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Editorial For The Special Issue “Advances And Applications Of Polymer Gels For Subsurface Energy And Storage”, Baojun Bai, Jingyang Pu

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Polymer gels are essential functional materials for subsurface energy operations, playing a critical role in conformance control, fluid diversion, hydraulic fracturing, and leakage mitigation. As reservoirs become increasingly complex and the demand for sustainable energy grows, continued innovation in gel technologies is crucial. This editorial introduces a Special Issue titled "Advances and Applications of Polymer Gels for Subsurface Energy and Storage," which compiles seven original research articles exploring recent developments in gel synthesis, characterization, and applications. The featured studies highlight the versatility of polymer gels, including nanoparticle-reinforced composites, foam–gel hybrids, recrosslinkable preformed particle gels, and advanced fracturing fluids. The contributions …

Collecting Holistic Evidence On Agroecology Performance To Accelerate Sustainable Food System Transitions, Sarah K. Jones, Andrea Cecilia Sánchez, Chris Dickens, Matthias S. Geck, Chaturangi Wickramaratne, Veronique Alary, Peter Bolo, Dennis Junior Choruma, Somphasith Douangsavanh, Modou Gueye Fall, Gatien Falconnier, Shweta Gupta, Chris Kettle, Smitha Krishnan, Sylvia Nyawira, Guillermo Orjuela-Ramirez, Boko Michel Orounladji, Piedad Pareja, Telma Sibanda, Christine Lamanna

All Peer-Reviewed Publications

A growing body of research shows the potential of agroecology for enabling a shift to planet-friendly and socially just agrifood systems through context-specific transition pathways. We conduct a scoping review to identify and evaluate the potential of 42 existing agroecology adherence and/or agricultural performance assessment tools to generate credible, legitimate, salient and transferable evidence to inform these transitions. Results show that while multiple relevant tools exist, each of them can be strengthened in at least one area. While most tools are transferable (low time and resource requirements) and have legitimacy (open access with transparent methods), tools have variable saliency (holistic …

Machine Learning-Enabled Chemical Ecology For Integrated Pest Management: From Volatiles To Field Applications, Steve B.S. Baleba, Victor O. Omondi, Pascal Aigbedion-Atalor, Emmanuel Peter, Souleymane Diallo, Komi Mensah Agboka

All Peer-Reviewed Publications

Machine learning is transforming chemical ecology by accelerating the discovery and deployment of semiochemical-based tools for precision pest management. These advances are particularly important in the face of climate change, pesticide resistance, and the growing need for sustainable agricultural intensification. This review synthesizes how machine learning can be applied across the semiochemical discovery and implementation pipeline, from chemical signal detection to field deployment and decision support for integrated pest management. We review major machine learning approaches and demonstrate how they extract biologically relevant information from high-dimensional chemical, electrophysiological, behavioral, sensor, and field datasets. These methods accelerate semiochemical discovery, prioritize candidate …

Pseudo-Traveling Waves And Bumps In Quantum And Classical Hierarchical Cellular Neural Networks, Wilson A. Zuniga-Galindo, B. A. Zambrano-Luna, Chayapuntika Indoung

School of Mathematical & Statistical Sciences Faculty Publications

We study the existence of pseudo-traveling waves and bump solutions for two classes of hierarchical cellular neural networks (CNNs) defined over the ring of p-adic integers Zp. The first type is a p-adic CNN described by a reaction-diffusion equation, while the second type is its quantum analog obtained via Wick rotation. The p-adic CNNs are hierarchical versions of the classical Chua-Yang CNNs; these networks have a tree-like hierarchical architecture with infinitely many cells and hidden layers. The states are governed by integro-differential equations on Zp. The p-adic traveling waves behave fundamentally differently from their Archimedean counterparts. A traveling wave restricted …

Generalizations Of The Dirichlet Problem For Bianalytic Functions, William L. Blair

Math Faculty Publications and Presentations

We prove the Dirichlet problem for second-order iterated Vekua equations, a natural generalization of the Bitsadze equation, is well-posed when the boundary condition is defined as a product of an exponential function and a polynomial on a non-degenerate conic that is not a circumference. Also, we extend this result, as well as other related results for the Dirichlet problem for polyanalytic and generalized analytic functions from the literature, to their analogues for bicomplex differential equations.

