Physical Sciences and Mathematics Commons^™

Don't Waste Heat! Innovations In Energy Conversion And Conservation From The London Tube, Noah Hamaoui

The Synapse: Intercollegiate science magazine

No abstract provided.

Meet The Staff

The Synapse: Intercollegiate science magazine

No abstract provided.

Back Cover, Julia Stuart

The Synapse: Intercollegiate science magazine

No abstract provided.

Density Functional Theory And Machine Learning Analysis Of C—C Reductive Coupling From Terminal Cyanido And Cyaphido Complexes, Roberto Magdiel Escobar

Theses and Dissertations

Carbon–carbon (C–C) bond activation has gained increased attention as a direct method for the synthesis of pharmaceuticals and industrial chemistry. Due to the thermodynamic stability and kinetic inaccessibility of the C–C bonds, however, activation of C–C bonds by homogeneous transition-metal catalysts under mild homogeneous conditions is still a challenge. Most of the systems in which the activation occurs either have aromatization or relief of ring strain as the primary driving force. The activation of unstrained C–C bonds of phosphaalkynes does not have this advantage. This study employs Density Functional Theory (DFT) calculations using the Gaussian16 package to elucidate and compare …

Universal Shape Replication Via Self-Assembly With Signal-Passing Tiles, Andrew Alseth, Daniel Hader, Matthew J. Patitz

Computer Science and Computer Engineering Faculty Publications and Presentations

In this paper, we investigate shape-assembling power of a tile-based model of self-assembly called the Signal-Passing Tile Assembly Model (STAM). In this model, the glues that bind tiles together can be turned on and off by the binding actions of other glues via “signals”. Specifically, the problem we investigate is “shape replication” wherein, given a set of input assemblies of arbitrary shape, a system must construct an arbitrary number of assemblies with the same shapes and, with the exception of size-bounded junk assemblies that result from the process, no others. We provide the first fully universal shape replication result, namely …

Landscaping Impacts On Urban Soil Development, Fry Lundgren

University Honors Theses

Urban soils can play a key role in flood mitigation, water filtration, and supporting urban plant communities, however many are highly disturbed due to development and management practices. Although full recovery will take time (centuries), in the short term managing soils in ways that conserve organic matter inputs and promote nutrient cycling can support important soil functions and likely promote long term development. To understand how landscaping impact soil development on PSU campus, organic matter content, moisture content, and respiration rates were measured in soils from four area types with different long term weed management strategies (mulch- M; sustained plant …

Assessing Reproducibility Challenges In Clinical Metabolics, Darcy Lin Cochran

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Current metabolomics research is expanding to be multiplatform and interdisciplinary. Metabolomic analysis has the potential to provide detailed insight into any biological system of interest to analyze the underlying molecular mechanisms of variables like environment, disease, and genetics. Human clinical metabolomics is a major research area that is often focused on finding biomarkers or panels of biomarkers for different diseases. However, results are difficult to compare across different laboratories since the field of metabolomics has yet to widely adopt best practices for sample preparation, data acquisition and data processing, results are difficult to compare across different laboratories.

This thesis focuses …

Controller Software: Evolution, Identification, And Implementation, Balaji Balasubramaniam

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

In the annals of automation history and advancement, one can find control technology is at the core. Modern-day controllers rely heavily on software capability to provide stability and improve the system's performance. In particular, drone flight controllers use autopilot control software to accomplish autonomous navigation from take-off to landing. However, we know very little about how the controller code modifications and its impact, particularly at the software level. No general framework has been developed to identify the control code changes and observe the real values of software control loops at the kernel layer.

