Matching Items (170)
Description
Though it is a widespread adaptation in humans and many other animals, parental care comes in a variety of forms and its subtle physiological costs, benefits, and tradeoffs related to offspring are often unknown. Thus, I studied the hydric, respiratory, thermal, and fitness dynamics of maternal egg-brooding behavior in Children's pythons (Antaresia childreni). I demonstrated that tight coiling detrimentally creates a hypoxic developmental environment that is alleviated by periodic postural adjustments. Alternatively, maternal postural adjustments detrimentally elevate rates of egg water loss relative to tight coiling. Despite ventilating postural adjustments, the developmental environment becomes increasingly hypoxic near the end of incubation, which reduces embryonic metabolism. I further demonstrated that brooding-induced hypoxia detrimentally affects offspring size, performance, locomotion, and behavior. Thus, parental care in A. childreni comes at a cost to offspring due to intra-offspring tradeoffs (i.e., those that reflect competing offspring needs, such as water balance and respiration). Next, I showed that, despite being unable to intrinsically produce body heat, A. childreni adjust egg-brooding behavior in response to shifts in nest temperature, which enhances egg temperature (e.g., reduced tight coiling during nest warming facilitated beneficial heat transfer to eggs). Last, I demonstrated that A. childreni adaptively adjust their egg-brooding behaviors due to an interaction between nest temperature and humidity. Specifically, females' behavioral response to nest warming was eliminated during low nest humidity. In combination with other studies, these results show that female pythons sense environmental temperature and humidity and utilize this information at multiple time points (i.e., during gravidity [egg bearing], at oviposition [egg laying], and during egg brooding) to enhance the developmental environment of their offspring. This research demonstrates that maternal behaviors that are simple and subtle, yet easily quantifiable, can balance several critical developmental variables (i.e., thermoregulation, water balance, and respiration).
ContributorsStahlschmidt, Zachary R (Author) / DeNardo, Dale F (Thesis advisor) / Harrison, Jon (Committee member) / McGraw, Kevin (Committee member) / Rutowski, Ronald (Committee member) / Walsberg, Glenn (Committee member) / Arizona State University (Publisher)
Created2011
Description
In this thesis I introduce a new direction to computing using nonlinear chaotic dynamics. The main idea is rich dynamics of a chaotic system enables us to (1) build better computers that have a flexible instruction set, and (2) carry out computation that conventional computers are not good at it. Here I start from the theory, explaining how one can build a computing logic block using a chaotic system, and then I introduce a new theoretical analysis for chaos computing. Specifically, I demonstrate how unstable periodic orbits and a model based on them explains and predicts how and how well a chaotic system can do computation. Furthermore, since unstable periodic orbits and their stability measures in terms of eigenvalues are extractable from experimental times series, I develop a time series technique for modeling and predicting chaos computing from a given time series of a chaotic system. After building a theoretical framework for chaos computing I proceed to architecture of these chaos-computing blocks to build a sophisticated computing system out of them. I describe how one can arrange and organize these chaos-based blocks to build a computer. I propose a brand new computer architecture using chaos computing, which shifts the limits of conventional computers by introducing flexible instruction set. Our new chaos based computer has a flexible instruction set, meaning that the user can load its desired instruction set to the computer to reconfigure the computer to be an implementation for the desired instruction set. Apart from direct application of chaos theory in generic computation, the application of chaos theory to speech processing is explained and a novel application for chaos theory in speech coding and synthesizing is introduced. More specifically it is demonstrated how a chaotic system can model the natural turbulent flow of the air in the human speech production system and how chaotic orbits can be used to excite a vocal tract model. Also as another approach to build computing system based on nonlinear system, the idea of Logical Stochastic Resonance is studied and adapted to an autoregulatory gene network in the bacteriophage λ.
