Physical Sciences and Mathematics Commons^™

Tempo: Training-Time Equilibration Of Modalities For Per-Sample Optimization In Multimodal Sentiment, Yi Zhao, Erik Cambria, Xiaosong E, Xianxun Zhu

Research Collection School Of Computing and Information Systems

Multimodal sentiment models often become over-reliant on the “easiest” modality (typically text), leading to three coupled sub-problems: (i) representation-level dominance, where weaker modalities contribute little to the fused representation; (ii) optimization-level dominance, where the strongest modality drives most gradient updates and suppresses learning in others; and (iii) robustness degradation, where audio or vision fail under noise or missing inputs at test time. We present TEMPO, a plug-and-play training framework that mitigates these issues by rebalancing learning pressure across modalities while leaving inference unchanged. For each mini-batch, TEMPO estimates relative modality strength and applies two synchronized, training-only controls: selective forward attenuation …

The Forensic Implications Of Hydrochloric Acid (Hcl) Exposure On Dna Preservation In Human Teeth, Shaelyn Lee Zimmerman

Graduate Student Theses, Dissertations, & Professional Papers

Identity is a human right, and forensic anthropological methods are often aimed at returning identity to decedents. Three main pathways are used for the identification of human remains: fingerprints, dental records, and DNA analysis. Identification efforts may be hindered when perpetrators attempt to obscure the victim’s identity. In cases where corrosive substances, such as hydrochloric acid (HCl) are used, dental comparison and fingerprinting often fail and genetic analysis becomes the best chance of achieving personal identification.

Previous studies have shown teeth are an excellent source of DNA. Large, multi-rooted teeth, such as molars, are often preferred because they contain the …

Hybrid Model For Phishing Website Detection Using Transfer Learning, Atul Dubal, Mansi Subhedar, Santosh Dhamala, Manasi Patil

ASEAN Journal on Science and Technology for Development

The rapid digitization of human activities has intensified reliance on internet-based platforms, creating fertile ground for cybercriminal exploits such as phishing. Despite advancements in detection mechanisms, phishing attacks continue to evolve, leveraging sophisticated visual mimicry to deceive users. This paper proposes a robust vision-based phishing detection system using ensemble deep learning to analyse webpage screenshots. The framework integrates transfer learning with pre-trained VGG16 and DenseNet121 models, extracting complementary low-level texture features (edges, gradients) and high-level hierarchical patterns (logos, layouts). These features are fused through a custom classifier with dropout regularization to mitigate overfitting. A balanced dataset of 3,000 webpage screenshots …

Design Of An Economic Order Quantity–Based Dashboard For Vaccine Packaging Inventory Management, Hadi Susanto, Susmitha Canny, Alexandra Elizabeth Callistha, Shakira Dwi Purwandari, Naila Davina Aurelia

ASEAN Journal on Science and Technology for Development

Background: Timely availability of vaccine packaging is critical to pharmaceutical supply chain reliability, as packaging materials play a key role in maintaining cold chain integrity during distribution. Inadequate availability of packaging materials can disrupt vaccine delivery even when finished vaccines are ready for shipment. The case company faces frequent delivery delays, insufficient safety stock, and irregular replenishment of vaccine packaging materials, which have jeopardized the organization’s target of achieving a 90% on-time delivery rate.

Objective: To design and evaluate an Economic Order Quantity (EOQ)–based inventory dashboard for vaccine packaging that standardizes ordering decisions and strengthens stock visibility in pharmaceutical distribution …

Labor Savings From Mechanical Rice Transplanting In Bangladesh, India, Nepal, And The Philippines: A Systematic Review And Meta-Analysis, Rodrigo S. David

ASEAN Journal on Science and Technology for Development

Rice production in South and Southeast Asia faces serious challenges, including acute labor shortages and rising wages, which threaten regional food security. Mechanical rice transplanting offers a promising solution; however, evidence on labor savings, especially region-specific data, remains inconsistent. This systematic review and meta-analysis aimed to quantify labor savings from mechanical versus manual transplanting across Bangladesh, India, Nepal, and the Philippines.

