Physical Sciences and Mathematics Commons^™

An Analysis Of The Predictability Of The February 2021 Cold Air Outbreak At S2s Timescales, Grace Dines

Legacy Theses & Dissertations (2009 - 2024)

The February 2021 cold air outbreak (CAO) brought long-lasting, freezing temperatures and extreme winter weather to the United States and Canada. The event resulted in major societal and economic impacts, including millions of power outages, hundreds of fatalities, and approximately $25 billion in losses. This thesis first analyzes the synoptic evolution of this CAO in order to explain how Arctic air arrived and stalled over central North America. Given the synoptic context, the analysis next considers the subseasonal-to-seasonal (S2S) predictability of this CAO by examining S2S ensemble model predictions from both high-top and low-top models. The S2S predictability analysis is …

An Assessment Of Coastal Impacts To The Natural And Built Assets Of The New York State Office Of Parks, Recreation And Historic Preservation, Kelsey Ruffino

Legacy Theses & Dissertations (2009 - 2024)

Climate change is expected to cause an increase in heavy precipitation events, flooding, and sea level rise in New York State, which will have significant impacts on coastal areas. Almost half of New York's state parks and historic sites, which are managed by the New York State Office of Parks, Recreation and Historic Preservation (OPRHP), are located in major coastal areas, including the Atlantic Ocean, Hudson River, Lake Erie, and Lake Ontario. As the effects of climate change become more pronounced in these areas, a selection of OPRHP’s infrastructure and natural, cultural, and historic resources will be at risk. To …

Variations And Approximations Of Interleaving Distances, Robert Cardona

Legacy Theses & Dissertations (2009 - 2024)

We present three different topics covering (1) presentation-based distances, (2) approximate nerve theorems, and (3) networks and persistence.We present seven main results (A) - (G).

Near-Optimal Motion Planning Algorithms Via A Topological And Geometric Perspective, Aakriti Kumari Upadhyay

Legacy Theses & Dissertations (2009 - 2024)

Motion planning is a fundamental problem in robotics, which involves finding a path for an autonomous system, such as a robot, from a given source to a destination while avoiding collisions with obstacles. The properties of the planning space heavily influence the performance of existing motion planning algorithms, which can pose significant challenges in handling complex regions, such as narrow passages or cluttered environments, even for simple objects. The problem of motion planning becomes deterministic if the details of the space are fully known, which is often difficult to achieve in constantly changing environments. Sampling-based algorithms are widely used among …

An Evaluation Of High-Resolution Model Simulations Of Orographic Precipitation, Snowpack, Atmospheric Rivers, And Their Climate Sensitivities In The Southern Andes, Erin Potter

Legacy Theses & Dissertations (2009 - 2024)

Orographic precipitation and snowpack are important for water resources and hydrometeorological hazards in the Andes Mountain range. As the climate warms, South American atmospheric rivers (ARs) are expected to increase in frequency and intensity, leading to increased orographic precipitation. Improved understanding of orographic precipitation and snowpack, and their association with ARs, in a warming climate is needed to provide a foundation for understanding future hydroclimate change over the Andes. The goal of this research is to evaluate how well variations in high-resolution regional climate simulations over the southern Andes (30-55°S), agree with observations of (1) orographic precipitation and (2) snowpack …

Exploiting Natural Rna 2′-5′-Linkage In Nucleic Acid Aptamers And Dna Nanostructures, Johnsi Mathivanan

Legacy Theses & Dissertations (2009 - 2024)

Developments in chemical synthesis of nucleic acid sequences and modifications have aided in the study of biomolecular interactions and the roles of modified nucleic acids in biology, medicine, and nanotechnology. This work describes some of the aspects of such developments, from synthesis of chemically modified RNA and DNA aptamers that can bind antibiotics and metal ions to the use of nucleic acids with novel backbone linkages in the construction of nanostructures. Analysis of such nucleic acid structures inform us on the basic structure of these molecules and the resulting functions that are applicable in fields such as biosensing, drug screening …

Scenario Planning In A Post-Pandemic World: Karner Blue Butterfly And The Albany Pine Bush, Senia Quevedo

Legacy Theses & Dissertations (2009 - 2024)

