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Constrained Sequence Alignment, Kyle Daling 2017 Western Washington University

Constrained Sequence Alignment, Kyle Daling

WWU Honors Program Senior Projects

Constrained Sequence Alignment: A new algorithm designed to help biologists produce better alignment for protein sequences.


Design And Implementation Of A Stand-Alone Tool For Metabolic Simulations, Milad Ghiasi Rad 2017 University of Nebraska-Lincoln

Design And Implementation Of A Stand-Alone Tool For Metabolic Simulations, Milad Ghiasi Rad

Computer Science and Engineering: Theses, Dissertations, and Student Research

In this thesis, we present the design and implementation of a stand-alone tool for metabolic simulations. This system is able to integrate custom-built SBML models along with external user’s input information and produces the estimation of any reactants participating in the chain of the reactions in the provided model, e.g., ATP, Glucose, Insulin, for the given duration using numerical analysis and simulations. This tool offers the food intake arguments in the calculations to consider the personalized metabolic characteristics in the simulations. The tool has also been generalized to take into consideration of temporal genomic information and be flexible ...


Examining The Electrical Excitation, Calcium Signaling, And Mechanical Contraction Cycle In A Heart Cell, Kristen Deetz, Nygel Foster, Darius Leftwich, Chad Meyer, Shalin Patel, Carlos Barajas, Matthias K. Gobbert, Zana Coulibaly 2017 Eastern University

Examining The Electrical Excitation, Calcium Signaling, And Mechanical Contraction Cycle In A Heart Cell, Kristen Deetz, Nygel Foster, Darius Leftwich, Chad Meyer, Shalin Patel, Carlos Barajas, Matthias K. Gobbert, Zana Coulibaly

Spora: A Journal of Biomathematics

As the leading cause of death in the United States, heart disease has become a principal concern in modern society. Cardiac arrhythmias can be caused by a dysregulation of calcium dynamics in cardiomyocytes. Calcium dysregulation, however, is not yet fully understood and is not easily predicted; this provides motivation for the subsequent research. Excitation-contraction coupling (ECC) is the process through which cardiomyocytes undergo contraction from an action potential. Calcium induced calcium release (CICR) is the mechanism through which electrical excitation is coupled with mechanical contraction through calcium signaling. The study of the interplay between electrical excitation, calcium signaling, and mechanical ...


Computational Fluid Dynamics Study Of Molten Steel Flow Patterns And Particle-Wall Interactions Inside A Slide-Gate Nozzle By A Hybrid Turbulent Model, Mahdi Mohammadi-Ghaleni, Mohsen Asle Zaeem, Jeffrey D. Smith, Ronald J. O'Malley 2017 Missouri University of Science and Technology

Computational Fluid Dynamics Study Of Molten Steel Flow Patterns And Particle-Wall Interactions Inside A Slide-Gate Nozzle By A Hybrid Turbulent Model, Mahdi Mohammadi-Ghaleni, Mohsen Asle Zaeem, Jeffrey D. Smith, Ronald J. O'Malley

Ronald J. O'Malley

Melt flow patterns and turbulence inside a slide-gate throttled submerged entry nozzle (SEN) were studied using Detached–Eddy Simulation (DES) model, which is a combination of Reynolds–Averaged Navier–Stokes (RANS) and Large–Eddy Simulation (LES) models. The DES switching criterion between RANS and LES was investigated to closely reproduce the flow structures of low and high turbulence regions similar to RANS and LES simulations, respectively. The melt flow patterns inside the nozzle were determined by k–ε (a RANS model), LES, and DES turbulent models, and convergence studies were performed to ensure reliability of the results. Results showed that ...


Perfect Ratings With Negative Comments: Learning From Contradictory Patient Survey Responses, Andrew S. Gallan, Marina Girju, Roxana Girju 2017 DePaul University

Perfect Ratings With Negative Comments: Learning From Contradictory Patient Survey Responses, Andrew S. Gallan, Marina Girju, Roxana Girju

Patient Experience Journal

This research explores why patients give perfect domain scores yet provide negative comments on surveys. In order to explore this phenomenon, vendor-supplied in-patient survey data from eleven different hospitals of a major U.S. health care system were utilized. The dataset included survey scores and comments from 56,900 patients, collected from January 2015 through October 2016. Of the total number of responses, 30,485 (54%) contained at least one comment. For our analysis, we use a two-step approach: a quantitative analysis on the domain scores augmented by a qualitative text analysis of patients’ comments. To focus the research, we ...


