Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Wayne State University (1161)
- COBRA (1105)
- Selected Works (946)
- SelectedWorks (497)
- Kansas State University Libraries (493)
-
- Missouri University of Science and Technology (419)
- University of Kentucky (367)
- Universitas Indonesia (342)
- Marquette University (333)
- Loma Linda University (324)
- Utah State University (289)
- University of Nebraska - Lincoln (248)
- Wright State University (241)
- University of South Carolina (229)
- University of Nevada, Las Vegas (215)
- Western University (209)
- Old Dominion University (185)
- Air Force Institute of Technology (161)
- University of South Florida (161)
- California Polytechnic State University, San Luis Obispo (159)
- Himmelfarb Health Sciences Library, The George Washington University (159)
- Roseman University of Health Sciences (152)
- Virginia Commonwealth University (152)
- Prairie View A&M University (136)
- University of New Mexico (134)
- Georgia Southern University (127)
- Brigham Young University (121)
- City University of New York (CUNY) (118)
- University of Texas at El Paso (109)
- Western Michigan University (109)
- Keyword
-
- Statistics (413)
- Humans (189)
- Female (133)
- Machine learning (129)
- Simulation (129)
-
- Male (128)
- Bayesian (97)
- Regression (92)
- Machine Learning (88)
- Logistic regression (85)
- Aged (83)
- Probability (79)
- Classification (76)
- Empirical legal studies (73)
- Middle Aged (73)
- COVID-19 (70)
- Forecasting (70)
- Prediction (67)
- Bootstrap (66)
- Epidemiology (63)
- Mathematics (60)
- Causal inference (59)
- Missing data (58)
- Survival analysis (58)
- Time series (58)
- Power (57)
- Estimation (56)
- Modeling (56)
- Bias (55)
- Reliability (55)
- Publication Year
- Publication
-
- Journal of Modern Applied Statistical Methods (1093)
- Theses and Dissertations (545)
- Conference on Applied Statistics in Agriculture (489)
- Mathematics and Statistics Faculty Research & Creative Works (347)
- Kesmas (334)
-
- Loma Linda University Electronic Theses, Dissertations & Projects (324)
- Mathematics, Statistics and Computer Science Faculty Research and Publications (317)
- Electronic Theses and Dissertations (286)
- U.C. Berkeley Division of Biostatistics Working Paper Series (242)
- UW Biostatistics Working Paper Series (215)
- Harvard University Biostatistics Working Paper Series (212)
- Johns Hopkins University, Dept. of Biostatistics Working Papers (178)
- Department of Statistics: Faculty Publications (162)
- Annual Research Symposium (152)
- Faculty Publications (145)
- Mathematics and Statistics Faculty Publications (145)
- Electronic Thesis and Dissertation Repository (141)
- Applications and Applied Mathematics: An International Journal (AAM) (136)
- USF Tampa Graduate Theses and Dissertations (127)
- All Graduate Theses and Dissertations, Spring 1920 to Summer 2023 (123)
- The University of Michigan Department of Biostatistics Working Paper Series (111)
- All Graduate Plan B and other Reports, Spring 1920 to Spring 2023 (110)
- Dissertations (107)
- Statistics (107)
- Epidemiology Faculty Publications (105)
- Open Access Theses & Dissertations (102)
- Mathematics & Statistics ETDs (98)
- International Conference on Gambling & Risk Taking (94)
- Doctoral Dissertations (93)
- COBRA Preprint Series (88)
- Publication Type
Articles 271 - 300 of 13244
Full-Text Articles in Physical Sciences and Mathematics
Modeling Of Covid-19 Clinical Outcomes In Mexico: An Analysis Of Demographic, Clinical, And Chronic Disease Factors, Livia Clarete
Modeling Of Covid-19 Clinical Outcomes In Mexico: An Analysis Of Demographic, Clinical, And Chronic Disease Factors, Livia Clarete
Dissertations, Theses, and Capstone Projects
This study explores COVID-19 clinical outcomes in Mexico, focusing on demographic, clinical, and chronic disease variables to develop predictive models. In the binary classification task, the Ada Boost Classifier distinguishes survivors from non-survivors, with age, sex, ethnicity, and chronic medical conditions influencing outcomes. In multiclass classification, the Gradient Boosting Classifier categorizes patients into outcome groups.
