Digital Commons Network^™

A Multiobjective Framework For Joint Coverage And Motion Planning In Uav Inspection Tasks, Luis Fernando Escobar Carvajal

Graduate Theses, Dissertations, and Problem Reports (ETD)

Unmanned Aerial Vehicles (UAVs) have become essential for data acquisition in complex 3D environments. However, traditional Coverage Path Planning (CPP) methodologies often rely on a sequential pipeline that isolates viewpoint generation from flight path routing. This decoupling fails to account for the interdependence between viewpoint distribution and minimum flight paths, effectively restricting the search space and preventing the identification of a global optimum. This dissertation proposes a unified multi-objective framework for joint coverage and motion planning. The primary contribution of this work is the transition from isolated, sequential steps to a simultaneous optimization of the number and position of viewpoints …

Multi-Grade Deep Learning, Yuesheng Xu

Mathematics & Statistics Faculty Publications

Deep learning requires solving a nonconvex optimization problem of a large size to learn a deep neural network (DNN). The current deep learning model is of a single-grade, that is, it trains a DNN end-to-end, by solving a single nonconvex optimization problem. When the layer number of the neural network is large, it is computationally challenging to carry out such a task efficiently. The complexity of the task comes from learning all weight matrices and bias vectors from one single nonconvex optimization problem of a large size. Inspired by the human education process which arranges learning in grades, we …

Multiple Changepoint Detection For Non-Gaussian Time Series, Robert Lund, Thomas J. Fisher, Norou Diawara, Michael Wehner

Mathematics & Statistics Faculty Publications

This article combines methods from existing techniques to identify multiple changepoints in non‐Gaussian autocorrelated time series. A transformation is used to convert a Gaussian series into a non‐Gaussian series, enabling penalized likelihood methods to handle non‐Gaussian scenarios. When the marginal distribution of the data is continuous, the methods essentially reduce to the change of variables formula for probability densities. When the marginal distribution is count‐oriented, Hermite expansions and particle filtering techniques are used to quantify the scenario. Simulations demonstrating the efficacy of the methods are given and two data sets are analyzed: 1) the proportion of home runs hit by …

Adaptive Multi-Grade Deep Learning For Highly Oscillatory Fredholm Integral Equations Of The Second Kind, Jie Jiang, Yuesheng Xu

Mathematics & Statistics Faculty Publications

This paper studies the use of Multi-Grade Deep Learning (MGDL) for solving highly oscillatory Fredholm integral equations of the second kind. We provide rigorous error analyses of continuous and discrete MGDL models, showing that the discrete model retains the convergence and stability of its continuous counterpart under sufficiently small quadrature error. We identify the DNN training error as the primary source of approximation error, motivating a novel adaptive MGDL algorithm that selects the network grade based on training performance. Numerical experiments with highly oscillatory (including wavenumber 500) and singular solutions confirm the accuracy, effectiveness and robustness of the proposed approach.

A New Functional Setting For Term Structure Modeling Using The Health-Jarrow-Morton Framework, Michael Pokojovy, Ebenezer Nkum, Thomas M. Fullerton Jr.

Mathematics & Statistics Faculty Publications

The well-known Heath–Jarrow–Morton (HJM) framework provides a universal and efficacious instrument for modeling the stochastic evolution of an entire yield curve by explaining the interest rate dynamics in continuous time under no-arbitrage conditions. Existing implementations involve exponentially weighted function spaces as theoretical settings for the former stochastic evolution. While the choice of weight can have a drastic effect on model calibration and subsequent forecasting, it cannot be estimated from market data and does not allow for any objective interpretation. The proposed approach does not have this shortcoming as it adopts a suitably designed unweighted function space. The HJM equation is …

The Adaptive Solution Of High-Frequency Helmholtz Equations Via Multi-Grade Deep Learning, Peiyao Zhao, Rui Wang, Tingting Wu, Yuesheng Xu

Mathematics & Statistics Faculty Publications

The Helmholtz equation is fundamental for modeling wave propagation in acoustics, electromagnetics, and geophysics; however, high-frequency regimes remain notoriously difficult due to the severe numerical “pollution effect.” We propose FD-MGDL, an adaptive framework that synergizes finite difference discretizations with Multi-Grade Deep Learning (MGDL) to efficiently resolve high-frequency wavefields. Unlike standard physics-informed neural networks (PINNs), which frequently suffer from spectral bias and heavy automatic differentiation overhead, FD-MGDL employs a progressive, grade-wise training strategy that incrementally incorporates shallow sub-networks to refine residual errors. By leveraging ReLU activations in refinement grades, the framework reformulates the highly non-convex global optimization problem into a sequence …

