Open Access

A growing share of modern trade policy instruments is shaped by non-tariff barriers (NTBs). Based on a structural gravity equation and the recently updated Global Trade Alert database, we empirically investigate the effect of NTBs on imports. Our analysis reveals that the implementation of NTBs reduces imports of affected products by up to 12%. Their trade dampening effect is thus comparable to that of trade defence instruments such as anti-dumping duties. It is smaller for exporters that have a free trade agreement with the importing country. Different types of NTBs affect trade to a different extent. Finally, we investigate the effect of behind-the-border measures, showing that they significantly lower the importer’s market access.

This work aims at investigating the magnetic effects of austenitc stainless steels which can occur after a low temperature carburisation depending on the alloy. Samples were prepared of different alloys and subjected to a multiple low temperature carburisation to obtain different treatment conditions for each alloy. The layer characterisation was carried out by light microscope and also by hardening profiles and shows that the layer develops with each additional treatment cycle. A lattice expansion could be detected in all treated samples by X-ray diffraction. Magnetisability was measured using Feritscope and SQUID measurements. Not all alloys showed magnetisability after treatment. In addition to MFM measurements, experiments with Ferrofluid were also used to visualize the magnetic areas. These studies show that only about half of the formed layer becomes magnetisable and has a domain-like structure.

Knot placement for curve approximation is a well known and yet open problem in geometric modeling. Selecting knot values that yield good approximations is a challenging task, based largely on heuristics and user experience. More advanced approaches range from parametric averaging to genetic algorithms. In this paper, we propose to use Support Vector Machines (SVMs) to determine suitable knot vectors for B-spline curve approximation. The SVMs are trained to identify locations in a sequential point cloud where knot placement will improve the approximation error. After the training phase, the SVM can assign, to each point set location, a so-called score. This score is based on geometric and differential geometric features of points. It measures the quality of each location to be used as knots in the subsequent approximation. From these scores, the final knot vector can be constructed exploring the topography of the score-vector without the need for iteration or optimization in the approximation process. Knot vectors computed with our approach outperform state of the art methods and yield tighter approximations.