Matching Items (3)
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- All Subjects: Systems science
- Creators: Wang, Xiao
- Member of: ASU Electronic Theses and Dissertations
Description
The power of science lies in its ability to infer and predict the
existence of objects from which no direct information can be obtained
experimentally or observationally. A well known example is to
ascertain the existence of black holes of various masses in different
parts of the universe from indirect evidence, such as X-ray emissions.
In the field of complex networks, the problem of detecting
hidden nodes can be stated, as follows. Consider a network whose
topology is completely unknown but whose nodes consist of two types:
one accessible and another inaccessible from the outside world. The
accessible nodes can be observed or monitored, and it is assumed that time
series are available from each node in this group. The inaccessible
nodes are shielded from the outside and they are essentially
``hidden.'' The question is, based solely on the
available time series from the accessible nodes, can the existence and
locations of the hidden nodes be inferred? A completely data-driven,
compressive-sensing based method is developed to address this issue by utilizing
complex weighted networks of nonlinear oscillators, evolutionary game
and geospatial networks.
Both microbes and multicellular organisms actively regulate their cell
fate determination to cope with changing environments or to ensure
proper development. Here, the synthetic biology approaches are used to
engineer bistable gene networks to demonstrate that stochastic and
permanent cell fate determination can be achieved through initializing
gene regulatory networks (GRNs) at the boundary between dynamic
attractors. This is experimentally realized by linking a synthetic GRN
to a natural output of galactose metabolism regulation in yeast.
Combining mathematical modeling and flow cytometry, the
engineered systems are shown to be bistable and that inherent gene expression
stochasticity does not induce spontaneous state transitioning at
steady state. By interfacing rationally designed synthetic
GRNs with background gene regulation mechanisms, this work
investigates intricate properties of networks that illuminate possible
regulatory mechanisms for cell differentiation and development that
can be initiated from points of instability.
existence of objects from which no direct information can be obtained
experimentally or observationally. A well known example is to
ascertain the existence of black holes of various masses in different
parts of the universe from indirect evidence, such as X-ray emissions.
In the field of complex networks, the problem of detecting
hidden nodes can be stated, as follows. Consider a network whose
topology is completely unknown but whose nodes consist of two types:
one accessible and another inaccessible from the outside world. The
accessible nodes can be observed or monitored, and it is assumed that time
series are available from each node in this group. The inaccessible
nodes are shielded from the outside and they are essentially
``hidden.'' The question is, based solely on the
available time series from the accessible nodes, can the existence and
locations of the hidden nodes be inferred? A completely data-driven,
compressive-sensing based method is developed to address this issue by utilizing
complex weighted networks of nonlinear oscillators, evolutionary game
and geospatial networks.
Both microbes and multicellular organisms actively regulate their cell
fate determination to cope with changing environments or to ensure
proper development. Here, the synthetic biology approaches are used to
engineer bistable gene networks to demonstrate that stochastic and
permanent cell fate determination can be achieved through initializing
gene regulatory networks (GRNs) at the boundary between dynamic
attractors. This is experimentally realized by linking a synthetic GRN
to a natural output of galactose metabolism regulation in yeast.
Combining mathematical modeling and flow cytometry, the
engineered systems are shown to be bistable and that inherent gene expression
stochasticity does not induce spontaneous state transitioning at
steady state. By interfacing rationally designed synthetic
GRNs with background gene regulation mechanisms, this work
investigates intricate properties of networks that illuminate possible
regulatory mechanisms for cell differentiation and development that
can be initiated from points of instability.
ContributorsSu, Ri-Qi (Author) / Lai, Ying-Cheng (Thesis advisor) / Wang, Xiao (Thesis advisor) / Bliss, Daniel (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2015
Description
Energy projects have the potential to provide critical services for human well-being and help eradicate poverty. However, too many projects fail because their approach oversimplifies the problem to energy poverty: viewing it as a narrow problem of access to energy services and technologies. This thesis presents an alternative paradigm for energy project development, grounded in theories of socio-energy systems, recognizing that energy and poverty coexist as a social, economic, and technological problem.
First, it shows that social, economic, and energy insecurity creates a complex energy-poverty nexus, undermining equitable, fair, and sustainable energy futures in marginalized communities. Indirect and access-based measures of energy poverty are a mismatch for the complexity of the energy-poverty nexus. The thesis, using the concept of social value of energy, develops a methodology for systematically mapping benefits, burdens and externalities of the energy system, illustrated using empirical investigations in communities in Nepal, India, Brazil, and Philippines. The thesis argues that key determinants of the energy-poverty nexus are the functional and economic capabilities of users, stressors and resulting thresholds of capabilities characterizing the energy and poverty relationship. It proposes ‘energy thriving’ as an alternative standard for evaluating project outcomes, requiring energy systems to not only remedy human well-being deficits but create enabling conditions for discovering higher forms of well-being.
Second, a novel, experimental approach to sustainability interventions is developed, to improve the outcomes of energy projects. The thesis presents results from a test bed for community sustainability interventions established in the village of Rio Claro in Brazil, to test innovative project design strategies and develop a primer for co-producing sustainable solutions. The Sustainable Rio Claro 2020 initiative served as a longitudinal experiment in participatory collective action for sustainable futures.
