Matching Items (59)
Description
Two urban flows are analyzed, one concerned with pollutant transport in a Phoenix, Arizona neighborhood and the other with windshear detection at the Hong Kong International Airport (HKIA).
Lagrangian measures, identified with finite-time Lyapunov exponents, are first used to characterize transport patterns of inertial pollutant particles. Motivated by actual events the focus is on flows in realistic urban geometry. Both deterministic and stochastic transport patterns are identified, as inertial Lagrangian coherent structures. For the deterministic case, the organizing structures are well defined and are extracted at different hours of a day to reveal the variability of coherent patterns. For the stochastic case, a random displacement model for fluid particles is formulated, and used to derive the governing equations for inertial particles to examine the change in organizing structures due to ``zeroth-order'' random noise. It is found that, (1) the Langevin equation for inertial particles can be reduced to a random displacement model; (2) using random noise based on inhomogeneous turbulence, whose diffusivity is derived from $k$-$\epsilon$ models, major coherent structures survive to organize local flow patterns and weaker structures are smoothed out due to random motion.
A study of three-dimensional Lagrangian coherent structures (LCS) near HKIA is then presented and related to previous developments of two-dimensional (2D) LCS analyses in detecting windshear experienced by landing aircraft. The LCS are contrasted among three independent models and against 2D coherent Doppler light detection and ranging (LIDAR) data. Addition of the velocity information perpendicular to the lidar scanning cone helps solidify flow structures inferred from previous studies; contrast among models reveals the intramodel variability; and comparison with flight data evaluates the performance among models in terms of Lagrangian analyses. It is found that, while the three models and the LIDAR do recover similar features of the windshear experienced by a landing aircraft (along the landing trajectory), their Lagrangian signatures over the entire domain are quite different - a portion of each numerical model captures certain features resembling those LCS extracted from independent 2D LIDAR analyses based on observations. Overall, it was found that the Weather Research and Forecast (WRF) model provides the best agreement with the LIDAR data.
Finally, the three-dimensional variational (3DVAR) data assimilation scheme in WRF is used to incorporate the LIDAR line of sight velocity observations into the WRF model forecast at HKIA. Using two different days as test cases, it is found that the LIDAR data can be successfully and consistently assimilated into WRF. Using the updated model forecast LCS are extracted along the LIDAR scanning cone and compare to onboard flight data. It is found that the LCS generated from the updated WRF forecasts are generally better correlated with the windshear experienced by landing aircraft as compared to the LIDAR extracted LCS alone, which suggests that such a data assimilation scheme could be used for the prediction of windshear events.
Lagrangian measures, identified with finite-time Lyapunov exponents, are first used to characterize transport patterns of inertial pollutant particles. Motivated by actual events the focus is on flows in realistic urban geometry. Both deterministic and stochastic transport patterns are identified, as inertial Lagrangian coherent structures. For the deterministic case, the organizing structures are well defined and are extracted at different hours of a day to reveal the variability of coherent patterns. For the stochastic case, a random displacement model for fluid particles is formulated, and used to derive the governing equations for inertial particles to examine the change in organizing structures due to ``zeroth-order'' random noise. It is found that, (1) the Langevin equation for inertial particles can be reduced to a random displacement model; (2) using random noise based on inhomogeneous turbulence, whose diffusivity is derived from $k$-$\epsilon$ models, major coherent structures survive to organize local flow patterns and weaker structures are smoothed out due to random motion.
A study of three-dimensional Lagrangian coherent structures (LCS) near HKIA is then presented and related to previous developments of two-dimensional (2D) LCS analyses in detecting windshear experienced by landing aircraft. The LCS are contrasted among three independent models and against 2D coherent Doppler light detection and ranging (LIDAR) data. Addition of the velocity information perpendicular to the lidar scanning cone helps solidify flow structures inferred from previous studies; contrast among models reveals the intramodel variability; and comparison with flight data evaluates the performance among models in terms of Lagrangian analyses. It is found that, while the three models and the LIDAR do recover similar features of the windshear experienced by a landing aircraft (along the landing trajectory), their Lagrangian signatures over the entire domain are quite different - a portion of each numerical model captures certain features resembling those LCS extracted from independent 2D LIDAR analyses based on observations. Overall, it was found that the Weather Research and Forecast (WRF) model provides the best agreement with the LIDAR data.
