OBJECTIVE: After comparing the top 10 menstrual tracking applications, there are no applications that address symptoms specific to Polycystic Ovary Syndrome (PCOS). This thesis demonstrates the need for a comprehensive menstrual tracking application that caters toward all populations, including patients with PCOS. METHODS: Mobile application usage was evaluated through data from the Ovulation and Menstruation (OM) Health Study to view relationships between PCOS/demographic groups and preference for tracking methods. The top 10 most popular mobile menstrual tracking applications were compiled through a search on the iOS operating system. Mobile menstrual tracking applications were then evaluated through an adapted APPLICATIONS system, which includes categories to score for PCOS-specific symptoms. RESULTS: PCOS groups showed clear preference for tracking methods overall, and are more likely to view their own health as fair/poor. P values for tests between other demographic variables in the OM Health study were insignificant, but there are observable trends in education, income, and age and usage of tracking methods. Evaluation of the top 10 mobile menstrual applications resulted in Clue scoring the highest, but no applications scored a complete PCOS-specific score. CONCLUSIONS: With continual variance in menstrual cycles, there needs to be development of a mobile menstrual application that is effective for all populations. Mobile menstrual applications have proven their popularity through PCOS groups and the rising usage within younger age groups. However, through the adapted APPLICATIONS system, major features are still missing, and are necessary to cater towards unique groups such as people with menstrual irregularities and PCOS.

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The purpose of this review of literature was to understand the role of mobile device applications in health related conditions and to analyze their effects on health outcomes related to the management of chronic illnesses. Implications for future use of applications in client-centered care and interpretation of the data by health care providers was also explored. Peer-reviewed, English-language research articles published from 2008 to present were included for synthesis. Study results revealed positive outcomes when health-related mobile applications were used in practice and support clinicians' use of mobile applications as a tool for monitoring symptoms and communicating with individuals. The literature indicated nurses play a significant role in providing feedback, which reinforces self-care strategies and adherence, with the potential for improving outcomes. Additional research is needed to evaluate the long-term effects of applications on patient outcomes, nurses' perspectives, and feasibility of implementation into practice.

Yes / Mixed reality (MR) is a powerful interactive technology for new types of user experience. We present a semantic‐based interactive MR framework that is beyond current geometry‐based approaches, offering a step change in generating high‐level context‐aware interactions. Our key insight is that by building semantic understanding in MR, we can develop a system that not only greatly enhances user experience through object‐specific behaviours, but also it paves the way for solving complex interaction design challenges. In this paper, our proposed framework generates semantic properties of the real‐world environment through a dense scene reconstruction and deep image understanding scheme. We demonstrate our approach by developing a material‐aware prototype system for context‐aware physical interactions between the real and virtual objects. Quantitative and qualitative evaluation results show that the framework delivers accurate and consistent semantic information in an interactive MR environment, providing effective real‐time semantic‐level interactions.

This paper documents the design and creation of an App Library for HydroShare water resources data and software discovery and sharing system. This App Library was developed to simplify the discovery process for using environmental web applications and to lower hosting requirements for such a repository. To accomplish this goal, I created the HydroShare App Library as a standalone web application using the React JavaScript framework. The App Library application uses the existing HydroShare resource connectors to allow the registration of all web applications within the App Library without having external software requirements. This allows the HydroShare App Library to be a centralized location for web app developers to register their tools and models using their preferred software while allowing water resources managers, engineers, scientists, and decision-makers to find these tools in a single location. The developed HydroShare App Library allows the discovery of all web applications that are included in the HydroShare ecosystem and not just CUAHSI-owned web apps. This is done by using a dynamic table with React that automatically updates the user interface without having to reload entire pages. This approach allows this web app to reduce processing for the App Library by only rendering web app entries that are relevant to the current user. This allows the App Library to grow and continue to be effective as more web applications are registered in HydroShare and are discoverable within the App Library.

