a known method for detecting and correcting these errors is presented. This method operates strictly in Fourier space so as to …

Analytic Solutions To Oceanographic Kinematic Property Equations, James Turbett

Theses and Dissertations

With the recent advent of large submesoscale drifter deployments, interpreting the timeseries of kinematic properties (KP) - divergence, vorticity, shear, and normal strain rates observed therein is a pressing issue. The evolution equations for the four KP are derived from the two-dimensional momentum conservation equations. The resulting equations, a nonlinear coupled system of ordinary differential equations, capture the submesoscale motion of drifters along fixed ocean surfaces, with time and space scales on the order of days and kilometers. This thesis builds theory around KP timeseries behavior by solving for the analytic solutions in the particular cases that one KP is …

Dynlp: Parallel Dynamic Batch Update For Label Propagation In Graph-Based Semi-Supervised Learning, S. M. Shovan, Arindam Khanda, S. M. Ferdous, Sajal K. Das, Mahantesh Halappanavar

Computer Science Faculty Research & Creative Works

Semi-supervised learning aims to infer class labels using only a small fraction of labeled data. In graph-based semi-supervised learning, this is typically achieved through label propagation to predict labels of unlabeled nodes. However, in real-world applications, new data often arrives in batches, and stale data often becomes irrelevant. Each time a new batch appears, reapplying the traditional label propagation algorithm to recompute all labels is redundant, computationally intensive, and inefficient. To address the absence of an efficient label propagation update method, we propose DynLP, a novel GPU-centric Dynamic Batched Parallel Label Propagation algorithm that performs only the necessary updates, propagating …

Synergistic Effects Of Arbuscular Mycorrhizal Fungi And Foliar Nitrogen–Phosphorus Application On Maize Productivity Under Irrigated And Rainfed Conditions, Mst. Lailatul Ferdows, Saima Biswas, Mizanur Rahman, F. M.Jamil Uddin, Md. Nayan, Aporna Tabassum Api, Mohaiminul Islam, Nadia Islam, Swapan Kumar Paul, Md. Harun Rashid

School of Earth, Environmental, & Marine Sciences Faculty Publications

A field experiment was conducted at the Agronomy Field Laboratory of Bangladesh Agricultural University to evaluate the effects of arbuscular mycorrhizal fungi (AMF) inoculation and foliar supplementation of nitrogen (N) and phosphorus (P) on the performance of maize (Zea mays L.) under irrigated and rainfed conditions. The experiment followed a randomised complete block design with two levels of AMF (inoculated and non-inoculated) and four foliar treatments (no N and P, N only, P only, and combined N + P). The recommended dose of fertiliser (RDF) was applied as a soil application to all treatments. AMF inoculation significantly increased grain yield …

Mapping And Diagnosing Local Climate Zones For Urban Heat Assessment And Planning In Tropical Cities, Inuwa Sani Sani, Adi Wibowo

Journal of Environmental Science and Sustainable Development

Rapid urbanization and rising heat risk are intensifying thermal stress and environmental degradation in tropical cities. This study applies the Local Climate Zone (LCZ) framework to examine how urban morphology influences land surface temperature (LST) and to support evidence-based urban planning and climate adaptation in Makassar, Indonesia. We hypothesized that compact built LCZs (1–3) exhibit significantly higher LST than vegetated LCZs (A–G). Landsat 8 OLI and MODIS LST datasets (2013–2023) were processed in Google Earth Engine to evaluate decade-long urban development and thermal dynamics. LCZs were classified using a Random Forest (RF) model trained with approximately 100 manually digitized polygons …

Coupled-Pendulum Modeling In An Ode Class: An Assignment On Fourier-Initialized Gradient Descent In Machine Learning, Huy Truong, Andrew Bennett

CODEE Journal

As data-driven methods are increasingly used in science and engineering, students benefit from learning to integrate machine learning techniques with traditional mathematical modeling. We present a hands-on extra-credit assignment for an undergraduate ordinary differential equations (ODE) course that enables students to compare classical analytical methods with data-driven approaches on the same physical system. Using a coupled-pendulum system---two pendulums connected by a spring---with real experimental data acquired via video tracking of a real physical setup, students work through three models in a guided Jupyter notebook with all code provided. First, they fit a neural network with Fourier features as a purely …

Toward Intelligent Machines: Conscious Learning And Hardware-Accelerated Ai, Pavia Bera

USF Tampa Graduate Theses and Dissertations

Artificial intelligence systems excel at narrow, well-defined tasks but remain brittle at theboundaries of their training distributions: they cannot quantify uncertainty, adapt continuously to non-stationary data, or operate efficiently on energy-constrained hardware. This dissertation addresses these limitations through four coordinated contributions spanning probabilistic learning algorithms, biologically inspired temporal memory, cross-entity warning propagation via distributed associative memory, and spintronic processing-in-memory.