This study outlines four future scenarios for the Karner blue butterfly and the Albany PineBush in a post-pandemic world. Management changes and modifications are becoming more necessary due to the changing climate conditions and habitat loss. The COVID-19 pandemic has changed certain conditions that affect the Karner blue butterfly and its habitats, such as noise and air pollution, increased outdoor recreation, and pausing development along the pine bush borders. A scenario planning model was used to better understand how policy and lockdown restrictions being lifted will affect the Karner blue butterfly population after the Albany Pine Bush surveyed a record-high …

The Development Of A Statistical Postprocessing Algorithm By Two-Step Space-Time Eof Analysis, William David Stikeleather

Legacy Theses & Dissertations (2009 - 2024)

Model prediction for extended range forecasts is fraught with shortcomings that generate forecast error and decrease skill. Biases in Numerical Weather Prediction (NWP) models generate patterns of error that grow larger with time. These inherent biases systematically alter the modeled positioning of atmospheric features that emerge as synoptic or subseasonal error. While individual weather events and anomalous patterns will generate model noise, it is computationally possible to separate the signal behind this error from noise. For 200 hPa Geopotential Height (Z200), systematic biases in model forecasts often manifest themselves as errors in atmospheric wave patterns in the high latitudes, including …

Novel Applications Of Enhanced Raman Spectroscopy For Drug Discovery, Mrna Vaccine Stability Assessment, And Forensic Purposes, Lamyaa M. Almehmadi

Legacy Theses & Dissertations (2009 - 2024)

The main focus of this dissertation was to apply enhanced Raman spectroscopy techniques to a variety of applications, ranging from drug discovery, mRNA vaccine stability, to forensic analysis. There are five chapters in this dissertation. The first chapter discusses the basics of enhanced Raman spectroscopy techniques and their relevance. The second chapter is focused on the use of surface-enhanced Raman spectroscopy (SERS) for drug discovery. The third chapter introduces the use of deep ultraviolet resonance Raman (DUVRR) spectroscopy for the detection of mRNA degradation in a mRNA vaccine model. The fourth chapter introduces the use of stand-off Raman spectroscopy in …

Distinguishing Between Living And Dead Plant Material From The Red Spruce Using Atr Ft-Ir Spectroscopy, Bailey Ann Hoplight

Legacy Theses & Dissertations (2009 - 2024)

Spectroscopic methods have recently been used to analyze plant species and maturation. However, there is a lack of research into employing spectroscopy for distinguishing living plant material and dead plant material, which is important for the agriculture and environmental health fields. Here in this study, we have shown the potential of ATR-FT IR (Attenuated total reflectance Fourier transformed infrared spectroscopy) to classify plant materials as living or dead through the help of multivariate statistical analysis methods. Plant samples were from the roots and needles of picea rubens, more commonly known as the red spruce tree. The samples were classified by …

Proceedings Of The 2023 Berry Summer Thesis Institute, University Of Dayton. University Honors Program

Proceedings of the Berry Summer Thesis Institute (2013-Present)

Thanks to a gift from the Berry Family Foundation and the Berry family, the University Honors Program launched the Berry Summer Thesis Institute in 2012. The institute introduces students in the University Honors Program to intensive research, scholarship opportunities and professional development. Each student pursues a 12-week summer thesis research project under the guidance of a UD faculty mentor. This contains the product of the students' research.

Contents:

• “How Porous Materials Affect the Boundary-Layer Transition of Hypersonic Flight Vehicles” (Megan C. Sieve)
• “Ultra-Stretchable, Self-Healing, DLP 3D-Printed Elastomers for Damage-Resistant Soft Robots: A Review” (Robert M. A. Drexler)
• “Extrapolation of Scalar …

Re: Approval Letter For The Butte Priority Soils Operable Unit (Bpsou) Revised 2023 Residential Metals Abatement Program (Rmap) Field Sampling Plan (Fsp) – Indoor Dust – Group 8 – Gold Hill Lutheran Church (Creativity Factory Preschool) And United Congregational (Crayon Academy Preschool) (Dated July 26, 2023), Nikia Greene

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Bayesian Optimization With Switching Cost: Regret Analysis And Lookahead Variants, Peng Liu, Haowei Wang, Wei Qiyu

