Digital Commons Network^™

Investigating The Impact Of An Elaborative Conversation Strategies (Ecs) Intervention On Parent-Child Interactions And Preschool Children's Science Learning Using A Single-Case Experimental Design, Yao Yao

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

The primary purpose of this study was to examine the efficacy of the elaborative conversation strategies (ECS) intervention in improving the quality of parents' science talk during everyday home learning interactions using a single-case experimental design. Specifically, the study explored whether training parents in ECS could increase their use of these high-quality conversation strategies and enhance the quality of their science talk when engaging their children in science activities. The study also investigated how the ECS intervention impacted children's science knowledge, skills, and interest, as well as parents' attitudes toward supporting their children's science learning.

Participants included five typically developing …

Variable Selection In Distance Metric Learning And Triplet Constraints For Deep Learning Based Ordinal Classification, James D. Clothier

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

The purpose of this research is to augment linear and kernelized ordinal distance metric learning (L/KODML) techniques with a proposed variable selection methodology that integrates the Sequential Multi-Response Feature Selection (SMuRFS) algorithm. Additionally, we aim to embed ordinal triplet constraints into a deep learning architecture, and to propose a general framework for deep learning-based ordinal classification. A variety of simulation studies and real data experiments were conducted to evaluate the various methodologies. For the distance metric learning and variable selection, results showed that the integration of SMuRFS performed effective variable selection and improved prediction accuracy. For the triplet constraints, incorporating …

Performance Of Acoustic Telemetry And Space Use Of Pallid Sturgeon In The Lower Platte River, Nebraska, Christopher F. Pullano

School of Natural Resources: Dissertations, Theses, and Student Research

Pallid Sturgeon (Scaphirhynchus albus) are centenarian, potamodromous, rheophiles that historically occupied the Missouri River and Mississippi River basins. Listed on the U.S. Endangered Species Act in 1990, population declines are attributed to habitat fragmentation and degradation, as well as overharvest, and hybridization. A knowledge gap exists regarding the extent to which tributaries facilitate key life stages for Pallid Sturgeon. This study evaluated the capacity of acoustic telemetry to monitor the movements of Pallid Sturgeon in a shallow, braided tributary to the Missouri River. The specific objectives were to (1) evaluate the environmental variables influencing the performance of acoustic …

Occupancy Of Fish Species Of Greatest Conservation Need And Temporal Patterns Of Assemblage Occurrence In Sandhills Ecoregion, Nebraska Streams, Braxton M. Newkirk

School of Natural Resources: Dissertations, Theses, and Student Research

Fish species’ occurrence is influenced by environmental conditions across multiple spatial and temporal scales. Alteration to stream systems has the potential to affect occurrence patterns of fish species. Stream systems of the North American Great Plains face threats originating from climate change and anthropogenic activities resulting in negative implications for many fish species. The Sandhills Ecoregion, Nebraska is considered one of the largest intact grasslands in North America, and thus may experience fewer threats than surrounding areas. Stream systems of the Sandhills Ecoregion are home to 12 fish species of greatest conservation need (SGCN). Little context exists regarding the environmental …

Spatial Ecology Of American Bison On Vermejo Park Ranch, New Mexico, Kate Ann Asmus

School of Natural Resources: Dissertations, Theses, and Student Research

American bison (Bison bison) are a keystone species that impact grassland productivity, plant diversity, and landscape heterogeneity. We used GPS telemetry and a replicate herbivore grazing experiment on Vermejo Park Ranch in northeastern New Mexico to advance understanding of bison spatial ecology and grazing dynamics. First, we evaluated the potentially disparate influences of forage productivity and site fidelity on the size and selection of home ranges of bison. Home range size was strongly negatively influenced by forage productivity. However, home range selection was influenced more strongly by site fidelity than spatial variation in forage, as bison selected home …

Regulating Robo-Advisors In An Age Of Generative Artificial Intelligence, Daniel Schwarcz, Tom Baker