Finally, the three-dimensional variational (3DVAR) data assimilation scheme in WRF is used to incorporate the LIDAR line of sight velocity observations into the WRF model forecast at HKIA. Using two different days as test cases, it is found that the LIDAR data can be successfully and consistently assimilated into WRF. Using the updated model forecast LCS are extracted along the LIDAR scanning cone and compare to onboard flight data. It is found that the LCS generated from the updated WRF forecasts are generally better correlated with the windshear experienced by landing aircraft as compared to the LIDAR extracted LCS alone, which suggests that such a data assimilation scheme could be used for the prediction of windshear events.
ContributorsKnutson, Brent (Author) / Tang, Wenbo (Thesis advisor) / Calhoun, Ronald (Committee member) / Huang, Huei-Ping (Committee member) / Kostelich, Eric (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2018
Description
Intervertebral Disc Degeneration (IVDD) is a complex phenomenon characterizing the desiccation and structural compromise of the primary joint in the human spine. The intervertebral disc (IVD) serves to connect vertebral bodies, cushion shock, and allow for flexion and extension of the vertebral column. Often presenting in the 4th or 5th decades of life as low back pain, this disease was originally believed to be the result of natural “wear and tear” coupled with repetitive mechanical insult, and as such most studies focus on patients between 40 and 50 years of age. Research over the past two decades, however, has demonstrated that environmental factors have only a modest effect on disc degeneration, with genetic influences playing a much more substantial role. Extensive research has focused on this process, though definitive risk factors and a clear pathophysiology have proven elusive. The aim of this study was to assemble a cohort of patients exhibiting definitive signs of degeneration who were well below the average age of presentation, with minimal or no exposure to suspected environmental risk factors and to conduct a targeted genome analysis in an attempt to elucidate a common genetic component. Through whole genome sequencing and analysis, the results corroborated findings in a previous study, as well as demonstrated a potential connection and influence between mutations found in IVD structural or functional genes, and the provocation of IVDD. Though the sample size was limited in scale and age, these findings suggest that further IVDD research into the association of variants in collagen, aggrecan and the insulin-like growth factor receptor genes of young patients with an early presentation of disc degeneration and minimal exposure to suspected risk factors is merited.
ContributorsFulton, Travis (Author) / Liebig, Juergen (Thesis advisor) / Neisewander, Janet (Committee member) / Theodore, Nicholas (Committee member) / Arizona State University (Publisher)
Created2016
Description
The role of environmental factors that influence atmospheric propagation of sound originating from freeway noise sources is studied with a combination of field experiments and numerical simulations. Acoustic propagation models are developed and adapted for refractive index depending upon meteorological conditions. A high-resolution multi-nested environmental forecasting model forced by coarse global analysis is applied to predict real meteorological profiles at fine scales. These profiles are then used as input for the acoustic models. Numerical methods for producing higher resolution acoustic refractive index fields are proposed. These include spatial and temporal nested meteorological simulations with vertical grid refinement. It is shown that vertical nesting can improve the prediction of finer structures in near-ground temperature and velocity profiles, such as morning temperature inversions and low level jet-like features. Accurate representation of these features is shown to be important for modeling sound refraction phenomena and for enabling accurate noise assessment. Comparisons are made using the acoustic model for predictions with profiles derived from meteorological simulations and from field experiment observations in Phoenix, Arizona. The challenges faced in simulating accurate meteorological profiles at high resolution for sound propagation applications are highlighted and areas for possible improvement are discussed.
