Physical Sciences and Mathematics Commons^™

Molecular Dynamics Simulations Of The Adenylate Kinase Enzyme And Its Mutant States, Beata Izabella Dulko Smith

Chemistry & Biochemistry Dissertations

Adenylate kinase (AK) is a small, monomeric enzyme present across all domains of life. It is responsible for maintaining homeostasis of an intracellular adenylate pool and catalyzes a reversible transfer of the phosphoryl group from NTP (nucleoside triphosphate) to NMP producing two NDP molecules. While most known isoforms take ATP as phosphate donor, AK3 present in mitochondrial matrix employs GTP. Crystallographic investigations revealed that the basis for its selectivity for GTP over ATP came from an additional hydrogen bond between guanosine’s O6 and backbone nitrogen of threonine is responsible for recognition of GTP. Molecular dynamics (MD) simulations have demonstrated that …

Senstive Enantiomeric Analysis Of N-Acyl Homoserine Lactones In Bacterial Matrices, Abiud E. Portillo

Chemistry & Biochemistry Dissertations

Communication between unicellular organism has long been studied with many facets yet to be discovered. A specific type of communication in gram-negative bacteria is quorum sensing (QS). N-acyl homoserine lactones (N-HLs) are the molecular signals in this process. At a critical concentration of N-HLs, all bacteria of the same population stop behaving like individuals and in a concerted manner induce a specific gene expression which ultimately benefits the entire population in major. This phenomenon has been coined with the term quorum sensing with the main signaling molecules dubbed L-N-acyl homoserine lactones (L-N-HL). Quorum sensing signaling pathways have their influence in …

Probing Short-Range Structural Distortion Of Stereochemically Active Lone Pairs In Extended Solid-State Materials, Uyen Phuong Dang

Chemistry & Biochemistry Dissertations

The structural and electronic effects of the lone pairs play an important role in magnetic, photocatalytic, and semiconducting behaviors of materials. Investigating the role of stereochemically active ns2 lone pairs derived from p-block cations in solid-state materials is important to phenomena associated with polarizable bonding, e.g., ferroelectricity. The effect of lone pairs in ferroelectric polarization has been studied on the lead metaniobate in which the covalency between Pb and O stabilizes the in-plane polarization which leads to orthorhombic symmetry. PbTiO3 has been widely studied for ferroelectric polarization associated with the off-centering of Ti in TiO6 due to the characteristic 6s2 …

Insights On The Behavior Of Nano-Copper In The Agroecosystem: Mycorrhizal Associations With Spearmint (Mentha Spicata), Suzanne Annette Apodaca

Open Access Theses & Dissertations

Nanotechnology offers significant potential benefits to our society, including the agriculture sector. With the advancement of nano-enabled agrochemicals towards sustainable and efficient agricultural practices, it is essential to address environmental issues associated with the use of nanoscale materials. The same properties that give promise to applications of nanotechnology in modern agriculture could have unintended consequences on ecosystem dynamics. A point of concern for risk management is the impact of engineered nanomaterials (ENMs) to beneficial microbial communities, which support a variety of ecosystem services.

Use of copper (Cu) products in agriculture are based on their abundance, role as a micronutrient, and …

Infiltration-Controlled Combustion Of Lithium And Magnesium Powders And Reactions Of Lithium With Oxygen And Carbon Dioxide, Kevin Samuel Estala Rodriguez

Open Access Theses & Dissertations

Chemical heat integrated power systems are of great interest for space missions where solar energy, nuclear energy, and batteries are not available or are not practical to use. A new concept of a power system is a metal combustor coupled with a chemical oxygen generator, where the generated oxygen infiltrates through the metal powder or combustion products. The combustion of lithium and magnesium powders under these conditions has not been studied yet. The present work investigates combustion of magnesium powder and stabilized lithium metal powder (SLMP) ignited by a laser inside a closed chamber filled with O2 or CO2. It …

Implementation Of Structure From Motion (Sfm) Technology For Edcuational Lessons, Valeria Veronica Martinez