Law & Economics Working Papers

New generative Artificial Intelligence (AI) tools can increasingly engage in personalized, sustained and natural conversations with users. This technology has the capacity to reshape the financial services industry, making customized expert financial advice broadly available to consumers. However, AI’s ability to convincingly mimic human financial advisors also creates significant risks of large-scale financial misconduct. Which of these possibilities becomes reality will depend largely on the legal and regulatory rules governing “robo-advisors” that supply fully automated financial advice to consumers. This Article consequently critically examines this evolving regulatory landscape, arguing that current U.S. rules fail to adequately limit the risk that …

A Review Of The Philosophy Of Flood Risk Communication And Education: New Insights Informed By Critical Complexity, Timothy Lutz

Earth & Space Sciences Faculty Publications

The approach to flooding in the U.S. is strongly influenced by research and practices that place the responsibility for managing risk on scientific and technical experts. But there is a recognition that the public generally needs to be more engaged and responsible. There is currently a disconnect between the objective knowledge of experts and the more subjective ways in which people experience floods and flood risk. To bridge this gap, I start with a review of the philosophy that informs government policies and the societal worldview. While societal leaders have maintained a mechanistic vision of a world that is ultimately …

Global Imaging Of The Magnetoshpere: An In-Depth Analysis Of The Magnetosphere-Ionosphere Coupling, Mayowa Adewuyi

Doctoral Dissertations

Magnetosphere – Ionosphere (MI) coupling plays an important role in Earth’s overall space environment. MI coupling influences ion dynamics in the plasma sheet, the evolution of ring current ions, and field aligned currents during storm time events. The coupling of these two regions makes it important to understand both as a whole, rather than individually. Understanding the importance of the magnetosphere-Ionospheric system and how the coupling affects earth’s space environment is made difficult due to the reliance on localized in-situ measurements and the spacecraft being at the right location at the right time.

The Two Wide-Angle Imaging Neutral-Atom Spectrometers (TWINS), …

A Physics-Based Material Model Incorporating Strain Gradient Plasticity Formulations In A Mean-Field Framework To Predict Spatial Fluctuation Of Material Properties, Zhangxi Feng

Doctoral Dissertations

With the market expansions and increasing number of scientific explorations, the demands for manufacturing metallic parts to satisfy specific requirements continue to increase. A slight improvement in the manufacturing process can result in great cost savings in time and energy. The research and development time to identify these areas of improvements can be greatly reduced using numerical models. One such model is the mean-field elastoplastic self-consistent (EPSC) framework studied extensively and advanced in this dissertation. In the past two decades, many material behavior formulations were implemented in EPSC, and the model has shown versatility and accuracy in predicting a wide …

“Why You Understand The Why”: A Qualitative Characterization Of The Metacognitive Competence Of Introductory Stem Peer Coaches, Jonathan Kustina

Doctoral Dissertations

The processes of metacognition are vital for students’ development of critical thinking skills and productive learning mindsets, yet many students never receive the direct instruction that would allow them to formally develop their metacognition. As instructors are typically not providing this instruction, there may be an alternate source to facilitate the learning of metacognition to students. This research study will investigate the viability of having peer coaches, undergraduate peer educators who help facilitate learning and problem solving in introductory STEM courses, facilitate metacognition to their students. Specifically, this research study aims to understand and characterize the metacognitive competence of the …

Navigating The Complexities Of Time: Innovative Approaches For Accurate And Resilient Time Series Modeling, Sepideh Koohfar

Doctoral Dissertations

The application of time series data has a rich history, and its relevance continues to expand due to recent advancements in artificial intelligence (AI) and autonomous systems. These technological developments have driven the collection and utilization of time series data to new heights, propelling its significance to unparalleled levels. Deriving insights from time series data has become indispensable for effective decision-making in various domains, as it enables the identification of patterns, trends, and fluctuations that inform strategic actions and optimize outcomes. The core challenge lies in developing precise and reliable predictive models that effectively leverage the input data for accurate …

Artificial Intelligence Driven Magnetic Materials Discovery: From Data Extraction To Property Prediction, Yibo Zhang

Doctoral Dissertations

This dissertation presents an integrated artificial intelligence framework aimed at accelerating magnetic materials discovery by addressing key challenges in materials science: improving experimental characterization techniques, efficiently extracting structured data from scientific literature, developing comprehensive materials databases, and creating reliable predictive models.

