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The primary goal of design is to provide effective and innovative solutions for solving design problems. Ideation, an initial idea generation for conceptualizing a design solution, is a key step that can lead design to an innovative design solution in the design process. Idea generation is a process that allows designers to explore many different areas of the design solution space. Due to the importance of ideation, many studies focused on understanding the cognitive processes in idea generation and evaluating ideation. This thesis focuses on the idea generation process based on conceptual similarity in a human-AI collaboration. Co-creative systems in design allow users to collaborate with an AI agent on open-ended creative tasks in the design process. Co-creative systems share the characteristics of both creativity support tools helping users achieve creative goals and algorithms that generate creative content autonomously. Co-creative systems support design creativity by encouraging the exploration of design solutions in the initial idea generation. However, there is a lack of studies about the effect of co-creative systems on the cognitive process during ideation. This thesis posits that the contribution of an AI partner in design is associated with specific properties of ideation such as novelty, variety, quality, and quantity of ideas.
This thesis presents a co-creative system that enhances design creativity in the initial idea generation process. The Collaborative Ideation Partner (CIP) is a co-creative design system that selects and presents inspirational images based on their conceptual similarity to the design task while the designer is sketching. This thesis addresses how the conceptual similarity of the contribution of the AI partner influences design ideation in a co-creative system. This thesis presents an experiment with a control condition in which the images are selected randomly from a curated database for inspiration and a treatment condition in which conceptual similarity is the basis for selecting the next inspiring image. To evaluate the ideation during the use of CIP, this thesis employed an aggregate analysis and a temporal analysis. The findings show that the AI model of conceptual similarity used in the treatment condition has a significant effect on the novelty, variety, and quantity of ideas during human design ideation.
Smart homes are more connected than ever before, with a variety of commercial internet of things devices available. The use of these devices introduces new security and privacy risks in the home and needs for helping users to understand and mitigate those risks by providing them some level of control over their data. For doing so, it is necessary to have a thorough understanding of smart home users' security and privacy perceptions, behaviors, preferences, and needs.
My thesis aims to investigate the current state of end-user knowledge of smart home device data practices, available privacy controls, and their security and privacy concerns and behaviors. I have utilized different research methods throughout this exploration, including semi-structured interviews, surveys, and experience sampling studies. The contributions of this dissertation are: 1) it uncovers several factors that contribute to the privacy perceptions, concerns, and behaviors of smart home users, 2) it provides in-depth analysis of the current interface support (or lack thereof) to address end-user privacy needs, and finally 3) it contributes several design guidelines to empower users with their privacy in the smart home.
Due to its reduced dimensionality, monolayer molybdenum disulfide (MoS2) exhibits many unique optical properties, making it an excellent candidate for future optoelectronic devices. Given the multitude of applications, understanding the optical limitations of MoS2 under intense excitation is essential to optimize its performance. To that end, we investigate the femtosecond laser-induced breakdown of monolayer MoS2 with a variety of techniques. In this study, the substrate is discovered to have a profound effect where the ablation threshold itself can vary by more than one order of magnitude due to a simple interference phenomenon within the monolayer. Via substrate engineering, the ablation threshold can be reduced such that laser patterning using pulse energies less than 100 pJ is possible. Similar to many other optical nonlinearities, absorption measurements and theoretical modeling reveal that avalanche ionization is also enhanced where more than 75% of the generated free carriers at breakdown are due to avalanche ionization alone. Finally, multi-shot studies demonstrate that MoS2 is one of the most optically robust materials with very weak incubation effects. Notably, the onset of optical damage results in the formation of nano-voids where clusters of atoms are removed while the overall integrity of the monolayer remains intact. All these findings help establish MoS2 as a promising candidate for strong field devices and provides foundational knowledge regarding the strong field physics of two-dimensional materials.
New modes of public transportation such as micromobility are rapidly growing in urban areas. Bike sharing and e-scooter sharing, for example, have been advanced to solve the first/last mile problem, providing quick access to bus stops and train stations for their users. This efficiency, however, may come at the cost of transmitting disease since the surfaces on the bicycles or scooters are subject to germs and harmful pathogens when they are left in contaminated places or used by infectious individuals. This dissertation aims to understand various facets of the role of micromobility transportation in the spread of viral disease within dense urban areas. I propose a novel micro-level and spatially-explicit agent-based modeling framework to model the spread of viral infectious diseases through micromobility systems and a baseline population. I use this simulation framework to study the role of micromobility in the spread of viral disease in urban areas by breaking down the problem into three directions. First, I want to study how surfaces on the new micromobility transportation systems contribute to the emergence and dynamics of viral epidemics in urban areas. Second, I seek to find out how geographic space and time are organized concerning the risk of exposure to a viral disease out of using micromobility vehicles. Third, to inform decision-making in response to the spread of viral disease through micromobility systems, I examine what intervention methods and strategies, including random or systematic intervention, are more effective in controlling the spread of infectious diseases through micromobility vehicles. In order to test the proposed model, a case study is conducted in Cook County, Illinois, and uses the Chicago City public bikesharing system. Results show that the emergence of viral disease through micromobility transportation in Cook County is possible, but the overall impact of the system on the disease dynamics in a worst-case scenario, especially with the current size of the system, is rather small. The proposed model, however, provides a better measure to evaluate the role of transportation in spread of disease compared to existing measures. The spatial pattern for the risk of exposure is higher in the central business district and in northern regions, where most of the shared bike transportation occurs. Moreover, the start day of exposure impacts the dynamics of the spread of disease through both micromobility and the baseline population. Finally, intervention success in a full-blown epidemic highly depends on human behavior, availability of disinfection equipment, and strategies to implement control methods. The proposed simulation framework can be used to assess the efficacy of interventions and make trade-offs between these factors when dealing with epidemics of the sort analyzed in this research.