Investigating The Impact Of Outreach Programmes On Second Level Students In Formal And Informal Computer Science Education In Ireland, Karen Nolan

Doctoral

Computer Science (CS) education at second-level in Ireland has undergone significant development, including the introduction of the Leaving Certificate Computer Science (LCCS). However, the existing literature examined demonstrated that access to and participation in CS education is uneven, resulting in many students having limited exposure CS at second-level before making educational choices. At the same time, students who elect to study LCCS require appropriate support as they engage with programming and other aspects of a relatively new formal curriculum. This thesis investigates the function of school-based CS outreach in helping students at these various phases of their second-level CS education …

Toward Equitable Energy Futures: The Influence Of Digital Government And Financial Development On Clean Energy Justice, Martin Spraggon, Muhammad Hafeez, Sana Ullah, Chanyanan Somthawinpongsai, Zurul Aisya Osman, Sidra Sohail

All Works

Fossil fuel energy is the largest source of carbon emissions and the main driver of climate change and global warming. Therefore, the world is switching towards clean and green energy sources as an alternative to fossil fuel-based energy sources. Nevertheless, the equitable distribution of clean energy resources in urban and rural areas among every segment of society has become a concern for policymakers. Thus, urban-rural clean energy justice is an investigation topic that needs our attention. In this analysis, our main focus is on investigating the influence of financial development and digital government on clean energy justice using an advanced …

Sea-Level Rise And Regional Economic Adjustment On The U.S. Gulf Coast, Md A M Hasif, Juhee Lee, Robin A. Choudhury

School of Earth, Environmental, & Marine Sciences Faculty Publications

Sea-level rise (SLR) poses escalating risks to the U.S. Gulf Coast, where critical industries, infrastructure, and populations are concentrated in low-lying areas. This study estimates the short- and long-run economic effects of SLR using a panel autoregressive distributed lag-pooled mean group framework applied to county-level data from 26 coastal counties across five Gulf Coast states from 2005 to 2021. Over this period, mean sea level increased by approximately 11 cm on average across counties, with cumulative changes ranging from about 4 cm to over 24 cm and substantial cross-county variation, providing a meaningful empirical setting for identifying economic adjustment. We …

Re: Butte Priority Soils Operable Unit (Bpsou) Final Bpsou Unreclaimed Sites Anderson Shaft Remedial Action Work Plan (Rawp) Request For Change Ur-2026-Ra-01, Mike Mcanulty

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Revised Draft Final Quarterly Operations And Maintenance Report: Butte Treatment Lagoon System – First Quarter 2026, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Re: Butte Priority Soils Operable Unit (Bpsou) Final Bpsou Unreclaimed Sites Anderson Shaft Remedial Action Work Plan (Rawp) Request For Change Ur-2026-Ra-01, Mike Mcanulty

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Remote Sensing Integrated Modeling Of Fall Armyworm Parasitoid Interactions In Africa Using Fractional Differential Equations, Marian Adan, Steeven Belvinos Affognon, Henri E.Z. Tonnang, Klaus Greve, Christian Borgemeister, Georg Goergen

All Peer-Reviewed Publications

The invasion of fall armyworm (Spodoptera frugiperda; FAW) into Africa poses a major threat to agricultural systems, particularly maize production, necessitating effective biological control strategies. This study presents an ecological modelling framework that integrates temperature data with fractional differential equations (FDEs) to investigate interactions between FAW and its parasitoid natural enemies. By incorporating temperature-driven effects on life-history traits and ecological memory dynamics, the model captures key features of host–parasitoid interactions under variable environmental conditions. Equilibrium states corresponding to FAW extinction, parasitoid extinction, and coexistence were analytically derived, and sensitivity analysis of the FAW reproduction number highlighted its central role in …

From Green Presence To Perceived Greenery: Everyday Mobility And Perceptual Availability In Singapore, Ziheng Zeng, Sonny Rosenthal, Orlando Woods

Research Collection College of Integrative Studies

Urban greenery may be abundant in transit-oriented cities, yet green presence does not necessarily become perceptually available to people moving through it. This paper distinguishes green supply, perceived greenery, and green experience, and examines the step at which greenery enters awareness during travel. The study combined 21 days of ecological momentary assessment from 68 participants in Singapore, yielding 6,794 in-trip moments, and 25 route-reconstruction interviews. A satellite normalized difference vegetation index (NDVI) provided a top-down baseline of green supply, and a street-level green view index (GVI) from Google Street View (GSV), matched to 5,824 moments, provided a co-located eye-level baseline. …