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search was conducted across four major scientific databases (2000–2026) using Boolean search terms for mechanical transplanting and labor outcomes. Of 284 initial records, 52 met eligibility …

Climate And Seasonal Influences On Indo-Pacific Sea Level: Insights From 30 Years Of Satellite And Tide Gauge Data, Karlina Triana

ASEAN Journal on Science and Technology for Development

We analyze Indo-Pacific sea-level variability using monthly satellite altimetry (1993–2025) validated against records from 16 tide-gauge stations. Cross-comparisons show strong agreement, especially in the western Pacific, confirming the reliability of altimetry for regional assessments. Seasonal SLA variability is largest in the Bay of Bengal and South China Sea and reflects monsoonal forcing, whereas interannual fluctuations in the eastern Indian and western Pacific oceans are dominated by ENSO and modulated by PDO. Harmonic decomposition isolates annual and semi-annual cycles, and an EOF/PCA framework identifies the leading modes: EOF1 (30.8%) captures basin-scale interannual variability and EOF2 (20.9%) reflects the seasonal cycle. Spectral …

Large Language Models For Neurology: A Mini Review, Donald C. Wunsch, Daniel B. Hier

Electrical and Computer Engineering Faculty Research & Creative Works

Large language models have the potential to transform neurology by augmenting diagnostic reasoning, streamlining documentation, and improving workflow efficiency. This Mini Review surveys emerging applications of large language models in Alzheimer's disease, Parkinson's disease, multiple sclerosis, and epilepsy, with emphasis on ambient documentation, multimodal data integration, and clinical decision support. Key barriers to adoption include bias, privacy, reliability, and regulatory alignment. Looking ahead, neurology-focused language models may develop greater fluency in biomedical ontologies and FHIR standards, improving data interoperability and supporting more seamless collaboration between clinicians and AI systems. Two future developments have the potential to be particularly impactful: (1) …

Safe Optimal Control Framework For Cooperative Manipulation Of Objects In Human–Robot Teams, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article introduces a distributed deep neural network (NN)-based adaptive control framework for cooperative object manipulation in human–robot teams with unknown agent dynamics by using three distinct multilayer NN observers (MNNOs). The first observer, termed the reference point estimator, enables each robotic agent to estimate the object's reference center using consensus-based learning, even without direct access to global reference trajectories. The second observer, referred to as the human force-to-trajectory estimator, uses human-applied forces to infer the intended position, velocity, and acceleration of the object, enabling real-time estimation of human intent. Together, these two observers allow distributed estimation of human-intended motion. …

Online Lifelong Optimal Adaptive Control Of Partially Uncertain Strict Feedback Discrete-Time Systems With Application To Quadrotor Uavs, Maxwell Geiger, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article considers the infinite time horizon optimal adaptive tracking control of partially uncertain strict feedback discrete-time (DT) systems with application to quadrotor uncrewed aerial vehicles (UAVs). First, the strict feedback DT system is transformed into an equivalent affine nonlinear DT system in terms of the tracking error dynamics. The optimal adaptive tracking control problem is solved using an augmented system approach, where a horizon of future bounded reference trajectory points is used in the augmented state, when compared to using a single point. It is assumed that the internal dynamics of the strict feedback system are unknown, but the …

New Metrics For Disambiguating Feature Overlap And Catastrophic Forgetting In Incremental Learning Contexts, Niklas M. Melton, Leonardo Enzo Brito Da Silva, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Catastrophic forgetting remains a central challenge in lifelong learning, where newly acquired knowledge interferes with previously learned tasks, degrading performance over time. Mitigation strategies such as rehearsal and regularization have been proposed, but both introduce limitations, either by retaining old data or by constraining model updates in ways that may impair learning. Complicating matters, recent findings show that feature-space overlap between tasks can produce similar performance drops even in models that memorize data, making it difficult to distinguish true forgetting from representational interference. Current accuracy-based metrics fail to disentangle these effects, undermining diagnostic clarity. In this work, we introduce the …

Fixed-Time Consensus Tracking For Nonlinear Multi-Agent Systems Under Aperiodically Intermittent Control, Lei Xue, Tong Wu, Wenwen Jia, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article explores the problem of fixed-time consensus tracking (FT-CT) for nonlinear multi-agent systems utilizing the a periodically intermittent control (AIC) strategy. In contrast to existing control algorithms, the proposed algorithm utilizes the AIC strategy instead of the conventional continuous-time control strategy, effectively reducing the consumption of communication resources. Moreover, the problem of intermittent FT-CT is well handled by proposing the average control rate of the AIC strategy. Two theorems based on the cases of directed and undirected graphs are proposed, respectively. Finally, the validity of these results is confirmed through numerical simulations on a general nonlinear system and a …

Rf-Attennet: A Hybrid Attention-Enhanced Network For Mixed Signal Classification In Uav Swarm Detection, Prajoy Podder, Mohammad Atikur Rahman, Maciej Zawodniok, Sanjay Madria

