If we calculate the change in entropy during gas mixing, we will find that this quantity is positive.
如果我们计算气体混合过程中熵的变化，我们会发现这个量是正的。
The present study is to investigate the application of sample entropy (SampEn) measures.
现在的研究采用了复杂性分析中的样品熵算法。
A score function for optimization based on maximum mutual information entropy with odditional restriction is proposed.
提出了基于最大互信息熵且具有奇数约束的优化得分函数。
So that's our initial entropy.
这样是我们初始的熵。
And it's all driven by entropy.
都是由熵驱动的。
Yeah. What about entropy, right?
对，熵！熵起了什么作用？
All driven by entropy of mixing.
所有这些都是由混合熵引起的。
So that's our entropy of mixing.
那就是我们混合物的熵。
Just from the entropy of mixing.
因为混合熵。
You're not worried about entropy.
你不用担心熵。
They're in your notes for entropy.
它们在你们关于熵的笔记中。
是的，是熵，对吗？
但是熵也会起作用。
But what has that to do with entropy?
不过那跟熵有什么关系？
Do systems like to have more entropy?
对吗？体系是不是倾向于具有更多的熵？
That's entropy and the arrow of time.
这就是熵和时间之箭。
Let's now look at the entropy of mixing.
让我们来看看混合熵。
熵增加。
And it's all driven by entropy of mixing.
这些的基本原因都是熵的混合。
Entropy s is a state function, generally.
熵是态函数，是普适的。
Entropy is the tendency of things to disorder.
熵是指物体失序的趋势。
So entropy of mixing is really super important.
所以混合中的熵是非常重要的。
Any questions about these entropy driven cases?
关于这些熵驱动的例子有什么问题吗？
What's the magic word? Entropy of mixing, right.
魔幻词汇是什么？混合熵。
And in that case, the entropy is absolutely zero.
在这种情况下，熵为零。
That is, the entropy term, of course, is still there.
那就是，熵的项，当然，仍然存在。
Why was it in such a weird low-entropy unusual state?
为什么它处于这么奇怪的、低熵的、不寻常的状态？
And continue to derive the macroscopic entropy changes.
继续得到宏观的熵变。
And continue to derive the macroscopic entropy changes.
继续得到宏观的熵变。
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