Modeling the complexity of sustainable cities: The interdependence between infrastructure systems and the socioeconomic environment

As a critical component of the city, urban infrastructures emerge through the interactions with the socioeconomic environment. Managing the complexity behind the interactions can make the city more sustainable. By this, we mean if we provide more sustainable amenities that people desire, a greater adoption of more sustainable infrastructures will likely occur. Two categories of infrastructure have emerged in recent years as exemplars of more sustainable development: green infrastructure and transit-oriented development. At the same time, new digital tools have emerged to better predict market acceptance of these infrastructures. This dissertation employs agent-based modeling, a latent-class analysis of survey results, and an online survey to model the potential of adoption of these infrastructures and the public benefits. The principal research content of the dissertation consists of two parts. First, understanding social preference and adoption of green infrastructure (e.g., low-impact development (LID) to control storm water), and transit-oriented development (TOD) to reduce car dependence and incentivize denser land use; Second, by developing an urban model that accounts for the complexity of the urban system, the purpose is to predict the emergent property of the city (e.g., land use, water consumption, tax revenues and carbon emissions). These two aspects constitute the research content of this dissertation. The principal findings of the dissertation are: 1) the use of digital feedback tools to inform the modeling of complex urban systems; 2) the future development of the metro Atlanta area can be more compact and sustainable with implementations of LID, TOD, and the proper policy. This dissertation consists of four sections. In the first section, I have developed an agent-based model (ABM) to predict the land use pattern. The ABM is an approach suited to simulating and understanding the dynamics of the complex system. To reduce the complexity and uncertainty of the ABM, the model simulates the decisions and interaction of agents (i.e., home buyer, the developer and the local government) at the neighborhood scale. The output of the ABM serves as the baseline scenario of land use pattern for evaluating the effect of tax investment and fees on the adoption of green infrastructure designs and more compact land use patterns. Second, with the help of the ABM, I evaluated and compared the policies (i.e., impact fees, subsidy) on the adoption of green infrastructure designs and more compact land use pattern. I developed a more sustainable development (MSD) scenario that introduces an impact fee that developers must pay if they choose not to use LID (i.e., rainwater harvesting, porous pavement) to build houses or apartment homes. Model simulations show homeowners selecting apartment homes 60% of the time after 30 years of development in MSD. In contrast, only 35% homeowners selected apartment homes after 30 years of development in a business as usual (BAU) scenario where there is no impact fee for LID. The increased adoption of apartment homes results from the lower cost of using LID (i.e., rain garden, native vegetation and porous pavements) in public spaces and improved quality of life for apartment homes relative to single-family homes. The MSD scenario generates more tax revenues and water savings than does BAU. Third, as an initial effort to calibrate the home buyer’s preference for community design in the ABM, I developed an analytic model based on an existing community preference survey. The data available for this effort is from National Association of Realtors’ 2011 community preference survey. I applied a latent class choice model to this data, and discovered four classes of individuals that reveal distinctive behaviors when choosing smart growth neighborhoods, based on the interplay between aspects of community design, socioeconomic characteristics and personal attitudes. Linking the results of the latent class choice to an agent-based market diffusion model enables planners to evaluate the effectiveness of a proposed smart growth neighborhood design in inducing less sprawling development. In the fourth section, I developed a survey that focuses on preferences of metropolitan Atlanta residents for LID and TOD. With the responses collected using Mechanical Turk, I developed a latent-class residential community choice model of four distinctive classes that reveal heterogeneous preferences for community designs. Spatial distribution of the four classes was mapped out to visualize the locations of the demand for different community designs in metropolitan Atlanta. The analysis of the impact of increase in housing price on the adoption of LID and TOD shows a low risk of investing in LID and TOD in metro area. Residents are willing to adopt the community with LID and TOD as compared to the corresponding one without LID and TOD. It turns out that LID and TOD have a great potential for adoption in metro Atlanta. Further, I integrated the individual residential community choice simulation into an agent-based market diffusion model to predict the emergent land use pattern and explore polices that can drive the adoption of more compact development. Results show that the current policy requiring single-family houses to implement LID based on individual sites should be switched to one that requires community-based LID for single-family houses. Such a policy switch will lead to a higher adoption of apartment homes with LID and TOD. Lastly, I estimated a 28% carbon emission reduction from more compact development driven by LID and TOD. This thesis is the very beginning of using digital feedback tools to anticipate market responses to more sustainable development alternatives. On the basis of the progress made in this dissertation, future work is recommended in terms of the development of an integrated platform that supports the integration of individual modules (e.g., land use, traffic simulation, air quality, and water resource management) for modeling the complexity, big data analytic techniques (e.g., Twitter, GPS data, sensors) for uncovering the interdependencies between infrastructures and socioeconomic development, and the exploration of sustainability metrics for public communication to build citizen capacity for sustainable cities.Ph.D

