Physical Sciences and Mathematics Commons^™

Effect Of Magnetic Draping On Satellite Galaxies In Clusters, Vanessa Brown

Dissertations, Theses, and Capstone Projects

Galaxy evolution has been observed to be influenced by environment. Satellite galaxies orbiting within clusters can experience changes in morphology and composition through various mechanisms such as ram-pressure stripping (RPS), which removes a galaxy’s interstellar medium as it passes through the cluster via direct interaction with the hot intracluster medium gas. An open question is whether intracluster magnetic fields affect galaxy evolution, for example by forming a magnetic layer around infalling galaxies (called magnetic draping) and mitigating gas removal by RPS. Using the code GADGET-3, we compare global properties and mass distributions within identical cluster simulations run with and without …

D-Hacking, Emily Black, Talia B. Gillis, Zara Hall

Faculty Scholarship

Recent regulatory efforts, including Executive Order 14110 and the AI Bill of Rights, have focused on mitigating discrimination in AI systems through novel and traditional application of anti-discrimination laws. While these initiatives rightly emphasize fairness testing and mitigation, we argue that they pay insufficient attention to robust bias measurement and mitigation — and that without doing so, the frameworks cannot effectively achieve the goal of reducing discrimination in deployed AI models. This oversight is particularly concerning given the instability and brittleness of current algorithmic bias mitigation and fairness optimization methods, as highlighted by growing evidence in the algorithmic fairness literature. …

Automated Sensor Node Malicious Activity Detection With Explainability Analysis, Md Zubair, Helge Janicke, Ahmad Mohsin, Leandros Maglaras, Iqbal H. Sarker

Research outputs 2022 to 2026

Cybersecurity has become a major concern in the modern world due to our heavy reliance on cyber systems. Advanced automated systems utilize many sensors for intelligent decision-making, and any malicious activity of these sensors could potentially lead to a system-wide collapse. To ensure safety and security, it is essential to have a reliable system that can automatically detect and prevent any malicious activity, and modern detection systems are created based on machine learning (ML) models. Most often, the dataset generated from the sensor node for detecting malicious activity is highly imbalanced because the Malicious class is significantly fewer than the …

Detecting Foot Strikes During Running With Earbuds, Changshuo Hu, Thivya Kandappu, Jake Stuchbury-Wass, Yang Liu, Anthony Tang, Cecelia Mascolo, Dong Ma

Research Collection School Of Computing and Information Systems

Running is a widely embraced form of aerobic exercise, offering various physical and mental benefits. However, improper running gaits (i.e., the way of foot landing) can pose safety risks and impact running efficiency. As many runners lack the knowledge or continuous attention to manage their foot strikes during running, in this work, we present a portable and non-invasive running gait monitoring system. Specifically, we leverage the in-ear microphone on wireless earbuds to capture the vibrations generated by foot strikes. Landing with different parts of the foot (e.g., forefoot and heel) generates distinct vibration patterns, and thus we utilize machine learning …

How Is Our Mobility Affected As We Age? Findings From A 934 Users Field Study Of Older Adults Conducted In An Urban Asian City, Yi Zhen Tan, Ngoc Doan Thu Tran, Sapphire Lin, Fang Zhao, Yee Sien Ng, Dong Ma, Jeonggil Ko, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

In this paper, we analyze the results of a large study involving 934 older adults living in an urban Asian city that collected their mobility patterns, in the form of logged GPS data, along with a multitude of demographic and health data. We show that mobility, in terms of average distance travelled per day, is greatly affected by age and by employment status. In addition, other factors such as type of day, household size, physical and financial conditions and the onset of retirement also play a significant role in determining the mobility of an individual. These results will have high …

Violet: Visual Analytics For Explainable Quantum Neural Networks, Shaolun Ruan, Zhiding Liang, Qiang Guan, Paul Robert Griffin, Xiaolin Wen, Yanna Lin, Yong Wang

Research Collection School Of Computing and Information Systems

With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum computing to significantly speed up classical machine learning tasks. Despite their increasing popularity, the quantum neural network is quite counter-intuitive and difficult to understand, due to their unique quantum-specific layers (e.g., data encoding and measurement) in their architecture. It prevents QNN users and researchers from effectively understanding its inner workings and exploring the model training status. To fill the research gap, we propose VIOLET , a novel visual analytics approach to improve the explainability …

