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Research collaborations provide opportunities for both practitioners and researchers: practitioners need solutions for difficult business challenges and researchers are looking for hard problems to solve and publish. Nevertheless, research collaborations carry the risk that practitioners focus on quick solutions too much and that researchers tackle theoretical problems, resulting in products which do not fulfill the project requirements.
In this paper we introduce an approach extending the ideas of agile and lean software development. It helps practitioners and researchers keep track of their common research collaboration goal: a scientifically enriched software product which fulfills the needs of the practitioner’s business model.
This approach gives first-class status to application-oriented metrics that measure progress and success of a research collaboration continuously. Those metrics are derived from the collaboration requirements and help to focus on a commonly defined goal.
An appropriate tool set evaluates and visualizes those metrics with minimal effort, and all participants will be pushed to focus on their tasks with appropriate effort. Thus project status, challenges and progress are transparent to all research collaboration members at any time.
Detection of Adrenaline Based on Bioelectrocatalytical System to Support Tumor Diagnostic Technology
(2017)
Biomechanical simulation of different prosthetic meshes for repairing uterine/vaginal vault prolapse
(2017)
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.