Physical Sciences and Mathematics Commons^™

Online Control Of Adaptive Large Neighborhood Search Using Deep Reinforcement Learning, Reijnen Reijnen, Yingqian Zhang, Hoong Chuin Lau, Zaharah Bukhsh

Research Collection School Of Computing and Information Systems

The Adaptive Large Neighborhood Search (ALNS) algorithm has shown considerable success in solving combinatorial optimization problems (COPs). Nonetheless, the performance of ALNS relies on the proper configuration of its selection and acceptance parameters, which is known to be a complex and resource-intensive task. To address this, we introduce a Deep Reinforcement Learning (DRL) based approach called DR-ALNS that selects operators, adjusts parameters, and controls the acceptance criterion throughout the search. The proposed method aims to learn, based on the state of the search, to configure ALNS for the next iteration to yield more effective solutions for the given optimization problem. …

Difference Of Convex Functions Programming For Policy Optimization In Reinforcement Learning, Akshat Kumar

Research Collection School Of Computing and Information Systems

We formulate the problem of optimizing an agent's policy within the Markov decision process (MDP) model as a difference-of-convex functions (DC) program. The DC perspective enables optimizing the policy iteratively where each iteration constructs an easier-to-optimize lower bound on the value function using the well known concave-convex procedure. We show that several popular policy gradient based deep RL algorithms (both for discrete and continuous state, action spaces, and stochastic/deterministic policies) such as actor-critic, deterministic policy gradient (DPG), and soft actor critic (SAC) can be derived from the DC perspective. Additionally, the DC formulation enables more sample efficient learning approaches by …

Final Butte Priority Soils Operable Unit Repository Screening Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final Repository Screening Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final 2022 Insufficiently Reclaimed Sites Sampling Bres No. 08 – Belle Of Butte Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

A Little Loud And A Little Alone: A Phenomenology Of Leadership Identity Construction Among Women In Higher Education Technology, Amy Barry

Department of Teaching, Learning, and Teacher Education: Theses and Other Student Research

This qualitative study is an exploration of how women in higher education information technology (IT) positions navigate constructing their leadership identities. This includes the messy, personal, internal identity work that occurs prior to claiming their leadership identities on the public stage, followed by an examination of what the experience of attempting to claim and negotiate a leadership identity is like in the social context of their organizations. This educational and sociological study employs an Interpretative Phenomenological Analysis approach with a series of three interviews per participant that allowed the researcher to deeply explore the personal identity experiences of participants. Findings …

Detection Of Deficiencies And Data Analysis Of Bridge Members With Deep Convolutional Neural Networks, Bennett Jackson

Department of Civil and Environmental Engineering: Dissertations, Theses, and Student Research

Concrete cracks and structural steel corrosion are two of the most common defects in bridges. Quantifying and classifying these defects provide bridge inspectors and engineers with valuable data for assessing deterioration levels. However, the bridge inspection process is typically a subjective, time intensive, and tedious task, as defects can be overlooked or in locations not easily accessible. Previous studies have investigated deep learning-based inspection methods, implementing popular models such as Mask R-CNN and U-Net. The architectures of these models offer certain advantages depending on the required task. This thesis aims to evaluate and compare Mask R-CNN and U-Net regarding their …

Asteroidal Sets And Dominating Targets In Graphs, Oleksiy Al-Saadi

School of Computing: Dissertations, Theses, and Student Research

The focus of this PhD thesis is on various distance and domination properties in graphs. In particular, we prove strong results about the interactions between asteroidal sets and dominating targets. Our results add to or extend a plethora of results on these properties within the literature. We define the class of strict dominating pair graphs and show structural and algorithmic properties of this class. Notably, we prove that such graphs have diameter 3, 4, or contain an asteroidal quadruple. Then, we design an algorithm to to efficiently recognize chordal hereditary dominating pair graphs. We provide new results that describe the …

Eco-Anxiety Effects College Students, Alexa Edmundson

Department of Environmental Studies: Undergraduate Student Theses

A survey was conducted on the UNL student population to evaluate whether college students, 18-25 year olds who are currently attending an accredited post-secondary school, are experiencing eco-anxiety and if eco-anxiety affects their pro-environmental behaviors. Climate change is taking a toll not only physically on us but also mentally. The mental health of people, especially college students, is not often studied in relation to climate change, but if we are to understand how to create change in this issue then we need to be looking at the whole picture. The hypothesis is that if college students are experiencing eco-anxiety, then …

Eco-Anxiety Effects College Students, Alexa Edmundson

Department of Environmental Studies: Undergraduate Student Theses

A survey was conducted on the UNL student population to evaluate whether college students, 18-25 year olds who are currently attending an accredited post-secondary school, are experiencing eco-anxiety and if eco-anxiety affects their pro-environmental behaviors. Climate change is taking a toll not only physically on us but also mentally. The mental health of people, especially college students, is not often studied in relation to climate change, but if we are to understand how to create change in this issue then we need to be looking at the whole picture. The hypothesis is that if college students are experiencing eco-anxiety, then …