Learning Dynamic Multimodal Network Slot Concepts From The Web For Forecasting Environmental, Social And Governance Ratings, Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Dynamic multimodal networks are networks with node attributes from different modalities where the at- tributes and network relationships evolve across time, i.e., both networks and multimodal attributes are dynamic; for example, dynamic relationship networks between companies that evolve across time due to changes in business strategies and alliances, which are associated with dynamic company attributes from multiple modalities such as textual online news, categorical events, and numerical financial-related data. Such information can be useful in predictive tasks involving companies. Environmental, social, and gov- ernance (ESG) ratings of companies are important for assessing the sustainability risks of companies. The process of …

New Examples Of Self-Dual Near-Extremal Ternary Codes Of Length 48 Derived From 2-(47,23,11) Designs, Sanja Rukavina, Vladimir Tonchev

Michigan Tech Publications, Part 2

In a recent paper (Araya and Harada, 2023), Araya and Harada gave examples of self-dual near-extremal ternary codes of length 48 for 145 distinct values of the number A12 of codewords of minimum weight 12, and raised the question about the existence of codes for other values of A12. In this note, we use symmetric 2-(47,23,11) designs with an automorphism group of order 6 to construct self-dual near-extremal ternary codes of length 48 for 150 new values of A12.

Predicting Seagrass Ecosystem Resilience To Marine Heatwave Events Of Variable Duration, Frequency And Re-Occurrence Patterns With Gaps, Paula Sobenko Hatum, Kathryn Mcmahon, Kerrie Mengersen, Kieryn Kilminster, Paul Pao Yen Wu

Research outputs 2022 to 2026

Background: Seagrass, a vital primary producer habitat, is crucial for maintaining high biodiversity and offers numerous ecosystem services globally. The increasing severity and frequency of marine heatwaves, exacerbated by climate change, pose significant risks to seagrass meadows. Aims: This study acknowledges the uncertainty and variability of marine heatwave scenarios and aims to aid managers and policymakers in understanding simulated responses of seagrass to different durations, frequencies and recurrence gaps of marine heatwaves. Materials and Methods: Using expert knowledge and observed data, we refined a global Dynamic Bayesian Network (DBN) model for a specific case study on Halophila ovalis in Leschenault …

Quality And Establishment Of Some Water-Conserving Turfgrass Species For Sustainable Development And Some Ecosystem Services In Arid Urban Environments, Fatemeh Kazemi, Mahmood Reza Golzarian, Seyedeh Maliheh Rabbani Kheir Khah

Research outputs 2022 to 2026

Turfgrasses are essential landscape plants with social, environmental, and aesthetic services for urban ecosystems. However, more is needed to know how to establish them so that they can benefit from their ecosystem services in urban environments. This research examined some quality and morphological and physiological factors for the establishment and social and environmental service assessment of three warm-season turfgrasses, including Kikuyu grass (Pennisetum clandestinum), bermuda grass (Cynodon dactylon), and buffalo grass (Buchloe dactyloides), compared to the cool-season grass of tall fescue (Festuca arundinacea Schreb.). The experiment was split-plot in time, based on a randomized complete block design with eight replications. …

Why Empirical Membership Functions Are Well-Approximated By Piecewise Quadratic Functions: Theoretical Explanation For Empirical Formulas Of Novak's Fuzzy Natural Logic, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Empirical analysis shows that membership functions describing expert opinions have a shape that is well described by a smooth combination of two quadratic segments. In this paper, we provide a theoretical explanation for this empirical phenomenon.

Synthesis Of Dyes Sulfamidazole: Characterization, Evaluation, Molecular Docking And Global Descriptors By Density Functional Theory (Dft)., Athra G. Sager, Jawad Kadhim Abaies, Zeena R. Katoof

Karbala International Journal of Modern Science

In the present work, novel azo compounds of sulfamidazole were created via the reaction of diazonium salt of sulfamidazole with several aromatic molecules including (resorcinol, 2-nitro phenol, 3-nitro phenol, and 4-nitro phenol)) (Z1–Z4). The new compounds (Z1-Z4) were identified using FTIR, ¹HNMR techniques, in addition to melting point measurements. The biological activity of compounds (Z1-Z4) was studied against four kinds of bacteria including E. coli, Klebsiella pneumonia, Salmonella, and Staphylococcus aureus. The findings showed that all compounds (Z1-Z4) were active against the examined bacteria. Theoretical studies of the antibacterial ability of the prepared compound against DNA gyrase enzyme …