Demographic variables, especially age, are crucial for predicting COVID-19 outcomes for both the binary and multiclass classification tasks. Clinical information about previous conditions, including chronic diseases, also holds relevance, especially diabetes, immunocompromise, and cardiovascular diseases. These insights inform public health measures and healthcare strategies, emphasizing …
Making Sense Of Making Parole In New York, Alexandra Mcglinchy
Making Sense Of Making Parole In New York, Alexandra Mcglinchy
Dissertations, Theses, and Capstone Projects
For many individuals incarcerated in New York, the initial step toward freedom begins with an interview with the Board of Parole. This process, however, is frequently a complex and challenging one, characterized by repeated denials and extended incarcerations. The disparity in outcomes – where one individual may receive over 20 denials and another is granted parole on their first attempt – highlights the ambiguity and inconsistency in the parole decision-making process. This project aims to clarify the factors that influence parole decisions by concentrating on measurable variables. These include age, race, duration of sentence served, proportion of sentence served, type …
Model Selection Through Cross-Validation For Supervised Learning Tasks With Manifold Data, Derek Brown
Model Selection Through Cross-Validation For Supervised Learning Tasks With Manifold Data, Derek Brown
The Journal of Purdue Undergraduate Research
No abstract provided.
Sensitivity Analysis Of Prior Distributions In Regression Model Estimation, Ayoade I Adewole, Oluwatoyin K. Bodunwa
Sensitivity Analysis Of Prior Distributions In Regression Model Estimation, Ayoade I Adewole, Oluwatoyin K. Bodunwa
Al-Bahir Journal for Engineering and Pure Sciences
Bayesian inferences depend solely on specification and accuracy of likelihoods and prior distributions of the observed data. The research delved into Bayesian estimation method of regression models to reduce the impact of some of the problems, posed by convectional method of estimating regression models, such as handling complex models, availability of small sample sizes and inclusion of background information in the estimation procedure. Posterior distributions are based on prior distributions and the data accuracy, which is the fundamental principles of Bayesian statistics to produce accurate final model estimates. Sensitivity analysis is an essential part of mathematical model validation in obtaining …
Statistical Consulting In Academia: A Review, Ke Xiao
Statistical Consulting In Academia: A Review, Ke Xiao
Major Papers
This paper reviews the state of statistical consulting in academia by performing a literature review on this topic in chapters 1 and 2. Chapter 1 overviews general aspects of statistical consulting and types of centers that conduct such services in academia. In Chapter 2 we summarise the literature about the common logistics and processes for conducting statistical consulting in academia. In Chapters 3 and 4, we analyze data on statistical consulting centers for the largest 100 universities in the USA. We also review the literature on the future of statistical consulting in academia in the era of big data and …
Time Scale Theory On Stability Of Explicit And Implicit Discrete Epidemic Models: Applications To Swine Flu Outbreak, Gülşah Yeni, Elvan Akın, Naveen K. Vaidya
Time Scale Theory On Stability Of Explicit And Implicit Discrete Epidemic Models: Applications To Swine Flu Outbreak, Gülşah Yeni, Elvan Akın, Naveen K. Vaidya
Mathematics and Statistics Faculty Research & Creative Works
Time scales theory has been in use since the 1980s with many applications. Only very recently, it has been used to describe within-host and between-hosts dynamics of infectious diseases. In this study, we present explicit and implicit discrete epidemic models motivated by the time scales modeling approach. We use these models to formulate the basic reproduction number, which determines whether an outbreak occurs, or the disease dies out. We discuss the stability of the disease-free and endemic equilibrium points using the linearization method and Lyapunov function. Furthermore, we apply our models to swine flu outbreak data to demonstrate that the …
On A Multivalued Prescribed Mean Curvature Problem And Inclusions Defined On Dual Spaces, Vy Khoi Le
On A Multivalued Prescribed Mean Curvature Problem And Inclusions Defined On Dual Spaces, Vy Khoi Le
Mathematics and Statistics Faculty Research & Creative Works
This article addresses two main objectives. First, it establishes a functional analytic framework and presents existence results for a quasilinear inclusion describing a prescribed mean curvature problem with homogeneous Dirichlet boundary conditions, involving a multivalued lower order term. The formulation of the problem is done in the space of functions with bounded variation. The second objective is to introduce a general existence theory for inclusions defined on nonreflexive Banach spaces, which is specifically applicable to the aforementioned prescribed mean curvature problem. This problem can be formulated as a multivalued variational inequality in the space of functions with bounded variation, which, …
Outpatient Fall Prevention In Ambulatory Adults 65 Years Old And Over, Dorothy L. Osborne-White
Outpatient Fall Prevention In Ambulatory Adults 65 Years Old And Over, Dorothy L. Osborne-White
Doctor of Nursing Practice (DNP) Scholarly Projects
Abstract
Background: In the United States (U.S.), falls are the leading cause of injury among adults 65 and over, resulting in 36 million falls yearly (Moreland et al., 2020). According to the Centers for Disease Control and Prevention (CDC, 2023), one in four older adults experiences a fall each year. Falls are the world's second most prominent cause of accidental deaths (World Health Organization [WHO], 2021). Falls are the leading cause of both fatal and non-fatal injuries among older adults (Moreland et al., 2020).