A detailed evaluation of the environmental forecast is conducted by investigating the Surface Energy Balance (SEB) obtained from observations made with an eddy-covariance flux tower compared with SEB from simulations using several physical parameterizations of urban effects and planetary boundary layer schemes. Diurnal variation in SEB constituent fluxes are examined in relation to surface layer stability and modeled diagnostic variables. Improvement is found when adapting parameterizations for Phoenix with reduced errors in the SEB components. Finer model resolution (to 333 m) is seen to have insignificant ($<1\sigma$) influence on mean absolute percent difference of 30-minute diurnal mean SEB terms. A new method of representing inhomogeneous urban development density derived from observations of impervious surfaces with sub-grid scale resolution is then proposed for mesoscale applications. This method was implemented and evaluated within the environmental modeling framework. Finally, a new semi-implicit scheme based on Leapfrog and a fourth-order implicit time-filter is developed.
A detailed evaluation of the environmental forecast is conducted by investigating the Surface Energy Balance (SEB) obtained from observations made with an eddy-covariance flux tower compared with SEB from simulations using several physical parameterizations of urban effects and planetary boundary layer schemes. Diurnal variation in SEB constituent fluxes are examined in relation to surface layer stability and modeled diagnostic variables. Improvement is found when adapting parameterizations for Phoenix with reduced errors in the SEB components. Finer model resolution (to 333 m) is seen to have insignificant ($<1\sigma$) influence on mean absolute percent difference of 30-minute diurnal mean SEB terms. A new method of representing inhomogeneous urban development density derived from observations of impervious surfaces with sub-grid scale resolution is then proposed for mesoscale applications. This method was implemented and evaluated within the environmental modeling framework. Finally, a new semi-implicit scheme based on Leapfrog and a fourth-order implicit time-filter is developed.
ContributorsShaffer, Stephen R. (Author) / Moustaoui, Mohamed (Thesis advisor) / Mahalov, Alex (Committee member) / Fernando, Harindra J.S. (Committee member) / Ovenden, Nicholas C. (Committee member) / Huang, Huei-Ping (Committee member) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2014
Description
Warning coloration deters predators from attacking prey that are defended, usually by being distasteful, toxic, or otherwise costly for predators to pursue and consume. Predators may have an innate response to warning colors or learn to associate them with a defense through trial and error. In general, predators should select for warning signals that are easy to learn and recognize. Previous research demonstrates long-wavelength colors (e.g. red and yellow) are effective because they are readily detected and learned. However, a number of defended animals display short-wavelength coloration (e.g. blue and violet), such as the pipevine swallowtail butterfly (Battus philenor). The role of blue coloration in warning signals had not previously been explicitly tested. My research showed in laboratory experiments that curve-billed thrashers (Toxostoma curvirostre) and Gambel's quail (Callipepla gambelii) can learn and recognize the iridescent blue of B. philenor as a warning signal and that it is innately avoided. I tested the attack rates of these colors in the field and blue was not as effective as orange. I concluded that blue colors may function as warning signals, but the effectiveness is likely dependent on the context and predator.
Blue colors are often iridescent in nature and the effect of iridescence on warning signal function was unknown. I reared B. philenor larvae under varied food deprivation treatments. Iridescent colors did not have more variation than pigment-based colors under these conditions; variation which could affect predator learning. Learning could also be affected by changes in appearance, as iridescent colors change in both hue and brightness as the angle of illuminating light and viewer change in relation to the color surface. Iridescent colors can also be much brighter than pigment-based colors and iridescent animals can statically display different hues. I tested these potential effects on warning signal learning by domestic chickens (Gallus gallus domesticus) and found that variation due to the directionality of iridescence and a brighter warning signal did not influence learning. However, blue-violet was learned more readily than blue-green. These experiments revealed that the directionality of iridescent coloration does not likely negatively affect its potential effectiveness as a warning signal.
