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Artificial Intelligence In Stem Education: A Transdisciplinary Framework For Engagement And Innovation, Cristo Leon Ph.D., James Lipuma Ph.D., Xavier Oviedo Torres

STEM for Success Resources

This peer-reviewed article, Artificial Intelligence in STEM Education: A Transdisciplinary Framework for Engagement and Innovation by Cristo León, James Lipuma, and Xavier Oviedo-Torres, published in Frontiers in Education, examines how artificial intelligence (AI) is transforming STEM education through a transdisciplinary lens. Based on a systematic review of 41 scholarly studies (2020–2025), it analyzes AI integration in STEM classrooms, focusing on learner agency, adaptive assessment, and ethical challenges such as algorithmic transparency, equity, and inclusion. Guided by a Transdisciplinary Communication (TDC) framework and grounded in PRISMA protocols, the study applies tools like Nvivo, Excel, and VOSviewer for thematic coding and …

Phys 121- 021, 121: Physics Ii Lecture, Physics Department

Physics Syllabi

No abstract provided.

Continuum Of Academic Collaboration: Issues Of Inconsistent Terminology In Multilingual Context, Cristo Leon Ph.D., James Lipuma Ph.D., Marcos O. Cabobianco, Maria B. Daizo

STEM for Success Resources

Journal of Systemics, Cybernetics and Informatics
1690-4524 (Online)

Methodological Memorandum For Michelle Llado-Wrzos' Phd Dissertation, Michelle Llado-Wrzos Ed.D., Cristo Leon Ph.D.

Journal of Roleplaying Studies and STEAM

This methodological memorandum outlines the research approach for Michelle Llado-Wrzos' PhD dissertation, which explores the underrepresentation of women in Science, Technology, Engineering, and Mathematics (STEM) fields. Despite multiple efforts to reduce the gender disparity, female students remain significantly less likely to pursue STEM majors compared to their male peers. The research focuses on assessing the impact of extrinsic motivators—specifically financial incentives and exposure to female role models—on the STEM major choices of female high school students. By adopting a mixed-methods approach, this study thoroughly integrates quantitative surveys and qualitative interviews to examine these motivators.

A quasi-experimental design will assess the …

Narrativas Y Convergencias Transdisciplinarias: Contribuciones Académicas Relevantes, Desafíos Y Horizonte Editorial, Cristo Leon Ph.D.

Journal of Roleplaying Studies and STEAM

La Revista de Estudios sobre Juegos de Rol y STEAM (JRPSSTEAM) se ha consolidado como un espacio transdisciplinario, multilingüe y de acceso abierto que articula investigación, práctica y comunidad en torno a los juegos de rol. En esta editorial, se reflexiona sobre los aprendizajes y patrones emergentes de los tres volúmenes y cinco números publicados hasta la fecha, abordando tanto aciertos como áreas de mejora. Se ofrecen lineamientos prácticos para autores potenciales, destacando errores comunes, criterios de relevancia temática y aspectos que enriquecen la comunicación entre disciplinas. Además, se revaloriza el carácter abierto e inclusivo del JRPSSTEAM, que acoge desde …

Journal Of Roleplaying Studies And Steam (Jrpssteam) Vol. 4 [2025], Número 1 (Issue 1), Ivan Ávila González Phd, Cristo Leon, Romano Ponce-Díaz Phd, Diogo A. Camelo, Alfonso Rincón Vaquero, Edgar Ávila González, Daira Pando Tamayo, Michelle Llado-Wrzos, Leonid Moyzhes, Andrea A. Echeverría Barrios

Journal of Roleplaying Studies and STEAM

La cultura es un diálogo constante con el pasado, el presente y el futuro. Todos nuestros objetos culturales son el resultado causal de las personas que estuvieron antes que nosotros, se actualizan en nuestro proceder en el presente y, les proyectamos al futuro con la esperanza de que aporten algo positivo a las generaciones que estarán delante de nosotros; de tal forma, este Volumen 4 [2025], número 1 (Issue 2) es el resultado directo de la labor de los editores fundadores Mauricio Rangel Jiménez, Miguel Angel Bastarrachea Magnani y todas aquellas personas que trabajaron antes que nosotros para que existiera …

Childhood Neuroanatomical Markers Of Familial And Nonfamilial Attention-Deficit/Hyperactivity Disorder, Rahman Baboli

Dissertations

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent neurodevelopmental disorder, characterized by developmentally inappropriate levels of inattention, hyperactivity, and impulsivity. Children with family history of ADHD are at an elevated risk of having ADHD as well as a higher risk of persistent ADHD into adulthood, reflecting a source of etiological heterogeneity in ADHD. This heterogeneity in terms of both biological and environmental risk factors may explain differences in neural correlates, outcomes, cognitive, behavioral as well as developmental trajectories. It is therefore critical to understand the influence of having, or not having positive family risk factors on the neuroanatomical structures of the …

