Physical Sciences and Mathematics Commons^™

Re: Approval Letter For The Butte Priority Soils Operable Unit (Bpsou) Draft Final Borrow Amendment Submittal #1 (Dated May 9, 2024), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Re: Approval Letter For The Butte Priority Soils Operable Unit (Bpsou) Request For Change (Rfc-Rmap-2024-001) (Dated May 9, 2024), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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 …

2-Tertbutylfuran At 550 And 700 K: A Multiplexed Photoionization Mass Spectrometric Investigation And Determination Of Cross Sections Of A Carbon- Bromine Bond And Various Brominated Organic Compounds, Ameyali Tapia

Master's Theses

This thesis presents the combustion study of 2-tertbutylfuran (2-TBF) in reaction with atomic oxygen (O(3P)). Data was collected at the Advanced Light Source (ALS) of the Lawrence Berkeley National Lab, using tunable vacuum ultraviolet radiation coupled with a multiplexed mass spectrometer to acquire the data. These data were taken at low pressures of 7 and 8 torr at temperatures of 550 K and 700 K, respectively. Primary products of the system were identified and characterized, while reaction pathways of the products are currently being studied to ensure thermodynamic feasibility. Branching fractions of the system were also calculated, to view the …

Charting A Path To The Quintuple Aim: Harnessing Ai To Address Social Determinants Of Health, Yash Shah, Zachary Goldberg, Erika Harness, David Nash

College of Population Health Faculty Papers

The Quintuple Aim seeks to improve healthcare by addressing social determinants of health (SDOHs), which are responsible for 70-80% of medical outcomes. SDOH-related concerns have traditionally been addressed through referrals to social workers and community-based organizations (CBOs), but these pathways have had limited success in connecting patients with resources. Given that health inequity is expected to cost the United States nearly USD 300 billion by 2050, new artificial intelligence (AI) technology may aid providers in addressing SDOH. In this commentary, we present our experience with using ChatGPT to obtain SDOH management recommendations for archetypal patients in Philadelphia, PA. ChatGPT identified …

Mapping For Tracking Sexually Transmitted Infections By Subdistricts In Surabaya, Indonesia, Destri Susilaningrum, Brodjol Sutijo Suprih Ulama, Fausania Hibatullah, Diandra Soja Anjani

Kesmas

The 2014 shutdown localization of prostitution in Surabaya City, East Java Province, Indonesia, has given rise to an illegal prostitution industry, resulting in the spread of uncontrolled sexually transmitted infections (STIs). Mapping needs to be done to track the spread of the disease. This study used secondary data on STIs in 2020 from the Surabaya City Health Office. By using biplot analysis, this study sought to offer a detailed understanding of the distribution and dynamics of STI cases in different parts of Surabaya. The early-stage syphilis was found in Tegalsari and Krembangan Subdistricts; then, gonorrheal urethritis was found in Tandes, …

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 …

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 …

Size-Constrained Weighted Ancestors With Applications, Philip Bille, Yakov Nekrich, Solon P. Pissis

Michigan Tech Publications, Part 2

The weighted ancestor problem on a rooted node-weighted tree T is a generalization of the classic predecessor problem: construct a data structure for a set of integers that supports fast predecessor queries. Both problems are known to require Ω(log log n) time for queries provided O(n poly log n) space is available, where n is the input size. The weighted ancestor problem has attracted a lot of attention by the combinatorial pattern matching community due to its direct application to suffix trees. In this formulation of the problem, the nodes are weighted by string depth. This research has culminated in …

What Climate Change Means For Nebraska

United States Environmental Protection Agency: Publications

Nebraska’s climate is changing. In the past century, most of the state has warmed by at least one degree (F). The soil is becoming drier, and rainstorms are becoming more intense. In the coming decades, flooding is likely to increase, yet summers are likely to become increasingly hot and dry, which would reduce yields of some crops, require farmers to use more water, and amplify some risks to human health.

