Digital Commons Network^™

Physiological Response To Concealed Information In A Mock-Crime Paradigm​, Ezra Koenigsberg, Tamya Mchenry, Jasmine Manzana, Taylor Powell, Samantha Jackson

Science University Research Symposium (SURS)

The Concealed Information Test (CIT), based on Lykken’s guilty knowledge framework, measures physiological responses when individuals recognize crime-related details compared to neutral alternatives. Previous studies show that variations in stimulus presentation enhance the CIT’s accuracy, especially through changes in electrodermal activity and heart rate. However, few studies have tested whether a Qualtrics-based mock-crime version of the CIT can replicate these physiological patterns. This study addresses that gap by adapting the CIT for a digital research environment. The goal is to determine whether participants show greater physiological arousal—measured through skin conductance, heart rate, and respiration—when exposed to crime-related “critical” stimuli compared …

Deconstructing The Black Box: An Explainability Analysis Of Deep Learning Architectures In Cytopathology, Chase A. Garrett

Master's Theses or Doctor of Nursing Practice

Deep learning shows strong potential in medical-image analysis, yet adoption in cyptopathology remains limited. Cytopathology could benefit from deep learning applications by improving diagnostic efficiency and accuracy. However deep learning comes with a notorious “black box” that keeps the models from being transparent and trustworthy for widespread clinical adoption. We conducted a comprehensive and comparative analysis of several deep learning architectures for multi-class classification of acute leukemia types, ALL, AML, and normal healthy cells from peripheral blood smear images. The models in this research include a Vision Transformer (ViT) and a diverse selection of Convolutional Neural Network (CNN) models. The …

Characterization Of Antiretroviral Therapy (Art) Adherence Phenotypes And Psychosocial Symptom Clusters Among Black/African American (Aa) And Hispanic/Latine Adolescents And Young Adults (Ayas) With Hiv In The Adherence Connection For Counseling, Education, And Support (Access-Ii) Trial, Ann-Margaret Navarra, Taehoon Ha, Eva Liang, Maurade Gormley, David R. Garcia, Jason Fletcher, Lloyd A. Goldsamt, Michael G. Rosenberg, Karin Hasegawa, Jie Yang

School of Nursing Faculty Publications

Antiretroviral therapy (ART) adherence behavior is heterogeneous among adolescents and young adults (AYAs) with HIV and influenced by individual and interpersonal psychosocial factors. The primary objective of this study is to characterize ART adherence phenotypes and psychosocial symptom clusters, as related to ART adherence and HIV viral load suppression. This analysis included 60 AYAs with HIV enrolled in an ART adherence support clinical trial. Self-reported ART adherence at baseline, 12-weeks, and 24-weeks was used to define four ART adherence phenotypes: consistently high adherence (YY), early-only adherence (YN), late-only adherence (NY), and consistently low adherence (NN). Symptom clusters were empirically derived …

Teacher Perceptions Of School Leadership: Trust, Fairness, And Support As Predictors Of Student Achievement, Trever B. Smith

Honors Theses

This quantitative study examined the relationship between staff–leadership working conditions and student achievement outcomes in Kentucky high schools. This research makes use of extant data from the Impact Kentucky Working Conditions Survey for the 2023-2024 school year. The researcher particularly studied the correlation between teacher perceptions of trust, fairness, and support from principals and student achievement in the Kentucky Summative Assessment (KSA). Teacher perceptions of working conditions were generally favorable, though trust and perceptions of school leadership emerged as weaker areas. Correlation analyses indicated only weak, nonsignificant associations between leadership working conditions and student achievement outcomes at the statewide level. …

For Pete's Sake: A Comparative Analysis On Quantum Review After Pete V. Boland, Joseph X. Kehrli

Louisiana Law Review

No abstract provided.