Integrating Genetic Modifier Genotype With Serum Proteomics In Duchenne Muscular Dystrophy Clinical Trials Links Ltbp4 Genetic Modifier To Il-23/Cd93 Pathways In Muscle, Utkarsh J. Dang, Yuan Fang, Daniele Sabbatini, Elena Pegoraro, Luca Bello, Paula R. Clemens, Michela Guglieri, John Van Den Anker, Jesse Damsker, Laura Hagerty, Yetrib Hathout, Michael Ziemba, Lauren Morgenroth, Surajit Bhattacharya, Kanneboyina Nagaraju, Jyoti K. Jaiswal, Eric P. Hoffman

Mathematics & Statistics Faculty Publications

Genetic modifiers of Duchenne muscular dystrophy (DMD) that alter disease severity or response to therapy have been reported using natural history or registry data sets of older corticosteroid-treated patients. We tested associations of genetic modifiers on motor function outcomes in young (4 to <  7 years) steroid naïve clinical trial participants. Participants in clinical trials (VBP15-002/003 [n = 48]; VBP15-004 [n = 121]; DNA available for n = 110) were genotyped for eight published genetic modifier loci, and associations of genotypes with baseline motor function defined via an age-adjusted linear model. Corticosteroid drug response was modeled by genotype-stratified placebo vs. steroid treatment at 12- and 24-weeks posttreatment (mixed model for repeated measures). …

Interpreting Anti-Boycott Laws In The Shadow Of The First Amendment, Joseph A. Tomain

Articles by Maurer Faculty

Thirty-eight states have enacted “anti-BDS” laws. These laws require people or entities doing business with the state to certify that they do not participate in the Boycott Divestment Sanction movement against Israel. Typically, these laws define boycott as a “refusal to deal, terminating business, or other actions” that limit commercial relations with Israel or Israeli entities. It is highly contested in the courts and commentary whether these anti-boycott laws violate the First Amendment. Assuming arguendo that the “refusal to deal” and “terminating business” clauses do not implicate the First Amendment, that leaves the residual clause. At least one court and …

Cultivating Free Speech Culture, Joseph A. Tomain

Articles by Maurer Faculty

Defending dissent to safeguard speech and political opposition is a challenging and necessary task for a self-governing democracy. Moreover, it is a collective task. Succeeding in this endeavor sometimes requires defending speech with which one disagrees, especially in a nation as pluralistic as the United States. Although imperfect and inconsistent, there are numerous examples where strange bedfellows join together to achieve this goal. While First Amendment law helps us reach the goal, law alone is not enough. We also need a cultural commitment to defending dissent. This Essay argues that cultivating a culture that values robust free speech protection is …

Spinning-Induced Exertional Rhabdomyolysis Without Acute Kidney Injury, Yolanda V. Gutierrez, Ronald Shaju, Alyssa L. Sepulveda, Joaquin Rivera, Aamir Ahmad

School of Medicine Publications

Rhabdomyolysis is a condition with a myriad of causes, including both traumatic and non-traumatic. Traumatic conditions are observed in crush injuries, compartment syndrome, and fractures, while non-traumatic rhabdomyolysis is commonly seen due to exertional heat stroke, extreme physical exercise, and dehydration. This case report explores two cases of extreme non-traumatic rhabdomyolysis caused by indoor cycling, also known as spinning. A common complication of severe rhabdomyolysis is acute kidney injury (AKI), which increases the risk of renal failure. Rapid interventions such as aggressive intravenous fluids are necessary to prevent kidney function loss. In this case report, two sisters, on two separate …

Accuracy Of The Phenotypic 1g Test To Detect Mycobacterium Tuberculosis And Drug Resistance From Sputa In The Us-Mexico Border, Mia Aguirre, Doris Ayala, Juan Ignacio Garcia, Yoscelina Estrella Martinez-Lopez, Amberlee Hicks, Nadine M. Chacon, Ashley Gay-Cobb, Alyssa Schami, Selena Zavala-Perez, Blanca I. Restrepo

School of Medicine Publications

Background: With >10 million new tuberculosis (TB) cases/year, a limitation to TB control is the lack of simple and accurate tests for TB diagnosis and drug-susceptibility testing (DST) in endemic regions. We evaluated the accuracy of the first-generation, low-complexity phenotypic TB test (1G test), designed for simultaneous Mtb detection and resistance to isoniazid, rifampicin and moxifloxacin, suitable for resource-limited settings.

