Physical Sciences and Mathematics Commons^™

Southwest Center On Resilience For Climate Change And Health (Scorch), Kacey C. Ernst, Huaqing Wang

Funded Research Records

No abstract provided.

Exploring The Correlation Between Climate Change And Influenza Activity In The Northeast United States, 2003-2023, Emily M. Posadas

Doctoral Dissertations and Projects

The influenza virus is influenced by a myriad of complex interactions with human and environmental factors, with changing climate patterns having significant effects on the health status of a population. This study investigates the correlation between climate change and influenza activity in the northeast United States from 2003 to 2023, providing insight and understanding into the interaction between the two variables. The anthropogenic factors that have contributed to climate change are recognized, and circulating seasonal and avian influenza viruses are discussed within the context of climate change. The investigation of the correlation between climate change and influenza activity across this …

Request For Change (Rfc-Rmap-2024-002), Mike Mcanulty

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Time-Series Feature Selection For Solar Flare Forecasting, Yagnashree Velanki, Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi

Computer Science Student Research

Solar flares are significant occurrences in solar physics, impacting space weather and terrestrial technologies. Accurate classification of solar flares is essential for predicting space weather and minimizing potential disruptions to communication, navigation, and power systems. This study addresses the challenge of selecting the most relevant features from multivariate time-series data, specifically focusing on solar flares. We employ methods such as Mutual Information (MI), Minimum Redundancy Maximum Relevance (mRMR), and Euclidean Distance to identify key features for classification. Recognizing the performance variability of different feature selection techniques, we introduce an ensemble approach to compute feature weights. By combining outputs from multiple …

Draft Final Revised 2024 Residential Metals Abatement Program (Rmap) Walkerville Park (Alley Of North Main And Alley Of West Daly) Soil Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

A Preliminary Fuzzy Inference System For Predicting Atmospheric Ozone In An Intermountain Basin, John R. Lawson, Seth N. Lyman

Mathematics and Statistics Faculty Publications

High concentrations of ozone in the Uinta Basin, Utah, can occur after sufficient snowfall and a strong atmospheric anticyclone creates a persistent cold pool that traps emissions from oil and gas operations, where sustained photolysis of the precursors builds ozone to unhealthy concentrations. The basin's winter-ozone system is well understood by domain experts and supported by archives of atmospheric observations. Rules of the system can be formulated in natural language ("sufficient snowfall and high pressure leads to high ozone"), lending itself to analysis with a fuzzy-logic inference system. This method encodes human expertise as machine intelligence in a more prescribed …

The Interplay Of Social Constraints And Individual Variation In Risk Tolerance In The Emergence Of Superspreaders, Earl Carr, Matthew J. Young, Matthew J. Silk, Alexander J. Pritchard, Nina F. Fefferman

Faculty Publications and Other Works -- Ecology and Evolutionary Biology

Individual host behaviours can drastically impact the spread of infection through a population. Differences in the value individuals place on both socializing with others and avoiding infection have been shown to yield emergent homophily in social networks and thereby shape epidemic outcomes. We build on this understanding to explore how individuals who do not conform to their social surroundings contribute to the propagation of infection during outbreaks. We show how non-conforming individuals, even if they do not directly expose a disproportionate number of other individuals themselves, can become functional superspreaders through an emergent social structure that positions them as the …

Search For Core Collapse Supernovae Signals In Ligo’S Third Observation Run Using A Network Of Gravitational Wave Detectors Integrated With A Multiclass Convolutional Neural Network, Shahrear Khan Faisal, Gaukhar Nurbek, Michael Benjamin, Bhawana Sedhai, Soma Mukherjee

