Digital Commons Network^™

A New Formulation Of Hardy-Type Dynamic Inequalities On Time Scales, Martin Bohner, Irena Jadlovská, Ahmed I. Saied

Mathematics and Statistics Faculty Research & Creative Works

In this paper, we introduce a novel formulation of dynamic Hardy-type inequalities on a time scale, motivated by a recently established convexity approach in the Haar measure. The classical Hardy inequality is refined so that the classical Lebesgue-measure constant is replaced by the sharp constant 1. We obtain time-scale analogues on finite intervals with best constants, and, for nonincreasing and nondecreasing functions, reversed inequalities with explicit weights described by incomplete β-functions. To establish our results, we employ two distinct time scales and apply the chain rule, together with the substitution rule, the derivative of inverse functions, and Fubini's theorem for …

Analysis Of Errors In The Management Of Cutaneous Disorders, Robert J. Pariser, Sarah Alnaif

Department Dermatology Faculty Publications

In this study, we prospectively and retrospectively evaluated the occurrence of errors in the management of cutaneous disorders from patient visits and medical records in a single dermatology practice in southeast Virginia over a 3-year period (June 2020-July 2023). Providers should be able to improve diagnostic accuracy by utilizing established rapid bedside diagnostic techniques.

The Economic And Environmental Impact Of Shinkansen And High-Speed Rail Infrastructure: A Comparative Analysis Of Economic Growth And Carbon Emissions Reduction, Dillan R. Victory

SPARK Symposium Presentations

The development of Japan's high-speed rail system, the Shinkansen, has played a pivotal role in the country's post-war economic resurgence. Introduced in 1964 with the Tokaidō Shinkansen, this transformative infrastructure investment significantly reduced travel times, bolstered economic activity around station hubs, and facilitated regional development by enabling urban decentralization. This paper explores the long-term economic benefits of high-speed rail, including its impact on land value, business expansion, and carbon emissions. The case study of the Linear Chuo Shinkansen, Japan's latest maglev project, underscores both the economic promise and the political resistance to expansion, particularly in regions such as Shizuoka.

Using …

Beyond Homogeneity: Exploring Causal Heterogeneity In Psychopathology, Philip B. Vinh

Theses and Dissertations

Traditional models in psychiatric research often impose assumptions of causal homogeneity, treating population-level associations as reflective of uniform underlying mechanisms. This dissertation challenges that assumption by introducing statistical and machine learning frameworks designed to detect and model causal heterogeneity in the development of psychopathology. Central to this approach is the advancement of finite mixture structural equation modeling (FM-SEM) to identify latent subgroups characterized by distinct, and sometimes opposing, causal pathways.

The dissertation comprises three integrated empirical studies. The first introduces mixDoC, a finite mixture extension of the classical Direction of Causation (DoC) model applied to twin data, enabling the detection …

Predictive Inference For Ion Concentration With Machine Learning And Bayesian Methods, Alexandra B. Ulbing

Theses and Dissertations

Ultraviolet--visible (UV--Vis) spectroscopy produces high-dimensional signals that are strongly collinear, shift with concentration, and exhibit heteroskedastic, non-Gaussian noise. These features make supervised regression from spectra to ionic concentrations statistically challenging and limit the reliability of methods that assume linear structure or homoscedastic errors.

This dissertation develops two complementary frameworks for prediction and uncertainty quantification in UV--Vis spectroscopic regression: (1) frequentist stacked ensembles combined with distribution-free conformal prediction, and (2) Bayesian hierarchical modeling and Bayesian stacking. Together, they provide a unified view of model-based and distribution-free uncertainty across nickel and nickel--cobalt datasets.