Advancements in robotics have the potential to aid humans in many realms of exploration as well as daily life: from search and rescue work, to space and deep sea exploration, to in-home assistance to improve the quality of life for those with limited mobility. One of the main milestones that needs to be met for robotics to achieve these ends is a robust ability to manipulate objects and locomote in cluttered and changing environments. A prerequisite to these skills is the ability to understand the current state of the world as well as how actions result in changes to the environment; in short, a robot needs a way to model itself and the world around it. With recent advances in machine learning and access to cheap and fast computation, one of the most promising avenues for creating robust models is to learn a neural network to approximate the dynamics of the system. Learning a data-driven model that accurately replicates the dynamics of a robot and its environment is an active area of robotics research. This model needs to be accurate, it needs to operate using sensors that are often high dimensional, and it needs to be robust to changes within the system and the surrounding environment. In this thesis, we investigate ways to improve the processof learning data-driven dynamics models as well as ways to reduce the dimensionality of a robot’s state space. We start by trying to improve the long-term accuracy of neural network based forward models. Learning forward models is more complicated than it appears on the surface. While it is easy to learn a model to predict the change of a system over a short horizon, it is challenging to assure this performance over a long horizon. We investigate the concept of adding temporal information into the loss function of the forward model during training; we demonstrate that this improves the accuracy of a model when it is used to predict over long horizons. While we are currently working with low dimensional systems, we eventually want to apply our learned models to robots with high dimensional state spaces. To make learning feasible, we need to find ways to learn a lower dimensional representation of the state space (also known as a latent space) to make learning models in the real world computationally feasible. We present a method to improve the usefulness of a learned latent space using a method we call context training: we learn a latent space alongside a forward model to encourage the learned latent space to retain the variables critical to learning the dynamics of the system. In all of our experiments, we spend significant time in analysis and evaluation. A large portion of literature demonstrating the effectiveness of data-driven forward models in robot control settings often only presents the final controller performance. We were often left curious about what the model was learning independent of the control scenario. We set out to do our own deep dive into exactly what data-driven forward models are predicting. We evaluate all of our models over long horizons. We also look deeper than just the mean and median loss values. We plot the full distribution of loss values over the entire horizon. The literature on data-driven models that do evaluate model prediction accuracy often focuses on the mean and median prediction errors; while these are important metrics, we found that looking at these metrics alone can sometimes obscure subtle but important effects. A high mean loss is often a result of poor performance on only a subset of the test dataset; one model can outperform other models with lower mean error values on a majority of the test set, but it can be skewed to look like the worst performer by having a few highly inaccurate outliers. We observe that models often have a subset of a test dataset on which they perform best; we seek to limit the use of a model to regions of the test dataset where it has high accuracy by using an ensemble of models. We find that if we train an ensemble of forward models, the accuracy of the models is higher when they all agree on a prediction. Conversely, when the ensemble of models disagrees, the prediction is often poor. We explore this relationship and propose future ways to apply it. Finally, we look into the application of improved model accuracy and context trained latent spaces. We start by testing the performance of our context training architecture as a method to reduce the state space dimensionality in a model-free reinforcement learning (MFRL) reaching task. We hypothesize that a policy trained with a latent space observation derived using our context trained encoder will outperform a policy trained with a latent space observation derived from a standard autoencoder. Unfortunately, we found no difference in task performance between the policies learned using either method. We end on a bright note by looking at the power of model-based control when we have access to an accurate model. We successfully use model predictive control (MPC) to generate robust locomotion for a simulated snake robot. With access to an accurate model, we are able to generate realistic snake gaits in a variety of environments with very little parameter tuning that are robust to changes in the environment.

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This report is a survey of Artificial Intelligence (AI) technology contributions to military training. It provides an overview of military training simulation and a review of instructional problems and challenges which can be addressed by AI. The survey includes current as well as potential applications of AI, with particular emphasis on design and system integration issues. Applications include knowledge and skills training in strategic planning and decision making, tactical warfare operations, electronics maintenance and repair, as well as computer-aided design of training systems. The report describes research contributions in the application of AI technology to the training world, and it concludes with an assessment of future research directions in this area.

The purpose of this study was to develop a simple one- dimensional finite element for the nonlinear analysis of symmetrically and unsymmetrically laminated composite beams including shear deformation. There is a need for a simple and efficient method for analyzing unsymmetrically laminated beams since no other study on this topic is currently available. The beam element has ten degrees of p freedom at each of the two nodes: the axial displacement, the transverse deflection due to bending and shear, the twisting angle, the inplane shear rotation, and their derivatives along the axial direction. The formulation, solution procedure, and the computer program have been evaluated by solving a series of examples on the static response, free vibration, buckling, and nonlinear vibrations of isotropic and laminated beams. For unsymmetrically laminated beams, the nonlinear vibrations were found to have a soft spring behavior for certain boundary conditions as opposed to a hard spring behavior observed in isotropic and symmetrically laminated beams. The inplane boundary conditions were found to have a significant effect on nonlinear responses. / Master of Science