Physical Sciences and Mathematics Commons^™

Attention-Based Multi-Omics Fusion For Drug Synergy Prediction, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug combination therapy in disease management gained popularity in the last few decades. Computational modeling of such combinations is an active area of research in the drug discovery domain. While earlier approaches solely emphasized on the structural features of participating drugs for designing synergistic models, they lack other crucial factors directly linked with drug administration - omics expressions. As differential omics expression is a downstream consequence of the administered drug combinations, utilizing such expressions while designing synergistic models promises robust and dynamic modeling. In this work, we propose SynergyLM that fuses multi-omics features with drug embeddings to build an omics-aware …

A Survey Of Ai Methods For Geometry Preparation And Mesh Generation In Engineering Simulation, Steven Owen, Nathan Brown, Nikos Chrisochoides, Rao Garimella, Xianfeng Gu, Franck Ledoux, Na Lei, Roshan Quadros, Navamita Ray, Nick Winovich, Yongjie Jessica Zhang

Computer Science Faculty Publications

Artificial intelligence is beginning to reduce the manual effort in the CAD-to-mesh pipeline. Written for meshing and geometry practitioners with limited AI background, this survey organizes recent work by workflow step. We cover part classification and segmentation, mesh quality prediction, and defeaturing. We review AI guidance for unstructured meshing, block-structured meshing in 2D and 3D, and volumetric parameterization, including reconstruction from implicit or sampled geometry. We also discuss parallel mesh generation and scripting automation via reinforcement learning and large language models. Across these topics, AI complements established geometry and meshing algorithms rather than replacing them. We conclude with practical lessons …

Environmental Pesticide Exposure In The Etiology Of Pediatric Brain Tumors And Leukemia: A Scoping Review Of Epidemiological Studies, G. N. Vandesteeg, A. R. Russman, M. R. Sandbulte, E. G. Rogan, M. G. Rhodes

School of Natural Resources: Faculty Publications

No abstract provided.

Mapping Robusta Coffee (Coffea Canephora) Cropping Systems In Uganda: A Two-Step Pixel And Sub-Pixel Based Approach With Sentinel-2 Data, Getachew Kebede, Bester Tawona Mudereri, Onisimo Mutanga, Tobias Landmann, John Odindi, Natacha Motisi, Fabrice Pinard, Henri E.Z. Tonnang, Elfatih M. Abdel-Rahman

All Peer-Reviewed Publications

Coffee is a highly valued commodity and a widely consumed beverage, playing an important role in global trade. However, coffee farming landscapes are increasing transitioning into smaller-scale agricultural setups. This transformation highlights the critical need for accurate classification and mapping of coffee cropping systems (CS), especially in countries like Uganda, where dense vegetation and complex terrain present substantial challenges to traditional land survey methods. Moreover, understanding the spatial distribution of Robusta coffee (Coffea canephora) CS is essential for developing site-specific management strategies, guiding extension services, and informing evidence-based policy decisions. To address this gap, the present study aimed to enhance …

Optimal Control Analysis Of Malaria Transmission In The Presence Of Insecticide Resistance And Climate Variability In Kenya, Lorna Chepkemoi, Titus Okello Orwa, Samuel Mwalili, Rachel Waema Mbogo, Steeven Belvinos Affognon, Daisy Salifu, Henri E.Z. Tonnang

All Peer-Reviewed Publications

Malaria remains a major public health concern in Kenya, where changing climatic conditions, insecticide resistance, and mosquito behavioral adaptations continue to challenge control efforts. This study develops and analyzes a climate-sensitive malaria transmission model that incorporates mosquito behavior, insecticide resistance, and vector–human ecological dynamics to identify optimal control strategies for the Kenyan context. The model’s well-posedness was established, the basic reproduction number (R0) computed using the next-generation matrix method, and the stability of equilibrium points assessed. Spatial analysis of R0 was performed using temperature and rainfall raster data to map transmission risk across Kenya under varying insecticide use scenarios. Results …

