Digital Commons Network^™

Pat Conroy First Edition Book Collection - Accession 1747, Donald Patrick Conroy

Manuscript Collection

The Pat Conroy First Edition Book Collection consists of first edition publications authored by Donald Patrick “Pat” Conroy (1945–2016), an acclaimed American novelist whose work is closely associated with the cultural, social, and physical landscapes of the South Carolina Lowcountry. Many of the volumes in this collection are signed by the author, enhancing their research and evidentiary value.

The collection spans Conroy’s literary career from his debut memoir The Boo (1970) through The Death of Santini (2013), a reflective work that revisits characters and themes central to his earlier writings. Included are major novels, memoirs, and nonfiction works such as …

The Normal Review, The Creative Arts Magazineof Montclair State University, Spring 2026, The Normal Review

The Normal Review

No abstract provided.

The Pure Yang-Mills Field, \, Iia: The Axioms, James Glimm

Department of Applied Mathematics & Statistics Faculty Publications

A previously constructed pure Yang-Mills quantum gauge field theory is shown to satisfy all Osterwalder Schrader axioms.

The Pure Yang-Mills Field, \, Iia: The Mass Gap, James Glimm

Department of Applied Mathematics & Statistics Faculty Publications

No abstract provided.

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 …

Machine Learning-Based Lifetime Prediction Of Lithium Batteries: A Comparative Assessment For Electric Vehicle Applications, Abdelilah Hammou, Raffaele Petrone, Demba Diallo, Boubekeur Tala-Ighil, Philippe Makany Boussiengue, Hicham Chaoui, Hamid Gualous

Electrical & Computer Engineering Faculty Publications

This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aims to find the most efficient prognosis method considering two constraints: the limitation of computational power and the unavailability of on-board capacity measurement that requires full charge and discharge conditions. The machine learning models are trained using capacity values estimated under vehicle conditions. The ageing data is collected from cycling tests of two battery chemistries, Lithium Fer Phosphate (LFP) and Nickel Manganese Cobalt (NMC), …

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 …

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 …

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, …

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 …

Fault Tolerance Of Accelerated Asynchronous Fixed-Point Iterations On Flexible Computing Infrastructure, Evan Coleman, Masha Sosonkina

Electrical & Computer Engineering Faculty Publications

Asynchronous iterative methods tolerate straggling processors by allowing workers to proceed with stale data, but at a cost: the iterates become inconsistent, potentially degrading convergence. We investigate whether convergence accelerators such as Anderson acceleration compensate for this degradation. We experimentally study three fixed-point iterations: the Jacobi method for sparse linear systems, value iteration for the Bellman equation, and the Hartree–Fock self-consistent field (SCF) iteration. The experiments are conducted using a high-performance execution framework, Ray, which abstracts the complexity of distributed systems and enables code parallelization and fault injection with minimal changes.

We establish two main results. First, straggler tolerance is …

Application Of A Hyaluronic Acid-Based Lotions Containing Hydroxyapatite Nano-Particles Approach For Treatment Of Initial Dental Caries, Zahra Gharavi, Zahra Namazi, Fatemehsadat Pishbin, Helia Givian, Maryam Torshabi, Farhood Najafi, Parisa Amdjadi

Electrical & Computer Engineering Faculty Publications

This study evaluated the remineralization potential of a ceramic–polyelectrolyte system, based on a novel combination of hydroxyapatite nanoparticles (HAp NPs) and a hyaluronic acid (HY) matrix, for dental enamel, a tissue that remains challenging to repair in clinical dentistry. Nano-Hydroxyapatite (nanoHAp) powder was synthesized and characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), field-emission scanning electron microscopy (FE-SEM), dynamic light scattering (DLS), and zeta potential analysis to confirm their crystalline structure, functional groups, morphology, particle size distribution, and colloidal stability. HY-based suspensions (remineralizing lotions) containing 5, 10, and 12 wt% of the synthesized nanoHAp particles were formulated via a …

First Report Of A Trematode Infection In An Invasive Population Of Pomacea Maculata: Evidence Of A Phaneropsolid (Trematoda: Microphalloidea) And Recommended Methods For Surveillance, Jonah A. Nguyen, Lori Tolley-Jordan, Annie P. Slayton, Bradley M. Richardson, Thomas G. Rosser

Research, Publications & Creative Work

Giant apple snails, Pomacea maculata (Caenogastropoda: Ampullariidae), are native to South America but are now invasively established in subtropical freshwater habitats worldwide. In May 2017, P. maculata from an urban pond in Mobile, Alabama, USA were found infected with a larval trematode, prompting further collections to determine prevalence and investigate effects of infection by snail sex and size. In total, 284 snails (n = 184 female, n = 100 male) were collected in May, August, and October of 2017 and 2018. Of these, 60 females and 23 males were infected with prevalence per sampling event ranging from 4 to …