In this thesis, we lay the foundation …

Mining Work Items To Streamline Software Maintenance Tasks, Salomé Perez-Rosero

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Software engineering maintenance tasks often require associating code changes into groupings of related units of work to have as much information as possible about the developments toward addressing a specific code task. A comprehensive understanding of how a code task has evolved helps developers make better decisions about changes in the overall codebase, where a commit represents the set of code changes made to the codebase at a specific time. While the concept of work items as logically related code changes has been primarily theoretical, its impact on software maintenance tasks, such as tracing the origins of bugs or fixes …

Analysis Of A Gravity Wave Horizontal Blocking Event Using Hodograph Techniques Applied To Sodium Lidar Data, Neal R. Criddle

All Graduate Theses and Dissertations, Fall 2023 to Present

Atmospheric gravity waves are ubiquitous and important events in Earth’s atmosphere with impacts on space weather and associated technological effects. The population of gravity waves is determined by their formation mechanisms, and their interactions with the background atmosphere. These gravity waves transport energy and momentum into the near Earth space environment, and are responsible for movement of chemical species. Predicting the occurrence of space weather effects, then, is in part a process requiring the characterization of gravity waves. Case studies of unique and interesting gravity wave events are an important part of this process. These allow for the investigation of …

Mathematical And Statistical Methods To Harness Limited Data In Models For Ecological Space Use Under Global Change, Sarah C. Bogen

All Graduate Theses and Dissertations, Fall 2023 to Present

The dynamics of how plants and animals use space in their habitats has important implications for the fields of ecology and conservation. However, understanding and responding to these spatial and temporal dynamics is often limited by data availability, financial resources and biases. As average global temperatures increase, suitable habitats shift poleward and require local populations to move with suitable habitat, adapt to the changing environment, or risk extinction. Capacity to persist without movement may be estimated by considering changes to a combination of habitat characteristics. Capacity to track suitable habitat may be modeled through synthesizing information on species demographic mechanisms …

Supervised Generative Adversarial Networks For Time Series Generation In Embedding Space, Mohammadreza Eskandarinasab

All Graduate Theses and Dissertations, Fall 2023 to Present

Time series data, such as weather forecasts, stock market trends, or heart rate monitors, plays a vital role in many areas of our lives. However, creating realistic synthetic time series data for research and testing purposes has been a significant challenge due to limitations in existing methods, which often struggle with accuracy and consistency. In this study, we developed two new approaches to generate high-quality time series data more effectively. The first method introduces a dual-feedback system that helps the model learn and replicate real data patterns more accurately by providing guidance at different stages of the learning process. The …

Leveraging Ai Tools In University Writing Instruction: Enhancing Student Success While Upholding Academic Integrity, Daisuke Akiba, Rebecca Garte

Publications and Research

The emergence of AI-powered Large Language Models (LLMs), such as ChatGPT and Google Gemini, presents both opportunities and challenges for higher education, particularly regarding academic integrity in writing instruction. This exploratory study examines a novel pedagogical approach that integrates LLMs as required feedback tools in a university-level psychology writing assignment. The exclusive online approach emphasizes improvement through revision, requiring students to obtain AI-generated feedback on ungraded initial drafts based on an instructor-provided rubric, with final assessment focused on the quality of subsequent revisions. Analysis of survey data from 39 undergraduate students, incorporating both quantitative measures and qualitative responses, revealed several …

Geochemical Evidence Of Drying During The 4.2 Ka Event In Sediment Cores From The Yucatán Peninsula, Mexico, Derek K. Gibson, Jonathan Obrist-Farner, Alex Correa-Metrio, Alejandra Rodriguez-Abaunza, Carlos Castañeda-Posadas

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Tropical hydroclimate variability during the Middle and Late Holocene was investigated using geochemical indicators of local-scale precipitation and evaporation preserved in sediment cores from two sites in the Mexican Yucatán Peninsula. Scanning X-Ray fluorescence spectroscopy data show generally decreasing precipitation trends during the Early and Middle Holocene. During the transition between the Middle and Late Holocene, geochemical evidence of reduced watershed erosion and increased evaporation indicate that a centennial-scale drying event impacted the region between 4.3 and 4.0 ka (kilo-anum; thousand years before present). These findings suggest that the 4.2 ka drying event, which has been previously recorded in Europe, …