Blue colors are often iridescent in nature and the effect of iridescence on warning signal function was unknown. I reared B. philenor larvae under varied food deprivation treatments. Iridescent colors did not have more variation than pigment-based colors under these conditions; variation which could affect predator learning. Learning could also be affected by changes in appearance, as iridescent colors change in both hue and brightness as the angle of illuminating light and viewer change in relation to the color surface. Iridescent colors can also be much brighter than pigment-based colors and iridescent animals can statically display different hues. I tested these potential effects on warning signal learning by domestic chickens (Gallus gallus domesticus) and found that variation due to the directionality of iridescence and a brighter warning signal did not influence learning. However, blue-violet was learned more readily than blue-green. These experiments revealed that the directionality of iridescent coloration does not likely negatively affect its potential effectiveness as a warning signal.
ContributorsPegram, Kimberly Vann (Author) / Rutowski, Ronald L (Thesis advisor) / Hoelldobler, Berthold (Committee member) / Liebig, Juergen (Committee member) / McGraw, Kevin (Committee member) / Smith, Brian H. (Committee member) / Arizona State University (Publisher)
Created2015
Description
The coordination of group behavior in the social insects is representative of a broader phenomenon in nature, emergent biological complexity. In such systems, it is believed that large-scale patterns result from the interaction of relatively simple subunits. This dissertation involved the study of one such system: the social foraging of the ant Temnothorax rugatulus. Physically tiny with small population sizes, these cavity-dwelling ants provide a good model system to explore the mechanisms and ultimate origins of collective behavior in insect societies. My studies showed that colonies robustly exploit sugar water. Given a choice between feeders unequal in quality, colonies allocate more foragers to the better feeder. If the feeders change in quality, colonies are able to reallocate their foragers to the new location of the better feeder. These qualities of flexibility and allocation could be explained by the nature of positive feedback (tandem run recruitment) that these ants use. By observing foraging colonies with paint-marked ants, I was able to determine the `rules' that individuals follow: foragers recruit more and give up less when they find a better food source. By altering the nutritional condition of colonies, I found that these rules are flexible - attuned to the colony state. In starved colonies, individual ants are more likely to explore and recruit to food sources than in well-fed colonies. Similar to honeybees, Temmnothorax foragers appear to modulate their exploitation and recruitment behavior in response to environmental and social cues. Finally, I explored the influence of ecology (resource distribution) on the foraging success of colonies. Larger colonies showed increased consistency and a greater rate of harvest than smaller colonies, but this advantage was mediated by the distribution of resources. While patchy or rare food sources exaggerated the relative success of large colonies, regularly (or easily found) distributions leveled the playing field for smaller colonies. Social foraging in ant societies can best be understood when we view the colony as a single organism and the phenotype - group size, communication, and individual behavior - as integrated components of a homeostatic unit.
ContributorsShaffer, Zachary (Author) / Pratt, Stephen C (Thesis advisor) / Hölldobler, Bert (Committee member) / Janssen, Marco (Committee member) / Fewell, Jennifer (Committee member) / Liebig, Juergen (Committee member) / Arizona State University (Publisher)
Created2014
Description
The tools developed for the use of investigating dynamical systems have provided critical understanding to a wide range of physical phenomena. Here these tools are used to gain further insight into scalar transport, and how it is affected by mixing. The aim of this research is to investigate the efficiency of several different partitioning methods which demarcate flow fields into dynamically distinct regions, and the correlation of finite-time statistics from the advection-diffusion equation to these regions.
For autonomous systems, invariant manifold theory can be used to separate the system into dynamically distinct regions. Despite there being no equivalent method for nonautonomous systems, a similar analysis can be done. Systems with general time dependencies must resort to using finite-time transport barriers for partitioning; these barriers are the edges of Lagrangian coherent structures (LCS), the analog to the stable and unstable manifolds of invariant manifold theory. Using the coherent structures of a flow to analyze the statistics of trapping, flight, and residence times, the signature of anomalous diffusion are obtained.