The Application Of Machine Learning And Deep Learning On Demand Forecasting Across Time-Critical Industries: A Systematic Review, Asmaa Seyam, Sujith Samuel Mathew, May El Barachi, Cheng Zhang, Jun Shen

All Works

The applications of machine learning and deep learning in demand forecasting have attracted increasing attention, as they offer remarkable predictive capabilities that help automate forecasting processes and achieve higher accuracy. While numerous review studies have examined solutions within specific industries, there is a lack of comprehensive literature review investigating these solutions across different sectors. Therefore, this study overviews machine learning and deep learning applications in demand forecasting across time-critical industries, including power, tourism, water, transportation, and food. A two-tier classification framework is proposed to categorize demand forecasting studies by both application industry and methodological architecture. In addition, the most popular …

Prune: A Patching Based Repair Framework For Certifiable And Privacy-Robust Unlearning Of Neural Networks, Xuran Li, Jingyi Wang, Xiaohan Yuan, Peixin Zhang

Research Collection School Of Computing and Information Systems

Machine unlearning has emerged as a key mechanism for enabling the “right to be forgotten” in neural network models, allowing the selective removal of specific training data upon request. Existing approaches typically rely on retraining models with the remaining data, which is computationally expensive and difficult to verify, especially when deployed models are distributed or resource-constrained. To address this challenge, our prior conference work introduced PRUNE, a patching-based framework that formulates unlearning as a neural network repair problem. PRUNE achieves targeted forgetting by learning lightweight patch networks that redirect model predictions on the data to be unlearned while preserving performance …

Neural Symphony Of Flow Experience: Evidence For High-Dimensional Metastable Dynamics, Abdelrahman B. M. Eldaly, Kris Zhangguang Kang, Fiona Fui-Hoon Nah, Leanne Lai-Hang Chan, Keng Siau, Xiao Fan Liu, Richard Huskey, Langtao Chen, Tejaswini Yelamanchili, Rene Weber

Research Collection School Of Computing and Information Systems

Flow, an optimal experience characterized by deep immersion and engagement in an activity, has been extensively studied in behavioral research. However, its neural dynamic mechanism remains poorly understood. In a within-subject video gaming experiment, we captured neural activity underlying flow, boredom, and anxiety using a 64-channel electroencephalogram (EEG) system. Compared to boredom and anxiety, flow exhibits the highest global functional connectivity, metastability, and dimensionality of dynamic functional connectivity patterns, suggesting that flow is a highly adaptable process that is supported by high-dimensional neural dynamics. Unlike previous studies that focused on identifying static or localized brain activity, we examine the neural …

Generalized Logit Adjustment: Improved Fine-Tuning By Mitigating Label Bias In Zero-Shot Vision Models, Beier Zhu, Qianru Sun, Xun Yang, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Foundation models like CLIP allow zero-shot transfer on various tasks without additional training data. Yet, the zero-shot performance is less competitive than a fully supervised one. Thus, fine-tuning and ensembling are also commonly adopted to better fit the downstream tasks. However, we argue that such prior work has overlooked the inherent biases in foundation models. Due to the highly imbalanced Web-scale training set, foundation models are inevitably skewed toward frequent semantics, and thus the subsequent fine-tuning or ensembling is still biased. In this study, we systematically examine the biases in foundation models and demonstrate the efficacy of our proposed Generalized …

Smart: When Is It Actually Worth Expanding A Speculative Tree?, Lifu Wang, Pan Zhou

Research Collection School Of Computing and Information Systems

Tree-based speculative decoding accelerates autoregressive generation by verifying a branching tree of draft tokens in a single targetmodel forward pass. However, existing methods prioritize maximizing token-level likelihood or the number of accepted tokens while ignoring a critical “efficiency paradox”: the computational overhead of drafting and verifying big trees can grow super-linearly, particularly at scale. This often leads to negative wall-clock speedup when batch sizes increase or hardware saturation limits are reached. To address this, we propose SMART, a System-aware Marginal Analysis framework for Runtime Tree construction. SMART reformulates tree expansion as a hardware-aware optimization problem that directly maximizes end-toend speedup. …

