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Articles 31 - 60 of 448
Full-Text Articles in Applied Mathematics
Enhancing Mathematical Models For Covid-19 Pandemic Response: A Philippine Study, Timothy Robin Teng, Elvira De Lara-Tuprio, Ma. Regina Justina Estuar, Christian Pulmano, Lu Christian S. Ong, Zachary Pangan, Lenard Paulo V. Tamayo, Jasper John V. Segismundo, Mark Anthony C. Tolentino, Alyssa Nicole N. Ty
Enhancing Mathematical Models For Covid-19 Pandemic Response: A Philippine Study, Timothy Robin Teng, Elvira De Lara-Tuprio, Ma. Regina Justina Estuar, Christian Pulmano, Lu Christian S. Ong, Zachary Pangan, Lenard Paulo V. Tamayo, Jasper John V. Segismundo, Mark Anthony C. Tolentino, Alyssa Nicole N. Ty
Mathematics Faculty Publications
Mathematical models supported by a robust automated data pipeline proved to be useful tools for a data-driven and science-based response and policy-making during the COVID-19 pandemic in the Philippines. In the first year of the pandemic, FASSSTER (Feasibility Analysis on Syndromic Surveillance using Spatio-Temporal Epidemiological modeleR) used a compartmental model to generate scenario-based projections of COVID-19 cases. The emergence of the Delta variant, however, and the administration of vaccines over the second half of 2021 caused significant changes in the Philippine pandemic landscape. This necessitated making adjustments to the model to better capture the local disease transmission dynamics and address …
Bayesian Lasso Regularized Quantile Regression And Its Applications, Priscilla Kissi-Appiah
Bayesian Lasso Regularized Quantile Regression And Its Applications, Priscilla Kissi-Appiah
Theses and Dissertations
Since the pioneering work of (Koenker and Bassett Jr 1978), quantile regression has been a popular regression technique that helps researchers investigate a whole distribution of the response variable. In addition, due to the quantile check loss function, it is robust against outliers and heavy-tailed distributions of the response variable and can provide a more comprehensive picture of modeling via exploring the conditional quantiles of the response variable. In this research, we study the lasso regularized quantile regression from a Bayesian perspective. We develop an efficient sampling algorithm to generate posterior samplings for making posterior inference by using a location-scale …
Bounding The Convex Hull Relaxation Of The Unit Commitment Problem With The Shapley-Folkman Theorem, Lauren Henderson
Bounding The Convex Hull Relaxation Of The Unit Commitment Problem With The Shapley-Folkman Theorem, Lauren Henderson
All Theses
The Unit Commitment (UC) problem finds an optimal schedule for a set of generators by minimizing the total operation cost subject to demand and operational constraints. The UC problem is often modeled with a mixed-integer linear program (MILP). We employ the Shapley-Folkman Theorem to provide a bound on the size of fractional solutions of its convex hull relaxation. This result is used to obtain a bound on the optimality gap between the MILP and the convex hull relaxation, which is further tightened using several problem-specific properties of UC. We conduct extensive numerical experiments to study the tightness of this threshold, …
Geoflood: Computational Model For Overland Flooding, Brian Kyanjo
Geoflood: Computational Model For Overland Flooding, Brian Kyanjo
Boise State University Theses and Dissertations
Overland flooding, a critical environmental phenomenon, poses significant challenges for computational modeling due to its complex hydrodynamics and the need for high-resolution data. This thesis presents GeoFlood, a new open-source software package for overland flooding simulations. The computational model solves shallow water equations (SWE) on a quadtree hierarchy of mapped, logically Cartesian grids managed by the parallel, adaptive library ForestClaw (Calhoun & Burstedde, 2017). The model is validated using standard benchmark tests from Neelz & Pender (2013) and against results from the GeoClaw software (George, 2011; Clawpack Development Team, 2020) for the historical Malpasset dam break problem. The benchmark test …
Question-Attentive Review-Level Explanation For Neural Rating Regression, Trung Hoang Le, Hady Wirawan Lauw
Question-Attentive Review-Level Explanation For Neural Rating Regression, Trung Hoang Le, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Recommendation explanations help to improve their acceptance by end users. Explanations come in many different forms. One that is of interest here is presenting an existing review of the recommended item as the explanation. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance or “attention” of each review to the recommendation objective. Our focus is improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution …
A Dynamical Systems Approach For Modeling Malware Propagating Through A Network And Potential Solutions Towards Mitigating Spread, James Johnson
A Dynamical Systems Approach For Modeling Malware Propagating Through A Network And Potential Solutions Towards Mitigating Spread, James Johnson
