Physical Sciences and Mathematics Commons^™

Contrastive Filtering And Dual-Objective Supervised Learning For Novel Class Discovery In Document-Level Relation Extraction, Nicholas Hansen

Master's Theses

Relation extraction (RE) is a task within natural language processing focused on the classification of relationships between entities in a given text. Primary applications of RE can be seen in various contexts such as knowledge graph construction and question answering systems. Traditional approaches to RE tend towards the prediction of relationships between exactly two entity mentions in small text snippets. However, with the introduction of datasets such as DocRED, research in this niche has progressed into examining RE at the document-level. Document-level relation extraction (DocRE) disrupts conventional approaches as it inherently introduces the possibility of multiple mentions of each unique …

Berkeley Pit And Discharge Pilot Project Discharge System Operations Assurance Plan 2024 Update, Revision 1, Pioneer Technical Services, Inc., Wsp Environment & Infrastructure, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Explicit Composition Identities For Higher Composition Laws In The Quadratic Case, Ajith A. Nair

Dissertations, Theses, and Capstone Projects

The theory of Gauss composition of integer binary quadratic forms provides a very useful way to compute the structure of ideal class groups in quadratic number fields. In addition to that, Gauss composition is also important in the problem of representations of integers by binary quadratic forms. In 2001, Bhargava discovered a new approach to Gauss composition which uses 2x2x2 integer cubes, and he proved a composition law for such cubes. Furthermore, from the higher composition law on cubes, he derived four new higher composition laws on the following spaces - 1) binary cubic forms, 2) pairs of binary quadratic …

Recombinant Bile Salt Hydrolase Enhances The Inhibition Efficiency Of Taurodeoxycholic Acid Against Clostridium Perfringens Virulence, Tahrir Alenezi, Bilal Alrubaye, Ying Fu, Janashrit Shrestha, Samar Algehani, Hong Wang, Rohana Liyanage, Xiaolun Sun

Chemistry & Biochemistry Faculty Publications and Presentations

Clostridium perfringens is the main pathogen of chicken necrotic enteritis (NE) causing huge economic losses in the poultry industry. Although dietary secondary bile acid deoxycholic acid (DCA) reduced chicken NE, the accumulation of conjugated tauro-DCA (TDCA) raised concerns regarding DCA efficacy. In this study, we aimed to deconjugate TDCA by bile salt hydrolase (BSH) to increase DCA efficacy against the NE pathogen C. perfringens. Assays were conducted to evaluate the inhibition of C. perfringens growth, hydrogen sulfide (H₂S) production, and virulence gene expression by TDCA and DCA. BSH activity and sequence alignment were conducted to select the …

On The Ratio-Type Family Of Copulas, Farid El Ktaibi, Rachid Bentoumi, Mhamed Mesfioui

All Works

Investigating dependence structures across various fields holds paramount importance. Consequently, the creation of new copula families plays a crucial role in developing more flexible stochastic models that address the limitations of traditional and sometimes impractical assumptions. The present article derives some reasonable conditions for validating a copula of the ratio-type form (Formula presented.). It includes numerous examples and discusses the admissible range of parameter (Formula presented.), showcasing the diversity of copulas generated through this framework, such as Archimedean, non-Archimedean, positive dependent, and negative dependent copulas. The exploration extends to the upper bound of a general family of copulas, (Formula presented.), …

Exporting Sysml Designs To Simulink, Drew Q. Broadbent

Theses and Dissertations

Various software systems have been developed to aid a systems engineer in evaluating system requirements, such as Dassault’s Magic System of Systems Architect (MSOSA) and MathWorks’ Simulink. Both software packages have different strengths; therefore, it is beneficial to export models from one software package to another. MSOSA provides a built-in tool that facilitates this transfer, built upon the Extension for Physical Interaction and Signal Flow Simulation (SysPhS) standard. However, the process is often unreliable and error prone and online documentation is largely lacking. This research used extensive trial and error to fill in the documentation gaps and create a method …

Ai Competency Acquisition Online? Engaging Undergraduate Students In An Ai 101 Course Through A Chatbot Workshop, Thomas Menkhoff, Lydia Teo