Light-Wdef: Lightweight Deepfake Evaluation Framework For Efficient And Reliable Visual Detection, Muhammad Yousaf, Sarwar Shah Khan, Babar Shah, Muhammad Shehzad Khan, Muhammad Awais Bacha, Mian Saeed Akbar, Ijaz Ali, Fernando Moreira

All Works

Deepfake technology synthesizes and manipulates digital content to produce artificial videos in which an individual’s identity, facial appearance, or actions are convincingly altered. These forgeries can be highly realistic, posing serious risks to trust in digital media and undermining personal reputation, dignity, and the integrity of public discourse. This paper presents the Lightweight Deepfake Evaluation Framework (Light-wDEF)– a resource-efficient, reproducible framework tailored for large-scale evaluation of visual deepfake detectors on modest hardware. Light-wDEF combines deterministic frame sampling, batched MTCNN face extraction, streaming append-only storage with lazy offset indexing, online Albumentations augmentations, stratified splits with class-aware sampling, and robust training (AdamW, …

Never Say Never: The Interdependence Of No-Till And Strategic Deep Tillage In Sustainable Production Systems, Therese Mcbeath, Stephen Davies, John A. Kirkegaard

Grain and Other Field Crops Research Articles

No-till (NT) farming systems are a defining feature of modern conservation agriculture (CA), providing benefits for soil and water conservation, operational efficiency, and resource use efficiency. However, long-term NT adoption across Australian dryland cropping systems has also intensified inherent and emergent soil constraints, including subsurface compaction, topsoil water repellence, acidity, and nutrient stratification. These constraints limit crop water use and yield potential. In the sandy soils of southern and western Australia, strategic deep tillage (SDT) has been developed in otherwise NT systems to alleviate these constraints. Advances in machinery design and controlled traffic systems have enabled deeper, more effective amelioration …

An Evaluation Of Next Generation Biological Control Methods For The Invasive Brown Treesnake In The Mariana Islands, Jonathan Q. Richmond, Tim Harvey-Samuel, Melia G. Nafus, Adam Perez, Renee Bellinger, Antoinette J. Piaggio, Riley Taitingfong, Margaret E. Hunter, Robert N. Fisher

United States Department of Agriculture Wildlife Services: Staff Publications

No abstract provided.

Constrained Multiview Contrastive Learning For Jointly Supervised Representation Learning, Siyuan Dai, Kai Ye, Kun Zhao, Yang Du, Haoteng Tang, Liang Zhan

Computer Science Faculty Publications

Purpose

Materials and Methods

Catalytic Synthesis Of Protoberberine Alkaloids And Their Evaluation As Selective Dopamine Receptor Ligands, Ashok Reddy Gudipally

Dissertations, Theses, and Capstone Projects

Dopamine regulates motor control, cognition, reward, motivation, and neuroendocrine signaling. Disruption of dopaminergic neurotransmission is closely linked to central nervous system disorders, including Parkinson’s disease, schizophrenia, addiction, and related neuropsychiatric conditions. Because these functions are mediated by multiple dopamine receptor subtypes with distinct roles in physiology and disease, the discovery of subtype-selective ligands remains an important goal in neuropharmacology. Within this context, protoberberine alkaloids, particularly tetrahydroprotoberberines (THPBs), are attractive molecular frameworks because they combine structural complexity with broad biological relevance, including measurable activity in CNS-related diseases. Prior work from our laboratory and others established (–)-stepholidine (1.60) as an important lead …

Bridging Data Gaps In Retinal Imaging: From Structural Domain Adaptation To Topology-Aware Synthesis, Gözde Merve Demirci

Dissertations, Theses, and Capstone Projects

Comprehensive visualization of the retina is essential for diagnosing and monitoring blinding diseases such as Diabetic Retinopathy and Retinopathy of Prematurity (ROP), where pathological changes often extend beyond a single field of view. Despite significant advances in automated retinal image analysis, clinical deployment remains limited by two fundamental data gaps: a structural learning gap, arising from scarce expert annotations and poor generalization across imaging domains, and a spatial coverage gap, caused by the difficulty of acquiring multi-view retinal images in fragile populations. Although these challenges are often addressed independently, this dissertation argues that they are tightly coupled: accurate, …

