京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21737号 / 工博第4554号 / 新制||工||1710(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 木村 亮, 准教授 肥後 陽介, 准教授 木元 小百合 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Betongpelare är en vanlig del i konstruktioner inom bygg-och anläggningsbranschen och kräver normalt en stor mängd armeringsjärn för att säkerställa dess styrka och stabilitet. Vid dimensionering av slanka betongpelare ska hänsyn inte bara tas till första ordningens moment och deformationer utan även andra ordningens teori ska beaktas. För att dimensionera förandra ordningens moment beskriver Eurokoderna tre olika metoder, en generell metod samt två förenklade metoder: nominell styvhet och nominell krökning. Dimensionering kan ske förhand eller med datorprogram. FEM-Design, som är ett avancerat analysprogram, baseras på finita elementmetoden som är en numerisk analysmetod och ett av de vanligaste sätten att beräkna fysikaliska fenomen. FEM-Design kan ofta ge ett bättre och säkrare resultat då handberäkningar approximerar för att de ska vara hanterbara.I arbetet jämförs beräknad armeringsmängd mellan handberäkningar med nominell styvhet,nominell krökning samt analysprogrammet FEM-Design. Arbetet har gjorts för att undersöka skillnaden i armering och därmed kunna avgöra vilken metod som ger minst respektive mest mängd armering. Betongpelarna som undersöks är slanka och har tre olika upplagsförhållanden, varje upplag belastas med tre olika belastningsfall. Beräkningarna är utförda enligt Eurokod 2 och resultatet blev att FEM-Design gav i sju av nio fall lägst andra ordningens moment. I åtta av nio fall gav FEM-Design lägst mängd armering medan nominell styvhet gav störst andra ordningens moment och störst mängd armering i samtliga fall. Nominell krökning gav ett andra ordningens moment som var nära FEM-Designs resultat medstörsta skillnaden på 30%. Beräknad armering för nominell krökning växlade mellan attstämma överens mest med nominell styvhet och FEM-Design. Utifrån resultatet har även skillnaden i pris på armering beräknats fram där nominell styvhet är det dyraste alternativet.FEM-Design är 61% billigare än nominell styvhet medan nominell krökning är 51% billigareän nominell krökning.Jämförelsen visar att nominell krökning kan i dessa belastningsfall som har undersökts ansesvara den bästa metoden av handberäkningarna men FEM-Design anses vara den bästa av samtliga metoder i detta arbete. Slutsatsen som kunde dras var att båda de förenklade handberäkningsmetoderna överdimensionerar armeringsmängden i pelarna och därmed anses FEM-Design som det bästa alternativet. FEM-Design gav inte bara minst armering och därmed lägsta armeringskostnaden utan dimensioneringen tog också kortast tid. / When designing slender concrete columns where the second-order theories need to be considered, the Eurocodes describe three different methods; a general method and two simplified methods - nominal stiffness and nominal curvature. Designing can be done manually or with programs. FEM-design, an advanced analysis program, is based on the finiteelement method, which is a numerical analysis method and is one of the most common ways to calculate big and complex problems. FEM-Design often provides more reliable results compared to calculations done by hand, which involve approximations to make them manageable.In this study the calculated reinforcement quantities are compared with hand calculationsusing the nominal stiffness, nominal curvature and FEM-Design. The purpose is to investigatethe difference in reinforcement and determine which method requires the least amount of reinforcement. The investigated columns are slender and have three different boundary conditions, each subject to three different load cases. The calculations are performed according to the Eurocode 2. The results show that in seven out of nine cases the FEM-Design method produced the lowest second-order moments. In eight out of nine cases, FEM-Design resulted in least amount of reinforcement, while nominal stiffness resulted in the highest second-order moments and greatest amount of reinforcement in all cases. Nominal curvature generally produced second-order moments that were close to FEM-Design, the largest difference being 30%. Regarding the calculated reinforcement , nominal curvature varied in agreement with nominal stiffness and FEM-Design. The cost of reinforcement was also analyzed, with nominal stiffness being 51% more expensive than nominal curvature and 61% more expensive than FEM-Design. Nominal curvature was the preferred manual method, but FEM-Design emerged as the best overall method, offering both minimal reinforcement and shorter design time.

