Digital Commons Network^™

Estrategias Lúdicas Como Didáctica En La Formación Especializada De Licenciados En Educación Infantil, Fe Rosalía Alba Veloz

Theses and Dissertations

El propósito de este estudio fue explorar la percepción que poseen los docentes acerca del uso de estrategias lúdicas como recurso didáctico en la formación especializada de los estudiantes de la Licenciatura en Educación Infantil, y cómo dichas percepciones inciden en sus prácticas áulicas dentro de una universidad pública en República Dominicana. La investigación se fundamentó en teorías constructivistas, particularmente en el enfoque Histórico-Cultural de Vygotsky y en la noción de zona de desarrollo próximo, además de los aportes de diversos autores que han resaltado la importancia de vincular teoría y práctica, reconocer el papel de las emociones en el …

La Influencia De Los Videojuegos En El Rendimiento Académico En El Condado De Fairfax Virginia, Wilmary Soto Vélez

Theses and Dissertations

Este estudio cuantitativo, de enfoque descriptivo y correlacional, examinó la influencia del uso de los videojuegos en el rendimiento académico de estudiantes de nivel elemental en una escuela pública del condado de Fairfax, Virginia. La investigación surge ante la preocupación institucional por los bajos resultados obtenidos en las pruebas estandarizadas Standards of Learning (SOL) durante los últimos ocho años, lo que motivó el interés por analizar la relación entre el uso de los videojuegos y el desempeño escolar de los estudiantes.

El propósito principal fue determinar si existe una correlación entre el tiempo y la forma en que los estudiantes …

Screen Time Use, Reading Achievement, And Thinking Maps: A Correlational Study Of Fifth-Grade Students, Morgan Brooke Unger

Theses and Dissertations

The problem addressed in this study was the fixation of screen time among fifth-grade students at a kindergarten to Grade 12 private school in South Florida. By the time children reach age 12, 69% of adolescents own a smartphone. The purpose of this study was designed to determine (a) the relationship between screen time use and reading achievement among fifth graders, (b) the relationship between parents’ awareness of their child’s screen time use and their child’s actual screen time use, and (c) how well fifth-grade students’ Thinking Maps illustrate the problems associated with screen time fixation at a kindergarten to …

A Case Study Of Employees’ Perception Of Leadership Styles In Law Firms, Naylet M. Sanchez

Theses and Dissertations

This applied dissertation explored employees' perceptions of their supervisors’ leadership styles and how those perceptions shape their job performance and satisfaction within a mid-sized personal injury law firm operating across multiple offices in the Southeastern United States. The effectiveness of leadership styles in law firms significantly influences employee performance, productivity, and organizational success, yet the unique contextual factors of legal practice environments have remained substantially underexplored in the leadership literature. Much of the existing research has focused on general organizational settings rather than the knowledge-intensive, hierarchical, and high-pressure dynamics that characterize law firms. This applied dissertation was designed to address …

Balancing Care And Chaos: A Phenomenological Study On The Emotional And Mental Health Challenges Of Emergency Department Nurses, Shantilee Green

Theses and Dissertations

The purpose of this qualitative, phenomenological study was to examine the experiences of emergency department nurses related to emotional and mental health challenges, coping with stress, and ways to provide them with better workplace support. Emergency department nurses are required to work 12-hour shifts in a high-stress environment with a significant patient load and an inadequate patient-to-nurse ratio. Emergency department nurses are exposed daily to patient events that can be traumatic and affect their mental and emotional health, and nurses may find it challenging to cope with the resultant stress.

The researcher created an interview protocol of 14 questions to …

Gain-Delay Decoupling In Human Stimulus Frequency Otoacoustic Emissions Reveals Constraints On Cochlear Nonlinear Amplification, Yoshita Sharma, Arturo Moleti, Renata Sisto, Teresa Botti, Hansapani Rodrigo, Sri Mishra

School of Mathematical & Statistical Sciences Faculty Publications

Purpose: The nonlinear cochlear amplifier, driven by outer hair cells, underlies the remarkable sensitivity and frequency selectivity of the mammalian auditory system. Stimulus frequency otoacoustic emissions (SFOAEs) provide a noninvasive window into these active cochlear processes, yet the relationship between emission gain and delay across stimulus levels remains incompletely understood. This study examined the level dependence of SFOAEs in normal-hearing human listeners to characterize cochlear nonlinear response properties. We tested how emission gain and delay vary with stimulus level and estimated the frequency of the apical–basal transition associated with the breakdown of the approximate local scaling symmetry.