Research Collection Lee Kong Chian School Of Business

In recent years, digital transformation has dominated industries at an unprecedented rate. Alongside the proliferation of Artificial ntelligence (AI) technologies in the workplace, institutions of higher learning are experiencing an unprecedented push to integrate AI into the education ecosystem (Popenici & Kerr, 2017; Renz & Hilbig, 2020). AI in education (AIED) has the propensity to enrich teaching and learning in higher education by personalising students’ learning courses, automating assessment tasks, or providing24/7 access to learning resources (Karandish, 2021). According to estimates by the AI Market in the US Education Report, the AIEDmarket will grow at a CAGR of 47.77% during …

Design, Synthesis And Characterization Of Zinc And Gold-Based Metal Organic Frameworks And Complexes For The Detection And Treatment Of Cancer, May Reda Mohamed

Dissertations

The early detection of cancer plays a pivotal role in improving patient outcomes, emphasizing the urgent need for highly sensitive and selective biosensing platforms. Metal-Organic Frameworks (MOFs) have emerged as promising candidates in this domain due to their tunable properties, large surface area, and high porosity. Herein, the utilization of zinc-based MOF as a sophisticated biosensing platform tailored for the precise detection of the HER2 cancer biomarker is presented. HER2, a critical protein marker in breast cancer diagnostics and treatment, demands highly selective and accurate detection methods for effective patient management. Simple hydrothermal synthesis methodology has been adopted to synthetize …

Development Of Highly Efficient Hybridfunctionalized Membranes For Sustainable Water Harvesting, Ahmed Z. Abuibaid

Dissertations

Water scarcity has emerged as a critical global challenge. This problem catches the world's interest through events such as the severe drought in Europe during the summer of 2022. While desalination plants offer a solution, their high energy consumption necessitates the exploration of alternative, sustainable water resources. Addressing the urgent need for alternative water resources in the face of increasing water scarcity. A viable option was presented by fog harvesting, which is a method to collect water from atmospheric fog. This method was initially developed to meet the demands of a variety of sectors such as agricultural purposes, and household …

Synthesis Of Dyes Sulfamidazole: Characterization, Evaluation, Molecular Docking And Global Descriptors By Density Functional Theory (Dft)., Athra G. Sager, Jawad Kadhim Abaies, Zeena R. Katoof

Karbala International Journal of Modern Science

In the present work, novel azo compounds of sulfamidazole were created via the reaction of diazonium salt of sulfamidazole with several aromatic molecules including (resorcinol, 2-nitro phenol, 3-nitro phenol, and 4-nitro phenol)) (Z1–Z4). The new compounds (Z1-Z4) were identified using FTIR, ¹HNMR techniques, in addition to melting point measurements. The biological activity of compounds (Z1-Z4) was studied against four kinds of bacteria including E. coli, Klebsiella pneumonia, Salmonella, and Staphylococcus aureus. The findings showed that all compounds (Z1-Z4) were active against the examined bacteria. Theoretical studies of the antibacterial ability of the prepared compound against DNA gyrase enzyme …

Cyberbullying Detection On Twitter Data Using Machine Learning Classifiers, Pradip Dhakal

Data Science and Data Mining

This study compares some of the popular machine learning techniques like Logistic Regression, Multinomial Naive Bayes, K-Nearest Neighbor, and Extreme Gradient Boosting to classify the tweets into three different categories: cyberbullying based on religion, cyberbullying based on ethnicity, or no cyberbullying. First, various data-cleaning approaches are used to clean the tweet data. After the data is clean and ready, the word embedding techniques, such as a bag of words and term frequency-Inverse document frequency, are used to convert the words into mathematical vectors. Finally, the model will be fitted using the combination of the above-mentioned word embedding techniques and machine …

Empirical Exploration Of Software Testing, Samia Alblwi

Dissertations

Despite several advances in software engineering research and development, the quality of software products remains a considerable challenge. For all its theoretical limitations, software testing remains the main method used in practice to control, enhance, and certify software quality. This doctoral work comprises several empirical studies aimed at analyzing and assessing common software testing approaches, methods, and assumptions. In particular, the concept of mutant subsumption is generalized by taking into account the possibility for a base program and its mutants to diverge for some inputs, demonstrating the impact of this generalization on how subsumption is defined. The problem of mutant …

