Physical Sciences and Mathematics Commons^™

Cmc Thesis Chatbot, Luis Gomez

CMC Senior Theses

This GitHub repo is a senior thesis for Claremont McKenna College; it is a thesis about theses. The project is an interactive RAG-based chatbot that helps students, researchers, and faculty explore Claremont McKenna College senior theses. The goal was to create a domain-specific chatbot to show that it is possible to combat the limitations of AI, including hallucinations, outdated data, and lack of domain expertise. The website link is:

CMCThesisChatbot

Artificial Intelligence And Digital Technologies In Finance: A Comprehensive Review, Soudeh Pazouki, Mohamad Jamshidi, Mirarmia Jalali, Arya Tafreshi

Finance Faculty Publications

This study explores the transformative impact of artificial intelligence (AI) and digital technologies on the financial technology (FinTech) industry, highlighting their role in fostering business growth, operational efficiency, and enhanced customer engagement. AI-driven strategies have unlocked new avenues for streamlining workflows, boosting productivity, and expanding financial inclusion by reaching underrepresented populations. However, these advancements also pose challenges, including navigating complex regulatory frameworks and adapting to the rapidly evolving technological landscape. This paper delves into the macroeconomic effects of AI, examining its influence on labor markets, consumer behavior, and organizational success. Furthermore, the paper discusses blockchain applications and their potential to …

Spatiotemporal Patterns Of Desertification Sensitivity And Influencing Factors Across The Western Inner Mongolia Plateau, China, Yang Chen, Long Ma, Xixi Wang, Tingxi Liu, Zixu Qiao

Civil & Environmental Engineering Faculty Publications

Desertification remains a critical global ecological and environmental challenge that threatens sustainable development. Although our understanding of desertification dynamics and their underlying drivers has improved, continued research is needed due to the region-specific nature of these processes. This study focuses on the Western Inner Mongolia Plateau in China as a case study to examine the evolution of desertification and its driving factors using a multifaceted approach, including the Mediterranean Desertification and Land Use (MEDALUS) model. Results show that the desertification sensitivity index (DSI) across the plateau ranged from 1.12 in prairie regions to 1.87 in desert areas, with a spatial …

Modification Of Nanoparticles For Designed Interfaces, Tony Lee Neely, Jr.

Theses and Dissertations

The work contained herein, is focused on the design, synthesis, and characterization of polymer nanocomposite interfaces and the property enhancement afforded from said interface design. Through the use of reversible addition fragmentation chain transfer (RAFT) polymerization for the grafting of polymer chains to silica nanoparticles, the surface of silica nanoparticles can be manipulated to tune the properties of the nanocomposite as a whole.

In the first part of this work, heterogeneity is introduced onto the surface of silica nanoparticles via a sequential RAFT polymerization to afford a bimodal brush system. A densely grafted, short brush population is polymerized from the …

Modified Equations Of Conformal Symplectic Exponential Time Differencing Methods, Taylore M. Keesler

Honors Undergraduate Theses

Planetary orbits, pendulums, and hurricanes are everyday examples of nonlinear systems, often studied using differential equations. However, their exact solutions can not always be computed, and thus, we use numerical methods to approximate their solutions. Certain methods are better suited to preserve special properties of the system like energy and geometry. Through previous numerical simulations, a conformal symplectic method proves more effective in this preservation. To understand why, we find the modified equation of a nonlinear system with damping, which is a differential equation for which the numerical solution is exact. Through backward error analysis, we obtain these modified equations, …

Prime Factorization And Unit Calculations Of Quadratic Integer Rings, Gabriel F. Roca

Honors Undergraduate Theses

The failure of unique factorization in a ring leads to the investigation of the closest algebraic structure, which are prime ideals. Using generalizations that have helped solve questions such as Fermat's Last Theorem, there is interest to study the elements with a multiplicative inverse (units) via the geometry and arithmetic patterns that arise in quadratic integer rings, since they provide tools for other questions in mathematics, ranging from pure algebra to applications in cryptography, and more. Overall, the following thesis provides a small exposition on the theory of integral domains and some specific calculations.