Our climate is changing because the earth is warming. People have increased the amount of carbon dioxide in the air by 40 percent since the late 1700s. Other heat-trapping greenhouse …

Try It Together - Qualitative Coding With Atlas.Ti, Danping Dong, Bryan Leow

AI for Research Week

This hands-on session introduces Atlas.ti, a well-established qualitative data analysis tool for analyzing your transcripts and textual data. The session will cover coding data, extracting insights, creating visualizations, and exploring the tool's latest AI features.

Try It Together: Transcribing Your Audio With Whisper Api, Bella Ratmelia

AI for Research Week

In this hands-on session, we will explore using the Whisper API to transcribe audio recordings from interviews, focus groups, and speeches. The session will delve into best practices and address common issues that may arise during the transcription process.

Effect Of Social Media On Shaping The Agenda Of The Communicator In The Jordanian Tv Channels, Amer Khaled Ahmad Dr, Hamza Mohammad Nahar, Maram Mohammad Naji Manajreh Dr

Middle East Journal of Communication Studies

The study aimed to assess the extent to which communicators at Jordanian television channels use social media platforms as an information source, and the impact of these platforms on their news and information selection priorities. This was achieved using a survey tool distributed on an equal quota sample of (150) communicators from Jordanian television channels (Jordan TV, AlMamlaka TV, Roya TV). The study found that all the study subjects used social media platforms as a source of information, with the "X platform (formerly Twitter)" being the most used platform. The statement "understanding the audience's interests and preferences to produce tailored …

Singleadv: Single-Class Target-Specific Attack Against Interpretable Deep Learning Systems, Eldor Abdukhamidov, Mohammed Abuhamad, George K. Thiruvathukal, Hyoungshick Kim, Tamer Abuhmed

Computer Science: Faculty Publications and Other Works

In this paper, we present a novel Single-class target-specific Adversarial attack called SingleADV. The goal of SingleADV is to generate a universal perturbation that deceives the target model into confusing a specific category of objects with a target category while ensuring highly relevant and accurate interpretations. The universal perturbation is stochastically and iteratively optimized by minimizing the adversarial loss that is designed to consider both the classifier and interpreter costs in targeted and non-targeted categories. In this optimization framework, ruled by the first- and second-moment estimations, the desired loss surface promotes high confidence and interpretation score of adversarial samples. By …

A Brief Introduction To General Topology, Richard P. Millspaugh

Open Educational Resources

The material in this text is intended to be accessible to undergraduates who have had an introduction to elementary set theory and proof techniques. It includes sufficient material from general topology to prove the two main topological results found in a standard first semester calculus course: the Intermediate Value Theorem and the Extreme Value Theorem. This material can be found in Chapters 2 through 6 and makes up the bulk of the text. Rather than approaching these topics through use of the standard euclidean metric, it defines the standard topology on R in terms of the usual order on R. …

Unveiling The Metaverse: A Survey Of User Perceptions And The Impact Of Usability, Social Influence And Interoperability, Mousa Al-Kfairy, Ayham Alomari, Mahmood Al-Bashayreh, Omar Alfandi, Mohammad Tubishat

All Works

This review explores the Metaverse, focusing on user perceptions and emphasizing the critical aspects of usability, social influence, and interoperability within this emerging digital ecosystem. By integrating various academic perspectives, this analysis highlights the Metaverse's significant impact across various sectors, emphasizing its potential to reshape digital interaction paradigms. The investigation reveals usability as a cornerstone for user engagement, demonstrating how social dynamics profoundly influence user behaviors and choices within virtual environments. Furthermore, the study outlines interoperability as a paramount challenge, advocating for establishing unified protocols and technologies to facilitate seamless experiences across disparate Metaverse platforms. It advocates for the adoption …

Anion Binding And Sensing Using Cs124-Sensitized Luminescent Terbium Complexes, Alessandro Rizzi, Minhee Lee, Wade Grabow, Olivia Brooks, Helena Nguyen, Neal Yakelis, Clarisse Vanderfeltz

Honors Projects

Two terbium complexes with varying degrees of intramolecular coordination, Tb:DO2A-Cs124 and Tb:DOTA-Cs124, were prepared. Their capacity to detect biologically and environmentally relevant anions through their luminescence changes was investigated. Tb:DOTA-Cs124 demonstrated exceptional selectivity as a sensor for nitrite, while Tb:DO2A-Cs124 detects nitrite, phosphates, and a range of carboxylate-containing anions.