Lifting surfaces of unmanned aerial vehicles (UAV) are often operated in low Reynolds number (Re) ranges, wherein the transition of boundary layer from laminar-to-turbulent plays a more significant role than in high-Re aerodynamics applications. This poses a challenge for traditional computational fluid dynamics (CFD) simulations, since typical modeling approaches assume either fully laminar or fully turbulent flow. In particular, the boundary layer state must be accurately predicted to successfully determine the separation behavior which significantly influences the aerodynamic characteristics of the airfoil. Reynolds-averaged Navier-Stokes (RANS) based CFD simulations of an elliptic airfoil are performed for time-varying angles of attack, and results are used to elucidate relevant flow physics and aerodynamic data for an elliptic airfoil under realistic operating conditions. Results are also used to evaluate the performance of several different RANS-based turbulence modeling approaches for this class of flowfield.

The far-reaching successes of deep neural networks in a wide variety of learning tasks have prompted research on how model properties account for high network performance. For a specific class of models whose activation functions are piecewise linear, one such property of interest is the number of linear regions that the network generates. Such models themselves define piecewise linear functions by partitioning input space into disjoint regions and fitting a different linear function on each such piece. It would be expected that the number or configuration of such regions would describe the model’s ability to fit complicated functions. However, previous works have shown difficulty in identifying linear regions as satisfactory predictors of model success. In this thesis, the question of whether the generation of linear regions due to training encode the properties of the learning problem is explored. More specifically, it is investigated whether change in linear region density due to model fitting is related to the geometric properties of the training data. In this work, data geometry is characterized in terms of the curvature of the underlying manifold. Models with ReLU activation functions are trained on a variety of regression problems defined on artificial manifolds and the change in linear region density is recorded along trajectories in input space. Learning is performed on problems defined on curves, surfaces and for image data. Experiments are repeated as the data geometry is varied and the change in density is compared with the manifold curvature measure used. In no experimental setting, was the observed change in density found to be clearly linked with curvature. However, density was observed to increase at points of discontinuity. This suggests that linear regions can in some instances model data complexities, however, the findings presented here do not support that data curvature is encoded by the formation of linear regions. Thus, the role that linear regions play in controlling the capacity of piecewise linear networks remains open. Future research is needed to gain further insights into how data geometry and linear regions are connected. / De breda framgångar som djupa neurala nätverk har uppvisat i en mängd olika inlärningsproblem har inspirerat ny forskning med syfte att förklara vilka modellegenskaper som resulterar i högpresterande nätverk. För neurala nätverk som använder styckvis linjära aktiveringsfunktioner är en intressant egenskap att studera de linjära regioner som nätverket genererar i det vektorrum som utgör träningsdatans definitionsmängd. Nätverk med styckvis linjära aktiveringsfunktioner delar upp definitionsmängden i distinkta regioner på vilka olika linjära funktioner avbildas. Dessa nätverk avbildar själva styckvis linjära funktioner. Genom att anpassa flera skilda linjära avbildningar går det att approximera funktioner som är icke-linjära. Därför skulle man kunna förvänta sig att antalet linjära regioner som en modell genererar och hur de är fördelade i rummet kunde fungera som mått på modellens förmåga att lära sig komplicerade funktioner. Tidigare efterforskingar inom detta område har dock inte kunnat demonstrera ett samband mellan antalet eller fördelningen av linjära regioner och modellens prestanda. I den här avhandlingen undersöks det vilken roll linjära regioner spelar i att förklara en modells kapacitet och vad den lär sig. Fångar de linjära regioner som ett nätverk lär sig de underliggande egenskaperna hos träningsdatan? Mer specifikt så studeras huruvida den lokala förändringen i antalet linjära regioner efter modellträning korrelerar med träningsdatans geometri. Träningsdata genereras från syntetiska mångfalder och datageometrin beskrivs i termer av mångfaldens krökning. På dessa mångfalder definieras regressionsproblem och träning upprepas för topologier av olika form och med olika krökning. Skillnaden i antalet linjära regioner efter träning mäts längs banor i definitionsdomänen och jämförs med datans krökning. Ingen av de experiment som utfördes lyckades påvisa något tydligt samband mellan förändring i antal regioner och datans krökning. Det observerades dock att antalet linjära regioner ökar i närheten av punkter som utgör diskontinuiteter. Detta antyder att linjära regioner under vissa omständigheter kan modellera komplexitet. Således förblir rollen som linjära regioner har i att förklara modellförmåga diffus.

