Physical Sciences and Mathematics Commons^™

Post-Fire Debris Flow Initiation Potential Based On Forensic Analysis Of Debris Flow Occurrence From The 2020 El Dorado Wildfire On Yucaipa Ridge, San Bernardino County California, Seth Clemen D. Saludez

Electronic Theses, Projects, and Dissertations

This study focuses on the potential debris flow initiation and effect in areas affected by the El Dorado Fire in the Southern San Bernardino Mountains. It uses novel technology combining drone-based photography and LiDAR imaging to create surface models of areas where potential debris flows could initiate, flow, and be deposited. The scans were repeated as geomorphic change occurred through the wet season to document spatial and volumetric changes resulted from the flows. These measurements were combined with traditional fire assessment and observational methods to characterize pertinent conditions.

As we investigated potential debris flow risk in this study area, we …

Network-Based Attacks In Cloud Computing In 2020-2024, Yaswanth Sai Manikanta Anguluri

Electronic Theses, Projects, and Dissertations

As the use of cloud technologies has increased in the past five years, the number of network attacks is also increasing. During 2020 to 2024, there are lot of changes in cloud technologies which led to various network attacks in the cloud computing environments from 2020 to 2024. This study investigates the evolution of network-based attacks in cloud environments from 2020 to 2024. Data was collected from Kaggle website to analyze the trends of the evolution of network-based attacks. The research questions are: (Q1) How do the trends change in network-based attack from 2020 to 2024 and why? (Q2) Which …

Modeling Language And Vision At Human Scales, Clayton Fields

Boise State University Theses and Dissertations

The impressive results that have recently been achieved in natural language processing and artificial intelligence have been primarily driven by the introduction of the transformer deep learning architecture, increasingly large models with many parameters and using enormous datasets. The size of models and their training data requirements present costly demands that freeze many researchers out of training with cutting edge models. Beyond these practical implications, current methods learn from text alone, without the rich array of sensory information that human beings use in learning language. This means that language models are often incapable of reasoning about the concrete world that …

2022 Annual Operations And Maintenance Report, Butte Silver Bow

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

2023 Annual Operations And Maintenance Report 2023 Bres Annual Summary Report For Work Completed In Quadrant 3, Butte-Silver Bow Department Of Reclamation And Environmental Services

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

2023 Annual Operations And Maintenance Report, Butte-Silver Bow Department Of Reclamation And Environmental Services

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Triple-Valued Neutrosophic Set, Quadruple-Valued Neutrosophic Set, Quintuple-Valued Neutrosophic Set, And Double-Valued Indetermsoft Set, Takaaki Fujita

Neutrosophic Systems with Applications

Concepts such as Fuzzy Sets, Neutrosophic Sets, Rough Sets, and Plithogenic Sets have been extensively studied to address uncertainty, finding diverse applications across various fields. A Double-Valued Neutrosophic Set (DVNS) extends traditional neutrosophic sets by introducing two distinct indeterminacy components: one leaning towards truth and the other towards falsity. In this paper, we explore Triple-Valued Neutrosophic Sets, Quadruple-Valued Neutrosophic Sets, and Quintuple-Valued Neutrosophic Sets, as well as an extension of the Indetermsoft Set, termed the Double-Valued Indetermsoft Set. Note that related concepts such as the Multi-Valued Neutrosophic Set and the n-Valued Refined Neutrosophic Set have already been established.

Towards Explainable And Robust Nlp: Neutrosophic Probability Augmentation In Text Classification, Nabil M. Abdel-Aziz, Mahmoud Ibrahim, Khalid A. Eldrandaly

Neutrosophic Systems with Applications

The rapid growth of textual data necessitates advanced text classification models. However, traditional methods struggle with ambiguity and uncertainty in natural language, reducing classification reliability. To address this, we integrate neutrosophic logic, which explicitly models truth, indeterminacy, and falsity, into a DistilBERT-based text classification framework. Additionally, we employ data augmentation using synonym replacement to enhance generalization. Our approach is evaluated on the AG News dataset, classifying articles into four categories: World, Sports, Business, and Science/Technology. By incorporating neutrosophic attributes, the proposed framework assesses text quality, mitigates uncertainty, and improves robustness against ambiguous inputs. Experimental results demonstrate an accuracy of 94.10%, …

Single Valued Neutrosophic Hypersoft Set Based On Merec-Moosra Mcdm Methods For Evolution Of Sustainable Strategies For The Circular Supply Chain Based On The Beverage Industry, Alaa Salem, Rayan Hussein, Ahmed Abdelmouty, Mohamed Abouhawwash

Neutrosophic Systems with Applications

Corporate enterprises around the world are facing increasing pressure to operate sustainably due to rising environmental concerns such as climate change, resource scarcity, and ecological degradation. In response, circular supply chain (CSC) practices have emerged as a promising solution, especially within the manufacturing and beverage sectors. CSC focuses on reusing materials, minimizing waste, and creating value from products that have reached the end of their lifecycle. This study investigates the challenges and opportunities of applying circular supply chain management (CSCM) in the beverage industry, which is known for generating significant waste due to high production volumes. The research identifies key …

Development Of Aczel-Alsina Aggregation Operators In Neutrosophic Cubic Sets For Multi-Expert And Multi-Criteria Weighting: Optimizing Alternative Fuel Technology Selection, Majid Khan, Muhammad Gulistan, Aitazaz A. Farooque, Mohammed M. Al-Shamiri, Witold Pedrycz