Decoding Emotions: Unveiling Facial Expressions Through Acoustic Sensing With Contrastive Attention, Guangjing Wang, Juexing Wang, Ce Zhou, Weikang Ding, Huacheng Zeng, Tianxing Li, Qiben Yan

Computer Science Faculty Research & Creative Works

Expression recognition holds great promise for applications such as content recommendation and mental healthcare by accurately detecting users’ emotional states. Traditional methods often rely on cameras or wearable sensors, which raise privacy concerns and add extra device burdens. In addition, existing acoustic-based methods struggle to maintain satisfactory performance when there is a distribution shift between the training dataset and the inference dataset. In this paper, we introduce FacER+, an active acoustic facial expression recognition system, which eliminates the requirement for external microphone arrays. FacER+ extracts facial expression features by analyzing the echoes of near-ultrasound signals emitted between the 3D facial …

Adan: Adaptive Nesterov Momentum Algorithm For Faster Optimizing Deep Models, Xingyu Xie, Pan Zhou, Huan Li, Zhouchen Lin, Shuicheng Yan

Research Collection School Of Computing and Information Systems

In deep learning, different kinds of deep networks typically need different optimizers, which have to be chosen after multiple trials, making the training process inefficient. To relieve this issue and consistently improve the model training speed across deep networks, we propose the ADAptive Nesterov momentum algorithm, Adan for short. Adan first reformulates the vanilla Nesterov acceleration to develop a new Nesterov momentum estimation (NME) method, which avoids the extra overhead of computing gradient at the extrapolation point. Then Adan adopts NME to estimate the gradient's first- and second-order moments in adaptive gradient algorithms for convergence acceleration. Besides, we prove that …

Ai Is A Viable Alternative To High Throughput Screening: A 318-Target Study, Izhar Wallach, Denzil Bernard, Kong Nguyen, Gregory Ho, Adrian Morrison, Adrian Stecula, Andreana Rosnik, Ann Marie O'Sullivan, Aram Davtyan, Ben Samudio, Bill Thomas, Brad Worley, Brittany Butler, Christian Laggner

Chemistry Faculty Research & Creative Works

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, …

Flexfl: Heterogeneous Federated Learning Via Apoz-Guided Flexible Pruning In Uncertain Scenarios, Zekai Chen, Chentao Jia, Ming Hu, Xiaofei Xie, Anran Li, Mingsong Chen

Research Collection School Of Computing and Information Systems

Along with the increasing popularity of Deep Learning (DL) techniques, more and more Artificial Intelligence of Things (AIoT) systems are adopting federated learning (FL) to enable privacy-aware collaborative learning among AIoT devices. However, due to the inherent data and device heterogeneity issues, existing FL-based AIoT systems suffer from the model selection problem. Although various heterogeneous FL methods have been investigated to enable collaborative training among heterogeneous models, there is still a lack of i) wise heterogeneous model generation methods for devices, ii) consideration of uncertain factors, and iii) performance guarantee for large models, thus strongly limiting the overall FL performance. …

Converting Vocal Performances Into Sheet Music Leveraging Large Language Models, Jinjing Jiang, Nicole Teo, Haibo Pen, Seng-Beng Ho, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Advanced natural language processing (NLP) models are increasingly applied in music composition and performance, particularly for generating vocal melodies and simulating singing voices. While NLP techniques have been effective in analyzing vocal performance data to assess quality and style, the automatic transcription of vocal performances into sheet music remains a significant challenge. Manual transcription tools often fall short due to the intricate dynamics of vocal expression. This study tackles the automation of vocal performance transcription into sheet music using innovative techniques, including large language models (LLMs). We propose a method to translate vocal audio input into display-ready sheet music effectively. …

A Strategic Insight Into The Market For Carbon Management Capacity, Mahelet G. Fikru, Ting Shen, Jennifer Brodmann, Hongyan Ma