On The Design Of A Framework For Large-Scale Exploratory Graph Analytics, Oliver Andres Alvarado Rodriguez

Dissertations

Large-scale exploratory graph analytics merges data science with high-performance computing to extract critical insights from network-representable data. Data scientists routinely analyze data from the natural, social, and computing sciences by representing it as networks, or graphs, where objects become vertices and their relationships become edges. This representation allows data scientists to add graph analytics to their toolbox. However, designing tools for large-scale exploratory graph analytics is challenging due to the complexities of graph algorithms, such as high communication in distributed systems and large memory demands. These challenges can lead to overly complex software, which limits usability and development to a …

Machine Learning And Optimization For Intelligent Decision-Making, Elson Cibaku

Dissertations

This dissertation presents a series of innovative machine learning and optimization model designs that address complex operational challenges across logistics and power systems. By integrating advanced neural architectures with robust optimization techniques, the work delivers scalable solutions designed to improve efficiency, reliability, and decision-making in dynamic and real-world environments. The first study introduces a two-stage approach to effective vaccine distribution. This framework tackles the capacitated vehicle routing problem by combining adaptive clustering techniques with reinforcement learning and a simulated annealing pickup policy. Through extensive computational experiments, the approach demonstrates substantial improvements in routing efficiency, reducing both computational time and logistical …

Model-Based Reinforcement Learning And Deep Learning For Power Converter Circuit Design Automation, Shaoze Fan

Dissertations

This dissertation presents a comprehensive automated framework for power converter design, leveraging reinforcement learning (RL) and graph-transformer networks (GTN) to address critical inefficiencies in traditional manual topology optimization. Motivated by the combinatorial increase of circuit design spaces and the computational cost of iterative simulations, this work develops a robust framework for generating energy-efficient topologies requiring rapid and reliable circuit design.

The framework integrates three key components: (1) an upper-confidence-bound-tree-based (UCT-based) RL model for circuit topology space exploration, (2) parallelized UCT algorithms to accelerate exploration processes, (3) a Graph-Transformer-based Network enabling fast circuit performance evaluation. Experimental validation demonstrates the whole framework …

The Role Of Excitatory Neuromodulation In Managing Variability Of Neural System Output, Omar Itani

Dissertations

Neural systems can generate consistent outputs across a population despite substantial variability in the underlying components of individuals. This dissertation aims to identify mechanisms through which neuromodulation influences the relationship between parametric and output variability in neural systems. Through a combination of theoretical analysis, computational modeling, and data-driven approaches, the research addresses how excitatory neuromodulation can shape population-level activity variability and identifies key patterns that govern the production of consistent neural population output despite underlying parameter variability.

The theoretical foundation is established by considering how excitatory neuromodulation affects population variability in simplified neuronal models. Two fundamental patterns of variability reduction …

Adversarial Robustness In Advanced Machine Learning Models Integrating Graph Neural Networks And Large Language Models, Mahmoud Nazzal

Dissertations

Artificial intelligence (AI) has achieved remarkable performances across various domains. In most real-world applications, data often takes relational forms, such as graphs and networks, or sequential forms, such as text and time series. As AI evolves, specialized models have emerged to handle these structures; Graph Neural Networks (GNNs) for relational mining and Large Language Models (LLMs) for sequential understanding. Despite their success, these models face challenges in security, robustness, and interpretability. GNNs excel in relational reasoning but are vulnerable to adversarial manipulation and lack interpretability, while LLMs are strong in linguistic reasoning and generalization yet struggle with relational data and …

An Inquiry Into The Physics Of Mixing And Floc Filtration, Andrew P. Pennock

Dissertations

Flocculation and clarification are two essential processes to deliver safe water at a reasonable cost to consumers. There are two major thrusts to the research presented in this dissertation. The first is to better characterize the physics and mixing parameters used for the design of hydraulic flocculators in the context of drinking water treatment plants. The second major thrust is to investigate floc filtration as a mechanism for the removal of primary particles during floc blanket clarification.

