京都大学 / 新制・課程博士 / 博士(理学) / 甲第24418号 / 理博第4917号 / 新制||理||1702(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)准教授 岩室 史英, 教授 嶺重 慎, 教授 太田 耕司 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

No description available.

The distribution of species in space is determined by the species dispersal capacity, adaptation to environmental conditions, and response to predators and competitors. To determine the importance of dispersal limitation, environmental filtering, and species interactions on the distribution of species in the Brazilian Amazonian forest, I sampled termites in a large area of Brazil. I investigated patterns in species occurrence that could indicate competition and predation structuring termite communities, and analyzed the association of termite abundance and species richness with the density of ant predators. The spatial distribution of termites, and their association with climatic and edaphic conditions were also used to infer about the effects of dispersal limitation and environmental filtering. A total of 271 termite species and 4,389 colonies was found in the 148 transects sampled. Predator density was the strongest predictor of termite abundance and species richness at small spatial scales, but the turnover in termite species composition was mostly associated with measures of soil texture. At broad spatial scales, soil chemistry, climate, and isolation by distance were associated with termite abundance, species richness, and species composition. These results suggest that both species interactions, their association with the environment, and their dispersal capacity determine their distribution. Nevertheless, dispersal limitation seem to be stronger over large areas, whereas environmental filtering can act both at small and large geographic scales.

The identification of the physical parameters (mass, stiffness, and damping) of structural, mechanical, and biomechanical systems is a major challenge in many applications, especially when dealing with old systems and biological systems with heavy damping and where environmental noises are presented. This work presents a novel methodology called eigenvector phase correction (EVPHC) to solve for the physical parameters of structural and biomechanical systems even with the existence of a significant amount of noise. The method was first tested on structural/mechanical systems and showed superior results when compared with an iterative method from the literature. EVPHC was then developed and used to identify the physical parameters of supine humans under vertical whole-body vibration. Modal parameters of fifteen human subjects, in the supine position, were first identified in this work using experimentation under vertical whole-body vibration. EVPHC was then used to solve an inverse modal problem for the identification of the stiffness and damping parameters at the cervical and lumbar areas of supine humans. The results showed that the resulting physical parameters were realistically close to those presented in the literature. The proposed human model was able to predict the time histories of the acceleration at the head, chest, pelvis, and legs very closely to those of the experimental measured values. A scaling methodology is also presented in this work, where an average human model was scaled to an individual subject using the body mass properties.

In this thesis, we will show how certain classes of quantum many-body Hamiltonians with $\su{2}_1 \oplus \su{2}_2 \oplus \ldots \oplus \su{2}_k$ spectrum generating algebras can be approximated by multi-dimensional shifted harmonic oscillator Hamiltonians. The dimensions of the Hilbert spaces of such Hamiltonians usually depend exponentially on $k$. This can make obtaining eigenvalues by diagonalization computationally challenging. The Shifted Harmonic Approximation (SHA) developed here gives good predictions of properties such as ground state energies, excitation energies and the most probable states in the lowest eigenstates. This is achieved by solving only a system of $k$ equations and diagonalizing $k\times k$ matrices. The SHA gives accurate approximations over wide domains of parameters and in many cases even across phase transitions. The SHA is first illustrated using the Lipkin-Meshkov-Glick (LMG) model and the Canonical Josephson Hamiltonian (CJH) which have $\su{2}$ spectrum generating algebras. Next, we extend the technique to the non-compact $\su{1,1}$ algebra, using the five-dimensional quartic oscillator (5DQO) as an example. Finally, the SHA is applied to a $k$-level Bardeen-Cooper-Shrieffer (BCS) pairing Hamiltonian with fixed particle number. The BCS model has a $\su{2}_1 \oplus \su{2}_2 \oplus \ldots \oplus \su{2}_k$ spectrum generating algebra. An attractive feature of the SHA is that it also provides information to construct basis states which yield very accurate eigenvalues for low-lying states by diagonalizing Hamiltonians in small subspaces of huge Hilbert spaces. For Hamiltonians that involve a smaller number of operators, accurate eigenvalues can be obtained using another technique developed in this thesis: the generalized Rowe-Rosensteel-Kerman-Klein equations-of-motion method (RRKK). The RRKK is illustrated using the LMG and the 5DQO. In RRKK, solving unknowns in a set of $10\times 10$ matrices typically gives estimates of the lowest few eigenvalues to an accuracy of at least eight significant figures. The RRKK involves optimization routines which require initial guesses of the matrix representations of the operators. In many cases, very good initial guesses can be obtained using the SHA. The thesis concludes by exploring possible future developments of the SHA.

In this thesis, we will show how certain classes of quantum many-body Hamiltonians with $\su{2}_1 \oplus \su{2}_2 \oplus \ldots \oplus \su{2}_k$ spectrum generating algebras can be approximated by multi-dimensional shifted harmonic oscillator Hamiltonians. The dimensions of the Hilbert spaces of such Hamiltonians usually depend exponentially on $k$. This can make obtaining eigenvalues by diagonalization computationally challenging. The Shifted Harmonic Approximation (SHA) developed here gives good predictions of properties such as ground state energies, excitation energies and the most probable states in the lowest eigenstates. This is achieved by solving only a system of $k$ equations and diagonalizing $k\times k$ matrices. The SHA gives accurate approximations over wide domains of parameters and in many cases even across phase transitions. The SHA is first illustrated using the Lipkin-Meshkov-Glick (LMG) model and the Canonical Josephson Hamiltonian (CJH) which have $\su{2}$ spectrum generating algebras. Next, we extend the technique to the non-compact $\su{1,1}$ algebra, using the five-dimensional quartic oscillator (5DQO) as an example. Finally, the SHA is applied to a $k$-level Bardeen-Cooper-Shrieffer (BCS) pairing Hamiltonian with fixed particle number. The BCS model has a $\su{2}_1 \oplus \su{2}_2 \oplus \ldots \oplus \su{2}_k$ spectrum generating algebra. An attractive feature of the SHA is that it also provides information to construct basis states which yield very accurate eigenvalues for low-lying states by diagonalizing Hamiltonians in small subspaces of huge Hilbert spaces. For Hamiltonians that involve a smaller number of operators, accurate eigenvalues can be obtained using another technique developed in this thesis: the generalized Rowe-Rosensteel-Kerman-Klein equations-of-motion method (RRKK). The RRKK is illustrated using the LMG and the 5DQO. In RRKK, solving unknowns in a set of $10\times 10$ matrices typically gives estimates of the lowest few eigenvalues to an accuracy of at least eight significant figures. The RRKK involves optimization routines which require initial guesses of the matrix representations of the operators. In many cases, very good initial guesses can be obtained using the SHA. The thesis concludes by exploring possible future developments of the SHA.

No description available.

We investigate the structure of trees that have greatest maximum eigenvalue among all trees with a given degree sequence. We show that in such an extremal tree the degree sequence is non-increasing with respect to an ordering of the vertices that is obtained by breadth-first search. This structure is uniquely determined up to isomorphism. We also show that the maximum eigenvalue in such classes of trees is strictly monotone with respect to majorization. (author´s abstract) / Series: Research Report Series / Department of Statistics and Mathematics

Hospital ambulatory patients are seen in outpatient departments (OPDs) located in the hospital. 83.3 million visits were made to these departments in 2002. Many sources of patient waiting time exist including: poor coordination of information, inefficient scheduling, inaccurate time estimation and others. Well-designed and executed patient scheduling has the potential to remedy some of these problems. To properly schedule patients, variability in demand must be addressed. Patients may cancel appointments, arrive late and arrive without appointments. We address this problem based on a Multi-attribute Decision Making (MADM) approach. Decision models are developed using the Simple Additive Weighting (SAW) method to address scheduling decisions for late-arrival and walk-in patients and the operational decision of calling back patients from the waiting room.The models are developed as part of a case study at H. Lee Moffitt Cancer Center and tested in a single-clinic computer simulation against the current clinic system decision process with respect to various performance measures.The proposed decision models successfully made walk-in and late patient scheduling decisions. The contributions of this research include identifying, defining and weighting of relevant decision making criteria at H. Lee Moffitt. Our decision models guaranteed all of the defined criteria are included every time a walk-in or late patient decision must be made. Based on the findings, implementation of the models with no reduction in number of patients would improve scheduling and operational decisions while not affecting clinic output measures.Using criteria to restrict the number of late and walk-in patients, on average, the clinic closed between 36.20 minutes and 47.95 minutes earlier. However, practitioner and room utilization suffered. The tradeoff among number of patients seen, resource utilization, waiting time and clinic close time should be considered but cannot be fully assessed solely on the information gathered in this research. As a case study of H. Lee Moffitt Cancer Center, the decision models successfully incorporated all relevant patient criteria without adversely affecting the clinic system. Future research is needed to better understand what factors will impact system measures and expand the decision models to other outpatient clinic settings.

Fully-polarimetric UAVSAR data of Hofsjokull Ice Cap, central Iceland, taken in June 2009 was decomposed using Pauli-based coherent decomposition as well as Cloude and H/A/alpha eigenvector-based decomposition methods. The goals of this research were to evaluate the effect of the near-surface conditions of temperate glaciers on polarized SAR data and investigate the potential of creating a model of the radar scattering mechanisms based on the decomposed elements and local temperature. The results of this data analysis show a strong relationship between the Pauli and H/A/alpha decomposition elements and the near-surface conditions. Fitting curves to the normalized Pauli decomposition elements shows consistent trends across several spatially independent regions of the ice cap suggesting that the Pauli elements might be useful for modeling the scattering mechanisms of temperate ice with various surface conditions. / text