ContributorsKia, Behnam (Author) / Ditto, William (Thesis advisor) / Huang, Liang (Committee member) / Lai, Ying-Cheng (Committee member) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2011
Description
Studies of animal contests often focus solely on a single static measurement of fighting ability, such as the size or the strength of the individual. However, recent studies have highlighted the importance of individual variation in the dynamic behaviors used during a fight, such as, assessment strategies, decision making, and fine motor control, as being strong predictors of the outcome of aggression. Here, I combined morphological and behavioral data to discover how these features interact during aggressing interactions in male virile crayfish, Faxonius virilis. I predicted that individual variation in behavioral skill for decision making (i.e., number of strikes thrown), would determine the outcome of contest success in addition to morphological measurements (e.g. body size, relative claw size). To evaluate this prediction, I filmed staged territorial interactions between male F. virilis and later analyzed trial behaviors (e.g. strike, pinches, and bout time) and aggressive outcomes. I found very little support for skill to predict win/loss outcome in trials. Instead, I found that larger crayfish engaged in aggression for longer compared to smaller crayfish, but that larger crayfish did not engage in a greater number of claw strikes or pinches when controlling for encounter duration. Future studies should continue to investigate the role of skill, by using finer-scale techniques such as 3D tracking software, which could track advanced measurements (e.g. speed, angle, and movement efficiency). Such studies would provide a more comprehensive understanding of the relative influence of fighting skill technique on territorial contests.
ContributorsNguyen, Phillip Huy (Author) / Angilletta, Michael (Thesis director) / McGraw, Kevin (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The cyber-physical systems (CPS) are emerging as the underpinning technology for major industries in the 21-th century. This dissertation is focused on two fundamental issues in cyber-physical systems: network interdependence and information dynamics. It consists of the following two main thrusts. The first thrust is targeted at understanding the impact of network interdependence. It is shown that a cyber-physical system built upon multiple interdependent networks are more vulnerable to attacks since node failures in one network may result in failures in the other network, causing a cascade of failures that would potentially lead to the collapse of the entire infrastructure. There is thus a need to develop a new network science for modeling and quantifying cascading failures in multiple interdependent networks, and to develop network management algorithms that improve network robustness and ensure overall network reliability against cascading failures. To enhance the system robustness, a "regular" allocation strategy is proposed that yields better resistance against cascading failures compared to all possible existing strategies. Furthermore, in view of the load redistribution feature in many physical infrastructure networks, e.g., power grids, a CPS model is developed where the threshold model and the giant connected component model are used to capture the node failures in the physical infrastructure network and the cyber network, respectively. The second thrust is centered around the information dynamics in the CPS. One speculation is that the interconnections over multiple networks can facilitate information diffusion since information propagation in one network can trigger further spread in the other network. With this insight, a theoretical framework is developed to analyze information epidemic across multiple interconnecting networks. It is shown that the conjoining among networks can dramatically speed up message diffusion. Along a different avenue, many cyber-physical systems rely on wireless networks which offer platforms for information exchanges. To optimize the QoS of wireless networks, there is a need to develop a high-throughput and low-complexity scheduling algorithm to control link dynamics. To that end, distributed link scheduling algorithms are explored for multi-hop MIMO networks and two CSMA algorithms under the continuous-time model and the discrete-time model are devised, respectively.