Cyberbullying Detection On Twitter Data Using Machine Learning Classifiers, Pradip Dhakal

Data Science and Data Mining

This study compares some of the popular machine learning techniques like Logistic Regression, Multinomial Naive Bayes, K-Nearest Neighbor, and Extreme Gradient Boosting to classify the tweets into three different categories: cyberbullying based on religion, cyberbullying based on ethnicity, or no cyberbullying. First, various data-cleaning approaches are used to clean the tweet data. After the data is clean and ready, the word embedding techniques, such as a bag of words and term frequency-Inverse document frequency, are used to convert the words into mathematical vectors. Finally, the model will be fitted using the combination of the above-mentioned word embedding techniques and machine …

Spatial Durbin Model On The Utilization Of Delivery At Health Facilities: A 2017 Indonesian Demographic And Health Survey Analysis, Indah Sri Wahyuni, Ira Gustina, Martya Rahmaniati Makful, Tris Eryando

Kesmas

The utilization of delivery at health facilities is a major intervention in reducing 16 to 33% of deaths. This study aimed to determine the model of utilization of delivery at health facilities in Indonesia in 2017 and its influential factors. This study used secondary data from the 2017 Indonesian Demographic and Health Survey using a Spatial Durbin Model (SDM) approach. The population was mothers aged 15 – 49 years, spread across 34 provinces of Indonesia, and had 15,321 samples. The results showed that the Moran’s I value was positive (0.146) and significant at p-value = 0.007, indicating clustered regions with …

Nonlinear Classifiers For Wet-Neuromorphic Computing Using Gene Regulatory Neural Network, Adrian Ratwatte, Samitha Somathilaka, Sasitharan Balasubramaniam, Assaf A. Gilad

School of Computing: Faculty Publications

The gene regulatory network (GRN) of biological cells governs a number of key functionalities that enable them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational principles resemble an artificial neural network (ANN), which can pave the way for the development of wet-neuromorphic computing systems. Genes are integrated into gene-perceptrons with transcription factors (TFs) as input, where the TF concentration relative to half-maximal RNA concentration and gene product copy number influences transcription and translation via weighted multiplication before undergoing a nonlinear activation function. This process yields protein concentration as the …

A Qualitative Exploration Of Factors Associated With Covid-19 Vaccine Uptake And Hesitancy In Selected Rural Communities In Kenya, Fletcher J. Njororai, Walter Amulla, Caleb Kogutu Nyaranga, Wilberforce Cholo, Toluwani Adekunle

Kinesiology Faculty Publications and Presentations

Purpose: The post-pandemic management of COVID-19 infections and any emergent outbreaks is because this endemic disease remains a public health concern. Vaccine hesitancy may continue to hamper efforts to respond to any new disease outbreaks and future epidemics. This qualitative study aimed to explore the factors influencing COVID-19 vaccine acceptance and hesitancy in Kenya to gain deeper insights into this issue. Methods: This study was implemented in western Kenya using key informant interviews. Fourteen (14) key informants were purposively selected for this study. All interviews were transcribed and analyzed using thematic analysis. The interpretation of findings was conducted within the …

Plas 439: Organic Farming And Food Systems Faculty-Led Inquiry Into Reflective Scholarly Teaching Benchmark Portfolio, Christian Stephenson

UNL Faculty Course Portfolios

Organic Farming and Food Systems is a senior and graduate level course for students in the Department of Agronomy and Horticulture. This course was previously offered but has been significantly modified as I have taken on responsibility for the course. Goals for the course include student comprehension of the methods of organic and regenerative farming and the impacts of those methods on economic, environmental, and social sustainability. An additional goal is to build student competency in the evaluation of primary, secondary, and tertiary information resources and critical thinking surrounding issues in food production. Assessment for the course was through diverse …

Morphometric Analysis And Taxonomic Re-Evaluation Of Pepsis Cerberus Lucas And P. Elegans Lepeletier (Hymenoptera: Pompilidae: Pepsinae: Pepsini), Frank E. Kurczewski, Akira Shimizu, Diane H. Kiernan