Cloud Condensation Nuclei Activity Of Fresh And Aged Phenolic Acid Aerosol Particles, Emily A. Nortmann

Honors Undergraduate Theses

In recent years, the incidence of wildfires has considerably increased, with fire seasons starting earlier and ending later than usual due to changes in snowpack, precipitation, and temperature, which are attributed in part to climate change. Wildfires are responsible for the emission of a variety of gases and organic aerosol particles in the atmosphere with profound impacts on air quality, visibility, and human health. The aerosol particles produced by wildfires can contribute to climate change by scattering or absorbing solar radiation, affecting the radiative balance of the planet through cooling or warming effects, or by acting as cloud condensation nuclei …

Exploring Electroweak Baryogenesis Within Real Scalar Models: Theoretical Foundations And Collider Phenomenology, Corine M. Smith

Honors Undergraduate Theses

The matter–antimatter asymmetry and the hierarchy problem related to the Higgs boson mass remain key open questions in high energy physics. Electroweak Baryogenesis offers a solution to the asymmetry by modifying the Higgs sector to allow a strongly first-order phase transition. This work investigates two real singlet scalar extensions of the Standard Model, incorporating novel quartic and triple couplings between the new scalar fields, providing a testable framework for vacuum-induced scalar mixing effects and enhanced multi-Higgs boson production. These interactions modify the scalar self-coupling and can induce resonant enhancements in multi-Higgs boson production processes. The theoretical constraints are derived from …

Ally With Ai: An Icebreaker To Unlock Career Aspirations For Online Or Hybrid Organizational Behavior Cohorts, Jyro B. Triviño

Leadership and Strategy Faculty Publications

Modern organizational behavior classroom, which are increasing in size, diversity, and complexity, are shifting to online and hybrid learning environments, challenging the use of traditional icebreaker activities. This paper introduces a 15-minute icebreaker designed to address these issues while integrating the principles of Kolb's experiential learning theory and fostering social capital through peerr engagement in an online setting. By leveraging the availability of generative AI, students prompt a template code to unlock their career aspirations and stimulate social connections among their classmates. This provides an innovative teaching model for meeting the foundational icebreaker goal while serving a suitable tool for …

Episodes In Computing History - Salon Talk, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

This talk (first given in 2004) presents a concise overview of key developments in the history of computing. It begins with early methods of counting and recordkeeping, such as tally sticks and the Inca quipu. It then traces the evolution of numeric systems, including Roman and Hindu-Arabic notation, and the mathematical contributions of figures like Al-Khwarizmi. Mechanical computing devices such as the abacus, Napier’s bones, and the Pascaline are examined, along with the Jacquard loom and its use of punch cards.

The talk continues through the rise of electronic computing, highlighting milestones such as ENIAC, the work of Alan Turing, …

Toward Embodied Navigation Through Vision And Language, Muraleekrishna Gopinathan

Theses: Doctorates and Masters

Embodied AI is a challenging but exciting field in which a robot learns to interact with human-living spaces to perform various tasks. This thesis studies the embodied navigation problem in which a robotic agent navigates in a previously unseen indoor environment based on a challenging task. In particular, the Vision-and-Language Navigation (VLN) task requires a robot to navigate based on a descriptive human-language instruction. This thesis aims to improve VLN agents on four key aspects - their understanding of the environment, training via additional data, correcting navigational errors, and predicting the layout of the environment for better planning.

First, we …

An Explainable Ai And Optimized Multi-Branch Convolutional Neural Network Model For Eye Anemia Diagnosis, Kamel K. Mohammed, Nadia Dahmani, Rania Ahmed, Ashraf Darwish, Aboul Ella Hassanien

All Works

This paper proposes a novel, non-invasive approach to diagnosing eye anemia using deep learning techniques. Traditional methods, reliant on invasive procedures like venipuncture, are costly and can cause patient discomfort. Our model leverages a multi-branch convolutional neural network (CNN) architecture, incorporating the Hippopotamus Optimization (HO) algorithm and multiclass support vector machines (SVMs) for enhanced accuracy. To address data imbalance, we employ the Synthetic Minority Oversampling Technique (SMOTE) and data augmentation. The model is trained and evaluated on a dataset of 211 eye images. The model achieves a remarkable 97.06% accuracy, with a Receiver Operating Characteristic (ROC) curve demonstrating an Area …

Digital Transformation Of Education: An Integrated Framework For Metaverse, Blockchain, And Ai-Driven Learning, Mousa Al-Kfairy, Omar Alfandi, Ravi S. Sharma, Saed Alrabaee

All Works

The integration of Metaverse, Blockchain, and Artificial Intelligence (AI) has the potential to revolutionize the educational landscape by providing immersive, secure, and personalized learning environments. This study proposes a conceptual framework that combines these technologies to address the key challenges faced by contemporary education systems, including accessibility, engagement, security, and personalization. The Metaverse serves as the immersive platform, offering virtual classrooms, interactive simulations, and gamified learning experiences. Blockchain provides the foundation for secure and transparent academic records, enabling tamper-proof credential verification and decentralized data management. AI enhances the educational experience by powering adaptive learning systems, predictive analytics, and intelligent tutoring …

