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Suburethral slings as well as different meshes are widely used treating stress urinary incontinence and prolaps in women. With the development of MiniSlings and special meshes using less alloplastic material anchorage systems become more important to keep devices in place and to put some tension especially on the MiniSlings. To date, there are many different systems of MiniSlings of different companies on the market which differ in the structure of the used meshes and anchors. A new objective measurement method to compare different properties of MiniSling systems (mesh and anchor) is presented in this article. Ballistic gelatine acts as soft tissue surrogate. Significant differences in parameters like pull-out strength of anchors or shrinkage of meshes under loading conditions have been determined. The form and size of the anchors as well as the structural stability of the meshes are decisive for a proper integration. The tested anchorings sytems showed markedly different mechanical function at their respective load bearing capacity. As the stable fixation of the device in tissue is a prerequisite for a permanet reinforcement, the proposed test system permits further optimisation of anchor and mesh devices to improve the success of the surgical treatment
We present new numerical results for shape optimization problems of interior Neumann eigenvalues. This field is not well understood from a theoretical standpoint. The existence of shape maximizers is not proven beyond the first two eigenvalues, so we study the problem numerically. We describe a method to compute the eigenvalues for a given shape that combines the boundary element method with an algorithm for nonlinear eigenvalues. As numerical optimization requires many such evaluations, we put a focus on the efficiency of the method and the implemented routine. The method is well suited for parallelization. Using the resulting fast routines and a specialized parametrization of the shapes, we found improved maxima for several eigenvalues.
New side-bands in ¹³⁴ Ce
(1981)
This paper presents NLP Lean Programming
framework (NLPf), a new framework
for creating custom natural language processing
(NLP) models and pipelines by utilizing
common software development build systems.
This approach allows developers to train and
integrate domain-specific NLP pipelines into
their applications seamlessly. Additionally,
NLPf provides an annotation tool which improves
the annotation process significantly by
providing a well-designed GUI and sophisticated
way of using input devices. Due to
NLPf’s properties developers and domain experts
are able to build domain-specific NLP
applications more efficiently. NLPf is Opensource
software and available at https://
gitlab.com/schrieveslaach/NLPf.
Nobody ever dies! / 1. ed.
(2000)