Physical Sciences and Mathematics Commons^™

Beyond Dialectics, Paradoxes, And Binary Logic, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

Philosophy, long defined by its pursuit of truth, has historically been a battleground for dichotomies: truth vs. falsehood, materialism vs. idealism, reason vs. emotion. These oppositions often provide a framework for understanding philosophical discourse, but they fail to capture the full nuances of reality. To challenge these binary oppositions, I introduced the neutrosophic perspective in philosophy, rooted in Mathematics, and Many-Valued Logics.1 By emphasizing the interrelation of affirmation, negation, and neutrality, neutrosophy allows for the reconciliation of seemingly irreconcilable viewpoints, providing a new lens through which to reinterpret age-old philosophical questions.

A New Simulation Framework For Analyzing Neutrosophic Data In Experimental Design, Muhammad Aslam, Nasrullah Khan, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

A recent simulation-based classical analysis has been developed for interval data. However, a review of the literature indicates that these existing simulations have notable limitations and fail to conform to the neutrosophic statistical framework. In this paper, we propose a novel simulation process designed to analyze neutrosophic data within an appropriate and rigorous neutrosophic framework. We demonstrate that the proposed simulation is more comprehensive and aligns closely with the principles of neutrosophic theory. The results will be obtained through simulation and compared with those of existing methods, with the expectation that the proposed approach provides substantial improvements and is better …

A Plausible Formal Correspondence Between Tetrahedral Condensates/Tsc And Pt-Symmetric Crystals Model Of Cmns (Aka. Low-Energy Nuclear Reactions), Victor Christianto, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

Akito Takahashi's Tetrahedral Symmetric Condensate (TSC) model, detailed in several of his earlier works1 proposes a mechanism for condensed matter nuclear science (CMNS) aka. low-energy nuclear reactions (LENR) within palladium lattices. The model centres on the formation of a tetrahedral cluster of deuterons, enhancing the probability of nuclear fusion. Here, we explore the possibility of extending this framework by considering the TSC within a more general crystalline solid with tetrahedral symmetry, and by approximating the screening potential experienced by the deuterons using PT-symmetric potentials.

When Emotions Flare: Solar Rhythms, Emotions And Cycles Of Political Revolution, Andreas Hernandez, Carolina Zilli Vieira, Alexandra Smith, Rebecca Olson

Geography and Environmental Studies Faculty Publications

Revolutions are among the most transformative events in human history. Analyzing 395 revolutionary episodes from 1900 to 2014, we find that major waves cluster around solar maxima - periods of intensified geomagnetic activity. Drawing on biomedical research linking geomagnetic disturbances to cardiovascular and stress regulation, and thus to emotional states, we propose that solar cycles modulate the affective ecologies within which revolutions arise. Solar activity may accelerate, shape, and amplify revolutionary cycles by heightening emotional climates and tipping fragile systems toward mass mobilization. This reframes revolutions as planetary events - entanglements of political conflict, embodied emotion, and cosmic …

Bibliography For Love Data Week 2025, Arianna Tillman, Isabella Piechota, Annikah Carpio

Library Displays and Bibliographies

A bibliography created to support a display about research data and Love Data Week during January/February 2025 at the Leatherby Libraries at Chapman University.

Geometric Modeling, Reconstruction And Evaluation Of Maize Leaf Morphology In 3d, Zhaocheng Xiang

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Maize is a vital crop for global food security, yet quantitative characterization of its three-dimensional (3D) morphology remains a major challenge due to the geometric complexity of curved leaf structures and occlusions during data collection. This work presents an integrated framework for the digital reconstruction, parametric modeling, and functional analysis of maize leaf morphology and canopy architecture, advancing the precision and interpretability of phenotyping and modeling in smart agriculture.

