Physical Sciences and Mathematics Commons^™

Mantle Flow And Transition Zone Discontinuities Beneath The Carribean Plate: Constraints From Shear Wave Splitting And Receiver Function Analyses, Tu Xue

Doctoral Dissertations

"Azimuthal anisotropy quantified by teleseismic SKS, SKKS, PKS (“XKS”) and local S wave splitting parameters is used to investigate lithospheric deformation and asthenospheric flow beneath the boundary zone of the North American and Caribbean plates and adjacent areas. A total of 4915 XKS and 1202 pairs of local S wave splitting parameters were obtained at 24 broad band seismic stations. The XKS observations can be divided into two groups based on the spatial distribution of the resulting fast polarization orientations. Those observed on the Caribbean Plate are mostly WNW-ESE which are roughly trench-parallel. In contrast, the fast orientations observed on …

Application Of Machine Learning In Geophysics: Ranking Teleseismic Shear Wave Splitting Measurements And Classifying Different Types Of Earthquakes, Yanwei Zhang

Doctoral Dissertations

"During the past decades, applications of Machine Learning have been explosively developed to solve various academic and industrial problems, and over-human performance has been shown in diverse areas. In geophysical research, Machine Learning, especially Convolutional Neural Network (CNN), has been applied in numerous studies and demonstrated considerable potential. In this study, we applied CNN to solve two geophysical problems, ranking teleseismic shear splitting (SWS) measurements and classifying different types of earthquakes.

For ranking teleseismic SWS measurements, we utilized a CNN-based method to automatically select reliable SWS measurements. The CNN was trained by human-verified teleseismic SWS measurements and tested using synthetic …

Disorder Effects In Frustrated Magnets And Absorbing State Transitions, Xuecheng Ye

Doctoral Dissertations

"Correlation, topology, and disorder can fundamentally affect the properties of interacting many-particle systems. After a short introduction which covers the basic concepts of phase transitions and scaling as well as the physics of Josephson junctions, the dissertation focuses on three separate projects.

The first project is motivated by the stripe and nematic phases observed e.g. in cuprate superconductors and iron pnictides. To understand the effects of disorder on such phases, we have investigated the behavior of the diluted J₁-J₂ Ising model. Spinless impurities generate a random-field disorder for the spin-density (stripe) order parameter, which destroys the stripe …

Deep Learning-Based Surrogate Models For Post-Earthquake Damage Assessment, Xinzhe Yuan

Doctoral Dissertations

"Seismic damage assessment is a critical step to enhance community resilience in the wake of an earthquake. This study aims to develop deep learning-based surrogate models for widely used fragility curves to achieve more accurate and rapid assessment in practice. These surrogate models are based on artificial neural networks trained from the labelled ground motions whose resulting damage classes on targeted structures are determined by nonlinear time history analyses. The development of various surrogate models is progressed in four phases. In Phase I, the multilayer perceptron (MLP) is used to develop multivariate seismic classifiers with up to 50 hand-crafted intensity …

Evaluating The Relationship Between Floodplain Topography And Channel Avulsion: Evidence From The Devonian Catskill Formation, North-Central Pennsylvania, Usa, Molly O'Halloran

Honors Theses

Topographic complexity on floodplains can route flow, control sediment dispersal, and influence channel behavior, but studying floodplain-channel interactions in modern rivers is challenging because of human modifications and the short timescales of observable data. This project assesses the link between different types of floodplain microtopography and avulsion style in the Devonian Catskill Formation, north-central Pennsylvania, where thick stacks of fluvial strata provide a lengthy record of channel-floodplain interaction. Using a combination of field observations and computer modeling, this study identifies sedimentary features indicative of floodplain complexity and analyzes their impact on avulsion style at fourteen Catskill Formation outcrops.