A Fast Trajectory Outlier Detection Approach Via Driving Behavior Modeling, Hao WU, Weiwei SUN, Baihua ZHENG 2017 Singapore Management University

A Fast Trajectory Outlier Detection Approach Via Driving Behavior Modeling, Hao Wu, Weiwei Sun, Baihua Zheng

Research Collection School Of Information Systems

Trajectory outlier detection is a fundamental building block for many location-based service (LBS) applications, with a large application base. We dedicate this paper on detecting the outliers from vehicle trajectories efficiently and effectively. In addition, we want our solution to be able to issue an alarm early when an outlier trajectory is only partially observed (i.e., the trajectory has not yet reached the destination). Most existing works study the problem on general Euclidean trajectories and require accesses to the historical trajectory database or computations on the distance metric that are very expensive. Furthermore, few of existing works consider some ...


Distributed Evolution Of Spiking Neuron Models On Apache Mahout For Time Series Analysis, Andrew Palumbo 2017 Cylance, Inc.

Distributed Evolution Of Spiking Neuron Models On Apache Mahout For Time Series Analysis, Andrew Palumbo

Annual Symposium on Biomathematics and Ecology: Education and Research

No abstract provided.


Systematic Analysis Of Enterprise Perception Towards Cloud Adoption In The African States: The Nigerian Perspective, George A. Oguntala, Prof. Raed A. Abd-Alhameed, Dr. Janet O. Odeyemi 2017 University of Bradford

Systematic Analysis Of Enterprise Perception Towards Cloud Adoption In The African States: The Nigerian Perspective, George A. Oguntala, Prof. Raed A. Abd-Alhameed, Dr. Janet O. Odeyemi

The African Journal of Information Systems

The desirous benefits of cloud computing such as high return on investment through efficient resource management, high application throughput and on-demand capabilities have resulted in the unprecedented global acceptance of the computing paradigm. However, research on cloud adoption indicates that fewer organisations in the African states are adopting cloud services. Thus, the purview of the paper is to examine the factors responsible for the poor adoption of cloud computing in most African enterprises using Nigeria as a case study. The study focus on the perception of IT and non-IT employees towards cloud computing. Moreover, the paper reviews the literature on ...


The Billion Object Platform (Bop): A System To Lower Barriers To Support Big, Streaming, Spatio-Temporal Data Sources, Devika Kakkar, Ben Lewis, David Smiley, Ariel Nunez 2017 Center for Geographic Analysis, Harvard University

The Billion Object Platform (Bop): A System To Lower Barriers To Support Big, Streaming, Spatio-Temporal Data Sources, Devika Kakkar, Ben Lewis, David Smiley, Ariel Nunez

Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings

With funding from the Sloan Foundation and Harvard Dataverse, the Harvard Center for Geographic Analysis (CGA) has developed a big spatio-temporal data visualization platform called the Billion Object Platform or "BOP". The goal of the project is to lower barriers for scholars who wish to access large, streaming, spatio-temporal datasets. Since once archived, streaming data gets big fast, and since most GIS systems don't support interactive visualization of millions of objects, a new platform was needed. The BOP is loaded with the latest billion geo-tweets and is fed a real-time stream of about 1 million tweets per day. The ...


Optimizing Spatiotemporal Analysis Using Multidimensional Indexing With Geowave, Richard Fecher, Michael A. Whitby 2017 Digital Globe

Optimizing Spatiotemporal Analysis Using Multidimensional Indexing With Geowave, Richard Fecher, Michael A. Whitby

Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings

The open source software GeoWave bridges the gap between geographic information systems and distributed computing. This is done by preserving locality of multidimensional data when indexing it into a single-dimensional key-value store, using space filling curves. This means that like values in each dimension are stored physically close together in the datastore. We demonstrate the efficiencies and benefits of the GeoWave indexing algorithm to store and query billions of spatiotemporal data points. We show how this indexing strategy can be used to reduce query and processing times by multiple orders of magnitude using publicly available taxi trip data published by ...