Grading Climate Action Plans In The Midwest, Megan Baker

Department of Environmental Studies: Undergraduate Student Theses

As agreed upon by international climate scientists, decarbonization in the American Midwest is critical to keeping the planet below the 1.5C increase in global temperature from pre-industrial levels. However, most previous research that evaluates climate action plans (CAPs) to find their weaknesses and provide policy recommendations neglects this crucial region of the United States. To fill this gap, this paper replicates Deetjen et al.’s (2018) review of CAPs in 29 US cities by applying their CAP rubric to 11 Midwestern cities. The results of both quantitative analysis were used to analyze the differences in plans in the Midwest versus the …

Framing Wildlife Conservation: Exploring Political Bias In States' Wildlife Action Plans, Ciara Nelson

Department of Environmental Studies: Undergraduate Student Theses

Climate change affects regions of the United States differently due to factors such as the country's diverse physical landscapes, political environments, and population distributions. This variation leads to varying impacts on humans, wildlife, and ecosystems, with anthropogenic climate change—acknowledging humans as a significant contributor—being a point of contention, especially among some prominent politicians. The relationship between political party affiliation and climate change acknowledgment is evident, with recent political discourse highlighting differing views on the severity of climate change. This political framing influences how environmental policies are perceived and accepted, with the choice of language and framing playing a crucial role …

Towards Sustainable Development: Civic Architecture As Material Banks, Kathleen O'Gara

Masters in Architecture Program: Theses

This thesis posed the question, “How can buildings be designed to be disassembled and reused to reduce construction and demolition waste and reduce our unsustainable reliance on raw materials?” Designing for disassembly and reuse has the potential to dramatically increase the life of building materials, thus decreasing construction and demolition waste relating to architecture. When designing with deconstruction and reuse in mind, designers must consider the next life of these materials. The thesis was further narrowed to push the adaptability of these materials by designing a structural kit of parts. The structure system is often seen as a stable and …

Learning Multi-Faceted Prototypical User Interests, Nhu Thuat Tran, Hady W. Lauw

Research Collection School Of Computing and Information Systems

We seek to uncover the latent interest units from behavioral data to better learn user preferences under the VAE framework. Existing practices tend to ignore the multiple facets of item characteristics, which may not capture it at appropriate granularity. Moreover, current studies equate the granularity of item space to that of user interests, which we postulate is not ideal as user interests would likely map to a small subset of item space. In addition, the compositionality of user interests has received inadequate attention, preventing the modeling of interactions between explanatory factors driving a user's decision. To resolve this, we propose …

Hjg: An Effective Hierarchical Joint Graph For Anns In Multi-Metric Spaces, Yifan Zhu, Lu Chen, Yunjun Gao, Ruiyao Ma, Baihua Zheng, Jingwen Zhao

Research Collection School Of Computing and Information Systems

Owing to the widespread deployment of smartphones and networked devices, massive amount of data in different types are generated every day, including numeric data, locations, text data, images, etc. Nearest neighbour search in multi-metric spaces has attracted much attention, as it can accommodate any type of data and support search on flexible combinations of multiple metrics. However, most existing methods focus on single metric queries, failing to answer multi-metric queries efficiently due to the complex metric combinations. In this paper, for the first time, we study the approximate nearest neighbour search (ANNS) in multi-metric spaces, and propose HJG, a hierarchical …

Conversion Of Microcrystalline Cellulose To Hexane Using Hydrogenated Metal Oxides At Low Temperature And Pressure, Mubarak Osman

Electronic Theses and Dissertations

Increasing global energy demand and environmental concerns have fueled the exploration of sustainable and efficient methods for renewable fuel production. The conversion of cellulosic waste to hydrocarbon fuels using hydrogenated metal oxides presents a novel and eco-friendly approach to sustainably address energy demand. In this study, hydrogenated metal oxides were used as catalysts in a relatively low-temperature and atmospheric-pressure environment to facilitate the conversion of cellulosic waste into hydrocarbon fuels. Hydrogenated metal oxides have been introduced as potential hydrogen transfer catalysts. The expected result is the selective conversion of cellulose to hexane at relatively low temperatures, which significantly reduces energy …

Interpreting Shift Encoders As State Space Models For Stationary Time Series, Patrick Donkoh

Electronic Theses and Dissertations

Time series analysis is a statistical technique used to analyze sequential data points collected or recorded over time. While traditional models such as autoregressive models and moving average models have performed sufficiently for time series analysis, the advent of artificial neural networks has provided models that have suggested improved performance. In this research, we provide a custom neural network; a shift encoder that can capture the intricate temporal patterns of time series data. We then compare the sparse matrix of the shift encoder to the parameters of the autoregressive model and observe the similarities. We further explore how we can …