Ai-Driven Real-Time Detection Of Zero-Day Browser Exploits Using Webassembly-Based Instrumentation, Temitope Damilola Elijah

College of Graduate Studies: Theses & Dissertations

The rapid evolution of web browsers into fully fledged application execution environments has significantly expanded their attack surface, making them prime targets for sophisticated zero-day exploits that evade traditional signature-based security mechanisms. To address this challenge, this research proposes an AI-driven framework for real-time detection and analysis of zero-day exploits in web browsers by integrating browser-level telemetry monitoring, unsupervised anomaly detection, and large language model–based threat interpretation. The framework introduces a lightweight WebAssembly telemetry agent embedded within the browser runtime to capture low-level execution behaviors, including WASM module instantiation, memory growth patterns, network interactions, and runtime API activity. These telemetry …

Assessing The Diversity And Composition Of Crayfish Assemblages In The Ogeechee River Basin, Reginald Turner

College of Graduate Studies: Theses & Dissertations

Crayfish assemblage composition in the southeastern United States is understudied relative to other aquatic taxa, such as aquatic insects and fishes, and the Coastal Plain watersheds of that region are particularly underrepresented in the contemporary literature on this topic. For example, although 38% of the crayfish species in Georgia are considered “species of greatest conservation need,” most of the distributional data used to make these designations are outdated, with some dating back over 50 years. This thesis sought to update our understanding of the contemporary distributions of crayfish species within the Ogeechee River Basin (ORB), a watershed in southeastern Georgia …

Swimming In Uncertainty: Filling Data Gaps And Providing An Educational Platform For Beach Water Quality At Tybee Island, Georgia, Lukas Roberson

College of Graduate Studies: Theses & Dissertations

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Swimming in beaches water contaminated with high levels of bacteria can make you sick. Current monitoring at the public beaches on Tybee Island consists of weekly monitoring and enumeration of fecal indicator bacteria that takes 24 hours for results. If the number of bacteria exceed regulatory limits, a public health advisory is issued, and affected waters are retested until …

Early Icu Physiological Subtyping From Time-Series Data: A Comparative Study Of Feature Complexity, Predictive Performance, And Model Interpretability, Rojeena Khadka

College of Graduate Studies: Theses & Dissertations

Intensive Care Unit (ICU) patients do not follow a single uniform physiological pattern. Patients admitted with the same diagnosis show different clinical trajectories over time making standardized classification and treatment approaches insufficient. The increasing availability of large-scale electronic health records in MIMIC-IV makes it possible to investigate such heterogeneity through data-driven approach that captures how physiology evolves during the early phase of ICU admission. This thesis compares two analytical pipelines designed to identify physiological subtypes from the first 48 hours of ICU time series data. This study then assesses how well these subtypes predict in-hospital mortality. The first approach, referred …

When The Gorenstein Projective Modules Are Precovering: A Survey On The Existence Of The Gorenstein Projective Precovers, Alexis L. Mcburney

College of Graduate Studies: Theses & Dissertations

This thesis surveys known results on the existence of Gorenstein projective precovers and studies conditions under which the class of Gorenstein projective modules is precovering. In classical homological algebra, projective resolutions are used to study modules, while relative homological algebra replaces projective modules with a different class that must be precovering for resolutions to exist. Gorenstein homological algebra extends these ideas using Gorenstein projective modules. We review key developments by Jorgensen, Murfet and Salarian, Estrada, Iacob, and Yeomans, and Cortés and Saroch. We discuss the strongest known existence result, showing that Gorenstein projective precovers exist under suitable conditions; in particular, …

Factorial Design: A New Look Through Overlap Measures, Sarah W. Liebenow

College of Graduate Studies: Theses & Dissertations

Traditional factorial analysis often relies on ANOVA, which assumes normality and equal variances. This thesis presents a nonparametric approach for assessing main and interaction effects in a 2 × 2 factorial design using the overlap coefficient, estimated through kernel density methods. A bootstrap procedure is used to approximate its sampling distribution for hypothesis testing. Simulation studies compare the overlap-based test with the ANOVA F-test, permutation F-test, and the Kruskal–Wallis test under heteroskedasticity and non-normal conditions. Results show that the overlap measure is highly sensitive to differences in spread and shape, detecting effects that traditional methods frequently miss.