Methods: SFOAEs were …

From Implementation To Impact: Schoolwide Mtss Systems And Student Growth, Amanda Kay Reinarts

All Electronic Theses and Dissertations

Multi-Tiered Systems of Support (MTSS) are widely used in schools to organize academic, behavioral, and social-emotional support for students. Although MTSS is intended to function as a comprehensive schoolwide framework, implementation often becomes fragmented when schools treat it as a standalone tiered-intervention model rather than an integrated system of support, instruction, assessment, and intervention. This fragmentation can limit the effectiveness of MTSS and reduce its impact on student achievement and organizational improvement. This literature review examines how school leaders leverage MTSS as a dynamic, schoolwide system to improve student achievement and organizational effectiveness. This review focuses on leadership structures, organizational …

Investigating The Relationship Between College Students’ Sense Of Belonging And Graduation Rates At A Private, Predominantly White Institution, Jamie Symonette

All Electronic Theses and Dissertations

This quantitative research analyzed the association between a sense of belonging and academic achievement among 514 undergraduates in a small, all-white institution (PWI), based on the 2025 Campus Life Survey and institutional data. Although national graduation disparities persist (64% of White students graduate within six years versus 54% of Latinx, 44% of Black, and 39% of Native American students), limited quantitative research has investigated belonging in private PWI settings. Variables were perceived sense of belonging, institutional climate, demographic factors (race/ethnicity, sex), academic advancement signs (cumulative GPA, earned credit hours), and seriously considered leaving. Multi-Regression (hierarchical and binary) and binary logistic …

Integrating Self-Organizing Maps And Explainable Artificial Intelligence To Unravel Surface Water-Groundwater Hydrochemical Evolution And Drinking-Water Suitability Controls In A Data-Limited Arid Basin, Hui Tong, Ruizhong Gao, Yinlong Wang, Xixi Wang, Limin Duan, Guoqiang Wang

Civil & Environmental Engineering Faculty Publications

Study region

The Dahei River Basin, located in the semi-arid northern region of China's Great Bend of the Yellow River.

Study focus

This study integrates hydrochemical characterization, stable hydrogen-oxygen isotopes, Self-Organizing Maps (SOM), the entropy-weighted water quality index (EWQI), and explainable ensemble learning (XGBoost-TreeSHAP) to investigate surface water-groundwater evolutionary patterns and identify key hydrochemical contributors to drinking-water suitability in the Dahei River Basin.

New hydrological insights for the region

Waters in the basin are predominantly weakly alkaline and are characterized by HCO₃^--Ca-Mg and HCO₃^--Na facies, indicating the combined effects of carbonate weathering and cation exchange, with …

Vocational Education And Employment Outcomes For Individuals With Disabilities: Trends, Barriers, And Supported Employment Practices, Taylor John Peterson

All Electronic Theses and Dissertations

This thesis examines literature that focuses on vocational education and employment outcomes for individuals with disabilities. This paper includes current vocational practices, barriers to employment, and the effectiveness of supported employment for individuals with disabilities. The studies used for this thesis show that work-based learning, career and technical education (CTE), and instruction in employability skills positively affect students’ employment outcomes. There are also barriers to employment for individuals with disabilities. They include employer misconceptions, limited accommodations, and a lack of transition services. Individuals with disabilities who receive supported employment have a much greater chance of obtaining employment. The findings in …

Lived Experiences Of Special Education Teachers Of Color In Minnesota Charter Schools: A Phenomenological Study Of Factors Influencing Retention, Gigi Krause

All Electronic Theses and Dissertations

Special education faces a critical shortage of licensed educators, a crisis intensified for teachers of color, who remain underrepresented and experience disproportionately high attrition rates. Although Minnesota has implemented initiatives to diversify the teaching workforce, the retention of novice special education teachers of color, particularly within charter school settings, remains underexamined. This qualitative phenomenological study explored the lived experiences of novice special education teachers of color in Minnesota charter schools to identify factors influencing their decisions to remain in or leave the profession. Guided by Critical Race Theory and Mentorship Theory, the study employed a transcendental phenomenological approach. Semi-structured interviews …