ContributorsQian, Dajun (Author) / Zhang, Junshan (Thesis advisor) / Ying, Lei (Committee member) / Zhang, Yanchao (Committee member) / Cochran, Douglas (Committee member) / Arizona State University (Publisher)
Created2012
Description
The Cape Floral Region (CFR) in southwestern South Africa is one of the most diverse in the world, with >9,000 plant species, 70% of which are endemic, in an area of only ~90,000 km2. Many have suggested that the CFR's heterogeneous environment, with respect to landscape gradients, vegetation, rainfall, elevation, and soil fertility, is responsible for the origin and maintenance of this biodiversity. While studies have struggled to link species diversity with these features, no study has attempted to associate patterns of gene flow with environmental data to determine how CFR biodiversity evolves on different scales. Here, a molecular population genetic data is presented for a widespread CFR plant, Leucadendron salignum, across 51 locations with 5-kb of chloroplast (cpDNA) and 6-kb of unlinked nuclear (nuDNA) DNA sequences in a dataset of 305 individuals. In the cpDNA dataset, significant genetic structure was found to vary on temporal and spatial scales, separating Western and Eastern Capes - the latter of which appears to be recently derived from the former - with the highest diversity in the heart of the CFR in a central region. A second study applied a statistical model using vegetation and soil composition and found fine-scale genetic divergence is better explained by this landscape resistance model than a geographic distance model. Finally, a third analysis contrasted cpDNA and nuDNA datasets, and revealed very little geographic structure in the latter, suggesting that seed and pollen dispersal can have different evolutionary genetic histories of gene flow on even small CFR scales. These three studies together caution that different genomic markers need to be considered when modeling the geographic and temporal origin of CFR groups. From a greater perspective, the results here are consistent with the hypothesis that landscape heterogeneity is one driving influence in limiting gene flow across the CFR that can lead to species diversity on fine-scales. Nonetheless, while this pattern may be true of the widespread L. salignum, the extension of this approach is now warranted for other CFR species with varying ranges and dispersal mechanisms to determine how universal these patterns of landscape genetic diversity are.
ContributorsTassone, Erica (Author) / Verrelli, Brian C (Thesis advisor) / Dowling, Thomas (Committee member) / Cartwright, Reed (Committee member) / Rosenberg, Michael S. (Committee member) / Wojciechowski, Martin (Committee member) / Arizona State University (Publisher)
Created2013
Description
A principal goal of this dissertation is to study stochastic optimization and real-time scheduling in cyber-physical systems (CPSs) ranging from real-time wireless systems to energy systems to distributed control systems. Under this common theme, this dissertation can be broadly organized into three parts based on the system environments. The first part investigates stochastic optimization in real-time wireless systems, with the focus on the deadline-aware scheduling for real-time traffic. The optimal solution to such scheduling problems requires to explicitly taking into account the coupling in the deadline-aware transmissions and stochastic characteristics of the traffic, which involves a dynamic program that is traditionally known to be intractable or computationally expensive to implement. First, real-time scheduling with adaptive network coding over memoryless channels is studied, and a polynomial-time complexity algorithm is developed to characterize the optimal real-time scheduling. Then, real-time scheduling over Markovian channels is investigated, where channel conditions are time-varying and online channel learning is necessary, and the optimal scheduling policies in different traffic regimes are studied. The second part focuses on the stochastic optimization and real-time scheduling involved in energy systems. First, risk-aware scheduling and dispatch for plug-in electric vehicles (EVs) are studied, aiming to jointly optimize the EV charging cost and the risk of the load mismatch between the forecasted and the actual EV loads, due to the random driving activities of EVs. Then, the integration of wind generation at high penetration levels into bulk power grids is considered. Joint optimization of economic dispatch and interruptible load management is investigated using short-term wind farm generation forecast. The third part studies stochastic optimization in distributed control systems under different network environments. First, distributed spectrum access in cognitive radio networks is investigated by using pricing approach, where primary users (PUs) sell the temporarily unused spectrum and secondary users compete via random access for such spectrum opportunities. The optimal pricing strategy for PUs and the corresponding distributed implementation of spectrum access control are developed to maximize the PU's revenue. Then, a systematic study of the nonconvex utility-based power control problem is presented under the physical interference model in ad-hoc networks. Distributed power control schemes are devised to maximize the system utility, by leveraging the extended duality theory and simulated annealing.