The first contribution introduces Boosted Bayesian Neural Networks (BBNNs), which extend standard mean-field variational inference by iteratively constructing a mixture posterior through Boosting Variational Inference. On five clinical medical classification datasets, BBNNs achieve superior uncertainty calibration—lower Negative Log-Likelihood and Expected Calibration …

Development And Evaluation Of A Community-Based Water Quality Monitoring Program In Mississippi, Beth H. Baker, Lily Langstaff, Mona Dominguez, Sergio Ruiz-Cordova, Eric Sparks, Madison Gnoose

Journal of Extension

Community-based water monitoring (CBWM) programs provide mechanisms for education and water quality data collection. In this pilot program, we adapted a long-standing CBWM program from Alabama to Mississippi. We assessed the efficacy of the initial program implementation in promoting changes in knowledge, skills, and behavior of participants. Results indicated that 94% of survey respondents reported increased knowledge, 96% reported learning a new skill, and 99% reported an intention to start monitoring, but only 33% engaged in active monitoring. This evaluation summary offers data-driven strategies to expand and adopt CBWM into Extension curricula that build knowledge, skills, and capacity for community …

Heterogeneous Graph-Augmented Contrastive Learning For Extreme Multi-Class Fiqh Classification, Ali A. Jalil

Al-Bahir

• Background/Introduction: Fine-grained text classification in the field of Islamic Jurisprudence (Fiqh) is difficult because of the structural interdependence of the legal concepts and the extremely multi-class long-tail data distribution (667 classes with 5,979 samples, 52.2% of which contain less than 5 samples). The main problem with traditional flat classifiers is that they assume that target classes are independent and orthogonal output neurons which discards very important relational semantics.
• Objectives: This paper seeks to remediate this extreme imbalance and maintain structural taxonomy by modeling the structural space of classification label space itself as an object to be learned, while giving a …

A Differential Equation–Based Epidemiological Model Of Post-Operative Chronic Pain In Scoliosis Patients With Data-Driven Analysis, Paige Zhu, Padmanabhan Seshaiyer

CODEE Journal

Chronic post-surgical pain (CPSP) is a common and often overlooked complication following surgical correction of idiopathic scoliosis, impacting long-term patient wellbeing despite improvements in surgical outcomes. This project introduces a novel epidemiological framework to model the progression of CPSP using a compartmental structure. By applying a coupled system of nonlinear differential equations, we simulate pain trajectories over time and assess the effectiveness of surgical interventions. The model is implemented for a single-cohort population and extended to a two-cohort design to compare outcomes between Posterior Spinal Fusion (PSIF) and Vertebral Body Tethering (VBT) procedures. Further stratification by patient age enables us …

Final Basinwide Quality Management Plan (Qmp) – 2026-2030, Revision 0, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Crustal Structure Of The Vøring Plateau And Vøring Spur From Joint Refraction And Reflection Travel-Time Tomography, Anika Nawar Mayeesha

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

Crustal architecture of the Vøring Plateau and Vøring Spur in the mid-Norwegian continental margin remains debated because the geophysical data in the area do not uniquely constrain whether the crust is continental, oceanic, or transitional. This study reanalyzes wide-angle ocean-bottom seismometer data from Profile 11-03 across the Vøring passive continental margin using joint travel-time tomography of crustal refractions and Moho reflections. A total of 6,760 first-arrival picks and 542 PmP picks from sixteen instruments were inverted for P-wave velocity and reflector depth. Three starting models were inverted independently to test sensitivity to the initial crustal configuration. The first two used …

Observing System Simulation Experiments To Investigate Impacts Of Deep-Tropospheric Uas Flights On Forecasts, Kenneth Alexander Swan

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

Uncrewed aircraft systems have emerged as a promising option to address meteorological gaps in observations in the lower troposphere. With recent advancements, UAS may be able to address gaps aloft in areas that lack coverage from radiosondes and commercial aircraft. UAS have shown the potential to significantly benefit NWP forecasts from profiles in the lowest 2 km AGL, but the impact of UAS profiles to the tropopause has yet to be quantified. Additionally, the impacts of hazardous flying conditions that UAS would encounter need to be addressed to guide future UAS capabilities. To address these questions, we designed an observing …

