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May 2026 Table Of Contents Newsletter - Accessibility, Victoria Peters

Table of Contents Newsletter

This month, the Library joins institutions worldwide in celebrating Global Accessibility Awareness Day (GAAD). The goal of this day is to get everyone talking, thinking, and learning about digital access and inclusion for the more than one billion people globally who live with disabilities or impairments. Whether you are designing a new syllabus, uploading course readings, or creating lecture slides, digital accessibility ensures that every student can engage with your content.

Perceptual And Geometric Advances In Crowd Simulation, Bilas Talukdar

Dissertations

Simulating realistic crowd motion remains a fundamental challenge in computer graphics and multi-agent systems, as it requires modeling both physically plausible interactions and perceptually natural behaviors. Existing crowd simulation methods typically employ simplified geometric abstractions, most commonly circular agent representations, and model navigation using either analytical interaction formulations (e.g., force, velocity, or constraint-based methods) or learned policies derived through reinforcement learning. Despite their effectiveness, these approaches often overlook detailed geometric structure and do not explicitly account for perceptual realism. This dissertation addresses these challenges by improving the realism of virtual crowd simulation through two key advancements: perceptual preference learning and …

Principles Of Privacy And Security In Artificial Intelligence And Applications, Khang Tran

Dissertations

Modern artificial intelligence (AI) systems have transformed critical domains such as healthcare, software engineering, finance, and the legal system. Despite their broad impact, concerns about trustworthiness, especially regarding privacy and security, remain major obstacles to wider adoption. Addressing these concerns requires both a systematic understanding of the privacy and security risks inherent in AI systems and the development of principled foundations for trustworthy AI that safeguard client privacy and security. This goal is particularly challenging because of the complexity of modern large-scale AI systems, the trade-offs between privacy and model utility, and the need to simultaneously ensure other important properties …

Mechanics And Physical Attributes Of Nature-Based Alterations: Rock Reinforcement And Urban Heat Island Assessment, Mary Chikondi Ngoma

Dissertations

Ground improvement is critical to geotechnical and geo-engineering systems, where modification of the properties of geomaterials (rocks and soils) is required to maintain stability and prevent failure of infrastructure installed within and around them. This need has become increasingly important with rapid urbanization and population growth, which intensify demands on surface and subsurface systems and further challenge the performance of supporting geomaterials. As a result, there is growing interest in nature-based solutions, particularly biologically mediated processes such as biocementation, which can enhance the physical, hydraulic, and mechanical properties of geomaterials while offering environmentally sustainable alternatives to conventional ground improvement techniques. …

Enabling Ml/Ai In 6g And Future Wireless Communication With Privacy Preservation, Mec Offloading And Quantum Computing, Changshi Zhou

Dissertations

The forthcoming sixth-generation (6G) and future wireless networks are envisioned to support an unprecedented range of services, delivering ultra-low latency, massive connectivity, and intelligent real-time responsiveness. These capabilities will enable emerging applications such as extended reality (XR), autonomous vehicles (AVs), industrial robotics, and the Internet of Things (IoT) to reach their full potential. Achieving this vision requires the integration of enabling technologies such as artificial intelligence and machine learning (AI/ML) and quantum computing, which are poised to play central roles in shaping the landscape of wireless communication systems.

In AI-native, data-driven, and computing-centric 6G networks, ML models will be deeply …

Differential-Geometric Methods For Neural Signed Distance Fields: Parameterized Surface Extraction And Curvature Regularization For Cad Models, Haotian Yin

Dissertations

Neural signed distance fields have emerged as a powerful framework for representing three-dimensional geometry through continuous and differentiable neural functions. Their flexibility, resolution independence, and compatibility with gradient-based optimization make them especially attractive for surface reconstruction and geometric learning. However, despite these advantages, two fundamental challenges remain for engineering-grade applications. First, higher-order geometric properties such as curvature are difficult to model reliably during training and often require computationally expensive second-order differentiation. Second, while neural signed distance fields provide implicit surface representations, they do not directly yield a globally consistent forward map or parameterization for downstream geometric processing.

This dissertation addresses …

Holistic Dram Enhancements: From Intrinsic In-Memory Operations To Robust Security Mechanisms, Ranyang Zhou

Dissertations

Dynamic Random-Access Memory (DRAM) is both the performance bottleneck and a critical security boundary of modern computing systems. Its physical properties make it an attractive substrate for near-data computation—yet those same properties expose it to disturbance-based hardware attacks. This dissertation argues that these two dimensions are not independent: the architectural choices that make DRAM efficient also reshape its threat landscape. Addressing both requires a unified approach to memory architecture and security co-design.

