Modification of Iron with Degradable Silver Phases Processed via Laser Beam Melting for Implants with Adapted Degradation Rate

In medical technology, implants are used to improve the quality of patients’ lives. The development of materials with adapted properties can further increase the benefit of implants. If implants are only needed temporarily, biodegradable materials are beneficial. In this context, iron-based materials are promising due to their biocompatibility and mechanical properties, but the degradation rate needs to be accelerated. Apart from alloying, the creation of noble phases to cause anodic dissolution of the iron-based matrix is promising. Due to its high electrochemical potential, immiscibility with iron, biocompatibility, and antibacterial properties, silver is suited for the creation of such phases. A suitable technology for processing immiscible material combinations is powder-bed-based procedure like laser beam melting. This procedure offers short exposure times to high temperatures and therefore a limited time for diffusion of alloying elements. As the silver phases remain after the dissolution of the iron matrix, a modification is needed to ensure their degradability. Following this strategy, pure iron with 5 wt% of a degradable silver–calcium–lanthanum alloy is processed via laser beam melting. Investigation of the microstructure yields achievement of the intended microstructure and long-term degradation tests indicates an impact on the degradation, but no increased degradation rate.     

Characterization of Cementitious Materials Exposed to Freezing and Thawing in Combination with Deicing Salts Using 3D Scans

Surface deterioration of concrete subjected to freezing and thawing in combination with deicing salts is one of the most important factors determining the durability of concrete infrastructure in cold climates. The freeze–thaw deicing salt (FTDS) resistance of cementitious materials can be determined by the capillary suction of de-icing chemicals and freeze–thaw (CDF) test. Specimens are subjected to repeated freeze–thaw cycles with simultaneous addition of deicing salt and the amount of material scaled off near the surface is determined. For concretes with adequate FTDS resistance, this test method works very well. However, specimens with unknown performance often experience increased edge scaling. This leads to a falsification of results and consequently to an underestimation of the actual freeze–thaw resistance. In materials research, however, concretes with high levels of surface deterioration are studied in order to investigate various factors of influence on the freeze–thaw resistance of concretes in a targeted manner. This article presents a novel methodology that delivers new information regarding surface deterioration of CDF samples using high-resolution 3D scan data. Change of volume is used to support deterioration results of the standard CDF methodology. Increase of surface area is used to estimate change in roughness of samples.     

GPU-based real-time approximation of the ablation zone for radiofrequency ablation

Percutaneous radiofrequency ablation (RFA) is becoming a standard minimally invasive clinical procedure for the treatment of liver tumors. However, planning the applicator placement such that the malignant tissue is completely destroyed, is a demanding task that requires considerable experience. In this work, we present a fast GPU-based real-time approximation of the ablation zone incorporating the cooling effect of liver vessels. Weighted distance fields of varying RF applicator types are derived from complex numerical simulations to allow a fast estimation of the ablation zone. Furthermore, the heat-sink effect of the cooling blood flow close to the applicator's electrode is estimated by means of a preprocessed thermal equilibrium representation of the liver parenchyma and blood vessels. Utilizing the graphics card, the weighted distance field incorporating the cooling blood flow is calculated using a modular shader framework, which facilitates the real-time visualization of the ablation zone in projected slice views and in volume rendering. The proposed methods are integrated in our software assistant prototype for planning RFA therapy. The software allows the physician to interactively place virtual RF applicator models. The real-time visualization of the corresponding approximated ablation zone facilitates interactive evaluation of the tumor coverage in order to optimize the applicator's placement such that all cancer cells are destroyed by the ablation.     

Modelling water dynamics with DNDC and DAISY in a soil of the North China Plain: A comparative study

The performance of the DNDC and Daisy model to simulate the water dynamics in a floodplain soil of the North China Plain was tested and compared. While the DNDC model uses a simple cascade approach, the Daisy model applies the physically based Richard's equation for simulating water movement in soil. For model testing a three years record of the soil water content from the Dong Bei Wang experimental station near Beijing was used. There, the effect of nitrogen fertilization, irrigation and straw removal on soil water and nitrogen dynamics was investigated in a three factorial field experiment applying a split-split-plot design with 4 replications. The dataset of one treatment was used for model testing and calibration. Two other independent datasets from further treatments were employed for validating the models. For both models, the simulation results were not satisfying using default parameters. After parameter optimisation and the use of site-specific van Genuchten parameters, however, the Daisy model performed well. But, for the DNDC model, parameter optimisation failed to improve the simulation result. Owing to the fact that many biological processes such as plant growth, nitrification or denitrification depend strongly on the soil water content, our findings bring us to the conclusion that the site-specific suitability of the DNDC model for simulating the soil water dynamics should be tested before further simulation of other processes.     