Based on …

A Contraction Based Approach To Tensor Isomorphism, Anh Kieu

Honors Theses

Tensor isomorphism is a hard problem in computational complexity theory. Tensor isomorphism arises not just in mathematics, but also in other applied fields like Machine Learning, Cryptography, and Quantum Information Theory (QIT). In this thesis, we develop a new approach to testing (non)-isomorphism of tensors that uses local information from "contractions" of a tensor to detect differences in global structures. Specifically, we use projective geometry and tensor contractions to create a labelling data structure for a given tensor, which can be used to compare and distinguish tensors. This contraction labelling isomorphism test is quite general, and its practical potential remains …

Voices Of The Often Unheard: The Environmental Impacts Of Catastrophic Wildfire Events On Individuals With Developmental Disabilities, Mary Madison Mckenzie

Graduate Student Theses, Dissertations, & Professional Papers

The Thomas Fire for a time was the largest wildfire in California history, burning 281,893 acres and destroying 1,063 structures. Within three years, the August Complex Fire, at 1,032,649 acres, almost quadrupled that record. Climate related disasters such as these have impelled social science researchers to heed calls for a paradigm shift in understanding the risks climate change poses to the social world, in particular, disaster risks for vulnerable groups. Existing research tends to focus on disasters such as hurricanes, featuring risks for vulnerable populations by race, class, and/or individuals with disabilities in general, but not for individuals with developmental …

Utilization Of The Yamamoto Cyclotrimerization Reaction Towards The Synthesis Of Polycyclic Aromatic Compounds, Josh Ledrew

Theses and Dissertations (Comprehensive)

The overall theme of this research was to synthesize and study the structure-property relationships of novel polycyclic aromatic compounds capable of self-assembling to form liquid crystalline, and microporous structures, as well as molecules capable of molecular recognition. The unifying theme that connects this research is the utilization of the Yamamoto coupling reaction to access the desired polycyclic aromatic hydrocarbon targets. More specifically, the scope of the Yamamoto coupling reaction was explored for the synthesis of electron-deficient triphenylene derivatives that are otherwise difficult to prepare. The molecules prepared via the Yamamoto coupling reaction are expected to be further utilized as intermediates …

Air Quality: Assessment Of Pollutant Levels And Chemistry In Kitchener, On Using Multisensor Pods, Wisam Mohammed

Theses and Dissertations (Comprehensive)

Air quality is a growing concern amongst governmental bodies worldwide. A large number of scientific studies accumulated over the past 25 years suggest that poor ambient air quality is attributed to adverse health effects, especially in vulnerable communities that exhibit pre-existing conditions. The United Nations Children’s Fund (UNICEF) reported around 600 000 deaths globally in children under the age of 5 as a result of acute lower respiratory infections caused by poor air quality. With the current statistics on air quality impacts, it is clear that more needs to be done. This MSc work aims to put into perspective the …

Using Neural Networks To Model Guitar Distortion, Caleb Koch, Scott Hawley, Andrew Fyfe

Science University Research Symposium (SURS)

Guitar players have been modifying their guitar tone with audio effects ever since the mid-20th century. Traditionally, these effects have been achieved by passing a guitar signal through a series of electronic circuits which modify the signal to produce the desired audio effect. With advances in computer technology, audio “plugins” have been created to produce audio effects digitally through programming algorithms. More recently, machine learning researchers have been exploring the use of neural networks to produce audio effects that yield strikingly similar results to their analog counterparts. Recurrent Neural Networks and Temporal Convolutional Networks have proven to be exceptional at …

Establishing A Biochemical System For The Purification And Atpase Activity Of Gst-Dbp5, Sarah R. Utley, Rachel E. Rigsby Phd, Rebecca L. Adams Phd

Science University Research Symposium (SURS)

The export of mRNA out of the nucleus is a crucial step for eukaryotic gene expression. The export of mRNA transcripts is aided by Mex67, which allows export through the nuclear pore complex doorways in the nuclear envelope. Once out of the nucleus, a protein known as Dbp5, bound to ATP, Gle1, and Nup42 aids in the directionality of mRNA export by helping remove Mex67 from the mRNA strand. Following interaction with RNA, Dbp5 then hydrolyzes ATP so that it unbinds the mRNA, allowing for enzyme recycling. Previous efforts worked towards the purification of Dbp5, but the attempts were unsuccessful …

The Amorphous Nature Of Hackers: An Exploratory Study, Kento Yasuhara, Daniel Walnycky, Ibrahim Baggili, Ahmed Alhishwan

Annual ADFSL Conference on Digital Forensics, Security and Law

In this work, we aim to better understand outsider perspectives of the hacker community through a series of situation based survey questions. By doing this, we hope to gain insight into the overall reputation of hackers from participants in a wide range of technical and non-technical backgrounds. This is important to digital forensics since convicted hackers will be tried by people, each with their own perception of who hackers are. Do cyber crimes and national security issues negatively affect people’s perceptions of hackers? Does hacktivism and information warfare positively affect people’s perception of hackers? Do individual personality factors affect one’s …

