Physical Sciences and Mathematics Commons^™

Updating Nuclear Recoil Models In The Noble Element Simulation Technique For The Lux-Zeplin Experiment, Ryan J. Mcmonigle

Electronic Theses & Dissertations (2024 - present)

There is a substantial amount of evidence that a majority of the mass in the universe is comprised of a non-luminous substance, given the name dark matter. Many candidates have been theorized as the solution to the dark matter mystery, with the Weakly Interacting Massive Particle (WIMP) being a favorable candidate. WIMPs may be detectable via interactions with nucleons, a method known as direct detection. The LUX-ZEPLIN (LZ) experiment is a xenon-based dual-phase Time Projection Chamber (TPC), that specializes in searching for WIMP-nucleon interactions. TPCs are used to reconstruct event information using scintillation and ionization, which are products of interactions …

Chemical Reduction Of Curved Molecular Nanographenes: Metal Intercalation And Core Transformation, Yikun Zhu

Electronic Theses & Dissertations (2024 - present)

The promising lithium storage capacity and fast charge transport rate make graphene a great candidate for energy storage applications. However, the harsh synthetic conditions lead to uneven size distribution and poor solubility, posing limitations on further applications and modifications of graphene and its derivatives. Molecular nanographenes, as graphene cutouts, possess outstanding chemical and physical properties stemming from their parent graphene but also demonstrate such unique features as controllable synthetic conditions, defined molecular structures, uniform size, and relatively good solubility. The improved solubility enables the use of solution chemistry methods for design modifications of molecular nanographenes and for systematical investigation of …

Assessment Of Spatial Distribution Patterns And Infestation Risk Over Time Of Spotted Lanterfly (Lycorma Delicatula) In New York State, Alanna Richman

Electronic Theses & Dissertations (2024 - present)

Spotted Lanternfly (Lycorma delicatula) is an invasive treehopper native to Asia. It was first introduced to Pennsylvania in 2014, and as of 2020 it has expanded to New York state. Their preferred host tree is the invasive tree of heaven (Ailanthus altissima), but they will feed on over 173 different plants worldwide. Preferred host species in New York state include agricultural vegetation such as grape vines and fruit trees. Positive Spotted Lanternfly site data collected by the New York Department of Agriculture and Markets and iMapInvasives were analyzed using ArcGIS Pro to assess spatial distribution patterns. …

Innovative Solutions In Medical Diagnostics, Seafood Industry, Environmental, And Forensic Applications Through Integrating Raman Spectroscopy And Machine Learning, Luis Perez Almodovar

Electronic Theses & Dissertations (2024 - present)

There is an urgent need for rapid and on-site analytical methods to address various environmental and forensic challenges. This dissertation explores the application of Raman spectroscopy combined with machine learning for three critical areas: evaluating perfluoroalkyl or polyfluoroalkyl substances (PFAS) exposure in wildlife, authenticating tuna species in the seafood industry, identifying body fluids at crime scenes, and medical diagnostics.

First, Raman spectroscopy and support vector machine discriminant analysis (SVMDA) were employed to detect PFAS levels in the blood plasma of smallmouth bass from two different tributaries, rivers, or streams that flow into larger bodies of water. The method achieved 100% …

Advancements In Forensic Analysis: Development Of Mass Spectrometric And Chemometric Approaches For The Identification Of Synthetic New Psychoactive Substances And Plant Materials, Mónica Ventura

Electronic Theses & Dissertations (2024 - present)

While forensic science is a well-established discipline, a number of federal agencies have highlighted challenges that continue to plague the field and the extent to which these challenges remain unaddressed. Examples are the illegal trade of wildlife timber and the drug epidemic. Characterization of these materials requires nuanced method development for compound determination, matrix material-specific protocols, and heavy use of expensive consumables. The application of a technique such as direct analysis in real time – high-resolution mass spectrometry (DART-HRMS) provides the opportunity to circumvent many of the challenges presented by conventional methods. In general, little to no sample preparation is …

Leveraging Redundancy As A Link Between Spreading Dynamics On And Of Networks, Felipe Xavier Costa

Electronic Theses & Dissertations (2024 - present)

