Physical Sciences and Mathematics Commons^™

Impact Of Magnetic Fields On Cosmic Ray Density In Molecular Clouds, Byron Jesus Encinas Velazquez

Open Access Theses & Dissertations

We investigate the effects caused by Cosmic Rays (CRs) in a Molecular Clouds (MCs) environment and the effects of varied magnetic field configurations in the core (r = 1 Pc ) of a MC. We take as initial conditions for the interstellar medium a product of various supernova explosions as injected initial perturbation and observe the evolution during the timescale of ~4.5 Myrs. We compute the CR reduction factor R as a measure of the density of a distribution of particles traveling with a guiding center along randomized field lines near the core and discuss their impact on the ionization …

A Profile Wald Test In M-Estimation, Reagan Kesseku

Open Access Theses & Dissertations

Despite the growing popularity of machine learning-based inference, classical statistical inference remains highly relevant in modern data science due to its interpretability and theoretical rigor. Among its core tools, the likelihood ratio test, Wald test, and score test are foundational methods for hypothesis testing within the maximum likelihood framework. Although these tests are asymptotically equivalent under regularity conditions, each offers distinct advantages depending on the context, computational demands, and the availability of parameter estimates. In this dissertation, we introduce a fourth method, the Profile Wald Test (PWT), within the broader M-estimation framework. The PWT is based on profile estimators of …

From Text To Utility: Distance-Aware Contrastive Learning For Detection-Ready And Shareable Malware Descriptions, Ivan Alejandro Montoya Sanchez

Open Access Theses & Dissertations

The rapid rise of sophisticated malware variants poses significant challenges for cybersecurity analysts, particularly due to the scarcity of data on newly emerging threats. Due to privacy, legal, and operational constraints, malware samples are often not shareable; instead, organizations publish cyber threat intelligence (CTI) in natural language. However, these reports are typically unstructured and inconsistent, limiting their utility in machine learning (ML) models. This thesis explores whether high-fidelity, shareable threat intelligence can be automatically generated from structured malware behaviors to supplement ML models when direct access to malware samples is limited. Two central questions are addressed:(i) How can descriptions be …

Towards Mitigation Of The Light Network Load Performance Penalty Of The Network Link Outlier Factor (Nlof), Jaime Merin Guzman

Open Access Theses & Dissertations

Detecting and localizing faults in communication networks is critical to maintaining reliable and efficient network operations. The Network Link Outlier Factor with Most Likely Link (NLOF: MLL) algorithm has demonstrated its potential to automate this task but suffers from significant performance degradation under low network load conditions, where limited network flow data reduces its ability to localize faults. This thesis proposes and evaluates the performance of a synthetic traffic generation algorithm to be used with NLOF:MLL. This algorithm strategically injects synthetic flows that supplement the insufficient real network flows, thereby improving NLOF:MLL's performance under low-load conditions. Specifically, we select network …

Hidden Layer Reshaping On Convolutional Neural Network, Alan Delgado

Open Access Theses & Dissertations

Artificial Intelligence (AI) technologies have become really popular in recent years. From ChatGPT to Tesla cars, many applications can benefit from these type of technologies. Automotive, healthcare, biomedical, cybersecurity, finances, and retail are some of the fields that take advantage of it. It has been seen that AI can solve complex problems, but there is still work to be done to optimize it. A deep learning neural network (DLNN) tries to simulate how a human brain operates. These DLNNs are made up of artificial neurons which are connected by weight that are modified when the network is trained. These networks …

Development Of Porous Separators For Lithium-Ion Batteries Via 3d Printing And Thermally Induced Phase Separation, Abraham Enchinton

Open Access Theses & Dissertations

Additive manufacturing processes allow the development of custom-shape rechargeable batteries, but research in custom filaments for fused deposition modeling (FDM) of battery separators has remained limited until now. This study discusses the development and optimization of a composite thermoplastic filament feedstock to 3D print separator membranes. A number of post-processing steps - leveraging the thermally induced phase separation (TIPS) of the composite filament - are introduced in tandem with FDM to promote microporosity formation through the removal of the sacrificial diluent phase within 3D printed samples. Three distinct compositions of varying polymer/diluent ratios were developed and thoroughly investigated through thermogravimetric …