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This thesis investigates some problems related to the form and shape of statistical distributions with the main focus on goodness of fit and bump hunting. A bump is a distinctive characteristic of distributional shape. A search for bumps, or bump hunting, in a probability density function (PDF) has long been an important topic in statistical research. We introduce a new definition of a bump which relies on the notion of the curvature of a planar curve. We then propose a new method for bump hunting which is based on a kernel density estimator of the unknown PDF. The method gives not only the number of bumps but also the location of their centers and base points. In quantitative risk applications, the selection of distributions that properly capture upper tail behavior is essential for accurate modeling. We study tests of distributional form, or goodness-of-fit (GoF) tests, that assess simple hypotheses, i.e., when the parameters of the hypothesized distribution are completely specified. From theoretical and practical perspectives, we analyze the limiting properties of a family of weighted Cramér-von Mises GoF statistics W2 with weight function psi(t)=1/(1-t)^beta (for beta<=2) which focus on the upper tail. We demonstrate that W2 has no limiting distribution. For this reason, we provide a normalization of W2 that leads to a non-degenerate limiting distribution. Further, we study W2 for composite hypotheses, i.e., when distributional parameters must be estimated from a sample at hand. When the hypothesized distribution is heavy-tailed, we examine the finite sample properties of W2 under the Chen-Balakrishnan transformation that reduces the original GoF test (the direct test) to a test for normality (the indirect test). In particular, we compare the statistical level and power of the pairs of direct and indirect tests. We observe that decisions made by the direct and indirect tests agree well, and in many cases they become independent as sample size grows. / Thesis / Doctor of Philosophy (PhD)

Cancer metastasis, the spread of cancer from the primary site to distant regions in the body, is the major cause of cancer mortality, accounting for almost 90% of cancer related deaths. During metastasis, cancer cells from the primary tumor initially probe the surrounding fibrous tumor microenvironment (TME) prior to detaching and subsequently migrating towards the blood vessels for further dissemination. It has widely been acknowledged that the biophysical cues provided by the fibrous TME greatly facilitate the metastatic cascade. Consequently, there has been a tremendous wealth of work devoted towards elucidating different modes of cancer cell migration. However, our knowledge of how cancer cells at the primary tumor site initially sense their fibrous surroundings prior to making the decision to detach and migrate remains in infancy. In part, this is due to the lack of a fibrous in vitro platform that allows for precise, repeatable manipulation of fiber characteristics. In this study, we use the non-electrospinning, Spinneret based Tunable Engineered Parameters (STEP) technique to manufacture suspended nanofiber networks with exquisite control on fiber dimensions and network architecture and use these networks to investigate how single cancer cells biophysically sense fibers mimicking in vivo dimensions. Using high spatiotemporal resolution imaging (63x magnification/1-second imaging interval), we report for the first time, that cancer cells sense individual fibers by coiling (i.e. wrapping around the fiber axis) at the tip of a cell protrusion. We find that coiling dynamics are mediated by both the fiber curvature and the metastatic capacity of the cancer cells with less aggressive cancer cells showing diminished coiling. Based on these results, we explore the possibility of using coiling in conjunction with other key biophysical metrics such as cell migration dynamics and forces exerted in the development of a genetic marker independent, biophysical predictive tool for disease progression. Finally, we identify the membrane curvature sensing Insulin Receptor tyrosine kinase Substrate protein of 53 kDa (IRSp53) as a key regulator of protrusive activity with IRSp53 knockout (KO) cells exhibiting significantly slower protrusion dynamics and diminished coil width compared to their wild-type (WT) counterparts. We demonstrate that the hindered protrusive activity ultimately translates to impaired contractility, alteration in the nucleus shape and slower migration dynamics, thus highlighting the unique role of IRSp53 