Machine Learning Algorithms For Automated Satellite Snow And Sea Ice Detection, George Bonev 2017 The Graduate Center, City University of New York

Machine Learning Algorithms For Automated Satellite Snow And Sea Ice Detection, George Bonev

All Graduate Works by Year: Dissertations, Theses, and Capstone Projects

The continuous mapping of snow and ice cover, particularly in the arctic and poles, are critical to understanding the earth and atmospheric science. Much of the world's sea ice and snow covers the most inhospitable places, making measurements from satellite-based remote sensors essential. Despite the wealth of data from these instruments many challenges remain. For instance, remote sensing instruments reside on-board different satellites and observe the earth at different portions of the electromagnetic spectrum with different spatial footprints. Integrating and fusing this information to make estimates of the surface is a subject of active research.

In response to these ...


Machine Learning Approach To Retrieving Physical Variables From Remotely Sensed Data, Fazlul Shahriar 2017 The Graduate Center, City University of New York

Machine Learning Approach To Retrieving Physical Variables From Remotely Sensed Data, Fazlul Shahriar

All Graduate Works by Year: Dissertations, Theses, and Capstone Projects

Scientists from all over the world make use of remotely sensed data from hundreds of satellites to better understand the Earth. However, physical measurements from an instrument is sometimes missing either because the instrument hasn't been launched yet or the design of the instrument omitted a particular spectral band. Measurements received from the instrument may also be corrupt due to malfunction in the detectors on the instrument. Fortunately, there are machine learning techniques to estimate the missing or corrupt data. Using these techniques we can make use of the available data to its full potential.

We present work on ...


Machine Learning Based Protein Sequence To (Un)Structure Mapping And Interaction Prediction, Sumaiya Iqbal 2017 University of New Orleans, New Orleans

Machine Learning Based Protein Sequence To (Un)Structure Mapping And Interaction Prediction, Sumaiya Iqbal

University of New Orleans Theses and Dissertations

Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs), and hence the involved dynamics, which help us explain complex interaction within a ...


Simulation Of Driven Elastic Spheres In A Newtonian Fluid, Shikhar M. Dwivedi 2017 The University of Western ontario

Simulation Of Driven Elastic Spheres In A Newtonian Fluid, Shikhar M. Dwivedi

Electronic Thesis and Dissertation Repository

Simulations help us test various restrictions/assumptions placed on physical systems that would otherwise be difficult to efficiently explore experimentally. For example, the Scallop Theorem, first stated in 1977, places limitations on the propulsion mechanisms available to microscopic objects in fluids. In particular, the theorem states that when the viscous forces in a fluid dominate the inertial forces associated with a physical body, such a physical body cannot generate propulsion by means of reciprocal motion. The focus of this thesis is to firstly, explore an adaptive Multiple-timestep(MTS) scheme for faster molecular dynamics(MD) simulations, and secondly, use hybrid MD-LBM ...


Machine Learning In Xenon1t Analysis, Dillon A. Davis, Rafael F. Lang, Darryl P. Masson 2017 Purdue University - North Central Campus

Machine Learning In Xenon1t Analysis, Dillon A. Davis, Rafael F. Lang, Darryl P. Masson

The Summer Undergraduate Research Fellowship (SURF) Symposium

In process of analyzing large amounts of quantitative data, it can be quite time consuming and challenging to uncover populations of interest contained amongst the background data. Therefore, the ability to partially automate the process while gaining additional insight into the interdependencies of key parameters via machine learning seems quite appealing. As of now, the primary means of reviewing the data is by manually plotting data in different parameter spaces to recognize key features, which is slow and error prone. In this experiment, many well-known machine learning algorithms were applied to a dataset to attempt to semi-automatically identify known populations ...