Methods: A cross-sectional study was conducted using sputa from 426 possible pulmonary TB subjects from two small Mexican cities bordering Texas. The 1G test was compared against phenotypic TB detection tests in the region [acid fast bacilli smear microscopy and …

Repurposing Of Fda-Approved Drugs Identifies Glipizide As A Potent Hmg-Coa Reductase Inhibitor: A High-Throughput Virtual Screening And Molecular Dynamics Approach For Targeting Hypercholesterolemia, Parvej Ahmad, Eswara N. Hanuma Kumar Ghali, Sahir S. Alvi

School of Medicine Publications

Purpose:

Atherosclerotic cardiovascular disease (ASCVD) is one of the leading causes of mortality worldwide. Several inhibitors of 3-hydroxy-3-methylglutaryl-CoA reductase (HMG-R), the key hepatic cholesterogenic enzyme, are prescribed to treat hypercholesterolemia and ASCVD. Statins, the established HMG-R inhibitors, exhibit promising cholesterol-lowering effects; however, their long-term use has prompted significant concerns, necessitating the search for additional therapeutic candidates with strong HMG-R inhibitory activity. Therefore, this study aims to find potential HMG-R inhibitor/s that may help reduce excess cholesterol levels via targeting HMG-R activity.

Methods: In this study, 1285 FDA-approved small molecules were assessed for their HMG-R inhibitory potential using …

Early Detection In Oral Cancer: Are We Prepared For Artificial Intelligence-Driven Precision Medicine?, Nikil Kumar, Sinnha Kumari, Sadia Sultana, Muhammad Saad Khan, Tirath Patel, Nikhilesh Anand

School of Medicine Publications

Oral cancer, particularly oral squamous cell carcinoma, remains a serious health concern, with a poor prognosis and a late diagnosis. Leukoplakia, erythroplakia, lichen planus, and submucous fibrosis are examples of oral potentially malignant disorders that must be detected early but are not always so by traditional, laborious, and subjective diagnostic techniques. In oral cancer, artificial intelligence (AI) and precision medicine are becoming game-changing technologies that enhance individualized care, treatment planning, and diagnostic precision. Complex imaging and histopathology data may be analyzed using machine learning and deep learning algorithms, particularly convolutional neural networks, which can identify patterns that are invisible to …

Beyond Hemostasis: A Narrative Review On The Multifaceted Therapeutic Potential Of Ankaferd Blood Stopper, Salima E. Tibi, Muhammad Hashir Nazir, Saleha Nazir, Bhumi Daishik Patel, Gayatri Misra, Tirath Patel, Fatima Nasir, Muhammad Farhan, Nana Sardarova, Nikhilesh Anand

School of Medicine Publications

Background: Effective hemorrhage control is essential in managing trauma and surgical patients, with uncontrolled bleeding being a leading cause of mortality. Ankaferd Blood Stopper (ABS), a hemostatic agent historically used in Anatolia, has gained recognition for its ability to modulate red blood cell-fibrinogen interactions to form a stable protein network for rapid bleeding cessation.

Objective: This review explores the multifaceted applications of ABS, evaluating its efficacy, safety, and potential therapeutic roles across various medical fields, including hemostasis, wound healing, antimicrobial activity, and antineoplastic effects.

Methods: A comprehensive synthesis of clinical trials, case reports, and experimental studies was conducted to assess …

Breaking The Resistance: A Narrative Review Of The Evolution From Traditional Drugs To Precision Therapies In Epilepsy, Tirath Patel, Fathimathul Henna, Ashfaq Ahmed, Ayesha Ahmad, Noor Ul Huda, Syed Aaraiz Ul Hassan, Aiman Muhammad, Amna Javed, Maryem Filal, Nikhilesh Anand

School of Medicine Publications

Background: Epilepsy is a chronic neurological disorder characterized by recurrent seizures, affecting approximately 50 million individuals globally. While conventional antiseizure medications (ASMs) control seizures in 70-80% of patients, about 30% experience drug resistance or intolerable side effects, necessitating alternative approaches.

Objective: To critically compare the efficacy, safety, and accessibility of conventional and emerging therapies for epilepsy, particularly in the context of treatment-resistant cases and global disparities in care.