The Effects Of Parental Involvement On A Child’S Educational Success, Samson Ghirmai

All Electronic Theses and Dissertations

The accessibility of education has opened doors for children to pursue interests and be prepared for their transition into adulthood. Education is a resource that opens doors, but it has been found that there are gaps in achievement. This opens the conversation on possible solutions to reduce this gap. Schools have been developing new systems and strategies to increase parental involvement, determining this as a possible solution towards bridging the gap. The purpose of this study is to determine whether the solution towards the achievement gap is identifying strategies to get parents more involved in their child's education. Research determined …

The Judicial Voice On The Courts Of Appeals, Allison Orr Larsen, Neal Devins

Faculty Publications

The judicial voice on an appellate court typically speaks in the collective, so when a judge chooses to go solo—either in a dissent or a concurrence—that act deserves a close look. Separate opinions on the U.S. Supreme Court are common because the Justices have strong incentives to articulate a distinctive personal jurisprudence. But lower court judges have always been more reluctant to write separately, and for good reason. The institutional design and longstanding practices of the U.S. courts of appeals are very different from the Supreme Court: Lower appellate court judges are bound by precedent in a different way, rarely …

A Comprehensive Survey Of Prompt Engineering Techniques In Large Language Models, Tonmoy Debnath, Md Nurul Absar Siddiky, Muhammad Enayetur Rahman, Prosenjit Das, Antu Kumar Guha, Muhammad Rezaur Rahman, H. M. Dipu Kabir

Electrical & Computer Engineering Faculty Publications

Prompt engineering has arisen as a pivotal discipline in optimizing the performance of Large Language Models (LLMs) by structuring inputs to enhance coherence, accuracy, and task alignment. This paper comprehensively surveys various prompting techniques, systematically categorizing them according to their application domains and methodological foundations. Fundamental approaches like zero-shot and few-shot prompting are examined along with advanced strategies, including chain-of-thought reasoning, retrieval-augmented generation, and self-consistency mechanisms. A rigorous qualitative analysis is conducted to evaluate each technique's strengths, limitations, and optimal use cases, offering a structured framework for selecting the most effective prompting strategies. Theoretical insights and empirical findings are consolidated …

Physics-Informed Temperature Prediction Of Lithium-Ion Batteries Using Decomposition-Enhanced Lstm And Bilstm Models, Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, François Allard

Electrical & Computer Engineering Faculty Publications

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally …

Markov Chain Wave Generative Adversarial Network For Bee Bioacoustic Signal Synthesis, Kumudu Samarappuli, Iman Ardekani, Mahsa Mohaghegh, Abdolhossein Sarrafzadeh

Electrical & Computer Engineering Faculty Publications

This paper presents a framework for synthesizing bee bioacoustic signals associated with hive events. While existing approaches like WaveGAN have shown promise in audio generation, they often fail to preserve the subtle temporal and spectral features of bioacoustic signals critical for event-specific classification. The proposed method, MCWaveGAN, extends WaveGAN with a Markov Chain refinement stage, producing synthetic signals that more closely match the distribution of real bioacoustic data. Experimental results show that this method captures signal characteristics more effectively than WaveGAN alone. Furthermore, when integrated into a classifier, synthesized signals improved hive status prediction accuracy. These results highlight the potential …

Microfluidic Encapsulation Of Sorafenib-Loaded Zif-8 Nanoparticles In Ph-Responsive Alginate Microparticles For Oral Chemotherapy Of Hepatocellular Carcinoma, Mojdeh Mirshafiei, Zahra Mahmoudi, Mehdi Mehrpouya, Mahdi Mahmoudi, Masoud Rezaeian, Mona Navaei-Nigjeh, Zahra Katoli, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality. Sorafenib is the current first-line oral therapy; however, its therapeutic efficacy is limited by poor aqueous solubility, low bioavailability, and gastrointestinal instability. This study aimed to develop a pH-responsive nano-in-microparticle delivery system using a single-step droplet-based microfluidic process to protect sorafenib in the gastric environment and achieve controlled release for enhanced oral chemotherapy. Sorafenib-loaded ZIF-8 nanoparticles (SZ NPs) were synthesized and characterized by scanning electron microscopy (SEM), Fourier-transform infrared (FTIR) spectroscopy, Energy-dispersive X-ray (EDX) spectroscopy, and X-ray diffraction (XRD), exhibiting a mean diameter of about 72 nm and …

