Institutional Context Drives Mobility: A Comprehensive Analysis Of Academic And Economic Factors That Influence International Student Enrollment At United States Higher Education Institutions, 2021 Old Dominion University
Institutional Context Drives Mobility: A Comprehensive Analysis Of Academic And Economic Factors That Influence International Student Enrollment At United States Higher Education Institutions, Natalie Cruz
College of Education & Professional Studies (Darden) Posters
International student enrollment (ISE) has become a hallmark of world-class higher education institutions (HEIs). Although the U.S. has welcomed the largest numbers of international students since the 1950s, ISE shrunk by 10% in the previous three years from an all-time high of 903,127 students in 2016/2017 (IIE, 2019). Research studies about international student mobility and enrollment highlights the significant role that academic and economic rationales play for international students. This quantitative, ex post facto study focused on the influence of ranking, tuition, Optional Practical Training, Gross Domestic Product, and the unemployment rate on ISE at 2,884 ...
Unsupervised Multivariate Time Series Clustering, 2021 Old Dominion University
Unsupervised Multivariate Time Series Clustering, Md Monibor Rahman, Lasitha Vidyaratne, Alex Glandon, Khan Iftekharuddin
College of Engineering & Technology (Batten) Posters
Clustering is widely used in unsupervised machine learning to partition a given set of data into non-overlapping groups. Many real-world applications require processing more complex multivariate time series data characterized by more than one dependent variables. A few works in literature reported multivariate classification using Shapelet learning. However, the clustering of multivariate time series signals using Shapelet learning has not explored yet. Shapelet learning is a process of discovering those Shapelets which contain the most informative features of the time series signal. Discovering suitable Shapelets from many candidates Shapelet has been broadly studied for classification and clustering of univariate time ...
Regression Analyses Assessing The Impact Of Environmental Factors On Covid-19 Transmission And Mortality, 2021 Wayne State University
Regression Analyses Assessing The Impact Of Environmental Factors On Covid-19 Transmission And Mortality, El Hussain Shamsa, Kezhong Zhang
Medical Student Research Symposium
No abstract provided.
Developing Farmer Typologies To Inform Conservation Outreach In Agricultural Landscapes, 2021 Iowa State University
Developing Farmer Typologies To Inform Conservation Outreach In Agricultural Landscapes, Suraj Upadhaya, J. Gordon Arbuckle, Lisa A. Schulte
Understanding factors that motivate conservation behavior among farmers is crucial to addressing societal, soil, water, and wildlife conservation goals. Farmers employ soil conservation practices to maintain agricultural productivity while minimizing impacts to water and wildlife in the long-term. The majority of conservation programs are voluntary in nature and some farmers are more willing and/or able to implement conservation practices than others. To inform the development of more effective conservation outreach and incentive programs, we created a farmer typology using data from three waves (2015, 2016, 2018) of a longitudinal survey of 358 farmers from Iowa, a highly productive agricultural ...
Self-Exciting Point Process For Modelling Terror Attack Data, 2021 Wilfrid Laurier University
Self-Exciting Point Process For Modelling Terror Attack Data, Siyi Wang
Theses and Dissertations (Comprehensive)
Terrorism becomes more rampant in recent years because of separatism and extreme nationalism, which brings a serious threat to the national security of many countries in the world. The analysis of spatial and temporal patterns of terror data is significant in containing terrorism. This thesis focuses on building and applying a temporal point process called self-exciting point process to fit the terror data from 1970 to 2018 of 10 countries. The data come from the Global Terrorism database. Further, an application in predicting the number of terror events based on the self-exciting model is another main innovative idea, in which ...
Improved Statistical Methods For Time-Series And Lifetime Data, 2020 Southern Methodist University
Improved Statistical Methods For Time-Series And Lifetime Data, Xiaojie Zhu
Statistical Science Theses and Dissertations
In this dissertation, improved statistical methods for time-series and lifetime data are developed. First, an improved trend test for time series data is presented. Then, robust parametric estimation methods based on system lifetime data with known system signatures are developed.
In the first part of this dissertation, we consider a test for the monotonic trend in time series data proposed by Brillinger (1989). It has been shown that when there are highly correlated residuals or short record lengths, Brillinger’s test procedure tends to have significance level much higher than the nominal level. This could be related to the discrepancy ...