Methods: This narrative review synthesized evidence from 120 peer-reviewed articles published between 2015 and 2025. Literature was retrieved from PubMed, Embase, Cochrane Library, and Scopus using predefined search terms related to epilepsy …

Una Nueva Herramienta Para Estimar El Volumen Muscular Independientemente De La Edad, El Sexo Y El Nivel De Actividad Física, José J. Rivera-Sánchez, Laura P. Burgos-Morelos, Daniel Castillo-Castillo, Gerardo Méndez-Suárez, Juan C. Lopez-Alvarenga, Damián Carrillo-Ruíz

School of Medicine Publications

Antecedentes: Actualmente, la evaluación del volumen muscular requiere técnicas invasivas o de imagen, lo que aumenta los costos y retrasa la evaluación, y las mediciones antropométricas con frecuencia son inexactas.

Objetivo: Desarrollar una fórmula matemática para estimar el volumen del cuádriceps.

Material y métodos: Se evaluaron 127 sujetos: 40 adultos sedentarios de 20 a 35 años, 47 atletas adultos de 20 a 45 años y 40 adultos sedentarios de 70 a 94 años. Se dividieron en grupos de desarrollo y de validación. Se realizaron mediciones de variables antropométricas y fuerza de agarre, y se desarrolló la fórmula de predicción del …

A Position Statement On Endovascular Models And Effectiveness Metrics For Mechanical Thrombectomy Navigation, On Behalf Of The Stakeholder Taskforce For Artificial Intelligence-Assisted Robotic Thrombectomy (Start), Harry Robertshaw, Anna Barnes, Phil Blakelock, Raphael Blanc, Robert Crossley, Rebecca Fahrig, Ameer E. Hassan, Benjamin Jackson, Lennart Karstensen

School of Medicine Publications

Although we are making progress in overcoming infectious diseases and cancer, one of the major medical challenges of the mid-21st century will be the increasing prevalence of stroke. Occlusions in large vessels are especially debilitating, yet effective treatment—needed within hours to achieve best outcomes—remains limited because of geographic accessibility. One solution for improving timely access to mechanical thrombectomy in geographically diverse populations is the widespread deployment of robotic surgical systems. Artificial intelligence assistance may enable the safe and effective upskilling of operators in this emerging therapeutic delivery approach. Our aim was to establish consensus frameworks for developing and validating artificial …

Training Tomorrow’S Hand Surgeons: Clinical, Leadership, And Structural Trends In Fellowship Education, Ogonna N. Nnamani Silva, Aaron I. Dadzie, Chinenye Ogbonnah, Lydia A. Helliwell, Abhiram R. Bhashyam, Catherine M. Curtin, Tamara D. Rozental, Krystle R. Tuaño

School of Medicine Publications

Purpose: Hand surgery fellowships include trainees from orthopedic, plastic, and general surgery. These pathways present an opportunity to examine variations in graduate medical education structures, such as program size, sponsoring department, and leadership composition. Limited research exists on the characteristics of these programs. The purpose of this study is to assess the structural and leadership characteristics of the Accreditation Council of Graduate Medical Education-accredited hand surgery fellowships in the United States. We hypothesized that characteristics (ie, faculty-to-fellow ratios and case volume) vary by specialty sponsorship. Leadership, although shaped by merit-based criteria, faces challenges in achieving broader demographic diversity.

Methods: A …

Tuberculosis Presenting As Multiple Pulmonary Nodules Mimicking Malignancy, Abdel Martinez Borrego, Fatimah Bello, Brandon Cantazaro, Luisa Montoya, Frank Mediola

School of Medicine Publications

Multiple pulmonary nodules often raise concern for metastatic malignancy; however, the differential diagnosis is broad and includes infectious, inflammatory, granulomatous, vascular, and benign etiologies. Tuberculosis (TB), although uncommon, can present with multiple nodules that closely mimic metastatic disease on advanced imaging, requiring careful clinicoradiologic and microbiologic correlation. We describe a woman in her 50s who presented with dyspnea and new-onset heart failure, in whom imaging revealed mediastinal lymphadenopathy and bilateral pulmonary nodules concerning for malignancy. Evaluation was limited by concurrent COVID-19 infection, and CT-guided biopsy demonstrated necrotizing granulomas without evidence of malignancy or infection. She later re-presented with fever and …