Measuring Confidentiality With Multiple Observables, John J. Utley

Computer Science Senior Theses

Measuring the confidentiality of programs that need to interact with the outside world can prevent leakages and is important to protect against dangerous attacks. However, information propagation is difficult to follow through a large program with implicit information flow, tricky loops, and complicated instructions. Previous works have tackled this problem in several ways but often measure leakage a program has on average rather than the leakage produced by a set of particularly compromising interactions. We introduce new methods that target a specific set of observables revealed throughout execution to cut down on the resources needed for analysis. Our implementation examines …

Exploring Applications Of Ai In Developer-Side Web Accessibility Practices, Maria H. Cristoforo

Computer Science Senior Theses

No abstract provided.

Urban And Rural Bmi Trajectories In Southeastern Ghana: A Space-Time Modeling Perspective On Spatial Autocorrelation, Hsiao-Chien Shih, Xiaoxiao Wei, Li An, John Weeks, Douglas Stow

International Journal of Geospatial and Environmental Research

Spatial autocorrelation in model residuals can have a significant impact on the results of spatial or space-time models. This can result in misleading estimates of the influence of different factors, potentially exaggerating or even reversing the perceived effects of these factors. This study also considers the potential implications of the Modifiable Areal Unit Problem (MAUP) in the context of spatial-temporal models. In this case study for southeastern Ghana, we examined whether and how spatial autocorrelation in model residuals might generate bias in regression coefficients when explaining women’s body mass index (BMI) across urban and rural areas. Eigenvector spatial filtering, with …

Predictive Analysis Of Local House Prices: Leveraging Machine Learning For Real Estate Valuation, Joey Hernandez, Danny Chang, Santiago Gutierrez, Paul Huggins

SMU Data Science Review

This paper presents a comprehensive study examining the real estate market potential in the dynamic urban landscapes of Frisco and Plano, Texas. Combining traditional real estate analysis with cutting-edge machine learning techniques, the study aims to predict home prices and assess investment feasibility. Leveraging these findings, the study proposes a strategic focus on predictive modeling and investment potential identification, emphasizing the continual refinement of machine learning models with updated data to accurately forecast changes in the real estate market. By harnessing the predictive power of these models, investors can identify high-growth areas and optimize their investment decisions, thus capitalizing on …

A Symbolic Approach To Nonlinear Time Series Analysis, Ranjan Karki, Nibhrat Lohia, Michael B. Schulte

SMU Data Science Review

Current nonlinear time series methods such as neural networks forecast well. However, they act as a black box and are difficult to interpret, leaving the researchers and the audience with little insight into why the forecasts are the way they are. There is a need for a method that forecasts accurately while also being easy to interpret. This paper aims to develop a method to build an interpretable model for univariate and multivariate nonlinear time series data using wavelets and symbolic regression. The final method relies on multilayer perceptron (MLP) neural networks as a form of dimensionality reduction and the …

Intelligent Solutions For Retroactive Anomaly Detection And Resolution With Log File Systems, Derek G. Rogers, Chanvo Nguyen, Abhay Sharma

SMU Data Science Review

This paper explores the intricate challenges log files pose from data science and machine learning perspectives. Drawing inspiration from existing methods, LAnoBERT, PULL, LLMs, and the breadth of recent research, this paper aims to push the boundaries of machine learning for log file systems. Our study comprehensively examines the unique challenges presented in our problem setup, delineates the limitations of existing methods, and introduces innovative solutions. These contributions are organized to offer valuable insights, predictions, and actionable recommendations tailored for Microsoft's engineers working on log data analysis.