Use of impact fees to incentivize low-impact development and promote compact growth

Low-impact development (LID) is an innovative stormwater management strategy that restores the predevelopment hydrology to prevent increased stormwater runoff from land development. Integrating LID into residential subdivisions and increasing population density by building more compact living spaces (e.g., apartment homes) can result in a more sustainable city by reducing stormwater runoff, saving infrastructural cost, increasing the number of affordable homes, and supporting public transportation. We develop an agent-based model (ABM) that describes the interactions between several decision-makers (i.e., local government, a developer, and homebuyers) and fiscal drivers (e.g., property taxes, impact fees). The model simulates the development of nine square miles of greenfield land. A more sustainable development (MSD) scenario introduces an impact fee that developers must pay if they choose not to use LID to build houses or apartment homes. Model simulations show homeowners selecting apartment homes 60% or 35% of the time after 30 years of development in MSD or business as usual (BAU) scenarios, respectively. The increased adoption of apartment homes results from the lower cost of using LID and improved quality of life for apartment homes relative to single-family homes. The MSD scenario generates more tax revenue and water savings than does BAU. A time-dependent global sensitivity analysis quantifies the importance of socioeconomic variables on the adoption rate of apartment homes. The top influential factors are the annual pay rates (or capital recovery factor) for single-family houses and apartment homes. The ABM can be used by city managers and policymakers for scenario exploration in accordance with local conditions to evaluate the effectiveness of impact fees and other policies in promoting LID and compact growth

Acute effects of vagus nerve stimulation parameters on gastric motility assessed with magnetic resonance imaging

BackgroundVagus nerve stimulation (VNS) is an emerging bioelectronic therapy for regulating food intake and controlling gastric motility. However, the effects of different VNS parameters and polarity on postprandial gastric motility remain incompletely characterized.MethodsIn anesthetized rats (N = 3), we applied monophasic electrical stimuli to the left cervical vagus and recorded compound nerve action potential (CNAP) as a measure of nerve response. We evaluated to what extent afferent or efferent pathway could be selectively activated by monophasic VNS. In a different group of rats (N = 13), we fed each rat a gadolinium- labeled meal and scanned the rat stomach with oral contrast- enhanced magnetic resonance imaging (MRI) while the rat was anesthetized. We evaluated the antral and pyloric motility as a function of pulse amplitude (0.13, 0.25, 0.5, 1 mA), width (0.13, 0.25, 0.5 ms), frequency (5, 10 Hz), and polarity of VNS.Key ResultsMonophasic VNS activated efferent and afferent pathways with about 67% and 82% selectivity, respectively. Primarily afferent VNS increased antral motility across a wide range of parameters. Primarily efferent VNS induced a significant decrease in antral motility as the stimulus intensity increased (R = - .93, P < .05 for 5 Hz, R = - .85, P < .05 for 10 Hz). The VNS with either polarity tended to promote pyloric motility to a greater extent given increasing stimulus intensity.Conclusions and InferencesMonophasic VNS biased toward the afferent pathway is potentially more effective for facilitating occlusive contractions than that biased toward the efferent pathway.We investigated a possible differential effect of primarily afferent versus efferent cervical VNS on gastric motility under a range of VNS parameters. Gastric MRI data revealed that primarily afferent VNS induced stronger antral contractions relative to primarily efferent VNS. These results could serve as an index for optimizing VNS parameters for promoting gastric motility. Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155957/1/nmo13853_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155957/2/nmo13853.pd

Task-evoked functional connectivity does not explain functional connectivity differences between rest and task conditions

