Digital Commons Network^™

Toward A Metric For Disciplinary Learning In The Age Of Ai: The Unblooms™ Metacognitive Awareness Scale And Discernment Rate In Ai-Mediated Learning, Tina R. Austin

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

Telling students to “reflect on their own thinking” has become insufficient in AI-mediated learning environments. When students are rewarded for polished outputs, cognitive offloading to AI tools becomes rational, and traditional snapshot assessments (single-moment evaluations of task completion) produce false signals about whether durable learning has occurred. This problem is sharpened by the broader shift in AI learning tools toward Socratic tutors, study modes, Khan Academy's Khanmigo, and agentic systems that can shape the learner’s process over time. Lodge and Loble (2026) distinguish beneficial cognitive offloading, which frees working memory for intrinsic learning, from detrimental outsourcing, which bypasses the cognitive …

Development Of An Adaptive Pelican Crossing Model Using Fuzzy Logic In Mixed Traffic Conditions, Manazil Adam, Andyka Kusuma, R. Jachrizal Sumabrata

Smart City

Traffic management at at-grade pedestrian crossing facilities (pelican crossings) in highly populated areas, such as the Universitas Indonesia Station, faces significant inefficiency challenges. During peak hours, the fixed-time system is frequently disabled and replaced with subjective manual control by security personnel, thereby triggering irregular stop-and-go cycles and a high accumulation of vehicle delays. This study aims to develop a hybrid adaptive control model integrating Computer Vision, Genetic Algorithm (GA), and Fuzzy Logic to optimize intersection performance under mixed traffic conditions. The research methodology begins with the extraction of traffic and pedestrian characteristic data, calculated manually through recorded field observations. This …

A Comprehensive Review Of The A* Algorithm: Evolution, Applications, And Future Trends In Path Planning, Saleel H. Abood, Hussein M. H. Al-Khafaji, Mohanned M. H. Al-Khafaji

Journal of Soft Computing and Computer Applications

Despite being a fundamental problem to autonomous robotics and intelligent navigation systems, path planning is still a challenge. The A^* algorithm is often used among search-based techniques for optimal search performance, as it's a tradeoff of computation. The above techniques have been developed for various applications as many versions of A^* Dynamic A^* (D^*), D^* Lite, Hybrid A^*, and Anytime A^* are suggested to deal with dynamic environments, real-time constraints, and kinematic restrictions. This paper comprehensively and structurally reviews the A^* algorithm and its major extensions, encompassing historical development, methodological …

Load Profile Analysis And Forecasting For Rural Mini Grids In Uganda, Prossy Mutesi, Santos L. Kihwele, Emmanuel S. Matee

Tanzania Journal of Science

Accurate load forecasting is essential for the reliable and cost-effective operation of rural mini grids, where constrained generation capacity and high penetration of renewable energy resources require well-informed operational decisions. This study examines electricity demand characteristics and forecasting performance for the Buzaami and Ssenyondo mini grids in Uganda, with particular focus on diurnal load profiles, peak demand behavior, and seasonal variability. 2022 operational data show extended peak demand from early morning to late evening, driven by socio-economic activities that strain resource scheduling and reliability management. To address these challenges, the study evaluates and compares Long Short-Term Memory (LSTM) networks, fuzzy …

The Dynamics Of Educational Change: A Complex Systems Perspective, Preethi Nanjundan, Lijo Thomas, Abith K. Sunil

Northeast Journal of Complex Systems (NEJCS)

The perception of university teachers toward educational reforms plays an important role in determining the success of changes introduced in the education sector. This study focuses on teachers’ attitudes toward change, their emotional responses, and their overall views on educational reforms. Across the world, many educational reforms have failed to achieve their expected outcomes in improving teaching practices and student learning. As education systems are highly complex, the approach toward implementing reforms has also changed over time. Some reforms are introduced gradually, while others involve major innovations within the system. Complexity theory provides useful insights and tools that help educators …

Bias Before Generation: Attention-Based Preemptive Fairness Signals In Large Language Models, Aniket Das

Master’s Dissertations

Warning: This paper includes examples of language that may be perceived as inappropriate or offensive. Large language models (LLMs) are known to propagate social biases embedded in their training corpora, producing outputs that disproportionately disadvantage individuals based on sensitive attributes such as gender, religion, race, sexual orientation and nationality. Existing mitigation strategies are either computationally prohibitive, require access to model parameters, or apply corrections only after biased content has already been generated. This work addresses a different question: can the model’s own internal attention dynamics, observed at inference time, serve as a reliable early-warning signal for bias, enabling intervention before …