The frequentist component builds ensembles of Functional Data Analysis …

Predicting Capture And Survival Probabilities Of The Arizona Tiger Salamander: A Comparison Of Capture-Recapture Models, Brittney Nelson

Murray State Theses and Dissertations

Capture-recapture models are essential tools for estimating population dynamics in ecological studies. A fundamental component of these models is the capture history matrix, which records individual detection over time and serves as the basis for estimating survival and capture probabilities. This presentation explores three statistical approaches to these estimations: the Cormack-Jolly-Seber (CJS) model, the Hidden Markov Model (HMM) for CJS, and the Bayesian CJS model. The CJS model provides a likelihood-based framework for estimation, and the HMM CJS incorporates latent states into the model to account for uncertainty in detection. The Bayesian CJS extends this same analysis by integrating prior …

Ms-Yolo: Infrared Object Detection For Edge Deployment Via Mobilenetv4 And Slideloss, Jiali Zhang, Thomas S. White, Haoliang Zhang, Wenqing Hu, Donald C. Wunsch, Jian Liu

Mathematics and Statistics Faculty Research & Creative Works

Infrared imaging has emerged as a robust solution for urban object detection under low-light and adverse weather conditions, offering significant advantages over traditional visible-light cameras. However, challenges such as class imbalance, thermal noise, and computational constraints can significantly hinder model performance in practical settings. To address these issues, we evaluate multiple YOLO variants on the FLIR ADAS V2 dataset, ultimately selecting YOLOv8 as our baseline due to its balanced accuracy and efficiency. Building on this foundation, we present MS-YOLO (MobileNetv4 and SlideLoss based on YOLO), which replaces YOLOv8's CSPDarknet backbone with the more efficient MobileNetV4, reducing computational overhead by 1.5% …

Optimizing Decision-Making In A Cerebral Palsy Model Using Reinforcement Learning, Richard Ampah

Pitzer Senior Theses

This study presents an original interdisciplinary investigation into how reinforcement learning (RL) can model motor and cognitive defects and potentially improve motor and cognitive functions in individuals with cerebral palsy (CP), a non-progressive neurological disorder that impairs movement and adaptability. Integrating computational neuroscience and machine learning, the research applies policy gradient methods and Markov Decision Processes (MDPs) to simulate adaptive learning in agents with and without CP-related constraints.

The central aim is to compare the cumulative rewards of optimal policies, derived from value iteration, and human-like learning policies using the REINFORCE algorithm, both with and without the Bellman baseline. The …

Local Limit Theorems On Finitely Generated Abelian Groups, Yutong Yan

Honors Theses

In this thesis, we classify the pointwise behavior of finite-range random walks on finitely generated abelian groups in terms of local limit theorems. Random walks are central objects of research in probability theory, and the theory has found applications in statistics, physics, and even card shuffling. One significant topic in this line of study is random walks on finitely generated groups. Starting from the pioneering work of G. Pólya and H. Kesten, random walks on finitely generated groups have been studied extensively. However, many notable results on the subject (local limit theorems, for example) make assumptions about periodicity and irreducibility …

Fault Tree Analysis For Robust Design, Jonathan Degroff, Gene Jean-Win Hou

Mechanical & Aerospace Engineering Faculty Publications

The objective of this research is to incorporate system failure into a robust design formation and solution process. The system failure referred to here will be built using fault tree analysis (FTA), which will take all lower-level failure events into consideration. Two examples are investigated here. One will directly treat the probabilities of the basis events as design variables, The other will be formulated in five different models: deterministic design optimization, the reliability index-based, the “and” gate-based, the “or” gate-based and the “inhibit” gate-based robust design. Their corresponding optimization solutions will be compared with each other. The post-optimality analysis of …

Comparative Study Of Single Imputation Techniques For The Prediction Of Missing Dairy Data, Ahmed M. Gad Prof, Ahmed Abdelhakim Ahmed Mr, Eman Manaa Prof, Basant Shafik Dr, Sakr Mostafa Prof

Business Administration

Dairy farm records are a crucial component of effective livestock business management. Record analysis allows a farm’s owner to make informed decisions. Incomplete records are less useful for data analysis, so it's important to handle missing values correctly. This study compares different imputation methods for handling missing values in a dataset of dairy records comprising 997 records collected from 234 cows between 2012 and 2022. The dataset was screened against records with missing values and then deleted, resulting in 858 observations from 200 animals. There were missing values in two variables, with a missing percentage of 13.9%: days in milk …