The first part of this dissertation attacks the memory wall through four processing-in-DRAM (PIM) frameworks. ReD-LUT and LT-PIM unify lookup-table arithmetic with charge-sharing logic, achieving up to 37.8x …

Vergence Task-Based Neural Pathways With Binocularly Normal Vision And Comorbid Persistent Post-Concussive Symptoms -Convergence Insufficiency, Ayushi Sangoi

Dissertations

Binocular dysfunctions are more prevalent in the persistent post-concussive symptoms (PPCS) population than in the general population. The most prevalent binocular disorder is convergence insufficiency (CI), affecting 3-17% of the general population and up to 10 times as many people with PPCS. CI makes it difficult to fuse or maintain fusion on targets at near, and its symptoms include double or blurry vision and headaches when performing close-range tasks such as reading, which can exacerbate PPCS symptoms. Given controversy over the subjectivity and effectiveness of diagnostic tools and symptom surveys for both PPCS and CI, understanding why CI has high …

Disentangling Non-Thermal Electron Injection And Decay In Solar Flares Using Multi-Wavelength Radio Observations, Brian Eugene O’Donnell

Dissertations

The broadband microwave imaging spectroscopy capability provided by the Expanded Owens Valley Solar Array (EOVSA) allows new diagnostics of high-energy processes in solar flares, providing spatially and temporally resolved spectra rich in information about the acceleration and transport of energetic electrons.

In this work, injections and transport of energy and particles into the solar corona during flares are studied. This is accomplished through the development and use of the PIP_Decomp Fitter, an automated fitting tool made by the author to fit injection and precipitation/decay parameters using the spatially resolved radio spectra obtained by EOVSA. These tools are used to study …

A Generative Ai-Driven Computational Framework For Industry-Scale Discovery Of Novel Battery Materials, Joy Datta

Dissertations

The growing demand for sustainable, high-energy-density electrochemical storage has motivated the exploration of multivalent-ion batteries based on earth-abundant elements such as aluminum, calcium, magnesium, and zinc. While multivalent charge carriers offer higher theoretical energy density than lithium, their practical deployment is hindered by sluggish ion transport, strong ion-host interactions, and structural degradation of electrode materials. Identifying host materials that can reversibly accommodate multivalent ions while maintaining structural integrity remains a fundamental challenge. The dissertation develops a scalable, end-to-end computational framework that integrates density functional theory (DFT), machine learning (ML), and generative artificial intelligence (GenAI) to accelerate the discovery of next-generation …

Toward Learning-Based Reconstruction And Part Decomposition Of Man-Made 3d Geometry: Neural Implicit Representations And Scalable Supervision, Shen Fan

Dissertations

Digital three-dimensional (3D) models are central to engineering design, analysis, and manufacturing, but learning pipelines for man-made geometry often operate on sampled carriers that do not preserve all of the structure present in exact CAD representations. This dissertation studies learning-based reconstruction and part decomposition for structured man-made 3D geometry, from general object benchmarks to CAD-derived datasets, with a focus on neural implicit representations trained from signed-distance samples, point clouds, and tessellated meshes. The goal is to make these models more accurate, more part-aware, and more consistently supervised.

First, signed distance function (SDF) reconstruction with implicit neural representations is improved through …

Significant Crash Characteristics Associated With E-Scooter And E-Bike Crashes, Aimee Jefferson

Dissertations

Micromobility devices—namely e-scooters and e-bikes—have rapidly gained popularity in the United Sates, rising from 35 million annual shared rides in 2017 to over 133 million in 2023 (NACTO 2024). But also increasing is the number of injuries associated with these devices; however, most research emphasizes injury and demographic patterns rather than crash characteristics that would inform prevention strategies. Most existing crash research also relies on small sample sizes, lacks nuance distinguishing between involved parties (motorists, pedestrians, single device), and fails to distinguish between bicycle and micromobility crash patterns despite micromobility devices often being instructed to use conventional bicycle facilities. These …

Anonymity And Accountability In Secure Messaging, Erin Kenney

Dissertations

Encypted messaging has become more and more prevalent as time moves on, and its benefits in assuring privacy cannot be overstated, but it also brings along with it concerns on how to moderate platforms where all messages are hidden. Message Franking, followed by Traceback systems, addressed these concerns by allowing the sender of a message to be proven when reported, even for forwarded messages in the case of Traceback, however these systems damage the privacy guarantees that originally motivated encrypted messaging to begin with.