Physics and Astronomy Faculty Publications and Presentations

In the existing body of literature, numerous waveforms of core collapse supernovae (CCSN) have emerged from extensive simulations conducted in high-performance computing facilities globally. These waveforms exhibit distinct characteristics related to their explosion mechanisms, influenced by parameters such as progenitor mass, angular momentum, gravitational wave energy, peak frequency, duration, and equation of state. Core collapse supernovae stand out as highly anticipated sources in LIGO’s fourth observation (O4) run, prompting dedicated efforts to detect them. The integration of machine learning, specifically convolutional neural networks (CNN), has become a pivotal avenue for analysis. This study addresses a fundamental query: how can a …

The Aimag Project: Using Machine Learning To Predict Crustal Magnetic Anomaly Values, Xavier Gobble, Marlie Mollett, Dr. Dawn King, Dr. Cory Reed, Erin Knese

Undergraduate Research Symposium

A detailed model of the Earth’s total magnetic field is important for acquiring the means for GPS-alternative, magnetic anomaly-based navigation. The Earth’s total magnetic field is an amalgam of 5 mechanisms: the geodynamo generated by the rotation of the Earth’s molten iron core, the fields induced by the flows of electric current in the atmosphere and oceans, the disturbance of the ionosphere by solar wind, and local anomalies attributable to ferromagnetic minerals present in the crust; the lattermost compose the crustal magnetic field. The EMAG2v3 dataset comprises a compilation of satellite, shipborne, and airborne magnetic measurements differenced from the Comprehensive …

Exploring The Early Solar System: Cometary Chemical Fingerprints: A Study Of Comet C/2022 E3 (Ztf) Via Near-Infrared Spectroscopy, Grace Puchalski

Undergraduate Research Symposium

Comets are small, icy remnants from the solar system formation (4.5 billion years ago). Their interior composition should reflect the composition and conditions presented in the mid-plane of the protoplanetary region where (and when) they formed. These small objects predominantly reside in two major reservoirs, the Oort cloud and the Kuiper belt. Comets coming from the Oort cloud have long orbital periods while comets from the Kuiper belt have short orbital periods (< 200 years). An overarching goal in astronomy is to understand the conditions presented in the planetary region in the early solar system. Since comets lack a known mechanism of self internal heating, any processes that have changed their composition should only affect a few meters deep, which is believed to be excavated over a course of a perihelion passage into the inner parts of the solar system. As comets get closer to the Sun, solar irradiation causes their ices to sublime, leaving a formation of a freely expanding atmosphere (coma). Depending on the science interest, astrophysicists use different techniques for data collection, a common one being spectroscopy. Using iSHELL spectrograph at the NASA-Near-Infrared Telescope Facility (IRTF), we examine the primary chemical composition (e.g., H2O, CO, CH4, C2H6, C2H2, H2CO, NH3, CH3OH, OCS, and OH) of cometary coma in bright comet C/2022 E3 (ZTF). Our preliminary results indicate the H2O production rate of ~3.4E28 (molecules per second), which corresponds to the rotational temperature of 86 (K). Cometary atmospheres are dense enough that molecules in the inner coma are thermalized by collision (Local Thermodynamic Equilibrium), thus 86 (K) is a physical parameter of coma. We compared the production of the rest of species with that of water (in %) and our results indicated that comet E3 was typical (close to average) in mixing ratios of all volatile species. By mapping the intensity of light with distance from the nucleus,we were able to examine the spatial distribution of volatiles and dust in E3’s coma which were consistent with production directly from the nucleus.

Understand Energy Issues With Chatgpt, Haochi Zheng

AI Assignment Library

This assignment is designed for the upper-level undergraduate Environmental and Natural Resource Economics class. The purpose of the assignment is to help students:

• Deepen understanding of the main differences between non-renewable and renewable resources through an experience of using AI technology;
• Collaborate with others to apply the basic Economics theories (consumer and producer principles) in analyzing energy issues and validating the accuracy of online information;