The intensity of mixing in environmental and chemical engineering applications is often characterized by the Camp and Stein velocity gradient. This parameter has …

Fact-Checking As A Multi-Step Process: From Ambiguity Resolution To Claim Validation, Wenbo Wang

Dissertations

The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. Fact-checking—the process of verifying claims against credible evidence—has emerged as a critical safeguard against misinformation. Yet, the task is fraught with challenges: claims are often ambiguous, context-dependent, or composed of multiple intertwined assertions, while automated systems struggle to replicate the nuanced reasoning of human experts. This dissertation addresses these challenges by reimagining fact-checking as a multi-step, knowledge-guided process that systematically resolves ambiguity, decomposes complexity, and validates claims through structured reasoning. Additionally, the proposed …

Enriching Vision Representation By Deep Neural Networks And Self-Supervised Learning, Yucong Shen

Dissertations

Nowadays, more and more interesting computer vision tasks are tackled by deep learning approaches. However, the increasing model complexity imposes significant computational and storage costs. To address this challenge, this dissertation explores efficient deep learning techniques, proposing morphological layer, an efficient feature extraction layer. It achieves competitive image classification accuracy with significantly decreased model parameters. Another attempt at efficient deep learning is a proposed channel pruning approach that compresses deep neural networks by identifying and removing redundant channels using optimal transport theory. This approach achieves significant reductions in model size and computational cost while maintaining or even improving performance across …

From Neural Networks To Large Language Models: Innovations In Financial Ai, Mathematical Reasoning, And Structured Data Representation, Junyi Ye

Dissertations

This dissertation explores the evolution and application of artificial intelligence techniques across three critical domains: financial modeling, mathematical reasoning, and structured data analysis. The dissertation presents seven research projects that chart a progression from specialized neural architectures to sophisticated large language models (LLMs), contributing novel methodologies and frameworks at each stage.

In the financial domain, the research first introduces TS-Mixer, a MLP-based architecture for time-series forecasting that captures both feature relationships and temporal dependencies through a simple yet effective design, outperforming more complex models in S&P500 index prediction. The dissertation then presents DySTAGE, a dynamic graph representation learning framework that …

Towards Explainable Ai On Graph Neural Networks: Xaig, Jiaxing Zhang

Dissertations

In the evolving landscape of artificial intelligence (AI), Graph Neural Networks (GNNs) have garnered growing prominence for their adeptness in processing graph-structured data. Despite this, the interpretability of their predictions often remains elusive. The demand for transparency and explainability in complex prediction models has reached unprecedented levels. To address this, post-hoc instance-level explanation techniques have emerged, aiming to unveil the rationale behind GNN predictions. These techniques endeavor to unearth substructures that elucidate the predictive behavior of trained GNNs.

This dissertation embarks on an exploration of Explainable AI (XAI) technologies within the realm of GNNs. Amid the challenges posed by the …

Phys 102-011: General Physics I, Keun Ahn

Physics Syllabi

No abstract provided.

Gamified Gait Rehabilitation Via Real-Time Biofeedback And Adaptive Hip-Exoskeleton Control, Mariya Huzaifa Tohfafarosh

Theses

Gait impairments arise from systemic diseases, age-related degeneration, musculoskeletal dysfunctions, or neurological conditions. While traditional rehabilitation can be effective, they often face challenges such as high costs, inaccessibility, and low patient engagement. To address these challenges, my work introduces a virtual reality-based rehabilitation (VRBR) system, integrating real-time motion and electromyographic (EMG) muscle activation feedback with a gamified virtual environment for enhanced adaptability and engagement. The system includes a custom-designed hip-exoskeleton that provides adaptive spring-like assistance or resistance, supporting both mobility-impaired users and strength training. Assistance levels can be tuned to match the user's progress. Additionally, a custom pressure insole was …

A Novel Framework For Dynamic Graph Representation Learning With Mamba, Ashish Pandey

Theses

Dynamic graph embedding is a key technique for modeling temporal dependencies in evolving networks. While transformer-based models perform well, their quadratic complexity limits scalability on long graph sequences. This thesis compares transformer approaches with the Mamba architecture-a linear-complexity state-space model—for temporal graph embedding.

Two frameworks are proposed: DG-Mamba and GDG-Mamba. DG-Mamba uses standard GCN-based spatial encoding, while GDG-Mamba incorporates domain-aware edge features using Graph Isomorphism Network with Edge Convolution (GraphGINE). Experiments on UCI, Reality Mining, Slashdot, Bitcoin-OTC, and SBM datasets show that Mamba-based models match or exceed transformer performance, especially on graphs with high temporal variability.

The thesis also applies …

Ce 321 - 141: Water Resources Engr, Nicole Delmonaco

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 332 -142: Structural Analysis, Avinash Prasad

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 341a -141: Soil Mechanics Lab, Ayodeji Aderibigbe

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 431 - 141: Steel Design, Avinash Prasad

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 432 - 141: Steel Design, Rajendra Navalurkar

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 461 - 131: Professional Practice In Cee, Avinash Prasad

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 495 - 141, Hm1: Civil Engr Design Ii, Joseph Baladi

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 495 - 142, Hm1: Civil Engr Design Ii, Joseph Baladi

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 611 - 850: Proj Planning & Control, Muhammad Elgammal

Civil and Environmental Engineering Syllabi

No abstract provided.

Ce 615 - 850: Infrastructure & Fac Rem, Giri Venkiteela

Civil and Environmental Engineering Syllabi

No abstract provided.