In practice, even without those concerns encrypted messaging alone is not enough to prevent the most …

Glass Transition Temperature Of Plga Nanoparticles And The Application In Drug Delivery, Guangliang Liu

Dissertations

The glass transition temperature (T_g) of poly(D,L-lactic-co-glycolic acid) (PLGA) nanoparticles plays a crucial role in governing molecular mobility, diffusion, and consequently, drug release kinetics. However, the interaction among residual surfactant, drug effect, nanoscale confinement, and release medium on T_g remains insufficiently characterized. This study aims to bridge this gap by correlating the thermal behavior of PLGA nanoparticles with their drug release behavior under physiologically relevant conditions.

In the present study, PLGA nanoparticles were synthesized using both nano-emulsion and surfactant-free nano-precipitation approaches. The influence of residual surfactants - poly(vinyl alcohol) (PVA) and didodecyldimethylammonium bromide (DMAB) - was systematically …

Membrane-Engineered Nanotherapeutic Platforms For Drug Delivery: Hollow Fiber Membrane Synthesis Of Lipid Nanoparticles, Biomimetic Nanocarriers, And Nanobubbles, Zhixiang Liu

Dissertations

Lipid-based nanocarriers have emerged as a cornerstone technology for RNA therapeutics, enabling effective intracellular delivery for applications ranging from vaccination to gene regulation. However, current manufacturing approaches, particularly microfluidic-based platforms, face inherent limitations in scalability, throughput, and structural tunability due to their reliance on confined channel geometries and restricted mixing architectures. Addressing these challenges requires fundamentally new strategies that decouple nanoparticle formation from traditional microscale flow constraints while maintaining precise control over physicochemical properties.

This dissertation presents a comprehensive framework for the design, engineering, and application of advanced lipid-based nanocarriers, centered on a hollow fiber membrane (HFM)—assisted nanopore-mediated assembly platform. …

Parameter Density Estimation For Cardiac Electrophysiology Models Using Data Consistent Deep Learning, Michael Luo

Dissertations

Mathematical models of biological rhythms and excitable systems can provide insights into mechanisms underlying cardiac electrical dynamics. However, estimating the parameters of these models from experimental observations is often difficult due to noise, heterogeneity, and unobserved variables. For example, in an electrocardiogram (ECG) recording, information about the electrical properties of different regions of the heart is compressed into a single voltage trace. Additionally, variability within these signals may contain important information about population heterogeneity, regional differences in electrophysiology, and time-dependent modulation.

This dissertation develops, explores, and evaluates methods that perform feature-based distributional inference for complex nonlinear dynamical systems. The objective …

Transcranial Ac Modulation Of Cerebellar Nuclear Activity In Awake Animals, Nuran Kavakli

Dissertations

Entrainment of cerebellar nuclear (CN) cells via cerebellar transcranial alternating current stimulation (ctACS) has been reported in animals under ketamine/xylazine anesthesia. Our main objective was to demonstrate modulation of CN activity in unanesthetized, freely moving animals using ctACS. Multi-channel carbon-fiber electrodes were implanted into the interpositus nucleus for recording multi-unit (MU) activity, and thin-film electrodes were implanted subcutaneously over the posterior cerebellum for stimulation. A frequency-domain-based metric was developed to quantify modulation from MU signals. The results demonstrated modulation in a wide range of frequencies (4 Hz-300 Hz) as in anesthetized animals. In contrast, the amplitude of the peak in …

Inaugural Lmu Prize For Social Justice For Fiction — Closing Ceremony Remarks: 20th Los Angeles Greek Film Festival, Katerina Zacharia

Classics and Archaeology Faculty Works

This document comprises the closing ceremony remarks delivered by Professor Katerina Zacharia on May 31, 2026, at the 20th Los Angeles Greek Film Festival (LAGFF). The award, funded by the Bellarmine College of Liberal Arts, Department of Classics & Archaeology at Loyola Marymount University, recognizes outstanding fiction films that engage with themes of migration, displacement, identity, and social inclusion in contemporary Greece and the Greek diaspora. The remarks present two inaugural LMU Prizes for Social Justice in the fiction categories. The first award — Best Fiction Short Film — goes to The Wolves Return, written and directed by Stelios …