Magnetic Properties Of Zgnrs With Nitrogen And Fluorine Adsorbates, A Computational Study, Justin Petit

Electronic Theses and Dissertations

Imposing dimensional restrictions on graphene sheets and adding impurities can give rise to carbon nanostructures with magnetic properties. In this work, zigzag graphene nanoribbons, zGNRs, with nitrogen and fluorine adatoms are investigated for magnetic properties of interest for spin devices. Geometry optimizations were done determining which position along a zGNR electrode that N and F would favorably attach to. Edge positions were determined as the most stable attachment site. M-cell zGNR electrodes (M = 1-3) edge-functionalized by N and F adatoms were investigated with respect to their band structures and spin densities in antiferromagnetic and ferromagnetic, AFM and FM, configurations. …

Traffic Analysis Of Cities In San Bernardino County, Sai Kalyan Ayyagari

Electronic Theses, Projects, and Dissertations

This research offers an in-depth analysis of vehicular traffic within San Bernardino County, California, aiming to spotlight congestion areas and suggest improvements for more efficient and sustainable transportation. Leveraging 2021 data from StreetLight Data, traffic patterns in 15 key cities were examined based on their population sizes, covering various vehicle types to dissect dynamics and flow. The methodology focused on analyzing trip purposes and metrics to calculate Vehicle Miles Traveled (VMT) and its influence on congestion and environmental factors.

Findings indicate considerable disparities in traffic volume, purposes, and timings across different urban areas, with population density and intercity connections significantly …

Truck Traffic Analysis In The Inland Empire, Bhavik Khatri

Electronic Theses, Projects, and Dissertations

This study undertakes a meticulous examination of truck traffic within the Inland Empire, focusing on the distribution and dynamics of medium and heavy-duty vehicles, to advocate for the region's transition to electric trucks. Utilizing advanced spatial analysis and data from Streetlight Data, it segments the region into six subregions, revealing distinct traffic patterns and environmental impacts. Notably, the research uncovers that the North Center and West zones, integral to the logistics and warehousing sectors, exhibit the highest traffic volumes, significantly influencing air quality and infrastructure.

Quantitative results from 2021 illustrate a pronounced disparity in truck activity: medium-weight vehicles accounted for …

On Cheeger Constants Of Knots, Robert Lattimer

Electronic Theses, Projects, and Dissertations

In this thesis, we will look at finding bounds for the Cheeger constant of links. We will do this by analyzing an infinite family of links call two-bridge fully augmented links. In order to find a bound on the Cheeger constant, we will look for the Cheeger constant of the link’s crushtacean. We will use that Cheeger constant to give us insight on a good cut for the link itself, and use that cut to obtain a bound. This method gives us a constructive way to find an upper bound on the Cheeger constant of a two-bridge fully augmented link. …

Crash Detecting System Using Deep Learning, Yogesh Reddy Muddam

Electronic Theses, Projects, and Dissertations

Accidents pose a significant risk to both individual and property safety, requiring effective detection and response systems. This work introduces an accident detection system using a convolutional neural network (CNN), which provides an impressive accuracy of 86.40%. Trained on diverse data sets of images and videos from various online sources, the model exhibits complex accident detection and classification and is known for its prowess in image classification and visualization.

CNN ensures better accident detection in various scenarios and road conditions. This example shows its adaptability to a real-world accident scenario and enhances its effectiveness in detecting early events. A key …

Pathology And Osteological Observations Of Early Pliocene Rhinoceros, Teleoceras Aepysoma (Perissodactyla, Rhinocerotidae) From Gray Fossil Site, Tennessee, Thomas Scaife

Electronic Theses and Dissertations

Rhinoceroses were an important part of North America’s Paleogene and Neogene ecosystems, with Teleoceras aepysoma being one of the last representatives of this family. Specimens of T. aepysoma from the Gray Fossil Site (GFS) possess distinct/peculiar pathologies: including a pair of fused ribs and ankylosed phalanges. A qualitative description of the pathologies in the GFS T. aepysoma, including new material, was conducted to accurately identify pathologies and make interpretations about the life history of the GFS rhinos. Analysis suggests that rheumatoid arthritis is common in the lower limb bones of GFS rhinos. Additionally, the rib and toe pathologies are more …

Developing A Suitability Model And Feasibility Report For The Tennessee Mesonet, Tristan Holmes