as a signal transducer – linking the protrusive activity at the cell membrane to changes in cytoskeletal contractility. Overall, these findings offer novel perspectives to our understanding of how cancer cells biophysically sense their fibrous surroundings. The results from this study could ultimately pave the way for elucidating the precise fiber configurations that either facilitate or hinder cancer cell invasion, allowing for the development of new therapeutics in the long term that could inhibit the metastatic cascade at a relatively nascent stage and yield a more promising prognosis in the perennial fight against cancer. / Doctor of Philosophy / Cancer is a leading cause of death worldwide. Almost ninety percent of cancer related deaths arise from the spreading of cancer cells from the primary tumor site to secondary sites in the body – a processed termed as metastasis. The environment surrounding a tumor (tumor microenvironment) is highly fibrous in nature and can assist in the metastatic process by providing biophysical cues to the cells at the tumor boundary. These cells sense the presence of the surrounding fibers by extending "arms" termed as protrusions, and then eventually detach from the primary tumor and start migrating through the fibrous microenvironment. While numerous studies have investigated the various modes of cell migration in fibrous environments, there is very little information regarding how cancer cells use protrusions to initially sense the fibers prior to detaching. In this study, we used the Spinneret based Tunable Engineered Parameters (STEP) technique to manufacture suspended nanofiber networks with robust control on fiber diameter and network architecture and use these networks to systematically investigate how single cancer cells biophysically sense fibers that mimic in vivo dimensions. We discovered that cancer cells sense individual fibers by "wrapping-around" the axis of the fiber at the tip of the protrusion – a phenomenon we refer to as coiling. We found both the fiber diameter as well as the invasive capacity of cells can influence the coiling mechanics. Based on these results, we explored the use of coiling in conjunction with other key biophysical metrics such as the cell migration speed and how much force a cell can exert to develop a biophysical predictor for cancer cell aggressiveness. Finally, given that cells sense the fiber curvature by coiling, we explored the role of a key curvature sensing protein Insulin Receptor tyrosine kinase Substrate protein of 53 kDa (IRSp53) in mediating coiling activity and found that knocking out (KO) IRSp53 results in reduced coiling and slower protrusions compared to wild-type (WT) cells. Furthermore, IRSp53 KO cells showed impaired contractility which led to an alteration in the nucleus shape and slower migration dynamics thus highlighting the role of IRSp53 in linking changes at the cell membrane to the underlying cell cytoskeleton. The results from this study could ultimately help us understand what type of fiber conditions around a primary tumor would either help or delay the emergence of the tumor boundary cells and thus allow for the development of therapeutics that could significantly slow down the metastatic process at a relatively early stage.

Final thesis describes two variants of computational models to simulate cross-roll leveling of rods, which are based on the Lagrangian approach to describe the continuum. Implementation of both variants was performed in ANSYS software, and their main difference lies in the choice of the type of elements for the discretization. An integral part of this thesis is the description of the principle, which is an evaluation of the curvature of the rod after completion of the simulation leveling. In the other part of the work are presented the results, which are then compared with realized experiment and simulation algorithm for cross-roll leveling based on the Euler approach. The final part is dedicated to the optimal settings of the leveller.

<p dir="ltr">In Chapter 1, we introduce geometric motions from the general perspective of gradient flows. Here we develop the basic framework in which to pose the two main results of this thesis.</p><p dir="ltr">In Chapter 2, we examine the pinch-off phenomenon for a tubular surface moving by surface diffusion. We prove the existence of a one parameter family of pinching profiles obeying a long wavelength approximation of the dynamics.</p><p dir="ltr">In Chapter 3, we study a diffusion-based numerical scheme for curve shortening flow. We prove that the scheme is one time-step consistent.</p>