Electrical and Computer Engineering Faculty Research & Creative Works

The continuous increase of UAVs, particularly in swarms, creates significant challenges for security and airspace regulation. Traditional RF fingerprinting methods struggle to detect and classify UAV swarms due to overlapping signals and interference. This study introduces RF-AttenNet, a hybrid deep learning model designed to classify mixed UAV signals by analyzing composite RF spectrograms. RF-AttenNet uses dual attention mechanisms, channel and spatial attention to focus on critical spectral features, enabling the model to effectively separate and identify overlapping UAV signals. We have developed custom composite UAV datasets that simulate real-world swarm interference, incorporating both single and mixed UAV classes. RF-AttenNet achieves …

Nash Equilibrium Strategies For Multicluster Pursuit–Evasion Game With Disturbances: A Prescribed-Time Convergence Approach, Lei Xue, Xian Yu, Yongbao Wu, Jian Liu, Changyin Sun, D. C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article investigates the problem of prescribed-time Nash equilibrium (NE) seeking for a multicluster pursuit–evasion game (PEG) subject to external disturbances. To mitigate the impact of disturbances and reach the NE within a user-defined prescribed time, a prescribed-time disturbance observer (PTDO) is devised to estimate and compensate for them. Based on this observation, a novel control algorithm is developed, which facilitates collaboration among multiple pursuers to capture multiple evaders within the prescribed time. It is theoretically demonstrated that the designed algorithm ensures prescribed-time convergence to the NE of the multicluster PEG with disturbances. Finally, numerical simulations are conducted to verify …

Event-Based Predefined-Time Synchronization For Complex Networks With Deception Attacks: An Asynchronously Intermittent Strategy, Lei Xue, Jiong Yu, Haoyu Zhou, Yongbao Wu, Jian Liu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article studies the practical predefined-time synchronization (PPTS) for complex networks (CNs) under deception attacks based on the asynchronously intermittent event-triggered control (AIE-TC). Notably, AIE-TC effectively integrates the advantages of asynchronously intermittent control (AIC) and event-triggered control, where AIC provides each subsystem node with independent control and rest intervals. Besides, all synchronization errors of the CNs converge to an adjustable neighborhood within the predefined time by designing a bounded time-varying function into the controller. Moreover, this article considers that the transmission network is subjected to stochastic deception attacks modeled by a Markov process, which captures the state-driven dynamic transition characteristics …

Infimum Dimension Nash Embeddings For 2d Projective Shape Analysis, Robert L. Paige, Vic Patrangenaru

Mathematics and Statistics Faculty Research & Creative Works

Vector embeddings make complicated data extracted from networks, words and images, more amendable to data science applications. At the present time, the Veronese-Whitney (VW) matrix embedding of the real projective space is the state of the art for making inference about digital images from an uncalibrated camera, such as a cell phone or security camera. In this work we consider vector embeddings for the projective shape data and in particular determine the minimum dimension isometric (distance-preserving or Nash) vector embedding for a projective space. We determine such an embedding for the projective plane in closed-form. From this embedding we determine …

A Fully Discrete Semi-Implicit Numerical Scheme And Its Optimal Error Estimates For Cahn-Hilliard-Mhd Model With Variable Density, Dongmei Duan, Fuzheng Gao, Xiaoming He, Yanping Lin

Mathematics and Statistics Faculty Research & Creative Works

This paper proposes and analyzes a fully discrete semi-implicit unconditionally energy stable numerical scheme to solve the Cahn-Hilliard Magnetohydrodynamics (Cahn-Hilliard-MHD) model with variable density. The unconditional energy stability and optimal L² error estimates are established for the fully discrete scheme. Major challenges in error estimation arise from the variable density, the strong nonlinearities, and the multi-physics coupling of the model. Under the mathematical induction framework, the Ritz quasi-projection and the Stokes quasi-projection, proposed in [SIAM J. Numer. Anal., 61(3):1218-1245, 2023], are utilized to avoid the gradient terms of the projection errors. The H⁻¹ superconvergence error estimates of Ritz …

A 1d Symmetric Interior Penalty Discontinuous Galerkin Solver In Rust, William Aey

