abstract: Healthcare is one of the most personal and complex services provided, and as such, designing healthcare environments is particularly challenging. In the last couple of decades, researchers have concentrated their efforts on exploring the elements of the hospital environment that affect patients' health and in finding ways to apply that knowledge in contemporary healthcare design. But despite the growing body of research, there is an element of utmost importance to healing environments that has not been studied very extensively: the patient experience. The interaction of patients with their environment shapes their personal experience, and inversely, focusing on designing experiences rather than services can inform the design of successful healing environments. This shift from designing services to designing experiences has deep implications in healthcare settings because of the stressful situations that patients have to go through; memorable experiences have a positive influence on a patient's emotional health because they help minimize stress and in healthcare environments this translates into improved outcomes. The concept of assembling experiences is not new, especially in the entertainment industry; it was, in fact, the underlying principle behind the creation of the first theme park more than fifty years ago: Disneyland. Today, Disney is an entertainment industry leader and their design concepts and practices have been perfected to achieve the Company's main purpose: to immerse Guests in a happy, unforgettable experience. This research study focuses on examining the principles used by Disney designers, or Imagineers, as they are called within the organization, to generate memorable experiences, and how those theories can be adopted and adapted by healthcare designers to create better healing environments. However, Disney's Imagineering is not the only approach considered in this research. A thorough analysis would not be complete without delving into the concept of experiential design as a design process and from an economical perspective, as well as without analyzing recent notions about the importance of authenticity in businesses and its implications on design. This study, therefore, suggests a new healing environment design model based on a comprehensive review of the literature related to three main design approaches: Disney Imagineering, experiential design and authenticity. / Dissertation/Thesis / M.S.D. Design 2012

<p>The design and discovery of novel materials are difficult not only due to expensive and time- consuming calculation and measurements of their properties, but also thanks to the infinite search spaces. With the increasingly abundant data from experiments and simulations, learning from data has the potential of bypassing complex physics-based simulations and experiments and providing fast approximations of the solution. Deep learning models are helpful in the design process that requires prohibitively expensive iterative computations. In addition, as efficient and accurate sur- rogate models, trained deep networks can incorporate techniques, such as sensitivity analysis and active learning, to provide guidance in searching promising candidates. Moreover, deep learning models need to account for the material structural information, such as molecule and atom align- ments, chemical bonds, and grain-level interactions, as it plays an important role in determining the macroscopic properties. In this thesis, we start with developing two standard deep learning model- based materials design frameworks for lithium-ion batteries and thermoelectric materials, and we then investigate the feasibility of standard deep learning models on data with graph-structured in- formation and identify the challenges. Finally, we propose a deep graph operator network that effectively capture the spatial dependency encoded in the graph structure to solve networked dy- namical systems.</p> <p><br></p> <p>In the first half of the thesis, we propose a hybrid convolutional neural network to infer lithium- ion battery microstructure properties, Bruggeman’s exponent and shape factor, given its voltage vs. capacity curves. The trained model accurately predicts the microstructural properties on both experimental and simulation data, and it can readily accelerate the processing-properties- performance and degradation characteristics of the existing and emerging chemistries of lithium- ion batteries. Also, we develop a AI-guided framework to discover and design thermoelectric materials, where we train classifiers based on the materials chemical and structural information embeddings and combine with variance-based sensitivity analysis to suggest candidates and con- duct fast screening.</p> <p><br></p> <p>In the second half of the thesis, we build a data-centric framework with a recurrent neural network-based classifier to achieve traffic incident detection on highway networks. We incorporate weak supervised learning and design labeling functions to create large amount of training data with probabilistic labels. The trained deep ensemble accurately detects incidents with predictive uncertainty. To capture the structural information in the network, we then propose a deep graph operator network that maps the input graph state function to the output graph state function. The proposed model enables resolution-independence and zero-shot transfer, where we do not require a set of fixed sensors to encode the graph trajectory and can use the trained model directly on larger graphs with high accuracy. We utilize the proposed model to solve power grid transient stability prediction and traffic forecasting problems.</p>