Educational Policy, Implementation, And Challenges: A Case Study Of Administrative Practice In Using Student Growth Measures For Instructional Improvement In A K-12 School District In Northern New Jersey, Dean Robinson

Theses and Dissertations

This qualitative single holistic case study explores how school administrators in a Northern New Jersey district implement student growth measures (SGMs) mandated by the New Jersey Department of Education. Grounded in Lipsky’s (1980) theory of street-level bureaucracy, Wright’s Connected Actions Roadmap (2022), and Argyris and Schön’s (1978) theory of action, this work examines how administrators in the case study navigate policy mandates while addressing the demands of leading their schools. Data was collected through two rounds of semi-structured interviews with eleven administrators, supplemented by field notes. Thematic analysis revealed five areas: administrative agency in balancing compliance and authenticity, collaboration as …

When Scripts Diverge: Strengthening Low-Resource Neural Machine Translation Through Phonetic Cross-Lingual Transfer, Ammon Shurtz

Theses and Dissertations

Multilingual Neural Machine Translation (MNMT) models enhance translation quality for low-resource languages by exploiting cross-lingual similarities during training--a process known as knowledge transfer. This transfer is particularly effective between languages that share lexical or structural features, often enabled by a common orthography. However, languages with strong phonetic and lexical similarities but distinct writing systems experience limited benefits, as the absence of a shared orthography hinders knowledge transfer. To address this limitation, we propose an approach based on phonetic information that enhances token-level alignment across scripts by leveraging transliterations. We systematically evaluate several phonetic transcription techniques and strategies for incorporating phonetic …

Spatial–Temporal Deep Learning For Electric-Vehicle Charging Demand: An Exploratory Study Of Graph Convolutional And Lstm Networks Performance, Maher Alaraj, Carla Martins, Mohammed Radi, Mohamed Darwish, Munir Majdalawieh

All Works

Electric-vehicle (EV) charging is a localized, time-varying load that challenges distribution networks. This study offers practical insights into when spatial graph structure adds value beyond temporal context, utilizing real-world data and a transparent evaluation. We compare Long Short-Term Memory (LSTM) and Graph Convolutional Network (GCN) models for hourly EV-charging energy forecasting, based on 145,778 sessions recorded in Boulder, Colorado (2018–2023). After preprocessing and temporal alignment, temporal covariates (hour, day, month, year) and, when applicable, ZIP-code indicators were engineered. LSTMs were trained with 1 h and 24 h input windows, with or without ZIP features, and evaluated through 5-fold cross-validation. GCNs …

Innovation Curse: The Wastefulness Of Technologies Believed To Mitigate Climate Change, Minh Hoang Nguyen, Valoree Gagnon, Thanh Tu Tran, Thi Mai Anh Tran

Michigan Tech Publications

Technological innovations are increasingly promoted as solutions to climate change. However, many innovations, including Carbon Capture and Storage, bioplastics, and glacier geo-engineering, suffer significant limitations from high costs, speculative efficacy, and adverse ecological consequences. Using Granular Interaction Thinking Theory (GITT), a transdisciplinary framework grounded in information theory, quantum mechanics, and mindsponge theory, in this study, we explain how such technological innovations become systemically favored despite their flaws. We introduce the concept of the “innovation curse,” which arises when institutional and cognitive filtering systems, operating under high informational entropy, default to familiar but ineffective techno-solutions while marginalizing Indigenous and Local Knowledges …

Optical, Electrical, And Structural Properties Of Nio Thin Films, Derived By Sol–Gel Method, Tatyana Ivanova, Antoaneta Harizanova, Nikolay Petkov

Physical Sciences Publications

NiO films were successfully deposited by sol–gel spin coating on Si, glass, and ITO-covered glass substrates. The impact of the film thickness (the different number of layers), annealing temperatures (from 300 to 500 °C), and the substrate type on the crystal structure, film morphology, optical, and vibrational properties was investigated. X-ray diffraction (XRD) revealed a polycrystalline structure and the appearance of the cubic NiO phase. Field Emission Scanning Electron Microscopy (FESEM) was applied to explore the surface morphology of NiO films, deposited on glass and ITO substrates. The oxidation states of Ni were determined by X-ray photoelectron spectroscopy (XPS). The …

Transformer Network On Global Self-Attention Mechanism For Brain Tumor Segmentation, Ammar Awni Abbas Baghdadi, Mohammed Al-Mukhtar, Ammar S Al-Zubaidi

Baghdad Science Journal

Transformers are a specific category of neural network design. Transformers often depend on extensive pre-training on a large scale and exhibit a notable degree of computational complexity. The disadvantage of using this method is a significant increase in computational complexity, which necessitates a significant commitment of time and computing resources in order to successfully work with these models. Transformer networks possess the desirable benefit of extracting distant characteristics effectively via their self-attention mechanism. In this paper, the Global Self-Attention Transformer module is applied to tackle these issues. The model is based on a segmentation problem called Brain-GS that works as …