Flexible Membrane Electrodes For Use In High-Capacity Lithium-Ion Batteries, David A. Denemark

College of Graduate Studies: Theses & Dissertations

Wearable electronic devices require high-capacity flexible batteries to improve user comfort and increase usage time per charge. In this thesis, flexible lithium-ion battery (LIB) electrodes are prepared using a novel and scalable phase inversion method, embedding one-dimensional SnO₂ nanowires and double-walled carbon nanotubes within a porous polyacrylonitrile asymmetric membrane. The optimized electrode delivered an initial specific discharge capacity of 915.93 mAh g⁻¹, approximately 2.5 times greater than conventional graphite anodes, with 61.99% capacity retention after 45 cycles. The incorporation of a carbon mesh substrate further enhanced both electrochemical and mechanical performance, achieving 89.46% capacity retention after 45 cycles and a …

First Record Of Arrhenophagus Chionaspidis Aurivillius (Hymenoptera: Encyrtidae) Parasitizing White Mango Scale In Kenya: Its Potential Distribution And Prospects In Biocontrol, Francis Obala, Abdelmutalab G.A. Azrag, Rehemah Gwokyalya, Shepard Ndlela, Ingo Grass, Georg Petschenka, Sunday Ekesi, Samira A. Mohamed

All Peer-Reviewed Publications

The white mango scale, Aulacaspis tubercularis Newstead (Hemiptera: Diaspididae), is one of the most destructive pests of mango worldwide. Its current management in most of the invaded areas largely depends on the intensive use of chemical insecticides. Biological control using parasitoids represents one of the most effective and environmentally sustainable management options. However, in Kenya, one of Africa's leading mango-producing countries, no effective parasitoid species associated with A. tubercularis had previously been recorded. This study aimed to identify encyrtid parasitoids associated with A. tubercularis in Kenya, and to assess how bioclimatic factors influence their occurrence and habitat suitability. Mango leaves …

Reinterpreting Stochastic Optimal Control Under Ecological Uncertainty: Inferring Decision Urgency From Vegetation Biomass Dynamics, Komi Mensah Agboka, Tobias Landmann, Elfatih M. Abdel-Rahman

All Peer-Reviewed Publications

Stochastic optimal control provides a rigorous framework for systems subject to uncertainty, yet its operational use in ecological crisis contexts remains limited by interpretability. We reinterpreted a stochastic control formulation in which selected parameters emerged as indicators of decision urgency rather than normative preferences. Vegetation biomass was modeled as a stochastic stock subject to nonlinear loss driven by feeding pressure (e.g., desert locust activity) and multiplicative noise, with uncertainty represented by a time-varying volatility term that integrated extreme rainfall anomalies and conflict-related disruption. Rather than prescribing an optimal policy, we inverted the closed-form solution of the control problem to infer …

Impact Of Environmental Microparticles On Insect Olfaction, Steve B.S. Baleba, Danube K.N. Wandji, Yves H. Tchiechoua, Komi Mensah Agboka, Iman B. Hassaballa, Victor O. Omondi, Beatrice T. Nganso, Saliou Niassy, Souleymane Diallo, Merid N. Getahun

All Peer-Reviewed Publications

Terrestrial insects underpin key ecosystem services, including pollination, herbivory regulation, decomposition, nutrient cycling, and disease control. These functions depend on chemical communication that guides insects to food, mates, hosts, shelters, and oviposition sites while helping them avoid threats. Environmental microparticles, such as micro- and nanoplastics, tyre wear particles, soot, mineral dust, and agricultural residues, are now widespread across air, soil, vegetation, and indoor environments, exposing insects through contact, deposition, and ingestion. Growing evidence shows that these particles disrupt insect olfaction by adsorbing volatile compounds, blocking antennal sensilla, and interfering with receptor and neuronal processes. These disruptions impair foraging, mating, oviposition, …