A constant quest in network science has been in the development of methods to identify the most relevant components in a dynamical system solely via the interaction structure amongst its subsystems. This information allows the development of control and intervention strategies in biochemical signaling and epidemic spreading. We highlight the relevant components in heterogeneous dynamical system by their patterns of redundancy, which can connect how dynamics affect network topology and which pathways are necessary to spreading phenomena on networks. In order to measure the redundancies in a large class of empirical systems, we develop the backbone of directed networks methodology, …

Sources And Significance Of Symmetric Instability In The Outflow Layer Of Tropical Cyclones, Minghao Zhou

Electronic Theses & Dissertations (2024 - present)

The tropical cyclone (TC) outflow layer is an integral part of the TC transverse circulation: warm and moist air converges in the boundary layer of the TC; rising motion and condensational heating occur in the TC eyewall and rainbands; the TC warm core and divergent anticyclone establish in the mid-upper troposphere, with the mass imported into the TC center exported away through outflow near the tropopause.

A series of state-of-the-art numerical simulations were utilized to investigate different scenarios of TC outflow evolution, from an idealized axisymmetric setup to a semi-idealized asymmetric setup, to a real-case setup of TC–trough interaction. Large …

Enhancing A Computer Model To Evaluate Rainwater Harvesting And Reuse Strategies For Landscape Irrigation Under Current And Future Climate Conditions, Siyu Li

Electronic Theses & Dissertations (2024 - present)

Urbanization and climate change have detrimental impacts on water quantity and quality. Rainwater harvesting/reuse for landscape irrigation with rain barrels/cisterns, which can assist urban hydrological/water quality restoration and provide a beneficial resource for irrigating landscape, holds significant potentials in urban areas under current and future climates. However, the impacts of rainwater harvesting/reuse strategies for landscape irrigation under a changing climate need to be comprehensively evaluated to assist decision-making. In addition, a comprehensive tool is needed to help stakeholders develop efficient strategies to harvest rainwater for landscape irrigation with rain barrels/cisterns.

To address the needs, the simulation of the sub-daily hydrological …

Exploring International Educators' Learning About Local And Global Social Justice In A Virtual Community Of Practice, Bima Sapkota, Xuwei Luo, Muna Sapkota, Murat Akarsu, Emmanuel Deogratias, Daphne Fauber, Rose Mbewe, Fidelis Mumba, Ram Krishna Panthi, Jill Newton

School of Mathematical & Statistical Sciences Faculty Publications

In this chapter, the authors report themes that emerged when a cross-cultural team of researchers involved in a virtual international community of practice (Global Social Justice in Education-GSJE) investigated reflections on activities focused on social justice in local and global contexts. The findings suggested that the activities elicited GSJE community members' understandings of the complexities of social justice associated with naming practices, privilege, and the arts within their own and across contexts. The authors discuss implications of the activities to advance diverse educators' understanding of social justice in global and local contexts. They also unpack the opportunities and challenges that …

Modeling The Effect Of Observational Social Learning On Parental Decision-Making For Childhood Vaccination And Diseases Spread Over Household Networks, Tamer Oraby, Andras Balogh

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we introduce a novel model for parental decision-making about vaccinations against a childhood disease that spreads through a contact network. This model considers a bilayer network comprising two overlapping networks, which are either Erdős–Rényi (random) networks or Barabási–Albert networks. The model also employs a Bayesian aggregation rule for observational social learning on a social network. This new model encompasses other decision models, such as voting and DeGroot models, as special cases. Using our model, we demonstrate how certain levels of social learning about vaccination preferences can converge opinions, influencing vaccine uptake and ultimately disease spread. In addition, …

Flow And Stability Of Three-Dimensional Rotating Viscoelastic Jet, Daniel N. Riahi

School of Mathematical & Statistical Sciences Faculty Publications

We consider rotationally driven nonlinear viscoelastic jet. In contrast to previous unrealistic planar studies with no gravity effect, we investigate here realistic three-dimensional jet with gravity effect. Using theoretical and computational methods, we develop models for such jet and its stability and determine the solutions. We find three-dimensional jet with gravity effect leads to faster and thinner jet. For jet arc length higher than its exit diameter, our calculation with typical parameter values shows that jet radius is smaller by a factor of more than 1.4 and jet speed is higher by factor of more than 2 as are compared …

Optimizing Energy Consumption In Smart Homes Using Ga-Lstm, Akibor Junior Chukwuka, Bakare-Bolaji Moyosoreoluwa, Baboucarr Dibba