Literature Landscape Of Government, Academic, And Industry Efforts In Responsible, Secure, And Trustworthy Ai, Jae C. Oh, Shiu-Kai Chin, Garrett Katz, William E. Young, Matt Clark

Electrical Engineering and Computer Science - All Scholarship

This report presents a systematic analysis of 54,628 AI research papers published between 2021 and 2025, examining the extent to which current government, academic, and industry efforts address the trustworthiness requirements of mission-critical AI systems. Papers were classified along two dimensions: organizational tier (from AI model to mission/business pro- cess) using NIST SP 800-39, and systems engineering lifecycle phase using ISO/IEC/IEEE 15288. Classification combined automated LLM-assisted screening with human validation, achieving inter-rater reliability measured by Krippendorff’s α and Gwet’s AC1. Only 16.8% of papers examined addressed any aspect of AI trustworthiness, reliability, or security, and virtually all were concentrated at …

Semantic Directing: A Human-Ai Workflow For Interative Virtual Cinematography, Lejie Liu

Dartmouth College Master’s Theses

This thesis presents AI Director, a Unity-based prototype for semantic directing in virtual cinematography. The system enables creators to describe cinematic intent in natural language, receive editable shot-strategy plans generated by a large language model, and refine specific shots through text and visual references. By separating semantic planning from deterministic camera execution, the workflow keeps user authorship centered on shot intent while making the resulting camera plans inspectable, editable, and executable in real time. This work explores semantic directing as a human-AI workflow for iterative cinematic authoring in dynamic 3D scenes.

Ai In Precision Medicine: Redefining Blindness Treatment Via Retinal Stem Cell Differentiation, Calissa Leong, Tiffany Nguyen

Interdisciplinary Design Senior Theses

Age Macular Degeneration (AMD) is currently the leading cause of blindness, driven by the degradation of Retinal Pigment Epithelial (RPE) cells. Stem-cell based therapies, particularly those involving induced pluripotent stem cells (iPSCs), typically consists of differentiating stem cells into RPE cells in vitro and transplanting them into the subretinal space. Our goal is to improve the accuracy of differentiation into RPE cells to better promote retinal regeneration. Traditional methods to alleviate symptoms of AMD is through therapeutic medications to slow disease progression, though stem cell therapy has emerged as a promising alternative. However, despite its potential, stem cell therapy presents …

Foundations' Deep, Emily Williams, Morgan Williams

University Honors Theses

Most historic buildings do not survive as they were built, yet preservation frameworks still tend to evaluate them as if they did.

Foundations' Deep begins with a structure located in Helena, Montana 59602, a building that sits uneasily within the categories it has been asked to meet. Built in the early twentieth century, it has persisted through alteration, abandonment, and use. What remains is not a stable artifact but a record of accumulated decisions, some careful and many indifferent, now embedded in the fabric of the building itself.

The thesis grows from this condition and from the limits of the …

Declination Of Groundwater Potential Zones Of Nashik, Maharashtra Using Geographic Information System With Analytic Hierarchy Process-Fuzzy Techniques, Madhusudhan M. Reddy, Bhogayya G. Naidu, Bharat N. Mulay, Ramanjaneyulu B, Ravi K. Kumar, Mirzi L. Betasolo, Praveen U. Goud, Snehal N. Chaudhari

Mansoura Engineering Journal

Groundwater is a critical resource for agriculture and domestic use in the semi-arid region of Nashik District, Maharashtra, where overexploitation and erratic climatic conditions have led to declining groundwater availability. This study integrates Geographic Information System (GIS) with Analytic Hierarchy Process (AHP) and Fuzzy-AHP techniques to delineate groundwater potential zones (GWPZs) and identify suitable sites for artificial recharge. A total of ten thematic layers drainage density, rainfall, geomorphology, elevation, distance from river, geology, slope, land use/land cover, topographic wetness index (TWI), and curvature were derived from SRTM DEM, Landsat-8, and IMD datasets, representing key controls on groundwater recharge, infiltration, and …

Bilingual Math For Kindergartners (Biki), Anna Aldrin, Caroline Tapia, Lillian Le

Computer Science and Engineering Senior Theses

For many children from bilingual households, kindergarten represents a critical, learning benchmark where the introduction of fundamental math concepts coincides with their first significant exposure to the English language. Traditional educational methods often fail to support English language learners, leading to a disconnect between their understanding of math in the home and its application in the classroom. This linguistic barrier frequently results in reduced engagement, diminished confidence, and an unstable educational foundation that can discourage students from pursuing STEM careers later in life. While existing platforms like Khan Academy and Duolingo address either language acquisition or mathematical proficiency independently, they …