Leveraging Sentiment Analysis Of Food Delivery Services Reviews Using Deep Learning And Word Embedding, Dheya Mustafa, Safaa M. Khabour, Mousa Al-Kfairy, Ahmed Shatnawi

All Works

Companies that deliver food (food delivery services, or FDS) try to use customer feedback to identify aspects where the customer experience could be improved. Consumer feedback on purchasing and receiving goods via online platforms is a crucial tool for learning about a company’s performance. Many English-language studies have been conducted on sentiment analysis (SA). Arabic is becoming one of the most extensively written languages on the World Wide Web, but because of its morphological and grammatical difficulty as well as the lack of openly accessible resources for Arabic SA, like as dictionaries and datasets, there has not been much research …

Performance Based Scheduling In Distributed Mixed Criticality Systems, Amjad Ali, Saud Wasly, Asad Masood Khattak, Ihsan Ali, Shahid Iqbal, Bashir Hayat

All Works

With a focus on computationally intensive, distributed, and parallel workloads, scheduling in mixed-criticality distributed systems presents significant challenges due to shared memory and resources, as well as the diverse demands of tasks. The system’s efficiency is heavily dependent on the overall scheduling duration (make span), while individual task deadlines impose strict timing constraints. When the tasks need to simultaneously access the shared memory, then these tasks interfere the execution of one another. For managing the scheduling of interfering tasks in distributed mixed-criticality systems, a novel Interference-Aware Partitioning Fixed Priority (IAP-FP) approach is proposed, which effectively handles task partitioning among cores …

Ai Innovations In Rppg Systems For Driver Monitoring: Comprehensive Systematic Review And Future Prospects, Soha G. Ahmed, Katrien Verbert, Nazar Zaki, Ashraf Khalil, Hamad Aljassmi, Fady Alnajjar

All Works

Advanced technologies, notably camera-based systems using remote photoplethysmography (rPPG), are increasingly used in automotive safety to non-invasively monitor driver well-being and fatigue by measuring physiological metrics like heart and respiration rates. This review examines recent advancements in machine learning algorithms and signal processing for rPPG in driver monitoring. A literature search up to April 2, 2024, across major databases, identified 344 studies; 29 were analyzed in depth, focusing on: 1) rPPG signal extraction and heart rate estimation, where deep learning improved accuracy; 2) fatigue detection, showing benefits of multimodal data fusion; 3) mental state monitoring, with machine learning classifying cognitive …

Llm-Driven Apt Detection For 6g Wireless Networks: A Systematic Review And Taxonomy, Muhammed Golec, Yaser Khamayseh, Suhib Bani Melhem, Abdulmalik Alwarafy

All Works

Sixth Generation (6G) wireless networks, which are expected to be deployed in the 2030s, have already created great excitement in academia and the private sector with their extremely high communication speed and low latency rates. However, despite the ultra-low latency, high throughput, and AI-assisted orchestration capabilities they promise, they are vulnerable to stealthy and long-term Advanced Persistent Threats (APTs). Large Language Models (LLMs) stand out as an ideal candidate to fill this gap with their high success in semantic reasoning and threat intelligence. This paper presents the first systematic review and taxonomy for LLM-assisted APT detection in 6G networks. It …

Deep Learning Approaches For Eeg-Based Biometrics: A Systematic Review, Ali E. Albaiati, Muhammad Firdaus Akbar, Murtadha D. Hssayeni, Ashraf Khalil, Mohd Nadhir Ab Wahab, Sundus Sulaiman Weli, Enas A. Raheema

All Works

Biometics such as fingerprint, face, and iris are vulnerable to spoof attacks. The unique characteristics of Electroencephalography (EEG) make it a promising biometric modality especially because of its resistance to spoofing attacks. Many deep learning methods have been proposed for EEG-based biometric systems. This systematic review examines these methods in terms of their feature extraction ability and authentication performance. We follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search IEEE Xplore, PubMed, Web of Science, ScienceDirect, and Springer databases. Initially, we identified 285 relevant articles published between 2018 and 2024. After removing duplicates and applying …

Automating International Human Rights Adjudication, Veronika Fikfak, Laurence R. Helfer

Faculty Scholarship

International human rights courts and treaty bodies are increasingly turning to automated decision-making (“ADM”) technologies to expedite and enhance their review of individual complaints. These tribunals have yet to consider many of the legal, normative, and practical issues raised by the use of different types of automation technologies for these purposes. This article offers a comprehensive and balanced assessment of the benefits and challenges of introducing ADM into international human rights adjudication. We argue in favor of using ADM to digitize documents and for internal case management purposes and to make straightforward recommendations regarding registration, inadmissibility, and the calculation of …