Human-Controlled Fuzzing With Afl, Maxim Grishin, Igor Korkin, Phd

Annual ADFSL Conference on Digital Forensics, Security and Law

Fuzzing techniques are applied to reveal different types of bugs and vulnerabilities. American Fuzzy Lop (AFL) is a free most popular software fuzzer used by many other fuzzing frameworks. AFL supports autonomous mode of operation that uses the previous step output into the next step, as a result fuzzer spends a lot of time analyzing minor code sections. By making fuzzing process more focused and human controlled security expert can save time and find more bugs in less time. We designed a new module that can fuzz only the specified functions. As a result, the chosen ones will be inspected …

Timestamp Estimation From Outdoor Scenes, Tawfiq Salem, Jisoo Hwang, Rafael Padilha

Annual ADFSL Conference on Digital Forensics, Security and Law

The increasing availability of smartphones allowed people to easily capture and share images on the internet. These images are often associated with metadata, including the image capture time (timestamp) and the location where the image was captured (geolocation). The metadata associated with images provides valuable information to better understand scenes and events presented in these images. The timestamp can be manipulated intentionally to provide false information to convey a twisted version of reality. Images with manipulated timestamps are often used as a cover-up for wrongdoing or broadcasting false claims and competing views on the internet. Estimating the time of capture …

Digital Forensics For Mobility As A Service Platform: Analysis Of Uber Application On Iphone And Cloud, Nina Matulis, Umit Karabiyik

Annual ADFSL Conference on Digital Forensics, Security and Law

Uber is a ride-hailing smartphone application (app) that allows users to order a ride in a highly efficient manner. The Uber app provides Mobility as a Service and allows users to easily order a ride in a private car with just a few clicks. Uber stores large amounts of data on both the mobile device the app is being used on, and in the cloud. Examples of this data include geolocation data, date/time, origin/destination addresses, departure/arrival times, and distance. Uber geolocation data has been previously researched to investigate the privacy of the Uber app; however, there is minimal research relating …

Microsoft Defender Will Be Defended: Memoryranger Prevents Blinding Windows Av, Denis Pogonin, Igor Korkin, Phd

Annual ADFSL Conference on Digital Forensics, Security and Law

Windows OS is facing a huge rise in kernel attacks. An overview of popular techniques that result in loading kernel drivers will be presented. One of the key targets of modern threats is disabling and blinding Microsoft Defender, a default Windows AV. The analysis of recent driver-based attacks will be given, the challenge is to block them. The survey of user- and kernel-level attacks on Microsoft Defender will be given. One of the recently published attackers’ techniques abuses Mandatory Integrity Control (MIC) and Security Reference Monitor (SRM) by modifying Integrity Level and Debug Privileges for the Microsoft Defender via syscalls. …

Smart Home Forensics: Identifying Ddos Attack Patterns On Iot Devices, Samuel Ho, Hope Greeson, Umit Karabiyik

Annual ADFSL Conference on Digital Forensics, Security and Law

Smart homes are becoming more common as more people integrate IoT devices into their home environment. As such, these devices have access to personal data on their homeowners’ networks. One of the advantages of IoT devices is that they are compact. However, this limits the incorporation of security measures in their hardware. Misconfigured IoT devices are commonly the target of malicious attacks. Additionally, distributed denial-of-service attacks are becoming more common due to applications and software that provides users with easy-to-use user interfaces. Since one vulnerable device is all an attacker needs to launch an attack on a network, in regards …

A Lightweight Reliably Quantified Deepfake Detection Approach, Tianyi Wang, Kam Pui Chow

Annual ADFSL Conference on Digital Forensics, Security and Law

Deepfake has brought huge threats to society such that everyone can become a potential victim. Current Deepfake detection approaches have unsatisfactory performance in either accuracy or efficiency. Meanwhile, most models are only evaluated on different benchmark test datasets with different accuracies, which could not imitate the real-life Deepfake unknown population. As Deepfake cases have already been raised and brought challenges at the court, it is disappointed that no existing work has studied the model reliability and attempted to make the detection model act as the evidence at the court. We propose a lightweight Deepfake detection deep learning approach using the …

