I have a situation where I have to clean up files that are older than 7 days, but from multiple locations like below.    /home/user1/Out/vendor /home/user2/Out/vendor /home/user3/Out/vendor /home/user4/Out/vendor         ︙ /home/usern/Out/vendor   Can someone tell me how to achieve this without deleting folders? I am thinking to call there users from a text file.
I have found the command to delete files older than 5 days in a folder  find /path/to/files* -mtime +5 -exec rm {} \;   But how do I also do this for subdirectories in that folder?
Finding roots/zeros for collision detection? For the most simple of 2D games, I have implemented a posteriori collision detection (overlapping rectangles) on the x/y Cartesian plane, but am now interested in understanding the basics of a priori collision detection...  In the link , I noticed the reference to Newton’s Method.  So, I want to better understand the link b/w finding zeros of an equation and detecting a posteriori collision.    Let's start with the most obvious case of finding roots of an equation:  If you have a projectile's trajectory modeled as a quadratic function, you can find the roots to predict at what time the height will equal zero (or any other constant height, actually, by just solving f(x)=C to be f(x)-C=0 and then finding roots of that expression).    So, as I understand, finding the root can tell me the time at which the object will be at any chosen height.  More broadly, we basically know the (x,y) location of the object for any given time.  But how does root finding, in particular, translate to detecting collisions with another object?  (The other object may be stationary, or be moving.  If the latter, do you also determine roots/location of this moving object as well? Calculate the (x,y) trajectory of both objects and determine where they will intersect?)  Again, the basic question is where does root finding come into play?  Any links to a basic primer on this topic, with a simple example would also be greatly appreciated. Show that $|z_1 + z_2|^2 < (1+C)|z_1|^2 + \left(1 + \frac{1}{C}\right) |z_2|^2$ Let $z_1$ and $z_2$ be two complex numbers. Show that there exists $C &gt; 0$ with   $$  |z_1 + z_2|^2 &lt; (1+C)|z_1|^2 + \left(1 + \frac{1}{C}\right) |z_2|^2. $$   I tried to simplify the L.H.S and R.H.S, SNF I was finally left to compare between a real number and a complex number  I really couldn't think of anything else. Please help. python ImportError: No module named primes "Unable to locate package sudo" when i try to install sudo on new Debian Allow horizontal scrolling of long math expressions Stack Exchange formats math, but often the formatted math is wider than the screen. It would be nice if formatted math were scrollable the same way that formatted code wider than the screen is scrollable. Changing subject and verb positions in statements and questions Most of something is plural or singular? I am writing a literature review. My question is which sentence is correct?   Most of the existing work focus on... Most of the existing work focuses on ... Most of the existing studies focus on ...   Thanks Move post to different user Plot electric potential and field Windows 10 upgrade led to grub rescue macOS app for showing external HDD usage statistics? I'm planning on selling a used external HDD on eBay.  Are there any applications for Mac out there (preferably free) that can show me statistics about the usage history for the drive so that I can let potential buyers know the condition more precisely? Find weak form of linear transport equation I am stuck on the following problem that says:       a) Find a weak formulation for the partial differential equation $${\partial u\over\partial t\ }+ c{\partial u \over \partial x\ }=0$$    b) Show that $u=f(x-ct)$ is a generalized solution of $${\partial u\over\partial t\ }+ c{\partial u \over \partial x\ }=0$$ for any distribution $f$    My Attempt:   I know that in order to find a weak form of a pde, we need to multiply it by a test function, then integrate it. Also, to find a generalized solution, we need to find a weak solution and just multiply it by the Heaviside function.  Let's take any test function $\phi $, then we have (integrating by parts second part of the integral) $$\int_{\Omega } ( {\partial u\over\partial t\ }+ c{\partial u \over \partial x\ })*\phi(x) dx= \int_{\Omega } {\partial u\over \partial t}\phi(x) dx - c\int_{\Omega } u(x,t)\phi'(x)dx$$ where $\phi $ vanishes at boundaries. So, is it the final form or can we proceed further? And how am I supposed to find a generalized solution?  Can someone help me out? Thanks in advance for your time. Multiple arguments in shebang I am wondering whether there is a general way of passing multiple options to an executable via the shebang line (#!).   I use NixOS, and the first part of the shebang in any script I write is usually /usr/bin/env. The problem I encounter then is that everything that comes after is interpreted as a single file or directory by the system.  Suppose, for example, that I want to write a script to be executed by bash in posix mode. The naive way of writing the shebang would be:  #!/usr/bin/env bash --posix   but trying to execute the resulting script produces the following error:  /usr/bin/env: ‘bash --posix’: No such file or directory   I am aware of , but I was wondering whether there was a more general and cleaner solution.    EDIT: I know that for  scripts, there is a way to achieve what I want, documented in  of the manual:   #!/usr/bin/env sh  exec guile -l fact -e '(@ (fac) main)' -s "$0" "$@"  !#   The trick, here, is that the second line (starting with exec) is interpreted as code by sh but, being in the #! ... !# block, as a comment, and thus ignored, by the Guile interpreter.  Would it not be possible to generalize this method to any interpreter?     Second EDIT: After playing around a little bit, it seems that, for interpreters that can read their input from stdin, the following method would work:  #!/usr/bin/env sh sed '1,2d' "$0" | bash --verbose --posix /dev/stdin; exit;   It's probably not optimal, though, as the sh process lives until the interpreter has finished its job. Any feedback or suggestion would be appreciated. Is the total variation function uniform continuous or continuous? What is the correct way to edit a crontab file? $A^TA+I$ is always invertible? In the book &quot;Quantum Mechanics and Path Integrals&quot; Feynman &amp; Hibbs state that  the probability $P(b,a)$ to go from point $x_a$ at time $t_a$ to the point $x_b$ at the time $t_b$ is $P(b,a) = \|K(b,a)\|^2$ of an amplitude $K(b,a)$ to go from $a$ to $b$. This amplitude is the sum of contributions $\phi[x(t)]$ from each path. $$ K(b,a) = \sum_{\text{paths from $a$ to $b$}} \phi[x(t)]$$ The contributions of a path has a phase proportional to the action $S$: $$ \phi[x(t)] = \text{const}\ e^{(i/\hbar)S[x(t)]}$$  Why must the contribution of a path be $\sim e^{(i/\hbar)S[x(t)]}$? Can this be somehow derived or explained? Why can't the contribution of a path be something else e.g. $\sim \frac{S}{\hbar}$, $\sim \cos(S/\hbar)$, $\log(S/\hbar)$ or $e^{- (S[x(t)]/\hbar)^2}$ ? Edit: I have to admit that in the first version of this question, I didn't exclude the possibility to derive the contribution of a path directly from Schrödinger's equation. So answers along this line are valid although not so interesting. I think when Feynman developed his formalism his goal was to find a way to quantize systems, which cannot be treated by Schrödinger's equation, because they cannot be described in terms of a Hamiltonian (e.g. the Wheeler-Feynman absorber theory). So I think a good answer would explain Feynman's Ansatz without referring to Schrödinger's equation, because I think Schrödinger's equation can only handle a specific subset of all the systems that can be treated by Feynman's more general principle. I have a layout similar to this:  &lt;div id="..."&gt;&lt;img src="..."&gt;&lt;/div&gt;   and would like to use a jQuery selector to select the child img inside the div on click.  To get the div, I've got this selector:  $(this)   How can I get the child img using a selector? Show that $E(X)=\int_{0}^{\infty}P(X\ge x)\,dx$ for non-negative random variable $X$ optimality of the Carnot cycle