Research Collection Lee Kong Chian School Of Business

Bayesian Optimization (BO) has recently received increasing attention due to its efficiency in optimizing expensive-to-evaluate functions. For some practical problems, it is essential to consider the path-dependent switching cost between consecutive sampling locations given a total traveling budget. For example, when using a drone to locate cracks in a building wall or search for lost survivors in the wild, the search path needs to be efficiently planned given the limited battery power of the drone. Tackling such problems requires a careful cost-benefit analysis of candidate locations and balancing exploration and exploitation. In this work, we formulate such a problem as …

A Multi-Method Inquiry Into The Cognitive Effort In Human Translation And Neural Machine Translation Post-Editing Processes, Yu Wang

Student Works (2020-2029)

Due to the inaccuracies of Machine Translation (MT), Post-editing (PE) by human translators is inevitable. While most studies have reviewed HT and PE as end products, questions concerning whether the effort made by the human to polish an MT is worth it and which texts would be more efficient to be post-edited remain unanswered in the area of the cognitive translation process, especially from an empirical perspective. This study, therefore, adopts a multi-method approach, incorporating quantitative data derived from keystroke-logging and eye-tracking, with qualitative data from retrospective interviews to examine a) the differences in cognitive effort between HT and PE …

Lightweight Blockchains And Their Network Impact On Vehicular Ad-Hoc Network-Based Blockchain Applications, Edgar Bowlin

Electronic Theses and Dissertations

Vehicular Ad-hoc Networks (VANETs) provide networks for smart vehicles and will enable future systems to provide services that enhance the overall transportation experience. However, these applications require consideration to possible damage to both property and human life. Communication between vehicles requires data immutability and user privacies to provide safe operation of the system. Blockchains can provide these properties and more to create a more secure and decentralized system. However, a chain’s security comes from the chain length. VANETs’ ephemeral connections provide harm limits how much data can be exchanged during vehicle rendezvous. This thesis investigates lightweight blockchains that operate with …

Geometric Morphometric Analysis Of Modern Viperid Vertebrae Facilitates Identification Of Fossil Specimens, Lance D. Jessee

Electronic Theses and Dissertations

Snake vertebrae are common in the fossil record, whereas cranial remains are generally fragile and rare. Consequently, vertebrae are the most commonly studied fossil element of snakes. However, identification of snake vertebrae can be problematic due to extensive variation. This study utilizes 2-D geometric morphometrics and canonical variates analysis to 1) reveal variation between genera and species and 2) classify vertebrae of modern and fossil eastern North American Agkistrodon and Crotalus. The results show that vertebrae of Agkistrodon and Crotalus can reliably be classified to genus and species using these methods. Based on the statistical analyses, four of the …

Age And Paleontology Of The Turin Pit Locality, Monona County, Iowa, Samantha Wright

Electronic Theses and Dissertations

The Turin Pit locality (Monona county, Iowa) has been known to paleontologists since 1908, yet the age of the fauna has been unclear. Early paleontologists considered Turin Pit to date to a pre-Illinoian interglacial (the “Aftonian).” Subsequent researchers suggested it dated to the last glaciation. This study provides a partial list of mammals in the Turin Pit fauna, and together with stratigraphic information, uses the known age ranges of taxa to estimate an age for the assemblage. The presence of Mammuthus, Aenocyon, and Castoroides combined with a magnetically-reversed till located stratigraphically above fossil-bearing deposits, suggest the Turin Pit …

A Low Cost, Compact Electrochemical Analyzer Based On An Open-Source Microcontroller, Michael Kofi Darko Addo

Electronic Theses and Dissertations

Compared to other instruments for chemical analyses, electrochemical analyzers are relatively simple, inexpensive, easy to miniaturize and require little-to-no maintenance. However, like all commercially available instruments, commercial electrochemical analyzers like potentiostats primarily operate as black boxes with manufacturers providing little or no information about internal circuitry and programming. This practice can limit a researcher’s ability to develop new techniques or adapt an instrument for applications outside its typical use. In contrast, creators of open-source instruments release all the necessary information for reproduction of the hardware and software – minimizing such barriers to innovation in chemical analyses. Here, we report a …

Extraction, Purification, And Characterization Of Potential Bioactive Compounds Produced By Janthinobacterium Lividum Tajx1901, Andy Elorm Agbakpo