Quantifying Riverine Plastic Pollution Using Participatory Science And Trash Traps, Nancy Lauer, Madison Haley, Emily Sutton, Rob Clark, Hannah Woodburn, Emma Crider, Kaity D’Angelo, Cecilia Kammire, Brandon Jones, David Caldwell, Taylor Register, Emily Fritz, Samantha Krop, Riley Lewis, Lisa Rider, Edgar Miller, Stephanie Stephens, Louisa Pitney, Emma Kaufman, Emma Stewart, Jason A. Somarelli, Michelle B. Nowlin

Faculty Scholarship

Local governments and environmental nonprofits are increasingly using trash traps to intercept and remove escaped plastics and other litter from stormwater systems and surface waters. In this paper, we demonstrate the utility of these devices for collecting data that provide insights into riverine litter sources and solutions. Between 2021 and 2024, seven Waterkeeper organizations in North Carolina maintained 21 in‐stream trash traps in watersheds across the state and trained staff and volunteers to record the types and quantities of litter during cleanouts. Over this period, Waterkeeper organizations and their volunteers documented 150,750 pieces of litter captured by traps. Captured litter …

The Future Of Ai Regulation In Drug Development: A Comparative Analysis, Gabriela Lenarczyk, Timo Minssen, Nicholson Price, Arti Rai

Faculty Scholarship

As artificial intelligence (AI) transforms drug development, regulatory frameworks are evolving to oversee its implementation, particularly at the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This paper makes three contributions to understanding emerging regulatory approaches. First, we offer a comparative analysis of how these agencies have responded to AI-driven advances, incorporating new US executive orders and the European Union (EU)’s AI Act. Second, we propose a novel analytical framework to understand regulatory divergence: the FDA’s flexible, dialog-driven model contrasts with the EMA’s structured, risk-tiered approach, reflecting broader institutional and political-economic differences. While the former encourages …

Artificial Intelligence And Procedural Due Process, Brandon L. Garrett

Faculty Scholarship

Artificial intelligence (AI) violates procedural due process rights if the government uses it to deprive people of life, liberty, and property without adequate notice or an opportunity to be heard. A wide range of government agencies deploy AI systems, including in courts, law enforcement, public benefits administration, and national security. If the government refuses to disclose the reasons why it denied a person bail, public benefits, or immigration status, serious due process concerns arise. If the government delegates such tasks to an AI system, the due process analysis does not change. One asks whether a person received adequate notice and …

Position: Benchmarking Is Broken - Don't Let Ai Be Its Own Judge, Zerui Cheng, Stella Wohnig, Ruchika Gupta, Samiul Alam, Tassallah Abdullahi, João Alves Ribeiro, Christian Nielsen-Garcia, Saif Mir, Siran Li, Jason Orender, Seyed Ali Bahrainian, Daniel Kirste, Aaron Gokaslan, Carsten Eickhoff, Pramod Viswanath, Ruben Wolff

Computer Science Faculty Publications

The meteoric rise of Artificial Intelligence (AI), with its rapidly expanding market capitalization, presents both transformative opportunities and critical challenges. Chief among these is the urgent need for a new, unified paradigm for trustworthy evaluation, as current benchmarks increasingly reveal critical vulnerabilities. Issues like data contamination and selective reporting by model developers fuel hype, while inadequate data quality control can lead to biased evaluations that, even if unintentionally, may favor specific approaches. As a flood of participants enters the AI space, this "Wild West" of assessment makes distinguishing genuine progress from exaggerated claims exceptionally difficult. Such ambiguity blurs scientific signals …

High Temporal Resolution Gene Expression Analysis Of Acute Heat Stress Response In Acropora Cervicornis And Porites Astreoides, Katherine Parker, Nicolas Evensen, Iliana Baums, Daniel Barshis

Biological Sciences Faculty Publications

Widespread coral bleaching and mortality events are becoming increasingly frequent due to rising ocean temperatures, though certain coral species and populations have displayed higher tolerance to elevated emperatures than others. As such, it is of high research priority to identify thermally tolerant coral populations for research and conservation and assess the biological mechanisms allowing these populations to survive exposure to higher temperatures. This research utilized the Coral Bleaching Automated Stress System (CBASS), a portable experimental aquaria system with precise temperature manipulation, to assess the thermal thresholds and heat stress response of two coral species from Summerland Key, FL, USA. Seven …

