Physical Sciences and Mathematics Commons^™

A Data-Driven Discovery System For Studying Extracellular Microrna Sorting And Rna-Protein Interactions, Sasan Azizian

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

Interactions between microRNAs (miRNAs) and RNA-binding proteins (RBPs) are pivotal in miRNA-mediated sorting, yet the molecular mechanisms underlying these interactions remain largely understudied. Few miRNA-binding proteins have been verified, typically requiring extensive laboratory work. This study introduces DeepMiRBP, a novel hybrid deep learning model designed to predict microRNA-binding proteins. The model integrates Bidirectional Long Short-Term Memory (Bi-LSTM) networks with attention mechanisms, transfer learning, and cosine similarity to offer a robust computational approach for inferring miRNA-protein interactions.

DeepMiRBP is implemented through two distinct architectures. The first architecture employs a Y-shaped model that uses Bi-LSTM networks and transfer learning to extract contextual …

On Neumann Boundary Conditions For Nonlocal Models With Finite Horizon, Scott Alex Hootman-Ng

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

Nonlocal models are have recently seen an explosive interest and development in the context of fracture mechanics, diffusion, image processing, population dynamics due to their ability to approximate differential-like operators with integral operators for inherently discontinuous solutions. Much of the work in the field focuses on how concepts from partial differential equations (PDEs) can be extended to the nonlocal domain. Boundary conditions for PDEs are crucial components for applications to physical problems, prescribing data on the domain boundary to capture the behavior of physical phenomena accurately with the underlying model. In this thesis we specifically examine a Neumann-type boundary condition …

Applications Of Artificial Intelligence On Drought Impact Monitoring And Assessment, Beichen Zhang

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

Drought, a prevalent and consequential natural disaster, poses widespread, indirect challenges across environmental and societal dimensions. Despite considerable focus on monitoring meteorological and hydrological drought and studying their characteristics, there is a gap in assessing its multifaceted impacts, especially on societal sectors. The dissertation comprises three research essays utilizing artificial intelligence to quantitatively study multi-dimensional drought impacts. The first essay leveraged deep learning and natural language processing to predict multi-dimensional drought impacts from textual datasets, including social media, news media, and citizen scientist reports. The findings demonstrate superior performance over traditional methods and unveil the spatial and temporal heterogeneity of …

Search For Physics Beyond The Standard Model In Top Quark Production With Additional Leptons In The Context Of Effective Field Theory, Furong Yan

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

The dissertation presents a search for new physics impacting top quark productions within the framework of effective field theory (EFT). Potential new physics effects are parameterized in terms of 26 dimension-six EFT operators into the event yields of six distinct top production processes in the detector level. The analysis targets multilepton final states consisting of two leptons of the same charge, three leptons and four leptons. The events are further categorized and binned in terms of kinematic distributions in order to gain sensitivity to the new physics effects. A likelihood function is formulated based on the predicted distribution in each …

Semigroup Well-Posedness And Finite Element Analysis Of A Biot-Stokes Interactive System, Sara Mcknight

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

The coupling of a porous medium modeled by the Biot equations and a fluid has many biological applications. There are numerous ways by which to model the fluid and to couple the porous medium with the fluid. This particular model couples the Biot equations to Stokes flow along the boundary, through the Beavers-Joseph-Saffman conditions. We address semigroup well-posedness of the system via an inf-sup approach, which along the way requires consideration of a related but uncoupled static Biot system. We also present the results of finite element analysis on both the uncoupled Biot system and the coupled system.

Advisor: Sara …

Perturbations Of Representations Of Cartan Inclusions, Catherine Zimmitti

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

A free semigroup algebra is the unital, weak operator topology closed algebra generated by a collection of Cuntz-Toeplitz isometries in B(H). Ken Davidson and David Pitts asked in [9] if a self-adjoint free semigroup algebra exists; Charles Read answered this question in [28] by constructing such an example, which Ken Davidson later simplified in [8]. The construction takes a standard representation of O₂ and multiplies it by a unitary operator in the diagonal MASA of the representation. This results in a new "perturbed" representation of O₂ generating a self-adjoint free semigroup algebra.