Electronic Theses and Dissertations

Underexplored environments such as soil samples continue to be an untapped source of bacterial strains with great potential to produce secondary metabolites for medicinal applications. As a result, these microorganisms represent a broad and yet unknown reservoir of new strains capable of producing these novel compounds. The current research primarily seeks to perform the isolation, purification, and characterization of secondary metabolites from a soil bacterium (Janthinobacterium lividum TAJX1901). The isolated soil bacterium was successfully cultured on rich media agar plates, followed by extraction using methanol and chloroform. The purification methods utilized include flash column chromatography, preparative thin-layer chromatography, and …

Isolation And Structural Determination Of Bioactive Metabolites From The Soil Bacterium, Arthrobacter Sp. Tajx1902, Amonah Arije

Electronic Theses and Dissertations

As antimicrobial resistance persistently disrupts the treatment of microbial infection, natural product chemistry has played a significant role in identifying novel drugs with novel modes of action critical to getting ahead of resistance. The primary goal of this project is to extract and identify potential novel antimicrobial natural products produced by Arthrobacter sp. Although underexplored, Arthrobacter sps. have been known to produce bioactive compounds with versatility; one such is a depsipeptide with quorum-sensing inhibitory activity.¹ In this research, an Arthrobacter sp. TAJX1902, isolated from a soil sample, showed inhibitory activity against a filamentous indicator bacterium and a violacein-producing Janthinobacterium …

Fostering A Sense Of Wonder: Promoting Experiential Learning Through Outdoor Discoveries On The Coast Of Maine, Joslyn Primicias

Honors Theses

Recent generations of children have been experiencing a decrease in connection to Nature, causing various negative effects on personal development and well-being. Researchers are studying the impact of environmental education programs as a potential resolution to this issue dubbed “nature deficit disorder.” Studies have shown that while citizen science by youth is rapidly expanding, little research addresses conservation science with environmental education. Recognizing this gap, the summer camps at Wells NERR are designed to ensure children’s environmental stewardship as they learn about estuarine ecology alongside environmental issues. This educational study focuses on how environmental education through estuarine activities at summer …

Knowledge Representation For Conceptual, Motivational, And Affective Processes In Natural Language Communication, Seng Beng Ho, Zhaoxia Wang, Boon-Kiat Quek, Erik Cambria

Research Collection School Of Computing and Information Systems

Natural language communication is an intricate and complex process. The speaker usually begins with an intention and motivation of what is to be communicated, and what outcomes are expected from the communication, while taking into consideration the listener’s mental model to concoct an appropriate sentence. Likewise, the listener has to interpret the speaker’s message, and respond accordingly, also with the speaker’s mental model in mind. Doing this successfully entails the appropriate representation of the conceptual, motivational, and affective processes that underlie language generation and understanding. Whereas big-data approaches in language processing (such as chatbots and machine translation) have performed well, …

Transferable Curricula Through Difficulty Conditioned Generators, Sidney Tio, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Advancements in reinforcement learning (RL) have demonstrated superhuman performance in complex tasks such as Starcraft, Go, Chess etc. However, knowledge transfer from Artificial "Experts" to humans remain a significant challenge. A promising avenue for such transfer would be the use of curricula. Recent methods in curricula generation focuses on training RL agents efficiently, yet such methods rely on surrogate measures to track student progress, and are not suited for training robots in the real world (or more ambitiously humans). In this paper, we introduce a method named Parameterized Environment Response Model (PERM) that shows promising results in training RL agents …

Proxy Hunting: Understanding And Characterizing Proxy-Based Upgradeable Smart Contracts In Blockchains, William E Iii Bodell, Sajad Meisami, Yue Duan

Research Collection School Of Computing and Information Systems

Upgradeable smart contracts (USCs) have become a key trend in smart contract development, bringing flexibility to otherwise immutable code. However, they also introduce security concerns. On the one hand, they require extensive security knowledge to implement in a secure fashion. On the other hand, they provide new strategic weapons for malicious activities. Thus, it is crucial to fully understand them, especially their security implications in the real-world. To this end, we conduct a large-scale study to systematically reveal the status quo of USCs in the wild. To achieve our goal, we develop a complete USC taxonomy to comprehensively characterize the …

Decompiling X86 Deep Neural Network Executables, Zhibo Liu, Yuanyuan Yuan, Shuai Wang, Xiaofei Xie, Lei Ma