Open Access Theses & Dissertations

Education is the most important obligation the humanity race has in order to evolve and thrive in the modern world. There are various applications for educators to teach valuable lessons to students, and not all deliveries will be the same for every lesson and for every student. Lesson being taught, the lesson delivery from educator to student often changes every time the lesson is implemented, depending on the learning ability of the students. Students have the ability to learn in different ways, such as the visual, auditory, kinesthetic, reading and writing learners (Oxford 2003). As educators aware of the four …

Rational Design Of Precisely Tailored Electrocatalysts For Efficient And Durable Electrocatalytic Devices, Mohmed Sanad Noufal

Open Access Theses & Dissertations

Due to their superior conversion efficiency, high power density, and green nature, sustainable energy technologies such as Fuel cells and Zinc-air batteries, once installed to produce green hydrogen fuel as well as energy storage devices, are expected to play an essential role in reducing the environmental effect of human transportation by replacing fossil fuels. However, the current bottleneck for the low-temperature polymer membrane fuel cells that dominate the fuel cell vehicle market is the sluggish kinetics of the oxygen reduction reaction (ORR) at the cathode. The slow ORR kinetics significantly reduce the efficiency of chemical-electrical energy conversion. Similarly, hydrogen production …

Hydrothermal Alteration Targeting And Geophysical Mineral Exploration Of Eureka And Sylvanite Mining Districts, Southwest New Mexico, Kenneth Singh

Open Access Theses & Dissertations

As part of the southwestern North American porphyry copper province, mining of natural resources in New Mexico has historically played a crucial role in economic development for over 150 years including this project area in the Little Hatchet Mountains. The northern half of the Little Hatchet Mountains lies in Grant County and includes the Eureka Mining District (EMD) whereas the southern half is in Hidalgo County and includes the Sylvanite Mining District (SMD), 38 miles southeast of Lordsburg. The EMD, a copper and silver-lead mining district from 1880-1961, had a total estimated production from the Laramide veins of 2.9 million …

Strong Constraints On New Physics From The Icecube South Pole Neutrino Observatory, Grant Kendrick Parker

Physics Dissertations

The IceCube Neutrino Observatory, a gigaton-scale ice Cherenkov detector located deep within the Antarctic glacier, has detected hundreds of thousands of atmospheric neutrinos at energies from a few GeV to 100 TeV. Above 100 GeV, where ordinary oscillation effects become vanishingly small, this data sample is particularly sensitive to a wide range of beyond-standard-model (BSM) neutrino oscillation mechanisms. This thesis presents two searches for such BSM physics: flavor-changing nonstandard neutrino interactions (NSI) and neutrino oscillation decoherence (decoherence). The first analysis constrains the mu-tau flavor-changing NSI parameter with eight years of IceCube atmospheric neutrino data ranging from 500 GeV to 1 …

Groundwater Modelling Of The Newman Area For Managed Aquifer Recharge Assessment, Wolfgang Schmid, Rodrigo Rojas, Michael J. Donn, Christopher Schelfhout Dr, Mathias Raiber, Olga Barron

Natural resources commissioned reports

This report contributes to the Transforming Agriculture in the Pilbara (TAP) initiative that has been developed to undertake the detailed studies in the Pilbara to bring suitable medium to large scale irrigation land to market for private investment. The research objective was to explore opportunities for irrigated agriculture north of Newman (Eastern Pilbara), deploying managed aquifer recharge (MAR) to secure water for irrigation. MAR source water identified was from mine dewatering surplus, generated from the large BHP Iron Ore operation in the Newman region. The objectives of this report were to:

• conceptualise the groundwater system and associated processes in the …

Human And Hydrologic Influences On Nebraska's Endangered Rainwater Basin Wetlands, Sarah Thompson

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

Over half of wetlands in North America have been lost or degraded. Rainwater Basin (RWB) wetlands, located in south-central Nebraska, are a primary example of such loss; an estimated 90% have been destroyed by land conversion for agriculture. Remaining RWB wetlands are often embedded in row-crop fields, where they are threatened by altered surface water runoff flow, drainage features, and excess sediment inputs. Efforts at the state and federal level have been made to preserve this wetland complex due to the critical stopover habitat these wetlands provide for migratory birds. Land managers work to maintain sufficient water levels during migratory …

2022 December - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Report

No abstract provided.