Network Slicing And Noma Enabled Mobile Edge Computing For Next-Generation Networks, Mohammad Arif Hossain

Dissertations

The advent of next-generation wireless networks ushers in a new era of potential, harnessing cutting-edge technologies like mobile edge computing (MEC), non-orthogonal multiple access (NOMA), and network slicing as pivotal drivers of transformation. Within this landscape, an innovative approach is proposed by introducing a NOMA-enabled network slicing technique within MEC networks. This approach aims to achieve multiple objectives: meeting stringent quality of service requirements, minimizing service latency, and enhancing spectral efficiency. By seamlessly integrating NOMA with network slicing in edge computing environments, significant reductions in overall latency are achieved, alongside ensuring optimal resource allocation for NOMA users. To address these …

On The Ubiquity, Properties And Evolution Of Small-Scale Magnetic Flux Ropes In The Heliosphere, Hameedullah Farooki

Dissertations

The solar wind is a plasma constantly blowing out from the Sun with a large-scale magnetic field having significant local complexity at small scales. Small-scale magnetic flux ropes (SMFRs), plasma structures with twisted field lines, are an important element of this complexity. This dissertation contributes several studies that further our understanding of SMFRs. The first study applies machine learning to measurements from Wind labeled by the presence of SMFRs and magnetic clouds (MCs). MCs were distinguished from non-MFRs with an AUC of 94% and SMFRs with an AUC of 89% and had distinctive plasma properties, whereas SMFRs appeared to be …

Computational Microscopy For Biomedical Imaging With Deep Learning Assisted Image Analysis, Yuwei Liu

Dissertations

Microscopy plays a crucial role across various scientific fields by enabling structural and functional imaging with microscopic resolution. In biomedicine, microscopy contributes to basic research and clinical diagnosis. Conventionally, optical microscopy derives its contrast from the amplitude of the optical wave and provides visualization of the physical structure of the sample qualitatively. To understand the function at the cellular or tissue level, there is a need to characterize the sample quantitatively and explore contrast mechanisms other than light intensity. Image enhancement or reconstruction from microscopic imaging systems is known as computational microscopy, and it involves the application of computational techniques …

Machine Learning-Based Design Of Doppler Tolerant Radar, Kyle Peter Wensell

Dissertations

In this work, machine learning theory is applied to the design of a radar detector in order to train a machine learning-based detector that is robust against Doppler shifts. The radar system is designed to work with data that would be otherwise intractable to conventional optimal detector design, such as transmitted noise waveforms and the effects of one-bit quantization at the receiver. The detection performance of the one-bit receiver is shown to match the performance of the derived square-law sign correlator detector. The resulting learning-based detector also introduces Doppler tolerance to the system, which allows for the successful detection of …

Information Theoretic Bounds For Capacity And Bayesian Risk, Ian Zieder

Dissertations

In this dissertation, the problem of finding lower error bounds on the minimum mean-squared error (MMSE) and the maximum capacity achieving distribution for a specific channel is addressed. Presented are two parts, a new lower bound on the MMSE and upper and lower bounds on the capacity achieving distribution for a Binomial noise channel. The new lower bound on the MMSE is achieved via use of the Poincare inequality. It is compared to the performance of the well known Ziv-Zakai error bound. The second part considers a binomial noise channel and is concerned with the properties of the capacity-achieving distribution. …

Financial Time Series Fusion, Completion, And Prediction With Deep Neural Networks, Dan Zhou

Dissertations

Time-series analysis is essential for a wide range of financial applications, including but not limited to bond valuation, firm earnings forecasts, firm fundamentals predictions, and firm characteristics imputations. Given its considerable value, the financial community has shown a strong interest in refining and advancing time-series analysis techniques. The study in this dissertation contributes to this field by employing advanced machine learning approaches, specifically graph neural networks, deep neural networks, and matrix/tensor methods. The primary objectives are twofold: first, to reveal complex correlations within financial time series to improve prediction accuracy, and second, to enhance the process of integrating and imputing …

Sensing With Integrity: Responsible Sensor Systems In An Era Of Ai, David Eisenberg