Research Collection School Of Computing and Information Systems

Due to their widespread use on heterogeneous hardware devices, deep learning (DL) models are compiled into executables by DL compilers to fully leverage low-level hardware primitives. This approach allows DL computations to be undertaken at low cost across a variety of computing platforms, including CPUs, GPUs, and various hardware accelerators. We present BTD (Bin to DNN), a decompiler for deep neural network (DNN) executables. BTD takes DNN executables and outputs full model specifications, including types of DNN operators, network topology, dimensions, and parameters that are (nearly) identical to those of the input models. BTD delivers a practical framework to process …

Hyperbolic Graph Topic Modeling Network With Continuously Updated Topic Tree, Ce Zhang, Rex Ying, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Connectivity across documents often exhibits a hierarchical network structure. Hyperbolic Graph Neural Networks (HGNNs) have shown promise in preserving network hierarchy. However, they do not model the notion of topics, thus document representations lack semantic interpretability. On the other hand, a corpus of documents usually has high variability in degrees of topic specificity. For example, some documents contain general content (e.g., sports), while others focus on specific themes (e.g., basketball and swimming). Topic models indeed model latent topics for semantic interpretability, but most assume a flat topic structure and ignore such semantic hierarchy. Given these two challenges, we propose a …

An Adaptive Large Neighborhood Search For Heterogeneous Vehicle Routing Problem With Time Windows, Minh Pham Kien Nguyen, Aldy Gunawan, Vincent F. Yu, Mustafa Misir

Research Collection School Of Computing and Information Systems

The heterogeneous vehicle routing problem with time windows (HVRPTW) employs various vehicles with different capacities to serve upcoming pickup and delivery orders. We introduce a HVRPTW variant for reflecting the practical needs of crowd-shipping by considering the mass-rapid-transit stations, as the additional terminal points. A mixed integer linear programming model is formulated. An Adaptive Large Neighborhood Search based meta-heuristic is also developed by utilizing a basic probabilistic selection strategy, i.e. roulette wheel, and Simulated Annealing. The proposed approach is empirically evaluated on a new set of benchmark instances. The computational results revealed that ALNS shows its clear advantage on the …

Experimental Study Of Linux Flightsize Estimation, Mingrui Zhang

School of Computing: Dissertations, Theses, and Student Research

Transmission Control Protocol (TCP) is a fundamental Internet protocol responsible for controlling and coordinating the Internet traffic. As a result, TCP significantly influences the overall performance and stability of the Internet. One critical information required by a TCP connection to make decisions is FlightSize, which is the total amount of outstanding data contributed by the connection to the Internet. The FlightSize information is used by a TCP connection to determine its future sending rate and also avoid traffic congestion and collapse in the Internet. Consequently, an inaccurate estimation of FlightSize can result in degraded performance and instability of the Internet. …

Seek And Classify: End-To-End Joint Multi-Signal Detection And Classification Using Deep Learning, Prashant Subedi

School of Computing: Dissertations, Theses, and Student Research

The rise in the use of wireless communication has led to the problem of spectrum scarcity in licensed bands. The popularity of Internet of Things (IoT) requires innovative solutions that maximize the use of available spectrum to support the increasing number of connected devices. This thesis tackles two significant problems in wireless communication: the need for efficient spectrum sensing techniques and the scarcity of large, diverse raw in-phase (I) and quadrature (Q) datasets.

The ability to detect and classify modulation of the signals efficiently can enable a cognitive radio to monitor the spectrum activity in real time and utilize unused …

Sparsity Brings Vulnerabilities: Exploring New Metrics In Backdoor Attacks, Jianwen Tian, Kefan Qiu, Debin Gao, Zhi Wang, Xiaohui Kuang, Gang Zhao

Research Collection School Of Computing and Information Systems

Nowadays, using AI-based detectors to keep pace with the fast iterating of malware has attracted a great attention. However, most AI-based malware detectors use features with vast sparse subspaces to characterize applications, which brings significant vulnerabilities to the model. To exploit this sparsityrelated vulnerability, we propose a clean-label backdoor attack consisting of a dissimilarity metric-based candidate selection and a variation ratio-based trigger construction. The proposed backdoor is verified on different datasets, including a Windows PE dataset, an Android dataset with numerical and boolean feature values, and a PDF dataset. The experimental results show that the attack can slash the accuracy …