Learnfca: A Fuzzy Fca And Probability Based Approach For Learning And Classification, Suraj Ketan Samal

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering.

This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide …

Bevers: A General, Simple, And Performant Framework For Automatic Fact Verification, Mitchell Dehaven

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Fact verification has become an important process, primarily done manually by humans, to verify the authenticity of claims and statements made online. Increasingly, social media companies have utilized human effort to debunk false claims on their platforms, opting to either tag the content as misleading or false, or removing it entirely to combat misinformation on their sites. In tandem, the field of automatic fact verification has become a subject of focus among the natural language processing (NLP) community, spawning new datasets and research. The most popular dataset is the Fact Extraction and VERification (FEVER) dataset. In this thesis an end-to-end …

Sequence-Based Bioinformatics Approaches To Predict Virus-Host Relationships In Archaea And Eukaryotes, Yingshan Li

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Viral metagenomics is independent of lab culturing and capable of investigating viromes of virtually any given environmental niches. While numerous sequences of viral genomes have been assembled from metagenomic studies over the past years, the natural hosts for the majority of these viral contigs have not been determined. Different computational approaches have been developed to predict hosts of bacteria phages. Nevertheless, little progress has been made in the virus-host prediction, especially for viruses that infect eukaryotes and archaea. In this study, by analyzing all documented viruses with known eukaryotic and archaeal hosts, we assessed the predictive power of four computational …

Attention In The Faithful Self-Explanatory Nlp Models, Mostafa Rafaiejokandan

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Deep neural networks (DNNs) can perform impressively in many natural language processing (NLP) tasks, but their black-box nature makes them inherently challenging to explain or interpret. Self-Explanatory models are a new approach to overcoming this challenge, generating explanations in human-readable languages besides task objectives like answering questions. The main focus of this thesis is the explainability of NLP tasks, as well as how attention methods can help enhance performance. Three different attention modules are proposed, SimpleAttention, CrossSelfAttention, and CrossModality. It also includes a new dataset transformation method called Two-Documents that converts every dataset into two separate documents required by the …

Bayesian Adaptive Clinical Trial Design, Mengyi Lu

Dissertations & Theses (Open Access)

The landscape of drug development in oncology has changed from conventional chemotherapies to molecular targeted therapies and immunotherapies, which provide innovative therapeutic modalities for treating cancers. These novel therapeutic agents work through mechanisms that fundamentally differ from standard chemotherapeutic agents, making the conventional trial design paradigm inefficient and dysfunctional. Specifically, the focus of dose-finding trials has shifted from finding the maximum tolerated dose (MTD) to the optimal biological dose (OBD), defined as the dose that optimizes the risk–benefit tradeoff. How to accurately identify the OBD and its dosing schedule is of great importance to maximize efficacy and safety of targeted …

Hydrogeologic Heterogeneity Identification: Using Inverse Modeling Of Synthetic Borehole Temperatures To Predict Groundwater Flux, Kevin Heintz

UNLV Theses, Dissertations, Professional Papers, and Capstones

Heat has been effectively used as a groundwater tracer for decades, and high-resolution temperature data can better identify and quantify discrete flow zones. Refinements to the numerical modeling of advective heat transfer in borehole temperature sensing deployments can improve understanding of dynamic hydrogeologic systems. In my thesis, I develop a novel two-dimensional coupled radial groundwater flow and heat transfer numerical model that considers intra-borehole vertical flow. To test the performance of this model, I used finite element analysis to generate synthetic data sets consisting of prescribed variable flow fields and resulting borehole temperatures. I input synthetic temperatures into the two-dimensional …

Estimation Of The Parameters In A Mixture Of Two Normal Distributions And The Generalized Pivotal Quantity Method, Md Faruk Hossain