A New Robust Imputation Method For Longitudinal Data With Non-Normal Continuous Outcomes, Ahmed M. Gad Prof, Yasmie A. Mohamed, Nesma M. Daewish Dr, Abdelnaser S. Abdrabou Prof, Wafaa M. Ibrahim Dr

Business Administration

Missing values is very common in longitudinal data and it is the main challenge in analysis of longitudinal data. Missing values have a significant effect on longitudinal data analysis because they lead to loss of information, biased estimates, and misleading results. In practice there is a need for an imputation method to deal with missing values.

Aim: In this study a new robust regression-based imputation method to deal with missing values in longitudinal data is proposed. This method utilizes the modified adaptive linear regression model and does not require the normality of the responses. It is a novel robust imputation …

Extraction Of Fine-Grained Research Methods In The Field Of Information Science, Jiayi Hao, Yuzhuo Wang, Chengzhi Zhang

Journal of Scientific Information Research

[Purpose/significance]Research methods in information science are one of the critical research directions in this field. Constructing a fine-grained research method corpus and extracting research method entities can help scholars quickly understand the research methods in this field, explore the evolution of methods and their future development trends, and lay the foundation for the service and application of the research method corpus in the subsequent digital wave. [Method/process]Firstly, based on academic articles published in the Journal of the China Society for Scientific and Technical Information from 2000 to 2023, this study randomly selected 50 articles and manually annotated the research methodology …

Identifying Research Paths Based On Main Path Analysis: A Case Study Of Knowledge Graphs, Ruibin Wei, Yidan Wang, Yan Xu

Journal of Scientific Information Research

[Purpose/significance]The main path analysis of citation networks can be used to identify important literature in specific fields and can achieve the extraction of mainstream research threads. This paper will use the main path analysis method to analyze the research path of knowledge graphs and sort out the context of their research development. [Method/process]This paper firstly obtains research papers in the field of knowledge graphs from the Web of Science platform, then uses the HistCite software to generate a direct citation network of the literature, and then imports the data into Pajek to generate multiple main paths of the dataset, and …

Association Between Lifetime Interpersonal Violence And Post– Covid-19 Condition Among Women In Kentucky, 2020-2022, Ayşe Güler, Heather M. Bush, Katie Schill, Nurlan Kussainov, Ann L. Coker

Biostatistics Faculty Publications

Objective: The COVID-19 pandemic increased the risk of interpersonal violence. We investigated the association between lifetime interpersonal violence experience and risk of post–COVID-19 condition (the persistence of symptoms of COVID-19 and severity of health problems associated with COVID-19 that last a few weeks, months, or years) among women with lifetime interpersonal violence experience.   Methods: Women participants aged ≥18 years in Kentucky’s Wellness, Health & You—COVID-19 study completed online quantitative surveys about the impacts of the pandemic, developing COVID-19, and symptoms of post–COVID-19 condition. We conducted cross-sectional analyses estimating rate ratios of developing COVID-19 and symptoms of post–COVID-19 condition during the …

Elements Of Statistics, Paul Flesher, Jeffrey Sadler, Jonathan Rehmert, Lanee Young

All Open Educational Resources

This undergraduate text, intended for an introductory statistics course, motivates and develops basic inferential statistics. The first chapter establishes the necessity of inferential statistics, lays the basis of a scientific worldview, and finishes by introducing sampling methods and variables. Subsequent chapters develop visualizations, descriptive statistics, probability, and random variables. Sampling distributions are treated in the fifth chapter which is immediately followed by the development of confidence intervals and hypothesis testing. The text concludes with a treatment of linear regression. The use of Excel is emphasized throughout. The text is best viewed online through LibreTexts.

Temporal Network Analysis Of Comorbidities Among People With Hiv In South Carolina, Yunqing Ma, Matthew Lohman Ph.D., Monique J. Brown Ph.D., Mph, Yichen Li, Xiaoming Li Ph.D., Bankole Olatosi Ph.D., Jiajia Zhang Ph.D.

