本文章並(!)不(!)可作為醫學建議。如需醫學意見，請諮詢專業人士。
（問：但是，在現今社會，無論是上班，或是讀書，完全不「開夜車」，又好像不切實際。）
其實，主要是講讀書時代。如果在工作時代，你的職位需要，你時常「開夜車」的話，你根本就應該另謀高就。
試問，世間上，有什麼工作，竟然值得你冒生命危險，去時常「開夜車」呢？
.
言歸正傳，讀書時代，如果時間管理得宜，需要「開夜車」的情況，其實是很少。
（問：那樣說有意思嗎？我正正是問你，在時間管理失宜，需要「開夜車」時，該如何自處？）
一定「開夜車」的話，你至少要做到以下幾點，去保障自己的安全：
- 只可以間中，不可以經常。
-
日間中途要有小睡。
-
平均而言，你仍必須要有，充足的睡眠。亦即是話，某一天睡少了，必須於在當個星期，還回「睡債」。
- 例如，如果你的充足睡眠是，每天七小時，而你在某一天只睡了六小時的話，你就有義務，在當個星期的另一天，睡多一小時。
.
另外，有時，只要跳出框框，破格思考，或者，只要你的時間表稍改一點，就根本毋須「開夜車」。
本文章並(!)不(!)可作為醫學建議。如需醫學意見，請諮詢專業人士。
— Me@2019-06-06 08:23:56 PM
.
.
2019.06.08 Saturday (c) All rights reserved by ACHK
and 
, as described above, are groups.
unitary matrices clearly coincides with the circle group; the unitary condition is equivalent to the condition that its element have absolute value 1. Therefore, the circle group is canonically isomorphic to 
is unitary if its conjugate transpose 
is also its inverse—that is, if
is the identity matrix.
) and the equation above becomes
 = \int_{t_1}^{t_2} L \circ \Gamma[q]}](https://s0.wp.com/latex.php?latex=%5Cdisplaystyle%7BS%5Bq%5D%28t_1%2C+t_2%29+%3D+%5Cint_%7Bt_1%7D%5E%7Bt_2%7D+L+%5Ccirc+%5CGamma%5Bq%5D%7D&bg=ffffff&fg=333333&s=0 "\displaystyle{S[q](t_1, t_2) = \int_{t_1}^{t_2} L \circ \Gamma[q]}")
![\displaystyle{\delta_\eta S[q] (t_1, t_2) = 0}](https://s0.wp.com/latex.php?latex=%5Cdisplaystyle%7B%5Cdelta_%5Ceta+S%5Bq%5D+%28t_1%2C+t_2%29+%3D+0%7D&bg=ffffff&fg=333333&s=0 "\displaystyle{\delta_\eta S[q] (t_1, t_2) = 0}")
![\delta_\eta S[q] (t_1, t_2) = \int_{t_1}^{t_2} \delta_\eta L \circ \Gamma[q]](https://s0.wp.com/latex.php?latex=%5Cdelta_%5Ceta+S%5Bq%5D+%28t_1%2C+t_2%29+%3D+%5Cint_%7Bt_1%7D%5E%7Bt_2%7D+%5Cdelta_%5Ceta+L+%5Ccirc+%5CGamma%5Bq%5D&bg=ffffff&fg=333333&s=0 "\delta_\eta S[q] (t_1, t_2) = \int_{t_1}^{t_2} \delta_\eta L \circ \Gamma[q]")
![\displaystyle{ \begin{aligned} \delta_\eta f[q] &= \lim_{\epsilon \to 0} \left( \frac{f[q+\epsilon \eta]-f[q]}{\epsilon} \right) \\ \end{aligned}}](https://s0.wp.com/latex.php?latex=%5Cdisplaystyle%7B+%5Cbegin%7Baligned%7D+%5Cdelta_%5Ceta+f%5Bq%5D+%26%3D+%5Clim_%7B%5Cepsilon+%5Cto+0%7D+%5Cleft%28+%5Cfrac%7Bf%5Bq%2B%5Cepsilon+%5Ceta%5D-f%5Bq%5D%7D%7B%5Cepsilon%7D+%5Cright%29+%5C%5C+%5Cend%7Baligned%7D%7D&bg=ffffff&fg=333333&s=0 "\displaystyle{ \begin{aligned} \delta_\eta f[q] &= \lim_{\epsilon \to 0} \left( \frac{f[q+\epsilon \eta]-f[q]}{\epsilon} \right) \\ \end{aligned}}")