School of Mathematical & Statistical Sciences Faculty Publications

The need to optimize energy consumption arises from the inadequate energy supply many homes face. However, to optimize energy consumption in a home, one must be equipped with the knowledge of the energy consumption rate and energy supply rate in the home. This paper proposed the use of a Long Short-Term Memory (LSTM) model optimized by Genetic Algorithm (GA) to optimize the energy consumption in a smart home. The model was designed using 8 input variables, which were observed weather information of a given region over a span of 350 days. The data set was split into a training data …

Ivermectin, Colleen Aldous, Eleftherios Gkioulekas, Philip Oldfield

School of Mathematical & Statistical Sciences Faculty Publications

No abstract provided.

Investigating Preservice Teachers’ Conceptualizations Of Mathematical Knowledge For Teaching Through Video Analysis, Bima Sapkota

School of Mathematical & Statistical Sciences Faculty Publications

Mathematics Preservice Teachers’ (M-PSTs) conceptions of Mathematical Knowledge for Teaching (MKT) enhance their reflective skills because they utilize such conceptions to reflect on how to contextualize content knowledge during secondary mathematics teaching. While previous studies suggested M-PSTs develop MKT, including pedagogical content knowledge by analyzing teaching in video lessons, how M-PSTs enhance their conceptions of MKT through such analysis is underexplored. I used a collective case study approach to investigate how four secondary M-PSTs conceptualized MKT when they analyzed and discussed teaching represented in a video lesson using the MKT framework. The findings indicated that the M-PSTs often described teacher …

On The Center And The Antipode Of The Super-Yangian Of Q(1), Elena Poletaeva

School of Mathematical & Statistical Sciences Faculty Publications

For the queer Lie superalgebra Q(1), we explicitly describe the center of the super-Yangian Y Q(1) of Q(1) in terms of generators of Y Q(1). We also describe the square of the antipodal map on Y Q(1) and use the antipode to construct an involutive automorphism of Y Q(1).

A Use Case Of Chatgpt: Summary Of An Expert Panel Discussion On Electronic Health Records And Implementation Science, Seppo T Rinne, Julian Brunner, Timothy P Hogan, Jacqueline M Ferguson, Drew A Helmer, Sylvia J Hysong, Grace Mckee, Amanda Midboe, Megan E Shepherd-Banigan, A Rani Elwy

Center for Medical Ethics and Health Policy Staff Publications

Objective: Artificial intelligence (AI) is revolutionizing healthcare, but less is known about how it may facilitate methodological innovations in research settings. In this manuscript, we describe a novel use of AI in summarizing and reporting qualitative data generated from an expert panel discussion about the role of electronic health records (EHRs) in implementation science.

Materials and methods: 15 implementation scientists participated in an hour-long expert panel discussion addressing how EHRs can support implementation strategies, measure implementation outcomes, and influence implementation science. Notes from the discussion were synthesized by ChatGPT (a large language model-LLM) to generate a manuscript summarizing the discussion, …

Conditional Constrained And Unconstrained Quantization For Probability Distributions, Megha Pandey, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we present the idea of conditional quantization for a Borel probability measure P on a normed space Rk. We introduce the concept of conditional quantization in both constrained and unconstrained scenarios, along with defining the conditional quantization errors, dimensions, and coefficients in each case. We then calculate these values for specific probability distributions. Additionally, we demonstrate that for a Borel probability measure, the lower and upper quantization dimensions and coefficients do not depend on the conditional set of the conditional quantization in both constrained and unconstrained quantization.

Atomically Dispersed Zeolite-Supported Rhodium Complex: Selective And Stable Catalyst For Acetylene Semi-Hydrogenation, Yuxin Zhao, Özge D. Bozkurt, Samira F. Kurtoğlu-Öztulum, Melisa Su Yordanli, Adam S. Hoffman, Jiyun Hong, Jorge E. Perez-Aguilar, Aylin Saltuk, Deniz Akgül, Tülay Ateşin

School of Integrative Biological & Chemical Sciences (Formerly Dept. of Chemistry)

Highlights

• Rhodium has heretofore been known to be unselective for acetylene semi-hydrogenation.

• Electronic properties of rhodium are tuned by choice of support and metal nuclearity.