Faculty Publications

Introduction: People with HIV experience a high rate of comorbidities that can complicate their health outcomes. Understanding the prevalence, interrelationships and temporal development of these comorbidities is crucial for improving health management and quality of life for people with HIV. Methods: We used a population-based cohort extracted from statewide electronic health record (EHR) data in South Carolina (SC), including 18 649 people with HIV who survived at least 1 year after HIV diagnosis between January 1, 2005, and December 31, 2020. Comorbidities and organ systems were classified using ICD-10 codes. Network analysis was performed to assess the closeness centrality among …

‘Waves Of Imagination’ Unconditional Spectogram Diffusion Using Diffusion Architecture., Rahul Vanukuri

Computer Science and Engineering Theses - Archive

The swift evolution of wireless communication technologies,particularly in the field of rf signals or in CBRS bands,demands increasingly sophisticated signal processing techniques to ensure efficient transmission, reception, and spectrum management.Traditional approaches to signal generation and reconstruction, although effective in controlled environments, often struggle to cope with the challenges presented by real-world noisy conditions, hardware constraints, and limited access to large-scale datasets. In response to these limitations, this thesis explores the application of diffusion models—a class of generative models known for their ability to produce high-fidelity samples—to the domain of spectrogram generation for communication signals.

Different from conventional strategies to simulate …

Understanding The Challenges And Satisfaction Among The Medical Professional Preceptors, Cornelia Ko

West Chester University Doctoral Projects

This study evaluates the Penn Medicine Family Medicine Clerkship program through a mixed-methods approach, exploring the challenges and factors influencing the satisfaction of medical professional preceptors when precepting students. A quantitative survey with 54 preceptor participants reveals the top challenges preceptors face: workload, conflicts with patient care, and limited clinical space. Despite this, there is strong support for the program, with 89% of preceptors enjoying the role and 85% expressing willingness to continue to precept for the next three years. Finally, data analysis shows no statistical correlation between preceptor motivation with gender, ethnicity, age group, years of serving, or total …

Highest Risk Density Region For The Communication Of The Impact Of A Treatment Covariate On The Time-To-Event Distribution, Giacomo Biganzoli, Giuseppe Marano Phd, Patrizia Boracchi Phd

COBRA Preprint Series

Analysis of time-to-event (TTE) data is central to clinical research, yet conventional summary measures like the hazard ratio (HR) and restricted mean survival time (RMST) present significant challenges. The HR is often misinterpreted, and its validity depends on the frequently violated proportional hazards assumption, while the RMST is highly sensitive to the choice of time horizon. This paper introduces two novel, assumption-free estimands to address these limitations: the Highest Risk Density Region (HRDR) and the Highest Net Risk Difference Region (HNRDR).

The HRDR identifies the narrowest time interval containing a pre-specified probability mass of events, directly answering the clinical question: …

Integrating Sentiment Analysis In Predictive Models: A Comparative Study On Game Popularity On Steam, Khaleefa Alhemeiri

CMC Senior Theses

Over the past decades, the gaming industry has managed to evolve into a multi-billion-dollar enterprise. Gaming platforms such as Steam foster unprecedented amounts of engagement among players worldwide daily. In this thesis, we investigate the effect of incorporating sentiment-driven metrics, specifically YouTube view counts and positive reviews, into predictive models for game popularity. In addition, by comparing our linear regression sentiment-based approach to the Bayesian hierarchical folded normal model used by De Luisa et al. (2021), we can understand the many differences, strengths, and limitations of each methodology. In our thesis, we focus on three games. Each is of varying …