We first develop a UNet-enhanced vector field electron tomography (VFET) approach for three-dimensional magnetic structure reconstruction. This method addresses the missing wedge problem in experimental data collection while maintaining computational efficiency, providing reliable experimental data crucial for materials research.

We introduce GPTArticleExtractor, a novel workflow leveraging large language models to automatically extract key information from scientific literature. Applied to …

Improving Understanding Of Fluvial Reach-Scale Solute Transport And Uptake Using A Particle Tracking Model And Random Forest Machine Learning Model, Jiaying Liu

Doctoral Dissertations

In addition to transporting flowing water, river systems carry dissolved nutrients and pollutants, which can experience chemical reactions, biological uptake, or retention in surface or hyporheic zones. This dissertation enhances our understanding of solute transport and uptake in river reaches through three main components. The first component involves the development of a novel curve-fitting model designed to capture the characteristics of pulse-release breakthrough curves (BTCs) from tracer studies with conservative solutes. The model, which was found to be applicable to a wide range of field studies, enables the rapid comparison of transport parameters among diverse field studies obtained under a …

Deformation-Induced Bonding Of Glassy Polymeric Films And The Diluent Effect, Ajay Vallabh

Doctoral Dissertations

Bonding between two polymer films can be achieved through various heat- or chemical-based methods. Recently, our group has discovered the novel phenomenon of deformation-induced bonding (DIB) for solid-state glassy polymers in time on the scale of a fraction of a second at ambient temperatures substantially below the bulk glass transition temperatures (T_g). DIB is achieved by bulk plastic compression, which triggers the molecular mobility of polymer chains to cause interpenetration across the interface even in a glassy regime.

First, we performed a series of experiments to evaluate solid-state, deformation-induced bonding in organic polymer blends composed of films of hydroxypropyl methylcellulose …

Counting Catalan: An Experimental Evaluation Of The Mixing Time For The Triangulation Markov Chain, Roy Gotlieb

Master's Theses

Monte Carlo Markov chains (MCMCs) are used in many areas as a way to model a system’s behavior. By running a probabilistic simulation on a system’s state space, we can estimate properties of the system that could be untenable to directly compute. It is of interest to determine how quickly a Markov chain mixes\textemdash that is, settles into its stationary distribution. One such chain is induced by taking a binary search tree and performing a rotation or flip on one of its edges. We know that this chain eventually settles into the uniform distribution, but the time complexity bounds on …

Enhancing Access To Inquiry-Based Art Education In Title I Schools: Addressing Inequities And Promoting Student Driven Learning, Rebecca Stone-Danahy

All Dissertations

This study investigates the enabling conditions that support inquiry-based art education programs in Title I schools, addressing a literature gap regarding effective practices for high school art educators working with economically disadvantaged students. Inquiry-based art education empowers students by fostering creativity and problem-solving skills and encouraging self-expression. These attributes are essential for equipping students with the tools they need for success in college and their future careers. While existing research has underscored the benefits of the arts for marginalized students, the literature lacks guidance on how to support an inquiry-based art education program in a Title I school.

The study …

Magnetic Resonance Imaging Trackable Pathology-Triggered Polymersomes Toward Enzyme Replacement Theranostics For Gm1 Gangliosidosis, Dorian Foster

All Dissertations

This dissertation investigates the development and optimization of pathology-responsive polymersomes for simultaneous enhanced magnetic resonance imaging (MRI) and enzyme replacement therapy (ERT) in the treatment of GM1 gangliosidosis (GM1). While therapeutic strategies exist for similar, non-neuropathic lysosomal storage disorders, implementation towards GM1 faces significant challenges due to the blood-brain barrier.