Gender issues in the landscape, for a long time, have belonged to the fields of social and political science, which remain relatively unfamiliar to both practitioners and students in the discipline of landscape architecture. Previous scholars have put effort into examining questions of gender, culture and landscape in order to clarify the issues that researchers may encounter in today’s field of study. Among these gender classifications, questions in feminist inquiry have provided a historical setting to this study: what are the forms, transitions and design approaches that women employ as creators of the built landscapes? Through reviewing the past literature and surveying today’s practitioners, an understanding emerges of how female landscape designers think about their gender identity as a variable in the design process. In addition, several issues are further identified, including the female awareness of their gender identity in the workplace, types of female work, transitions in design approach since the 1899 American Society of Landscape Architects was founded to the present day, cultural discourse in female landscape forms, and so on. The major goal of this study is not to build a description of history that asks how women may design differently than men, but to reexamine the idea that has made such stereotypes invalid; gender may influence design approaches but not outcome. Furthermore, this study also attempts to identify the potential gender issues in today’s profession, and to provide a viewpoint to landscape designers of any gender: How does our innate gender identity potentially influence design thinking? Finally, as a designer who is drawn to the cultural dimension of landscape architecture, I hope this study will be helpful to landscape professionals in developing a more complex approach and critical eye for looking at designed landscape forms as cultural vehicles for gender construction.

The purpose of this project was to describe a novel design approach for a digital computer peripheral controller, then design and construct a case study controller. This document consists of three chapters and an appendix. Chapter II presents the design approach chosen; a variation to a design presented by Charles R. Richards in an article published in Electronics magazine. Richards' approach consists of a finite state machine circuitry controlling all the functions of a controller. The variation to Richards' approach consists of considering the various logically independent processes which a controller carries out and assigning control of each process to a separate finite state machine. The appendix contains the documentation of the design and construction of the controller.

Online learning courses are taken by nearly 31% of college students (Allen & Seaman, 2011). The majority of those enrolled in online learning are graduate and non-traditional undergraduate students. Survey data from multiple sources show a growing number of traditional students enrolling in online courses or online only universities. There is a lack of information about younger college students enrolling in online courses and those attending online only institutions. Without more research on the perceptions of this population, it is difficult to design an effective online learning environment. The Community of Inquiry (CoI) framework has been used as a process model that defines, describes and measures the tasks supporting online learning. The core elements are the three overlapping presences of teaching, social and cognitive and the interrelationships among those presences. Through more than a decade of work on the framework, a methodology and survey instrument emerged for studying the potential and effectiveness of online learning. Will younger college students enrolling today perceive the CoI framework and the presences from the model, the same way that non-traditional students have in the last decade? Most CoI studies sample non-traditional adults aged 25 and older. American Public University System (APUS) is a for-profit online only institution which publishes research studies that contribute to the growing number of CoI studies available. In the last four years APUS has provided large samples to validate the CoI model and investigate how CoI relates to retention and course design. The limited purpose of the research is to determine whether the CoI framework and its current results, is applicable to a select group of traditional students aged 21 and under who enroll in only online courses APUS. Through an exploratory study using statistical tests including a factorial analysis, the first sample population (n=2,019) consisted of students', 21 and under, responses to the CoI questionnaire and the second sample (n=125,039) was the responses of students older than 21. The samples were compared to determine if there was any significant difference between the perceptions of non-traditional and younger college students on the CoI model. Results indicated that the comparative means of the two populations are highly correlated at .924 but the p value is .000 at the 95% confidence interval. The two populations are different. A factor analysis showed that both samples perceived a three factor solution. The total variance explained was very similar for both samples. For the students who were 21 and under, the three factors accounted for 77.16% of the total and for the older students, three factors accounted for 74.17% of the total. The factor analysis results from the younger students also show that each item from the questionnaire is associated with the appropriate factor. The factor analysis results correspond to previous validated research conducted on the CoI model. The results continue to support the validity of the CoI model, but the differences in the populations are significant. The significance tests are useful but may not be as meaningful as the factor analysis due to the size of the samples. This research adds to the body of knowledge on the CoI model, a dominant theory that describes what learners perceive in an online environment. The results inform the understanding of the CoI model as it applies to younger college learners' perceptions of an effective online learning environment.