Jsd: Novel Methodology For Synthetic Data Evaluation, Jeffrey Lane, Scott Wang, Vincent Chang, Bojun Zhang

Computer Science and Engineering Senior Theses

The proliferation of sensitive Personally Identifiable Information (PII) on dark web marketplaces has created an urgent need for robust data protection systems, especially for vulnerable populations such as minors. Traditional PII redaction often fails to identify implicit privacy risks—such as author gender indicators or non-fictional child-related context—hidden within large-scale e-commerce datasets. This paper presents JSD, a dual-stage framework for the detection and protection of sensitive text data. The Detection phase utilizes Transformer and CNN-based architectures and Human-in-the-Loop AI to surpass the "semantic ceiling" of traditional NER approaches, enabling context-aware identification of implicit PII. The Protection phase introduces GASE (Genetic Algorithm …

Mimica, Sean Lai, Gurprasaad Hora, Stephanie Campos

Computer Science and Engineering Senior Theses

Language barriers remain a significant obstacle to natural human communication, limiting access to healthcare, business, travel, and daily social interaction for billions of people worldwide. Existing translation solutions such as smartphone applications and earpiece devices address the functional problem of converting words between languages but fail to preserve the speaker’s voice identity, require hands-on interaction, or depend on proprietary device ecosystems. These limitations disrupt the natural flow of conversation and reduce the human quality of cross-lingual communication.

Mimica is a real-time wearable translation necklace designed to address these shortcomings. Built around an ESP32-S3 microcontroller with an integrated microphone and speaker, …

Reinforced Concrete Design Fundamentals, Belen Stephanie Martinez Espinoza, Estevan Jacob Perez

Architectural Engineering

Hispanic workers make up a large and growing portion of the construction workforce in the United States; particularly in California, the percentage of construction workers who are Hispanic is estimated to be about 55% as of 2021 (Elkins). Many of these workers are Spanish-speaking, yet there is still a gap between technical engineering design and what is understood and applied in the field. One major reason for this gap is the lack of clear bilingual resources. To bridge the gap, a 48-page bilingual booklet titled Reinforced Concrete Design Fundamentals (Fundamentos del Diseño de Concreto Reforzado in Spanish) was created. This …

Design And Optimization Of A Bluff Body For Energy Harvesting Of Transverse Galloping Induced By Low-Speed Wind Using Bernstein Polynomial Equations, Youssef Wael Abdelmoneim

Theses and Dissertations

In this thesis, a computational framework is proposed for optimizing the aerodynamic shape of bluff bodies used in galloping-based wind energy harvesters. The system targets low-wind speed environments, where normal wind turbines are not effective, offering a potential alternative to batteries used for powering small electronic devices such as wireless sensors. The design relies on the galloping effect, where airflow around a bluff body induces transverse oscillations that drive an energy conversion mechanism. To generate efficient bluff body geometry, the Class-Shape Transformation (CST) method is used to define a wide range of candidate shapes with minimal design parameters. These shapes …

Water Turbine Dynamometer For Low-Power Turbomachinery Testing, Carmen Alaichamy, Yosef Huynh Torres, Michael Duperly, Michael Thomas Duff

Mechanical Engineering

Students in ME 443: Introduction to Turbomachinery compete to design and build efficient small impulse turbines. The course lacks a standardized method to experimentally calculate turbine power output and mechanical efficiency from accurate measurements of torque and speed, for example. To support the learning goals of ME 443, a dedicated water-turbine dynamometer is needed to provide controlled hydraulic power, accurate mechanical and electrical measurements, and clear data for performance evaluation.

Our proposed system delivers a safe, repeatable, and educational testing environment that can be integrated into the Fluids Lab or used as a standalone instructional tool. Key stakeholders include Professor …

A Review On Credit Card Electronic Fraud Detection Methodologies, Titilayo Mary Sayikanmi, Ibrahim Adepoju Adeyanju, Bolaji Abigail Omodunbi

Mansoura Engineering Journal

Credit card fraud remains a critical and escalating challenge within the global financial ecosystem, driving substantial annual losses and necessitating the continuous evolution of detection methodologies. This paper presents a systematic literature review, conducted via the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, which comprehensively analyzes the state-of-the-art in electronic credit card fraud detection. Through a rigorous examination of 49 high-quality studies, this review maps the methodological evolution from traditional rulebased systems and statistical models to advanced artificial intelligence techniques, including machine learning, deep learning, and graph-based approaches. The analysis reveals that while individual methods possess distinct advantages …