Butte Reclamation Evaluation System Corrective Action Plans For Reclaimed Sites 2025 Bres Site Specific Corrective Action Plans For Quadrant 2, Butte-Silver Bow Department Of Reclamation And Environmental Services

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Data-Driven Characterization Of Counties In The Prison Industrial Complex Using Clustering Analysis, Riley N. Tuccio

Capstone Projects

This project investigates the complex relationship between counties that house prisons in the United States and the rurality associated with them. The central research question explores how both county characteristics, such as variables corresponding to cost of living and demographics of a county, and prison characteristics, such as programming available to inmates and staffing levels, differ across the census-designated rural-urban distinctions. Furthermore, the study examines whether modern data science methods can more accurately define and distinguish these characteristics, providing a nuanced understanding of the Prison Industrial Complex (PIC) and its manifestation across various American communities. The motivation for this research …

Numerical Range Of Rank-1 Partial Isometries And Elliptical Range Theorem, Lucas Saone

Theses

The Elliptical Range Theorem states that the numerical range of a 2x2 complex matrix A is an elliptical disc with foci at the eigenvalues of A and center at tr(A)/2. Many proofs have been offered for this theorem. In this paper, we provide another one. The strategy we will use is to decompose the matrix A into the sum of rank-1 operators of the form YxX*, where X and Y are in 2-D complex vectors. When analyzing the numerical range of a rank-1 operator of this form, three results can happen: i) the numerical range forms a disc at the …

Insurgent Until Proven Innocent: Land, Power, And The Right To Defend Rights, Kelsey Jost-Creegan

Michigan Journal of International Law

As the climate crisis intensifies, advocates worldwide have stood up to defend their communities, territories, and environment. In response, they have faced violent retaliation. This repression persists despite evolving international, regional, and national-level legal frameworks around the “right to defend rights” and the “right to protection.” This article begins by providing a comprehensive overview of this evolving legal landscape. Against this backdrop, the article examines the tactics and architectures of violence used to repress land and environmental defenders through the lens of counterinsurgency theory.

The article proposes a typology of counterinsurgent violence including: surveillance; stigmatization; criminalization; arbitrary detention; infiltration; cooptation; …

Prediction Of Satellite Temperature During An Orbit Of A Cubesat, Michael Reynolds

Honors Theses

This thesis develops a thermal-simulation strategy for predicting CubeSat component temperatures, applied to Jag-Sat-1, a CubeSat developed at the University of South Alabama and deployed from the International Space Station in 2022. The orbit was reconstructed from two-line element (TLE) data using simplified general perturbations (SGP4) propagation, and spacecraft attitude was recovered from onboard gyroscope measurements. Sunlight, penumbra, and umbra intervals were computed geometrically, and the external radiative environment — direct solar, Earth infrared, and albedo heat fluxes — was modeled using orientation-dependent view factors. These time-varying fluxes drove a transient finite-element thermal simulation of the full satellite geometry in …

On The Side And Angle Mixtures Expressed By Triangles: Phase Diagrams Based On Intensive State Properties, Daniel Graham

Chemistry: Faculty Publications and Other Works

This is a lecture packet focused on the side and angle mixtures of structures at the center of the planar geometry universe. The intensive states of triangles are charted with the help of phase diagrams—long-standing tools in chemical thermodynamics and materials science. The intended audience consists of students and teachers—college and high school—seeking fresh perspectives, projects, and discussion topics surrounding age-old systems.

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks For In-Home Activity Learning Of Mild Cognitive Impairment, Seng Khoon Teh, Ah-Hwee Tan, Kar Way Tan, Iris Rawtaer

Research Collection School Of Computing and Information Systems

In-home spatiotemporal data, such as the movement trajectory data and the spatial time series data, contains potential predictive utility for detection of geriatric conditions including Mild Cognitive Impairment (MCI), frailty, and cognitive frailty. However, few have explored spatiotemporal learning models for learning and fusion of such disparate spatiotemporal data, owing to the lack of a generalized machine learning model that can jointly model these different spatiotemporal data types. This work reports a multimodal spatiotemporal machine learning model based on a class of self-organizing neural networks that can integrate different spatiotemporal data types for MCI detection. Specifically, Episodic Memory Adaptive Resonance …