First, we propose a descriptive and parametric model that represents maize leaves through three fundamental components: midrib, cross-section, and blade contour. Each is described by geometric curves and controlled by biologically …

Leaf-Based Varietal Categorization Of Sweetpotato (Ipomoea Batatas L. Lam.), A Potentially Healthful Vegetable, Using Image Processing And K-Means Clustering, Shahidul Islam, Md Towfiqur Rahman, Md Hamidul Rahman, Abdul Momin

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Sweetpotato (Ipomoea batatas Lam) leaves contain higher concentrations of phenolic compounds, flavonoids, and carotenoids that are remarkable in health promotion. However, the nutrient content in sweetpotato leaves varies from variety to variety, and leaf shape and color are the key identifying factors for the varietal classification of sweetpotatoes. So, detecting sweetpotato leaves is essential for the in-situ identification of sweetpotato varieties and for developing intelligent agricultural systems. This study aimed to create a leaf-shape-based varietal classification technique for sweetpotato using image processing techniques coupled with a K-means clustering algorithm. 38 leaf images (RGB) of two sweetpotato cultivars were collected …

In-Season Nitrogen Mmanagement: Leveraging Data Visualization For Precision Agriculture, Chathurika Harshani Narayana

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Effective nitrogen management is vital for sustainable agriculture, impacting both crop yield and environmental health. Traditional methods often use fixed application rates set before planting, which do not adapt to changing crop needs during the season. This can lead to over- or under-application, reducing efficiency and sustainability. While modern tools like sensors, satellites, and UAVs provide valuable real-time data on crop and field conditions, integrating and using this data to guide timely nitrogen decisions remains a major challenge. In-season nitrogen management offers a solution by allowing for dynamic adjustments to nitrogen applications, addressing crop needs as they arise. This approach …

Distinct Composition-Dependent Topological Hall Effect In Mn2-Xznxsb, Md Rafique Un Nabi, Yue Li, Suzanne G.E. Te Velthuis, Santosh Karki Chhetri, Dinesh Upreti, Rabindra Basnet, Gokul Acharya, Charudatta Phatak, Jin Hu

Physics Faculty Publications and Presentations

Spintronics, an evolving interdisciplinary field at the intersection of magnetism and electronics, explores innovative applications of electron charge and spin properties for advanced electronic devices. The topological Hall effect (THE), a key component in spintronics, has gained significance due to emerging theories surrounding noncoplanar chiral spin textures. This study focuses on Mn_2-xZn_xSb, a material crystalizing in centrosymmetric space group with rich magnetic phases tunable by Zn contents. Through comprehensive magnetic and transport characterizations, we found that the high-Zn (x > 0.6) samples display THE which is enhanced with decreasing temperature, while THE in the low-Zn ( …

Boden Lecture: Taxation Of Autonomous Artificial Intelligence, Reuven S. Avi-Yonah, Lucas Brasil Salama, Herbert Snitz, W. Robert Thomas

Articles

This Article proposes that tax can be a useful supplement to other measures to regulate Autonomous Artificial Intelligence (AAI) and limit its potential harmful effects. This proposal differs from command-and-control regulation of AAI along the lines of European Union legislation that may unduly limit the development of AAI. It also differs from existing proposals to tax AAI to generate revenue to help workers displaced by AAI programs, or to tax the data used by AAI The proposal is based on granting AAI programs like ChatGPT separate legal personhood, like corporate personhood, while incentivizing or requiring their corporate owner to place …

Improving The Performance Of Multi-Stakeholder Partnerships For Sustainable Development In Coastal Areas : Sweden (Hanö Bay) As A Case Study, Jennie Larsson

World Maritime University Ph.D. Dissertations

Coastal areas are vital for both ecosystems and human societies. Comprising diverse terrestrial, freshwater, and marine ecosystems, coastal areas provide us with essential resources and services. However, these areas are under threat from human activities and climate change, necessitating new governance structures to ensure their sustainable management and conservation. This research investigates how to improve the performance of local multi-stakeholder partnerships (M-SPs) in coastal areas, promoted as key mechanisms for achieving sustainable development goals. By drawing on stakeholder theory and using Pattberg & Widerberg’s (2014) analytical framework with nine building blocks for successful M-SPs as a foundation, the study examined …

Safety And Sustainability In The Domestic Ferry Sector : A Pci Framework For Esg-Aligned Maritime Governance, Mirza Zeeshan Baig

World Maritime University Ph.D. Dissertations

The domestic ferry sector is a significant component of maritime transportation. It strengthens social ties, supports economic growth and connects communities. Despites its importance, this industry faces ongoing challenges. These include fragmented governance, operational adequacies, safety risks and environmental concerns. The study deals with these issues by combining a systems-based approach with governance and change management theories. It examines the interplay between human, technical and organizational aspects. The study integrates the rule-based and rights-based maritime governance along with the Lewin’s three stage change management model (Unfreeze, Change, and Refreeze).