Informed Applicants: Anti-Discrimination Enforcement Based On The Use Of Criminal Records, Benjamin David Pyle

Faculty Scholarship

Navigating the labor market with a criminal record is treacherous. Countless job applicants will be turned down immediately after a potential employer learns they have had contact with the criminal legal system. Criminal records reflect and exacerbate long-standing racial and economic inequalities in the United States. Thus, the use of records in employment decisions will often implicate anti-discrimination laws. However, successfully enforcing these protections requires job applicants to, at minimum, understand why they were not hired. Many applicants never learn why they were not hired and thus will never be able to protect their rights. This Article argues that understanding …

Spending Programs And The New Roberts Court, Nicole Huberfeld

Faculty Scholarship

The Supreme Court is poised to place new limits on Congress’s spending power at the urging of states vying for regulatory dominance, particularly in health care and public health. This article is the first to highlight and catalog the throughlines of argumentation, which sparked after states successfully challenged the ACA but have grown into opposing more than Medicaid’s cooperative federalism. This means federal and state programs that heavily rely on federal spending power are at risk, so studying this pattern is imperative. The article begins with a descriptive account of increasing spending power challenges, which make use of converging factors …

Timing Whistleblowing, Andrew K. Jennings

Faculty Articles

Whistleblower programs (WBPs) expose hidden corporate wrongdoing. They do so by offering retaliation protection and financial bounties to those who bring original information to law enforcement about unseen, and often complex, misconduct. Under a standard account, whistleblowing serves the public interest by increasing the ex post detection of illegal activity and the ex ante risk of its exposure. That standard view—which centers whistleblowing on detecting wrongdoing—is incomplete, however. To present a fuller view, this Article introduces a complementary model that refocuses whistleblowing as preventing wrongdoing. Working together, these models show that a WBP can do more than enable detection at …

Catholic Solidarity In American Jesuit Education Through The Radical Kinship Of Fr. Greg Boyle: A Conceptual Analysis, Sajit U. Kabadi

Journal of Catholic Education

This is a conceptual analysis that examines how American Jesuit secondary education can pursue greater Catholic solidarity in a polarized world through a framework of radical kinship, as espoused by Fr. Greg Boyle. Boyle is the founder of Homeboy Industries, the world’s largest gang rehabilitation program, in the city of Los Angeles. The purpose of this conceptual analysis is to delve deeper into Boyle’s conceptual framework of radical kinship in the context of American Jesuit secondary education. This conceptual analysis will use Catholic Social Teaching, Biblical references, and the values of the Jesuit mission.

Christmas Peace, St. Norbert College Music Department

Music Performances

See Program

Skin Type Diversity In Image Datasets, Neda Alipour

Doctoral

Image-based AI systems that analyse human skin are increasingly used in healthcare and computer vision applications. However, many human skin-based image datasets do not provide reliable information about skin type, making it difficult to assess whether these systems perform consistently across the full spectrum of skin colour. The objective of this thesis is to examine how skin type diversity is represented and measured in image datasets, and to evaluate the reliability of image-based skin type measurement methods under different imaging conditions. Using publicly available skin lesion image datasets as a well-defined and widely used sub-class of skin image datasets, this …

The Role Of Artificial Intelligence And Virtual Reality In Postgraduate Medical Education, Yasir Hamandi

Iraqi Postgraduate Medical Journal

No abstract provided.

Steady As She Goes: Remote Work Benefits Daily Goal Progress Through Reduced Energy Variability, Andrew A. Bennett, Kathleen R. Keeler, Emily D. Campion, Sheila K. Keener

Management Faculty Publications

As the prevalence and allowance of remote and hybrid work evolves, research has shown consistently mixed findings about how work location is related to worker energy and perceptions of daily performance. We aim to resolve this ambiguity by integrating affective events theory with ideas about emotion dynamics to develop new theorizing explaining why energy variability, rather than mean energy level, is an important predictor of daily goal progress. We test our hypotheses using a sample of 219 employees from one organization allowing remote and hybrid work, capturing individual hourly energy levels for five consecutive workdays (4,812 hourly observations). Our …

Stories Are Weird, Hadley Kirschner

Voice/Craft

Grade 8

Why Should You Try Jarling's Custard Cup?, Elijah Thrapp

Voice/Craft

Grade 8

Analyzing An Ai Summary Of An Executive Order, Natalie D. Ebolum

Open Educational Resources

This assignment asks students to critically analyze the Executive Order on Incarceration in Private Prisons by producing an independent summary of its intent, purpose, and implementation, along with best-practice recommendations for improving its effectiveness. Students then use the Grammarly AI Summary Tool to generate an alternative summary and compare it with their own analysis. Through structured self-assessment and revision, students evaluate differences in interpretation, identify gaps or insights, and reflect on the strengths and limitations of AI-assisted analysis. The assignment emphasizes critical policy analysis, ethical use of AI tools, and student-centered judgment.