Cybersecurity Undergraduate Research Showcase
Many people draw close parallels between malware propagating through a network and an epidemic spreading through a population. Epidemics are often modeled by a Susceptible-Infected-Recovered (SIR) model, in which a similar system of equations can model the spread of a virus through a computer network, and can be simplified when making assumptions about the network itself and its fixed number of nodes and edges. In this instance, malware propagating in a network also should reflect the network it is propagating through, in which the dynamical system will factor in the nodes of the network and their properties. The system itself …
Leveraging Quantitative Systems Pharmacology For Dose Optimization In Oncology Drug Development, Blerta Shtylla
Leveraging Quantitative Systems Pharmacology For Dose Optimization In Oncology Drug Development, Blerta Shtylla
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Kreig: Gaining Insight Into Epidemics Through Use Of Mathematical Modeling, Christina Edholm
Kreig: Gaining Insight Into Epidemics Through Use Of Mathematical Modeling, Christina Edholm
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Shevtsov: Growing Pains And Growing Gains: The Arizona Experience, Tynan Lazarus, Jane Shevtsov
Shevtsov: Growing Pains And Growing Gains: The Arizona Experience, Tynan Lazarus, Jane Shevtsov
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Early Ctdna Kinetics As A Dynamic Biomarker Of Cancer Treatment Response, Aaron Li, Emil Lou, Kevin Leder, Jasmine Foo
Early Ctdna Kinetics As A Dynamic Biomarker Of Cancer Treatment Response, Aaron Li, Emil Lou, Kevin Leder, Jasmine Foo
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
A Paradigm For Ecological Dynamics In Predator-Prey Systems: Applications To Climate Change, Grayson D. Adams, Aditi Ghosh Dr.
A Paradigm For Ecological Dynamics In Predator-Prey Systems: Applications To Climate Change, Grayson D. Adams, Aditi Ghosh Dr.
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Kreig: Examining Affinity Maturation And Antigenic Drift With An Agent-Based Model, Jasmine Af Kreig, Jannatul Ferdous, Ruian Ke, Ruy M. Ribeiro
Kreig: Examining Affinity Maturation And Antigenic Drift With An Agent-Based Model, Jasmine Af Kreig, Jannatul Ferdous, Ruian Ke, Ruy M. Ribeiro
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Modelling Saccharomyces Cerevisiae For The Production Of Fermented Beverages, Paul A. Valle Dr., Yolocuauhtli Salazar Dr., Luis N. Coria Dr., Oscar N. Soto Dr., Jesus B. Paez Dr.
Modelling Saccharomyces Cerevisiae For The Production Of Fermented Beverages, Paul A. Valle Dr., Yolocuauhtli Salazar Dr., Luis N. Coria Dr., Oscar N. Soto Dr., Jesus B. Paez Dr.
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Network Analysis Of Progress In Mathematics Research, Anna Singley
Network Analysis Of Progress In Mathematics Research, Anna Singley
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
From Ecology To Modeling Apps - Newts, Crayfish, And Slopes, Timothy Lucas
From Ecology To Modeling Apps - Newts, Crayfish, And Slopes, Timothy Lucas
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Bodine: Exploring The Versatility Of Agent-Based Modeling In Netlogo: Lessons From Education And Research, Anne E. Yust
Bodine: Exploring The Versatility Of Agent-Based Modeling In Netlogo: Lessons From Education And Research, Anne E. Yust
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Bayesian Networks And Machine Learning For Predicting Breast Cancer Growth From In Vitro Cell Count Data, Widodo Samyono
Bayesian Networks And Machine Learning For Predicting Breast Cancer Growth From In Vitro Cell Count Data, Widodo Samyono
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Seshaiyer: Data-Driven Machine Learning Framework To Predict Dynamics Of Infectious Diseases Incorporating Human Behavior, Alonso Gabriel Ogueda Oliva, Dr. Padmanabhan Seshaiyer
Seshaiyer: Data-Driven Machine Learning Framework To Predict Dynamics Of Infectious Diseases Incorporating Human Behavior, Alonso Gabriel Ogueda Oliva, Dr. Padmanabhan Seshaiyer
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Bodine: Enhancing Netlogo3d Simulations: Computational Efficiency And Online Accessibility Of A Pain Model, Rachael Miller Neilan
Bodine: Enhancing Netlogo3d Simulations: Computational Efficiency And Online Accessibility Of A Pain Model, Rachael Miller Neilan
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Modeling Opioid Addiction In Hand Surgery Patients, Eli Goldwyn, Grace Bowman, Kathryn Montovan, Julie Blackwood
Modeling Opioid Addiction In Hand Surgery Patients, Eli Goldwyn, Grace Bowman, Kathryn Montovan, Julie Blackwood
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Modeling The Synergistic Interplay Between Malaria Dynamics And Economic Growth, Ruijun Zhao, Hope Enright, Calistus Ngonghala, Olivia Prsoper
Modeling The Synergistic Interplay Between Malaria Dynamics And Economic Growth, Ruijun Zhao, Hope Enright, Calistus Ngonghala, Olivia Prsoper
