Thermodynamic analysis of the δ ferrite in the systems Fe-Cr-N and Fe-Cr-Mn-N

The solubility of nitrogen in ferritic iron-chromium alloys with mass contents of between 6.08 to 23.94 % chromium was measured in the temperature range 1523 to 1773 K. Combining the results with published data for the nitrogen solubility in iron-chromium, improved parameters describing the chromium-nitrogen interaction were derived. The Gibbs free excess energy can be described by . Calculations of the nitrogen solubility using this equation and calculations using parameters published by the Swedish Institute of Metals Research differ the more the higher the temperature. A repeated analysis of measurements of the nitrogen solubility in ferritic Fe-Cr-Mn alloys using the Gibbs free excess energy derived in this investigation for the chromium-nitrogen interaction leads to the manganese-nitrogen interaction energy .     

Stability and passivity preserving Petrov–Galerkin approximation of linear infinite-dimensional systems

This contribution presents two approximation methods for linear infinite-dimensional systems that ensure the preservation of stability and passivity. The first approach allows one to approximate internal source free infinite-dimensional systems such that the resulting approximation is a port-controlled Hamiltonian system with dissipation. The second method deals with the class of systems that are not required to have conjugated outputs but only a dissipative system operator. It yields approximations with a dissipative system matrix for which bounds of their stability margin are provided. Both approaches are based on a state space formulation of the infinite-dimensional system. This makes it possible to use the Petrov–Galerkin approximation whose free parameters are partly used for achieving the structure preservation. Since still free parameters remain, further application specific objectives, such as, e.g., moment matching, can be achieved. Both approaches are applied to the approximation of an Euler–Bernoulli beam.     

A Comparison of Static Analysis and Evolutionary Testing for the Verification of Timing Constraints

This paper contrasts two methods to verify timing constraints of real-time applications. The method of static analysis predicts the worst-case and best-case execution times of a task's code by analyzing execution paths and simulating processor characteristics without ever executing the program or requiring the program's input. Evolutionary testing is an iterative testing procedure, which approximates the extreme execution times within several generations. By executing the test object dynamically and measuring the execution times the inputs are guided yielding gradually tighter predictions of the extreme execution times. We examined both approaches on a number of real world examples. The results show that static analysis and evolutionary testing are complementary methods, which together provide upper and lower bounds for both worst-case and best-case execution times.     

GCD of Random Linear Combinations

We show that for arbitrary positive integers with probability the gcd of two linear combinations of these integers with rather small random integer coefficients coincides with This naturally leads to a probabilistic algorithm for computing the gcd of several integers, with probability via just one gcd of two numbers with about the same size as the initial data (namely the above linear combinations). This algorithm can be repeated to achieve any desired confidence level.     

Complex logarithmic views for small details in large contexts

Commonly known detail in context techniques for the two-dimensional Euclidean space enlarge details and shrink their context using mapping functions that introduce geometrical compression. This makes it difficult or even impossible to recognize shapes for large differences in magnification factors. In this paper we propose to use the complex logarithm and the complex root functions to show very small details even in very large contexts. These mappings are conformal, which means they only locally rotate and scale, thus keeping shapes intact and recognizable. They allow showing details that are orders of magnitude smaller than their surroundings in combination with their context in one seamless visualization. We address the utilization of this universal technique for the interaction with complex two-dimensional data considering the exploration of large graphs and other examples     

A generic and scalable pipeline for GPU tetrahedral grid rendering

Recent advances in algorithms and graphics hardware have opened the possibility to render tetrahedral grids at interactive rates on commodity PCs. This paper extends on this work in that it presents a direct volume rendering method for such grids which supports both current and upcoming graphics hardware architectures, large and deformable grids, as well as different rendering options. At the core of our method is the idea to perform the sampling of tetrahedral elements along the view rays entirely in local barycentric coordinates. Then, sampling requires minimum GPU memory and texture access operations, and it maps efficiently onto a feed-forward pipeline of multiple stages performing computation and geometry construction. We propose to spawn rendered elements from one single vertex. This makes the method amenable to upcoming Direct3D 10 graphics hardware which allows to create geometry on the GPU. By only modifying the algorithm slightly it can be used to render per-pixel iso-surfaces and to perform tetrahedral cell projection. As our method neither requires any pre-processing nor an intermediate grid representation it can efficiently deal with dynamic and large 3D meshes.     

Probabilistic Modeling and Recognition of 3-D Objects

This paper introduces a uniform statistical framework for both 3-D and 2-D object recognition using intensity images as input data. The theoretical part provides a mathematical tool for stochastic modeling. The algorithmic part introduces methods for automatic model generation, localization, and recognition of objects. 2-D images are used for learning the statistical appearance of 3-D objects; both the depth information and the matching between image and model features are missing for model generation. The implied incomplete data estimation problem is solved by the Expectation Maximization algorithm. This leads to a novel class of algorithms for automatic model generation from projections. The estimation of pose parameters corresponds to a non-linear maximum likelihood estimation problem which is solved by a global optimization procedure. Classification is done by the Bayesian decision rule. This work includes the experimental evaluation of the various facets of the presented approach. An empirical evaluation of learning algorithms and the comparison of different pose estimation algorithms show the feasibility of the proposed probabilistic framework.     

Resource allocation with time intervals

We study a resource allocation problem where jobs have the following characteristic: each job consumes some quantity of a bounded resource during a certain time interval and induces a given profit. We aim to select a subset of jobs with maximal total profit such that the total resource consumed at any point in time remains bounded by the given resource capacity.While this case is trivially NP-hard in general and polynomially solvable for uniform resource consumptions, our main result shows the NP-hardness for the case of general resource consumptions but uniform profit values, i.e. for the case of maximizing the number of performed jobs. This result applies even for proper time intervals.We also give a deterministic (1/2−ε)-approximation algorithm for the general problem on proper intervals improving upon the currently known 1/3 ratio for general intervals. Finally, we study the worst-case performance ratio of a simple greedy algorithm.     

Mesoscale river restoration enhances the diversity of floodplain vegetation

Effective river restoration aims for the recovery of ecosystem functions by restoring processes and connectivity to the floodplain. At the straightened lowland river Stör in northern Germany, a sequence of 15 new meanders was created in 2008, with wavelengths up to 70 m. The newly created areas within the meander bends range in size from 215 to 1,115 m² and function as a series of 15 restored floodplain sites, which are subject to succession. After 7 years of restoration measures, we investigated the vegetation dynamics on the (a) restored floodplains and compared them with adjacent floodplain sites that were used as (b) low‐intensity grazed grassland or as (c) abandoned grassland. We analysed the species diversity, functional vegetation parameters, and plant communities of 200 plots within the floodplain area of the three floodplain types and of 246 plots at their river banks. Plant species diversity and composition differed with respect to restoration measure and site management. Restored floodplains revealed a higher coverage in species of wet grasslands and softwood forests and higher species diversity than abandoned grasslands. Grazed grasslands showed the highest species number and coverages of pioneer vegetation. The banks indicated fewer differences in species composition between floodplain types. The construction of restored floodplains revealed greater overall plant diversity due to promoting the development of typical floodplain vegetation. Shallow meanders with increased flooding intensity and the creation of a varying microreliefs are recommended as combined river/floodplain measures in order to foster processes and connectivity between valley components.