Towards Interpretable Transfer Learning With Limited Data, Youxiang Zhu

Graduate Doctoral Dissertations

Modern foundation models are typically trained with large-scale data to ensure good performance. However, certain tasks cannot scale with large amounts of data due to cost and practical constraints, resulting in limited performance. To address this, in this dissertation, I introduce model-task alignment, a general methodology for making a foundation model work well with tasks with limited data. The methodology comprises two parts: aligning downstream tasks with foundation models and aligning foundation models with downstream tasks. To study and validate this methodology, I first focus on speech-based dementia detection, a representative task with limited data, and then extend to general …

Bridging Images And Language In Radiology: A Comprehensive Prisma Systematic Review Of Transformer Vision-Language Models And Clinical Readiness, Mohammad T. Khasawneh Dr., Sadaf Tabatabaee

Systems Science and Industrial Engineering Student Scholarship

Transformer vision-language models (VLMs) promise end-to-end automation of radiology reporting and related multimodal tasks. However, evidence remains fragmented across datasets, architectures, evaluation practices, and levels of clinical validation, limiting fair comparison and safe translation into practice. Following PRISMA 2020/PRISMA-S, search engines including PubMed, IEEE Xplore, Web of Science, and Google Scholar were systematically searched for peer-reviewed, English-language studies published between 2019 and 2025 that used paired radiology images and free-text reports. Dual reviewers screened records and extracted data using a locked schema covering datasets, modalities, architectures, training objectives, evaluation metrics, and indicators of clinical readiness. Free-text model descriptions were normalized …

Uncovering Discrete States From Multimodal Psychophysiological Data Using Gaussian Latent Dirichlet Allocation (Glda), Congyu Wu, Aaron Fisher, David Schnyer

Northeast Journal of Complex Systems (NEJCS)

In this article we explore and validate the utility of an unsupervised probabilistic model, Gaussian Latent Dirichlet Allocation (GLDA), for discovering discrete states from repeated, multimodal psychophysiological samples collected from multiple individuals. Psychology and medical research heavily involves measuring potentially related but individually inconclusive variables from a cohort of participants to derive diagnosis, necessitating clustering analysis for state identification. Traditional probabilistic clustering models such as Gaussian Mixture Model (GMM) assume a global mixture of component distributions, which may not be realistic for observations from different patients. The GLDA model borrows the individual-specific mixture structure from a popular topic model Latent …

Somatic Prosthetics For Planetary Resonance, Disha Dharesh Kumar R

Masters Theses

This thesis investigates how industrial design can transcend extractive technological paradigms in favor of relational interfaces that foster planetary attunement. Framing the climate crisis as a 'crisis of imagination', the research challenges the Western bifurcation of nature and culture by drawing on Indic cosmologies- which recognize stones, plants, and ecosystems as conscious at different levels and participants in a shared cosmic field. By synthesizing research in Biosemiotics, Quantum Information Pansycishm, and Neuroscience, the project redefines intelligence as a distributed, more-than-human phenomenon.

The research materializes as a speculative design artifact: a device that functions as a somatic prosthetic for planetary resonance. …

Visualization And Marker-Less Tracking Of User-Defined Pre-Processed Mri Articulator Data Using Deep Learning, Michael De George

Student Theses

This thesis presents a comprehensive framework for the automated tracking and visualization of articulatory movements based on magnetic resonance imaging (MRI) data. A well-known data analysis tool for markerless pose estimation, known as DeepLabCut, is investigated for this purpose. The performance of this tool is enhanced through the design and implementation of a pre-processor. DeepLabCut is a markerless pose estimation toolbox based on deep learning, which overcomes the issue of making manual annotations frame-by-frame. Limitations from manually marking the MRI images are addressed by implementing transfer learning with convolutional neural networks to achieve accurate, user-defined articulator tracking without markers. Current …

Predictive Natural Language Metrics Of Alzheimer's Disease And Cognitive Decline Trend Analysis, Zerui Ma

Computer Science and Engineering Theses and Dissertations

Inspired by Dr. David Snowden's Nun Study, which linked early-life Propositional Idea Density (PID) to later-life Alzheimer's disease, this thesis investigates two questions: whether fine-tuned Transformer-based large language models (LLM) can detect cognitive decline from patient speech transcripts with meaningful feature attribution, and whether longitudinal PID trends are observable across large-scale internet and academic text corpora. We evaluate dementia prediction on the DementiaBank Pitt Corpus and conduct an exploratory longitudinal PID analysis across seven diverse datasets spanning up to 29 years and over 12.6 million documents. This work suggests that linguistic ability metrics, traditional PID metrics and novel LLM-based analysis, …

