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The vaginal prolapse after hysterectomy (removal of the uterus) is often associated with the prolapse of the vaginal vault, rectum, bladder, urethra or small bowel. Minimally
invasive surgery such as laparoscopic sacrocolpopexy and pectopexy are widely performed for the treatment of the vaginal prolapse with weakly supported vaginal vault after hysterectomy using prosthetic mesh implants to support (or strengthen) lax apical ligaments. Implants of different shape, size and polymers are selected depending on the patient’s anatomy and the surgeon’s preference. In this computational study on pectopexy, DynaMesh®-PRP soft, GYNECARE GYNEMESH® PS Nonabsorbable PROLENE® soft and Ultrapro® are tested in a 3D finite element model of the female pelvic floor. The mesh model is implanted into the extraperitoneal space and sutured to the vaginal stump with a bilateral fixation to the iliopectineal ligament at both sides. Numerical simulations are conducted at rest, after surgery and during Valsalva maneuver with weakened tissues modeled by reduced tissue stiffness. Tissues and prosthetic meshes are modeled as incompressible, isotropic hyperelastic materials. The positions of the organs are calculated with respect to the pubococcygeal line (PCL) for female pelvic floor at rest, after repair and during Valsalva maneuver using the three meshes.
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.
In energy economy forecasts of different time series are rudimentary. In this study, a prediction for the German day-ahead spot market is created with Apache Spark and R. It is just an example for many different applications in virtual power plant environments. Other examples of use as intraday price processes, load processes of machines or electric vehicles, real time energy loads of photovoltaic systems and many more time series need to be analysed and predicted.
This work gives a short introduction into the project where this study is settled. It describes the time series methods that are used in energy industry for forecasts shortly. As programming technique Apache Spark, which is a strong cluster computing technology, is utilised. Today, single time series can be predicted. The focus of this work is on developing a method to parallel forecasting, to process multiple time series simultaneously with R and Apache Spark.
We propose a stochastic programming method to analyse limit and shakedown of structures under random strength with lognormal distribution. In this investigation a dual chance constrained programming algorithm is developed to calculate simultaneously both the upper and lower bounds of the plastic collapse limit or the shakedown limit. The edge-based smoothed finite element method (ES-FEM) using three-node linear triangular elements is used.
Electromechanical model of hiPSC-derived ventricular cardiomyocytes cocultured with fibroblasts
(2018)
The CellDrum provides an experimental setup to study the mechanical effects of fibroblasts co-cultured with hiPSC-derived ventricular cardiomyocytes. Multi-scale computational models based on the Finite Element Method are developed. Coupled electrical cardiomyocyte-fibroblast models (cell level) are embedded into reaction-diffusion equations (tissue level) which compute the propagation of the action potential in the cardiac tissue. Electromechanical coupling is realised by an excitation-contraction model (cell level) and the active stress arising during contraction is added to the passive stress in the force balance, which determines the tissue displacement (tissue level). Tissue parameters in the model can be identified experimentally to the specific sample.
The overall objective of this study is to develop a new external fixator, which closely maps the native kinematics of the elbow to decrease the joint force resulting in reduced rehabilitation time and pain. An experimental setup was designed to determine the native kinematics of the elbow during flexion of cadaveric arms. As a preliminary study, data from literature was used to modify a published biomechanical model for the calculation of the joint and muscle forces. They were compared to the original model and the effect of the kinematic refinement was evaluated. Furthermore, the obtained muscle forces were determined in order to apply them in the experimental setup. The joint forces in the modified model differed slightly from the forces in the original model. The muscle force curves changed particularly for small flexion angles but their magnitude for larger angles was consistent.