Major And Institution Transfer Path Combinations And The Relaxed Lasso Method: Parsimonious Prediction Of Vertical Transfer Student Success, David Wutchiett, A. W. Logue, Martin Kurzweil, Colin Chellman

Publications and Research

The majors, institutions, and their combinations, which characterize transfer between higher education institutions relate to student success but introduce high-dimensionality and multicollinearity to statistical modeling. Relaxed lasso regression, a procedure blending regularized lasso models with unregularized refit models, selects variables and facilitates unbiased coefficient estimation in high-dimensional settings. Using a large sample of students transferring from associate to bachelor’s programs (n=28,166), we compared lasso and ridge regression parsimonious models predicting bachelor’s graduation and posttransfer GPA improvement, evaluating associations using unregularized parsimonious predictive models. Relaxed lasso parsimonious optimal models outperformed standard lasso and ridge regression models while including few of 702 …

The Colorado River: Searching For Solutions In The Face Of Uncertainty: Conference Summary, Emilia Endara, Douglas Kenney

Books, Reports, and Studies

The Colorado River is facing unprecedented change and uncertainty. Average annual flows continue to decline, and negotiations to craft post-2026 guidelines to manage the river have repeatedly bogged down in disagreements about delivery obligations, sharing shortages, and mandatory conservation. While some clarity may emerge in the coming months, the ability to sustain many existing municipal and agricultural water users is already being compromised. Meanwhile, there is growing recognition that a durable resolution must also address the water rights of sovereign Tribal Nations and better integrate environmental values into river management. All sovereigns, stakeholders, and sectors must work together now more …

Thirst For Data: State Responses To Data Center Water Use, Daniel Anderson, Sydney Stellato

Books, Reports, and Studies

No abstract provided.

Markov Chain Wave Generative Adversarial Network For Bee Bioacoustic Signal Synthesis, Kumudu Samarappuli, Iman Ardekani, Mahsa Mohaghegh, Abdolhossein Sarrafzadeh

Electrical & Computer Engineering Faculty Publications

This paper presents a framework for synthesizing bee bioacoustic signals associated with hive events. While existing approaches like WaveGAN have shown promise in audio generation, they often fail to preserve the subtle temporal and spectral features of bioacoustic signals critical for event-specific classification. The proposed method, MCWaveGAN, extends WaveGAN with a Markov Chain refinement stage, producing synthetic signals that more closely match the distribution of real bioacoustic data. Experimental results show that this method captures signal characteristics more effectively than WaveGAN alone. Furthermore, when integrated into a classifier, synthesized signals improved hive status prediction accuracy. These results highlight the potential …

Interpretable Battery Soh Prediction: A Comparative Interpretability Framework For Multi-Architecture Ml Models, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

This work introduces a unified interpretability-efficiency framework for lithium-ion battery state of health (SOH) prediction using hybrid deep learning architectures. We comparatively analyze four hybrid models: CNN LSTM MultiHead, CNN Feature Extractor LSTM, DNN LSTM, and DNN BiLSTM to disentangle how network topology, feature composition, and computational design influence both predictive fidelity and physical interpretability. By integrating Monte Carlo Shapley (MC Shapley), background occlusion SHAP (BoSHAP), and ablation analysis, we quantify the contribution and robustness of five electrochemical feature groups: time, capacity, voltage, dQ/dV and peaks of dQ/dV from NASA battery dataset. The results reveal a consistent dominance of differential …

Generative Ai-Driven Optimization In Flexible And Reconfigurable Manufacturing Systems, Salah Hammedi, Hicham Chaoui, Lotfi Nabli

Electrical & Computer Engineering Faculty Publications

Flexible and Reconfigurable Manufacturing Systems (FRMSs) are essential for coping with variability in modern production environments; however, efficient scheduling and rapid reconfiguration remain challenging. This paper presents a hybrid optimization framework that integrates Colored Petri Net (CPN) modeling with Generative Artificial Intelligence (GenAI) to enhance scheduling performance and system adaptability. The CPN formalism ensures verifiable modeling of system dynamics, while a transformer-based generative model produces candidate scheduling and reconfiguration strategies. Simulation experiments were conducted under static, dynamic, and adaptive scenarios, including machine breakdowns and dynamic job arrivals. Performance was evaluated using makespan, mean flow time, machine utilization, and reconfiguration latency. …