Identifying The Best Cutoff Value Of The Fecal Occult Blood Immunochemical Test In The Detection Of Advanced And Neoplastic Colorectal Lesions [Identificación Del Mejor Punto De Corte De La Prueba Inmunoquímica De Sangre Oculta En Heces En La Detección De Lesiones Colorrectales Avanzadas Y Neoplásicas], Patricia Gaggero, Eduardo Fenocchi, Cecilia Silva, Juan C. Lopez-Alvarenga, Natalia Lambert, Fabian Batista, Sergio Sobrino-Cossio, Jonathan R. White, Adolfo Parra-Blanco

School of Medicine Publications

Objective: Screening is an effective tool for detecting colorectal lesions in asymptomatic subjects. There is a positive correlation between fecal immunochemical test (FIT) values and the size of tumors. Despite the efficacy of screening, the detection of colorectal cancer (CRC) remains low. The primary objective was to evaluate the best FIT cutoff value for detecting advanced adenomas and CRC among individuals at average risk in a country with a high incidence and morbidity from CRC.

Methods: This observational and prospective study analyzed consecutive cases in 1461 asymptomatic subjects with a positive FIT (≥ 100 ng hemoglobin [Hb]/ mL) referred for …

Deep Learning-Based Co-Current Upward Gas-Liquid Two-Phase Flow Regime Identification In An Annular Conduit, Joshua Robert Macomber

Graduate Theses, Dissertations, and Problem Reports (ETD)

Flow regime identification in co-current upward gas-liquid flow through annular conduits remains a significant challenge in petroleum engineering, with major safety and operational implications. It is also important across industries involving the transport of multiphase fluids. Misidentifying flow regimes can introduce major operational risk, yet regime boundaries in annular gas-liquid flow are often visually complex and context dependent.

The objective of this study was to evaluate the utility of convolutional neural network (CNN) classifiers for flow regime identification. The CNN was trained using annular flow image dataset published by Texas A&M University. The dataset consists of approximately 947 RGB images …

Addressing Algorithmic Bias In Ai Journalism: A Toolkit For More Equitable Coverage Of Marginalized Communitie, Sierra Monet Worden

Graduate Theses, Dissertations, and Problem Reports (ETD)

This project addresses a critical gap in journalism reporting concerning the use of artificial intelligence (AI) when covering stories on marginalized communities. Partially as it relates to the bias that exists in artificial intelligence algorithms, and the lack of training that exists for the implementation of these tools. Journalists play a crucial role in providing accurate and fair information on marginalized communities, yet the implementation of AI presents a set of all new challenges and allows opportunities for more bias to make its way into news coverage. Existing resources for reporting focus more on ethics and less on how to …

Studies On The Depth Formula And On Reducing Dimensions, Brian Mccourt Laverty

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation presents the author’s recent research, conducted under the supervision of Professor Olgur Celikbas, and based on two articles—one published and one in progress. These works develop two closely related research directions in commutative algebra. Together, they contribute to the subject by addressing aspects of existing conjectures, establishing new results, and introducing methods for studying homological invariants.

The first research direction concerns the depth formula, namely the equality \[ \depth_R(M)+\depth_R(N)=\depth(R)+\depth_R(M\otimes_RN) \] where $M$ and $N$ are finitely generated $R$-modules. A classical result of Auslander \cite{Aus} shows that the depth formula holds provided that either $M$ or $N$ has finite …

Machine Learning-Based Prediction Of Heavy-Duty Diesel Engine Emissions And Comparative Analysis Against Statistical Models, Abdullah Ahmad Khan

Graduate Theses, Dissertations, and Problem Reports (ETD)

The increasing stringency of emissions regulations for heavy-duty (HD) diesel engines necessitates the development of accurate, efficient, and general predictive models for engine-out emissions under steady-state operating conditions. Traditional statistical approaches, while computationally efficient, often fail to capture the inherent nonlinear relationships between the engine operating parameters and emissions formation. In contrast, machine learning (ML) techniques offer a promising alternative by learning the complex patterns directly from experimental data. The present thesis summarizes a comprehensive study on the prediction of steady-state, HD diesel engine emissions using ML models, along with a comparative analysis against conventional statistical modeling approaches.