Detection Of Overlapping Passive Manipulation Techniques In Image Forensics, Gianna S. Lint, Umit Karabiyik

Annual ADFSL Conference on Digital Forensics, Security and Law

With a growing number of images uploaded daily to social media sites, it is essential to understand if an image can be used to trace its origin. Forensic investigations are focusing on analyzing images that are uploaded to social media sites resulting in an emphasis on building and validating tools. There has been a strong focus on understanding active manipulation or tampering techniques and building tools for analysis. However, research on manipulation is often studied in a vacuum, involving only one technique at a time. Additionally, less focus has been placed on passive manipulation, which can occur by simply uploading …

Anatomy Of An Internet Hijack And Interception Attack: A Global And Educational Perspective, Ben A. Scott, Michael N. Johnstone, Patryk Szewczyk

Annual ADFSL Conference on Digital Forensics, Security and Law

The Internet’s underlying vulnerable protocol infrastructure is a rich target for cyber crime, cyber espionage and cyber warfare operations. The stability and security of the Internet infrastructure are important to the function of global matters of state, critical infrastructure, global e-commerce and election systems. There are global approaches to tackle Internet security challenges that include governance, law, educational and technical perspectives. This paper reviews a number of approaches to these challenges, the increasingly surgical attacks that target the underlying vulnerable protocol infrastructure of the Internet, and the extant cyber security education curricula; we find the majority of predominant cyber security …

A Low-Cost Machine Learning Based Network Intrusion Detection System With Data Privacy Preservation, Jyoti Fakirah, Lauhim Mahfuz Zishan, Roshni Mooruth, Michael L. Johnstone, Wencheng Yang

Annual ADFSL Conference on Digital Forensics, Security and Law

Network intrusion is a well-studied area of cyber security. Current machine learning-based network intrusion detection systems (NIDSs) monitor network data and the patterns within those data but at the cost of presenting significant issues in terms of privacy violations which may threaten end-user privacy. Therefore, to mitigate risk and preserve a balance between security and privacy, it is imperative to protect user privacy with respect to intrusion data. Moreover, cost is a driver of a machine learning-based NIDS because such systems are increasingly being deployed on resource-limited edge devices. To solve these issues, in this paper we propose a NIDS …

Lithofacies Characterization Of Marine Shelf Shale And Implicaitons On Depositional Conditions And Processes On The Continental Shelf — A Case Study Of The Upper-Cretaceous Tuscaloosa Marine Shale In Louisiana And Mississippi, U.S.A., Efren Mendez Jr

Masters Theses

"The Upper Cretaceous Tuscaloosa Marine Shale (TMS) is an unconventional shale reservoir deposited on the continental shelf of the northern Gulf of Mexico Basin. Previous studies have focused on sequence stratigraphy, thermal modeling, and well-log correlations. However, a limited understanding of the stratigraphic heterogeneity of the TMS is yet to be studied. This study aims to characterize the stratigraphic heterogeneity of the TMS using core, petrographic, and well-log analyses to better understand the depositional conditions and processes that occur on the continental shelf. The TMS has been classified into four lithofacies of very fine sands – coarse silts (LF1), medium-fine …

Indoor Air Quality Through The Lens Of Outdoor Atmospheric Chemistry, Jonathan P.D. Abbatt, Douglas B. Collins

Faculty Contributions to Books

Outdoor atmospheric chemistry and air quality have been the topic of research that intensified in earnest around the mid-20^th century, while indoor air quality research has only been a key focus of chemical researchers over the last 30 years. Examining practices and approaches employed in the outdoor atmospheric chemistry research enterprise provides an additional viewpoint from which we can chart new paths to increase scientific understanding of indoor chemistry. This chapter explores our understanding of primary chemical sources, homogeneous and multiphase reactivity, gas-surface partitioning, and the coupling between the chemistry and dynamics of indoor air through the lens of …

Characterization Of A Three-Way Catalyst For High Efficiency Spark Ignition Engines, Cavin Hesketh

Electronic Theses and Dissertations

The push for environmental protection and sustainability has led to strict emission regulations for automotive manufacturers as evident in EURO VII and 2026 EPA requirements set to take effect in the coming years. The modern gasoline spark ignition (SI) engine typically employs various in-cylinder emission reduction techniques along with an exhaust after-treatment system to comply with emission standards. The three-way catalyst (TWC) is wholly responsible for removing the engine-out emissions including hydrocarbons (HC), carbon monoxide (CO) and nitrogen oxides (NOx). The main objective of this thesis is to investigate the impact of extensive exhaust gas recirculation (EGR) on …

The Wright Message, 2021-2022, University Of Northern Iowa. Department Of Mathematics.