In this thesis, …

Further Developing Drought Early Warning Information Systems Using Mixed-Methods And Multiple Streams Of Data, Caily Claire Schwartz

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

Drought is a complex phenomenon with varying degrees of impacts and monitoring methods. No drought is alike, creating a challenge for both water managers and communities. No area is immune to a drought. Due to the cyclical nature of drought events, clear information to those impacted is necessary to reduce risk and move towards proactive responses, as opposed to reactive responses. To better provide communication and mitigation tools, Drought Early Warning Information Systems (DEWIS) have been developed in various regions and contexts. To improve early warning, an understanding of the end user’s perceptions of risk, and the applicability of data …

Integrating Water And Nitrogen Management For Sustainable Agriculture: Optimizing Resource Use Efficiency And Maximizing Crop Productivity, Jiaming Duan

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

Advisors: Derek Heeren and Daran Rudnick Maize, accounting for over 95% of national grain production in the United States, is highly sensitive to water and nitrogen (N) inputs. Conventional agricultural practices often lead to excessive application, causing groundwater contamination through nitrate leaching. Therefore, there is a demand for integrating water and nitrogen management with innovative scheduling methods for sustainable agricultural development.

This dissertation first reviewed two decades of U.S.-based research, highlighting the optimal management of water and N to enhance yield, water use efficiency (WUE), and nitrogen use efficiency (NUE). Findings indicate that maintaining optimal levels of N and water …

Long Term Ultrasonic Monitoring And Machine Learning Investigation Of Micro-Crack Damaged Concrete, Yalei Tang

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

The thermal modulation method is a recently developed nonlinear ultrasonic technique for evaluating material damage. This method utilizes thermal strain changes resulting from temperature variations to excite the nonlinear behavior of materials and modulate high-frequency ultrasonic waves within them. Its working principle suggests significant potential for application in large-scale concrete structures and in-situ monitoring of real structures. Despite numerous laboratory demonstrations of its effectiveness, several gaps remain before it can be applied to in-service large concrete structures.

This study investigates the potential of the thermal modulation technique for evaluating concrete structures in ambient conditions, addressing key uncertainties for practical implementation. …

Development Of Feature Extraction Models To Improve Image Analysis Applications In Cancer, Yu Shi

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

Cancer poses a significant global health challenge. With an estimated 20 million new cases diagnosed worldwide in 2022 and 9.7 million fatalities attributable to the disease, the economic burden of cancer is immense. It impacts healthcare systems and imposes substantial costs for its care on patients and their families. Despite advancements in early detection, prevention, and treatment that have reduced overall cancer mortality rates, the growing prevalence of cancer, particularly among younger individuals, remains a pressing issue.

Recent advancements in medical imaging technology have progressed significantly with the help of emerging computer vision and artificial intelligence (AI) technology. Despite these …

Optimizing Scalability For Formal Analysis With Evolutionary Algorithm, Jianghao Wang

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

Predominantly employed to tackle hardware validation challenges in the early years, formal methods have since expanded to software engineering, introducing a significant level of rigor and precision to software analysis. Its use of mathematical notations and logical reasoning allows for abstract modeling of programs, enabling researchers and engineers to perform a multitude of analysis tasks to verify system dependability and rigorously prove the correctness of system properties. Despite the availability of many automated analysis tools including those considered lightweight, the practical adoption of formal methods in software development has been limited due to scalability concerns, especially when applied to large …

Virtual Unknotting Numbers For Families Of Virtual Torus Knots, Kaitlin R. Tademy

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

A virtual torus knot T(p,q,VC) sits in the intersection of the well-understood torus knot and the not-so-well-understood virtual knot, making it an intriguing object to study.

The unknotting number of a classical knot K is defined unambiguously. However, "the" unknotting number when K is a virtual knot is not as clear to define, since virtual knots have both classical and virtual crossings. We will define virtual unknotting number vu(K) as the minimum number of (classical) crossing changes required to unknot K. Under this definition of virtual unknotting, not all …

Transformer-Based Deep Learning Prediction Of 10-Degree Humphrey Visual Field Tests From 24-Degree Data, Min Shi, Anagha Lokhande, Yu Tian, Yan Luo, Mohammad Eslami, Saber Kazeminasab, Tobias Elze, Lucy Shen, Louis Pasquale, Sarah Wellik, Carlos Gustavo De Moraes, Jonathan Myers, Nazlee Zebardast, David Friedman, Michael Boland, Mengyu Wang

Wills Eye Hospital Papers

PURPOSE: To predict 10-2 Humphrey visual fields (VFs) from 24-2 VFs and associated non-total deviation features using deep learning.