UNLV Theses, Dissertations, Professional Papers, and Capstones

A pivotal quantity is a random variable that is a function of both the random data and the unknown population parameters and whose probability distribution does not depend on any of the unknown parameters. The population parameters here may include nuisance parameters. Historically, pivotal quantities have been used for the construction of test statistics for hypothesis testing of some of these unknown parameters. They have also been used for the construction of confidence intervals for some of these parameters.Generalized pivotal quantities (GPQ) were introduced by Tsui and Weerahandi (1989) and Weerahandi (1993). A GPQ is a function, not only of …

Day ‘N’ Nite: Habitability Of Tidally Locked Planets With Sporadic Rotation, Cody James Shakespeare

UNLV Theses, Dissertations, Professional Papers, and Capstones

Tidally locked worlds provide a unique opportunity for constraining the climates of detected exoplanets. They are unique in that few exoplanet spin and obliquity states are known or will be determined in the near future. The TRAPPIST-1 exoplanet system has multiple habitable zone planets that, in the past, have been presumed to be tidally locked. However, a recent study shows the dynamical conditions present in the TRAPPIST-1 system make rotation and large librations possible spin states for these planets. I confirm the tendency for these planets to sporadically transition from tidally locked libration to slow rotation using N-body simulations independent …

What Can General Chemistry Students Learn From External Representations Of Acid- Base Titrations?, Nicole Baldwin

UNLV Theses, Dissertations, Professional Papers, and Capstones

Laboratory activities are a prevalent and essential part of chemistry learning because of their potential to help students develop problem solving abilities, visualize chemistry concepts learned in lecture, and gain practical skills. However, learning in the laboratory environment is not without its challenges. For example, cookbook-style chemistry laboratories can promote superficial learning, and cognitive overload can result from the study of new concepts and the use of new procedures in this environment. Multiple pedagogies and supports have been developed to address challenges such as these. The current research focuses upon external representations that are commonly used to support learning in …

Cold Calls To Enhance Class Participation And Student Engagement, Manoj Thulasidas, Aldy Gunawan

Research Collection School Of Computing and Information Systems

The question whether cold calls increase student engagement in the classroom has not been conclusively answered in the literature. This study describes the automated system to implement unbiased, randomized cold calling by posing a question, allowing all students to think first and then calling on a particular student to respond. Since we already have a measure of the level of student engagement as the self-reported classparticipation entries from the students, its correlation to cold calling is also further studied. The results show that there is a statistically significant increase in the class participation reported, and therefore in student engagement, in …

Gamified Online Industry Learning Platform For Teaching Of Foundational Computing Skills, Yi Meng Lau, Rafael Jose Barros Barrios, Gottipati Swapna, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

Online industry learning platforms are widely used by organizations for employee training and upskilling. Courses or lessons offered by these platforms can be generic or specific to an enterprise application. The increased demand of new hires to learn these platforms or who are already certified in some of these courses has led universities to look at the opportunities for integrating online industry learning platforms into their curricula. Universities hope to use these platforms to aid students in their learning of concepts and theories. At the same time, these platforms can equip students with industryrecognized certifications or digital badges. This paper …

Authentic Assessments For Digital Education: Learning Technologies Shaping Assessment Practices, Tristan Lim, Swapna Gottipati, Michelle L. F. Cheong

Research Collection School Of Computing and Information Systems

Assessment is a powerful lever that affects learning. To better inform educators on authentic assessment practices within digital education in the higher education landscape, this paper takes us through a meta-analysis of existing literature between 2011 to 2021. The study evaluates the following research question: “How are emerging technologies shaping authentic assessment practices within digital education for higher education for the period between 2011 and 2023”. To aid with the forecasting, we utilize the EDUCAUSE Horizon Reports, which provide the predictions of emerging technology. This study affirms the importance of immersive learning technologies, followed by ubiquitous and adaptive learning technologies …

S-Prompts Learning With Pre-Trained Transformers: An Occam's Razor For Domain Incremental Learning, Yabin Wang, Zhiwu Huang, Xiaopeng. Hong