Herein, we developed and characterized a range of low molecular weight hyaluronic acid-b-polylactic acid (HA-PLA) polymersomes (PSs) capable of encapsulating therapeutic enzymes in their hydrophilic core. These polymersomes exhibit pathophysiology-triggered responsiveness, degrading in the presence of acidic conditions, which is characteristic of lysosomes, and enzymes such as hexosaminidase A, which …

Bounds On 𝑎hvp,Lo Using Hölder’S Inequalities And Finite-Energy Qcd Sum Rules, Siyuan Li, T. G. Steele, Jason N.E. Ho, R. Raza, K. Williams, R. T. Kleiv

Faculty Work Comprehensive List

This study establishes bounds on the leading-order (LO) hadronic vacuum polarization (HVP) contribution to the anomalous magnetic moment of the muon (𝑎HVP,LO 𝜇 ,𝑎𝜇 =(𝑔 −2)𝜇∕2) by using Hölder’s inequality and related inequalities in Finite-Energy QCD sum rules. Considering contributions from light quarks (𝑢, 𝑑, 𝑠) up to five-loop order in perturbation theory within the chiral limit, leading-order light-quark mass corrections, next-to-leading order for dimension-four QCD condensates, and leading-order for dimension-six QCD condensates, the study finds QCD lower and upper bounds as (657.0±34.8)×10−10 ≤𝑎HVP,LO 𝜇 ≤(788.4±41.8)×10−10.

The Price Of Precision: Significant Figures And The Student Experience, Ryan Michael Britt

Dissertations

For students pursuing careers in medicine and science, general chemistry is often viewed as a gatekeeper course. Students struggle with general chemistry for a variety of reasons, one of the most common being mathematical difficulties. Compared to other lower division STEM courses, mathematical computation in general chemistry represents a unique challenge for the student due to the special emphasis placed on reporting answers to the proper degree of precision using significant figure (SF) rules. Although these rules feature in nearly every undergraduate chemistry course, their impact on the student experience of learning chemistry has never before been studied.

The purpose …

Evaluation Of Joint Model Imputation Of Multilevel Models, Gabriel Owusu

Dissertations

Missing data is a pervasive challenge in research, particularly in studies involving multilevel models. When left unaddressed or handled poorly, missing data can reduce statistical power and introduce bias, undermining the validity of findings. Although multiple imputation has emerged as a preferred method for addressing missing data due to its ability to provide unbiased and efficient estimates, its application in multilevel modeling remains underexplored. This gap is particularly significant given the hierarchical nature of multilevel data, which requires specialized approaches to imputation. The purpose of the study is to evaluate the effectiveness of joint model imputation in handling missing data …

Costly Signaling Theory, Hui Ying Ivy Koh, Norman P. Li

Research Collection School of Social Sciences

Costly signaling theory is the idea that the high costs incurred by an individual to convey information about underlying qualities keeps this signal reliable, as only individuals who truly possess the quality signaled can afford to send the signal.

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. …

Ali-Agent: Assessing Llms’ Alignment With Human Values Via Agent-Based Evaluation, Jingnan Zheng, Han Wang, Tai D. Nguyen, An Zhang, Jun Sun, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) can elicit unintended and even harmful content when misaligned with human values, posing severe risks to users and society. To mitigate these risks, current evaluation benchmarks predominantly employ expertdesigned contextual scenarios to assess how well LLMs align with human values. However, the labor-intensive nature of these benchmarks limits their test scope, hindering their ability to generalize to the extensive variety of open-world use cases and identify rare but crucial long-tail risks. Additionally, these static tests fail to adapt to the rapid evolution of LLMs, making it hard to evaluate timely alignment issues. To address these challenges, …

A Comprehensive Study On Static Application Security Testing (Sast) Tools For Android, Jingyun Zhu, Kaixuan Li, Sen Chen, Lingling Fan, Junjie Wang, Xiaofei Xie

Research Collection School Of Computing and Information Systems

To identify security vulnerabilities in Android applications, numerous static application security testing (SAST) tools have been proposed. However, it poses significant challenges to assess their overall performance on diverse vulnerability types. The task is non-trivial and poses considerable challenges. Firstly, the absence of a unified evaluation platform for defining and describing tools’ supported vulnerability types, coupled with the lack of normalization for the intricate and varied reports generated by different tools, significantly adds to the complexity. Secondly, there is a scarcity of adequate benchmarks, particularly those derived from real-world scenarios. To address these problems, we are the first to propose …

The Learning Crisis: Three Years After Covid-19, Tomasz Gajderowicz, Maciej Jakubowski, Alec Kennedy, Christian Christrup Kjeldsen, Harry Anthony Patrinos, Rolf Strietholt