METHODS: We included 5189 reliable 24-2 and 10-2 VF pairs from 2236 patients, and 28,409 reliable pairs of macular OCT scans and 24-2 VF from 19,527 eyes of 11,560 patients. We developed a transformer-based deep learning model using 52 total deviation values and nine VF test features to predict 68 10-2 total deviation values. The mean absolute error, root mean square error, and the R2 were evaluation metrics. We further evaluated whether the predicted 10-2 VFs can improve the structure-function relationship …

An Llm-Assisted Easy-To-Trigger Poisoning Attack On Code Completion Models: Injecting Disguised Vulnerabilities Against Strong Detection, Shenao Yan, Shen Wang, Yue Duan, Hanbin Hong, Kiho Lee, Doowon Kim, Yuan Hong

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have transformed code completion tasks, providing context-based suggestions to boost developer productivity in software engineering. As users often fine-tune these models for specific applications, poisoning and backdoor attacks can covertly alter the model outputs. To address this critical security challenge, we introduce CODEBREAKER, a pioneering LLM-assisted backdoor attack framework on code completion models. Unlike recent attacks that embed malicious payloads in detectable or irrelevant sections of the code (e.g., comments), CODEBREAKER leverages LLMs (e.g., GPT-4) for sophisticated payload transformation (without affecting functionalities), ensuring that both the poisoned data for fine-tuning and generated code can evade strong …

Self-Chats From Large Language Models Make Small Emotional Support Chatbot Better, Zhonghua Zheng, Lizi Liao, Yang Deng, Libo Qin, Liqiang Nie

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have shown strong generalization abilities to excel in various tasks, including emotion support conversations. However, deploying such LLMs like GPT-3 (175B parameters) is resource-intensive and challenging at scale. In this study, we utilize LLMs as “Counseling Teacher” to enhance smaller models’ emotion support response abilities, significantly reducing the necessity of scaling up model size. To this end, we first introduce an iterative expansion framework, aiming to prompt the large teacher model to curate an expansive emotion support dialogue dataset. This curated dataset, termed ExTES, encompasses a broad spectrum of scenarios and is crafted with meticulous strategies …

Di­Chlorido­Tetra­Kis­(3-Meth­­Oxy­Aniline)Nickel(Ii), Benjamin A. Mukda, Mark M. Turnbull

Chemistry

The reaction of nickel(II) chloride with 3-meth­oxy­aniline yielded di­chlorido­tetra­kis­(3-meth­oxy­aniline)nickel(II), [NiCl₂(C₇H₉NO)₄], as yellow crystals. The Ni^II ion is pseudo-octa­hedral with the chloride ions trans to each other. The four 3-meth­oxy­aniline ligands differ primarily due to different conformations about the Ni—N bond, which also affect the hydrogen bonding. Inter­molecular N—H⋯ Cl hydrogen bonds and short Cl⋯Cl contacts between mol­ecules link them into chains parallel to the b axis.

3d-Magnetometer Arrays In Physics Experiments, Shaun G. Vavra

Masters Theses

This study presents the design and experimental evaluation of a magnetometer array utilizing LIS3MDL chips integrated with an Arduino microcontroller. Magnetometer arrays find crucial applications in various fields, including physics research, geophysics, and navigation systems. The goal of this research is to create an affordable and versatile magnetometer array for scientific investigations and practical applications. The paper begins by outlining the hardware and software components of the array. The LIS3MDL chips, known for their high sensitivity and low power consumption, are employed as the core sensing elements. The Arduino microcontroller is utilized for data acquisition and processing. The integration of …

Optimization Of Learning Algorithms In Neuromorphic Computing Systems., Oyinpere S. Ameli

Masters Theses

Spiking Neural Networks (SNNs) are a type of artificial neural network that aim to more closely mimic the data processing processes observed in biological neural systems. However, one major challenge in training these networks has been their non-differentiable nature, which makes it difficult to apply traditional gradient-based learning techniques. Different approaches have been proposed to address this challenge, ranging from supervised learning - largely inspired by error backpropagation in Deep Neural Networks - to unsupervised learning, which closely emulates biological learning approaches such as spike-timing dependent plasticity (STDP). Neuromorphic hardware platforms such as Intel's Loihi offer programmable plasticity that allows …

