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Recently, a new Quicksort variant due to Yaroslavskiy was chosen as standard sorting
method for Oracle's Java 7 runtime library. The decision for the change was based on
empirical studies showing that on average, the new algorithm is faster than the formerly
used classic Quicksort. Surprisingly, the improvement was achieved by using a dual pivot
approach — an idea that was considered not promising by several theoretical studies in the
past. In this thesis, I try to find the reason for this unexpected success.
My focus is on the precise and detailed average case analysis, aiming at the flavor of
Knuth's series “The Art of Computer Programming”. In particular, I go beyond abstract
measures like counting key comparisons, and try to understand the efficiency of the
algorithms at different levels of abstraction. Whenever possible, precise expected values are
preferred to asymptotic approximations. This rigor ensures that (a) the sorting methods
discussed here are actually usable in practice and (b) that the analysis results contribute to
a sound comparison of the Quicksort variants.
Palladium-Catalyzed C–C Bond Formations via Activation of Carboxylic Acids and Their Derivatives
(2013)
Applications of carboxylic acids and their derivatives in transition metal-catalyzed cross-coupling reactions regio-selectively forming Csp3-Csp2, and Csp2-Csp2 bonds were explored in this thesis. Several important organic building blocks such as aryl acetates, diaryl acetates, imines, ketones, biaryls, styrenes and polysubstituted alkenes were successfully accessed from carboxylic acids and their derivatives by the means of C–H activation and decarboxylative cross-couplings.
An efficient and practical protocol for the synthesis of biologically important ethyl 2-arylacates through the dealkoxycarbonlative cross-coupling reaction between aryl halides and malonates was developed. Activation of the alpha-proton of alkyl esters by a copper catalyst allowed the deprotonation of esters even in the presence of mild bases, leading to a straightforward and efficient approach to alkyl alpha-diarylacetate from simple alkyl acetates and aryl halides.
The addition of a primary amine into the coupling reaction of alpha-oxocarboxylic acids and aryl halides led to an unprecedented low-temperature redox-neutral decarboxylative coupling process, providing a green and efficient method for the preparation of azomethines, in which all the three substituents can be independently varied. A minor modification of this protocol allowed us to easily access the corresponding ketones.
The decarboxylative coupling of robust aryl mesylates as well as polysubstituted alkenyl mesylates using our customized imidazolyl phosphine ligands was realized, further expanding the scope of carbon electrophiles in decarboxylative coupling reactions. Variation of the ligands led to two complementary protocols, providing the corresponding biaryls and polysubstituted olefins in high yields.
The use of a new class of pyrimidinyl phosphine ligands dramatically reduced the reaction temperatures of decarboxylative cross-coupling reactions between aromatic carboxylic acids and aryl or alkenyl triflates. The new catalyst system for the first time allowed the efficient decarboxylative biaryls synthesis at only 100 °C, representing a significant achievement in redox-neutral decarboxylative coupling reactions.
In this paper we consider a multivariate switching model, with constant states means
and covariances. In this model, the switching mechanism between the basic states of
the observed time series is controlled by a hidden Markov chain. As illustration, under
Gaussian assumption on the innovations and some rather simple conditions, we prove
the consistency and asymptotic normality of the maximum likelihood estimates of the model parameters.
There is growing international concern about the necessity to re-think the university so that it might remain relevant in a modern society. In the traditional task division at universities, knowledge is the main resource. Universities make use of both the cognitive and the informational approach. It was expected that universities use each approach to improve overall university performance. To effectively use the informational approach, universities should apply the tools from knowledge management. To effectively use the cognitive approach, universities must update their teaching-learning strategies to incorporate some of the recent advances in neuroscience and biology of knowledge, specifically from neurobiology and autopoiesis. With this frame, the main contribution of this work is the result of merging pedagogy and biology, towards an ideal future university. This goal was achieved through an exploratory study conducted to identify opportunities and difficulties in improving the teaching-learning process for the future of higher education in Honduras. The Delphi Study was used as a predictive method. Nineteen Honduran experts participated in this study, and two rounds were necessary to achieve consensus.
The multi-disciplinary approach of this research addresses three different fields whose core element is knowledge. First, input from the present field of higher education is used to speak about the future. Second, input is taken from the biology of knowledge, and its contributions from neurobiology and autopoiesis that allow modifying and completing the already existing learning theories with a biological basis. Third, input is taken from the knowledge process, which is traditionally used as an organizational tool and know is translated to the individual level. The exploration shows that experts are concerned about all the missions and responsibilities of universities, but they agree that changes should primarily take place in the teaching dimension. Even though they are not aware of the possible contributions of biology, they suggest new forms of teaching that more favor skills development, promotes values, pertinent knowledge, and personal development over short-term contents. The resulting BRAIN Model encompasses the ideal future of higher education regarding teaching and learning, according to experts’ answers. It provides a useful guide that any reform in teaching should take into account for a holistic, integral, and therefore more efficient learning task.
Congress Report 2012.11-12
(2012)
Congress Report 2012.09-10
(2012)
Congress Report 2012.03-05
(2012)
Fluid extraction is a typical chemical process where two types of fluids are mixed together. The high complexity of this process which involves droplet coalescence, breakup, mass transfer, and counter-current flow often makes design difficult. The industrial design of these processes is still based on expensive mini-plant and pilot plant experiments. Therefore, there is a strong need for research into the stimulation of fluid-fluid interaction processes using computational fluid dynamics (CFD).
Previous multi-phase fluid simulations have focused on the development of models that couple mass and momentum using the Navier-Stokes equation. Recent population balance models (PBM) have proved to be important methods for analyzing droplet breakage and collisions. A combination of CFD and PBM facilitates the simulation of flow property by solving coupling equations, and the calculation of the droplet size and numbers. In our study, we successfully coupled an Euler-Euler CFD model with the breakup and coalescence models proposed by Luo and Svendsen (59).
The simulation output of extraction columns provides a mathematical understand- ing of how fluids are mixed inside a mixing device. This mixing process shows that the dispersed phase of a flow generates large blobs and bubbles. Current mathemati- cal simulation results often fail to provide an intuitive representation of how well two different types of fluid interact, so intuitive and physically plausible visualization tech- niques are in high demand to help chemical engineers to explore and analyze bubble column simulation data. In chapter 3, we present the visualization tools we developed for extraction column data.
Fluid interfaces and free surfaces are topics of growing interest in the field of multi- phase computational fluid dynamics. However, the analysis of the flow field relative to the material interface shape and topology is a challenging task. In chapter 5, we present a technique that facilitates the visualization and analysis of complex material interface behaviors over time. To achieve this, we track the surface parameterization of time-varying material interfaces and identify locations where there are interactions between the material interfaces and fluid particles. Splatting and surface visualization techniques produce an intuitive representation of the derived interface stability. Our results demonstrate that the interaction of a flow field with a material interface can be understood using appropriate extraction and visualization techniques, and that our techniques can help the analysis of mixing and material interface consistency.
In addition to texture-based methods for surface analysis, the interface of two- phase fluid can be considered as an implicit function of the density or volume fraction values. High-level visualization techniques such as topology-based methods can re- veal the hidden structure underlying simple simulation data, which will enhance and advance our understanding of multi-fluid simulation data. Recent feature-based vi- sualization approaches have explored the possibility of using Reeb graphs to analyze scalar field topologies(19, 107). In chapter 6, we present a novel interpolation scheme for interpolating point-based volume fraction data and we further explore the implicit fluid interface using a topology-based method.