Dissertations

Deep and machine learning now offer immense benefits for consumer choice, decision-making, medicine, mental health and education, smart cities, and intelligent transportation and driver safety. However, as communication and Internet technology further advances, these benefits have the potential to be outweighed by compromises to privacy, personal freedom, consumer trust, and discrimination. While ethical consequences for personal freedom and equity rise from these technological advances, the issue may not be the technology itself but a lack of regulation and policy that allow abuses to occur. A first study examines how emerging sensor-based technologies, limited to only accelerometer and gyroscope data from …

A Molecular Dynamics Study On The Destruction Mechanism Of Per And Polyfluoroalkyl Substances Due To Ultrasound Technology, Bruno Bezerra De Souza

Dissertations

Per- and polyfluoroalkyl substances (PFAS) are a group of stable synthetic chemicals that are highly persistent and harmful pollutants to the environment and human health. PFAS have caused a strong public and regulatory response due to their ubiquitous presence in the environment and toxicity to humans. The application of ultrasound is one of the most effective treatment technologies for the mineralization of PFAS in contaminated water. However, this technology is treated as a black box causing the inability to be optimized. Therefore, there is a pressing need to investigate the intricate dynamics of PFAS degradation under ultrasound to unlock its …

Molecular-To-Continuum Scale Modeling Of Aerosols: Atmospheric Application And Beyond, Ella Ivanova

Dissertations

Aerosol modeling is critical for various applications, such as climate forecasting, air quality, and human health impact assessment. During their lifetime, aerosols undergo a complex evolution, usually divided into several stages — formation, processing, transport, and removal — that occur on different scales. Thus, the choice of modeling methods depends on the stage considered. For example, certain stages of particle formation may require nano-scale modeling while aerosol-cloud interactions span from microscale to mesoscale. This study examines the modeling of aerosol behavior over a wide range of scales, from nano to microscale, with implications to mesoscale.

This dissertation focuses on two …

A Machine Learning-Assisted Steering And Scheduling Framework For Big-Data Scientific Workflows On Heterogeneous Computing Platforms, Yijie Zhang

Dissertations

In next-generation scientific applications, the exponential growth of big data necessitates advanced techniques for efficient data storage, processing, and analysis. This has led to the construction of intricate computing workflows, managed and orchestrated by powerful engines in big data systems as exemplified by Hadoop. As scientific applications increasingly shift towards simulation-centric approaches, traditional methodologies face new challenges in accommodating the complexity of extreme-scale numerical modeling with numerous tunable parameters. To address these challenges, this dissertation propose to develop a machine learning-assisted framework that enables autonomous computational steering of scientific simulations and optimized execution of big-data workflows on heterogeneous platforms. This …

Development Of Super Hydrophobic Membranes And Their Applications, Sumona Paul

Dissertations

Effective liquid phase separation is vital across industries, especially in aviation, chemicals, fuels, and textiles. Membrane technology offers advantages over traditional methods like distillation, with low energy consumption and protection for heat-sensitive molecules. Membrane performance is assessed based on factors such as materials, surface modification, and interactions. This research targets superhydrophobic membranes for liquid-liquid filtration and antiwetting omniphobic membranes for membrane distillation, focusing on properties like contact angle and thermal resistance.

This study presents a highly hydrophobic membrane achieved by immobilizing carbon nanotubes (CNTs) onto a PTFE microfiltration membrane. It's designed for dewatering organic-water mixtures with trace water content via …

Application Of Secant Span In Medical Diagnosis, R. Narmadhagnanam, A. Edward Samuel

Neutrosophic Systems with Applications

Many common and specific characteristics engrave most diseases. Water-borne diseases differ slightly in their characteristics. Erroneous diagnoses can be attributed to shared characteristics. Current approaches tend to rely on imprecise diagnoses and lack robust techniques for differentiating between characteristics. Every illness also presents with specific symptoms. To assist doctors in approaching a likely diagnosis, the suggested method is successful in determining the connection between a class of sickness and the people with a specific pathology to the indications. Among n-valued interval neutrosophic sets, a secant span is proposed in this paper and a few of its attributes are talked about …

Rough Fermatean Neutrosophic Sets And Its Applications In Medical Diagnosis, P. Dhanalakshmi

Neutrosophic Systems with Applications

This paper introduces the concept of rough fermatean neutrosophic sets and investigates their properties. Additionally, a cosine similarity measure between these sets is proposed. By applying this measure to a medical diagnosis example, the paper illustrates how the method can be used in practical situations, highlighting its effectiveness in complex decision-making scenarios. This innovation holds promise for improving decision-making processes, especially in critical areas like medical diagnosis, where making accurate assessments amidst uncertainty is crucial.