Item Response Theory Evaluation Of Mental Health Sentiment Scales, Muhammad Amzad Hossain

Graduate Theses and Dissertations

Mental health is an essential part of overall well-being. Accurate measurement of constructs such as loneliness, anxiety, depression, and life satisfaction is extremely important in research and practice. Traditional approaches based on Classical Test Theory (CTT) are limited. They rely on total test scores and assume constant measurement error across individuals. They may not reflect true variability in measurement precision (Zein & Akhtar, 2025; Marvianto, 2023). In contrast, Item Response Theory (IRT) provides an advanced approach by examining item-level performance. It models the relationship between latent traits and responses (Zein & Akhtar, 2025 ). To address these limitations, this study …

Beyond Semantic Matching: Integrating Graph Structures, Boolean Logic, And Interactive Aggregation In Modern Retrieval, Quan Mai

Graduate Theses and Dissertations

Modern Natural Language Processing (NLP) and Information Retrieval (IR) systems have achieved remarkable success in semantic understanding; however, they continue to struggle with complex logical reasoning, structural interactions, and dynamic aggregation. This dissertation presents a comprehensive framework to enhance the structural, logical, and interactive capabilities of language models and retrieval systems across four progressive studies. First, we address the challenge of modeling dynamic conversational logic in online debates. We introduce a Sequence Graph Network (SGN) that captures the temporal and interactive exchange of ideas—such as counterarguments and reinforcements—by updating node features sequentially through a novel Sequence Graph Attention (SGA) layer. …

Potassium Fertilization Timing And Critical Stalk Potassium Concentration As Tools To Improve Corn Production, Gabriela Andrade Leite Mengez

Graduate Theses and Dissertations

Potassium (K) is a common yield-limiting nutrient in Mid-Southern U.S. corn (Zea mays L.) production, emphasizing the importance of effective diagnostic tools to support fertilizer decision-making. This study integrated two approaches to improve K management in irrigated corn systems: (i) evaluating K fertilization timing to identify in-season opportunities for correcting K deficiency, and (ii) developing a corn stalk-based diagnostic test to assess end-of-season K and nitrate-nitrogen (NO₃-N) status and fertilizer adequacy. Seven (2024-2025) and eight (2023-2024) field trials were conducted across Arkansas for the K application timing and stalk testing studies, respectively. For the K timing study, fertilizer-K (112 kg …

Benchmarking Current Progress In 3d Content Generation, Vuong Ho

Graduate Theses and Dissertations

In recent years, 3D generation has rapidly advanced with the development of powerful generative AI models capable of producing high-quality 3D content from various modalities, including text, images, and multi-view inputs. These advancements have significantly accelerated progress in applications such as gaming, virtual reality, robotics, and digital content creation. Despite this progress, there is still a lack of standardized and fair benchmarking protocols for evaluating 3D generation methods. Existing approaches are often assessed under inconsistent experimental settings, using different datasets, evaluation metrics, and processing pipelines. Such inconsistencies make reliable and objective comparisons difficult, limiting our understanding of the strengths and …

Iterative Generation Of Feature-Based Cad Models Using An Llm With Domain Specific Constraints, Ammon I. Hepworth

Graduate Theses and Dissertations

This paper presents a method to generate feature-based parametric mechanical CAD models from text input. The method uses domain-specific constraints (DSCs) to guide a large language model (LLM) in the generation of scripts to build models from the CAD API. It also presents a framework for an iterative text-based system that produces parametric CAD models. The method was implemented by developing DSCs for FreeCAD and Open AI GPT-5.2 and incorporated into a web-based software application which generates models that can be visualized with an interactive viewer after each iteration. Tests were run comparing various models with and without this method. …

A Framework For Top-K Queries With Constrained Preferences, Kyriakos Mouratidis, Nikolaos Chaloulakos, Bo Tang

Research Collection School Of Computing and Information Systems

Traditional rank-aware processing assumes a dataset that contains available options to cover a specific need (e.g., restaurants, hotels, etc) and users who browse that dataset via top-k queries with linear scoring functions, i.e., by ranking the options according to the weighted sum of their attributes, for a set of given weights. In practice, however, user preferences (weights) may only be estimated with bounded accuracy, or may be inherently imprecise due to the inability of a human user to specify exact weight values with absolute accuracy. Motivated by this, we define the constrained-preference top-k (CT) query. Given an approximate description of …