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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.
Ein Drittel der Mitarbeiter der Saint-Gobain Glass Deutschland
GmbH hat drei Jahre lang regelmäßig seinen Rücken trainiert.
Mit Erfolg, wie eine abschließende Evaluation in Zusammenarbeit
mit der FH Aachen zeigt. Die Fehltage der Trainingsteilnehmer
sind enorm zurückgegangen, während die untrainierten Kollegen
weiterhin unter Rückenbeschwerden leiden.
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.
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.
Wind is closely associated with the discussion of fairness in ski jumping. To counter-act its influence on the jump length, the International Ski Federation (FIS) has introduced a wind compensation approach. We applied three differently accurate computer models of the flight phase with wind (M1, M2, and M3) to study the jump length effects of various wind scenarios. The previously used model M1 is accurate for wind blowing in direction of the flight path, but inaccuracies are to be expected for wind directions deviating from the tangent to the flight path. M2 considers the change of airflow direction, but it does not consider the associated change in the angle of attack of the skis which additionally modifies drag and lift area time functions. M3 predicts the length effect for all wind directions within the plane of the flight trajectory without any mathematical simplification. Prediction errors of M3 are determined only by the quality of the input data: wind velocity, drag and lift area functions, take-off velocity, and weight. For comparing the three models, drag and lift area functions of an optimized reference jump were used. Results obtained with M2, which is much easier to handle than M3, did not deviate noticeably when compared to predictions of the reference model M3. Therefore, we suggest to use M2 in future applications. A comparison of M2 predictions with the FIS wind compensation system showed substantial discrepancies, for instance: in the first flight phase, tailwind can increase jump length, and headwind can decrease it; this is opposite of what had been anticipated before and is not considered in the current wind compensation system in ski jumping.