Saussure’S Universal Distinctive Feature Dimensions, Thomas M. Eccardt

Publications and Research

This paper revisits Saussure’s scalar analysis of phonemes, offering a modern reinterpretation that replaces binary features with phonological dimensions grounded in measurement theory. By redefining Saussure’s [openness] and [backness] as ordered scales and Chomsky and Halle’s “combination and concatenation” as partially ordered scales, the paper introduces a notion of “[phono]logical simultaneity.” This framework renders the variously-defined phoneme and segment redundant, and shifts the focus of distinctiveness to the linguistic sign, the morpheme. It demonstrates how phonological dimensions link continuous articulations and acoustic signals (e.g., F1 and F2) with discrete linguistic signs through calibrated distinctive features. The analysis identifies Saussure’s two …

Modified Artificial Neural Networks In Stochastic Differential Equations To Estimate The Occurrence Rate Of A New Process For Software Reliability Growth Modelling, Adel Sufyan Hussain, Zedan Zubair Mashikhin, Noora Taha Abduirazzaq

Baghdad Science Journal

This paper aims to utilize modified artificial neural networks under stochastic differential equations to improve the estimation of occurrence rates on models towards the growth of software reliability through such models. There has never been a time when reliable assessment methods have been more important as software systems grow ever more sophisticated. This study covers the risks arising from software bugs, security weaknesses, and the need to meet legal requirements. The authors use a new method that combines artificial intelligence techniques with classical reliability models to achieve their goal of a more efficient computational tool for predicting software reliability. It …

On Iterative Method For The Solution Of Non-Linear Equations Using Anti-Lobatto Four Point Quadrature Rule, Tusar Singh, Dwiti Krushna Behera

Baghdad Science Journal

By harnessing the classic anti-Lobatto four-point quadrature rule of degree of precision three, the order of convergence of the Newton-Raphson method has been profoundly enhanced. This newly developed iterative scheme reveals a remarkable order of convergence, reaching three and far surpassing the original Newton-Raphson method. It operates as a two-step iterative method that elevates Newton’s approach by integrating the quadrature rule. A theorem related to this innovative iterative method has been formulated, confirming that its order of convergence is indeed three. In the numerical computation section, insightful scenarios demonstrate the remarkable numerical efficiency of the new method. Through the power …

Enhancing Brain Mri Classification With Gray Wolf Optimization And Swin Transformers, Lamyaa Fahem Katran, Ebtesam N. Alshemmary, Waleed A. Al-Jawher

Baghdad Science Journal

With the rapid advancement of computational power, deep learning has become integral to medical imaging. Convolutional Neural Networks (CNNs) are widely applied to brain MRI scans for tumor classification due to their strong localization and recognition capabilities. However, their limited ability to capture long-range dependencies often leads to information loss and reduced performance in complex feature extraction. To overcome this, the Swin Transformer introduces a hierarchical attention mechanism with shifted windows that preserves spatial relationships and enhances the modeling of global contexts. This architecture improves the detection of intricate tissue textures and supports high-resolution reconstruction of MRI images. Furthermore, integrating …

Forecasting Big Data Daily Silver Price Based On Hybridizing Support Vector Regression And Meta-Heuristic Algorithms, Ibtihal Muneeb Yahya, Zakariya Yahya Algamal

Baghdad Science Journal

Because commodities are prone to large price swings, forecasting their prices can be extremely difficult. Similar to other commodities, the price of silver is determined by a complicated interplay between a number of factors, including time-lag. Precise forecasting of the silver price is essential for the stable and effective functioning of silver markets. The time series that metal prices follow is non-stationary, nonlinear, and contains periods that fluctuate due to possible growth. Accurate forecasting of silver prices is difficult since they are consistently highly nonlinear and non-stationary. Forecasting metal prices, in general, have been predicted by support vector regression (SVR). …

Optimizing Artificial Neural Networks For Smart Grid Stability Analysis Using Tabu Search-Based Hyperparameter Tuning, Salwa Khalid Abdulateef, Armaneesa Naaman Hasoon, Moceheb Lazam Shuwandy

Baghdad Science Journal

Smart grids are supposed to make electricity networks more reliable, yet in practice their behavior can be surprisingly unstable when the load changes too quickly. During this work, we tried to understand that instability and to build a model that predicts when a grid remains stable or begins to drift. We used an Artificial Neural Network and tuned it with a Tabu Search routine that keeps adjusting the number of neurons and the activation functions until the results stop improving. It was not a single run; several training attempts were needed before the model settled. After applying the Tabu optimization, …

The Environmental Impacts Of Super Bowl Host City Location, Joshua Sheibley, Charles Mango

Montserrat Annual Writing Prize

No abstract provided.