Williams Honors College, Honors Research Projects

This honors project will build a 1D Symmetric Interior Discontinuous Galerkin (SIPDG) solver in Rust for Stum-Liouville type problems such as the Poisson equation, with Robin, Dirichlet, and Neumann boundary conditions. The work will cover the full pipeline: starting from the strong form of the PDE, deriving the DG weak form, implementing element and interface operators, and assembling or apply the discrete operator. Rust's safety and concurrency (e.g, via Rayon) will be used to explore serial and parallel performance. A test-driven development approach will be used to maintain a strong suite of tests. The project will result in a documented …

Printer For Music Box, Chad Lewis, Caleb Murawski, Zion Smith, Bryan Tibbs

Williams Honors College, Honors Research Projects

The traditional method of creating music box sheet music involves manually punching holes into a paper strip using a hand-operated hole punch. This process involves precise knowledge of each note’s location and the ability to achieve perfect accuracy for hours.

The goal of this system is to automate this process, significantly reducing the time required while greatly improving the accuracy of the resulting music box playback. The user simply uploads a MIDI file of their choice into a user-friendly application. Here, the file is modified based on the user’s needs and sent to an automated hole-punching system to punch the …

An Investigation Into The Potential Role Of Thioester Bonds In Prebiotic Atp Synthesis, Adam J. Ruf

Williams Honors College, Honors Research Projects

In this project I will attempt to show a potential prebiotic pathway for the synthesis of ATP. This pathway involves the use of thioester bond containing molecules, as derivatives of these bonds have been shown to exhibit enzyme-like catalytic properties. Nuclear magnetic resonance spectroscopy will be utilized to characterize each step of the pathway to ensure it is successful.

Defending A Soho Network Against Mitm Attacks, Braeden J. Wise

Williams Honors College, Honors Research Projects

Cybersecurity is a vast domain that consists of many threats that target sensitive information found on wired and wireless networks. One of those threats is a man-in-the-middle (MITM) attack, which involves an attacker situating themselves between a sender and a receiver to intercept or redirect network traffic. These kinds of attacks can run rampant on a small office home office (SOHO) network due to the vulnerabilities and lack of enterprise level tools. The intent of this project is to perform and defend against MITM attacks for a SOHO network. In the context of the project, three MITM attacks will be …

Analyzing Network Traffic And Data Exfiltration Via Smb In Post-Vm Escape Scenarios, Noah M. Disanza

Williams Honors College, Honors Research Projects

Virtual machines (VMs) play a crucial role in modern IT infrastructure environments by providing isolation and enhanced security, among other things, for both personal and corporate systems. VMs are heavily rely upon to safely test malware, manage infrastructure, and reduce risk to host systems. This reliance is so substantial that the idea of reducing risk to the host system is believed to be erasing risk entirely. However, this mindset has shown to be challenged time and time again by the emergence of exploits known as virtual machine escapes. These exploits allow malicious actors to break out of the virtualized environment …

A Decade Of Programming Languages: Trends In Popularity And Influence, Jonathan C. Erb

Williams Honors College, Honors Research Projects

Programming languages play a central role in open-source software ecosystems, yet their adoption, visibility, and influence shift over time as technologies, developer communities, and industry practices evolve. The study aims to investigate long-term trends in programming-language usage on GitHub from 2014 through 2024, focusing on ten major languages that represent diverse domains and ecosystems. Using repository metadata, engagement metrics such as stars and forks, and language-level code statistics measured with cloc, the analysis will examine changes in repository creation, code contribution volume, and popularity. Since popularity remains an unsettled and multidimensional concept, part of this research involves determining how it …

Reconstructing Lost Voices, Lana Tamim

Williams Honors College, Honors Research Projects

This project uses digital text mining tools (OCR, NLP, sentiment analysis, and topic modeling) to analyze 19th–20th-century newspaper archives, focusing on how marginalized groups (women, immigrants, or labor workers) were historically portrayed. Many historical newspapers were dominated by elite voices, so this project aims to recover silenced or misrepresented perspectives by identifying hidden patterns in language, frequency of coverage, sentiment, and shifts in public perception over time. Using machine learning and visualization tools, the project will create interactive maps and timelines showing how representation evolved across regions.

Codezip: A University-Based Coding Practice And Collaboration Platform, Manoj Khatri

Williams Honors College, Honors Research Projects

This project proposes the development of CodeZip, a university-exclusive web platform designed to help University of Akron students practice coding problems, prepare for technical interviews, and engage in collaborative learning. CodeZip ensures a secure environment by restricting access to users with @uakron.edu email addresses via Clerk API authentication.