Improving Semantic Precision In Text-To-Image Diffusion Models Via Latent-Space Optimization And Semantically-Parsed Evaluation, Mohammad Rouie Miab

McKelvey School of Engineering Graduate Student Theses & Dissertations

Text-to-image diffusion models can produce visually impressive images from natural-language prompts, but they often fail to satisfy the detailed semantic constraints expressed in compositional prompts. Typical failure modes include omitted objects, merged entities, incorrect quantities, incorrect attribute binding, and leakage of one entity's attributes onto another. This thesis studies the problem of semantic precision in text-to-image generation: how faithfully a generated image satisfies the structured meaning of its prompt.   The thesis makes two linked contributions. First, it presents a training-free inference-time refinement method for diffusion-based image generation. The method operates directly in latent space during denoising and uses noun-phrase-aware cross-attention …

Comparing Llm Architectures In Street Fighter Iii, Andrew Hinh, Hyemin Doo, Jack Wu

Computer Science and Engineering Senior Theses

We built a browser-based Street Fighter III benchmark for comparing large language models (LLMs) and visionlanguage models (VLMs) under real-time constraints. The game runs through a Gymnasium-like control loop that exposes both frames and structured state while the agent emits button actions. We deploy the system on Modal and stream matches overWebRTC, which lets us compare ability, latency, and cost in one setting instead of spreading them across unrelated benchmarks. Because the benchmark is narrow, architectural tradeoffs that are easy to blur in offline evaluation show up quickly during live play.

Rgfc: Registry Grounded Function Calling With Stage-Decomposed Evaluation, Ritvik Garimella, Vedant Khandelwal, Anvi Kohli, Amit Sheth

Publications

Tool-calling evaluations often report whether the final call matches a reference answer, but this single outcome merges different failures: choosing a function outside the available registry, violating the selected schema, or filling arguments with values not supported by the user request. These failures matter more when models must choose from larger tool registries. We introduce RFCD, a registry-scaled function-calling benchmark built from BFCL, Glaive Function Calling v2, and Tool-Call-Data, with standardized JSON-schema registries ranging from 5 to 3,000 functions. We also introduce TAAG, a deterministic stage-decomposed evaluator for registry conformance, structural completeness, and argument grounding. Across six locally served sub-4B …

Robustness Of Vision Language Models For Pedestrian Detection Tasks, Ostonya K. Thomas

All Theses

Autonomous vehicle (AV) systems typically employ modular systems in which discrete components handle separate tasks such as perception, computation, and path planning. While flexible, this approach allows errors to propagate and compound across the pipeline, and many AI systems offer little transparency into their internal decision-making. Such limitations are particularly concerning in safety-critical domains where failures can carry lethal consequences. Vision Language Models (VLMs) have emerged as a promising alternative because they support end-to-end implementations that bypass compounding error risks and provide natural language explanations of their outputs. Despite these advantages, prior research has demonstrated that both computer vision systems …

Enhanced Situational Awareness Of Solar Pv Plants For Near Real-Time Planning And Operation In Modern Power Systems, Michael A. Walters

All Theses

Solar photovoltaic (PV) plant development and utilization is increasing worldwide but remains intrinsically challenged by its large dependence on highly variable weather conditions and operating states. This paper presents a framework to leverage three new situational awareness indices (SAIs), namely: weather condition index (WCI) to gauge operational performance based on environmental states, operational complexity index (OCI) to indicate the severity of power generation reductions, and photovoltaic generation index (PVGI) to provide a final determination of the impact on power generation and to bolster situational awareness in planning and operational contexts for solar PV plants. This is accomplished by exploiting the …

Autonomous Deficiency Detection And Vision-Language Summarization For Underground Infrastructure On Embedded Edge Systems, Johny Lopez

LSU New Orleans Theses and Dissertations

Aging underground infrastructure poses significant risks to public health and environmental safety, yet structural condition assessment remains bottlenecked by labor-intensive manual CCTV inspections. This thesis proposes a comprehensive algorithmic framework enabling fully autonomous, real-time deficiency detection, geometric assessment, and natural language reporting on resource- constrained edge computing platforms. Three core components address this challenge. First, RAPID-SCAN, a novel semantic segmentation architecture utilizing a Dynamic Feature Pyramid Network and Channel-Spatial Attention, achieves real-time, pixel-precise defect localization with dramatically reduced parameters. Second, an Edge-Optimized Vision-Language Model pipeline employing LoRA and 4-bit QLoRA quantization compresses Phi-3.5 for local deployment, en- abling autonomous technical …