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A Note On The Periodic Orbits Of Wolbachia Spread Dynamics In Mosquito Populations In Periodic Environments, Jose S. Cánovas

Turkish Journal of Mathematics

We consider the periodic model introduced by B. Zheng and J. Yu, and disprove the conjectures on the number of periodic orbits the model can have. We rebuild the conjecture to prove that for periodic sequences of maps of any period, the number of nonzero periodic trajectories is bounded by two.

Characterizations Of Bi-Riordan Arrays, Nai̇m Tuğlu, Fatma Yeşi̇l Baran

Turkish Journal of Mathematics

In this study, we introduce the concept of a bi-Riordan array. As part of this construction, we define new composition and power operations. We then establish an analogue of the well-known fundamental theorem of Riordan arrays (FTRA), which we call the fundamental theorem of bi-Riordan arrays. Next, we show that the product of two bi-Riordan matrices is again a bi-Riordan matrix. Using this product structure, we prove that the set of bi-Riordan matrices forms a monoid.

Exponential Stability For A Strongly Damped Wave Equation With Distributed Internal Delay, Manal Alotaibi, Nasser-Eddine Tatar, Waled Al-Khulaif

Turkish Journal of Mathematics

This paper investigates the exponential stability of a strongly damped wave equation subject to an internal distributed time delay. Two distinct analytical frameworks are developed: the first employs a modified energy method based on auxiliary functionals, while the second introduces a transformation that rewrites the delay term as the derivative of a convolution integral. Both approaches yield explicit decay conditions and establish exponential convergence of the energy under weaker assumptions on the damping coefficient and the delay kernel. Notably, the transformation method allows for a wider class of admissible delay weights than those permitted by classical techniques. Numerical simulations based …

A Delayed Siamr Prostate Cancer Model With Vertical Transmission And Treatment: Stability Analysis And Numerical Simulation, Hüseyi̇n Demir, İnci̇ Çi̇li̇ngi̇r Süngü, İbrahi̇m Keles

Turkish Journal of Mathematics

​In this paper, we propose and analyse a delayed-compartmental mathematical model of prostate cancer dynamics that incorporates vertical transmission and therapeutic intervention. The population is stratified into five compartments: susceptible (S), infected/virally activated (I), asymptomatic infectious under treatment (A), malignant untreated (M), and recovered (R). A discrete time delay τ > 0 is introduced in the force-of-infection term to model the age-dependent onset of susceptibility in middle-aged adult males. Vertical transmission of viral etiology at a rate qΛ ensures that a fraction of newborns enter the infected compartment directly, rendering the disease persistent regardless of the basic reproduction number R₀. We …

Hyers-Ulam And Hyers-Ulam Rassias Stability Of Caputo Fractional Hahn Difference Equations, Karima Mohamed Oraby, Alaa E. Hamza, Afrah Al-Bossly

Turkish Journal of Mathematics

​In this paper, we investigate Hyers–Ulam and Hyers–Ulam–Rassias stability for fractional linear Hahn difference equations of Caputo Type. To the best of our knowledge, this is the first work concerning Hyers–Ulam type stability in the framework of Caputo fractional Hahn difference equations, thereby filling a significant gap in the literature on fractional stability theory. Our approach is based on establishing an equivalence between the fractional Hahn difference initial value problem and a corresponding fractional integral equation, via the Caputo–Hahn inversion formula. These results lay a strong groundwork for analyzing the stability of Hahn fractional systems, which could be useful in …

Final 2023 Unreclaimed Sites Sampling: Ur-42 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Governing Agentic Ai: The Human Values Alignment Framework (Hvaf-A) As A Policy Tool, Mousa Al-Kfairy

All Works

Agentic artificial intelligence (AI) systems—software that plans, acts, and decides without step-by-step human approval—are already deployed in finance, healthcare, recruitment, and public services. They differ from earlier AI in one critical way. They do not produce outputs for a human to accept or reject. They take actions. Those actions may be irreversible. They may affect people who never used the system. Existing governance frameworks were not designed for this. Current alignment approaches—including reinforcement learning from human feedback, constitutional AI, and preference aggregation—assume that a well-aligned system satisfies what users ask for. This paper argues that the assumption fails in agentic …