The goal of this honors project is to expand CodeZip into an intelligent, interactive system. Planned enhancements include AI-generated problem hints, automated grading with performance analytics, and a visual dashboard to track learning progress. These features will provide personalized guidance, encourage collaboration between students and alumni, and create a dynamic platform that …

Machine Learning For Recession Prediction, Ethan Reusser

Williams Honors College, Honors Research Projects

Macroeconomic predictions present challenges in machine learning due to the rarity of economic recessions, the constantly-changing matter of global markets, and severe class imbalance in historical data. This project focuses on predicting the onset of United States economic recessions within a 12-month window using Python and Jupyter Notebook. A machine learning pipeline was developed utilizing multiple models: Logistic Regression, Random Forest, XGBoost, and Long Short-Term Memory (LSTM) neural networks. For class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied strictly to training data, paired with Platt scaling for calibration on thresholds. The resulting models were evaluated in the 2005 …

Pipeline Corrosion And Relevant Microbial Influence, Renee E. Bamberger

Williams Honors College, Honors Research Projects

This study investigated the role of microbial activity and oxygen availability in the corrosion of carbon steel pipelines with a focus on microbially influenced corrosion (MIC). Sediment collected from Old Woman Creek, Ohio, was used to simulate natural soil conditions, and carbon steel coupons were incubated under four treatments: nonsterile oxic, nonsterile anoxic, autoclaved oxic, and autoclaved anoxic over 85 days. Corrosion was assessed through mass loss measurements and the analysis of pH, sulfate, chloride, and dissolved iron concentrations. The greatest mass loss was observed in autoclaved oxic conditions, contrary to the initial hypothesis that microbial activity would greatly enhance …

Secure The Database: A Red Team, Blue Team Analysis Of Sql Injection, Andrew N. Miller

Williams Honors College, Honors Research Projects

SQL injection (SQLi) attacks are a type of cyberattack that seeks to bypass website logins and gain entry to sensitive information. These pose a significant danger to organizations holding confidential user information. Personally Identifiable Information (PII) like physical addresses, emails, phone numbers, social security numbers are at risk of theft. Login credentials like usernames, passwords, and other sensitive information like financial details and social security numbers are also exposed through SQLi attacks. SQLi attacks harm the confidentiality, integrity, and availability of people’s identity. Additionally, data breaches that reach public battention harm the reputation and trust of organizations. SQLi attacks rank …

Large Telescope Mount For Long Exposure Photos, Payne M. Landis, Anna J. Gray

Williams Honors College, Honors Research Projects

When viewing the stars through a telescope, it is common that the object you are viewing drifts out of view. Many amateur astronomers seek to capture long exposure images of the stars, planets, and galaxies with commercially available sky tracking telescope mounts. The complication is that these telescope mounts are both incredibly expensive and have low weight limits. These issues limit accessibility to these products based on the telescope. For each person, telescope parameters determine the amount of light and magnification that can be viewed, which then determines exposures time for adequate photo results. The options on the market for …

Computer-Aided Molecular Design To Identify More Environmentally Friendly Pfas, Melvin M. Keita

Williams Honors College, Honors Research Projects

The goal of this project is to inversely design alternatives to per- and polyfluoroalkyl substances (PFAS) using Computer-Aided Molecular Design (CAMD). PFAS, also described as “forever chemicals”, have been used in industry and consumer products since the 1940s. PFAS can be found in drinking water, food, food packaging, waste sites, and other sources. Exposure to different PFAS can lead to increased risks of some cancers, immune effects, and reproductive effects. Pulling from existing data, this project will use quantitative structure-property relationships (QSPRs) to design PFAS alternatives that possess optimal properties to prevent adsorption into drinking water and other potential sources …

Corannulene: Optimized Synthesis And Four-Fold Reduction, Guy Rozenman

Electronic Theses & Dissertations (2024 - present)

Corannulene is a bowl-shaped π-conjugated fragment of the ball-shaped C₆₀-fullerene that undergoes stepwise multielectron reduction and can accept four electrons to form the aromatic tetraanion, C₂₀H₁₀⁴⁻. Prior literature has established this charge state in donor-solvated alkali-metal assemblies: early solution studies identified the formation of the tetraanion under lithium reduction in THF, and later X-ray diffraction crystallography demonstrated a triple-decker lithium sandwich in which two corannulene tetraanion decks encapsulate five Li⁺ ions. The central objective of this thesis is to determine whether the corannulene tetraanion can instead be isolated as a bulk solvent-free alkali-metal salt containing only corannulene and …