Model-Theoretic Arguments In Philosophy, Peter Susanszky

Dissertations, Theses, and Capstone Projects

This dissertation is on model-theoretic arguments in philosophy, especially those of Quine, Davidson, and Putnam. In the first part, to ground the debate, I give a rigorous introduction to the salient parts of first-order model theory. I start the second part by giving an introduction to Quine's philosophy, and how the model-theoretic arguments fit into it. After considering how Donald Davidson adopted the Quinean lesson, I move on to Putnam's model-theoretic arguments. Putnam's spin on these model-theoretic considerations significantly departs from Quine and Davidson, while retaining many of the core ideas. Most importantly, I argue that the target of Putnam's …

Potential Energy Landscape Formalism For Quantum Liquids, Yang Zhou

Dissertations, Theses, and Capstone Projects

Atomic delocalization due to nuclear quantum effects (NQE) remains poorly understood in low-temperature liquids near the glass state and during vitrification. Many liquids can be described accurately by treating their nuclei as classical particles, but this approximation fails for light elements such as He and H₂, small hydrogen-containing molecules such as water, and systems in which zero-point motion or isotope-substitution effects are important. Developing a general thermodynamic and statistical-mechanical description of such liquids has been challenging. This dissertation extends the potential energy landscape (PEL) formalism, originally developed for classical liquids and glasses, to liquids that obey quantum mechanics and exhibit …

Mapping The Water Quality Of Jamaica Bay, New York (1996-2024): Principal Component Analysis And K-Means Clustering, Sneha Srivastava

Dissertations, Theses, and Capstone Projects

Jamaica Bay, located along the southeastern coast of New York City, acts as a biodiverse estuary of wetlands, meadows, and salt marsh islands. The purpose of this study is to analyze the water quality conditions of the region over time, comparing locations around the bay to identify hyperlocal features that influence larger trends in the hydrological system. Ten variables were used as water quality indicators, including total Kjeldahl nitrogen, salinity, pH, Secchi disk depth, and total phosphorus, among others, across five stations in the bay, between 1994 and 2024. After data cleaning and standardization methods were applied, principal component analysis …

Rethinking Road Racing Standards: Comparing New York Road Runners Race Results To Running Industry Standards, Jennie Coughlin

Dissertations, Theses, and Capstone Projects

Running has undergone a third boom in participation since 2020, driven largely by people seeking fitness options that were outside and socially distanced during the COVID-19 pandemic. Social media has also allowed runners from groups that did not traditionally participate to find community. As the road racing population has expanded to include more people, some road racing industry standards have not kept up. This project assesses what the road racing community looks like and measures industry standards against the community of participants to see what changes in those standards would be needed to make them inclusive for all participants.

To …

The Role Of External Factors In Shaping Carbohydrate Conformational Landscapes, Murat Yaman

Dissertations, Theses, and Capstone Projects

Carbohydrates (glycans) are among the most structurally diverse biomolecules and participate in a wide range of biological processes, including molecular recognition, cell adhesion, immune response, host-pathogen interactions, and cellular signaling. Their biological functions are governed by a complex interplay between sequence, stereochemistry, conformational flexibility, and environmental factors. However, the structural heterogeneity and dynamic nature of glycans present significant challenges for both experimental characterization and theoretical modeling. In particular, the relationships between glycan structure, conformational dynamics, and the resulting potential energy surfaces remain incomplete.

This dissertation investigates how external factors modulate the potential energy surfaces of carbohydrates through a combination of …

Mesoscopic Scaling Of Microwave Transmittance And Disorder-Induced Alignment, Israel Kurtz

Dissertations, Theses, and Capstone Projects

This thesis presents microwave measurements and numerical simulations of wave propagation and transmission through random mesoscopic waveguides. We analyze the scaling of mesoscopic microwave conductance, including departures from Ohm’s law near channel openings, which are frequencies at which new propagating modes enter the system, and the evolution of modal phase alignment within the medium, which leads to the observed diversity of transmission eigenvalues. We extend the transmission matrix formalism to all sample depths using the flux matrix, which relates the flux amplitude within the sample to the incident flux, and decompose the flux at each sample depth into forward- and …