This project explores the optimization of HVAC and renewable energy systems of new, modular and portable off grid systems like the Recycling Microfactory, a joint project between Arizona State University and the Department of Defense (DOD). There has been a growing push for innovative solutions to address the underlying deficiencies in United States supply chains and energy infrastructure. This paper seeks to elaborate on the proposed solutions of portable and modular infrastructure to support neglected sectors of the economy: energy grid modernization and waste management specifically. This will be done by analyzing the Microfactory’s operations and optimizing the site’s energy efficiency. Background knowledge and context behind the current state of supply chains and of both energy and waste management sectors are briefly explained in the introduction followed by a high-level overview of the concept of modular infrastructure such as the Recycling Microfactory. The body of the thesis is organized into two sections. The first section focuses on the methods for planning the structure, layout, and workflow of the Recycling Microfactory for when it is out for transport and organized for operation. A series of 3D parametric models were used for the high-fidelity layouts of the Microfactory and was developed in conjunction with user experience gained from evaluating the custom-built processing equipment. The second section further expands the initial energy simulation models of the Microfactory generated from the first simulations of the project. Utilizing the building energy modeling (BEM) software EnergyPlus/OpenStudio, more advanced models accounting for HVAC sizing requirements, climate building standards (i.e., building insulation), and human comfort standards for workspaces are generated. A more realistic simulation of the energy requirements of the Microfactory to maintain temperature and humidity standards is presented through a comprehensive review of the OpenStudio building model design flow.

This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.

This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.