Shape from Shading Using Probability Functions and Belief Propagation

Shape-from-shading (SFS) aims to reconstruct the three-dimensional shape of an object from a single shaded image. This article proposes an improved framework based on belief propagation for computing SFS. The implementation of the well-known brightness, integrability and smoothness constraints inside this framework is shown. We implement the constraints as probability density functions. For example, the brightness constraint is a two-dimensional probability density function that relates all possible surface gradients at a pixel to their probability given the pixel intensity. A straightforward extension of the framework to photometric stereo is presented, where multiple images of the same scene taken under different lighting conditions are available. The results are promising, especially since the solution is obtained by iteratively applying simple operations on a regular grid of points. The presented framework therefore can be implemented in parallel and is a reasonably likely biological scheme.     

Cognitive agents — a procedural perspective relying on the predictability of Object-Action-Complexes (OACs)

Embodied cognition suggests that complex cognitive traits can only arise when agents have a body situated in the world. The aspects of embodiment and situatedness are being discussed here from the perspective of linear systems theory. This perspective treats bodies as dynamic, temporally variable entities, which can be extended (or curtailed) at their boundaries. We show how acting agents can, for example, actively extend their body for some time by incorporating predictably behaving parts of the world and how this affects the transfer functions. We suggest that primates have mastered this to a large degree increasingly splitting their world into predictable and unpredictable entities. We argue that temporary body extension may have been instrumental in paving the way for the development of higher cognitive complexity as it is reliably widening the cause-effect horizon about the actions of the agent. A first robot experiment is sketched to support these ideas.We continue discussing the concept of Object-Action Complexes (OACs) introduced by the European PACO-PLUS consortium to emphasize the notion that, for a cognitive agent, objects and actions are inseparably intertwined. In another robot experiment we devise a semi-supervised procedure using the OAC-concept to demonstrate how an agent can acquire knowledge about its world. Here the notion of predicting changes fundamentally underlies the implemented procedure and we try to show how this concept can be used to improve the robot’s inner model and behaviour. Hence, in this article we have tried to show how predictability can be used to augment the agent’s body and to acquire knowledge about the external world, possibly leading to more advanced cognitive traits.     

Aerodynamic optimization via multi-objective micro-genetic algorithm with range adaptation,knowledge-based reinitialization,crowding and ε-dominance

In this study, an optimization of the airfoil of a sailplane is carried out by a recently developed multi-objective genetic algorithm based on microevolution, containing crowding, range adaptation, knowledge-based reinitialization and ε-dominance. Its efficiency was tested on a set of test problems. The results are encouraging, suggesting that very small populations can be used effectively to solve real-world multi-objective optimization problems in many cases of interest.     

First-order and second-order statistical analysis of 3D and 2D image structure

In the first part of this article, we analyze the relation between local image structures (i.e., homogeneous, edge-like, corner-like or texture-like structures) and the underlying local 3D structure (represented in terms of continuous surfaces and different kinds of 3D discontinuities) using range data with real-world color images. We find that homogeneous image structures correspond to continuous surfaces, and discontinuities are mainly formed by edge-like or corner-like structures, which we discuss regarding potential computer vision applications and existing assumptions about the 3D world. In the second part, we utilize the measurements developed in the first part to investigate how the depth at homogeneous image structures is related to the depth of neighbor edges. For this, we first extract the local 3D structure of regularly sampled points, and then, analyze the coplanarity relation between these local 3D structures. We show that the likelihood to find a certain depth at a homogeneous image patch depends on the distance between the image patch and a neighbor edge. We find that this dependence is higher when there is a second neighbor edge which is coplanar with the first neighbor edge. These results allow deriving statistically based prediction models for depth interpolation on homogeneous image structures.     

Kernel least-squares models using updates of the pseudoinverse

Sparse nonlinear classification and regression models in reproducing kernel Hilbert spaces (RKHSs) are considered. The use of Mercer kernels and the square loss function gives rise to an overdetermined linear least-squares problem in the corresponding RKHS. When we apply a greedy forward selection scheme, the least-squares problem may be solved by an order-recursive update of the pseudoinverse in each iteration step. The computational time is linear with respect to the number of the selected training samples.