Methods: A quality improvement project that included a fall bundle was implemented in a primary clinic. …
Predicting Superconducting Critical Temperature Using Regression Analysis, Roland Fiagbe
Predicting Superconducting Critical Temperature Using Regression Analysis, Roland Fiagbe
Data Science and Data Mining
This project estimates a regression model to predict the superconducting critical temperature based on variables extracted from the superconductor’s chemical formula. The regression model along with the stepwise variable selection gives a reasonable and good predictive model with a lower prediction error (MSE). Variables extracted based on atomic radius, valence, atomic mass and thermal conductivity appeared to have the most contribution to the predictive model.
Judging Our New Judges: Why We Must Remove Artificial Intelligence From Our Courtrooms Now, Kieran Duffy Newcomb
Judging Our New Judges: Why We Must Remove Artificial Intelligence From Our Courtrooms Now, Kieran Duffy Newcomb
Honors Theses and Capstones
In this paper, I explore some of the ways in which artificial intelligence might enhance the sentencing process through recidivism prediction technology. Notably, this technology can increase the accuracy of risk predictions and the speed with which sentencing decisions are reached. I then show, however, that the recidivism prediction technology is likely to turn into what data scientist Cathy O’Neil calls a Weapon of Math Destruction. The potential harmfulness of this technology is due not to the inherent nature of the technology, but the symbiotic relationship it will have with our already harmful criminal justice system. I argue that the …
Accounting For Variability Due To Resampling Using Bootstrapping, Dipendra Phuyal
Accounting For Variability Due To Resampling Using Bootstrapping, Dipendra Phuyal
Electronic Theses and Dissertations
Bradley Efron (1979) introduced bootrapping. Typically a researcher is interested in studying a process which generates individuals. The collection of individuals the process has(actual) or could have (conceptual) generated is the population. The collection of conceptual members of the population is an uncountable collection. Hence, the population is anuncountable collection of individuals. The collection of individuals the process has generated (actual individuals) is representative of what the process can generate and will bereferred to as the representative sample. The size of this sample is a nonnegative integervalued random variable N which may be a constant random variable such as in …
Classification In Supervised Statistical Learning With The New Weighted Newton-Raphson Method, Toma Debnath
Classification In Supervised Statistical Learning With The New Weighted Newton-Raphson Method, Toma Debnath
Electronic Theses and Dissertations
In this thesis, the Weighted Newton-Raphson Method (WNRM), an innovative optimization technique, is introduced in statistical supervised learning for categorization and applied to a diabetes predictive model, to find maximum likelihood estimates. The iterative optimization method solves nonlinear systems of equations with singular Jacobian matrices and is a modification of the ordinary Newton-Raphson algorithm. The quadratic convergence of the WNRM, and high efficiency for optimizing nonlinear likelihood functions, whenever singularity in the Jacobians occur allow for an easy inclusion to classical categorization and generalized linear models such as the Logistic Regression model in supervised learning. The WNRM is thoroughly investigated …
Numerical Investigation And Statistical Analysis Of The Flow Patterns Behind Square Cylinders Arranged In A Staggered Configuration Utilizing The Lattice Boltzmann Method, M. Abid, N. Yasin, M. Saqlain, S. Ul-Islam, S. Ahmad
Numerical Investigation And Statistical Analysis Of The Flow Patterns Behind Square Cylinders Arranged In A Staggered Configuration Utilizing The Lattice Boltzmann Method, M. Abid, N. Yasin, M. Saqlain, S. Ul-Islam, S. Ahmad
Mathematics & Statistics Faculty Publications
Flow past bluff bodies like square cylinders is important in engineering applications, but flow patterns behind staggered cylinder arrangements remain poorly understood. Existing studies have focused on tandem or side-by-side configurations, while offset orientations have received less attention. The aim of this paper is to numerically investigate flow dynamics and force characteristics behind two offset square cylinders using the single relaxation time lattice Boltzmann method. The effects of changing both the Reynolds number (Re = 1-150) and gap spacing ratio (g* = 0.5-5) between the cylinders are analyzed. Instantaneous vorticity contours, time histories of drag and lift coefficients, power spectra …