Investigation Of High-Frequency Electron Spin Dynamics In The Kagome-Lattice Ymn6sn6 Crystal, Lovia Ofori

Open Access Theses & Dissertations

Kagome magnetic crystals received a great deal of research attention in the recent past for their intriguing magnetic properties. Recently, YMn6Sn6 (Y166) has shown to exhibit several nontrivial magnetic phases (such as the Distorted Spiral (DS) phase, Transverse Conical Spiral (TCS) phase, Fan-like (FL) phase and the Forced-Ferromagnetic (FF) phase) and complex magnetic interactions. In this work, we employed very high-frequency electron paramagnetic resonance (VHF-EPR) spectroscopy to investigate the local microscopic magnetic interactions of Mn ions in the Kagome Crystal Y166 to better understand the new observed magnetic phases. Particularly, we studied the temperature-dependent EPR behavior at variable very-high microwave …

A Numerical Study Of Self-Assembling Amphiphilic Systems, Joshua Albert Sackey

Open Access Theses & Dissertations

Mixing processes in ternary mixtures involve immiscible fluids such as oil and water, and a surface-active molecule called surfactant. These physical processes find applications in various fields, including enhanced oil recovery, drug delivery design systems, and the formulation of cleaning products. Even though this process has several applications, the mathematical models describing it and the numerical methods solving it are not well understood. The underlying mathematical model is a nonlinear initial-boundary value problem involving sixth-order derivatives and belongs to the class of sixth-order Cahn-Hilliard equations. Authors Sharma and Tierra recently proposed a numerical method to approximate its solutions in two …

Machine Learning And Time Series Forecasting For Hydropower Predictions, Jose Reynaldo Vega

Open Access Theses & Dissertations

Recent advancements in machine learning have led to the design of many neural network architectures aimed at solving real-world problems. Each network works to make predictions by finding patterns in the provided data. One common application is time series forecasting, where a model predicts future events based on historical time series data. Time series forecasting is used in a variety of fields, one example being in predicting water releases of Hybrid Floating Photovoltaic-Hydropower (HFPVH) systems. As global population growth drives an increase in energy demand, the need for resilient and sustainable energy generation has become urgent. HFPVH systems have emerged …

Generative Ai For 3d Printed Antenna Design, Jennifer Ann Chavez

Open Access Theses & Dissertations

This research explores the integration of generative artificial intelligence (AI) with a physics-informed particle swarm optimizer (PSO) to develop 3D printable microstrip patch antennas. A neural network was trained on a dataset of microstrip patch antenna geometries and their corresponding performance metrics: return loss and gain. The PSO used a fitness function prioritizing low return loss in potential antennas, eventually yielding novel antenna geometries with parasitic components. 3D printing constraints were also hard coded into the framework, thus preventing any geometries being generated that cannot be fabricated. When simulated using Ansys HFSS, the AI generated microstrip patch antennas exceeded the …

Yield Prediction Of Pv Solar Energy Systems And Its Application In The Energy Grid For Operational Efficiency, Pablo Bustamante

Open Access Theses & Dissertations

The generation of power from photovoltaic (PV) solar panels is influenced by a multitude of factors. These include the tilt and orientation of the solar panels, the latitude of their location, and the prevailing climate and weather conditions. Additionally, shading at specific locations, particularly if the panels are not part of a solar facility, can significantly impact their efficiency. The quality and efficiency of the panels themselves, along with the preventive maintenance of both the solar panels and associated components such as inverters and trackers, are also critical. Finally, the overall system design and installation play a vital role in …

Cyber Resiliency Framework And Mechanisms For Software Defined Tactical Networks, Anthony Castanares

Open Access Theses & Dissertations

Traditional tactical networks fail to achieve cyber resiliency for many reasons, but the most prevalent causes include flat designs, the absence of cyber detection capabilities at the lowest level, and immutable resource allocations after instantiation. These design choices allow network threats direct visibility to each device on the network, and the lack of detection allows infections to proliferate. Furthermore, tactical battlefield networks are difficult to secure because of the lack of persistent oversight by an intelligent agent that can exercise control over the network's topology or resources in real-time. However, advances in software-defined networking (SDN) provide an opportunity to address …