Individualized Bayesian Inference Identifies Novel Genetic Variants For Parkinson's Disease, Jin Ren, Yasaman J. Soofi, Md Asad Rahman, Qing Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Parkinson's disease (PD) is a complex neurodegenerative disorder with a significant genetic component. While genome-wide association studies (GWAS) have been instrumental in identifying genetic variants associated with PD, the reliance on large sample sizes and population-level analyses may overlook variants with lower minor allele frequencies or individual-specific relevance. Individualized Bayesian Inference (IBI) offers a promising method to complement GWAS by identifying and prioritizing candidate genetic markers at both the individual and patients-like-me subgroup levels. This study evaluates the application of IBI to PD genetics, using GWAS as a baseline for comparison. We analyzed genetic data from the Fox Insight online …

Karhunen-Loève Modes For Coherent Arrays, Jack E. Mccrae, Santasri R. Bose-Pillai, Steven T. Fiorino

Faculty Publications

The optimal modes for correcting atmospheric turbulence on coherent arrays are determined. These Karhunen-Loève modes are eigenvectors of a covariance matrix. Creating this covariance matrix requires knowledge of the power spectrum of the turbulence, the aperture geometry, and a basis set for the matrix. The Kolmogorov power law is ordinarily chosen here for the turbulence spectrum. By choosing the phase on each array subaperture element minus the phase averaged over all subapertures as this basis, infinite values in the variances and covariances can be avoided. The piston mode of the whole array, which would otherwise also be infinite, is thus …

Bayesian And Multi-Objective Decision Support For Incident Mitigation In Cyber-Physical Systems, Shaofei Huang, Christopher M. Poskitt, Lwin Khin Shar

Research Collection School of Computing and Information Systems

Cyber-physical systems increasingly rely on interconnected physical and digital systems whose security incidents can escalate rapidly into safety and operational failures. Existing decision-support approaches struggle to support incident response because they rely on static assumptions, incomplete vulnerability data, and single-objective risk models that do not adequately capture trade-offs between attack success likelihood, impact severity, and system availability. This paper proposes an adaptive decision-support framework for incident mitigation in cyber-physical systems that integrates hierarchical Bayesian Network modelling, confidence-calibrated exposure estimation, and multi-objective optimisation into a unified, adaptive pipeline. The framework constructs probabilistic models from system architecture and vulnerability data, incorporating complementary …

Bayesian Network: An Explainable Artificial Intelligence (Xai) Approach To Human Performance Modelling For Control Room Operations, Houda Briwa

Theses

Alarm systems in process industry control rooms routinely exceed the performance targets set by standards such as EEMUA 191, placing operators under conditions where reliable performance is most difficult to achieve. Predicting how operators respond under such conditions is central to risk management, yet current Human Reliability Assessment (HRA) methods depend on expert judgement that is rarely tested against operational evidence, assume independence among factors known to interact, and do not explicitly represent the cognitive processes through which performance emerges. In Resilience Engineering terms, these methods encode Work-as-Imagined with limited means to assess how far expectations hold when work is …

Stochastic Control Of Inventory And Scheduling Decisions In An Open Network Of Repairables, Erhun Özkan

Turkish Journal of Mathematics

We study maintenance operations for capital goods with repairable components. Because the downtime of a capital good is costly, the maintenance provider keeps an inventory of spare parts to quickly replace broken parts of the capital goods. After the replacement, the broken part is added to the repair facility’s repair queue. Upon a part breakdown, if there is no spare part in the inventory, then the maintenance provider has two options: (i) it can backorder the spare part demand, (ii) it can execute an emergency repair, which is done immediately and quickly, and is followed by a quick installation of …

A Full-Scale Watertight Workflow Numerical Study For A Triangular Fin And Tube Heat Exchanger, Hamdi̇ Selçuk Çeli̇k, Bahadir Doğan, Lati̇fe Berri̇n Erbay