At the core of the study is the development of a …

Development Of An Ecg-Based Deep Learning Model For Pediatric Congenital Heart Disease (Chd) Diagnosis, Annbar Mekouar

Selected Full-Text Master Theses 2021-

Congenital heart disease (CHD) stands as the leading congenital anomaly which affects pediatric populations throughout the world. The effectiveness of treatment depends on both early diagnosis and accurate identification but echocardiography requires manual interpretation which proves time-consuming and inconsistent especially when examining pediatric patients with their distinct cardiac systems. The research aims to create a deep learning-based diagnostic framework which uses ECG data to identify coronary artery disease subtypes in pediatric patients. The model uses high-quality datasets from Dr. Ignacio Lugones to extract R-R intervals and QRS durations through convolutional neural networks (CNNs). The system addresses pediatric-specific challenges while enhancing …

A Machine Learning Based Framework For Predicting Drug Cardiotoxicity Using A Combination Of Ecg Biomarkers And Drug Dosage Data, Jamie Wong

Selected Full-Text Master Theses 2021-

Drug-induced cardiotoxicity presents a significant challenge in clinical practice and drug clinical development, particularly with medications that modulate calcium, potassium, and sodium channels that influence cardiac electrophysiology. Clinical practice often relies on QTc prolongation alone as a predictor, which lacks specificity and may lead to excluding other safe therapeutic options. To address this limitation, this study integrates electrocardiogram (ECG) biomarkers with normalized drug dosage data to improve the accuracy of cardiotoxicity risk prediction using machine learning techniques. ECG features, including QT, QRS, RR, and PR intervals, were analyzed alongside normalized dosage data to account for dose-dependent cardiac effects. A physiologically …

Strategic Identification Of Prognostic Biomarkers For Knee Osteoarthritis Via Optimized Regression Techniques, Varun Sri Sai Vemuri

Selected Full-Text Master Theses 2021-

Knee Osteoarthritis (KOA) is a progressive musculoskeletal disease involving cartilage matrix degradation, subchondral bone remodeling, and systemic inflammation, significantly impairing joint function and mobility. Existing KOA prediction models are not designed to account for nonlinear multimodal biomarker interactions or to integrate biochemical and imaging data, thus limiting their clinical utility. The current method for early detection and prediction of KOA disease progression is primarily based on machine learning-based approaches using radiographic imaging data, static feature selection, and deterministic outputs. These machine learning approaches often fail to capture the pathophysiology of KOA disease progression, which involves a complex cascade of processes, …

Ai And Tribal Court Practice, Matthew L.M. Fletcher

Articles

American Indian tribal court practice resides at the intersection of two difficult legal problems. First, because tribal justice systems are usually very young and dynamic, awareness and analysis of tribal law is underdeveloped. Second, because tribal nations are not governed by state or federal law, tribal law is culturally unique. Tribal court practitioners often find that even routine legal matters will involve questions of first impression in the jurisdiction. All of this is to say tribal court jurisprudence is intensely jurisgenerative.

Because tribal law is often unsettled or indeterminate, the costs of discovering and applying this law are occasionally high. …

The Future Of Ai Regulation In Drug Development: A Comparative Analysis, Gabriela Lenarczyk, Timo Minssen, W. Nicholson Price Ii, Arti Rai

Articles

As artificial intelligence (AI) transforms drug development, regulatory frameworks are evolving to oversee its implementation, particularly at the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This paper makes three contributions to understanding emerging regulatory approaches. First, we offer a comparative analysis of how these agencies have responded to AI-driven advances, incorporating new US executive orders and the European Union (EU)’s AI Act. Second, we propose a novel analytical framework to understand regulatory divergence: the FDA’s flexible, dialog-driven model contrasts with the EMA’s structured, risk-tiered approach, reflecting broader institutional and political-economic differences. While the former encourages …

Multi-Modal Graph Learning For Vision Language Model In General And Medical Domains, Xinyue Hu

Computer Science and Engineering Dissertations - Archive

Multi-modal learning has gained significant attention in deep learning for its ability to integrate and process information from multiple modalities, such as text, images, and videos. By leveraging complementary information from different modalities, it enables a more comprehensive understanding of complex data in various tasks. Simultaneously, graph learning, a prominent paradigm that models structured data as graphs, captures both local and global dependencies, providing a natural framework to represent intricate interactions and contextual relationships. When combined with multi-modal learning, these graph-based approaches have the potential to enhance feature representation and reasoning by effectively fusing heterogeneous data, leading to more robust …