The Wright Message

Contents

From the Dean --- 1
Around Wright Hall --- 3
In Memoriam: Diane Lee Baum --- 4
Faculty Spotlight --- 4
Retiring Faculty --- 5
Statistical Consulting Center staff changes --- 7
Alumni Spotlight --- 8
News from the CTLM --- 10
Student Spotlights --- 11
Donor Spotlight --- 18
First Woman to Win International Meshing Award --- 19
Contribution Recognition --- 20
Department Funds --- 22
Contribution Forms --- 23

Developing A Robust Defensive System Against First Order Adversarial Attacks Using Siamese Neural Network Methods, Ahamd Khalil

Electronic Theses and Dissertations

Both Convolutional neural networks (CNN) and Deep neural networks (DNN)have recently demonstrated state-of-the-art performance in various real-world ap- plications. However, in recent research the Deep neural networks are shown to be sensitive to adversarial attacks .[32]. Furthermore, it was evidenced that inputs that are almost invariant to the human eye from natural data can be classified incorrectly by deep neural networks. [32]. Although adversarial training improves the model’s robustness significantly, it eventually devolves into a whack-a-mole game in which defenders and attackers try to outdo each other. Because of recent advancements in computer applications, the security aspects of machine learning …

Femtosecond Pulse Compression Via Self-Phase Modulation In 1-Decanol, Jacob A. Stephen

Electronic Theses and Dissertations

Ultrafast science is a branch of photonics with far reaching applications in and outside the realm of physics. Ultrashort laser pulses on the order of femtoseconds (1 fs = 1 × 10−15 s) are widely used for ultrafast science. Many lasers can produce pulses on the order of 100 fs, with state of the art, high end lasers being capable of producing pulses around 30 fs. However, many experiments require pulses around 10 fs or shorter. Femtosecond pulses are typically generated using spectral broadening via self-phase modulation, followed by dispersion compensation. The most common spectral broadening technique exploits the nonlinear …

Simple Measurement For Ultrafast Field Reconstruction, Chathurangani Jayalath Arachchige

Electronic Theses and Dissertations

Ultrashort pulses are important for resolving electron motion in semiconductors to measure electronic transport properties. Electron wave packet motion is on the order of attoseconds, requiring temporal resolution on this time scale. However, a major constraint on femtosecond (1 fs = 10−15 s) and attosecond (1 as = 10−18 s) science is how well we can control and compress the excitation and measurement pulses in pump-probe experiments. Such ultrashort pulses require a broad spectrum with careful phase control across its bandwidth to minimize the duration. We discuss a new optical measurement technique that can directly measure the electric field that …

Convolutional Neural Networks For Breast Ultrasound Image Segmentation, Yahya Abdullah Alzahrani

Electronic Theses and Dissertations

Medical Image Segmentation is the process of segmenting and detecting boundaries of anatomical structures in various types of 2D and 3D medical images. The latter come from different modalities, such as Magnetic Resonance Imaging (MRI), X-Rays, Positron Emission Tomography (PET)/Single-Photon Emission Computed Tomography, Computed Tomography (CT), and Ultrasound (US). It is a key supporting technology for medical applications including diagnostics, planning, monitoring, and guidance. Hence, a large number of segmentation methods have been published in past decades. This dissertation presents four contributions to the field d of medical images and for segmentation in general and to Breast Ultrasound (BUS) image …

Mosquito Species Distribution Models: Limitations And Best Practices, Justin Barker

Electronic Theses and Dissertations

Understanding species distribution is fundamental to ecology and biogeography, with important implications for species management. This is especially true for species of public health concern—such as mosquitoes—which can impart both economic and health impacts. Species distribution models (SDMs) are powerful tools to accomplish this goal. However, obtaining reliable conclusions, those which are accurate and applicable for policy development, from SDMs is not straightforward owing to the many required considerations such as objective with respect to limitations of the available data. The goal of this thesis was to determine recommended methods to address known and unknown mosquito SDM limitations. First, I …