Turkish Journal of Mathematics

In this study, the thermo-hydraulic characteristics of an air-cooled triangular finned tube heat exchanger were analyzed numerically. The full-scale heat exchanger was modeled using Ansys Fluent watertight workflow, considering water as the primer fluid to precisely investigate the effects of geometrical factors. The effects of fin height, fin width, and fin spacing of the triangular fins on the performance of the heat exchanger were examined using k-ω turbulence model. A basic finned-tube heat exchanger model, which was manufactured as a monolithic structure without tube-fin contact resistance by milling from aluminum 5083 material, was tested in an air tunnel to verify …

Aligning Tenure With Institutional Values: Supporting And Rewarding Engaged Scholarship In College Tenure Policies, Sian Bareket, Alexander R. Barron, Abigail O’Meara, Charavee Basnet Chettri

Environmental Science and Policy: Faculty Publications

Scholars have described the current era as a polycrisis, with climate change overlapping and interacting with other social justice and environmental challenges. While the expertise and capacity housed in academia has tremendous potential to help society face these threats, scholars and others have long critiqued academia as too isolated from the practical problems of society and front-line communities. While engaged scholarship (ES) connects academia and real-world problems, tenure policies are usually designed to primarily reward “traditional” scholarship focused on journal articles and academic books. This study sought to investigate the contrast between higher education institutions’ publicly shared values aligned with …

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 …

Behind The Score: The Influence Of Neighborhood Policy Contexts On New York Regents Exam Performance Across New York City Public High Schools, Philip J. Kovacevic

Dissertations, Theses, and Capstone Projects

This study used a two-level hierarchical linear model (HLM) to test whether New York City neighborhood zoning and land-use characteristics independently predict school-level performance on the New York State Regents Examinations, after controlling for school demographic and organizational characteristics, and Regents mean scores, both drawn from the NYC DOE's Regents Exam Results file (NYC Public Schools InfoHub, 2024). Using NYC Primary Land Use Tax Lot Output (PLUTO) 2025 data, a Neighborhood Educational Quality (NEQ) index was built for each of the city’s 42 community districts by combining the commercial floor area ratio (CommFAR) and the proportion of lots zoned residential …

Cavity Optomechanics Via Mechanically Induced Coupling Of Optical Modes, Satyam S. Jha

Dissertations, Theses, and Capstone Projects

This thesis introduces a transverse, symmetry-breaking mode of optomechanical coupling that departs from the conventional dispersive and dissipative interactions, taking its motivating platform from whispering-gallery-mode (WGM) resonators, where a mechanical displacement lifts the near-degeneracy of azimuthal mode pairs and couples them. After reviewing the standard optomechanical model and the sideband-cooling phenomenology it underlies, we derive the Hamiltonian for the transverse-coupling system and first study its Hamiltonian dynamics, without drive or dissipation. Recast in terms of Schwinger pseudospin operators, the model exhibits a Hamiltonian Hopf bifurcation separating stable and unstable dynamical regimes. In the stable regime, the mechanical nonlinearity permits controlled …

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 …

Which Factors Are Most Important For Understanding Neighborhood-Scale Electrification Transitions In New York City, And How Do These Factors Interact Within Urban Energy Systems?, Aaryan Nair

Publications and Research

Building electrification has emerged as a central strategy for reducing greenhouse gas emissions from urban areas, yet its implementation remains uneven across cities due to differences in buildings, infrastructure, and local conditions. This literature review examines the factors that influence neighborhood-scale electrification transitions in New York City and explores how these factors interact within urban energy systems. Drawing on research from building science, energy systems, infrastructure planning, and sustainability transitions, the paper identifies five recurring categories of influence: building characteristics, demand characteristics, infrastructure conditions, planning and investment capacity, and socioeconomic conditions. The review finds that building age, typology, density, and …

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 …

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.

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.