A Framework For Developing Collaborative Community Building Tools For Novice Computer Science Students, Daniel Olivares, Jakob Kubicki, Katie Imhof

Computer Science Faculty Scholarship

Students enrolled in introductory computer science courses tend towards individual work because of pedagogical practices discouraging collaboration and a focus on individual assignments. This can discourage new computer science students and may negatively affect persistence in computer science. In contrast, social learning theory research suggests a connection between student success and their level of involvement with peers, instructors, and in the greater learning community. Motivated by these contrasting conclusions, the research presented in this paper puts forth a framework based on social learning theories and teaching and learning methodologies to leverage social computing as a learning tool. This framework’s primary …

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 …

Distribution And Tooth Morphoguilds Of Mosasaurids (Squamata) Of The Campanian Western Interior Seaway With Implications For Controls On Mosasaur Paleobiogeography, Alec Zaborniak

Master's Theses or Doctor of Nursing Practice

The Western Interior Seaway (WIS) of North America is well known for its mosasaurid squamate diversity, particularly during the Campanian of the Late Cretaceous. Mosasaurid diversity has historically been examined at a fine scale, with many studies investigating the faunal composition of specific assemblages; however, studies focusing on mosasaur communities within the Western Interior Seaway during specific Ages are currently lacking. Mosasaur tooth morphology has also been extensively studied, with tooth characteristics commonly being used as phylogenetic characters. However, many studies examining mosasaur communities and tooth morphology have been focused on broader spatiotemporal or phylogenetic scales. This study investigates the …

Exploring The Impact Of Generative Ai Chatgpt On Critical Thinking In Higher Education: Passive Ai-Directed Use Or Human-Ai Supported Collaboration?, Nesma Ragab Nasr, Chih-Hsiung Tu, Jennifer Werner, Tonia Bauer, Cherng-Jyh Yen, Laura Sujo-Montes

STEMPS Faculty Publications

Generative AI is weaving into the fabric of many human aspects through its transformative power to mimic human-generated content. It is not a mere technology; it functions as a generative virtual assistant, raising concerns about its impact on cognition and critical thinking. This mixed-methods study investigates how GenAI ChatGPT affects critical thinking across cognitive presence (CP) phases. Forty students from a four-year university in the southwestern United States completed a survey; six provided their ChatGPT scripts, and two engaged in semi-structured interviews. Students’ self-reported survey responses suggested that GenAI ChatGPT improved triggering events (M = 3.60), exploration (M = 3.70), …

The Effects Of Return To Prairie In Barber County, Kansas: A Case Study Using Ndvi And Dendrochronology, Elijah O. Joy

Master's Theses or Doctor of Nursing Practice

This study aims to evaluate the impact of prairie restoration efforts in Barber County, Kansas, with a focus on removal of Eastern Red Cedar. Restoration effectiveness was assessed using remote sensing of satellite scenes via NDVI, density analysis, and dendrochronological methods. The tree density analysis revealed a 66% reduction across the study area and a 99% reduction in the fully restored zones. NDVI analysis from 2010 to 2024 showed higher summer vegetation health and seasonal fluctuations influenced by climate variability and fire events. Dendrochronological data from tree cores reveals growth patterns and establishes a baseline for future dendrochronological chronologies. The …

Oil-Oil Correlation Across Sedimentary Basins In Kansas Using Molecular Geochemistry And Fourier Transform Infrared Spectroscopy, Oluwaseun V. Omoyemi

Master's Theses or Doctor of Nursing Practice

Hydrocarbons are produced from different subbasins and stratigraphic intervals in Kansas. However, the geochemical characteristics and sources of these hydrocarbons are still poorly understood. While reservoir rocks, seals, and traps that are essential for petroleum systems are well defined within the state, the possible source rocks are often considered thermally immature, raising questions about their ability to generate the volume of hydrocarbons currently being produced. This research project tries to evaluate the geochemical characteristics of hydrocarbons produced in the state of Kansas and determine if the hydrocarbons from the different subbasins and stratigraphic units are genetically related. Thirty-seven crude oil …

A Histological Survey Of A Large, Sub-Adult Tylosaurus Nepaeolicus With Implications On The Validity Of Tylosaurus Kansasensis, Carson Cope