Node-Attributed Spatial Graph Partitioning: A Kullback-Leibler Divergence Based Approach, Valentin Nechita

Electronic Theses and Dissertations 2020 - Present

This thesis presents a novel approach to node-attributed spatial graph partitioning (NSGP) that addresses limitations in existing homogeneity measures. We introduce a Kullback-Leibler (KL) divergence-based formulation we name KLD that measures attribute similarity by comparing each node’s distribution to its subgraph centroid, providing a computationally efficient method of measuring homogeneity. Integrated within a multilevel graph partitioning framework and Fiduccia-Mattheyses refinement, our approach achieves approximately 90% runtime reduction compared to the baseline Clustering and Local Refinement (CLR) algorithm while maintaining comparable partition quality. Applied to COVID-19 infection data combined with American Community Survey demographics across U.S. FIPS codes, KLD successfully identifies …

Low-Energy Multiphoton Scattering At Tree-Level And One-Loop Order In A Homogeneous Electromagnetic Field, James Edwards, Ivan Ahumada Hernandez, Patrick Copinger

School of Engineering, Computing and Mathematics

We study low energy photons coupled to scalar and spinor matter in the presence of an arbitrary homogeneous electromagnetic field in a first-quantized (worldline) approach. Utilizing a Fock-Schwinger gauge for both the scattering photons and homogeneous background, simple compact expressions are found for both the photon- and background-dressed effective action and propagator in scalar and spinor quantum electrodynamics. The low-energy limit allows identification of the coupling of the scattering photons as one of an effective homogeneous superposition of their field strengths, with amplitudes following from application of a suitable linearization operator. To treat the linearization, several techniques are employed, including …

Final Butte Priority Soils Operable Unit (Bpsou) Insufficiently Reclaimed Sites Remedial Action Work Plan (Rawp): Bres No. 104 (Colorado Dump) North Slope, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Flow Control Tactics For Photovoltaic Modules: Coherent Structure And Heat Shedding Amplification, Sarah Elizabeth Smith

Dissertations and Theses

Solar photovoltaic systems have become the fastest growing renewable technology in the last several years. However, large-scale systems are at the mercy of their external environments, where extreme heating and wind-flung debris impact their efficiency and useable life. The work in this dissertation presents studies aimed to both understand and mitigate for these effects by identifying the underlying physics which govern them. Exploring flow control as a tactic to combat environmental heating, collective work discusses the role of turbulent fluid dynamics within large-scale systems as pertains to convective heat transfer, turbulent structure formation, and particle-laden environments. Wind tunnel experiments on …

Constraints On Axionlike Particles From Veritas Observations Of A Flaring Radio Galaxy In The Perseus Cluster, Avery Archer, C. B. Adams, P. Bangale, J. T. Bartkoske, W. Benbow, J. L. Christiansen, A. J. Chromey, A. Duerr

Physics & Astronomy Faculty publications

Axion-like particles (ALPs) are hypothetical particles that emerge in numerous theoretical extensions to the Standard Model. Their coupling to electromagnetic field implies that ALPs would mix with photons in the presence of external magnetic fields. As ALP phenomenology is governed by the mass and strength of its coupling, there is a subset of this parameter space in which this mixing would be expected to leave an imprint on the spectra of TeV γ-ray sources. In 2017, the VERITAS γ-ray observatory recorded the second day of a dramatic flare of the radio galaxy NGC 1275, embedded at the center of the …

Modulation Of The Semi-Annual Oscillation By Stratospheric Sudden Warmings As Seen In The High-Altitude Jawara Re-Analyses, Jiarong Zhang, Yvan Orsolini, Kaoru Sato

All Physics Faculty Publications

The semi-annual oscillation (SAO) dominates seasonal variability in the equatorial stratosphere and mesosphere. However, the seasonally dependent modulation of the SAO in the stratosphere (SSAO) and mesosphere (MSAO) by sudden stratospheric warmings (SSWs) in the Arctic has not been investigated in detail. In this study, we examine the seasonal evolution of the SAO during 16 major SSW events spanning 2004 to 2024 using the Japanese Atmospheric General Circulation Model for Upper Atmosphere Research Data Assimilation System Whole Neutral Atmosphere Re-analysis (JAWARA). Basic features of the SAO are well captured by JAWARA, as evidenced by the SSAO and MSAO appearing at …

2025 - The Sixth Annual Fall Symposium Of Student Scholars

Symposium of Student Scholars Program Books

The full program book from the Fall 2025 Symposium of Student Scholars, held in November 2025. Includes abstracts from the presentations and posters.