Stabilized Multi-Objective Optimization For Camouflage Attack In Remote Sensing, Omid Rajabi Rostami, Kazi Aminul Islam, Liuwan Zhu, Rui Ning, Chungsheng Xin, Hongyi Wu, Jiang Li

Electrical & Computer Engineering Faculty Publications

We investigate the vulnerability of multi-task maritime surveillance systems in which object detection and semantic segmentation operate concurrently. We propose Stabilized Multi-Objective Optimization (SMO), a framework that jointly targets Mask R-CNN and U-Net with a single universal adversarial texture in a digital white-box setting, with additional cross-model transfer evaluation. SMO combines a logarithmic-inversion loss transformation, feature inconsistency, and a systematic comparison of six optimization strategies for seven heterogeneous objectives. The loss transformation controls loss-dependent gradient scaling, while a U-Net generator supplies a spatially coherent texture and a hull-aligned application function accommodates target geometry without explicit Expectation over Transformation (EoT) sampling. …

A Comprehensive Survey Of Prompt Engineering Techniques In Large Language Models, Tonmoy Debnath, Md Nurul Absar Siddiky, Muhammad Enayetur Rahman, Prosenjit Das, Antu Kumar Guha, Muhammad Rezaur Rahman, H. M. Dipu Kabir

Electrical & Computer Engineering Faculty Publications

Prompt engineering has arisen as a pivotal discipline in optimizing the performance of Large Language Models (LLMs) by structuring inputs to enhance coherence, accuracy, and task alignment. This paper comprehensively surveys various prompting techniques, systematically categorizing them according to their application domains and methodological foundations. Fundamental approaches like zero-shot and few-shot prompting are examined along with advanced strategies, including chain-of-thought reasoning, retrieval-augmented generation, and self-consistency mechanisms. A rigorous qualitative analysis is conducted to evaluate each technique's strengths, limitations, and optimal use cases, offering a structured framework for selecting the most effective prompting strategies. Theoretical insights and empirical findings are consolidated …

Application Of Organic Geochemistry To The Characterization Of Hydrochar And Biochar: Insights Into Composition And Optimization, Michael A. Kruge, Teresa A. Centeno, Alvaro Amado-Fierro, José Manuel González-Lafuente, Rubén Forján, José Luis Gallego

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Hydrothermal carbonization has garnered significant attention in recent studies due to potential applications of the hydrochar produced, which overlap with those of torrefied biochar, including soil improvement, carbon sequestration, and energy production. However, unlike the relatively uniform properties of biochar, hydrochar characteristics can vary greatly depending on the feedstock used. Advanced characterization techniques are essential to better understand these materials and enhance their practical applications. Research on hydrochar requires a multi-faceted approach, including molecular-level studies, to optimize production processes and identify specific applications.

In this regard, analytical methods commonly used in organic geochemistry could play a crucial role in deepening …

The Influence Of Scale In Modeling Social Vulnerability And Disaster Assistance, Sina Razzaghi Asl, Oronde Drakes, Eric Tate, Samuel Brody, Wesley Highfield, Kayode Atoba

Political Science & Geography Faculty Publications

Understanding how social vulnerability relates to disaster impacts is critical for addressing social equity, yet the role of spatial scale in this relationship is often overlooked. Most studies use aggregated data, risking ecological fallacy-misinterpreting individual outcomes from group-level data. This study examines how spatial scale influences the relationship between social vulnerability and federal disaster assistance after Hurricane Harvey. Using spatial econometric models at both household and census tract levels, we assessed the strength of key vulnerability indicators in explaining disaster assistance. Results show that disability, housing tenure, household size, and income predict assistance at the household level, but their influence …