The Gut-Heart Axis: Exploring The Role Of The Gut Microbiome In Cardiovascular Health-A Focused Systematic Review, Yahya Makkieh, Haider Hussain Shah, Sakan Binte Imran, Shadman Mahmood Mahmood Khan Pathan, Anagha Chirayath Saju, Mounica Majooju, Aastha Garg, Tarun Naag, Rabeeul Islam, Cheeranthodika Fahima, Ramsha Ali

Electrical & Computer Engineering Faculty Publications

This focused systematic review examines the role of the gut microbiota in cardiovascular disease (CVD). The review explores mechanisms linking gut dysbiosis with CVD via microbial metabolites such as trimethylamine-Noxide (TMAO) and short-chain fatty acids (SCFAs), which affect inflammation, endothelial function, and lipid metabolism. Interventions including dietary modifications, probiotics, prebiotics, fecal microbiota transplantation, and pharmacological agents such as statins, rifaximin, and empagliflozin are evaluated for their impact on microbial composition and cardiovascular outcomes. Probiotic strains and fiber-rich diets demonstrated modest improvements in blood pressure, lipid profiles, and inflammatory markers. Studies revealed that gut microbiome alterations influence drug metabolism and bleeding …

Mtl_Tx: A Multi-Task Transformer Model For Improved Radiation Time-Series Estimation, Hongfang Zhang, Adam Stavola, Hal Ferguson, Bence Budavari, Hongyi Wu, Chiman Kwan, Jiang Li

Electrical & Computer Engineering Faculty Publications

Controlling radiation doses at potential radioactive facilities is critical to ensuring the safety of both personnel and the public. At the Thomas Jefferson National Accelerator Facility (JLab), multiple sensors are deployed around the three experimental halls to monitor key parameters, including single-beam current, energy levels, current leakage, and radiation values during accelerator operations. In this study, we developed a Multi-task Transformer model, MTL_TX, to accurately estimate radiation doses at sensor locations based on historical data, with the aim of enhancing safety in accelerator facilities and surrounding public areas. To improve estimation accuracy, we integrated two innovative components into the proposed …

Photobiomodulation Mitigates The Inhibitory Effects Of Pirfenidone On Cancer-Associated Fibroblasts And Their Pro-Tumorigenic Properties: A Cause For Concern, Nima Rastegar-Pouyani, Alireza Nasirpour, Fatemeh Javani Jouni, Hossein Vazini, Ahmad Moshaii

Electrical & Computer Engineering Faculty Publications

Cancer-associated fibroblasts (CAFs) within the tumor microenvironment highly contribute to cancer progression and poor prognosis. Recent findings introduce pirfenidone, a medication for pulmonary fibrosis, as a promising repurposed candidate for inhibiting CAFs. Moreover, photobiomodulation (PBM), or low-level laser therapy, has surfaced as a potential modality against cancer; however, its impact on CAFs is not thoroughly comprehended. The present study scrutinized the effects of PBM, pirfenidone, and PBM+pirfenidone on CAFs, and their consequent impact on cancer cells using the CAF-conditioned media. After treating CAFs with PBM at wavelengths 660 and 980 nm with/without PFD and then culturing cancer cells in the …

Recent Insight And Perspective Of Marine-Inspired Biopolymers For Wound Healing Applications - A Review, Muhammad Umar Aslam Khan, Saiqa Yousaf, Abdalla Abdal-Hay, Mohd Faizal Bin Abdullah, Sahar Madani, Muhammad Shahzad Zafar, Goran M. Stojanović, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