Assessment Of Enzyme Stability In Subsurface Sediments By Computational Methods, Kambiz Kalhor

Masters Theses

The microorganisms found in marine subseafloor sediment play a vital role in global carbon and nitrogen cycles, with an estimated 2.9×10²⁹ cells, accounting for about 0.6% of Earth’s total living biomass. These microbes grow at a very slow rate, with carbon turnover occurring over the course of years to thousands of years, about six orders of magnitude slower than sulfate reducing bacteria in pure culture. These slow metabolic rates suggest that the enzymes they produce must also have extended lifespans in order to be effective over such long periods of time. As a result, these enzymes are likely to …

2d Temperature Map Acquisition Using Hyperspectral Imaging System (Hsis), Anthony Kim

Masters Theses

Imaging techniques are close to our lives and are used for various applications. In the engineering field, one of the dominant techniques is hyperspectral imaging. It is a necessary tool that combines spectroscopy and digital photography and provides additional information on what is imaged by the imaging system. Hyperspectral imaging has been applied to various fields including remote sensing, cultural relic conservation, food microbiology, forensic science, biomedicine, etc.

In particular, work was done to apply hyperspectral imaging to measure the temperature and emissivity of an object. Due to its ability to measure temperature and emissivity without being in contact with …

Transfer Learning For Predictive Maintenance: A Case Study, Colter A. Swanson

Masters Theses

In light of recent strides in high-performance computing, the concept of transfer learning has emerged as a prominent paradigm within the realm of Artificial Intelligence and Machine Learning methodologies. Analogous to the human brain's capacity to assimilate information across related domains for pattern recognition, transfer learning has swiftly asserted its dominance, particularly in deep learning applications such as image classification and natural language processing. Despite its ascendancy in these domains, there exists a lack of comprehensive investigations in alternative domains, notably those encompassing tabular data formats. This thesis seeks to redress this gap by conducting an empirical examination of transfer …

The Geometry Of Ancient Solutions To Curvature Flows, Sathyanarayanan Rengaswami

Doctoral Dissertations

Following the tremendous success of the mean curvature flow, other variants such as the Gauss curvature flow, inverse mean curvature flow have been investigated in great detail, leading to interesting applications to other fields including partial differential equations, convex geometry etc. This calls for an investigation of curvature flow as a general phenomenon. While basic existence and uniqueness results, roundness estimates etc have been obtained, there isn't a substantial body of work that addresses the geometry of solutions of curvature flows and their relation to the choice of speed function used. It is therefore interesting to investigate curvature flows as …

Neural-Network-Based Detection Of Radiopharmaceutical Extravasation In Pet/Ct Data, Elijah D. Berberette

Masters Theses

The immediate identification of PET/CT radiopharmaceutical extravasation can eliminate many adverse effects such as misdiagnosis and improper therapy. Radiopharmaceutical extravasation is the leakage of an injected radiotracer from the patient’s intended vein into surrounding tissues. The detection of this phenomenon often requires the use of an external monitoring device; due to a lack of robust visual features that can provide indication that it has occurred. In this thesis, the feasibility of using neural networks trained on PET/CT data to identify extravasation is explored. This approach begins with a novel preprocessing methodology that automatically extracts body weight normalized standard uptake values …

Koopman-Inspired Proximal Policy Optimization (Kippo), Andrei Cozma

Masters Theses

Reinforcement Learning (RL) has made significant strides in various domains, yet developing effective control policies for environments with complex, nonlinear dynamics remains a challenge, particularly for policy gradient methods. These methods often struggle due to high-variance in gradient estimates, non-convex optimization landscapes, and sample inefficiency, resulting in unstable learning, suboptimal policies, and trade-offs between performance and reproducibility. The quest for more robust, stable, and effective methods has led to numerous innovations and remains a critical area of research. Proximal Policy Optimization (PPO) has gained popularity in recent years due to its balance in performance, training stability, and computational efficiency. In …

Spontaneous Plant Colonization Of Newly Established Green Roofs: An Experimental Approach, Braden Matthew Coats