Economics Faculty Research & Creative Works

This study presents the market for carbon management capacity via carbon capture, utilization, and storage technologies, identifying demand and supply forces, as well as clarifying the potential impact of market and non-market-based shocks on technology developers versus adopters. The paper addresses a prevailing gap in market analysis, introducing a microeconomic framework and unique dataset to identify key players, market forces, and policy incentives shaping the carbon capture, utilization, and storage landscape. The analysis equips industry stakeholders, policymakers, and investors with valuable insights regarding (1) leaders in the design, development, and manufacture of carbon capture, utilization, and storage technologies (supply), (2) …

Development Of Uranium Fluoride Microstructures, Harry Jang

UNLV Theses, Dissertations, Professional Papers, and Capstones

Uranium microstructured materials with controlled size and shape have found applications in several branches of the nuclear industry including as targets for medical isotopes production, fuels for nuclear reactors, standards for analytical measurements, and energy sources for space exploration. Morphological studies of actinide materials are also highly relevant to nuclear forensics-related work. Other potential applications include information storage, catalysis, sensors, and luminescent devices. Most studies on uranium microstructured materials have focused on binary oxides, nitrides, carbides, and fluorides, with the spherical shape being the dominant morphology. Prior to the work reported in this dissertation, aside from UF4 microspheres (ms) and …

Fast Neutron & Gamma-Ray Scintillation And Broadband Photoluminescence From Gallium Nitride & Gallium Oxide: Developing Radiation-Hard Diagnostic Platforms, Daniel Jesus Valdes

UNLV Theses, Dissertations, Professional Papers, and Capstones

Integrated scintillation and electronics processing materials are of critical importance in environments exposed to high levels of radiation, such as nuclear fusion diagnostics and space missions. This dissertation focuses on the radiation detection capabilities of gallium oxide (Ga2O3) and gallium nitride (GaN), exploring their responses to fast neutrons and gamma rays. Using high-energy neutron beam facilities at Los Alamos Neutron Science Center (LANSCE) Flight Path 4FP60R, we exposed both Ga2O3 and GaN crystals to neutron irradiation spanning an energy spectrum ranging from 1 to 400 MeV. A Pi-Max 4 fast-gated Intensified CCD (ICCD) camera captured the transient scintillation responses for …

A Novel Approach To Sustainable Behavior Enhancement Through Ai-Driven Carbon Footprint Assessment And Real-Time Analytics, Ahmad Jasim Jasmy, Heba Ismail, Noof Aljneibi

All Works

This research introduces an Artificial Intelligence-driven mobile application designed to help users calculate and reduce their Carbon Footprint (CFP). The proposed system employs an Intelligent Sustainable Behavior Tracking and Recommendation System, analyzing users' carbon emissions from daily activities and suggesting eco-friendly alternatives. It facilitates sustainability discussions through its chat community and educates users on sustainable practices via an intelligent chatbot powered by a sustainability knowledge base. To promote social engagement around sustainability, the application incorporates a competition and reward system. Additionally, it aggregates behavioral data to inform government sustainability policies and address challenges. Emphasizing individual responsibility, the proposed system stands …

Sustainable Energysense: A Predictive Machine Learning Framework For Optimizing Residential Electricity Consumption, Murad Al-Rajab, Samia Loucif

All Works

In a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate and computing the total electricity consumption based on the number of hours the appliance was used. The application …

Comparison Of Statistical And Machine Learning Genomic Prediction Methods In Plant Breeding: Case Studies In Maize And Soybean, Igor Kuivjogi Fernandes

Graduate Theses and Dissertations

Plant breeding is essential to increase genetic gain and food production worldwide. This study was conducted to evaluate new ways to use machine learning (ML) to tackle plant breeding challenges, where two ideas were tested — the first chapter focuses on how to combine genetic and environmental data using ML to improve the prediction of maize grain yield in multi-environment trials, while the second chapter centers on how to couple feature selection of molecular markers with ML to enhance prediction of yield in soybean, and, in both cases, ML approaches were compared to well-established statistical methods greatly adopted by the …

Oceano Dunes Community Survey Results, Michele Romolini, Eric G. Strauss, Marni Goldenberg, Keri Schwab