Insecta Mundi

Hurd (1952) separated Pepsis cerberus Lucas from P. elegans Lepeletier (Hymenoptera: Pompilidae: Pepsinae: Pepsini) based on external morphology and biogeography. Vardy (2005) synonymized the familiar and historically well-documented P. cerberus and P. elegans, combining these Nearctic taxa with several Neotropical variants in an extremely broad definition of P. menechma Lepeletier. In doing so, Vardy (2005) breached the principle of nomenclatural stability. He ignored the prevailing usage and clearly violated articles 23.2, 23.3 and 23.9.1.2 of the ICZN (1999). Morphological differences, ecological divergence, and narrow sympatric geographic distribution of P. cerberus and P. elegans …

Comparison Of Methods For Creating Populations Of Models By Solving Stochastic Inverse Problems, Elizabeth Epstein

Theses

Given a parametric family of models and observational data, a researcher may be faced with an inverse problem: what distribution of parameters best creates a set of models that produce the observed data? Traditionally, Markov Chain Monte Carlo (MCMC) has commonly been used as a method to solve these stochastic inverse problems. In recent years, however, Generative Adversarial Networks (GANs) have been employed. The effectiveness of Markov Chain Monte Carlo methods as compared to a conditional generative adversarial network (cGAN) when applied to a family of models produced by a system of ordinary differential equations that model viral load over …

A Survey Of Practical Haskell: Parsing, Interpreting, And Testing, Parker Landon

Honors Projects

Strongly typed pure functional programming languages like Haskell have historically been confined to academia as vehicles for programming language research. While features of functional programming have greatly influenced mainstream programming languages, the imperative programming style remains pervasive in practical software development. This paper illustrates the practical utility of Haskell and pure functional programming by exploring “hson,” a scripting language for processing JSON developed in Haskell. After introducing the relevant features of Haskell to the unfamiliar reader, this paper reveals how hson leverages functional programming to implement parsing, interpreting, and testing. By showcasing how Haskell’s language features enable the creation of …

Internet-Based Data Platforms Re-Define The Distributions Of Some Large Crabronid Wasps In Arkansas (Hymenoptera: Crabronidae), David E. Bowles

Insecta Mundi

The geographic distributions of three large wasps, Sphecius speciosus (Drury), Stictia carolina Fabricius, and Stizus brevipennis Walsh (Hymenoptera: Crabronidae), occurring in Arkansas are defined using museum specimens and three internet-based data platforms. The internet-based data platforms generally provided more county location records than museum records. Using data from internet sources for easily identified species can better serve to illustrate the known distributions for some species thus making for a powerful tool elucidating distributional patterns and conservation planning.

ZooBank registration. urn:lsid:zoobank.org:pub:DCAE9192-1765-40CD-952B-0A094F413991

The Next Strike: Pioneering Forward-Thinking Attack Techniques With Rowhammer In Dram Technologies, Nakul Kochar

Theses

In the realm of DRAM technologies this study investigates RowHammer vulnerabilities in DDR4 DRAM memory across various manufacturers, employing advanced multi-sided fault injection techniques to impose attack strategies directly on physical memory rows. Our novel approach, diverging from traditional victim-focused methods, involves strategically allocating virtual memory rows to their physical counterparts for more potent attacks. These attacks, exploiting the inherent weaknesses in DRAM design, are capable of inducing bit flips in a controlled manner to undermine system integrity. We employed a strategy that compromised system integrity through a nuanced approach of targeting rows situated at a distance of two rows …

Empirical Exploration Of Software Testing, Samia Alblwi

Dissertations

Despite several advances in software engineering research and development, the quality of software products remains a considerable challenge. For all its theoretical limitations, software testing remains the main method used in practice to control, enhance, and certify software quality. This doctoral work comprises several empirical studies aimed at analyzing and assessing common software testing approaches, methods, and assumptions. In particular, the concept of mutant subsumption is generalized by taking into account the possibility for a base program and its mutants to diverge for some inputs, demonstrating the impact of this generalization on how subsumption is defined. The problem of mutant …

Network Slicing And Noma Enabled Mobile Edge Computing For Next-Generation Networks, Mohammad Arif Hossain