Ecu-Malnett V2, Matthew G. Gaber, Mohiuddin Ahmed, Michael N. Johnstone

Research Datasets

ECU-MALNETT (ECU MALware NETwork Traffic) is a real world, reproducible dataset of labeled benign and malicious network flows built from the Peekaboo execution corpus. Peekaboo runs evasive malware with dynamic binary instrumentation and records raw host-level PCAPs while granting full Internet access, yielding noisy, real-world captures with background OS activity and concurrent processes. To derive trustworthy labels from these traces, we apply Construct, a baseline aware, zero-trust labeling framework. Construct first ingests a baseline capture to establish reference sets (DNS qnames, HTTP hosts, TLS SNIs, and socket endpoints) and grows a conservative benign IP pool only via whitelisted DNS resolutions. …

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 …

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 …

Ultrafast Spectroscopy Of Organic Materials In Strong Light–Matter Interaction And Terahertz Optics, Kamyar Rashidi

Dissertations, Theses, and Capstone Projects

Ultrafast spectroscopy is a powerful tool for investigating phenomena that occur on extremely short timescales and for probing ultrabroadband spectral responses enabled by its ultrashort laser pulses. These capabilities are essential for advancing both the fundamental understanding of light–matter interactions and the development of next-generation optoelectronic technologies. The first part of this thesis investigates the ultrafast dynamics of organic exciton–polaritons and their application to controlling molecular excited-state processes using momentum-resolved pump–probe spectroscopy. A major challenge in these systems is the dark-state problem, where photoexcitation predominantly populates long-lived excitonic reservoir states that obscure the in trinsic dynamics of the hybrid light–matter …

Complementing Topographic Wetness With Sentinel-2 Phenological Indices To Map Soil Moisture Gradients In A Snowmelt-Driven Mountain Floodplain, Edward Turk

Master's Projects and Capstones

Soil moisture in mountain floodplains varies over a few meters, far finer than ground-based sensors or coarse satellite images resolve; conventional optical and microwave methods sense only the top few centimeters, missing the root zone that governs plant function and biogeochemistry. Similarly, topography-derived moisture variations miss micro-heterogeneities in moisture related to intrinsic soil heterogeneity and/or the root zone. This has important implications for our ability to characterize local hotspots of biogeochemical reactions, like those related to the generation of methane. In this work, we read vegetation as a proxy for the subsurface, using Sentinel-2 land-surface phenology to infer root-zone moisture, …

Exposing And Addressing Machine Learning Brittleness Through Constraint Solving, Muyeed Ahmed

Dissertations

Machine Learning (ML) implementations are fundamentally brittle: nondeterministic, inconsistent, and prone to overfitting; however, constraint solving can be used to systematically expose, quantify, and address this brittleness.

This dissertation first establishes that widely-used implementations of popular ML algorithms are nondeterministic (producing different outputs on the same input, across different runs) and inconsistent (different implementations of the same algorithm producing different outputs on the same input). This is more prevalent in Unsupervised Learning (UL) implementations where, due to the lack of a ground truth, subtle execution errors can go unnoticed and are difficult to verify. Nondeterminism and inconsistency also introduce security …

Development Of Ultrafast Protein Digestion And Standards Free Quantitation Methods For Mass Spectrometric Analysis, Praneeth Ivan Joel Fnu

Dissertations

A wide variety of biologically important molecules, such as enzymes, antibodies, hormones, transporters and receptors are proteins by composition and they play key roles in biological functions such as cellular regulation, communication, metabolism and physiological function. Protein dysfunctions and abnormalities are associated with numerous diseases, making proteins critical targets for understanding disease mechanisms and developing therapeutic interventions. Protein-based therapeutics such as monoclonal antibodies, hormones and vaccines gained popularity due to their high specificity, efficacy, and ability to treat complex diseases that are often difficult to address with small-molecule drugs.

Given the important role of proteins in a variety of biological …

The Inverse Elasto-Acoustic Problem, Patrick Grice

Dissertations

A stable and numerically efficient boundary integral method formulation of the elasto-acoustic problem is presented, based on Fourier analysis. The method generalizes well to multiple scattering. The Frechet derivative of the elasto-acoustic problem with respect to shape perturbations is derived, and geometric flow theory is used to design stable numerical methods for the simulation of moving boundaries. The shape derivative is used to define a regularized Gauss-Newton algorithm for shape fitting of elasto-acoustic scatterers.