Fish-Spec: Fast Identification System For Handheld Spectroscopy And Species Classification, Mitchell Sueker, Nicholas Mackinnon, Gregory Bearman, Amanda Tabb, Diane Kim, Rosalee S. Hellberg, Alireza Akhbardeh, Hamid Reza Marateb, Jianwei Qin, Moon Kim, Fartash Vasefi, Hossein Kashani Zadeh

Food Science Faculty Articles and Research

Accurate fish species identification is critical to prevent mislabeling and fraud in the seafood industry. We present a handheld multi-mode point spectroscopy system that combines fluorescence (365 and 395 nm excitation) and reflectance measurements in the visible to near-infrared (∼350–900 nm) and short-wave infrared (∼900–1700 nm) regions for rapid, non-destructive classification of fish fillets. Tissue spectra were acquired at 25 positions on 68 fillets from 11 species, in both frozen and thawed states. Feature-level fusion across all four modes enabled higher classification accuracy than any single mode alone. A global machine-learning model classified all species with 85 ± 2.8 %, …

Proud Larry's Owners Support Southern Culture, Rebecca Lauck Cleary

University of Mississippi News

OXFORD, Miss. – Enjoying a slice of pizza or a burger with seasoned fries while listening to a live band at Proud Larry's is a quintessential Oxford experience. A gift from Lisa and Scott Caradine, who opened the restaurant and music venue in 1993, promises to help document and preserve such iconic Southern experiences.

Gboost-Ctl: A Novel Method In Multi-Tissue Transcriptome-Wide Associations Studies In Cross-Tissue Learner Incorporating Gwas Information, Md Mutasim Billah, Hairong Wei, Fengzhu Sun, Kui Zhang

Michigan Tech Publications

Genome-wide association studies (GWAS) have uncovered numerous genetic variants linked to complex human diseases, yet linking these variants to transcripts and tissues that drive pathology remains difficult. Multi-tissue transcriptome-wide association studies (TWAS) offer a powerful bridge, but existing analytical methods have some limitations, either by discarding important signals by separately analyzing and then aggregating results across tissues, implying imputation models in individual tissues, or fusing them with weights that ignore how much GWAS signal each tissue actually carries. Therefore, most of the existing methods do not work uniformly across different GWAS cohorts. Here, we propose GBoost-CTL - a GWAS-boosted cross-tissue …

Leveraging Consistency: Evaluating Ai Coders For Qualitative Research, Saríah López-Fierro, Carlos Chiriboga-Calderón, José López-Fierro, Rubén Pacheco-Villamar

Instructional Technology and Learning Sciences Faculty Publications

This study provides insights of using Large Language Models (LLMs) for deductive coding tasks in market research. Deductive coding, which applies predefined codes to text, often demands consistency rather than interpretive nuance. 

We compare five closed-weight LLMs with a group of human coders in tagging instances of “Expansive Framing” across qualitative excerpts, using Fleiss’ Kappa, ANOVA, and Estimated Marginal Means. Results reveal that LLMs exhibit higher consistency and faster processing than humans. These findings suggest that integrating LLMs can improve efficiency, making them an attractive asset for firms managing large datasets.

Harnessing Hyperspectral Imaging And Deep Learning For Terrestrial Habitat Mapping In Arid Landscapes: A Case Study In Saudi Arabia, Ali Elgendy, Hesham Morgan, Brandon Tran, Rejoice Thomas, Tamer Ismail, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Muburak, Khaled Allam Harhash, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Arid ecosystems remain under-mapped at actionable scales despite their ecological importance. Decision makers lack reliable, high-resolution habitat maps in drylands to prioritize protection and target restoration. This research integrates spaceborne hyperspectral imaging from the Environmental Mapping and Analysis Program (EnMAP) with deep learning semantic segmentation models to produce an updated level of habitat classification based on the International Union for Conservation of Nature (IUCN) for part of the Imam Turki bin Abdullah Royal Reserve, Saudi Arabia. Using ground control points and the full EnMAP spectral cube without band selection, U-Net and DeepLabV3+ architectures were each implemented with VGG19 and ResNet-101 …