Scalar-On-Function Regression: Estimation And Inference Under Complex Survey Designs, Ekaterina Smirnova, Erjia Ciu, Lucia Tabacu, Andrew Leroux
Scalar-On-Function Regression: Estimation And Inference Under Complex Survey Designs, Ekaterina Smirnova, Erjia Ciu, Lucia Tabacu, Andrew Leroux
Mathematics & Statistics Faculty Publications
Increasingly, large, nationally representative health and behavioral surveys conducted under a multistage stratified sampling scheme collect high dimensional data with correlation structured along some domain (eg, wearable sensor data measured continuously and correlated over time, imaging data with spatiotemporal correlation) with the goal of associating these data with health outcomes. Analysis of this sort requires novel methodologic work at the intersection of survey statistics and functional data analysis. Here, we address this crucial gap in the literature by proposing an estimation and inferential framework for generalizable scalar-on-function regression models for data collected under a complex survey design. We propose to: …
Applications Of Independent And Identically Distributed (Iid) Random Processes In Polarimetry And Climatology, Dan Kestner
Applications Of Independent And Identically Distributed (Iid) Random Processes In Polarimetry And Climatology, Dan Kestner
Dissertations, Master's Theses and Master's Reports
The unifying theme of this thesis is the characterization of “perfect randomness,” i.e., independent and identically distributed (IID) stochastic processes as these are applied in physical science. Two specific and mathematically distinct applications are chosen: (i) Radar and optical polarimetry; (ii) Analysis of time series in meteorology. In (i), IID process of a special kind, namely, with a distribution defined by symmetry, is used to link its multivariate Gaussian density to uniformity on the Poincaré sphere. This “statistical ellipsometry” approach is then used to relate polarimetric mismatches or imbalances to ellipsometric variables and suitably chosen cross-correlation measures. In (ii), recently …
Existence Of Solutions By Coincidence Degree Theory For Hadamard Fractional Differential Equations At Resonance, Martin Bohner, Alexander Domoshnitsky, Seshadev Padhi, Satyam Narayan Srivastava
Existence Of Solutions By Coincidence Degree Theory For Hadamard Fractional Differential Equations At Resonance, Martin Bohner, Alexander Domoshnitsky, Seshadev Padhi, Satyam Narayan Srivastava
Mathematics and Statistics Faculty Research & Creative Works
Using the Coincidence Degree Theory of Mawhin and Constructing Appropriate Operators, We Investigate the Existence of Solutions to Hadamard Fractional Differential Equations (FRDEs) at Resonance
Developing Machine Learning And Time-Series Analysis Methods With Applications In Diverse Fields, Muhammed Aljifri
Developing Machine Learning And Time-Series Analysis Methods With Applications In Diverse Fields, Muhammed Aljifri
Theses and Dissertations
This dissertation introduces methodologies that combine machine learning models with time-series analysis to tackle data analysis challenges in varied fields. The first study enhances the traditional cumulative sum control charts with machine learning models to leverage their predictive power for better detection of process shifts, applying this advanced control chart to monitor hospital readmission rates. The second project develops multi-layer models for predicting chemical concentrations from ultraviolet-visible spectroscopy data, specifically addressing the challenge of analyzing chemicals with a wide range of concentrations. The third study presents a new method for detecting multiple changepoints in autocorrelated ordinal time series, using the …
Open Diameter Maps On Suspensions, Hussam Abobaker, Włodzimierz J. Charatonik, Robert Paul Roe
Open Diameter Maps On Suspensions, Hussam Abobaker, Włodzimierz J. Charatonik, Robert Paul Roe
Mathematics and Statistics Faculty Research & Creative Works
It is shown that if X is a metric continuum, which admits an open diameter map, then the suspension of X, admits an open diameter map. As a corollary, we have that all spheres admit open diameter maps.
