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In addition to these new developments, we automated a series of REFMAC refinement extensions: utilization of automatic de-twinning of experimental data, optimization of the B-factor restraint weight and refinement of anisotropic B-factors when the ratio of experimental data and model parameters is sufficient.
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It also enables automatic utilization of appropriate modules from among the previously learned ones.
Additionally, more effective behavioral choices may be outlined and subsequently reinforced, to help patients be better prepared to deal with health-related stressors that, upon arising, might otherwise trigger the automatic utilization of coping strategies that could make the situation worse.
We propose a similar utilization of context information in the automatic sound event detection process.
The main benefits of this approach include its lightweight design, automatic configuration, and, utilization of widely available and tested network protocols of TCP/IP and HTTP.
It uses the Automatic Positive Rotor Identification system of automatic rotor detection to ensure complete safety.
The systems are fully automated, consisting of automatic train protection (ATP), automatic train operation (ATO) and automatic train supervision.
This enables modification of automatic behavior.
To evaluate the efficiency of automatic tool.
This automatic segmentation routine can be used as part of automatic diagnostic tools.
Since manual segmentation of these layers is time consuming and prone to bias, automatic segmentation methods are critical for full utilization of this technology.
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