Beyond Discrete Indicators: Modeling Intersectional Flood Vulnerability, Sina Razzaghi Asl, Eric Tate, Christopher T. Emrich, Md Asif Rahman, Kaeleb Royster

Political Science & Geography Faculty Publications

Social vulnerability to flooding is shaped by intersectional social marginalization, yet most quantitative assessments employ indicators of single populations. This study applies spatial machine learning to examine how the intersectional social vulnerability indicators of poverty-race, poverty-housing tenure, and race-housing tenure compare with traditional discrete indicators of single populations in predicting flood exposure in California. Using geographically weighted random forests and partial dependence plots, we model spatial heterogeneity and non-linear relationships between social vulnerability and exposure. We quantified flood exposure using a population-adjusted measure derived from building footprints and modeled 500-year fluvial and pluvial flood hazard. The results reveal distinct explanatory …

Autonomous Weapons And Strategic Stability, Chick Edmond

Political Science & Geography Faculty Publications

The increasing number of artificial intelligence (AI) elements within military systems has introduced new forms of security dilemmas related to speed, level of secrecy, and transfer of responsibility from humans to machines. This article addresses the question of whether or how AI enabled autonomous weapons can lead to greater levels of strategic instability. Three causal mechanisms were determined by this study to potentially create destabilizing effects due to the introduction of autonomy; the first mechanism is a reduction in time available for decision making. The second mechanism involves the creation of multiple pathways of escalation. The third mechanism is the …

Global Evidence On Scaling Nature-Based Solutions For Compound Hazards, Sina Razzaghi Asl, Anahita Azadgar

Political Science & Geography Faculty Publications

Nature-based solutions (NbS) are increasingly promoted for climate adaptation to compound hazards, but systematic evidence of their effectiveness is still scarce. We address this gap with a systematic review of 38 studies. Each study was sorted by what its measurement design can support, not by what its authors claim. This produced four groups. Direct studies attribute a measured hazard reduction to the intervention, proxy studies infer protection from ecosystem condition, prioritization studies map suitable locations without estimating performance, and qualitative studies describe effectiveness without measuring it. We detect trends in geographic distribution, hazard combinations, intervention types, methodological approaches, and strength …

Small-𝑥 Asmptomatics Of The Leading-Twist Flavor Singlet Quark Tmds, Daniel Adamiak, M. Gabriel Santiago, Yossathorn Tawabutr

Physics Faculty Publications

In this paper, we investigate the small-𝑥 behavior of the flavor-singlet, leading-twist quark transverse-momentum-dependent parton distribution functions (TMDs) using the light-cone operator treatment. This formalism allows us to express TMD operators at small 𝑥 in terms of polarized dipole amplitudes, enabling a systematic approach to their small-𝑥 evolution. We derive the evolution equations for these TMDs and solve them within the large-𝑁𝑐 approximation under the linearized, double-logarithmic approximation, where 𝑁𝑐 represents the number of quark colors. Expanding on previous work on unpolarized and helicity TMDs, we present the small-𝑥 asymptotics for a comprehensive set of TMDs, including the Sivers function, …

Trigonometric Continuous-Variable Gates And Hybrid Quantum Simulations Of The Sine-Gordon Model, Tommaso Rainaldi, Victor Ale, Matt Grau, Dmitri Kharzeev, Enrique Rico, Felix Ringer, Pubasha Shome, George Siopsis

Physics Faculty Publications

Hybrid qubit-qumode quantum computing platforms provide a natural setting for simulating interacting bosonic quantum field theories. However, existing continuous-variable gate constructions rely predominantly on polynomial functions of canonical quadratures. In this work, we introduce a complementary universality paradigm based on trigonometric continuous-variable gates, which enable a Fourier-like representation of bosonic operators and are particularly well suited for periodic and non-perturbative interactions. We present an ancilla-based framework for implementing trigonometric gates with arguments given by arbitrary Hermitian functions of qumode quadratures. The protocol yields unitary gates deterministically, and non-unitary gates through probabilistic post-selection. As a concrete application, we develop a hybrid …