ContributorsYang, Lei (Author) / Zhang, Junshan (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Xue, Guoliang (Committee member) / Ying, Lei (Committee member) / Arizona State University (Publisher)
Created2012
Description
Human-induced rapid environmental change (HIREC) influences nearly all of Earth's ecosystems through processes such as urbanization. Previous studies have found that urbanization influences biodiversity patterns, often yielding an increase in the abundance of a few urban-adapted taxa at the expense of native species diversity. The western black widow spider, Latrodectus hesperus, is a medically-important pest species that often forms dense urban subpopulations (i.e., infestations) relative to the low-density subpopulations found throughout undisturbed, desert habitat. Here, I employ field and laboratory studies to examine the population ecology and stoichiometry of this urban pest to increase our understanding of the mechanisms underlying its success. The population ecology of ten black widow subpopulations spread across metropolitan Phoenix, AZ was examined during the peak breeding season (June-August). This study revealed that arthropod prey abundance, female mass and population density of females showed significant spatial variation across the ten subpopulations. Additionally, prey abundance and foraging success, measured as the number of carcasses found in webs, were a strong determinant of female mass and population density within each subpopulation. To test the mechanisms that drive black widow infestations, I used ecological stoichiometry to examine the nutrient (nitrogen and phosphorus) composition of spiders and arthropod prey from urban habitat, desert habitat and a laboratory diet regime. These studies revealed that (1) spiders are more nutrient rich than cricket prey in the field, (2) spider subpopulations exhibit significant spatial variation in their nitrogen composition, (3) nutrient composition of urban spider subpopulations does not differ significantly from Sonoran desert subpopulations, (4) laboratory-reared spiders fed a diet of only laboratory-reared crickets are more nitrogen and phosphorus limited than field-captured spiders, and (5) cannibalism by laboratory-reared spiders alleviated phosphorus limitation, but not nitrogen limitation, when compared to field-captured spiders. This work highlights the need to examine the population ecology of species relationships, such as predator-prey dynamics, to fully understand the fecundity and population growth of urban pest species. Moreover, the integration of population ecology and stoichiometry illustrates the need to address mechanisms like nutrient limitation that may explain why urban pest populations thrive and native species diversity suffers following HIREC.
ContributorsTrubl, Patricia (Author) / Johnson, James C. (Thesis advisor) / Rutowski, Ronald (Thesis advisor) / McGraw, Kevin (Committee member) / Arizona State University (Publisher)
Created2012
Description
The rapid advancement of wireless technology has instigated the broad deployment of wireless networks. Different types of networks have been developed, including wireless sensor networks, mobile ad hoc networks, wireless local area networks, and cellular networks. These networks have different structures and applications, and require different control algorithms. The focus of this thesis is to design scheduling and power control algorithms in wireless networks, and analyze their performances. In this thesis, we first study the multicast capacity of wireless ad hoc networks. Gupta and Kumar studied the scaling law of the unicast capacity of wireless ad hoc networks. They derived the order of the unicast throughput, as the number of nodes in the network goes to infinity. In our work, we characterize the scaling of the multicast capacity of large-scale MANETs under a delay constraint D. We first derive an upper bound on the multicast throughput, and then propose a lower bound on the multicast capacity by proposing a joint coding-scheduling algorithm that achieves a throughput within logarithmic factor of the upper bound. We then study the power control problem in ad-hoc wireless networks. We propose a distributed power control algorithm based on the Gibbs sampler, and prove that the algorithm is throughput optimal. Finally, we consider the scheduling algorithm in collocated wireless networks with flow-level dynamics. Specifically, we study the delay performance of workload-based scheduling algorithm with SRPT as a tie-breaking rule. We demonstrate the superior flow-level delay performance of the proposed algorithm using simulations.
