Modeling the Total Allowable Area for Coastal Reclamation : a case study of Xiamen, China

Author Posting. © The Author(s), 2013. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Ocean & Coastal Management 76 (2013):38-44, doi:10.1016/j.ocecoaman.2013.02.015.This paper presents an analytical framework to estimate the Total Allowable Area for Coastal Reclamation (TAACR) to provide scientific support for the implementation of a coastal reclamation restriction mechanism. The logic of the framework is to maximize the net benefits of coastal reclamation subject to a set of constraints. Various benefits and costs, including the ecological and environmental costs of coastal reclamation, are systematically quantified in the framework. Model simulations are developed using data from Tongan Bay of Xiamen. The results suggest that the TAACR in Tongan Bay is 5.67 km2, and the area of the Bay should be maintained at least at 87.52 km2.The study was funded by the National Oceanic Public Welfare Projects (No. 201105006) and the Fujian Natural Science Foundation (No. 2010J01360

Operational quantum resource theories : unified framework and applications

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, September, 2020Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 209-229).A major goal of quantum information science is to understand the relation between the properties of quantum features and the enhancements to information processing tasks enabled by them. In particular, precise quantitative descriptions of quantum phenomena have become increasingly important not only for theoretical interest but also from a practical point of view as the recent technological advances have provided access to systems on small scales, in which quantum effects play major roles. As a platform to offer such quantitative treatments, quantum resource theory has been developed. This is an operationally motivated framework, which systematically deals with quantication and manipulation of the quantum effects by considering the quantities of interest as precious "resources" that cannot be freely created by the given sets of operations.This thesis develops quantum resource theories from two perspectives. The first part advances the framework of general resource theories, which encompass various types of quantum phenomena such as quantum entanglement, quantum superposition, and many others. We find that common structures universally shared by a wide class of resources can be extracted by employing operational viewpoints. Specically, we consider fundamental operational tasks in quantum information theory -- state/channel discrimination, resource distillation/dilution, implementation of unitary evolution -- and establish quantitative connections between the resource contents and their operational capabilities. Our general results contribute to building a unified picture of quantum resources, allowing us to gain a deeper understanding of the characterization of quantum mechanics. The second half of the thesis applies the resource theory to specic settings such as continuous-variable systems, systems with conserved additive quantities, and communication via quantum channels. We show that operational perspectives offered by resource theories provide effective ways of quantifying the underlying resources and concise arguments to solve concrete problems of interest, suggesting the further potential of resource theory as a useful theoretical tool. The resource objects considered in this thesis span from quantum states to quantum measurements and channels, extending the consideration beyond static resource theories that have been a major focus in the field, and paving the way for the development of dynamic resource theories.by Ryuji Takagi.Ph. D.Ph. D. Massachusetts Institute of Technology, Department of Physic

On the analysis of self-adaptive evolutionary algorithms

Due to the flexibility in adapting to different fitness landscapes, self-adaptive evolutionary algorithms (SA-EAs) have been gaining popularity in the recent past. In this paper, we postulate the properties that SA-EA operators should have for successful applications. Specifically, population mean and variance of a number of SA-EA operators, such as various real-parameter crossover operators and self-adaptive evolution strategies, are calculated for this purpose. In every case, simulation results re shown to verify the theoretical calculations. The postulations and population variance calculations explain why self-adaptive GAs and ESs have shown similar performance in the past and also suggest appropriate strategy parameter values which must be chosen while applying and comparing different SA-EAs. (orig.)Available from TIB Hannover: RR 8071(99-69)+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Self-adaptive genetic algorithms with simulated binary crossover

Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored only with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly-used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need of emphasizing further studies on self-adaptive GAs. (orig.)SIGLEAvailable from TIB Hannover: RR 8071(99-61)+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman