Digital Commons Network^™

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 …

Clairaut Conformal Submersions From Ricci Solitons, Murat Polat

Turkish Journal of Mathematics

In this study, we investigate Clairaut conformal submersions in the context of total manifolds that support a Ricci soliton structure. We begin by deriving the scalar curvature and Ricci tensor expressions associated with these manifolds, and we establish criteria under which the fibres can be characterized as almost Ricci solitons or Einstein manifolds. Additionally, we identify the conditions required for the base manifold to admit Ricci soliton and Einstein structures. We also determine the necessary conditions for a vector field ζ to be conformal or Killing. Moreover, we demonstrate that when the potential vector field of the Ricci soliton is …

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 …

​Compact And Am-Compact Orthogonally Additive Operators, Bahri̇ Turan, Bi̇rol Altin

Turkish Journal of Mathematics

We show that compactness of orthogonally additive operators between Banach lattices is not preserved under taking the modulus by providing a nonlinear analogue of Krengel’s example. In contrast, for operators acting from a Banach lattice into an AM-space, AM-compactness is stable under lattice operations. Moreover, the space of absolutely norm bounded AM-compactorthogonally additive operators from a Banach lattice into an AM-space is a Banach lattice, and on such AM-spaces with the weak Fatou property, norm boundedness coincides with absolute norm boundedness.

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 …

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 …

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 Withdrawal To Impact: Smu Libraries’ Book Rehoming Initiatives, Kai Leong Heng, Eng Ling Lynn Yeo

Research Collection Library

Academic libraries are no strangers to large-scale deselection exercises. Driven by space constraints, evolving curricula, and the shift towards digital resources, the question is no longer whether to withdraw print materials, but what comes next. In the past year, SMU Libraries explored a different answer: instead of recycling withdrawn books, could we reimagine their next chapter, and rehome them to create meaningful impact for the community?

Quantitative Analysis Of Blue 1 Dye In Commercial Sports Drinks By Uv -Vis Spectroscopy And Hplc-Dad, Mikayla Bush

Forensic Science Master's Projects

Food dyes are natural or synthetic substances added to foods, beverages, and cosmetics to enhance color and visual appeal. Although they provide no nutritional value, they are widely used to attract consumers, particularly children. Concerns have been raised about the potential health effects of synthetic dyes, including links to hyperactivity in children, tumor formation at high doses, and possible impacts on gut and brain function. In response, regulatory efforts are ongoing. In April 2025, the U.S. Department of Health and Human Services (HHS) and the Food and Drug Administration (FDA) announced an initiative to phase out petroleum-based food dyes, including …

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. …

2026 Report On The State Of Great Salt Lake Social Science, Creed Jones, Avyrlie Smith, Shae Barber, Stacia Ryder, Mufti Nadimul Quamar Ahmed, Georgie Corkery, Jennifer Givens, Anna Mcentire, Carla Trentelman, Jessica Ulrich-Schad, Kirsten Vinyeta, Bryn Watkins

Publications

Great Salt Lake (GSL) faces rapid decline that presents more than just a hydrological risk; it poses an existential social hazard. As water levels decline, ecosystems, economies, cultures, and societies face irreversible harm. Since the lake reached its lowest levels in 2022, this issue has specifically gained increased attention in Utah and beyond. Given both the degree to which human consumption contributes to the problem, and the potential risks that the drying of the lake poses for community health, social science research is a crucial for informing successful water management policies and mitigating impacts. The purpose of this report is …

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 …

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 …

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 …

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 …

Self-Organized Criticality In Biased Activated Random Walk, Joshua Meisel

Dissertations, Theses, and Capstone Projects

We demonstrate many of the conjectured universality and self-organized criticality (SOC) properties of the interacting particle system Activated Random Walk (ARW) in the setting of one-dimensional biased walks, as formulated for instance in Levine and Silvestri’s 2024 survey. Namely, there exists the same limiting critical state for two finite ARW models, as well as for the infinite model as the density approaches its critical value from below. Many of the desired properties hold, including heavy-tailed avalanches, but spatial correlations decay exponentially fast, a deviation from the SOC paradigm. Nonetheless, the obtained decay rate vanishes with the bias. Since the critical …

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 …

Building Wellbeing Through Nature And Adventure For Justice-Involved Youth, Lewis Kogan, Meagan Ricks, Janna Coulter, Miranda Margetts, Lori Butterfield, Ben Ukoh-Eke

Outcomes and Impact Quarterly

Justice-involved youth often experience elevated levels of stress, mental health challenges, and reduced access to positive developmental opportunities. A nature- and adventure-based youth development program designed to strengthen resilience, self-efficacy, nature-connectedness, and well-being among justice-involved youth was piloted in 2025 to address these challenges. Pilot findings indicated improvements in resilience, self-efficacy, hopefulness, and connectedness to nature.