Long‐Run Impacts Of Trade Shocks And Export Competitiveness: Evidence From The U.S. Bse Event, 2020 Iowa State University
Long‐Run Impacts Of Trade Shocks And Export Competitiveness: Evidence From The U.S. Bse Event, Chen-Ti Chen, John M. Crespi, William Hahn, Lee L. Schulz, Fawzi Taha
This paper examines how comparative advantages of major beef exporters changed following the 2003 bovine spongiform encephalopathy (BSE) outbreak, which significantly disrupted the U.S. beef trade until approximately 2007. Using longitudinal data on beef export values and constructed revealed comparative advantage measures, we show that while some measures of the long‐run impacts of BSE on U.S. beef export competitiveness have returned to pre‐2003 levels, the U.S.’s comparative advantage has not. We also examine a hypothetical scenario of no BSE event in 2003 and predict that in the absence of the BSE outbreak, the U ...
Time Series Analysis Of Offshore Buoy Light Detection And Ranging (Lidar) Windspeed Data, 2020 Southern Methodist University
Time Series Analysis Of Offshore Buoy Light Detection And Ranging (Lidar) Windspeed Data, Aditya Garapati, Charles J. Henderson, Carl Walenciak, Brian T. Waite
SMU Data Science Review
In this paper, modeling techniques for the forecasting of wind speed using historical values observed by Light Detection and Ranging (LIDAR) sensors in an offshore context are described. Both univariate time series and multivariate time series modeling techniques leveraging meteorological data collected simultaneously with the LIDAR data are evaluated for potential contributions to predictive ability. Accurate and timely ability to predict wind values is essential to the effective integration of wind power into existing power grid systems. It allows for both the management of rapid ramp-up / down of base production capacity due to highly variable wind power inputs and integration ...
Estimating Vehicular Traffic Intensity With Deep Learning And Semantic Segmentation, 2020 Purdue University
Estimating Vehicular Traffic Intensity With Deep Learning And Semantic Segmentation, Logan Bradley-Trietsch
The Journal of Purdue Undergraduate Research
No abstract provided.
Snow-Albedo Feedback In Northern Alaska: How Vegetation Influences Snowmelt, 2020 CUNY Hunter College
Snow-Albedo Feedback In Northern Alaska: How Vegetation Influences Snowmelt, Lucas C. Reckhaus
School of Arts & Sciences Theses
This paper investigates how the snow-albedo feedback mechanism of the arctic is changing in response to rising climate temperatures. Specifically, the interplay of vegetation and snowmelt, and how these two variables can be correlated. This has the potential to refine climate modelling of the spring transition season. Research was conducted at the ecoregion scale in northern Alaska from 2000 to 2020. Each ecoregion is defined by distinct topographic and ecological conditions, allowing for meaningful contrast between the patterns of spring albedo transition across surface conditions and vegetation types. The five most northerly ecoregions of Alaska are chosen as they encompass ...
A Novel Correction For The Adjusted Box-Pierce Test — New Risk Factors For Emergency Department Return Visits Within 72 Hours For Children With Respiratory Conditions — General Pediatric Model For Understanding And Predicting Prolonged Length Of Stay, 2020 Chapman University
A Novel Correction For The Adjusted Box-Pierce Test — New Risk Factors For Emergency Department Return Visits Within 72 Hours For Children With Respiratory Conditions — General Pediatric Model For Understanding And Predicting Prolonged Length Of Stay, Sidy Danioko
Computational and Data Sciences (PhD) Dissertations
This thesis represents the results of three research projects that underline the breadth and depth of my interests.
Firstly, I devoted some efforts to the well-known Box-Pierce goodness-of-fit tests for time series models which has been an important research topic over the last few decades. All previously proposed tests are focused on changes of the test statistics. Instead, I adopted a different approach that takes the best performing test and modifying the rejection region. Thus, I developed a semiparametric correction of the Adjusted Box-Pierce test that attains the best I error rates for all sample sizes and lags and outperforms ...
Analyzing The Fractal Dimension Of Various Musical Pieces, 2020 University of Arkansas, Fayetteville
Analyzing The Fractal Dimension Of Various Musical Pieces, Nathan Clark
Industrial Engineering Undergraduate Honors Theses
One of the most common tools for evaluating data is regression. This technique, widely used by industrial engineers, explores linear relationships between predictors and the response. Each observation of the response is a fixed linear combination of the predictors with an added error element. The method is built on the assumption that this error is normally distributed across all observations and has a mean of zero. In some cases, it has been found that the inherent variation is not the result of a random variable, but is instead the result of self-symmetric properties of the observations. For data with these ...
D-Vine Pair-Copula Models For Longitudinal Binary Data, 2020 Old Dominion University
D-Vine Pair-Copula Models For Longitudinal Binary Data, Huihui Lin
Mathematics & Statistics Theses & Dissertations
Dependent longitudinal binary data are prevalent in a wide range of scientific disciplines, including healthcare and medicine. A popular method for analyzing such data is the multivariate probit (MP) model. The motivation for this dissertation stems from the fact that the MP model fails even the binary correlations are within the feasible range. The reason being the underlying correlation matrix of the latent variables in the MP model may not be positive definite. In this dissertation, we study alternatives that are based on D-vine pair-copula models. We consider both the serial dependence modeled by the first order autoregressive (AR(1 ...