Optimal Accelerated Life-Testing Plans Under Progressive Type Ii First Failure Censoring Scheme, Emmanuella Duah

Open Access Theses & Dissertations

Designing optimal accelerated life testing (ALT) plans under progressive Type-II first failure censoring involves complex computational challenges, especially when trying to balance efficiency, precision, and practicality. This research introduces a novel optimization framework aimed at determining the best test configuration that minimizes estimation uncertainty within constrained experimental conditions. We developed a tailored meta-heuristic strategy based on an enhanced Variable Neighborhood Search (VNS) algorithm, which efficiently navigates the complex landscape of censoring schemes and stress allocations. Unlike conventional methods that typically focus on simpler censoring or stress-level structures, this approach simultaneously optimizes both the censoring points and sample allocations across various …

Computing Optimal Multi-Level Stress Testing Plans Using A Combined Variable Neighborhood Search Algorithm Under Progressive Type-Ii Censoring Scheme, Michael Obuobi

Open Access Theses & Dissertations

In multi-level stress life tests under Type-II progressive censoring, determining optimal allocation poses significant computational challenges due to the vast solution space. Efficient methods are essential for exploring the admissible censoring schemes effectively. This thesis introduces a novel meta-heuristic algorithm, the Combined Variable Neighborhood Search (CVNS), which computes optimal schemes at different stress levels simultaneously. Unlike methods focusing on marginal stress levels or one-step progressive censoring, this approach leverages a unified framework to ensure enhanced computational efficiency and solution quality. By integrating the components of the design parameters into a cohesive optimization process, the algorithm effectively reduces computational time while …

Computing Optimal Progressive Hybrid Censoring Schmes Using An Mcmc Type Probabilistic Approach, Irene Yemotiorkor Odoi

Open Access Theses & Dissertations

Progressive hybrid censoring schemes play a crucial role in optimizing life-testing experiments by balancing test duration and statistical efficiency. This study presents a Markov Chain Monte Carlo (MCMC)-based probabilistic approach for determining optimal progressive hybrid censoring schemes, incorporating a time-dependent component that enhances traditional progressive censoring methods.We implement our approach using three distinct probability distributions the multinomial, hypergeometric, and uniform distributions to simulate censoring schemes. The optimal censoring schemes are then identified based on three optimality criteria: A-optimality, D-optimality, and T-optimality, ensuring robust selection by minimizing estimator variance, maximizing Fisher information, and optimizing test duration, respectively..To evaluate the effectiveness of …

Clinicogenomic Insights For Prostate Cancer Progression, Kelvin Ofori-Minta

Open Access Theses & Dissertations

Prostate cancer (PrCa) remains a critical challenge in precision oncology due to several reasons including its apparent heterogenous condition, recurrence following treatment and rapid progressive forms. Therefore, identifying patients at risk of progression is essential to fast-track therapeutic decisions and improve outcomes. Despite recent advances in genomic and molecular profiling, conventional PrCa risk assessment tools heavily rely on a few clinical parameters, neglecting the prognostic potential of genomic biomarkers in the presence of clinical biomarkers. This study presents a computational pipeline to harmonize and evaluate the prognostic value of clinicogenomic profiles of patients in modelling progression free survival (PFS). PFS, …

From Land To Lagoon: The Spatiotemporal Biogeochemical Dynamics Of Arctic Coastal Erosion In Elson Lagoon, Alaska, Sasha Victoria Peterson

Open Access Theses & Dissertations

TThe Arctic is warming rapidly, driving widespread environmental change across coastal landscapes, including permafrost thaw, shoreline erosion, and shifts in sediment and carbon dynamics, pose growing challenges for predicting carbon cycling and climate feedbacks. This dissertation investigates the spatiotemporal variability of erosion, decomposition, and nearshore turbidity in Elson Lagoon, Alaska (1955–2023), to better understand how carbon, nitrogen, and sediment are mobilized, transformed, and potentially emitted as greenhouse gases in Arctic coastal systems. Chapter 2 presents a high-resolution analysis of shoreline change and erosion-driven material fluxes spanning nearly seven decades. Drawing on 795 soil data points, airborne LiDAR, and five shoreline …

Multiscale Integration Of Receptor-Ligand Dynamics Into Discrete And Continuous Tumor Growth Models With Application To Tyrosine Kinase Inhibitor Treatment, Romasa Qasim

Open Access Theses & Dissertations

The epidermal growth factor (EGF) receptor cascade plays a crucial role in the survival and proliferation of tumor cells. Tyrosine kinase inhibitors (TKIs) are a class of drugs that inhibit epidermal growth factor receptors (EGFRs), thereby preventing the downstream signal transduction. Despite their importance, models that link spatial receptor dynamics to tumor growth remain scarce. Further, TKIs act through selective mechanisms, inhibiting active, inactive, or all receptor states, which poses a challenge to traditional modeling approaches.