A New G Family: Properties, Characterizations, Different Estimation Methods And Port-Var Analysis For U.K. Insurance Claims And U.S. House Prices Data Sets, Ahmad M. Aboalkhair, Gholamhossein Hamedani, Nazar Ali Ahmed, Mohamed Ibrahim, Mohammad A. Zayed, Haitham M. Yousof

Mathematical and Statistical Science Faculty Research and Publications

This paper introduces a new class of probability distributions, termed the generated log exponentiated polynomial (GLEP) family, designed to enhance flexibility in modeling complex real financial data. The proposed family is constructed through a novel cumulative distribution function that combines logarithmic and exponentiated polynomial structures, allowing for rich distributional shapes and tail behaviors. We present comprehensive mathematical properties, including useful series expansions for the density, cumulative, and quantile functions, which facilitate the derivation of moments, generating functions, and order statistics. Characterization results based on the reverse hazard function and conditional expectations are established. The model parameters are estimated using various …

Interplay Between Computational Techniques And Quantum Theory: Advancing Quantum Chaos And Quantum Error Correction, Refaat Ismail

Theses and Dissertations--Physics and Astronomy

This dissertation investigates computational techniques addressing critical problems arising within quantum chaos and quantum error correction (QEC). First, we investigate quantum dynamics via the Lanczos algorithm, establishing how its computational features — branching patterns, convergence, and complexity saturation — relate to physical properties like spectral statistics and operator matrix elements. We derive a formula connecting Lanczos coefficients to spectral densities for two-branched Lanczos sequences, revealing how branching patterns encode level repulsion—a hallmark of quantum chaos. Furthermore, we develop an analytic framework predicting Krylov complexity saturation from static system properties alone. Specifically, we decompose the saturation value into a spectral density-dependent …

Laser–Assisted Free–Free (Laff) Electron Scattering: An Elastic Angular Distribution In Argon And The Use Of A Novel Pulsed-Laser Multipass System In Laff Experiments, Charles M. Weaver

Theses and Dissertations--Physics and Astronomy

Laser–assisted free–free experiments investigate the emission or absorption of photons when an electron scatters from an atom in the presence of a laser field.

We measured the relative differential cross sections for 350 eV electrons scattered by an argon target in the presence of 1.17 eV photons from a Nd:YAG laser. The angular distribution for elastically scattered 350 eV electrons was measured over scattering angles from 4◦ to 80◦. This distribution exhibits a pronounced maximum near 47◦ and is symmetric about this angle, as predicted by a Kroll–Watson approximation (KWA) calculation for the experimental kinematics. These measurements directly test a …

Quantum Hall Phases In Monolayer Graphene, Jincheng An

Theses and Dissertations--Physics and Astronomy

In the presence of a perpendicular magnetic field, monolayer graphene at and near charge neutrality forms a quantum Hall ferromagnet—a correlated electronic state where the interplay of interactions, spin, and valley degrees of freedom leads to spontaneous symmetry breaking. While the dominant Coulomb interaction has $SU(4)$ symmetry, the ground state is ultimately determined by subdominant terms: residual lattice-scale anisotropic interactions, Zeeman, and sublattice couplings. Relaxing the ultra-short-range limit of anisotropic interactions unveils diverse symmetry-breaking phases in both integer and fractional quantum Hall regimes. Haldane pseudopotentials, which quantify interactions between particle pairs with fixed relative angular momentum, provide a readily parameterizable …

Computational Bridges: Enhancing Natural Language Processing Of Swahili., Joyce Murungi

Harrisburg University Other Works

Swahili remains significantly underrepresented in natural language processing (NLP) despite being one of the most widely spoken languages in Africa. Computational Bridges: Enhancing Natural Language Processing of Swahili addresses this gap through computational linguistics, corpus creation, and large-scale analysis of Swahili syntax and lexical structure. Central to this study is GUMZO, a novel corpus developed from spontaneous conversational data collected from YouTube videos, television panel discussions, political speeches, religious discourse, and unscripted broadcasts. Unlike many existing datasets that rely on formal or translated text, GUMZO captures authentic language use and provides a stronger foundation for NLP research involving low-resource languages. …

Parallel Algorithms For Large Scale Dynamic Graph Analysis, Arindam Khanda

Doctoral Dissertations

A complex system of interacting entities in contemporary scenarios, be it biological, technological, or social, can be represented using graphs. Dynamic graphs, unlike their static counterparts, are ones in which the underlying topology changes over time. These networks act as a model for numerous systems, from transportation to social interactions, capturing the ever-evolving nature of real-world phenomena. However, the inherent temporality of these networks presents a unique set of challenges and the traditional static graph algorithms often fall short in efficiency and applicability. In our research, we delve into the complexities presented by large dynamic networks and suggest various methodologies …