This research also investigates the use of linear models for approximating the elements of the covariance matrix of nonlinear flows, and then applying the covariance matrix approximation over coherent regions. The first and second-order moments can be used to fully describe an ensemble evolution in linear systems, however there is no direct method for nonlinear systems. The problem is only compounded by the fact that the moments for nonlinear flows typically don't have analytic representations, therefore direct numerical simulations would be needed to obtain the moments throughout the domain. To circumvent these many computations, the nonlinear system is approximated as many linear systems for which analytic expressions for the moments exist. The parameters introduced in the linear models are obtained locally from the nonlinear deformation tensor.
For autonomous systems, invariant manifold theory can be used to separate the system into dynamically distinct regions. Despite there being no equivalent method for nonautonomous systems, a similar analysis can be done. Systems with general time dependencies must resort to using finite-time transport barriers for partitioning; these barriers are the edges of Lagrangian coherent structures (LCS), the analog to the stable and unstable manifolds of invariant manifold theory. Using the coherent structures of a flow to analyze the statistics of trapping, flight, and residence times, the signature of anomalous diffusion are obtained.
This research also investigates the use of linear models for approximating the elements of the covariance matrix of nonlinear flows, and then applying the covariance matrix approximation over coherent regions. The first and second-order moments can be used to fully describe an ensemble evolution in linear systems, however there is no direct method for nonlinear systems. The problem is only compounded by the fact that the moments for nonlinear flows typically don't have analytic representations, therefore direct numerical simulations would be needed to obtain the moments throughout the domain. To circumvent these many computations, the nonlinear system is approximated as many linear systems for which analytic expressions for the moments exist. The parameters introduced in the linear models are obtained locally from the nonlinear deformation tensor.
ContributorsWalker, Phillip (Author) / Tang, Wenbo (Thesis advisor) / Kostelich, Eric (Committee member) / Mahalov, Alex (Committee member) / Moustaoui, Mohamed (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2018
Description
Efforts to treat prostate cancer have seen an uptick, as the world’s most commoncancer in men continues to have increasing global incidence. Clinically, metastatic
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
ContributorsReckell, Trevor (Author) / Kostelich, Eric (Thesis advisor) / Kuang, Yang (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2020
Description
A complex social system, whether artificial or natural, can possess its macroscopic properties as a collective, which may change in real time as a result of local behavioral interactions among a number of agents in it. If a reliable indicator is available to abstract the macrolevel states, decision makers could use it to take a proactive action, whenever needed, in order for the entire system to avoid unacceptable states or con-verge to desired ones. In realistic scenarios, however, there can be many challenges in learning a model of dynamic global states from interactions of agents, such as 1) high complexity of the system itself, 2) absence of holistic perception, 3) variability of group size, 4) biased observations on state space, and 5) identification of salient behavioral cues. In this dissertation, I introduce useful applications of macrostate estimation in complex multi-agent systems and explore effective deep learning frameworks to ad-dress the inherited challenges. First of all, Remote Teammate Localization (ReTLo)is developed in multi-robot teams, in which an individual robot can use its local interactions with a nearby robot as an information channel to estimate the holistic view of the group. Within the problem, I will show (a) learning a model of a modular team can generalize to all others to gain the global awareness of the team of variable sizes, and (b) active interactions are necessary to diversify training data and speed up the overall learning process. The complexity of the next focal system escalates to a colony of over 50 individual ants undergoing 18-day social stabilization since a chaotic event. I will utilize this natural platform to demonstrate, in contrast to (b), (c)monotonic samples only from “before chaos” can be sufficient to model the panicked society, and (d) the model can also be used to discover salient behaviors to precisely predict macrostates.