Modeling The Development & Expression Of Political Opinion: A Zallerian Approach, Avery C. Ellis
Modeling The Development & Expression Of Political Opinion: A Zallerian Approach, Avery C. Ellis
Honors Projects
Research focused on John Zaller's famous RAS model of political opinion formation and change from "The Nature and Origins of Mass Opinion" (1992). Analyzed the mathematical and psychological underpinnings of the model, the first paper to do so in over fifteen years and the first to do so through an analysis of motivated reasoning and Bayesian reasoning. Synthesized existing critiques of Zaller's model and other literature to suggest ways to build on Zaller, utilizing fundamental reunderstandings of opinions and messages from political and mathematical perspectives. Found verification for Zaller's model, confirming its value, but also found support for the proposed …
Formulating An Efficient Statistical Test Using The Goodness Of Fit Approach With Applications To Real-Life Data, S. A. Qaid, S. E. Abo Youssef Prof., Mahmoud Mansour
Formulating An Efficient Statistical Test Using The Goodness Of Fit Approach With Applications To Real-Life Data, S. A. Qaid, S. E. Abo Youssef Prof., Mahmoud Mansour
Basic Science Engineering
Statistical tests are very important for researchers to make decisions. In particular, when the tests are non-parametric, they are of greater importance because they can be applied to a wide range of data sets regardless of knowing the distribution of these data. Researchers are therefore racing to obtain efficient tests for making good decisions based on the results of these tests. In this study, NBU (2)L was used based on the goodness of fit approach to present an efficient statistical test. The efficiency of the proposed test was computed, and the results were compared to those of other tests. Critical …
Imputation Strategies For Different Categories Of Missing Data, Karthik Chalumuri
Imputation Strategies For Different Categories Of Missing Data, Karthik Chalumuri
Honors Theses and Capstones
Addressing missing data in research is crucial for ensuring the reliability and validity of study findings, yet it remains a significant challenge. This study investigates the impact of missing data on research outcomes and explores the underutilization of existing tools for managing missingness, potentially leading to gaps in critical information with tangible implications for decision-making processes (Dziura et al.).
Focusing on the different categories of missing data—Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR)—this research examines various imputation strategies tailored to each category. Specifically, we compare the efficacy of several model-based imputation methods, …
Defensive Impact Wins: Developing A New Method To Rate Individual Defense In Nba Games, Dylan J. Stiles
Defensive Impact Wins: Developing A New Method To Rate Individual Defense In Nba Games, Dylan J. Stiles
Honors Theses and Capstones
With the analytics revolution in sports in the past 20 years, it seems that everything that can be quantified is. In basketball though, trying to break the game down into a set of numbers comes with a unique problem. While we've come up with a good set of advanced numbers to measure offensive efficiency, defense is fundamentally harder to quantify. The game is played five on five, but it has often been popular or convenient to model defense as a set of five one on one games. As defenses became more complex into the 2010s, this methodology became more insignificant. …
An Analysis Of Corporate Social Responsibility And Real Earnings Management, Rachel Brassine
An Analysis Of Corporate Social Responsibility And Real Earnings Management, Rachel Brassine
Theses, Dissertations and Capstones
Real earnings management (REM) is costly in the form of intense loan restrictions, increased interest expense, and public scrutiny. Nevertheless, companies still practice REM. Based on agency and stakeholder theories, this research predicts that as a company’s CSR score increases, REM will decrease, and this association will become more negative when a critical mass of females on the board of directors exists and when a board-level CSR committee is present. This study also predicts that when a company offers an executive incentive plan based on CSR metrics, REM will decrease, and the relationship will become more negative with a critical …
The Performance Of Marginal Modeling Methods For Rare Events With Application To Opioid Overdose Mortality And Morbidity, Shawn Nigam
Theses and Dissertations--Epidemiology and Biostatistics
Opioid misuse is a nationwide epidemic, with Kentucky having one of the highest opioid overdose-related fatality rates across all US states. These rates have increased significantly over the past decade, with particularly large increases during the COVID-19 pandemic. This dissertation aims to study the behavior of these increases and the methods for the marginal modeling of count outcomes related to opioid overdose.