A Network-Cascade Framework For Short-Run Production Failure Under Maritime-Energy Chokepoint Disruption, Siyao Liu

Double Helix Methodology

Abrupt maritime-energy disruption can generate system-wide production losses before firms and policymakers can adjust. Existing assessments usually emphasize direct exposure or long-run equilibrium responses, which makes them less suitable for short-run risk assessment in energy-dependent production systems. We develop a threshold-cascade framework that combines dual-track dependence topology, edge-level inventories, smooth operability bands, and a separate price-validation step to identify the blockade intensity at which a localized chokepoint shock becomes systemic production loss. The framework is evaluated against the March 2021 Suez blockage and the 2022 Russia–Ukraine producer-price episode, and then applied to a 2026 Strait of Hormuz stress scenario using …

Mapping The Future Of Digital Logistics: A Multi-Layered Analysis Of Technological Evolution And Integration, Jiang Yu

Double Helix Methodology

In the context of accelerating digital transformation and increasingly complex global supply chains, this study develops a multi-layered analytical framework to investigate the technological evolution and future trajectory of digital logistics. Using 107,202 patent records from the IncoPat database covering 1960–2023, the study integrates the entropy weight method, grey relational analysis, Latent Dirichlet Allocation (LDA), and a Hidden Markov Model (HMM) to identify core patents, extract technological topics, and forecast their dynamic evolution through 2028. The findings identify 27 major technological topics and reveal a significant transition from traditional operational optimization toward integrated intelligence and autonomous execution. Technologies related to …

Evaluating Flood Risk Management Performance Under Climate Change Based On Nature-Based Solutions — Empirical Evidence From Taihu Lake Basin, Shaofeng Chen, Conglin Zhang

Double Helix Methodology

Identifying a flood risk management (FRM) performance evaluation method that aligns with the sustainable development goals of the natural-social complex ecosystem is essential for effective and timely responses to flood risks. Using the Taihu Lake Basin (TLB) as a case study, this research systematically examines the evolving needs of FRM under climate change and proposes a performance evaluation method based on Nature-based Solutions (NbS). The method covers multiple dimensions including flood resources, socio-economic, and environmental factors. Principal component analysis (PCA) is employed to assess changes in the FRM level in TLB from 2010 to 2020. The key findings are as …

Enhancing Physiological Complexity Through Mindfulness: A Wearable-Based Intervention For First Responders And Their Partners, Thurmon Lockhart, Rahul Soangra, Christopher W. Frames, Sun-Kyung Lee, Amberlee Martin, Susanne Lee, Abigail H. Gewirtz

Physical Therapy Faculty Articles and Research

Background/Objectives: First responders frequently encounter high-stress environments that challenge physiological resilience and autonomic regulation. Heart rate variability (HRV) complexity is a critical marker of adaptive capacity and stress regulation. This study assessed the impact of a wearable-based mindfulness intervention on HRV complexity among first responders using a smartwatch. Methods: A total of 87 first responders participated in a one-month wearable-based intervention. Participants wore Garmin Vivosmart 5 devices to continuously collect PPG data (photoplethysmogram), focusing on beat-to-beat intervals (BBIs). The intervention involved daily Ecological Momentary Assessments (EMAs) and individual randomization to either a mindfulness message, a prompt to access an audio …

Edge Co-Occurrence Regularization For Node Classification, Kadir Altunel

Theses

We propose a simple yet effective regularization technique for node classification on graphs that leverages edge-based label co-occurrence patterns. We first train an MLP on node features to produce class probability distributions, then compute a fixed penalty matrix from edge-based co-occurrence statistics of these predictions. This penalty matrix, which captures unlikely class combinations on connected nodes, is then used to regularize GNN training without further updates. We evaluate this approach across multiple homophilic datasets (Cora, CiteSeer, PubMed, ogbn-arxiv) and heterophilic benchmarks (Chameleon, Squirrel, Actor, Roman-Empire) using three GNN architectures: GCN, GraphSAGE, and H2GCN. Results show consistent improvements on homophilic graphs, …

The Effects Of Osha State Plans And State Gdp On Occupational Injuries And Illnesses In The United States, Ahmad Abdel Rahman Mohammad Amoorah