• Selectivity is maximized when Rh is atomically dispersed on a weak electron donor.

• Zeolite-supported Rh complex with C2H2 ligands offers a stable C2H4 selectivity > 90 %.

• Results demonstrate unprecedented catalytic properties of Rh for semi-hydrogenation.

Abstract

Supported rhodium catalysts are known to be unselective for semi-hydrogenation reactions. Here, by tuning the electronic structure of supported mononuclear rhodium sites determined by the metal nuclearity and the electron-donor properties of the support, we report that atomically dispersed …

Symmetries And Integrable Systems, Sen-Yue Lou, Bao-Feng Feng

School of Mathematical & Statistical Sciences Faculty Publications

Symmetry plays key roles in modern physics especially in the study of integrable systems because of the existence of infinitely many local and nonlocal generalized symmetries. In addition to the fundamental role to find exact group invariant solutions via Lie point symmetries, some important new developments on symmetries and conservation laws are reviewed. The recursion operator method is important to find infinitely many local and nonlocal symmetries of (1+1)-dimensional integrable systems. In this paper, it is pointed out that a recursion operator may be obtained from one key symmetry, say, a residual symmetry. For (2+1)-dimensional integrable systems, the master-symmetry approach …

Structure Of Fine Selmer Groups In Abelian P-Adic Lie Extensions, Debanjana Kundu, Filippo Alberto Edoardo Nuccio Mortarino Majno Di Capriglio, Sujatha Ramdorai

School of Mathematical & Statistical Sciences Faculty Publications

This paper studies fine Selmer groups of elliptic curves in abelian p -adic Lie extensions. A class of elliptic curves are provided where both the Selmer group and the fine Selmer group are trivial in the cyclotomic Z p -extension. The fine Selmer groups of elliptic curves with complex multiplication are shown to be pseudonull over the trivializing extension in some new cases. Finally, a relationship between the structure of the fine Selmer group for some CM elliptic curves and the Generalized Greenberg's Conjecture is clarified.

Ultradeep Atca Imaging Of 47 Tucanae Reveals A Central Compact Radio Source, Alessandro Paduano, Arash Bahramian, James C. A. Miller-Jones, Adela Kawka, Tim J. Galvin, Liliana E. Rivera Sandoval, Sebastian Kamann, Jay Strader, Laura Chomiuk, Craig O. Heinke

Physics & Astronomy Faculty Publications

We present the results of an ultradeep radio continuum survey, containing ∼480 hr of observations, of the Galactic globular cluster 47 Tucanae with the Australia Telescope Compact Array. This comprehensive coverage of the cluster allows us to reach rms noise levels of 1.19 μJy beam−1 at 5.5 GHz, 940 nJy beam−1 at 9 GHz, and 790 nJy beam−1 in a stacked 7.25 GHz image. This is the deepest radio image of a globular cluster and the deepest image ever made with the Australia Telescope Compact Array. We identify ATCA J002405.702-720452.361, a faint (6.3 ± 1.2 μJy at 5.5 …

Logarithmic Algorithms For Fair Division Problems, Alexandr Grebennikov, Xenia Isaeva, Andrei V. Malyutin, Mikhail Mikhailov, Oleg R. Musin

School of Mathematical & Statistical Sciences Faculty Publications

We study the algorithmic complexity of fair division problems with a focus on minimizing the number of queries needed to find an approximate solution with desired accuracy. We show for several classes of fair division problems that under certain natural conditions on sets of preferences, a logarithmic number of queries with respect to accuracy is sufficient.

On Combinatorics Of Voronoi Polytopes For Perturbations Of The Dual Root Lattices, Alexey Garber

School of Mathematical & Statistical Sciences Faculty Publications

The Voronoi conjecture on parallelohedra claims that for every convex polytope P that tiles Euclidean d-dimensional space with translations there exists a d-dimensional lattice such that P and the Voronoi polytope of this lattice are affinely equivalent. The Voronoi conjecture is still open for the general case but it is known that some combinatorial restrictions for the face structure of P ensure that the Voronoi conjecture holds for P. In this article, we prove that if P is the Voronoi polytope of one of the dual root lattices Dd*, E6*, E7* or E8*=E8 or their small perturbations, then every parallelohedron …