The Limits Of Location Privacy In Mobile Devices, 2020 University of Massachusetts Amherst
The Limits Of Location Privacy In Mobile Devices, Keen Yuun Sung
Mobile phones are widely adopted by users across the world today. However, the privacy implications of persistent connectivity are not well understood. This dissertation focuses on one important concern of mobile phone users: location privacy.
I approach this problem from the perspective of three adversaries that users are exposed to via smartphone apps: the mobile advertiser, the app developer, and the cellular service provider. First, I quantify the proportion of mobile users who use location permissive apps and are able to be tracked through their advertising identifier, and demonstrate a mark and recapture attack that allows continued tracking of users ...
Immunosenescence And Its Influence On Reproduction In A Long-Lived Vertebrate, 2020 Iowa State University
Immunosenescence And Its Influence On Reproduction In A Long-Lived Vertebrate, Jessica M. Judson, Dawn M. Reding, Anne M. Bronikowski
Ecology, Evolution and Organismal Biology Publications
Immunosenescence is a well-known phenomenon in mammal systems, but its relevance in other long-lived vertebrates is less understood. Further, the influence of age and reproductive effort on immune function in long-lived species can be challenging to assess, as long-term data are scarce and it is often difficult to sample the oldest age classes. We used the painted turtle (Chrysemys picta) to test hypotheses of immunosenescence and a trade-off between reproductive output and immune function in a population of a long-lived vertebrate that has been monitored for over 30 years. These long-term data are utilized to employ a unique approach of ...
Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, 2020 Washington University in St. Louis
Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim
Engineering and Applied Science Theses & Dissertations
Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross- sectional nature of training and prediction processes. Finding temporal patterns in EHR is ...
Gait Characterization Using Computer Vision Video Analysis, 2020 College of William and Mary
Gait Characterization Using Computer Vision Video Analysis, Martha T. Gizaw
Undergraduate Honors Theses
The World Health Organization reports that falls are the second-leading cause of accidental death among senior adults around the world. Currently, a research team at William & Mary’s Department of Kinesiology & Health Sciences attempts to recognize and correct aging-related factors that can result in falling. To meet this goal, the members of that team videotape walking tests to examine individual gait parameters of older subjects. However, they undergo a slow, laborious process of analyzing video frame by video frame to obtain such parameters. This project uses computer vision software to reconstruct walking models from residents of an independent living retirement ...
Rdc Data Alternatives: Conducting Research During Covid-19, 2020 Western University
Rdc Data Alternatives: Conducting Research During Covid-19, Kristi Thompson, Elizabeth Hill
Western Libraries Presentations
Recent physical distancing protocols pertaining to the COVID-19 Pandemic have meant that RDC researchers need to find alternatives ways of carrying out their research. The Real Time Remote Access (RTRA) program offers one alternative way to access confidential Statistics Canada data. Other options include using the Statistics Canada public use files and analyzing data from other sources.
The presenters, data librarians from Western Libraries will discuss the differences between the data that can be accessed through the RTRA the RDC. RTRA data is a very useful option for some types of questions but also has some important limitations. We will ...
Forecasting San Francisco Bay Area Rapid Transit (Bart) Ridership, 2020 Southern Methodist University (SMU)
Forecasting San Francisco Bay Area Rapid Transit (Bart) Ridership, Swee K. Chew, Alec Lepe, Aaron Tomkins, Peter Scheirer
SMU Data Science Review
In this paper, we present a forecasting analysis of the San Francisco Bay Area Rapid Transit (BART) ridership data utilizing a number of different time series methods. BART is a major public transportation system in the Bay Area and it relies heavily on its riders' fares; having models that generate accurate ridership numbers better enables the agency to project revenue and help manage future expenses. For our time series modeling, we utilized autoregressive integrated moving average (ARIMA), deep neural networks (DNN), state space models, and long short-term memory (LSTM) to predict monthly ridership. As there is such a wide range ...
Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach, 2020 Southern Methodist University
Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach, Tanvi Arora, Rajat Chandna, Stacy Conant, Bivin Sadler, Robert Slater
SMU Data Science Review
In this paper, historical data from a wholesale alcoholic beverage distributor was used to forecast sales demand. Demand forecasting is a vital part of the sale and distribution of many goods. Accurate forecasting can be used to optimize inventory, improve cash ow, and enhance customer service. However, demand forecasting is a challenging task due to the many unknowns that can impact sales, such as the weather and the state of the economy. While many studies focus effort on modeling consumer demand and endpoint retail sales, this study focused on demand forecasting from the distributor perspective. An ensemble approach was applied ...