Research Collection School Of Computing and Information Systems

State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i.e., domain increment learning (DIL). The key idea of the paradigm is to learn prompts independently across domains with pre-trained transformers, avoiding the use of exemplars that commonly appear in conventional methods. This results in a win-win game where the prompting can achieve the best for each domain. The independent prompting across domains only …

Prompting For Multimodal Hateful Meme Classification, Rui Cao, Roy Ka-Wei Lee, Wen-Haw Chong, Jing Jiang

Research Collection School Of Computing and Information Systems

Hateful meme classification is a challenging multimodal task that requires complex reasoning and contextual background knowledge. Ideally, we could leverage an explicit external knowledge base to supplement contextual and cultural information in hateful memes. However, there is no known explicit external knowledge base that could provide such hate speech contextual information. To address this gap, we propose PromptHate, a simple yet effective prompt-based model that prompts pre-trained language models (PLMs) for hateful meme classification. Specifically, we construct simple prompts and provide a few in-context examples to exploit the implicit knowledge in the pretrained RoBERTa language model for hateful meme classification. …

A Recommendation On How To Teach K-Means In Introductory Analytics Courses, Manoj Thulasidas

Research Collection School Of Computing and Information Systems

We teach K-Means clustering in introductory data analytics courses because it is one of the simplest and most widely used unsupervised machine learning algorithms. However, one drawback of this algorithm is that it does not offer a clear method to determine the appropriate number of clusters; it does not have a built-in mechanism for K selection. What is usually taught as the solution for the K Selection problem is the so-called elbow method, where we look at the incremental changes in some quality metric (usually, the sum of squared errors, SSE), trying to find a sudden change. In addition to …

Curiosity-Driven And Victim-Aware Adversarial Policies, Chen Gong, Zhou Yang, Yunpeng Bai, Jieke Shi, Arunesh Sinha, Bowen Xu, David Lo, Xinwen Hou, Guoliang Fan

Research Collection School Of Computing and Information Systems

Recent years have witnessed great potential in applying Deep Reinforcement Learning (DRL) in various challenging applications, such as autonomous driving, nuclear fusion control, complex game playing, etc. However, recently researchers have revealed that deep reinforcement learning models are vulnerable to adversarial attacks: malicious attackers can train adversarial policies to tamper with the observations of a well-trained victim agent, the latter of which fails dramatically when faced with such an attack. Understanding and improving the adversarial robustness of deep reinforcement learning is of great importance in enhancing the quality and reliability of a wide range of DRL-enabled systems. In this paper, …

A Unified Dialogue User Simulator For Few-Shot Data Augmentation, Dazhen Wan, Zheng Zhang, Qi Zhu, Lizi Liao, Minlie Huang

Research Collection School Of Computing and Information Systems

Pre-trained language models have shown superior performance in task-oriented dialogues. However, existing datasets are on limited scales, which cannot support large-scale pre-training. Fortunately, various data augmentation methods have been developed to augment largescale task-oriented dialogue corpora. However, they heavily rely on annotated data in the target domain, which require a tremendous amount of data collection and human labeling work. In this paper, we build a unified dialogue user simulation model by pre-training on several publicly available datasets. The model can then be tuned on a target domain with fewshot data. The experiments on a target dataset across multiple domains show …

Learning Generalizable Models For Vehicle Routing Problems Via Knowledge Distillation, Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, Yeow Meng Chee

Research Collection School Of Computing and Information Systems

Recent neural methods for vehicle routing problems always train and test the deep models on the same instance distribution (i.e., uniform). To tackle the consequent cross-distribution generalization concerns, we bring the knowledge distillation to this field and propose an Adaptive Multi-Distribution Knowledge Distillation (AMDKD) scheme for learning more generalizable deep models. Particularly, our AMDKD leverages various knowledge from multiple teachers trained on exemplar distributions to yield a light-weight yet generalist student model. Meanwhile, we equip AMDKD with an adaptive strategy that allows the student to concentrate on difficult distributions, so as to absorb hard-to-master knowledge more effectively. Extensive experimental results …