During complex tasks, patterns of functional connectivity differ from those in the resting state. However, what accounts for such differences remains unclear. Brain activity during a task reflects an unknown mixture of spontaneous and task-evoked activities. The difference in functional connectivity between a task state and the resting state may reflect not only task-evoked functional connectivity, but also changes in spontaneously emerging networks. Here, we characterized the differences in apparent functional connectivity between the resting state and when human subjects were watching a naturalistic movie. Such differences were marginally explained by the task-evoked functional connectivity involved in processing the movie content. Instead, they were mostly attributable to changes in spontaneous networks driven by ongoing activity during the task. The execution of the task reduced the correlations in ongoing activity among different cortical networks, especially between the visual and non-visual sensory or motor cortices. Our results suggest that task-evoked activity is not independent from spontaneous activity, and that engaging in a task may suppress spontaneous activity and its inter-regional correlation

The interface states in gate-all-around transistors (GAAFETs)

The atomic-level structural detail and the quantum effects are becoming crucial to device performance as the emerging advanced transistors, representatively GAAFETs, are scaling down towards sub-3nm nodes. However, a multiscale simulation framework based on atomistic models and ab initio quantum simulation is still absent. Here, we propose such a simulation framework by fulfilling three challenging tasks, i.e., building atomistic all-around interfaces between semiconductor and amorphous gate-oxide, conducting large-scale first-principles calculations on the interface models containing up to 2796 atoms, and finally bridging the state-of-the-art atomic level calculation to commercial TCAD. With this framework, two unnoticed origins of interface states are demonstrated, and their tunability by changing channel size, orientation and geometry is confirmed. The quantitative study of interface states and their effects on device performance explains why the nanosheet channel is preferred in industry. We believe such a bottom-up framework is necessary and promising for the accurate simulation of emerging advanced transistors

An efficient magnetic carbon-based solid acid treatment for corncob saccharification with high selectivity of xylose and enhanced enzymatic digestibility

An effective method for corncob saccharification was investigated over a magnetic carbon-based solid acid (MMCSA) catalyst in the aqueous phase. MMCSA was synthesized through a simple and inexpensive impregnation-carbonization-sulfonation process. Under the optimal reaction conditions (150 °C, 2 h, 0.5 g corncob, 0.5 g catalyst and 50 ml deionized water), 74.9% of xylose yield was directly obtained from corncob, with 91.7% cellulose retention in the residue. After reaction, the MMCSA was easily separated from the residue by an external magnet and reused 4 times showing high stability and catalytic activity. The enzymatic digestibility of the pretreated residue reached 95.2%, with a total sugar yield of 90.4%. The morphologic and structural properties of the natural and treated corncobs were analyzed primarily through 3D X-ray microscopy to characterize the cell wall thickness, porosity, and pore surface area distribution. The increase of macropores (pore surface areas > 200 μm2) was beneficial to the accessibility of cellulose to cellulosic enzymes, so the enzymatic digestibility was enhanced immediately. Compared with other traditional hydrolysis methods, this two-step hydrolysis approach represents an environmentally friendly and sustainable saccharification of lignocellulose to produce xylose and glucose while achieving the same level of reaction efficiency

Preparation of reducing sugars from corncob by solid acid catalytic pretreatment combined with in situ enzymatic hydrolysis

The efficient conversion of hemicellulose and cellulose into reducing sugars remains as one major challenge for biorefinery of lignocellulosic biomass. In this work, saccharification of corncob in the aqueous phase was effectively realized via pretreatment by magnetic carbon-based solid acid (MMCSA) catalyst, combined with the subsequent in situ enzymatic hydrolysis (occurring in the same pretreatment system after separation of MMCSA). Through the combined two-step hydrolysis of corncob, the total sugar (xylose and glucose) yield of 90.03% was obtained, including a xylose yield of 86.99% and an enzymatic digestibility of pretreatment residue of 91.24% (cellulase loading of 20 FPU/g, 24 h). Compared with the traditional enzymatic hydrolysis of pretreated residue, the presented in situ enzymatic hydrolysis system can reach a comparable enzymatic digestibility in one-third reacting time with a half cellulase loading and save about 31% water consumption, which provides a more sustainable and low-cost method for the saccharification of lignocellulose