A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …

Learning Optimal Inter-Class Margin Adaptively For Few-Shot Class-Incremental Learning Via Neural Collapse-Based Meta-Learning, Hang Ran, Weijun Li, Lusi Li, Songsong Tian, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Few-Shot Class-Incremental Learning (FSCIL) aims to learn new classes incrementally with a limited number of samples per class. It faces issues of forgetting previously learned classes and overfitting on few-shot classes. An efficient strategy is to learn features that are discriminative in both base and incremental sessions. Current methods improve discriminability by manually designing inter-class margins based on empirical observations, which can be suboptimal. The emerging Neural Collapse (NC) theory provides a theoretically optimal inter-class margin for classification, serving as a basis for adaptively computing the margin. Yet, it is designed for closed, balanced data, not for sequential or few-shot …

Robots Still Outnumber Humans In Web Archives In 2019, But Less Than In 2015 And 2012, Himarsha R. Jayanetti, Kritika Garg, Sawood Alam, Michael L. Nelson, Michele C. Weigle

Computer Science Faculty Publications

The significance of the web and the crucial role of web archives in its preservation highlight the necessity of understanding how users, both human and robot, access web archive content, and how best to satisfy this disparate needs of both types of users. To identify robots and humans in web archives and analyze their respective access patterns, we used the Internet Archive’s (IA) Wayback Machine access logs from 2012, 2015, and 2019, as well as Arquivo.pt’s (Portuguese Web Archive) access logs from 2019. We identified user sessions in the access logs and classified those sessions as human or robot based …

Building Datasets To Support Information Extraction And Structure Parsing From Electronic Theses And Dissertations, William A. Ingram, Jian Wu, Sampanna Yashwant Kahu, Javaid Akbar Manzoor, Bipasha Banerjee, Aman Ahuja, Muntabir Hasan Choudhury, Lamia Salsabil, Winston Shields, Edward A. Fox

Computer Science Faculty Publications

Despite the millions of electronic theses and dissertations (ETDs) publicly available online, digital library services for ETDs have not evolved past simple search and browse at the metadata level. We need better digital library services that allow users to discover and explore the content buried in these long documents. Recent advances in machine learning have shown promising results for decomposing documents into their constituent parts, but these models and techniques require data for training and evaluation. In this article, we present high-quality datasets to train, evaluate, and compare machine learning methods in tasks that are specifically suited to identify and …

Triphlapan: Predicting Hla Molecules Binding Peptides Based On Triple Coding Matrix And Transfer Learning, Meng Wang, Chuqi Lei, Jianxin Wang, Yaohang Li, Min Li

Computer Science Faculty Publications

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding …

Osfs-Vague: Online Streaming Feature Selection Algorithm Based On A Vague Set, Jie Yang, Zhijun Wang, Guoyin Wang, Yanmin Liu, Yi He, Di Wu

Computer Science Faculty Publications

Online streaming feature selection (OSFS), as an online learning manner to handle streaming features, is critical in addressing high-dimensional data. In real big data-related applications, the patterns and distributions of streaming features constantly change over time due to dynamic data generation environments. However, existing OSFS methods rely on presented and fixed hyperparameters, which undoubtedly lead to poor selection performance when encountering dynamic features. To make up for the existing shortcomings, the authors propose a novel OSFS algorithm based on vague set, named OSFS-Vague. Its main idea is to combine uncertainty and three-way decision theories to improve feature selection from the …

A-Disetrac Advanced Analytic Dashboard For Distributed Eye Tracking, Yasasi Abeysinghe, Bhanuka Mahanama, Gavindya Jayawardena, Yasith Jayawardena, Mohan Sunkara, Andrew T. Duchowski, Vikas Ashok, Sampath Jayarathna

Computer Science Faculty Publications

Understanding how individuals focus and perform visual searches during collaborative tasks can help improve user engagement. Eye tracking measures provide informative cues for such understanding. This article presents A-DisETrac, an advanced analytic dashboard for distributed eye tracking. It uses off-the-shelf eye trackers to monitor multiple users in parallel, compute both traditional and advanced gaze measures in real-time, and display them on an interactive dashboard. Using two pilot studies, the system was evaluated in terms of user experience and utility, and compared with existing work. Moreover, the system was used to study how advanced gaze measures such as ambient-focal coefficient K …