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Mathematical Modeling: Instructor And Student Resources, Marnie Phipps, Patty Wagner 2020 University of North Georgia

Mathematical Modeling: Instructor And Student Resources, Marnie Phipps, Patty Wagner

Mathematics Ancillary Materials

This collection of student and instructor materials for Mathematical Modeling contains lesson plans, lecture slides, homework, learning goals, and student notes for the following major topics:

  • Linear Functions
  • Quadratic Functions
  • Exponential Functions
  • Logarithmic Functions

This is a materials update for a collection of materials created for a Round Nine ALG Textbook Transformation Grant.


Constitutive Model Of Lateral Unloading Creep Of Soft Soil Under Excess Pore Water Pressure, Wei Huang, Kejun Wen, Xiaojia Deng, Junjie Li, Zhijian Jiang, Yang Li, Lin Li, Farshad Amini 2020 Chongqing University of Science and Technology

Constitutive Model Of Lateral Unloading Creep Of Soft Soil Under Excess Pore Water Pressure, Wei Huang, Kejun Wen, Xiaojia Deng, Junjie Li, Zhijian Jiang, Yang Li, Lin Li, Farshad Amini

Civil and Architectural Engineering Faculty Research

Presented in this paper is a study on the lateral unloading creep tests under different excess pore water pressures. The marine sedimentary soft soil in Shenzhen, China, was selected in this study. The results show that the excess pore water pressure plays a significant role in enhancing the unloading creep of soft soil. Higher excess pore water pressure brings more obvious creep deformation of soft soil and lower ultimate failure load. Meanwhile, the viscoelastic and the viscoplastic modulus of soft soil were found to exponentially decline with creep time. A modified merchant model and a combined model of the modified ...


Seasonal Warranty Prediction Based On Recurrent Event Data, Qianqian Shan, Yili Hong, William Q. Meeker 2020 Iowa State University

Seasonal Warranty Prediction Based On Recurrent Event Data, Qianqian Shan, Yili Hong, William Q. Meeker

Statistics Publications

Warranty return data from repairable systems, such as home appliances, lawn mowers, computers and automobiles, result in recurrent event data. The nonhomogeneous Poisson process (NHPP) model is used widely to describe such data. Seasonality in the repair frequencies and other variabilities, however, complicate the modeling of recurrent event data. Not much work has been done to address the seasonality, and this paper provides a general approach for the application of NHPP models with dynamic covariates to predict seasonal warranty returns. The methods presented here, however, can be applied to other applications that result in seasonal recurrent event data. A hierarchical ...


Estimating Thermal Conductivity Of Frozen Soils From Air‐Filled Porosity, Zhengchao Tian, Tusheng Ren, Joshua L. Heitman, Robert Horton 2020 Huazhong Agricultural University

Estimating Thermal Conductivity Of Frozen Soils From Air‐Filled Porosity, Zhengchao Tian, Tusheng Ren, Joshua L. Heitman, Robert Horton

Agronomy Publications

Soil thermal conductivity (λ) is an important thermal property for environmental, agricultural, and engineering heat transfer applications. Existing λ models for frozen soils are complicated to use because they require estimates of both liquid water content and ice content. This study introduces a new approach to estimate λ of partially frozen soils from air‐filled porosity (n a), which can be determined by using an oven‐drying method. A λ and n a relationship was established based on measurements for 28 partially frozen soils. A strong exponential relationship between λ and n a was found (with R2 of 0.82 ...


Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh 2020 The University of Western Ontario

Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh

Electronic Thesis and Dissertation Repository

Rapid growth in numbers of connected devices, including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for ...


Statistical Models And Analysis Of Univariate And Multivariate Degradation Data, Lochana Palayangoda 2020 Southern Methodist University

Statistical Models And Analysis Of Univariate And Multivariate Degradation Data, Lochana Palayangoda

Statistical Science Theses and Dissertations

For degradation data in reliability analysis, estimation of the first-passage time (FPT) distribution to a threshold provides valuable information on reliability characteristics. Recently, Balakrishnan and Qin (2019; Applied Stochastic Models in Business and Industry, 35:571-590) studied a nonparametric method to approximate the FPT distribution of such degradation processes if the underlying process type is unknown. In this thesis, we propose improved techniques based on saddlepoint approximation, which enhance upon their suggested methods. Numerical examples and Monte Carlo simulation studies are used to illustrate the advantages of the proposed techniques. Limitations of the improved techniques are discussed and some possible ...