We propose to numerically study two mathematical models incorporating receptor-dynamics into cancer models to describe the impact of EGFR overexpression and TKIs. The first …

Enhancing Security And Resiliency In Operational Technology Environments Through Network Slicing And Federated Learning, Brian Giovanni Rodiles Delgado

Open Access Theses & Dissertations

The growing convergence of Information Technology (IT) and Operational Technology (OT) within Industry 4.0 environments has introduced new demands on industrial network infrastructure. As cyber-physical systems become increasingly interconnected, ensuring the secure, timely, and efficient exchange of critical data is essential. This thesis explores how network slicing, a method of creating isolated virtual network segments, can be applied within OT environments to address challenges such as latency, security, and resource allocation.

The first research question addressed in this thesis is: How can OT networks take advantage of NFV and SDN technology to become cyber resilient? This study examines the operational, …

Machine Learning And Protein Engineering Approaches To Understanding Kinesin-5 Activity, Jason Eden Sanchez

Open Access Theses & Dissertations

Cancer is a term describing a collection of diseases that result in uncontrolled cell growth. Cancer has manifold etiologies and underlying cancers are rouge biochemical pathways involving many different proteins. In the current work, two approaches are used to enhance knowledge of kinesin-5, a potential cancer target involved in cell division. Kinesin-5 promotes cell division by cross-linking and separating microtubules in dividing cells. The first approach uses machine learning (ML) to identify small molecule inhibitors for kinesin-5. Though decades of research have uncovered classes of small-molecules which inhibit kinesin-5 in vitro and in vivo, no candidates have reached phase III …

A Sociocognitive Perspective On The Activation Of Productive Epistemic Resources In College Students' Understanding Of Chemical Equilibrium, Henk Steven Van Den Bogaard

Open Access Theses & Dissertations

Science education, particularly in higher education, has a content coverage problem: it has long been described as "a mile wide, an inch deep" in various consensus documents from the National Academies. In chemistry education, the most common general chemistry curriculum covers topics as chapters based on the popular Sienko and Plane 1960's general chemistry textbook. The limited time available in such fast-paced coverage may limit the instructor's modeling and assessment of key performances in developing a mechanistic understanding of complex chemistry phenomena. In this sociocognitive, qualitative investigation, general chemistry II students' responses to assessment prompts were used to activate cognitive …

7 Plus Minus 2 Law Revisited: Alternative Geometric Explanation, Mayan Arithmetic, And Using 9- And 18-Based Numbers In Jewish Tradition, Julio C. Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

A recent paper showed that to make sure that the movements in the crowd are not chaotic, the directions of all the motions should deviate from some fixed direction by no more than 13 degrees. We show that this results provides a new geometric explanation for the seven plus minus two law in psychology, according to which we can keep in mind no more than 7 plus minus 2 items. We also show that all this is related to the somewhat mysterious appearance of 9- and 18-based number systems in Jewish and Mayan traditions.

Why Um And U*Log(U) Are The Most Effective Nonlinear Functions In Fuzzy Clustering: Theoretical Explanation Of The Empirical Fact, Olga Kosheleva, Vladik Kreinovich, Yuchi Kanzawa

Departmental Technical Reports (CS)

In fuzzy clustering, we need to have non-linear functions of the membership degrees. Different nonlinear functions have been tried. Empirical evidence shows that for fuzzy clustering, the most effective nonlinear functions are um and u*log(u). In this paper, we provide a theoretical explanation for this empirical fact.