Re: Approval Letter For The Butte Priority Soils Operable Unit (Bpsou) Revised Draft Final Insufficiently Reclaimed Sites Field Sampling Plans (Fsps) And Response To Agency Comments – Bres No. 34e Eveline Dump East, Bres No. 52 Moscow Dump (Dated August 13, 2024) And The Bres No. 78 Original Mine Yard (Updated On September 6, 2024), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final Blacktail Creek Groundwater Hydraulic Control System Pre-Design Investigation Evaluation Report, Stantec Consulting Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

On Signifiable Computability: Part I: Signification Of Real Numbers, Sequences, And Types, Vladimir A. Kulyukin

Computer Science Faculty and Staff Publications

Signifiable computability aims to separate what is theoretically computable from what is computable through performable processes on computers with finite amounts of memory. Real numbers and sequences thereof, data types, and instances are treated as finite texts, and memory limitations are made explicit through a requirement that the texts be stored in the available memory on the devices that manipulate them. In Part I of our investigation, we define the concepts of signification and reference of real numbers. We extend signification to number tuples, data types, and data instances and show that data structures representable as tuples of discretely finite …

Biochar And Biosorbents Derived From Biomass For Arsenic Remediation, Gaurav Sharma, Yaksha Verma, Chin Wei Lai, Mu Naushad, Jibran Iqbal, Amit Kumar, Pooja Dhiman

All Works

Global groundwater contamination by Arsenic (As) presents a grave danger to the health of living beings and wildlife, demanding comprehensive remediation strategies. This review delves into the complex landscape of arsenic remediation, encompassing its chemical forms, occurrences, sources, and associated health risks. Advanced techniques, notably biomass-derived adsorbents, emerge as promising and cost-effective solutions. The exploration spans preparing and modifying biomass-derived adsorbents, unraveling their adsorption capacity, influencing factors, isotherms, kinetics, and thermodynamics. Noteworthy attention is given to plant-agricultural waste, algal-fungal-bacterial, and iron-modified biomass-derived adsorbents. The comprehensive discussion of the adsorption mechanism highlights the efficacy of low-cost biomass, particularly from plant, animal, …

Human Capital At Home: Evidence From A Randomized Evaluation In The Philippines, Noam Angrist, Sarah Kabay, Dean S. Karlan, Lincoln Lau, Kevin M. Wong

Education Division Scholarship

Children spend most of their time at home in their early years, yet efforts to promote human capital at home in many low- and middle-income settings remain limited. We conduct a randomized controlled trial to evaluate an intervention which encourages parents and caregivers to foster human capital accumulation among their children between ages 3 and 5, with a focus on math and phonics skills. Children gain 0.52 and 0.51 standard deviations relative to the control group on math and phonics tests, respectively (p<0.001). A year later effects persist, but math gains dissipate to 0.15 (p=0.06) and phonics to 0.13 (p=0.12). Effects appear to be mediated largely through instructional support by parents and not other parent investment mechanisms, such as more positive parent-child interactions or additional time spent on education at home beyond the intervention. Our results show that parents can be effective conduits of educational instruction even in low-resource settings.

Q-Rational Functions And Interpolation With Complete Nevanlinna–Pick Kernels, Daniel Alpay, Paula Cerejeiras, Uwe Kaehler, Baruch Schneider

Mathematics, Physics, and Computer Science Faculty Articles and Research

In this paper we introduce the concept of matrix-valued q-rational functions. In comparison to the classical case, we give different characterizations with principal emphasis on realizations and discuss algebraic manipulations. We also study the concept of Schur multipliers and complete Nevanlinna–Pick kernels in the context of q-deformed reproducing kernel Hilbert spaces and provide first applications in terms of an interpolation problem using Schur multipliers and complete Nevanlinna–Pick kernels.