Master's Theses or Doctor of Nursing Practice

Tylosaurus nepaeolicus is a basal, medium-sized tylosaurine mosasaur that lived during the late Coniacian and early Santonian in the Western Interior Seaway. Despite being a well-studied species, there is debate over whether Tylosaurus kansasensis represents the juvenile form of T. nepaeolicus, or if the two species are distinct. Osteohistology can determine organism age at time of death and can be used to help resolve this debate and provide a better understanding of the growth of T. nepaeolicus. Thin-sections were taken from a humerus, rib, and vertebra of a single individual (FHSM VP-2209). All thin-sections show primarily parallel-fibered cortical …

Development And Application Of Computational Tools For Data-Driven Materials Science., Logan L. Lang

Graduate Theses, Dissertations, and Problem Reports (ETD)

Modern materials science generates vast amounts of data from computational simulations and experiments, creating significant challenges for data processing and analysis. This thesis addresses these challenges through the development and application of computational tools within the framework of Material Data Science (MDS). Contributions span the four pillars of MDS: Material/Molecular Data, Algorithms, Databases, and High-Throughput Processes—with a primary focus on the Algorithm, Data, Database pillars.

For the Algorithm pillar, two Python libraries were developed to streamline common analysis tasks. PyProcar simplifies the post-processing and visualization of electronic structure data (band structures, density of states, Fermi surfaces) obtained from various Density …

Three-Dimensional Spreading Of Magnetic Reconnection Between Non-Parallel Flux Ropes With A Guide Field, Regis John

Graduate Theses, Dissertations, and Problem Reports (ETD)

Magnetic reconnection is a fundamental plasma process that facilitates the rapid conversion of magnetic energy into particle acceleration, plasma flows, and heating. It plays a central role in explosive astrophysical events such as solar flares, where vast amounts of magnetic energy are released on short time scales. A key structure in many reconnection sites is the magnetic flux rope, a column of plasma carrying current threaded by helical magnetic fields, which is frequently involved in or generated by reconnection. Understanding how reconnection unfolds in such flux rope systems is critical for interpreting both space weather phenomena and laboratory plasma dynamics. …

Forensic Characterization And Comparison Of Modern Nail Polish Products By Their Physical Features And Chemical Composition, Madison Meredith Lindung

Graduate Theses, Dissertations, and Problem Reports (ETD)

Nail polish can serve as valuable evidence in forensic investigations as it is often found on the hands and feet of individuals. Nail polish can break off and transfer easily during self-defense actions or forceful contact, such as in cases of kidnapping, sexual assault, or homicide. These residues can provide clues about who was at the scene, as well as where, when, and how the event evolved. However, compared to other types of paints commonly analyzed in forensic laboratories, limited forensic research has been conducted on the chemical composition and discrimination capabilities of popular nail products. The lack of information …

Systematic Methodologies For Magnetic Materials Design, Andres Tellez Mora

Graduate Theses, Dissertations, and Problem Reports (ETD)

Understanding and predicting the magnetic behavior of materials from first principles is one of the central challenges in condensed matter physics. This dissertation presents a systematic framework that bridges ab initio calculations, many-body physics, and effective spin models to analyze magnetic materials in particular, but also more general quantum systems. Starting from the electron many-body Hamiltonian and the second quantization formalism, we derive Density Functional Theory (DFT) and explain how magnetic properties emerge from exchange interactions and can be interpreted as perturbations to the magnetization density. To capture these effects efficiently, we construct Heisenberg models from first principles using the …

Approaches To The Synthesis Of Highly Substituted Arenes, Kh Tanvir Ahmed

Graduate Theses, Dissertations, and Problem Reports (ETD)

While benzene rings and their derivatives are among the most frequently encountered ring systems in natural products, pharmaceuticals, agrochemicals, dyes, and functional materials, polysubstituted arenes are relatively scarce. The analysis of FDA-approved small molecules provides valuable insights into the characteristics of successful drugs while also highlighting gaps in the availability of synthetic methods capable of efficiently accessing diverse substitution patterns on aromatic rings. Highly substituted arenes are typically synthesized through sequential modification of a pre-existing benzene core; however, this stepwise approach becomes increasingly challenging with each additional substitution. An alternative strategy involves the direct construction of benzene rings via annulation …