Center for Urban Resilience Reports

This report presents the findings of a 42-question survey conducted by Loyola Marymount University and Cal Poly San Luis Obispo from June 2023-May 2024 among visitors and local residents regarding their perceptions and experiences at California’s Oceano Dunes State Vehicular Recreation Area (SVRA). The survey aimed to gather insights into visitor and community demographics, frequency and types of use, barriers to use, environmental awareness and perceptions of use, and familiarity with park opportunities. In particular, we sought to seek responses from communities local to the parks, especially from the community of Oceano itself, and Latino and Hispanic community members. In …

Enhancing Mathematical Models For Covid-19 Pandemic Response: A Philippine Study, Timothy Robin Teng, Elvira De Lara-Tuprio, Ma. Regina Justina Estuar, Christian Pulmano, Lu Christian S. Ong, Zachary Pangan, Lenard Paulo V. Tamayo, Jasper John V. Segismundo, Mark Anthony C. Tolentino, Alyssa Nicole N. Ty

Mathematics Faculty Publications

Mathematical models supported by a robust automated data pipeline proved to be useful tools for a data-driven and science-based response and policy-making during the COVID-19 pandemic in the Philippines. In the first year of the pandemic, FASSSTER (Feasibility Analysis on Syndromic Surveillance using Spatio-Temporal Epidemiological modeleR) used a compartmental model to generate scenario-based projections of COVID-19 cases. The emergence of the Delta variant, however, and the administration of vaccines over the second half of 2021 caused significant changes in the Philippine pandemic landscape. This necessitated making adjustments to the model to better capture the local disease transmission dynamics and address …

Community Co-Production Of The Meaning Of Local Climate Resilience And Action: A Case Study, Michele Romolini, Mary Hillemeier, Dustin L. Herrmann

Center for Urban Resilience Scholarship

In the face of a rapidly urbanizing and warming planet, there is urgency for local governments to develop community resilience strategies. Successful climate change adaptation will likely require the inclusion of local knowledge, especially from historically marginalized communities. One pathway for this is through research projects and government planning activities that employ processes of co-production to generate actionable knowledge relevant to the community. This paper describes the approach, results, and reflections of a multi-sectoral partnership organized to engage residents of a small municipality to co-produce the meaning of community climate resilience, through methods including individual interviews, cognitive mapping, and a …

Populations Digitally Excluded From Education: Issues, Factors, Contributions And Actions For Policy, Practice And Research In A Post-Pandemic Era, Don Passey, Jean Gabin Ntebutse, Manal Yazbak Abu Ahmad, Janet Cochrane, Simon Collin, Asmaa Ganayem, Elizabeth Langran, Sadaqat Mulla, Ma. Mercedes T. Rodrigo, Toshinori Saito, Miri Shonfeld, Saunand Somasi

Department of Information Systems & Computer Science Faculty Publications

This conceptual paper draws on a wide range of research and policy literature, providing a contemporary view of issues, factors and practices that affect education for digitally excluded populations. Concern for how education for digitally excluded populations can be supported is focal to this paper, with different sections offering key related perspectives. From an analysis of issues, factors and practices, actions for policy, practice and research are identified. Given a key finding that power issues can have major effects on plans, implementation processes and outcomes when addressing needs of education for digitally excluded populations, the paper concludes by offering frameworks …

Understanding The Influence Of Traffic And Noise On The Wildlife Community Surrounding Fm106 And Sh100 In South Texas, Anna Rose Mehner

Theses and Dissertations

In South Texas, highways create landscape fragmentation, habitat degradation, and result in wildlife vehicle collisions. Vehicle collisions are the highest known cause of mortality of the endangered ocelot (Leopardus pardalis), which led Texas Department of Transportation to install nine wildlife crossing structures (WCS) on Farm-to-Market 106 and five on State Highway 100. These highways are along Laguna Atascosa National Wildlife Refuge, which is the location of the southernmost breeding population of ocelots in the United States. Many other wildlife species including bobcat (Lynx rufus) and coyote (Canis latrans), are present along these highways and …