Theses

Workplace safety in the United States is governed by a system in which some states operate under federal OSHA oversight, while others administer their own OSHA-approved safety and health plans. This duality raises questions about whether state-level control leads to variation in safety and health outcomes. Economic conditions have also been shown to influence workplace injury rates, yet the interaction between safety and health regulations and local economies remains underexplored. Understanding these relationships is critical to protecting workers in occupational settings. The effects of OSHA state-plan status and state-level economic activity on occupational injury and illness rates across the United …

Deep Learning Approaches For Ctenophore Identification And Tracking, Anagha Bharadwaj

Theses

Ctenophores are translucent marine organisms with nearly invisible tentacles and pose significant challenges due to their transparent morphology and ambiguous structural features. This research addresses the classification and tracking of these organisms and evaluates the performance of current computer vision models under sparse-data environments.

A dataset from the NJIT Life History Lab consisting of microscopic laboratory videos and photographs of different growth stages is used to train and assess a number of convolutional neural network designs, including VGG16, ResNet, BioCLIP2, YOLO, and DeepLabCut. Additionally, a web-based interface is developed to evaluate expert-labeled ground truth with the model's performance.

The findings …

Sound Detection Of Memory Leaks In Llvm Ir Programs Using Accumulation Analysis, Robert Blacha

Theses

Resource leaks occur when a limited resource such as memory is allocated by a program and needlessly held past the point of use. Leaks can lead to a degradation of services which can be specifically triggered with malicious behavior, for example abusing a memory leak in a program to cause a server to slow down and crash for a denial-of-service attack.

Prior work has demonstrated that accumulation analysis provides a sound detection of resource leaks with a working implementation for programs written in Java. While useful, current implementations are limited to programs written in Java, which has a garbage collector, …

Adaptive Multimodal Smart Home Control On A Raspberry Pi 5 Using Hand Gestures, Voice Cues, And User Feedback, Vaibhav Bora

Theses

A real time multimodal smart home control system deployed on a Raspberry Pi 5 is presented. The system combines hand gestures, short voice cues, and proximity aware interaction to execute household commands such as light brightness control, fan speed adjustment, and stop or kill switch actions. Lightweight gesture and keyword spotting voice classifiers were trained offline and exported to TensorFlow Lite for efficient on device inference. For more natural spoken phrases, the system additionally integrates a locally deployed pretrained offline ASR component rather than a speech recognizer trained from scratch. Using a USB camera and microphone, the system operates fully …

Evaluation Of Medical Instrument Reliability For The Heath And Maddox Components Of The Accommodative-Vergence System, Jacob Chabuel

Theses

Binocular vision relies on the coordination of near response triad composed in part by the vergence and accommodative systems. Binocular dysfunctions linked to these systems impact the quality of life of those affected by complicating day-to-day tasks and leading to further health concerns. Clinical evaluations that diagnose dysfunctions and tailor therapies rely on subjective methods, creating a need for a reliable, quantitative, measurement tool. This is achieved using a haploscope, a tool that quantitatively measures and observes binocular vision to identify deficiencies within the eye. Modernization of a haploscope system, measurement of the Heath and Maddox components of the visual …

Polarimetric Terahertz Imaging For The Measurement Of Birefringence In Plastic, Rachel Cohen

Theses

Birefringence offers a promising way to observe stress concentration in materials such as glass and plastic, and thereby to identify weaknesses. Polarimetric imaging can be used to measure the birefringence of material, so long as the material is transparent to the light being used for the imaging. In this research, 2D Terahertz imaging was investigated as a means of measuring the birefringence of plastics that are opaque to visible light but transparent to THz radiation, for the eventual purpose of analyzing the residual stress present. In order to do so, two separate terahertz cameras were characterized for potential use in …

Autonomous Exploration Of An Environment With Static Obstacles Using A Ppo Agent, Brandon Knight

Theses

Research in autonomous exploration has created many effective algorithms that have been tested and proven to work in many different virtual and physical environments. Many optimizations have also been developed to reduce computational effort and increase exploration speed.

However, despite optimizations, these algorithms can still require considerable computational effort and time to explore even small environments. To obtain further improvements in computation and exploration speed, a reinforcement learning agent using actor-critic style proximal policy optimization (PPO) is trained to explore various environments efficiently, then compared to an algorithm using contemporary exploration methods.

Testing is performed in virtual environments with ideal …