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Seshaiyer: Understanding Non-Linear Dynamics Of Interacting Subpopulations And Implicit Human Behavior Using Physics-Informed Neural Networks, Naima Aubry-Romero, Alonso Ogueda-Oliva, Padmanabhan Seshaiyer
Seshaiyer: Understanding Non-Linear Dynamics Of Interacting Subpopulations And Implicit Human Behavior Using Physics-Informed Neural Networks, Naima Aubry-Romero, Alonso Ogueda-Oliva, Padmanabhan Seshaiyer
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Harvesting Modulation In Three Species Food Chains Using A Robust Control Approach, Hector Puebla, Mariana Rodriguez-Jara, Priti Kumar Roy
Harvesting Modulation In Three Species Food Chains Using A Robust Control Approach, Hector Puebla, Mariana Rodriguez-Jara, Priti Kumar Roy
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Mathematically Modeling How Trapping Specific Crayfish Life Stages Impacts Removal Efficacy, Relena Pattison, Courtney L. Davis
Mathematically Modeling How Trapping Specific Crayfish Life Stages Impacts Removal Efficacy, Relena Pattison, Courtney L. Davis
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Improving Infectious Disease Predictions Through The Use Of Metapopulation Sir Modeling And Graph Convolutional Neural Networks, Petr Kisselev, Padmanabhan Seshaiyer
Improving Infectious Disease Predictions Through The Use Of Metapopulation Sir Modeling And Graph Convolutional Neural Networks, Petr Kisselev, Padmanabhan Seshaiyer
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Modeling Insurance Impact On Opioid Addiction, Ihlara K. Williamson, Eli E. Goldwyn
Modeling Insurance Impact On Opioid Addiction, Ihlara K. Williamson, Eli E. Goldwyn
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
On The Work Of Cartan And Münzner On Isoparametric Hypersurfaces, Thomas E. Cecil, Patrick J. Ryan
On The Work Of Cartan And Münzner On Isoparametric Hypersurfaces, Thomas E. Cecil, Patrick J. Ryan
Mathematics and Computer Science Department Faculty Scholarship
A hypersurface Mn in a real space form Rn+1, Sn+1, or Hn+1 is isoparametric if it has constant principal curvatures. This paper is a survey of the fundamental work of Cartan and Münzner on the theory of isoparametric hypersurfaces in real space forms, in particular, spheres. This work is contained in four papers of Cartan [3]–[6] published during the period 1938–1940, and two papers of Münzner [47]–[48] that were published in preprint form in the early 1970’s, and as journal articles in 1980–1981. These papers of Cartan and Münzner have been the …
Learning Problems Related To Stochastic Differential Equations, Jinpu Zhou
Learning Problems Related To Stochastic Differential Equations, Jinpu Zhou
LSU Doctoral Dissertations
Stochastic differential equations (SDEs) are essential for modeling systems influenced by both deterministic dynamics and random fluctuations, with applications in a wide variety of disciplines. This thesis develops a Bayesian framework for nonparametric learning in SDEs, addressing key challenges in inference, particularly when dealing with complex systems and incomplete data. The thesis begins by establishing a theoretical foundation in optimization over Hilbert spaces, including a generalized representer theorem to address infinite-dimensional optimization problems encountered in nonparametric inference. Building on this, we introduce a Bayesian framework with shrinkage priors to learn drift functions from high-frequency data. Bayesian approach incorporates low-cost sparse …
Cp Decomposition Initialization Schemes For Speeding-Up Of Convolutional Neural Networks, Mollee M. Swift
Cp Decomposition Initialization Schemes For Speeding-Up Of Convolutional Neural Networks, Mollee M. Swift
LSU Master's Theses
While machine learning and convolutional neural networks (CNNs) are making strides, a persistent effort remains to optimize classification techniques and target redundancies from a large number of parameters naturally present in CNNs. While CNNs have become more accessible across machine learning, the aim is to make their use optimal for central processing unit (CPU) schemes with varying degrees of computational power. Tensor decomposition methods, specifically canonical polyadic (CP) decomposition, look to reduce parameters by compressing specific layers in a CNN. However, they have certain inconsistencies, and their full potential remains untouched as decomposition research is endless, with many facets one …
An Introduction To The Time-Independent Schrödinger Equation And Methods To Solve It, Vu Giang, Alex Gnech
An Introduction To The Time-Independent Schrödinger Equation And Methods To Solve It, Vu Giang, Alex Gnech
OUR Journal: ODU Undergraduate Research Journal
The Time-Independent Schrödinger Equation is a linear elliptic PDE that describes quantum-mechanical systems. Its significance in the science of submicroscopic phenomena, particularly quantum mechanics, is as central as Newton’s laws of motion are to classical mechanics. This study uses various methods, including novel neural networks and finite difference schemes, to solve the one-dimensional two-body equation.