There is an increasing necessity for advanced, sustainable, and biocompatible materials for wound healing as therapeutic and diagnostic products. Marine environments, characterized by high biodiversity, offer an underutilized source of natural resources with enormous potential for creating novel materials for dressings. This review highlights the revolutionary nature of polymeric biomaterials of marine origin, with a focus on polysaccharides, like alginate, chitosan, and carrageenan; proteins, such as collagen and gelatin. These biopolymers are outstanding in their physicochemical properties, such as biodegradability, bioactivity, and modifiable mechanical strength, which enable their use in wound-healing systems. Besides, these biomaterials may be easily chemically and …

Qubit Lattice Algorithm Simulations Of The Scattering Of A Bounded Two Dimensional Electromagnetic Pulse From The Infinite Planar Dielectric Interface, Min Soe, George Vahala, Linda Vahala, Efstratios Koukoutsis, Abhay K. Ram, Kyriakos Hizanidis

Electrical & Computer Engineering Faculty Publications

Qubit lattice algorithm (QLA) simulations are performed for a two-dimensional spatially bounded pulse propagating onto a plane interface between two dielectric slabs. QLA is an initial value scheme that consists of a sequence of unitary collision and streaming operators, with appropriate potential operators, that recover Maxwell equations in inhomogeneous dielectric media to the second order in the lattice discreteness. For the case of total internal reflection, there is transient energy transfer into the second medium due to the evanescent fields as the Poynting unit vector of the pulse is rotated from its incident to reflected direction. Because of the finite …

Pin-Plane Electrical Discharge Driven By A Mosfet Dc Current Source, Myles Perry, Sidmar Holoman, Daniel Wozniak, Shirshak Kumar Dhali

Electrical & Computer Engineering Faculty Publications

The generation of atmospheric pressure nonequilibrium plasma using electrical discharges is an active area of research due to its significance in a wide spectrum of applications including medicine, combustion, and manufacturing. In our attempt to create a helium plasma jet in a pin-plane discharge with a constant current source, we observed self-pulsating behavior. We present the results of the electrical, optical, and spectroscopic measurements carried out to characterize the discharge. The duration of the discharge is a few tens of nanoseconds, and the repetition rate is in the few tens of kHz. The effect of the gap distance and gas …

Healthcare Digital Twins: A Methodological Literature Review On Integrating Iot And Ai For Personalized Medicine And Predictive Care, Sara Shahnazinia, Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Ali Karimoddini

Electrical & Computer Engineering Faculty Publications

Digital Twin (DT) technology has the potential to revolutionize healthcare delivery and enhance patient outcomes through personalized and precision medicine, simulation models for operations and interventions, and drug discovery. However, successful implementation of DTs in Internet of Things (IoT) and artificial intelligence (AI) healthcare is contingent upon addressing key challenges such as privacy, ethics, and robust data security. This paper presents a methodological literature review of DT applications in healthcare, systematically analyzing the current state of research, key enabling technologies, and implementation challenges. The review summarizes DT categorization approaches (application-based, technology-based, and real-time function-based); delineates core DT components such as …

Gem-Can: A Real-World Dataset Of Can-Bus Attack Scenarios On An Autonomous Vehicle For Intrusion-Detection Research, Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Ali Karimoddini, Tienake Phuapaiboon, Milad Khaleghi, Daniel Tobias

Electrical & Computer Engineering Faculty Publications

This paper presents GEM-CAN, a labelled Controller Area Network (CAN) dataset captured from an autonomous GEM e6 platform under both normal operation and controlled cyber-attack conditions.

The dataset contains ∼143 K frames comprising (i) ∼ nominal autonomous operation (∼100k messages), (ii) DoS floods using arbitration ID 0 × 00000000 (∼41 K messages), and (iii) data-tampering injections that reuse legitimate IDs for brake and steering-lock (∼1.3 K messages). Each record includes timestamp, arbitration ID (11/29-bit), DLC, eight payload bytes, and a Normal/Attack label. A companion metadata file enumerates attack windows, PCAN bus-load traces, bitrate, and test conditions. Data were collected with …

Multimodal Machine Learning For Alzheimer's Disease Classification Using Adni Biomarker Fusion, Hesameddin Mostaghimi, Hamid R. Okhravi, Bahar Niknejad, Daniel A. Cohen, Michel A. Audette, Alzheimer's Disease Neuroimaging Initiative