![\displaystyle{ \begin{aligned} g( \epsilon ) &= f[q + \epsilon \eta] \\ \delta_\eta f[q] &= \lim_{\epsilon \to 0} \left( \frac{g(\epsilon) - g(0)}{\epsilon} \right) \\ &= D g(0) \\ \end{aligned}}](https://s0.wp.com/latex.php?latex=%5Cdisplaystyle%7B+%5Cbegin%7Baligned%7D+g%28+%5Cepsilon+%29+%26%3D+f%5Bq+%2B+%5Cepsilon+%5Ceta%5D+%5C%5C+%5Cdelta_%5Ceta+f%5Bq%5D+%26%3D+%5Clim_%7B%5Cepsilon+%5Cto+0%7D+%5Cleft%28+%5Cfrac%7Bg%28%5Cepsilon%29+-+g%280%29%7D%7B%5Cepsilon%7D+%5Cright%29+%5C%5C+%26%3D+D+g%280%29+%5C%5C+%5Cend%7Baligned%7D%7D&bg=ffffff&fg=333333&s=0 "\displaystyle{ \begin{aligned} g( \epsilon ) &= f[q + \epsilon \eta] \\ \delta_\eta f[q] &= \lim_{\epsilon \to 0} \left( \frac{g(\epsilon) - g(0)}{\epsilon} \right) \\ &= D g(0) \\ \end{aligned}}")
![\displaystyle{f[q]}](https://s0.wp.com/latex.php?latex=%5Cdisplaystyle%7Bf%5Bq%5D%7D&bg=ffffff&fg=333333&s=0 "\displaystyle{f[q]}")
that depends on a path 
. How does the function vary as the path is varied? Let 
be a varied path, where the function 
is a path-like function that can be added to the path 
is a scale factor. We define the variation ![\displaystyle{ \delta_\eta f[q]}](https://s0.wp.com/latex.php?latex=%5Cdisplaystyle%7B+%5Cdelta_%5Ceta+f%5Bq%5D%7D&bg=ffffff&fg=333333&s=0 "\displaystyle{ \delta_\eta f[q]}")
of the function 
on the path ![\displaystyle{\delta_\eta f [q] = \lim_{\epsilon \to 0} \left( \frac{f[q + \epsilon \eta] - f[q]}{\epsilon} \right)}](https://s0.wp.com/latex.php?latex=%5Cdisplaystyle%7B%5Cdelta_%5Ceta+f+%5Bq%5D+%3D+%5Clim_%7B%5Cepsilon+%5Cto+0%7D+%5Cleft%28+%5Cfrac%7Bf%5Bq+%2B+%5Cepsilon+%5Ceta%5D+-+f%5Bq%5D%7D%7B%5Cepsilon%7D+%5Cright%29%7D&bg=ffffff&fg=333333&s=0 "\displaystyle{\delta_\eta f [q] = \lim_{\epsilon \to 0} \left( \frac{f[q + \epsilon \eta] - f[q]}{\epsilon} \right)}")
![\displaystyle{\delta_\eta (fg)[q]}](https://s0.wp.com/latex.php?latex=%5Cdisplaystyle%7B%5Cdelta_%5Ceta+%28fg%29%5Bq%5D%7D&bg=ffffff&fg=333333&s=0 "\displaystyle{\delta_\eta (fg)[q]}")
is![\displaystyle{\delta_\eta (f[q]g[q])}](https://s0.wp.com/latex.php?latex=%5Cdisplaystyle%7B%5Cdelta_%5Ceta+%28f%5Bq%5Dg%5Bq%5D%29%7D&bg=ffffff&fg=333333&s=0 "\displaystyle{\delta_\eta (f[q]g[q])}")
and a 50% it is in the state 
. There is a 0% chance that the system is in either of those states, and a 100% chance the system is in the state 
.
for measuring eigenvalues 
, for any observable you want. The difference is the way you combine probabilities, in a quantum superposition you have complex numbers that can interfere. In a classical probability distribution things only add positively.