Forecasting Equity Betas Using Option-Implied Moments, Ivan Kolesnikov

CMC Senior Theses

Traditional beta estimates are constructed from historical stock‑and‑market returns and therefore adjust only as fast as realized data accrue. This thesis investigates whether the forward‑looking information embedded in equity‑option prices can enhance beta forecasts. Using near‑end‑of‑day quotes for 236 S&P 500 firms between 2007 and 2024, I extract risk‑neutral variance and skewness, construct five alternative beta estimators (historical, option‑implied, and three hybrids), and evaluate them against realized betas over six‑, twelve‑, and twenty‑four‑month windows. Rolling‑OLS beta remains the most accurate benchmark at short horizons, yet option‑implied moments add economically and statistically significant value when systematic exposure is expected to change …

Analyzing Political Sentiment On Micro-Blogging Data: A Lexicon And Machine Learning Approach To The 2024 U.S. Presidential Election, Ava Grey

CMC Senior Theses

This paper explores the trends in sentiment towards U.S. presidential candidates Kamala Harris and Donald Trump through micro-blogging social media text during the five months leading up to the election. Two datasets of varying sizes and origins were used to contextualize and validate analysis findings. The analyses include both a lexicon-based approach and a machine learning predictive method. Common sentiment analysis techniques like term frequency, term frequency inverse, various lexicons, and n-grams were utilized during the lexicon approach. During the modeling, a random forest was utilized in addition to the methods used during the lexicon approach. Results showed that overall …

การเปรียบเทียบการทำนายผลการแข่งขันฟุตบอลโดยใช้การเรียนรู้เชิงลึกจากการผสานข้อมูลทางสถิติและแผนที่ความร้อนของผู้เล่น, ภานุพันธุ์ พันธุ์พฤกษ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

งานวิจัยนี้มีวัตถุประสงค์เพื่อเปรียบเทียบประสิทธิภาพของแบบจำลอง Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN) และ Multimodal Neural Network (MMNN) ในการทำนายผลการแข่งขันฟุตบอลพรีเมียร์ลีกอังกฤษ โดยใช้ข้อมูลทางสถิติ ข้อมูลแผนที่ความร้อนของผู้เล่น และการผสานข้อมูลทั้งสองรูปแบบ ผ่านแนวทาง Early Fusion, Late Fusion และการแปลง Heatmap เป็นข้อมูลเชิงตัวเลขแบบแบ่งพื้นที่สนาม ผลการวิจัยพบว่า แบบจำลอง MMNN ที่ใช้แนวทาง Late Fusion ให้ค่าความแม่นยำที่ 55.68% ซึ่งสูงกว่า Early Fusion ที่ได้ความแม่นยำ 53.45% และ CNN ที่ใช้ข้อมูลทางสถิติร่วมกับข้อมูลตำแหน่งเชิงพื้นที่จากแผนที่ความร้อนที่แบ่งสนามออกเป็น 4 ส่วน มีค่าความแม่นยำสูงสุดที่ 63.50% และให้คะแนน Precision, Recall และ F1 Score สูงกว่าแบบจำลองอื่น ๆ นอกจากนี้ ยังได้วิเคราะห์ความสำคัญของฟีเจอร์ด้วยเทคนิค SHAP ซึ่งชี้ให้เห็นว่าฟีเจอร์ที่เกี่ยวข้องกับเกมรับของทีมเหย้า เช่น การบล็อก การเคลียร์บอล และการดวลลูกกลางอากาศ มีผลต่อการทำนายมากที่สุด ขณะที่ตำแหน่งของผู้เล่นทีมเยือนมีอิทธิพลน้อยที่สุด

Adverse Childhood Experiences, Depression And Subjective Cognitive Decline By Gender: A Moderated Mediation Analysis, Monique J. Brown, Darlingtina K. Esiaka, Jaya Viswanathan

Behavioral Science Faculty Publications

Studies assessing depression as a mediating factor between adverse childhood experiences (ACEs) and subjective cognitive decline (SCD) are lacking. Therefore, the aims of this study were to: (1) determine the mediating role of depression in the association between ACEs and SCD; and (2) assess the moderating role of gender. Data were obtained from the 2023 Behavioral Risk Factor Surveillance Study (BRFSS) survey (N = 38,600). Crude and adjusted path analyses were used to determine the mediating role of depression between ACEs and SCD. Adjusted analyses controlled for sociodemographic confounders. ACEs were positively associated with depression (B = 0.129, p …