Trustworthy And Explainable Malware Threat Intelligence Through Social Media Analytics And Nature-Inspired Optimization, Feras Al-Obeidat, Muhammad Saad Rashad, Muhammad Amin, Waqas Ali, Bilal Khan, Sajid Anwar

All Works

The convergence of media analytics, Cyber threat Intelligence (CTI) and trustworthy artificial intelligence has become essential for modern cybersecurity systems operating over large-scale, heterogenous data sources. In particular, Social Media Intelligence (SOCMINT) and Open Source Intelligence (OSINT) provide high-volume, real-time signals that complement structured CTI frameworks for early-stage malware and adversarial threat detection. However, integrating these unstructured and dynamic sources with Structured Threat Information Expression (STIX) remains challenging due to its hierarchical complexity, semantic redundancy, and computational overhead in resource-constrained environments. This paper proposes an explainable and optimized intelligence pipeline (BERT-STIX) that unifies SOCMINT, OSINT, and STIX-based CTI using deep …

Braids On The Stranded Cellular Automata Model, Alexa Renner

Mathematical Sciences Technical Reports (MSTR)

The Stranded Cellular Automata (SCA) model is a grid of cells such that each cell can contain 0, 1, or 2 strands, together with two cellular automata that control when and how strands turn and cross. It was developed to study patterns occurring in fiber arts. We define a notion of what it means for a braid, in the sense of an element of a braid group, to be represented by an SCA pattern, and provide several algorithms to determine when a braid has an SCA representation with certain additional properties.

Bathurst Burr And Its Control, Department Of Primary Industries And Regional Development, Western Australia

Biosecurity factsheets

Bathurst burr (Xanthium spinosum) is a declared pest in Western Australia (WA). This factsheet describes the nature of the plant, requirements land owners/occupiers must adhere to, control methods, and how to search, detect, and report it.

Bathurst burr is a summer growing annual shrubby plant originating in South America. In south west Australia, some mature plants survive well into winter, with the result that burrs may be found for most of the year. The weed spreads entirely by burrs, which are usually carried by people or animals, attached to clothes, fur, or other fibre by their hooked spines. They may …

Noogoora Burr And Its Control, Department Of Primary Industries And Regional Development, Western Australia

Biosecurity factsheets

Noogoora burr, also known as cocklebur (Xanthium strumarium), is a declared pest in Western Australia (WA). This factsheet describes the nature of the plant with links to requirements that land owners/occupiers must adhere to and control methods.

Native to western United States of America. Locally abundant in parts of the Kimberley, especially near water courses. Occasionally found in the South West region of WA.

Shadowless In A World Of Shadows: Diminishing The Refraction Of Light Via Nanoconfinement, Vuk Uskoković

Administration and Staff Articles and Research

This paper presents a physicochemical study that merges science and art in the context of exploring the refractive index of water under nanoconfinement. Using a simple and economical experimental approach, it is shown that confinement within the micropores of a zeolitic structure, characterized using electron microscopy and X-ray diffraction, paradoxically reduces the refractive index of water, bringing confined water droplets closer to a shadowless state. Counterintuitively, water under confinement, as indicated by vibrational spectroscopic analyses performed in the transmittance mode in the IR range and reflectance mode in the near-IR range, exhibits greater structural freedom than in its bulk or …

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

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 …

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 …

Methodological Optimization Of Microplastic Visualization For The Comparison Of Eastern Oysters Cultivated In Traditional Plastic Or Novel Non-Plastic Aquaculture Farm Gear, Mikayla Straube

All Theses And Dissertations

Microplastics (< 5 mm) are a pervasive physical pollutant. This study was predominately focused on the development and utilization of fluorescence microscopy to locate and identify microplastics and other particles in the presence of a background autofluorescing biogenic precipitate. Oyster digest samples are visualized with fluorescent microscopy using Nile Red (NR). Photobleaching, or concentrated light exposure, of the filters was tested to determine if it can quench the autofluorescence signal. It was found that photobleaching significantly decreased autofluorescence intensity of the biogenic precipitate in emission wavelengths 518, 580, and 670 nm even with the application of NR, while minimally impacting the fluorescence behavior when bound to plastic particles. In application, fluorescence microscopy still yields some gaps in microplastic identification  when compared to Raman spectral results. This methodology was broadly developed to be able to quantify ambient microplastic quantities in farmed oyster samples. The specific level of aquaculture’s contribution to plastic pollution in global waterways is difficult to estimate. The methodology was applied to a local case study that compared microplastic loading in cultured eastern oysters raised in traditional plastic farm gear and novel plastic-free farm gear. Marginal trends in the gear type treatment and microparticles identified across the harvest season were identified, however effect sizes were too weak, and evidence was too inconsistent to currently suggest that gear type and harvest date affect microplastics and other particle quantities g^-1 oyster tissue (d.w.).

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 …