A high-quality …

Simulation Optimization Of Intermodal Freight Transportation Under Disruptions, Israt Humayra

Graduate Theses, Dissertations, and Problem Reports (ETD)

Rapid growth in freight transportation in modern supply chains has led to increased operational costs, congestion, and severe environmental impacts, especially greenhouse gas emissions. Combining different modes, such as highway, railway, and waterway, intermodal transportation could thus offer considerable benefit to improve efficiency, sustainability, and resilience. However, most existing planning approaches rely on simplified assumptions, fixed schedules, and average cost estimates, making them less relevant to dealing with real-world uncertainties and disruptions. This study develops a simulation-optimization framework for intermodal freight transportation under disruption. We develop a mixed-integer programming model to represent an intermodal logistics planning framework on a multi-layered …

Do You Walk On By? The Effects Of Donor Fatigue On Willingness To Donate, Shawn Michael Henry

Graduate Theses, Dissertations, and Problem Reports (ETD)

Donation solicitation literature validates the successful usage of various solicitation tactics that lead to achieving set donation goals for their associated cause. Yet, while the financial success of “checkout charity” is well-documented, a significant gap remains regarding the impact of high-frequency solicitations on long-term consumer-brand relationship. This research addresses this gap. Over the span of three surveys, this study finds that donation request frequency overall erodes donation likelihood through donor fatigue when donation frequency changes (occasional vs. always). However, this relationship is moderated by the perceived consumer base, with a larger perceived consumer base exacerbating the “diffusion of responsibility.” Further, …

Microwave Calcination Techniques For Electrode And Electrolyte Powders In Fuel Cells, Logan Scott Proud

Graduate Theses, Dissertations, and Problem Reports (ETD)

This work investigates microwave-assisted calcination as a rapid, energy-efficient alternative to conventional resistive heating for the synthesis of complex oxide powders used in solid oxide electrochemical cells. Ce₀.₉Gd₀.₁O₁.₉₅ (GDC10) and La₀.₆Sr₀.₄Co₀.₂Fe₀.₈O₃–δ (LSCF6428) were synthesized via solid-state and co-precipitation routes to evaluate the influence of precursor chemistry and dielectric behavior on microwave coupling, reaction kinetics, and powder characteristics. Calcination was performed in single-mode and multimode microwave systems using a silicon carbide/alumina susceptor–insulation package to ensure controlled high-temperature processing. A parametric study examining ramp rate, sample size, and temperature revealed that microwave processing reduced calcination time to ~1 hour compared to >21 …

Public Perception Of Using Public Lands For Climate Change Mitigation, Casey Marie Zaitzew

Graduate Theses, Dissertations, and Problem Reports (ETD)

Biological carbon sequestration provides a way to integrate carbon initiatives while meeting sustainable forestry management needs. In the United States, woody plants capture about 13% of annual carbon emissions, highlighting the importance of forests and other ecosystems in national climate strategies.With over 640 million acres of public land, management strategies that incorporate carbon sequestration could significantly influence national carbon storage. Because public lands are taxpayer-funded, public acceptance is critical in carbon-focused policies. While prior studies have examined public attitudes toward carbon capture and storage techniques, fewer studies have focused on biological sequestration within federally managed lands.

This study analyzes public …

Exploring The Role Of Occupational Therapy In A Pediatric Specialty Care Office, Sierra Leigh Hays

Graduate Theses, Dissertations, and Problem Reports (ETD)

Introduction: The role of OT in primary and specialty care clinics is a rising area of interest in the field. There are also many multidisciplinary clinics that OTs can be or are apart of. This study aims to evaluate the consulting role of OT in primary and specialty care clinics, as well as to evaluate data collected by an OT in a pediatric brachial plexus and peripheral nerve clinic. Methods: The first objective was focused on analyzing functional outcomes, using the Active Movement Scale (AMS), of children following neurosurgery or onabotulinum toxin A injections. This was a retrospective study with …

Data-Driven Methodologies For Mapping Cultural Heritage: The Case Of The National Coal Heritage Area, West Virginia, Usa, Hossain Mohammad Nahyan

Graduate Theses, Dissertations, and Problem Reports (ETD)

The objective of this dissertation was to develop a comprehensive, data-driven spatial framework for characterizing the complex cultural landscape of the National Coal Heritage Area (NCHA) in West Virginia. By transitioning away from traditional, heuristic spatial mapping, this research integrates advanced spatial statistics, machine learning, and GIS-based methodologies to objectively quantify the physical, visual, and cultural dimensions of the post-mining environment. The research is structured around three interconnected empirical studies, each addressing a specific scale of the Landscape Character Assessment (LCA) framework to support heritage conservation and sustainable spatial planning. The first paper focused on landform classification, developing an automated …