Opioid overdose-related fatality rates in Kentucky increased significantly during the COVID-19 pandemic. In this chapter, we characterize the changes in opioid overdose fatality rates in Kentucky and identify associations between potential factors and fatality rates. County-level opioid overdose …
Machine Learning Approaches For Cyberbullying Detection, Roland Fiagbe
Machine Learning Approaches For Cyberbullying Detection, Roland Fiagbe
Data Science and Data Mining
Cyberbullying refers to the act of bullying using electronic means and the internet. In recent years, this act has been identifed to be a major problem among young people and even adults. It can negatively impact one’s emotions and lead to adverse outcomes like depression, anxiety, harassment, and suicide, among others. This has led to the need to employ machine learning techniques to automatically detect cyberbullying and prevent them on various social media platforms. In this study, we want to analyze the combination of some Natural Language Processing (NLP) algorithms (such as Bag-of-Words and TFIDF) with some popular machine learning …
The Effect Of Social Determinants Of Health On End-Stage Kidney Disease Mortality Across Diverse Adult Populations: Systematic Review And Meta-Analysis, Prince Agyapong
The Effect Of Social Determinants Of Health On End-Stage Kidney Disease Mortality Across Diverse Adult Populations: Systematic Review And Meta-Analysis, Prince Agyapong
Electronic Theses and Dissertations
Background: This systematic review and meta-analysis aimed to examine the influence of social determinants of health (SDOH) on End-Stage Kidney Disease (ESKD) mortality among diverse racial populations. Given the high morbidity and mortality associated with ESKD, understanding the impact of various SDOH factors across different racial groups is crucial for improving patient outcomes.
Methods: A comprehensive literature search was conducted to identify studies reporting on the relationship between SDOH and ESKD mortality using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) format. Citations were collated in EndNote 21 and screened in Covidence by two independent reviewers, with inter-rater …
Refining The Inverse Lipschitz Constant For Injective Relu Networks, Cole Rausch
Refining The Inverse Lipschitz Constant For Injective Relu Networks, Cole Rausch
Electronic Theses and Dissertations
In this thesis, we study the Inverse Lipschitz Constant (ILC) of injective ReLU layers. We study the tightness of the ILC lower bound established in Puthawala et al. Our approach has three components. First, we find that the conditions for injectivity on lines yield a weaker condition than the general condition given in Puthawala et al. Second, we perform numerical experiments to judge the tightness of the existing ILC lower bound and find that bound is overly conservative. Third, we identify the source of the potential slack in the proof of the existing ILC bound, and perform further numerical experiments …
Assessing The Utility Of Breast Cancer Polygenic Risk Scores And Association With Clinical Factors In A Population Of Breast Cancer Patients, John L. Slunecka
Assessing The Utility Of Breast Cancer Polygenic Risk Scores And Association With Clinical Factors In A Population Of Breast Cancer Patients, John L. Slunecka
Dissertations and Theses
INTRODUCTION: Breast cancer (BC) is the most common cancer among women and is classified as a complex disease. Advances in population genomics have led to the development of polygenic risk scores (PRSs) with the potential to enhance current risk models, but replication is often limited. OBJECTIVE: We sought to assess the predictive capabilities of two high-powered BC PRSs in a sample population selected for breast cancer. In addition, the capacity of the PRSs to predict clinical variables that could improve BC screening and treatments was explored. METHODS: Two published PRS algorithms (313 vs 3820) were used to score female subjects …
Sparse Representation Learning For Temporal Networks, Maxwell Mcneil
Sparse Representation Learning For Temporal Networks, Maxwell Mcneil
Electronic Theses & Dissertations (2024 - present)
Temporal networks arise in many domains including activity of social network users, sensor network readings over time, and time course gene expression within the interaction network of a model organism. Data of this type contains a wealth of prior information such as the connectivity among nodes (e.g., a friendship graph), and prior knowledge of expected temporal patterns (e.g., periodicity). Modeling these temporal and network patterns jointly is essential for state-of-the-art performance in temporal network data analysis and mining. Sparse dictionary encoding is one modeling approach for such underlying patterns. However, most classical approaches consider only one dimension of the data …
Climate Change's Effect On Flow Regime, Alexander Ialenti
Climate Change's Effect On Flow Regime, Alexander Ialenti
Williams Honors College, Honors Research Projects
This project will test to see if there is a percent increase in non-perennial streams sampled from 2003-2021. Using data provided by The Cleveland Metroparks, sampling events will be separated by date, flow regime classification, and rain data. Current literature supports the claim that many perennial streams, streams that flow year-round, will become non-perennial streams over time. This shift is predicted to be caused by a change in rain patterns. Both the interval between rain events and the intensity of rainfall per event are predicted to increase. My hypothesis is that there will be an increase in the percentage of …