Egyptian Triangle And Geometry Of Airplane Wings: A Simplified Explanation, Julio C. Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In historically first planes, wings were orthogonal to the fuselage. However, later it turned out that from the aerodynamic viewpoint, it is most efficient to place the wings at about 37 degrees from this orthogonal direction -- and this is where wings are placed in most modern planes. There exist theoretical explanations for this optimality -- explanations based on solving the equations of aerodynamics. In such situations when only a complex not-very-intuitive explanation exists, it is desirable to come up with a simpler more intuitive explanation. For the wing angles, such an explanation is provided in this paper. Namely, we …

"At Least K Out Of N" Under Fuzzy Uncertainty: Efficient Algorithm For General "And"-Operations, Olga Kosheleva, Vladik Kreinovich, Klaus-Peter Adlassnig

Departmental Technical Reports (CS)

In medicine, many diagnoses are made when, for some value k, at least k of n possible symptoms are present. Many of such symptoms -- such as fever -- are, in reality, fuzzy. For example, it makes no sense that say that 38.0 is fever while 37.9 is not a fever, both are fever to some degree. Once such degrees are given, we need to use them to estimate the degree to which the patient has the corresponding disease. For this problem, the usual fuzzy techniques require exponentially many computational steps -- so it is desirable to have a more …

Why Interval-Valued (And Type-2) Fuzzy Methods Are Often More Effective, Christian Servin, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Interval-valued and type-2 fuzzy techniques were designed to provide a more adequate representation of expert knowledge than the traditional (type-1) fuzzy techniques. Somewhat unexpectedly, they also often turn out to be more effective even when there is no expert knowledge at all -- when we are simply using fuzzy rules to fit experimental data. In precise terms, for the same number of parameters, interval-valued and type-2 systems often provide a better fit for the data and/or better quality control than traditional (type-1) fuzzy techniques. In this paper, we provide a theoretical explanation for this surprising phenomenon.

Why Convex Combinations Of Interval Endpoints: Related Explanations For Cases Of Data Processing And Decision Making, Christian Servin, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

There are two cases in which it has been empirically shown that a convex combination of the interval's endpoints works better than any other combination: processing interval data and dealing with situations in which we know both approximate probability and possibility and we need to make a decision. In this paper, we provide an explanation of both phenomena.

Control Of Industrial Robots Based On Artificial Intelligence, Bryan Lara Medrano

Open Access Theses & Dissertations

Industrial robots are vital in developing smart factories, creating the need for more efficient and modern control systems. As a result, investigators and scholars are dedicating great effort to advancing this field et al. [27]. Literature showcases significant progress in various areas, including the control of articulated arms and advancements in human-robot interfaces, self-decision-making, object recognition, decision-making, and routing planning. This manuscript describes a novel technique for predicting the movement of a robotic arm based on artificial neural networks. We have implemented an artificial intelligence method based on artificial neural networks to analyze the possible routing of a robotic arm …

A Bland–Altman Comparison Of The Lead Care® System And Inductively Coupled Plasma Mass Spectrometry For Detecting Low-Level Lead In Child Whole Blood Samples, Christina Sobin, Tanner Schaub, Natali Parisi, Eva De La Riva

Departmental Papers (PH)

Chronic childhood lead exposure, yielding blood lead levels consistently below 10 μg/dL, remains a major public health concern. Low neurotoxic effect thresholds have not yet been established. Progress requires accurate, efficient, and cost-effective methods for testing large numbers of children. The LeadCare® System (LCS) may provide one ready option. The comparability of this system to the “gold standard” method of inductively coupled plasma mass spectrometry (ICP-MS) for the purpose of detecting blood lead levels below 10 μg/dL has not yet been examined. Paired blood samples from 177 children ages 5.2–12.8 years were tested with LCS and ICP-MS. Triplicate repeat tests …

From Machine Learning To Human Learning: What Can Pedagogy Learn From Ai Successes, Victor L. Timchenko, Yury P. Kondratenko, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Many machine learning techniques -- including many techniques behind the current AI-based boom in machine learning -- come from the analysis of successful human learning strategies (and researchers expect that other human learning experiences can lead to even more effective AI-based systems). At this moment, so much experience have been accumulated in AI-based machine learning that it is time to start the analysis in the opposite direction -- to see what can human-based pedagogy learn from AI successes. In this chapter, we provide the first results of such an analysis -- some of which go somewhat against the current pedagogical …