Masters Theses

Urban Heat Island Effect (UHI) increases heat risks in densely developed environments. Ecosystem services provided by green spaces are known to mitigate UHI. Green roofs, designed as novel ecosystems, transform less-utilized spaces like rooftops into functional areas, executing ecosystem functions in densely populated urban environments where traditional green spaces are less common. Research has examined ways to reduce the obstacles of implementing green roofs to maximize accessibility and efficiency of the performed ecosystem services. Research involving plant community dynamics found that maximizing biodiversity on green roofs enhances the ecosystem services provided. Utilizing spontaneous colonizing species, or species that are not …

The Mechanical Strength Of Sedimentary Rocks At Meridiani Planum, Mars, Tyler J. Seyglinski

Masters Theses

The physical properties of rocks such as their strength, hardness, and density can help inform our understanding of the formation and modification history of rock units. For sedimentary rocks, their strength is inherently linked to factors such as porosity and degree of induration, which are in turn controlled by factors such as burial depth and water-rock interaction. On Earth, rock strength is typically assessed as unconfined compressive strength (UCS) and can be measured directly or inferred via indirect strength tests (e.g., the Schmidt hammer test). On Mars, instruments such as the Rock Abrasion Tool (RAT) onboard the Opportunity rover can …

Solving Long-Run Average Reward Robust Mdps Via Stochastic Games, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Mehrdad Karrabi, Petr Novotný, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Markov decision processes (MDPs) provide a standard framework for sequential decision making under uncertainty. However, MDPs do not take uncertainty in transition probabilities into account. Robust Markov decision processes (RMDPs) address this shortcoming of MDPs by assigning to each transition an uncertainty set rather than a single probability value. In this work, we consider polytopic RMDPs in which all uncertainty sets are polytopes and study the problem of solving long-run average reward polytopic RMDPs. We present a novel perspective on this problem and show that it can be reduced to solving long-run average reward turn-based stochastic games with finite state …

Certified Policy Verification And Synthesis For Mdps Under Distributional Reach-Avoidance Properties, S. Akshay, Krishnendu Chatterjee, Tobias Meggendorfer, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Markov Decision Processes (MDPs) are a classical model for decision making in the presence of uncertainty. Often they are viewed as state transformers with planning objectives defined with respect to paths over MDP states. An increasingly popular alternative is to view them as distribution transformers, giving rise to a sequence of probability distributions over MDP states. For instance, reachability and safety properties in modeling robot swarms or chemical reaction networks are naturally defined in terms of probability distributions over states. Verifying such distributional properties is known to be hard and often beyond the reach of classical state-based verification techniques. In …

Task Scheduling Strategy For 3dpcp Considering Multidynamic Information Perturbation In Green Scene, Jianjia He, Jian Wu, Keng Siau

Research Collection School Of Computing and Information Systems

The 3D printing cloud platform (3DPCP) plays a pivotal role in breaking down the information silos between supply and demand, effectively reducing waste through information integration and intelligent production. However, due to the complexity of 3DPCP scheduling in green scenes and the multidynamic information perturbations, unveils problems in traditional task scheduling methods in 3DPCP. These issues manifest as incomplete considerations, subpar green performance, and weak adaptability to dynamic changes. There is an urgent need to design practical methods to realize the multidynamic information perturbations in green scenes within 3DPCP. Therefore, this article first defines the 3DPCP task scheduling problem for …

Mesenchymal Stem Cells In Autoimmune Disease: A Systematic Review And Meta-Analysis Of Pre-Clinical Studies, Hailey N. Swain, Parker D. Boyce, Bradley A. Bromet, Kaiden Barozinksy, Lacy Hance, Dakota Shields, Gayla R. Olbricht, Julie A. Semon

Mathematics and Statistics Faculty Research & Creative Works

Mesenchymal Stem Cells (MSCs) Are of Interest in the Clinic Because of their Immunomodulation Capabilities, Capacity to Act Upstream of Inflammation, and Ability to Sense Metabolic Environments. in Standard Physiologic Conditions, They Play a Role in Maintaining the Homeostasis of Tissues and Organs; However, there is Evidence that They Can Contribute to Some Autoimmune Diseases. Gaining a Deeper Understanding of the Factors that Transition MSCs from their Physiological Function to a Pathological Role in their Native Environment, and Elucidating Mechanisms that Reduce their Therapeutic Relevance in Regenerative Medicine, is Essential. We Conducted a Systematic Review and Meta-Analysis of Human MSCs …