Dissertations

The advent of next-generation wireless networks ushers in a new era of potential, harnessing cutting-edge technologies like mobile edge computing (MEC), non-orthogonal multiple access (NOMA), and network slicing as pivotal drivers of transformation. Within this landscape, an innovative approach is proposed by introducing a NOMA-enabled network slicing technique within MEC networks. This approach aims to achieve multiple objectives: meeting stringent quality of service requirements, minimizing service latency, and enhancing spectral efficiency. By seamlessly integrating NOMA with network slicing in edge computing environments, significant reductions in overall latency are achieved, alongside ensuring optimal resource allocation for NOMA users. To address these …

On The Ubiquity, Properties And Evolution Of Small-Scale Magnetic Flux Ropes In The Heliosphere, Hameedullah Farooki

Dissertations

The solar wind is a plasma constantly blowing out from the Sun with a large-scale magnetic field having significant local complexity at small scales. Small-scale magnetic flux ropes (SMFRs), plasma structures with twisted field lines, are an important element of this complexity. This dissertation contributes several studies that further our understanding of SMFRs. The first study applies machine learning to measurements from Wind labeled by the presence of SMFRs and magnetic clouds (MCs). MCs were distinguished from non-MFRs with an AUC of 94% and SMFRs with an AUC of 89% and had distinctive plasma properties, whereas SMFRs appeared to be …

Computational Microscopy For Biomedical Imaging With Deep Learning Assisted Image Analysis, Yuwei Liu

Dissertations

Microscopy plays a crucial role across various scientific fields by enabling structural and functional imaging with microscopic resolution. In biomedicine, microscopy contributes to basic research and clinical diagnosis. Conventionally, optical microscopy derives its contrast from the amplitude of the optical wave and provides visualization of the physical structure of the sample qualitatively. To understand the function at the cellular or tissue level, there is a need to characterize the sample quantitatively and explore contrast mechanisms other than light intensity. Image enhancement or reconstruction from microscopic imaging systems is known as computational microscopy, and it involves the application of computational techniques …

Machine Learning-Based Design Of Doppler Tolerant Radar, Kyle Peter Wensell

Dissertations

In this work, machine learning theory is applied to the design of a radar detector in order to train a machine learning-based detector that is robust against Doppler shifts. The radar system is designed to work with data that would be otherwise intractable to conventional optimal detector design, such as transmitted noise waveforms and the effects of one-bit quantization at the receiver. The detection performance of the one-bit receiver is shown to match the performance of the derived square-law sign correlator detector. The resulting learning-based detector also introduces Doppler tolerance to the system, which allows for the successful detection of …

Information Theoretic Bounds For Capacity And Bayesian Risk, Ian Zieder

Dissertations

In this dissertation, the problem of finding lower error bounds on the minimum mean-squared error (MMSE) and the maximum capacity achieving distribution for a specific channel is addressed. Presented are two parts, a new lower bound on the MMSE and upper and lower bounds on the capacity achieving distribution for a Binomial noise channel. The new lower bound on the MMSE is achieved via use of the Poincare inequality. It is compared to the performance of the well known Ziv-Zakai error bound. The second part considers a binomial noise channel and is concerned with the properties of the capacity-achieving distribution. …

Financial Time Series Fusion, Completion, And Prediction With Deep Neural Networks, Dan Zhou

Dissertations

Time-series analysis is essential for a wide range of financial applications, including but not limited to bond valuation, firm earnings forecasts, firm fundamentals predictions, and firm characteristics imputations. Given its considerable value, the financial community has shown a strong interest in refining and advancing time-series analysis techniques. The study in this dissertation contributes to this field by employing advanced machine learning approaches, specifically graph neural networks, deep neural networks, and matrix/tensor methods. The primary objectives are twofold: first, to reveal complex correlations within financial time series to improve prediction accuracy, and second, to enhance the process of integrating and imputing …

Sensing With Integrity: Responsible Sensor Systems In An Era Of Ai, David Eisenberg

Dissertations

Deep and machine learning now offer immense benefits for consumer choice, decision-making, medicine, mental health and education, smart cities, and intelligent transportation and driver safety. However, as communication and Internet technology further advances, these benefits have the potential to be outweighed by compromises to privacy, personal freedom, consumer trust, and discrimination. While ethical consequences for personal freedom and equity rise from these technological advances, the issue may not be the technology itself but a lack of regulation and policy that allow abuses to occur. A first study examines how emerging sensor-based technologies, limited to only accelerometer and gyroscope data from …