Finally, results are discussed from a collaborative project with grassroots practitioners to understand the energy-poverty nexus, map the social value of energy and develop energy thriving solutions. Partnering with local private and non-profit organizations in Uganda, Bolivia, Nepal and Philippines, the project evaluated and refined methods for designing and implementing innovative energy projects using the theoretical ideas developed in the thesis, subsequently developing a practitioner toolkit for the purpose.
First, it shows that social, economic, and energy insecurity creates a complex energy-poverty nexus, undermining equitable, fair, and sustainable energy futures in marginalized communities. Indirect and access-based measures of energy poverty are a mismatch for the complexity of the energy-poverty nexus. The thesis, using the concept of social value of energy, develops a methodology for systematically mapping benefits, burdens and externalities of the energy system, illustrated using empirical investigations in communities in Nepal, India, Brazil, and Philippines. The thesis argues that key determinants of the energy-poverty nexus are the functional and economic capabilities of users, stressors and resulting thresholds of capabilities characterizing the energy and poverty relationship. It proposes ‘energy thriving’ as an alternative standard for evaluating project outcomes, requiring energy systems to not only remedy human well-being deficits but create enabling conditions for discovering higher forms of well-being.
Second, a novel, experimental approach to sustainability interventions is developed, to improve the outcomes of energy projects. The thesis presents results from a test bed for community sustainability interventions established in the village of Rio Claro in Brazil, to test innovative project design strategies and develop a primer for co-producing sustainable solutions. The Sustainable Rio Claro 2020 initiative served as a longitudinal experiment in participatory collective action for sustainable futures.
Finally, results are discussed from a collaborative project with grassroots practitioners to understand the energy-poverty nexus, map the social value of energy and develop energy thriving solutions. Partnering with local private and non-profit organizations in Uganda, Bolivia, Nepal and Philippines, the project evaluated and refined methods for designing and implementing innovative energy projects using the theoretical ideas developed in the thesis, subsequently developing a practitioner toolkit for the purpose.
ContributorsBiswas, Saurabh (Author) / Miller, Clark A. (Thesis advisor) / Wiek, Arnim (Committee member) / Janssen, Marcus A (Committee member) / Arizona State University (Publisher)
Created2020
Description
Complex dynamical systems are the kind of systems with many interacting components that usually have nonlinear dynamics. Those systems exist in a wide range of disciplines, such as physical, biological, and social fields. Those systems, due to a large amount of interacting components, tend to possess very high dimensionality. Additionally, due to the intrinsic nonlinear dynamics, they have tremendous rich system behavior, such as bifurcation, synchronization, chaos, solitons. To develop methods to predict and control those systems has always been a challenge and an active research area.
My research mainly concentrates on predicting and controlling tipping points (saddle-node bifurcation) in complex ecological systems, comparing linear and nonlinear control methods in complex dynamical systems. Moreover, I use advanced artificial neural networks to predict chaotic spatiotemporal dynamical systems. Complex networked systems can exhibit a tipping point (a “point of no return”) at which a total collapse occurs. Using complex mutualistic networks in ecology as a prototype class of systems, I carry out a dimension reduction process to arrive at an effective two-dimensional (2D) system with the two dynamical variables corresponding to the average pollinator and plant abundances, respectively. I demonstrate that, using 59 empirical mutualistic networks extracted from real data, our 2D model can accurately predict the occurrence of a tipping point even in the presence of stochastic disturbances. I also develop an ecologically feasible strategy to manage/control the tipping point by maintaining the abundance of a particular pollinator species at a constant level, which essentially removes the hysteresis associated with tipping points.
Besides, I also find that the nodal importance ranking for nonlinear and linear control exhibits opposite trends: for the former, large degree nodes are more important but for the latter, the importance scale is tilted towards the small-degree nodes, suggesting strongly irrelevance of linear controllability to these systems. Focusing on a class of recurrent neural networks - reservoir computing systems that have recently been exploited for model-free prediction of nonlinear dynamical systems, I uncover a surprising phenomenon: the emergence of an interval in the spectral radius of the neural network in which the prediction error is minimized.
My research mainly concentrates on predicting and controlling tipping points (saddle-node bifurcation) in complex ecological systems, comparing linear and nonlinear control methods in complex dynamical systems. Moreover, I use advanced artificial neural networks to predict chaotic spatiotemporal dynamical systems. Complex networked systems can exhibit a tipping point (a “point of no return”) at which a total collapse occurs. Using complex mutualistic networks in ecology as a prototype class of systems, I carry out a dimension reduction process to arrive at an effective two-dimensional (2D) system with the two dynamical variables corresponding to the average pollinator and plant abundances, respectively. I demonstrate that, using 59 empirical mutualistic networks extracted from real data, our 2D model can accurately predict the occurrence of a tipping point even in the presence of stochastic disturbances. I also develop an ecologically feasible strategy to manage/control the tipping point by maintaining the abundance of a particular pollinator species at a constant level, which essentially removes the hysteresis associated with tipping points.
Besides, I also find that the nodal importance ranking for nonlinear and linear control exhibits opposite trends: for the former, large degree nodes are more important but for the latter, the importance scale is tilted towards the small-degree nodes, suggesting strongly irrelevance of linear controllability to these systems. Focusing on a class of recurrent neural networks - reservoir computing systems that have recently been exploited for model-free prediction of nonlinear dynamical systems, I uncover a surprising phenomenon: the emergence of an interval in the spectral radius of the neural network in which the prediction error is minimized.
ContributorsJiang, Junjie (Author) / Lai, Ying-Cheng (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Wang, Xiao (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2020