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Multi-dimensional Optimal Order Detection (MOOD) breaks away from classical limitations employed in high-order methods.
It is second-order accurate in space and is combined with the a posteriori Multidimensional Optimal Order Detection (MOOD) limiting strategy to ensure robustness and stability at shock waves.
Efficient higher order interpolation schemes based on a multi-dimensional optimal order detection (MOOD) paradigm are developed coupled with the implicit time discretization scheme and further investigated for implicit large eddy simulation of compressible turbulence.
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Early detection of mood disorders in primary health care can help prevent progression.
We evaluated several commonly used screening instruments for the detection of mood disorders, anxiety disorders, and attention-deficit hyperactivity disorder (ADHD).
The detection of mood and anxiety disorders is of great clinical importance in patients with chronic disease but data on the occurrence of affective dysfunction is lacking among people with epilepsy (PWE) in non-western populations.
This method can allow a prompt detection of mood episodes (Grof et al. 1993), and possibly allow intermittent treatment, with lithium salts, for example, in those patients with a classical type of bipolar illness, which will decrease side effects and improve compliance.
Even without a mood detection algorithm, you can classify that emotion: skepticism.
Microsoft added support for automatic mood detection in Windows Live Messenger.
Like in every machine learning problem it is crucial for the success of mood detection to select suitable features.
It has also been applied to identification tasks, such as vocal identification [41] and mood detection [42].
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