The core problem addressed in this research is the current lack of an ability to repurpose and curate scientific data among interdisciplinary scientists within a research enterprise environment. Explosive growth in sensor technology as well as the cost of collecting ocean data and airborne measurements has allowed for exponential increases in scientific data collection as well as substantial enterprise resources required for data collection. There is currently no framework for efficiently curating this scientific data for repurposing or intergenerational use. There are several reasons why this problem has eluded solution to date to include the competitive requirements for funding and publication, multiple vocabularies used among various scientific disciplines, the number of scientific disciplines and the variation among workflow processes, lack of a flexible framework to allow for diversity among vocabularies and data but a unifying approach to exploitation and a lack of affordable computing resources (mostly in past tense now). Addressing this lack of sharing scientific data among interdisciplinary scientists is an exceptionally challenging problem given the need for combination of various vocabularies, maintenance of associated scientific data provenance, requirement to minimize any additional workload being placed on originating data scientist project/time, protect publication/credit to reward scientific creativity and obtaining priority for a long-term goal such as scientific data curation for intergenerational, interdisciplinary scientific problem solving that likely offers the most potential for the highest impact discoveries in the future. This research approach focuses on the core technical problem of formally modeling interdisciplinary scientific data provenance as the enabling and missing component to demonstrate the potential of interdisciplinary scientific data repurposing. This research develops a framework to combine varying vocabularies in a formal manner that allows the provenance information to be used as a key for reasoning to allow manageable curation. The consequence of this research is that it has pioneered an approach of formally modeling provenance within an interdisciplinary research enterprise to demonstrate that intergenerational curation can be aided at the machine level to allow reasoning and repurposing to occur with minimal impact to data collectors and maximum impact to other scientists.

In this thesis, the author gives a description his journey creating and developing a Student Life Application for East Tennessee State University. This thesis will document his process with development as well as reflect on the struggles and victories in creation of this application.

Electronic gadgets such as touchpads and smartphones are becoming more popular in business and everyday life. The main advantage of mobile devices over personal computers is their portability. Cellular data plans allow Internet access without having permanent access point. There is a number of web-based applications available for gadgets. The primary goal of these apps is to provide their services through constant Internet access. However, it may affect the operation of both devices and applications. The objective of this thesis is to find a better way of client/server data synchronization in iOS development that can reduce the negative consequences of constant Internet access such as Internet traffic consumption, reduced battery life and cover area limitation of cellular services. As the result of the conducted research, the "FollowQTM" application was developed. Despite the fact, that the application is synchronized with web server, it does not require constant Internet access to operate.

This work presents heuristics for searching large sets of molecular structures for low-energy, stable systems. The goal is to find the globally optimal structures in less time or by consuming less computational resources. The strategies intermittently evaluate and rank structures during molecular dynamics optimizations, culling possible weaker solutions from evaluations earlier, leaving better solutions to receive more simulation time. Although some imprecision was introduced from not allowing all structures to fully optimize before ranking, the strategies identify metrics that can be used to make these searches more efficient when computational resources are limited.

In this work, the algorithm used by AlphaZero is adapted for dots and boxes, a two-player game. This algorithm is explored using different numbers of convolutional filters and training loops, in order to better understand the effect these parameters have on the learning of the player. Different board sizes are also tested to compare these parameters in relation to game complexity. AlphaZero originated as a Go player using an algorithm which combines Monte Carlo tree search and convolutional neural networks. This novel approach, integrating a reinforcement learning method previously applied to Go (MCTS) with a supervised learning method (neural networks) led to a player which beat all its competitors.

Processing real-world data requires the ability to analyze data in real-time. Data processing engines like Hadoop come short when results are needed on the fly. Apache Spark's streaming library is increasingly becoming a popular choice as it can stream and analyze a significant amount of data. To showcase and assess the ability of Spark various metrics were designed and operated using data collected from the USGODAE data catalog. The latency of streaming in Apache Spark was measured and analyzed against many nodes in the cluster. Scalability was monitored by adding and removing nodes in the middle of a streaming job. Fault tolerance was verified by stopping nodes in the middle of a job and making sure that the job was rescheduled and completed on other node/s. A full stack application was designed that would automate data collection, data processing and visualizing the results. Google Maps API was used to visualize results by color coding the world map with values from various analytics.

Transposable Elements (TEs) or jumping genes are the DNA sequences that have an intrinsic capability to move within a host genome from one genomic location to another. Studies show that the presence of a TE within or adjacent to a functional gene may alter its expression. TEs can also cause an increase in the rate of mutation and can even promote gross genetic arrangements. Thus, the proper classification of the identified jumping genes is important to understand their genetic and evolutionary effects. While computational methods have been developed that perform either binary classification or multi-label classification of TEs, few studies have focused on their hierarchical classification. The existing methods have limited accuracy in classifying TEs. In this study, we examine the performance of a variety of machine learning (ML) methods and propose a robust augmented Stacking-based ML method, ClassifyTE, for the hierarchical classification of TEs with high accuracy.

Custom T-shirt Designs is a web-based application. The purpose of this project is to provide a website that would allow customers to be able to customize T-shirt and place an order of custom T-shirt. Customers can sign up, sign in, select T-shirt color, add text, choose Font, choose Font color, upload an image, apply filters to images, transform text or images, choose T-shirt size and save designs for future references. Customers would be able to add the design to a cart, manage cart and checkout with their credit card to purchase for the order and view their previous orders. The project focuses on object-oriented programming paradigm using PHP framework Laravel along with JavaScript, CSS, and HTML, Bootstrap and jQuery. Custom T-shirt Designs application uses Model-view-controller architecture. Stripe API is used to handle the credit card payments tokens providing a secure way for customers to purchase a T-shirt. Google SMTP is used to send confirmation of purchase order to administrator and customer. The project implements AJAX technology to send and receive parts of data from the server asynchronously making Custom T-shirt Designs a fast and efficient real-time web application.

The main aim of the course recommendation system is to build a course recommendation path for students to help them plan courses to successfully graduate on time. The Model-View-Controller (MVC) architecture is used to isolate the user interface (UI) design from the business logic. The front-end of the application develops the UI using AngularJS. The front-end design is done by gathering the functionality system requirements -- input controls, navigational components, informational components and containers and usability testing. The back-end of the application involves setting up the database and server-side routing. Server-side routing is done using Express JS.

As quantum computers edge closer to viability, it becomes necessary to create logic synthesis and minimization algorithms that take into account the particular aspects of quantum computers that differentiate them from classical computers. Since quantum computers can be functionally described as reversible computers with superposition and entanglement, both advances in reversible synthesis and increased utilization of superposition and entanglement in quantum algorithms will increase the power of quantum computing. One necessary component of any practical quantum computer is the computation of irreversible functions. However, very little work has been done on algorithms that synthesize and minimize irreversible functions into a reversible form. In this thesis, we present and implement a pair of algorithms that extend the best published solution to these problems by taking advantage of Product-Sum EXOR (PSE) gates, the reversible generalization of inhibition gates, which we have introduced in previous work [1,2]. We show that these gates, combined with our novel synthesis algorithms, result in much lower quantum costs over a wide variety of functions as compared to our competitors, especially on incompletely specified functions. Furthermore, this solution has applications for milti-valued and multi-output functions.