Alphafold2 Modeling And Molecular Dynamics Simulations Of The Conformational Ensembles For The Sars-Cov-2 Spike Omicron Jn.1, Kp.2 And Kp.3 Variants: Mutational Profiling Of Binding Energetics Reveals Epistatic Drivers Of The Ace2 Affinity And Escape Hotspots Of Antibody Resistance, Nishank Raisinghani, Mohammed Alshahrani, Grace Gupta, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

AlphaFold2-based atomistic predictions of structures and conformational ensembles of the SARS-CoV-2 spike complexes with the host receptor ACE2 for the most dominant Omicron variants JN.1, KP.1, KP.2 and KP.3 to examine the mechanisms underlying the role of convergent evolution hotspots in balancing ACE2 binding and antibody evasion. Using the ensemble-based mutational scanning of the spike protein residues and computations of binding affinities, we identified binding energy hotspots and characterized the molecular basis underlying epistatic couplings between convergent mutational hotspots. The results suggested the existence of epistatic interactions between convergent mutational sites at L455, F456, Q493 positions that protect and restore …

Institutional Data Repositories Are Vital, Jen Darragh, Mikala R. Narlock, Halle Burns, Peter A. Cerda, Wind Cowles, Leslie M. Delserone, Seth Erickson, Joel Herndon, Heidi Imker, Lisa R. Johnston, Sherry Lake, Michael Lenard, Alicia Hofelich Mohr, Jennifer Moore, Jonathan Petters, Brandie Pullen, Shawna Taylor, Briana Wham

UNL Libraries: Faculty Publications

As funding agencies and publishers reiterate research data sharing expectations (1), many higher-education institutions have demonstrated their commitment to the long-term stewardship of research data by connecting researchers to local infrastructure, with dedicated staffing, that eases the burden of data sharing. Institutional repositories are an example of this investment (2). They provide support for researchers in sharing data that might otherwise be lost: data without a disciplinary repository, data from projects with limited funding, or data that are too large to sustainably store elsewhere. The staffing and technical infrastructure provided by institutional repositories ensures responsible access to information while considering …

Review Of Data Bias In Healthcare Applications, Atharva Prakash Parate, Aditya Ajay Iyer, Kanav Gupta, Harsh Porwal, P. C. Kishoreraja, R. Sivakumar, Rahul Soangra

Physical Therapy Faculty Articles and Research

In the area of medical artificial intelligence (AI), data bias is a major difficulty that affects several phases of data collection, processing, and model building. The many forms of data bias that are common in AI in healthcare are thoroughly examined in this review study, encompassing biases related to socioeconomic status, race, and ethnicity as well as biases in machine learning models and datasets. We examine how data bias affects the provision of healthcare, emphasizing how it might worsen health inequalities and jeopardize the accuracy of AI-driven clinical tools. We address methods for reducing data bias in AI and focus …

Exploring Artificial Intelligence: A Collaborative Small Group Analysis And Application, Ellamarie Powell

AI Assignment Library

In this small group project, students will collaborate to explore the principles and applications of Artificial Intelligence (AI). Each group will research, analyze, and present on a specific AI topic, highlighting its real-world implications and ethical considerations. The project involves team members contributing to various roles, including research, technical analysis, and presentation. The final deliverable will be a video presentation integrating individual contributions, showcasing a comprehensive understanding of AI and its impact on society. This assignment fosters teamwork, critical thinking, and effective communication skills.

Groundwater Model Technical Memorandum, Stantec Consulting Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Response To Agency Comments On The Butte Priority Soils Operable Unit (Bpsou) Draft Blacktail Creek Groundwater Hydraulic Control System Pre-Design Investigation Evaluation Report Prepared For Atlantic Richfield Company By Stantec Consulting Services, Inc. Dated September 12, 2024 Date Of Comments: February 1, 2024, Stantec Consulting Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Blacktail Creek (Btc) Pumping Test Interpretation Technical Memorandum, Stantec Consulting Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Neurosymbolic Cognitive Methods For Enhancing Foundation Model-Based Reasoning, Kaushik Roy, Siyu Wu, Alessandro Oltramari