Statistical Inference Of Adaptation At Multiple Genomic Scales Using Supervised Classification And A Hidden Markov Model, Lauren A. Sugden 2020 Duquesne University

Statistical Inference Of Adaptation At Multiple Genomic Scales Using Supervised Classification And A Hidden Markov Model, Lauren A. Sugden

Biology and Medicine Through Mathematics Conference

No abstract provided.


Decision Tree For Predicting The Party Of Legislators, Afsana Mimi 2020 CUNY New York City College of Technology

Decision Tree For Predicting The Party Of Legislators, Afsana Mimi

Publications and Research

The motivation of the project is to identify the legislators who voted frequently against their party in terms of their roll call votes using Office of Clerk U.S. House of Representatives Data Sets collected in 2018 and 2019. We construct a model to predict the parties of legislators based on their votes. The method we used is Decision Tree from Data Mining. Python was used to collect raw data from internet, SAS was used to clean data, and all other calculations and graphical presentations are performed using the R software.


Semiparametric Optimal Estimation With Nonignorable Nonresponse Data, Kosuke Morikawa, Jae Kwang Kim 2020 Osaka University

Semiparametric Optimal Estimation With Nonignorable Nonresponse Data, Kosuke Morikawa, Jae Kwang Kim

Statistics Preprints

When the response mechanism is believed to be not missing at random (NMAR), a valid analysis requires stronger assumptions on the response mechanism than standard statistical methods would otherwise require. Semiparametric estimators have been developed under the model assumptions on the response mechanism. In this paper, a new statistical test is proposed to guarantee model identifiability without using any instrumental variable. Furthermore, we develop optimal semiparametric estimation for parameters such as the population mean. Specifically, we propose two semiparametric optimal estimators that do not require any model assumptions other than the response mechanism. Asymptotic properties of the proposed estimators are ...


Modeling Species Distribution And Habitat Suitability Of American Ginseng (Panax Quinquefolius) In Virginia, Jacob Peters 2020 James Madison University

Modeling Species Distribution And Habitat Suitability Of American Ginseng (Panax Quinquefolius) In Virginia, Jacob Peters

Masters Theses, 2020-current

American ginseng (Panax quinquefolius) is a well-known and sought-after medicinal plant native to North America that is facing increased threat of extinction due to overharvesting, herbivory, and habitat loss. Species distribution and habitat suitability models may be valuable to landowners interested in sustainable harvest or to institutions interested in the conservation and restoration of the species. With unequal sampling efforts across a region of interest, it is likely that some locations with appropriate habitat may be misrepresented in model predictions. This study refined a state-derived species distribution model for ginseng through increased sampling effort across the Cumberland Plateau of Virginia ...


Analyzing Competitive Balance In Professional Sport, Kevin Alwell 2020 University of Connecticut - Storrs

Analyzing Competitive Balance In Professional Sport, Kevin Alwell

Honors Scholar Theses

In this paper we review several measures to statistically analyze competitive balance and report which leagues have a wider variance of performance amongst its competitors. Each league seeks to maintain high levels of parity, making matches and overall season more unpredictable and appealing to the general audience. Here we quantify competitive advantage across major sports leagues in numbers using several statistical methods in order for leagues to optimize their revenue.


Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim 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 ...


Ragweed And Sagebrush Pollen Can Distinguish Between Vegetation Types At Broad Spatial Scales, Hannah M. Carroll, Alan D. Wanamaker, Lynn G. Clark, Brian J. Wilsey 2020 Iowa State University

Ragweed And Sagebrush Pollen Can Distinguish Between Vegetation Types At Broad Spatial Scales, Hannah M. Carroll, Alan D. Wanamaker, Lynn G. Clark, Brian J. Wilsey

Ecology, Evolution and Organismal Biology Publications

Patterns of vegetation distribution at regional to subcontinental scales can inform understanding of climate. Delineating ecoregion boundaries over geologic time is complicated by the difficulty of distinguishing between prairie types at broad spatial scales using the pollen record. Pollen ratios are sometimes employed to distinguish between vegetation types, although their applicability is often limited to a geographic range. The Neotoma Paleoecology Database offers an unparalleled opportunity to synthesize a large number of pollen datasets. Ambrosia (ragweed) is a genus of mesic‐adapted species sensitive to summer moisture. Artemisia (sagebrush, wormwood, mugwort) is a genus of dry‐mesic‐adapted species resilient ...


Modeling Movement: A Machine-Learning Approach To Track Migration Routes After Displacement, Ethan Harrison 2020 William & Mary

Modeling Movement: A Machine-Learning Approach To Track Migration Routes After Displacement, Ethan Harrison

Undergraduate Honors Theses

Over the past decade, the number of individuals internally displaced by conflict (IDPs) has reached unprecedented levels. Humanitarian actors and first-responders face persistent information gaps in meeting the needs of these populations. Specifically, they face challenges in understanding where and how IDPs move after they are displaced, which is necessary to locate them in conflict-affected situations and provide them with life-saving assistance. In this paper, I propose a framework, using established machine-learning methods, to forecast the migration routes of these displaced populations (Chapter 1). In a case study of displacement in Yemen, my models predict 80% of IDPs' migration routes ...