ContributorsZhou, Shan (Author) / Ying, Lei (Thesis advisor) / Zhang, Yanchao (Committee member) / Zhang, Junshan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2013
Description
Complex dynamical systems consisting interacting dynamical units are ubiquitous in nature and society. Predicting and reconstructing nonlinear dynamics of units and the complex interacting networks among them serves the base for the understanding of a variety of collective dynamical phenomena. I present a general method to address the two outstanding problems as a whole based solely on time-series measurements. The method is implemented by incorporating compressive sensing approach that enables an accurate reconstruction of complex dynamical systems in terms of both nodal equations that determines the self-dynamics of units and detailed coupling patterns among units. The representative advantages of the approach are (i) the sparse data requirement which allows for a successful reconstruction from limited measurements, and (ii) general applicability to identical and nonidentical nodal dynamics, and to networks with arbitrary interacting structure, strength and sizes. Another two challenging problem of significant interest in nonlinear dynamics: (i) predicting catastrophes in nonlinear dynamical systems in advance of their occurrences and (ii) predicting the future state for time-varying nonlinear dynamical systems, can be formulated and solved in the framework of compressive sensing using only limited measurements. Once the network structure can be inferred, the dynamics behavior on them can be investigated, for example optimize information spreading dynamics, suppress cascading dynamics and traffic congestion, enhance synchronization, game dynamics, etc. The results can yield insights to control strategies design in the real-world social and natural systems. Since 2004, there has been a tremendous amount of interest in graphene. The most amazing feature of graphene is that there exists linear energy-momentum relationship when energy is low. The quasi-particles inside the system can be treated as chiral, massless Dirac fermions obeying relativistic quantum mechanics. Therefore, the graphene provides one perfect test bed to investigate relativistic quantum phenomena, such as relativistic quantum chaotic scattering and abnormal electron paths induced by klein tunneling. This phenomenon has profound implications to the development of graphene based devices that require stable electronic properties.
ContributorsYang, Rui (Author) / Lai, Ying-Cheng (Thesis advisor) / Duman, Tolga M. (Committee member) / Akis, Richard (Committee member) / Huang, Liang (Committee member) / Arizona State University (Publisher)
Created2012
Description
What can classical chaos do to quantum systems is a fundamental issue highly relevant to a number of branches in physics. The field of quantum chaos has been active for three decades, where the focus was on non-relativistic quantumsystems described by the Schr¨odinger equation. By developing an efficient method to solve the Dirac equation in the setting where relativistic particles can tunnel between two symmetric cavities through a potential barrier, chaotic cavities are found to suppress the spread in the tunneling rate. Tunneling rate for any given energy assumes a wide range that increases with the energy for integrable classical dynamics. However, for chaotic underlying dynamics, the spread is greatly reduced. A remarkable feature, which is a consequence of Klein tunneling, arise only in relativistc quantum systems that substantial tunneling exists even for particle energy approaching zero. Similar results are found in graphene tunneling devices, implying high relevance of relativistic quantum chaos to the development of such devices. Wave propagation through random media occurs in many physical systems, where interesting phenomena such as branched, fracal-like wave patterns can arise. The generic origin of these wave structures is currently a matter of active debate. It is of fundamental interest to develop a minimal, paradigmaticmodel that can generate robust branched wave structures. In so doing, a general observation in all situations where branched structures emerge is non-Gaussian statistics of wave intensity with an algebraic tail in the probability density function. Thus, a universal algebraic wave-intensity distribution becomes the criterion for the validity of any minimal model of branched wave patterns. Coexistence of competing species in spatially extended ecosystems is key to biodiversity in nature. Understanding the dynamical mechanisms of coexistence is a fundamental problem of continuous interest not only in evolutionary biology but also in nonlinear science. A continuous model is proposed for cyclically competing species and the effect of the interplay between the interaction range and mobility on coexistence is investigated. A transition from coexistence to extinction is uncovered with a non-monotonic behavior in the coexistence probability and switches between spiral and plane-wave patterns arise. Strong mobility can either promote or hamper coexistence, while absent in lattice-based models, can be explained in terms of nonlinear partial differential equations.
ContributorsNi, Xuan (Author) / Lai, Ying-Cheng (Thesis advisor) / Huang, Liang (Committee member) / Yu, Hongbin (Committee member) / Akis, Richard (Committee member) / Arizona State University (Publisher)
Created2012