Faculty Publications

Foundation models have emerged as powerful tools, exhibiting extraordinary performance across various tasks, such as language processing, visual recognition, code generation, and human-centered engagement. However, recent studies have highlighted their limitations when grounded, abstract, and generalized reasoning capabilities are required. Complex tasks often involve multiple hierarchical reasoning steps, which are typical features of human thinking processes. In fact, in this chapter we claim that cognitively-inspired computational models, such as the so-called Common Model of Cognition, are key to enable complex reasoning within foundation model-based artificial intelligence (AI) systems. We investigate neurosymbolic approaches for mapping AI system components to those of …

Assessing The Adequacy Of A Prediction Model, Abhaya Indrayan, Sakshi Mishra Ms

COBRA Preprint Series

No abstract provided.

Could A Carbon Tax Improve Utah's Air Quality And Mitigate Climate Change?, Rose Cameron, Kendall Becker, Alexi Elizabeth Lamm, Scott Hotaling

All Current Publications

Urban areas in Utah have suffered from poor air quality since Euro-American settlement in 1847. Because today’s harmful air pollutants and carbon dioxide—the most significant driver of contemporary climate change—are often emitted together, a carbon tax could improve air quality while also mitigating climate change. Here, we explore how Sweden has successfully implemented a carbon tax and consider the viability of a similar approach for Utah. While a carbon tax would pressure Utah’s transportation and energy sectors to reduce reliance on fossil fuels, revenue from the tax could finance renewable energy infrastructure to support companies transitioning away from fossil fuels …

Building Community Resillience To Extreme Heat: Lessons Learned From Spokane, Wa Community Conversations, Anna Reed, Brian G. Henning, Marina Cortes Espinosa, Hannah Mckinley, Tania Busch Isaksen

Climate, Water, and the Environment Research

Heat is the primary cause of weather-related mortality in the United States. The 2021 Northwest Heat Dome highlighted this susceptibility. In Washington State, 159 excess deaths were attributed to the 7-day period of unprecedented extreme heat between June 26th and July 2nd. This impact was felt even in some of the more acclimatized parts of the state, like Spokane County, where 19 heat-related deaths were reported. As climate change increases the frequency, duration, and intensity of extreme heat events, creating and sustaining heat-resilient communities has become an urgent public health priority. On 6 June 2023, Gonzaga University, in partnership with …

Malware Classification Through Abstract Syntax Trees And L-Moments, Anthony J. Rose [*], Christine M. Schubert Kabban, Scott R. Graham, Wayne C. Henry, Christopher M. Rondeau

Faculty Publications

The ongoing evolution of malware presents a formidable challenge to cybersecurity: identifying unknown threats. Traditional detection methods, such as signatures and various forms of static analysis, inherently lag behind these evolving threats. This research introduces a novel approach to malware detection by leveraging the robust statistical capabilities of L-moments and the structural insights provided by Abstract Syntax Trees (ASTs) and applying them to PowerShell. L-moments, recognized for their resilience to outliers and adaptability to diverse distributional shapes, are extracted from network analysis measures like degree centrality, betweenness centrality, and closeness centrality of ASTs. These measures provide a detailed structural representation …

Interoperability In Deep Learning: A User Survey And Failure Analysis Of Onnx Model Converters, Purvish Jajal, Wenxin Jiang, Arav Tewari, Erik Kocinare, Joseph Woo, Anusha Sarraf, Yung-Hsiang Lu, George K. Thiruvathukal, James C. Davis

Computer Science: Faculty Publications and Other Works

Software engineers develop, fine-tune, and deploy deep learning (DL) models using a variety of development frameworks and runtime environments. DL model converters move models between frameworks and to runtime environments. Conversion errors compromise model quality and disrupt deployment. However, the failure characteristics of DL model converters are unknown, adding risk when using DL interoperability technologies.
This paper analyzes failures in DL model converters. We survey software engineers about DL interoperability tools, use cases, and pain points (N=92). Then, we characterize failures in model converters associated with the main interoperability tool, ONNX (N=200 issues in PyTorch and TensorFlow). Finally, we formulate …