Estimating affinities of calcium ions to proteins

Ca2+-ions have a range of affinities to different proteins, depending on the various functions of these proteins. This makes the determination of Ca2+-protein affinities an interesting subject for functional studies. We have investigated the performance of two methods – Fold-X and AutoDock vina – in the prediction of Ca2+-protein affinities. Both methods, although based on different energy functions, showed virtually the same correlation with experimental affinities. Guided by insight from experiment, we further derived a simple linear model based on the solvent accessible surface of Ca2+ that had practically the same performance in terms of absolute errors as the more complex docking methods

Predictive Cost Analytics of Vehicle Assemblies Based on Machine Learning in the Automotive Industry

Due to the high pace of development in the automotive industry, there is a need for innovating cost engineering. A methodology for intelligent cost estimation in the early stages of the product life cycle is introduced. In a first step it is shown how significant economic and technical parameters for cost prediction can be prepared and filtered from historical calculation data. Subsequently, it is shown how cost prediction models can be developed using machine learning algorithms. Learning data and practical use cases come from a large automotive manufacturer in Germany. The models predict the costs of car parts and assemblies of increasing complexity. Seven different machine learning models are trained and optimized. Based on the test data of the use cases these models are assessed and compared. Finally, the prediction results obtained are evaluated from different perspectives, demonstrating the practical applicability of the most suitable methods explored

Application of Thomson scattering at 1.06mm as a diagnostic for spatial profile measurements of electron temperature and density on the TCV tokamak

The variable configuration tokamak, TCV, in operation at CRPP since the end of 1991, is a particularly challenging machine with regard to the experimental systems that must provide essential information regarding properties of confined plasmas with strongly shaped, non-circular cross-sections. Although TCV is unique in its capacity for the study of magnetic equilibria not previously examined in modem, large tokamaks, this flexibility poses serious problems for the experimentalist who may be required, for example, to make measurements in completely different configurations from one discharge to the next. Highly shaped plasmas also render more complex, or even impossible, the application of inversion techniques for the recovery of plasma profiles based on chordal measurements which necessarily yield line averaged quantities. The importance of the energy confinement issue in a machine designed specifically for the investigation of the effect of plasma shape on confinement and stability is self-evident, as is the necessity for a diagnostic capable of providing the profiles of electron temperature and density required for evaluation of this confinement. For TCV, a comprehensive Thomson Scattering (TS) diagnostic was the natural choice, specifically owing to the resulting spatially localized and time resolved measurement. The details of the system installed on TCV, together with the results obtained from the diagnostic comprise the subject matter of this thesis. A first version of the diagnostic was equipped with only ten observation volumes. In this case, adequate spatial resolution can only be maintained if measurements are limited to plasmas located in the upper half of the highly elongated TCV vacuum vessel. The system has recently been upgraded through the addition of a further fifteen observation volumes, together with major technical improvements in the scattered light detection system. This new version now permits TS observations in all TCV plasma configurations, including equilibria produced in the lower and upper halves of the vacuum vessel and the highly elongated plasmas now routinely created (κ=2.47 is the maximum elongation achieved at the time of writing). Whilst a description of the new detection system along with some results obtained using the extended set of observation volumes are included, this thesis reports principally on the hardware details of and the interpretation of data from the original, ten observation volume system. The complexity of the TCV Thomson Scattering system can only be effectively conveyed through considerable descriptive effort and such details can be found in the earlier chapters of this work. Effort is also required if the set of discrete data points constituting the profile is to be effectively fitted over the wide range of profile shapes encountered in TCV. For this purpose, a number of analysis routines have been developed during the course of this research with which TS profile data can be reliably fitted with a minimum of user intervention. These routines are based on cubic spline interpolation within a normalized poloidal flux coordinate system facilitating the comparison of TS data with the results of other TCV diagnostics. However complex a given tokamak diagnostic may be, its primary purpose is, of course, to provide relevant data for use in understanding the results obtained from any particular experimental campaign. The hardware descriptions and data analysis techniques of the earlier chapters thus give way, in the second half of this thesis, to a series of studies dedicated to the use of TS data for physics understanding. The absence of an additional heating system on TCV throughout the duration of this research, necessarily limits the scope of such studies to the case of ohmic plasmas only. Some effort is devoted to an investigation of the extent to which the phenomena of profile consistency in ohmically heated discharges is observed in TCV. In general, if a form for the edge safety factor appropriate to shaped plasmas is adopted, the effect does appear to prevail, at least for elongations up to κ= 1.9 and for plasma triangularities in the range -0.4<δ<0.7. An area constituting high priority in the TCV experimental programme is the study of the effect of plasma shape on energy confinement. In this case, TS profiles are of the utmost importance since the current absence of experimental information regarding the ion temperature and density in TCV precludes a reliable estimate of anything but the electron energy confinement time. Analysis of changes in the electron temperature gradient near the plasma edge as a function, in particular, of plasma triangularity, shows that the observed decrease in energy confinement time with increasing δ can be explained in terms of a combination of geometrical effects and heat flux degradation. The important question of how the inclusion of TS electron pressure profiles may modify or improve the results of the TCV equilibrium reconstruction algorithm, LIUQE, is also addressed. Such reconstructions are presently computed solely on the basis of magnetic measurements, but often lead to reconstituted total pressure profiles (and hence energy confinement times) in clear contradiction with the TS electron pressure profiles. Since ion pressure profile measurements are unavailable, the use of TS data as input to LIUQE can only be performed if the Thomson profiles are combined with assumed ion pressure profiles. These theoretical profiles depend, in turn, on the assumed mechanism of ion transport. An attempt to model this transport, together with a presentation of the effect of additional experimental constraints on the results of equilibrium reconstructions constitute the material of the final chapter of this thesis

Charakterisierung und Regulation der stofflichen Zusammensetzung der Apoplastenlösung in den Gefäßleitbahnen der Sprossachsen von Populus balsamifera L.

(Keine Zusammenfassung hinterlegt.

The Cityscapes Dataset for Semantic Urban Scene Understanding

Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations; 20000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.Comment: Includes supplemental materia

Non-Sequential Ensemble Kalman Filtering using Distributed Arrays

This work introduces a new, distributed implementation of the Ensemble Kalman Filter (EnKF) that allows for non-sequential assimilation of large datasets in high-dimensional problems. The traditional EnKF algorithm is computationally intensive and exhibits difficulties in applications requiring interaction with the background covariance matrix, prompting the use of methods like sequential assimilation which can introduce unwanted consequences, such as dependency on observation ordering. Our implementation leverages recent advancements in distributed computing to enable the construction and use of the full model error covariance matrix in distributed memory, allowing for single-batch assimilation of all observations and eliminating order dependencies. Comparative performance assessments, involving both synthetic and real-world paleoclimatic reconstruction applications, indicate that the new, non-sequential implementation outperforms the traditional, sequential one