Optimization And Control Of Production Of Graphene, Atharva Hans, Nimish M. Awalgaonkar, Majed Alrefae, Ilias Bilionis, Timothy S. Fisher 2017 Purdue University

Optimization And Control Of Production Of Graphene, Atharva Hans, Nimish M. Awalgaonkar, Majed Alrefae, Ilias Bilionis, Timothy S. Fisher

The Summer Undergraduate Research Fellowship (SURF) Symposium

Graphene is a 2-dimensional element of high practical importance. Despite its exceptional properties, graphene’s real applications in industrial or commercial products have been limited. There are many methods to produce graphene, but none has been successful in commercializing its production. Roll-to-roll plasma chemical vapor deposition (CVD) is used to manufacture graphene at large scale. In this research, we present a Bayesian linear regression model to predict the roll-to-roll plasma system’s electrode voltage and current; given a particular set of inputs. The inputs of the plasma system are power, pressure and concentration of gases; hydrogen, methane, oxygen, nitrogen and ...


Improving The Accuracy For The Long-Term Hydrologic Impact Assessment (L-Thia) Model, Anqi Zhang, Lawrence Theller, Bernard A. Engel 2017 Purdue University

Improving The Accuracy For The Long-Term Hydrologic Impact Assessment (L-Thia) Model, Anqi Zhang, Lawrence Theller, Bernard A. Engel

The Summer Undergraduate Research Fellowship (SURF) Symposium

Urbanization increases runoff by changing land use types from less impervious to impervious covers. Improving the accuracy of a runoff assessment model, the Long-Term Hydrologic Impact Assessment (L-THIA) Model, can help us to better evaluate the potential uses of Low Impact Development (LID) practices aimed at reducing runoff, as well as to identify appropriate runoff and water quality mitigation methods. Several versions of the model have been built over time, and inconsistencies have been introduced between the models. To improve the accuracy and consistency of the model, the equations and parameters (primarily curve numbers in the case of this model ...


Structure-Force Field Generator For Molecular Dynamics Simulations, Carlos M. Patiño, Lorena Alzate, Alejandro Strachan 2017 Universidad de Los Andes - Colombia

Structure-Force Field Generator For Molecular Dynamics Simulations, Carlos M. Patiño, Lorena Alzate, Alejandro Strachan

The Summer Undergraduate Research Fellowship (SURF) Symposium

Atomistic and molecular simulations have become an important research field due to the progress made in computer performance and the necessity of new and improved materials. Despite this, first principle simulations of large molecules are still not possible because the high computational time and resources required. Other methods, such as molecular dynamics, allow the simplification of calculations by defining energy terms to describe multiple atom interactions without compromising accuracy significantly. A group of these energy terms is called a force field, and each force field has its own descriptions and parameters. The objective of this project was to develop a ...


Predictive Power And Validity Of Connectome Predictive Modeling: A Replication And Extension, Michael Wang, Joaquin Goni, Enrico Amico 2017 Purdue University

Predictive Power And Validity Of Connectome Predictive Modeling: A Replication And Extension, Michael Wang, Joaquin Goni, Enrico Amico

The Summer Undergraduate Research Fellowship (SURF) Symposium

Neuroimaging, particularly functional magnetic resonance imaging (fMRI), is a rapidly growing research area and has applications ranging from disease classification to understanding neural development. With new advancements in imaging technology, researchers must employ new techniques to accommodate the influx of high resolution data sets. Here, we replicate a new technique: connectome-based predictive modeling (CPM), which constructs a linear predictive model of brain connectivity and behavior. CPM’s advantages over classic machine learning techniques include its relative ease of implementation and transparency compared to “black box” opaqueness and complexity. Is this method efficient, powerful, and reliable in the prediction of behavioral ...


Predicting Locations Of Pollution Sources Using Convolutional Neural Networks, Yiheng Chi, Nickolas D. Winovich, Guang Lin 2017 Purdue University

Predicting Locations Of Pollution Sources Using Convolutional Neural Networks, Yiheng Chi, Nickolas D. Winovich, Guang Lin

The Summer Undergraduate Research Fellowship (SURF) Symposium

Pollution is a severe problem today, and the main challenge in water and air pollution controls and eliminations is detecting and locating pollution sources. This research project aims to predict the locations of pollution sources given diffusion information of pollution in the form of array or image data. These predictions are done using machine learning. The relations between time, location, and pollution concentration are first formulated as pollution diffusion equations, which are partial differential equations (PDEs), and then deep convolutional neural networks are built and trained to solve these PDEs. The convolutional neural networks consist of convolutional layers, reLU layers ...


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