Electrical & Computer Engineering Faculty Publications

Despite ongoing advances, accurate diagnosis of Alzheimer’s disease (AD) remains challenging due to its multifactorial nature, comorbidities, and clinical heterogeneity. Accordingly, approaches that combine multimodal data may improve AD classification by integrating complementary information. To investigate this, we evaluated classification performance using a preprocessed ADNI-3 dataset comprising a shared set of clinical/cognitive features along with four imaging modality-based cohorts: trimodal (MRI + amyloid PET + tau PET), MRI + amyloid PET, MRI + tau PET, and MRI-only. We trained a range of supervised machine learning (ML) and deep learning (DL) classifiers using stratified five-fold cross-validation and evaluated performance using accuracy, …

Interpretable Battery Soh Prediction: A Comparative Interpretability Framework For Multi-Architecture Ml Models, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

This work introduces a unified interpretability-efficiency framework for lithium-ion battery state of health (SOH) prediction using hybrid deep learning architectures. We comparatively analyze four hybrid models: CNN LSTM MultiHead, CNN Feature Extractor LSTM, DNN LSTM, and DNN BiLSTM to disentangle how network topology, feature composition, and computational design influence both predictive fidelity and physical interpretability. By integrating Monte Carlo Shapley (MC Shapley), background occlusion SHAP (BoSHAP), and ablation analysis, we quantify the contribution and robustness of five electrochemical feature groups: time, capacity, voltage, dQ/dV and peaks of dQ/dV from NASA battery dataset. The results reveal a consistent dominance of differential …

An Explainable Cs-Mitigation Triangular (Ecsmt) Framework To Secure Graph Neural Networks, Sabah Ettahri, Sergio Pallas Enguita, Chung-Hao Chen, Wen-Chao Yang

Electrical & Computer Engineering Faculty Publications

This research addresses cyber risk by defending against backdoor attacks on Graph Neural Networks (GNNs). We propose the Explainable Complex System-Mitigation Triangular (ECSMT) Framework, which integrates Robust Training, Graph Regularization, and Data Sanitization into a lightweight, hardware-efficient defense layer. To evaluate structural generalizability, we conducted empirical evaluations across three distinct benchmark domains (AIDS, MUTAG, and PROTEINS) using a Graph Isomorphism Network (GIN) backbone. Under a baseline 5% backdoor subgraph trigger injection ratio, ECSMT achieves excellent utility retention, securing a Clean Accuracy (CA) of 97.33% (±0.62%) while reducing the Attack Success Rate (ASR) from 97.00% down to 69.45% on the primary …

Dynamic Direct Voltage Control Under Maximum Torque Per Ampere For Interior Pmsms, Mohamad Alzayed, Hicham Chaoui, Alaref Elhaj

Electrical & Computer Engineering Faculty Publications

A novel method for controlling the speed of interior permanent magnet synchronous motors (IPMSMs), known as the current-sensing-based dynamic direct voltage control method under the maximum torque per ampere (MTPA) concept, is introduced. This technique achieves precise tracking of machine velocity by determining the optimal combination of voltage amplitude and angle for each specific motor velocity and current/load condition. Unlike previous studies, this approach takes into account the transient model of the machine, resulting in improved accuracy during dynamic operating conditions compared with existing methods in the literature. Moreover, a comparative analysis is conducted involving different direct voltage MTPA speed …

Generative Ai-Driven Optimization In Flexible And Reconfigurable Manufacturing Systems, Salah Hammedi, Hicham Chaoui, Lotfi Nabli

Electrical & Computer Engineering Faculty Publications

Flexible and Reconfigurable Manufacturing Systems (FRMSs) are essential for coping with variability in modern production environments; however, efficient scheduling and rapid reconfiguration remain challenging. This paper presents a hybrid optimization framework that integrates Colored Petri Net (CPN) modeling with Generative Artificial Intelligence (GenAI) to enhance scheduling performance and system adaptability. The CPN formalism ensures verifiable modeling of system dynamics, while a transformer-based generative model produces candidate scheduling and reconfiguration strategies. Simulation experiments were conducted under static, dynamic, and adaptive scenarios, including machine breakdowns and dynamic job arrivals. Performance was evaluated using makespan, mean flow time, machine utilization, and reconfiguration latency. …