Expectations regarding leadership practices are changing and evolving as the expectations for school leaders and, thus, central office leaders, to lead and support the creation of equitable outcomes for all students. School systems are recognizing methods to acquire and strengthen a critical lens for identifying the inequities within their school systems so that they can tackle barriers to advancement and root causes more directly (Cheatham et al., 2020). Central office leaders should exemplify specific critical roles for school reform (Rorrer et al., 2008). For this phenomenological study, six equity officers from five urban districts were interviewed about their perceptions of how they define equity-driven central office leadership and their perception of the skills needed for central office leaders to actualize their definition of equity-driven central office leadership and to also reflect upon their roles as equity officers. Districts may benefit from learning more about the practical core skills, behaviors, and comprehensive leadership development practices to develop equity-driven central office leaders who impact equitable outcomes for students. The findings indicate that when equity officers have support from the district, including time, financial resources, and access to school leaders, they believe they can have a more significant impact on schools and leaders.
Subjective perceptions of social mobility are critical for defending societal system and maintain political stability (Day and Fiske 2017; Houle 2019). This dissertation enhances our understanding of factors that shape beliefs in opportunity for upward mobility by focusing on the living environments in American neighborhoods. Inspired by the research from psychology and development economics, I developed and tested the Opportunity Beliefs Theory to explain how the built environment in neighborhoods affects individuals’ opportunity beliefs. The theory aims to elucidate how environmental factors psychologically affect people’s beliefs and behavior. The Opportunity Beliefs theory argues that the living environment can rouse positive or negative emotions. These emotional incentives shape residents’ self-efficacy. These emotions and self-efficacy largely affect people’s expectations for the future. According to the Opportunity Beliefs Theory, for people with low/middle income, those who live in a neighborhood with a better-maintained built environment are more likely to possess positive emotions and hold a high-level of self-efficacy. Furthermore, these residents will perceive more opportunities for themselves and their children for getting ahead in life, and they are more likely to agree that the opportunities are distributed equally in the society.
I have designed three studies which can support each other to explore the valid causal inferences between the built environment in neighborhoods and opportunity beliefs. First, In order to understand how the built environment in neighborhoods affects Americans’ opportunity beliefs, I designed a conventional survey which can obtain samples nation-wide and has high external validity. Next, I conducted two-round survey experiments to explore the causal inference. The results support my hypotheses.
This dissertation explores the interaction between the living environment and human psychological states and enriches the knowledge of emotions, self-efficacy, and opportunity beliefs. This research has important implications for poverty reduction and redistributive policy.
The task of visually tracking multiple objects remains an active field of algorithm development even after several decades of research in the computer vision community. It remains an active research area because identifying and maintaining the location of multiple targets in a video recording can be approached from several perspectives. Another reason is simply that the general problem of automated tracking can be very challenging. Challenges within visual tracking collectively manifest into three broader design decisions often faced by multiple object tracking (MOT) algorithms. First is how to handle what one could think of as "easy" and "hard" regions of a trajectory. The second is how to handle the sheer number of possible explanations of the data. The third is how do you model certainty. This dissertation aims to better model the uncertainty among possible answers to the tracking data in offline tracking scenarios. Furthermore, the method does so in a way that utilizes the information within the "hard to track" regions — information that is typically not used. The way we do this results in accurate tracking that is better suited for video analysis pipelines that may need to filter or correct any tracking errors.