Nonlinear Classifiers For Wet-Neuromorphic Computing Using Gene Regulatory Neural Network, Adrian Ratwatte, Samitha Somathilaka, Sasitharan Balasubramaniam, Assaf A. Gilad

School of Computing: Faculty Publications

The gene regulatory network (GRN) of biological cells governs a number of key functionalities that enable them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational principles resemble an artificial neural network (ANN), which can pave the way for the development of wet-neuromorphic computing systems. Genes are integrated into gene-perceptrons with transcription factors (TFs) as input, where the TF concentration relative to half-maximal RNA concentration and gene product copy number influences transcription and translation via weighted multiplication before undergoing a nonlinear activation function. This process yields protein concentration as the …

Internet-Based Data Platforms Re-Define The Distributions Of Some Large Crabronid Wasps In Arkansas (Hymenoptera: Crabronidae), David E. Bowles

Insecta Mundi

The geographic distributions of three large wasps, Sphecius speciosus (Drury), Stictia carolina Fabricius, and Stizus brevipennis Walsh (Hymenoptera: Crabronidae), occurring in Arkansas are defined using museum specimens and three internet-based data platforms. The internet-based data platforms generally provided more county location records than museum records. Using data from internet sources for easily identified species can better serve to illustrate the known distributions for some species thus making for a powerful tool elucidating distributional patterns and conservation planning.

ZooBank registration. urn:lsid:zoobank.org:pub:DCAE9192-1765-40CD-952B-0A094F413991

Morphometric Analysis And Taxonomic Re-Evaluation Of Pepsis Cerberus Lucas And P. Elegans Lepeletier (Hymenoptera: Pompilidae: Pepsinae: Pepsini), Frank E. Kurczewski, Akira Shimizu, Diane H. Kiernan

Insecta Mundi

Hurd (1952) separated Pepsis cerberus Lucas from P. elegans Lepeletier (Hymenoptera: Pompilidae: Pepsinae: Pepsini) based on external morphology and biogeography. Vardy (2005) synonymized the familiar and historically well-documented P. cerberus and P. elegans, combining these Nearctic taxa with several Neotropical variants in an extremely broad definition of P. menechma Lepeletier. In doing so, Vardy (2005) breached the principle of nomenclatural stability. He ignored the prevailing usage and clearly violated articles 23.2, 23.3 and 23.9.1.2 of the ICZN (1999). Morphological differences, ecological divergence, and narrow sympatric geographic distribution of P. cerberus and P. elegans …

Production, Utilization And Quality Of Irrigated Grasses And Legumes In The Mountain West Usa Under Mob Stocking Or Clipping, Jennifer W. Macadam, Brody Maughan, Xin Dai

Browse all Datasets

Feed is the most costly input for US ruminant livestock production systems, and increasing the utilization efficiency of irrigated forage systems can improve system profitability. This study assessed the production, utilization and quality of 22 intensively managed perennial grasses and legumes. Forages were cultivated as monocultures under irrigation and subjected to similarly intense clipping or mob stocking for two years at six-week intervals between May and September. Grasses and legumes were randomly assigned to 22 adjacent 1.5-m-wide by 9-m-long subplots within each whole plot, and pairs of whole plots were randomly assigned to grazing or clipping management. Seven grasses did …

A Survey Of Practical Haskell: Parsing, Interpreting, And Testing, Parker Landon

Honors Projects

Strongly typed pure functional programming languages like Haskell have historically been confined to academia as vehicles for programming language research. While features of functional programming have greatly influenced mainstream programming languages, the imperative programming style remains pervasive in practical software development. This paper illustrates the practical utility of Haskell and pure functional programming by exploring “hson,” a scripting language for processing JSON developed in Haskell. After introducing the relevant features of Haskell to the unfamiliar reader, this paper reveals how hson leverages functional programming to implement parsing, interpreting, and testing. By showcasing how Haskell’s language features enable the creation of …