Education Reform Faculty and Graduate Students Publications

The COVID-19 pandemic caused widespread disruptions to education, with school closures affecting over one billion children. These closures, aimed at reducing virus transmission, resulted in significant learning losses, particularly in mathematics and science. Using data from TIMSS 2023, which assesses fourth and eighth-grade achievements across 71 education systems, this study analyzes the impact of school closure duration on learning outcomes. Mixed-effect models estimate deviations from pre-pandemic trends, adjusting for demographic factors. Results show a global average decline of 0.11 standard deviations (SD) in student achievement, with longer closures linked to more severe losses. The effects on low performers, girls, and …

Time Series Decomposition Of Land Surface Temperature For Long-Term Trend Forecasting And Impact On Nesting Sea Turtle Habitats In The Arabian Gulf, Sachi Perera, Rommel H. Maneja, Mohamed Allali, Cyril Rakovski, Erik Linstead, Daniele Struppa, Ali Qasem, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Improving land surface temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats’ long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, …

Detecting Fraudulent Medicare Claims With Imbalanced Data Classification, Akbota Aitbayeva

Mathematics Theses, Dissertations, Research and Student Creative Activity

Medicare, a public health insurance program, provides essential coverage to older adults and individuals with certain disabilities but suffers significant financial losses from fraudulent activities such as false claims and upcoding. To combat this, machine learning techniques, including XGBoost and Random Forest, were employed to detect fraudulent behavior in Medicare data provided by Mutual of Omaha. A key challenge in fraud detection is the imbalance in datasets, with legitimate claims vastly outnumbering fraudulent ones. This study addressed the issue using cost-sensitive methods and sampling techniques, including Random Oversampling, SMOTE, and Random Undersampling. A baseline model without any imbalance techniques was …

Characterization Of Sediment-Flow Interactions In Tidally Driven Salt Marsh Environments, Elijah Rousseau

Master's Theses and Capstones

Salt marshes are effective sediment traps and under static sea levels are known to increase in elevation over time. However, current sedimentation rates are insufficiently high to keep up with the estimated sea level rise. Strategies such as the mud motor, which proposes the injection of dredged sediment into incoming flood tides, require a deeper understanding of how tidal flows evolve in complex estuarine systems and how suspended sediment is dispersed and deposited in vegetated environments. Two field deployments occurred in the summer of 2023 to study the mechanisms of sediment transport and accretion along a horizontal marsh transect in …

Building Bridges: A Cohort Model For Content-Based Language Learning And Multilingual Learner Success, Marry E. Royers

Teacher Education Theses, Dissertations, and Student Creative Activity

Building Bridges: Cohort Model Preliminary Steps

1: Identify the need within the school.

2: Cohort Selection

3.Attitudes and experiences pre-test for both teachers and students

Teachers gather student pre-PD survey data

PLC CYCLE STEP 1 Review student survey data Micro PD part 1 with ML teacher Set goals for the PLC cycle.

Professional Development Cycle

PLC CYCLE STEP 2 Reflection or progress, affirmations, and group troubleshooting. Micro PD part 2 with ML teacher

PLC CYCLE STEP 3 Reflection or progress, affirmations, and group troubleshooting.

Focus of Professional Development:

1.Content-Based Language Learning 2.Asset-based Pedagogy & Culturally Relevant Teaching 3.Implementing Academic Conversations …

Where And How Are Undergraduate Students Developing Dispositions Needed For The Geoscience Workforce?, Kaelyn Lagerwall

Boise State University Theses and Dissertations

Dispositions, such as the desire to learn, work ethic, initiative, and organization, are defined as internal attributes, attitudes, or values that stimulate action. Rather than being intrinsic traits, dispositions can be learned and are considered a dynamic and evolving arrangement of interconnected components. Dispositions are highly desired and valuable to the workforce, playing a critical role in gaining and maintaining employment; they are frequently mentioned in job advertisements for bachelor-level geoscientists. Therefore, it is important to understand where and how geoscience students are developing these dispositions as part of their degree programs. We surveyed faculty in geoscience departments at two …