Electronic Theses and Dissertations

Mesoscale networks, also known as Mesonets, are advanced environmental monitoring systems that consist of multiple high-quality weather stations that collect meteorological data at frequent intervals (5 minutes or less). Tennessee currently lacks a Mesonet system, and this study identifies suitable and feasible locations for stations in all 95 counties. Layers needed to develop the suitability model included land cover, road centerlines, elevation, flood zones, building footprints, and hydrologic soil types. Geospatial analysis techniques were used to process and combine these layers to identify suitable areas. Once suitable areas were determined, a feasibility assessment that examined land ownership, accessibility, and cell …

Characterizing Modulators Of Protease-Activated Receptors With A Calcium Mobilization Assay Using A Plate Reader, Jacob T. Derousse, Chris Dockendorff

Chemistry Faculty Research and Publications

Changes in calcium concentration in cells are rapidly monitored in a high-throughput fashion with the use of intracellular, fluorescent, calcium-binding dyes and imaging instruments that can measure fluorescent emissions from up to 1,536 wells simultaneously. However, these instruments are much more expensive and can be challenging to maintain relative to widely available plate readers that scan wells individually. Described here is an optimized plate reader assay for use with an endothelial cell line (EA.hy926) to measure the protease-activated receptor (PAR)-driven activation of G_αq signaling and subsequent calcium mobilization using the calcium-binding dye Fluo-4. This assay has been used to …

Many Direct-To-Consumer Canine Genetic Tests Can Identify The Breed Of Purebred Dogs, Halie M. Rando, Kiley Graim, Greg Hampikian, Casey S. Greene

Computer Science: Faculty Publications

OBJECTIVE To compare pedigree documentation and genetic test results to evaluate whether user-provided photographs influence the breed ancestry predictions of direct-to-consumer (DTC) genetic tests for dogs.

ANIMALS 12 registered purebred pet dogs representing 12 different breeds. METHODS Each dog owner submitted 6 buccal swabs, 1 to each of 6 DTC genetic testing companies. Experimenters registered each sample per manufacturer instructions. For half of the dogs, the registration included a photograph of the DNA donor. For the other half of the dogs, photographs were swapped between dogs. DNA analysis and breed ancestry prediction were conducted by each company. The effect of …

Advancement Of Iterative Optimization Technology Algorithms Toward Calibration-Free Process Analytical Technology Applications, Adam Rish

Electronic Theses and Dissertations

The expansion of spectroscopic process analytical technology (PAT) tools within the pharmaceutical industry has the potential to elevate the current state-of-the-art of pharmaceutical manufacturing by offering opportunities for reduced quality testing times, enhanced process control, and greater production flexibility. Spectroscopic PAT tools are dependent on multivariate models to extract the relevant information from the spectral outputs. However, there is a substantial calibration burden for developing and maintaining these multivariate models that discourages the application of PAT, despite the encouragement from regulators. This has led to an interest in calibration-free methods such as iterative optimization technology (IOT) for spectroscopic PAT that …

Spatiotemporal Negative Inventory Outlier Decomposition For Supply Chain Applications In Consumer-Packaged Goods (Cpg), Hayden Mcdonald

Data Science Undergraduate Honors Theses

Coca-Cola is a popular soft drink brand with sales occurring in every Walmart store across the world, which generates large quantities of data and requires a robust supply chain system. However, the company does not currently have a sophisticated, automated, and/or prescriptive system for detecting where, when, and why inventory outages occur and applying preventative measures to avoid loss of revenue from the absence of inventory on store shelves. This thesis proposes and applies a novel, prescriptive system for this purpose. An inventory outage can be seen as a ‘negative’ statistical outlier in a time series of inventory for an …

The Importance Of Data Preparation In A Data Science Problem, Sophia Beard

Data Science Undergraduate Honors Theses

This study is going to be based on an inventory outlier automation data science problem that is being solved to identify and prescribe inventory level outliers to help keep shelves stocked in terms of beverages. The objective of this paper will address why it is so important to understand the data that is involved in a particular data science problem and how planning ahead ensures a successful outcome in the data science world. In this data science project, Spatiotemporal Outlier Analysis for Inventory Intervention Automation, it was crucial for the team to understand, research, and visualize the data we were …

Employing Natural Language Processing To Link Customer Survey Feedback With Net Promoter Scores, Gerardo Moreno

Data Science Undergraduate Honors Theses

This project leverages Natural Language Processing (NLP) to analyze customer feedback from Sam’s Club, aiming to pinpoint key factors influencing Net Promoter Score (NPS). Using sentiment analysis, bigram, and trigram techniques, the project analyses textual data to identify underlying themes and patterns that affect customer satisfaction. These analyses reveal actionable insights into customer preferences and pain points, facilitating a deeper understanding of what drives customer satisfaction in retail environments. By correlating these findings with NPS, this paper details strategies to enhance customer experiences at Sam’s Club, ultimately aiming to improve both satisfaction levels and NPS.