ContributorsChoi, Taeyeong (Author) / Pavlic, Theodore (Thesis advisor) / Richa, Andrea (Committee member) / Ben Amor, Heni (Committee member) / Yang, Yezhou (Committee member) / Liebig, Juergen (Committee member) / Arizona State University (Publisher)
Created2020
Description
The flexibility and robustness of social insect colonies, when they cope with challenges as integrated units, raise many questions, such as how hundreds and thousands of individual local responses are coordinated without a central controlling process. Answering such questions requires: 1. Quantifiable collective responses of colonies under specific scenarios; 2. Decomposability of the collective colony-level response into individual responses; and 3. Mechanisms to integrate the colony- and individual-level responses. In the first part of my dissertation, I explore coordinated collective responses of colonies in during the alarm response to an alarmed nestmate (chapter 2&3). I develop a machine-learning approach to quantitatively estimate the collective and individual alarm response (chapter 2). Using this methodology, I demonstrate that colony alarm responses to the introduction of alarmed nestmates can be decomposed into immediately cascading, followed by variable dampening processes. Each of those processes are found to be modulated by variation in individual alarm responsiveness, as measured by alarm response threshold and persistence of alarm behavior. This variation is modulated in turn by environmental context, in particular with task-related social context (chapter 3). In the second part of my dissertation, I examine the mechanisms responsible for colonial changes in metabolic rate during ontogeny. Prior studies have found that larger ant colonies (as for larger organisms) have lower mass-specific metabolic rates, but the mechanisms remain unclear. In a 3.5-year study on 25 colonies, metabolic rates of colonies and colony components were measured during ontogeny (chapter 4). The scaling of metabolic rate during ontogeny was fit better by segmented regression or quadratic regression models than simple linear regression models, showing that colonies do not follow a universal power-law of metabolism during the ontogenetic development. Furthermore, I showed that the scaling of colonial metabolic rates can be primarily explained by changes in the ratio of brood to adult workers, which nonlinearly affects colonial metabolic rates. At high ratios of brood to workers, colony metabolic rates are low because the metabolic rate of larvae and pupae are much lower than adult workers. However, the high colony metabolic rates were observed in colonies with moderate brood: adult ratios, because higher ratios cause adult workers to be more active and have higher metabolic rates, presumably due to the extra work required to feed more brood.
ContributorsGuo, Xiaohui (Author) / Fewell, Jennifer H (Thesis advisor) / Kang, Yun (Thesis advisor) / Harrison, Jon F (Committee member) / Liebig, Juergen (Committee member) / Pratt, Stephen C (Committee member) / Pavlic, Theodore P (Committee member) / Arizona State University (Publisher)
Created2021
Description
The effects of urbanization on ozone levels have been widely investigated over cities primarily located in temperate and/or humid regions. In this study, nested WRF-Chem simulations with a finest grid resolution of 1 km are conducted to investigate ozone concentrations O3 due to urbanization within cities in arid/semi-arid environments. First, a method based on a shape preserving Monotonic Cubic Interpolation (MCI) is developed and used to downscale anthropogenic emissions from the 4 km resolution 2005 National Emissions Inventory (NEI05) to the finest model resolution of 1 km. Using the rapidly expanding Phoenix metropolitan region as the area of focus, we demonstrate the proposed MCI method achieves ozone simulation results with appreciably improved correspondence to observations relative to the default interpolation method of the WRF-Chem system. Next, two additional sets of experiments are conducted, with the recommended MCI approach, to examine impacts of urbanization on ozone production: (1) the urban land cover is included (i.e., urbanization experiments) and, (2) the urban land cover is replaced with the region's native shrubland. Impacts due to the presence of the built environment on O3 are highly heterogeneous across the metropolitan area. Increased near surface O3 due to urbanization of 10–20 ppb is predominantly a nighttime phenomenon while simulated impacts during daytime are negligible. Urbanization narrows the daily O3 range (by virtue of increasing nighttime minima), an impact largely due to the region's urban heat island. Our results demonstrate the importance of the MCI method for accurate representation of the diurnal profile of ozone, and highlight its utility for high-resolution air quality simulations for urban areas.
ContributorsLi, Jialun (Author) / Georgescu, Matei (Author) / Hyde, Peter (Author) / Mahalov, Alex (Author) / Moustaoui, Mohamed (Author) / Julie Ann Wrigley Global Institute of Sustainability (Contributor)
Created2014-11-01