Applications Of Machine Learning In High-Frequency Trade Direction Classification, Jared E. Hansen 2020 Utah State University

Applications Of Machine Learning In High-Frequency Trade Direction Classification, Jared E. Hansen

All Graduate Theses and Dissertations

The correct assignment of trades as buyer-initiated or seller-initiated is paramount in many quantitative finance studies. Simple decision rule methods have been used for signing trades since many data sets available to researchers do not include the sign of each trade executed. By utilizing these decision rule methods, as well as engineering new variables from available data, we have demonstrated that machine learning models outperform prior methods for accurately signing trades as buys and sells, achieving state-of-the-art results. The best model developed was 4.5 percentage points more accurate than older methods when predicting onto unseen data. Since finance and ...


Point Process Modelling Of Objects In The Star Formation Complexes Of The M33 Galaxy, Dayi Li 2020 The University of Western Ontario

Point Process Modelling Of Objects In The Star Formation Complexes Of The M33 Galaxy, Dayi Li

Electronic Thesis and Dissertation Repository

In this thesis, Gibbs point process (GPP) models are constructed to study the spatial distribution of objects in the star formation complexes of the M33 galaxy. The GPP models circumvent the limitations of the two-point correlation function employed in the current astronomy literature by naturally accounting for the inhomogeneous distribution of these objects. The spatial distribution of these objects serves as a sensitive probe in understanding the star formation process, which is crucial in understanding the formation of galaxies and the Universe. The objects under study include the CO filament structure, giant molecular clouds (GMCs) and young stellar cluster candidates ...


484— Modeling Social Distancing Methods And Their Effectiveness In Combating The Spread Of Ebola, Rachel Fair 2020 SUNY Geneseo

484— Modeling Social Distancing Methods And Their Effectiveness In Combating The Spread Of Ebola, Rachel Fair

GREAT Day

Ebola Virus Disease (EVD) is a rare but severe disease that is transmitted among humans through direct-contact with, and close proximity to, infected bodily fluids. From 2014-16, West Africa experienced the largest Ebola outbreak ever recorded, infecting over 28,000 people, and killing over 11,000. Although the symptoms of EVD are treatable, the disease can be extremely deadly, with an average of 50% EVD cases resulting in fatality. In areas where healthcare is scarce and vaccinations are not readily available, the practices of social distancing and self-quarantining have been shown to be highly effective in combating the spread of ...


483— Effectiveness Of Mmr Vaccination In Orthodox Jewish Neighborhoods, Meenu Mundackal 2020 SUNY Geneseo

483— Effectiveness Of Mmr Vaccination In Orthodox Jewish Neighborhoods, Meenu Mundackal

GREAT Day

Measles is a highly contagious disease, where large outbreaks arise by direct contact between susceptible (unvaccinated) and infectious individuals. Many Orthodox Jewish neighborhoods were affected by measles from 2018-2019. To quantify the vaccination effort on this susceptible population, a retrospective analysis was used to study the NYC and Rockland County populations using a differential equations model. A subsequent model, known as a realistically-structured network model, studied only the NYC population, in relation to typical household size. Vaccination strategies were applied to three cohorts: unvaccinated family members, members with 1 prior MMR dose, and members with 2 prior MMR doses. The ...


Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach, Tanvi Arora, Rajat Chandna, Stacy Conant, Bivin Sadler, Robert Slater 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 ...


An Analysis Of Dredge Efficiency For Surfclam And Ocean Quahog Commercial Dredges, Leanne Poussard 2020 The University of Southern Mississippi

An Analysis Of Dredge Efficiency For Surfclam And Ocean Quahog Commercial Dredges, Leanne Poussard

Master's Theses

Between 1997 and 2011, The National Marine Fisheries Service conducted 50 depletion experiments to estimate survey gear efficiency and stock density for Atlantic surfclam (Spisula solidissima) and ocean quahog (Arctica islandica) populations using commercial hydraulic dredges. The Patch Model was formulated to estimate gear efficiency and organism density from the data. The range of efficiencies estimated is substantial, leading to uncertainty in the application of these estimates in stock assessment. Analysis of depletion experiment simulations showed that uncertainty in the estimates of gear efficiency from depletion experiments was reduced by higher numbers of dredge tows per experiment, more tow overlap ...


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