Gentrification research almost exclusively focuses on traditional postindustrial cities. Despite a growing number of scholars emphasizing the importance of understanding gentrification outside of traditional urban areas, its presence and modalities in mid-sized cities remains underexplored. This holds particularly true in the U.S. South where unique historical processes of industrialization, segregation, and immigration form low-density spatial patterns of urbanization that set Southern cities apart from other U.S. regions. A group of rapidly emerging mid-size U.S. Sunbelt cities – known as the New South – share concerns over a number of converging and interrelated trends: urban core revitalization, rising housing costs, lagging economic mobility, investing in public infrastructure, and shifting demographics. In this context, the New South is an ideal region for investigating longitudinal neighborhood development trends within a gentrification framework. Using a case study approach in Charlotte, my dissertation explores the spatial, temporal, and spatial-temporal aspects of contemporary gentrification. A survival analysis also tests the relationships between gentrification and changes in housing renovation, urban amenities, proximity to light rail development, and other factors. Results reveal that administrative data at the parcel level is more precise at pinpointing where gentrification occurs and how it diffuses overtime. Findings also identify substantial differences between area estimates of gentrification hot spots calculated from parcel data, demonstrating that spatial aggregation error may lead to significant errors in measuring gentrification. Findings suggest that aggregating data to census blocks or tax parcel spatial unit provide more precise measurements of gentrification. Key findings from the survival analysis identify that neighborhood parks and greenways increase the likelihood of gentrification. Results also highlight a strong spatial effect, demonstrating that neighborhood effects do influence spatial patterns of gentrification. Unexpectedly, light rail variables do not increase the likelihood of gentrification. Additional variables that increase the likelihood of gentrification include parcels with older homes, parcels in and around historical areas, lower home values per square foot, proximity to quality education, proximity to highways, and proximity to commercial areas increase the likelihood of gentrification. Thus, at a time when urban areas are rapidly changing and considering how to accommodate future growth, a local level understanding of gentrification aids policy makers and community organizers to tailor more effective public policy.
Ocean Current Turbines (OCTs), which function similarly to wind and tidal turbines, represent a promising technology for harnessing the energy from oceanic currents such as the Gulf Stream. In planning the deployment of arrays of OCT devices, it is critical to consider the two-way interactions between the turbines and the ocean environment: temporally and spatially nonuniform flow fields are expected in the dynamic flow environments of western boundary currents, and include the presence of upstream shear and turbulence. These nonuniform flow conditions will affect power extraction, and the efficiency of the turbines when operating in isolation or as part of an array. Furthermore, models that are used in a predictive capability to compute the levelized cost of energy obtainable from such devices, or to optimize the layout of an array of turbines must be modified to account for the effects of such spatially and temporally inhomogeneous conditions. Similarly, the operation of OCT arrays can in turn influence the background flow in two significant ways, namely by contributing to the production of turbulence and through the generation of internal gravity waves that are radiated away from the point of origin. In this thesis, we have studied using detailed numerical simulations, the above two-way interaction between arrays of OCTs and the ocean environment. Insights developed from the simulations have guided the development of low-order wake interaction models capable of describing the effects of inhomogeneous flow conditions on array performance.
A new, wake interaction modeling framework capable of capturing the detailed effects of turbulence and upstream shear on various performance parameters associated with OCTs arranged in any arbitrary configuration has been developed. The model accounts for the effects of turbulence and shear on the structure of the turbine wakes, specifically the extents of near- and far-wake regions. The analytical description for turbine wake is combined with an existing wake interaction model, the Unrestricted Wind Farm Layout Optimization model to predict the global power output from an array of OCTs. The resulting modelling framework accurately captures the effect of inlet turbulence and shear on the OCT farm power and efficiency, and can be applied to any array configuration. Results from the model were validated against both Large Eddy Simulations and Reynolds Averaged Navier-Stokes simulations, in which the OCTs were modeled using a Blade Element Momentum model. The dispersion of OCT wake turbulence through the background stratification of the ocean was investigated using Large Eddy Simulations for different levels of the density stratification. The effects of varying the strength of the stratification as well as the turbulent forcing were studied. Finally, the wake turbulence associated with OCT operation can drive the formation and radiation of internal gravity waves in the density-stratified background flow of ocean currents. Through detailed numerical simulations, the effect of the propagation of the internal waves on the background turbulent diffusivity was studied, and found to alter the transport properties of the ambient flow. The properties of the internal wave field, and its impact on background turbulent mixing was found to depend both on the Richardson number and the ambient, upstream turbulence.
Several urban areas are building new facilities to encourage users of alternative modes of transportation (e.g., public transportation, walking, and bicycling). The existing infrastructure is changed to accommodate/ encourage these alternative mode users. However, there is not enough evidence to justify whether such plans are instrumental in improving mobility and enhancing safety of the transportation system from a multimodal perspective Therefore, the goal of this research is to model the operational performance and safety of urban roads with heterogeneous traffic conditions to improve the safety, reliability, and mobility of people and goods. A two-step approach is used to assess the operational performance of the urban roads with heterogeneous conditions. Firstly, a travel time reliability-based modeling and analysis was conducted to analyze the transportation system performance comprehensively accounting for all the modes of transportation. Secondly, a simulation-based modeling and analysis was conducted to assess the effect of light rail transit (LRT), pedestrian activity, bicyclist activity, and traffic, individually or combined, on the operational performance of the transportation system. Further, surrogate safety assessment was conducted to check the effect of LRT, pedestrian activity, bicyclist activity, and traffic, individually or combined, on the overall safety performance of the transportation system. Notable effects on operational and safety performance were observed between the modeled and evaluated hypothetical scenarios, emphasizing the need to plan and build infrastructure by evaluating complex mobility patterns and interactions between all the mode users. The proposed methodological framework is cross-disciplinary, transferable, and can be applied to other regions.