Neutrosophic Systems with Applications

Managing vague and uncertain data has long been a challenge in decision-making (DM), particularly in scenarios where criteria and expert assessments play a critical role. This paper introduces operational laws based on Aczel-Alsina (AA) norms within Neutrosophic Cubic Sets (NCS) to more effectively handle uncertainty. Leveraging these operational laws, we propose two aggregation operators: the Neutrosophic Cubic Aczel-Alsina Weighted Averaging (NCAAWA) and the Neutrosophic Cubic Aczel-Alsina Weighted Geometric (NCAAWG) operators. These provide a comprehensive approach to data aggregation, preserving both additive and multiplicative influences on outcomes in complex systems. In DM, the importance of weights is paramount, and we introduce …

New Bayesian Methods For Longitudinal Data Analysis With Complex Dependence Structures., Anish Mukherjee

Electronic Theses and Dissertations

Longitudinal data in real-world settings are frequently found to be heterogeneous and exhibit intricate spatio-temporal dependence structures. Analyzing such complex data to obtain reliable estimation while quantifying uncertainty necessitates using sophisticated Bayesian methodology. In this work, we present novel Bayesian methods developed to address these challenges. We often observe heterogeneity in longitudinal data, where the mean and variance for certain profiles meaningfully differs from the rest. Some profiles may also exhibit outliers at a limited number of measurements. Using a standard mixed effects model, which assumes homogeneity, can lead to overestimating the residual variance and inefficient estimation. In this work, …

Studies Of Pathways To T2d And Interventions Through A Dynamical System Model., Rafiqul Islam

Electronic Theses and Dissertations

Existing mathematical models investigating the progression of type 2 diabetes (T2D) over time primarily focus on glucose, insulin, β-cell mass, and other related factors, while often omitting fatty acids (FA) as an explicit variable—despite FA being a major energy source for the body. There exists a complex network of dynamical interactions among glucose, insulin, FA, and β-cell mass. To gain deeper insights into the metabolic dynamics and pathophysiology of T2D, it is essential to incorporate FA into such models. In this study, we extend the classic Topp’s GIβ model by explicitly incorporating FA and exploring its interactions with glucose, insulin, …

Thin-Film Optical Filters For Selective Transmission And Black-Body Reflection In Pyroelectric Energy Systems., Cedric Cruz Silva

Electronic Theses and Dissertations

Essential to pyroelectric energy systems is the ability for a pyroelectric material to achieve high temperatures rapidly. In pursuit of augmenting this process, several thin-film optical filters were designed for transmitting laser excitation in the near-infrared region, while simultaneously reflecting black-body radiation for pyroelectric materials. Designs include a classical Fabry-Perot structure with gold and tantalum pentoxide, an induced transmittance filter with silica, hafnia, and gold, and an indium-tin-oxide filter with silica, alumina, and ITO. Performance varies depending on the design. The Fabry-Perot filter experimentally showed a maximum transmittance of 42% at 1.04 μm and an infrared reflectance of 98%. The …

Empirically Exploring The Physical Realizability Of Adversarial Examples, Ruoyao Wen

McKelvey School of Engineering Graduate Student Theses & Dissertations

The development of autonomous vehicles (AVs) has been accelerated by advancements in deep neural networks (DNNs), which power the complex perception systems necessary for safe and efficient real-world navigation. However, as AVs increasingly integrate into public transportation networks, the robustness of their perception systems against potential vulnerabilities is critical. Among these threats, adversarial attacks—particularly through the use of adversarial patches—pose significant risks. These patches are carefully crafted perturbations designed to mislead DNNs, potentially compromising AV safety by causing incorrect object recognition or misclassification.

While extensive research has demonstrated high attack success rates for adversarial patches in controlled digital environments, their …

Leveraging Machine Learning And Deep Learning Techniques For Voter Registration Fraud Detection, Nahid Anwar

Boise State University Theses and Dissertations

The primary objective of this research is to develop an advanced framework for detecting voter registration anomalies, with a specific focus on fraud detection, using the Idaho Voter Registration Election Dataset. The data set contains both anonymized real voter data and synthetically generated fraudulent instances, allowing for a comprehensive examination of potential vulnerabilities in voter registration systems. The real data was obtained from the Idaho Secretary of State's office. The initial part of the research involved data analysis and identification of misinformation and potential disinformation using statistical analysis and approximate string matching algorithms. Subsequently, we have created the aforementioned anonymized …

Functional Data Analysis Of Weekly Confirmed Covid-19 Cases Per Million In Relation To Unemployment Rates And Emergency Declaration Responses, Elaina Kay Mcgovern

Boise State University Theses and Dissertations

At the onset of the COVID-19 pandemic, U.S. states implemented policies that attempted to mitigate the spread and effects of the disease, such as a state of emergency declaration. This study explores the application of functional data analysis (FDA) as a means to investigate the impact of U.S. state-level emergency declarations on weekly confirmed COVID-19 cases per million during the first year of the pandemic. We focus on the duration and extent to which proactive states showed significantly different patterns of COVID-19 spread than those that implemented policies reactively. Additionally, we examine patterns in monthly state-level unemployment rates over the …

Leading Digits Of Some P-Adic Numbers, Jinha Park

Boise State University Theses and Dissertations

I investigated the leading digits of some sequences in the p-adic numbers Q_p, hoping to find some sequences following Benford's law, or following something completely different. We found that the sequence of partial sums Sum^N_{n=1}\frac{1}{n^s}, where s\in\N is fixed, shows uneven distribution of leading digits, except for s\equiv 0 (mod{p,/em>-1}), where the distribution of leading digits is eventually even. We were able to characterize the distribution of this partial sum for almost all s.