Physical Sciences and Mathematics Commons^™

An Integrated Machine Learning Approach For Identifying Emergency Rescue Messages On Social Media During Natural Disasters, Wael Khallouli, Jiang Li, Jingwei Huang, Ghaith Rabadi, Samuel Kovacic

School of Cybersecurity Faculty Publications

During large-scale disasters, emergency call centers are often overwhelmed by the large volume of rescue requests and calls for help. Consequently, people are turning to social media platforms to seek assistance. Rescue information posted on these platforms is extremely valuable for first responders to make informed rescue decisions. Therefore, the automatic identification of these requests from the vast amount of data posted on social media during crises is critical yet challenging. This work presents our ongoing research on applying deep learning techniques to extract actionable rescue information from social media during crises. We proposed a novel deep learning model that …

High-Resolution Modeling Of Extreme Heat Events With Socioeconomic Consideration: A Real-Case Wrf-Les Approach, Maryam Golbazi, Frank Liu, Yin-Hsuen Chen, Timothy W. Juliano, Heather Richter

ODU Articles

The overarching goals of this work is to explore best practices for micro-scale modeling of a real case, identify relevant phenomena by using high-resolution modeling, and to explore their implications for public health, and climate resilience strategies in Hampton Roads, VA, USA. This project employs the Weather Research and Forecasting (WRF) model to conduct a comprehensive study of Hampton Roads, utilizing a coupled mesoscale to microscale modeling capable of resolving boundary layer turbulence. This study has three primary objectives: (1) to establish the optimal mesoscale to Large-Eddy Simulation (LES) configurations for complex geographical regions such as the Hampton Roads (HR) …

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

Uncertain Labeling Graphs And Uncertain Graph Classes (With Survey For Various Uncertain Sets), Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

Graph theory, a branch of mathematics, studies the relationships between entities using vertices and edges. Uncertain Graph Theory has emerged within this field to model the uncertainties present in real-world networks. Graph labeling involves assigning labels, typically integers, to the vertices or edges of a graph according to specific rules or constraints. This paper introduces the concept of the Turiyam Neutrosophic Labeling Graph, which extends the traditional graph framework by incorporating four membership values—truth, indeterminacy, falsity, and a liberal state—at each vertex and edge. This approach enables a more nuanced representation of complex relationships. Additionally, we discuss the Single-Valued Pentapartitioned …

Symbolic Hyperplithogenic Set, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

Concepts such as Fuzzy Sets, Neutrosophic Sets, and Plithogenic Sets have been widely investigated for tackling uncertainty, with numerous applications explored across various domains. As extensions of the Plithogenic Set, the HyperPlithogenic Set and the SuperHyperPlithogenic Set are also recognized. A Symbolic Plithogenic Set (SPS) is a structured set defined by symbolic components 𝑃𝑖 and coefficients 𝑎𝑖 , enabling flexible algebraic operations under a specified prevalence order. In this paper, we examine concepts including the Symbolic HyperPlithogenic Set and the Symbolic 𝑛-SuperhyperPlithogenic Set.

Some Types Of Hyperneutrosophic Set (4): Cubic, Trapozoidal, Q-Rung Orthopair, Overset, Underset, And Offset, Florentin Smarandache, Takaaki Fujita

Branch Mathematics and Statistics Faculty and Staff Publications

This paper builds upon the foundational work presented in [38–40]. The Neutrosophic Set provides a comprehensive mathematical framework for managing uncertainty, defined by three membership functions: truth, indeterminacy, and falsity. Recent advancements have introduced extensions such as the Hyperneutrosophic Set and the SuperHyperneutrosophic Set, which are specifically designed to address increasingly complex and multidimensional problems. The formal definitions of these sets are available in [30]. In this paper, we extend the Neutrosophic Cubic Set, Trapezoidal Neutrosophic Set, q-Rung Orthopair Neutrosophic Set, Neutrosophic Overset, Neutrosophic Underset, and Neutrosophic Offset using the frameworks of the Hyperneutrosophic Set and the SuperHyperneutrosophic Set. Furthermore, …

Some Types Of Hyperneutrosophic Set (3): Dynamic, Quadripartitioned, Pentapartitioned, Heptapartitioned, M-Polar, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

This paper builds upon the foundation established in [50, 51]. The Neutrosophic Set provides a robust mathematical framework for handling uncertainty, defined by three membership functions: truth, indeterminacy, and falsity. Recent developments have introduced extensions such as the Hyperneutrosophic Set and SuperHyperneutrosophic Set to tackle increasingly complex and multidimensional problems. In this study, we explore further extensions, including the Dynamic Neutrosophic Set, Quadripartitioned Neutrosophic Set, Pentapartitioned Neutrosophic Set, Heptapartitioned Neutrosophic Set, and m-Polar Neutrosophic Set, to address advanced challenges and applications.

Plithogenic Duplets And Plithogenic Triplets, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

A Neutrosophic Set is a mathematical framework that represents degrees of truth, indeterminacy, and falsehood to address uncertainty in membership values [41, 42]. In contrast, a Plithogenic Set extends this concept by incorporating attributes, their possible values, and the corresponding degrees of appurtenance and contradiction [50]. Among the related concepts of Neutrosophic Sets, Neutrosophic Duplets and Neutrosophic Triplets are well-known. This paper defines Plithogenic Duplets and Plithogenic Triplets as extensions of these concepts using the Plithogenic Set framework and briefly examines their relationship with existing concepts.

Some Types Of Hyperneutrosophic Set (2): Complex, Single-Valued Triangular, Fermatean, And Linguistic Sets, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

This paper is a continuation of the work presented in [35]. The Neutrosophic Set provides a mathematical framework for managing uncertainty, characterized by three membership functions: truth, indeterminacy, and falsity. Recent advancements have introduced extensions such as the Hyperneutrosophic Set and SuperHyperneutrosophic Set to address more complex and multidimensional challenges. In this study, we extend the Complex Neutrosophic Set, Single-Valued Triangular Neutrosophic Set, Fermatean Neutrosophic Set, and Linguistic Neutrosophic Set within the frameworks of Hyperneutrosophic Sets and SuperHyperneutrosophic Sets. Furthermore, we investigate their mathematical structures and analyze their connections with other set-theoretic concepts.

Soft Directed N-Superhypergraphs With Some Real-World Applications, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

This paper introduces the Directed Soft Super Hyper Graph, a unified framework for modeling complex, multi-layered directed networks. It combines directionality, recursive hyperstructure, and soft-set parameterization to address the integration of Soft Super HyperGraphs and Directed SuperHyperGraphs, which remains largely unexplored. The paper provides formal definitions, core operations, and real-world examples, such as urban infrastructure and transportation networks, to demonstrate the framework's effectiveness in managing deep hierarchies and uncertain relationships simultaneously.

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 …