<div>Accelerating the development of novel materials is one of the central goals of the Materials Genome Initiative and improving the predictive power of computational</div><div>material science methods is critical to attain this goal. Molecular dynamics (MD) is one such computational technique that has been used to study a wide range of materials since its invention in the 1950s. In this work we explore some examples of using and increasing the predictive power of MD simulations to understand materials phenomena and provide guidelines to design tailored materials. We first demonstrate the use of MD simulations as a tool to explore the design space of shape memory alloys, using simple interatomic models to identify characteristics of an integrated coherent second phase that will modify the transformation characteristics of the base shape memory alloy to our desire. Our approach provides guidelines to identify potential coherent phases that will achieve tailored transformation temperatures and hysteresis. </div><div><br></div><div>We subsequently explore ideas to enhance the length and time scales accessible via MD simulations. We first discuss the use of kinetic Monte Carlo methods in MD simulations to predict the microstructure evolution of carbon fibers. We ?find our approach to accurately predict the transverse microstructures of carbon fibers, additionally predicting the transverse modulus of these fibers, a quantity difficult to measure via experiments. Another avenue to increase length and time scales accessible via MD simulations is to explore novel implementations of algorithms involved in machine-learned interatomic models to extract performance portability. Our approach here results in significant speedups and an efficient utilization of increasingly common CPU-GPU hybrid architectures.</div><div><br></div><div>We finally explore the use of machine learning methods in molecular dynamics, specifically developing machine learning methods to discover interpretable laws directly from data. As examples, we demonstrate the discovery of integration schemes for MD simulations, and the discovery of melting laws for perovskites and single elements. Overall, this work attempts to illustrate how improving the predictive capabilities of molecular dynamics simulations and incorporating machine learning ideas can help us design novel materials, in line with the goals of the Materials Genome Initiative.</div>

There are various approaches available for the durability design of non-metallic reinforcement in concrete, such are ACI 440.1R-15, BISE-99, CHBDC-07, NS3473-98, and JSCE-97.These available design approaches are very general and consolidate all effects into a single factor for each type of Fiber Reinforced Polymer (FRP) material depending on the type of fiber, which does not actually resemble the real concrete service life. Thus, more reliable design approaches are in need and have been developed in recent years which tried to simulate real-life conditions. They provide safety factors by not only considering the type of fiber, but also the moisture conditions, temperature conditions, diameter of the bar, and service life of the structure. Such design approaches need to be considered in the standards that can be applied in the concrete design context. This study was a part of my master’s thesis.

This thesis paper addresses a problem of motivation that interaction designers experience when managing multiple design processes while keeping track of many design considerations. Once this issue is described at length, the paper focuses towards a possible solution in the form of a hybrid between creativity and productivity support tools: an accomplishment support tool. This tool is meant to support interaction designers in their experience with managing multiple processes.This paper does not suggest that interaction design is the only profession that suffers from the motivation issue that is described, nor does it deny that other professions could benefit from the use of such a tool. The paper merely attempts to narrow the issue down to one profession so that it may be addressed within the limits of the thesis project.The paper explores the need for such a tool by inspecting and analyzing current methods and digital applications used by interaction designers and mentions how this need is addressed with solutions based on relevant theories from diverse areas of interest. As defining qualities emerge from a combination of theoretical and practical research, case studies are described from a preparation perspective and then as experienced by workshop participants and interviewees.The case studies (which include workshops and prototype modules) are then reflected upon and discussed in terms of their impact on the overall goals of the thesis project. A final prototype in the form of a web application, IxD Companion, is then described through scenarios of use and assessed in the conclusion. Suggestion to future work on accomplishment support tools such as IxD Companion, as well as others, is provided at the end.