Model-Free Organization Of Patient Reported Outcomes Data: Geometrical Rep-Resentation Of The Modified Compartmen-Talization Method, Manasi Sheth, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

There is a recent advancement in the field of mathematics and statistics to understand the geometry or connectedness of the data due to the massive amounts of data being generated. The data provided for analyses are usually very large and need to be organized and minimized in order to make it more useful and meaningful. In biostatistics or medical field, it is important for patients to have access to high-quality, safe and effective and/ or efficacious medical products. It is quite necessary to ascertain that the patients and their care-partners stay at the center of the regulatory decision-making process. In …

Understanding The Impact Of The Medicaid Expansion On Hospital Length Of Stay And Emergency Department Use In Kentucky, Cameron Bushling

Theses and Dissertations--Epidemiology and Biostatistics

Three related analyses were performed to try to understand the immediate effects of Medicaid expansion on hospital systems in Kentucky. Medicaid expansion led to a large, sudden increase in the number of individuals eligible for various health care needs. This sudden increase in demand for health services lead to initial hypotheses regarding hospitals’ ability to handle this demand. The first analysis examined hospital average length of stay (LOS) using a linear mixed-effects model. Length of stay was modeled longitudinally between 2013 and 2015 to determine if significant changes in the slope of LOS could be detected after expansion (2014). Separate …

Changes In Cancer Diagnosis And Survival In The United States During The Covid-19 Pandemic, Justin T. Burus

Theses and Dissertations--Epidemiology and Biostatistics

The COVID-19 Pandemic led to global societal disruptions as political leaders and public health authorities attempted to control the spread of the newly discovered SARS- CoV-2 virus. While these measures were designed to lessen morbidity and mortality from a novel pathogen, their impact was also felt in many other, often unintended ways. The purpose of this dissertation is to use cancer surveillance research methods to examine the association between COVID-19 Pandemic-related disruptions and changes in the normal diagnosis and care of cancer in the United States.

The first two studies of this dissertation analyzed reductions in cancer diagnoses in the …

Students’ Perceptions Of Self And Peers Predict Self-Reports Of Cheating, Amber M. Henslee, Luke Settles, Sara E. Johnson, Gayla R. Olbricht

Psychological Science Faculty Research & Creative Works

Academic dishonesty and how to address it are common concerns across higher education disciplines, but engineering students admit to higher rates of academic dishonesty than other students. However, first-year students may be particularly receptive to prevention efforts. Considering self-perception, social norming, and behavioral choice theories, we hypothesized that 1.) Students who perceived themself as ethical and more knowledgeable of the consequences for misconduct would be less likely to self-report cheating and 2.) Students who perceived cheating and plagiarism to be common would be more likely to self-report cheating. For this study, freshmen engineering students (N=703) reported their self-perception, perception of …

From Data To Insight: A Machine Learning Approach In Classifying Dairy Cow Productivity Level And Identifying Important Influencing Variables, Fatkhurokhman Fauzi, Achmad Fauzan, Rhendy K P Widiyanto, Khairil Anwar Notodiputro, Bagus Sartono

Knowledge Engineering and Data Science

Identifying influential predictor variables is crucial for enhancing model interpretability in supervised classification. This study applies Permutation Variable Importance (PVI), a model-agnostic approach, to evaluate variable relevance after model fitting. Using data from the 2024 Indonesia Dairy Cow Productivity Survey, this research investigates five classification techniques: (1) Support Vector Machine (SVM), (2) Neural Network (NN), (3) k-Nearest Neighbors (kNN), (4) Naïve Bayes Classifier (NB), and (5) Logistic Regression (LR), to identify which method(s) yield the best performance based on evaluation metrics such as accuracy, sensitivity, and specificity. PVI is employed to identify the most influential predictor variables within the best-performing …