We have a vcenter database on an AG setup. The log file keeps growing sometime due to metrics which are enabled on the application end. They will be moving this soon to another db vendor to probably mitigate the situation, but when I try to run tlog backup by myself it is taking hours to finish(this is not the case when I setup a scheduled tlog backup via sql agent)  Also, when I try to shrink the log file after the backup, it is not getting shrunk. Is there another way to shrink the logs? Please let me know
This one seems to be a common question in most forums and all over the web, it is asked here in many formats that typically sound like this:  In SQL Server -  What are some reasons the transaction log grows so large? Why is my log file so big? What are some ways to prevent this problem from occurring? What do I do when I get myself on track with the underlying cause and want to put my transaction log file to a healthy size?
as of 4/2015 OS X Kindle App: Where are the books located? After I updated my Kindle for Mac app 2 mos. ago I can no longer find the azw book files on my Mac to convert them so I can read them on my KOBO ereader. I use to find them in a My Kindle Content file but that has disappeared. I have Ult. DRM &amp; Calibre but they won't work without the azw files, and I can't find them on my mac anymore. Can anyone help me? The answers on this site about this are years old &amp; do not apply to my problem. Using Ubuntu 11.10, how do I format an external or USB drive? I don't find an option to 'format' when I right click. In the new version, the drives don't come up on the desktop, but instead come on the Launcher. How to calculate the prediction interval for an OLS multiple regression? What is the algebraic notation to calculate the prediction interval for multiple regression?  It sounds silly, but I am having trouble finding a clear algebraic notation of this. This is a basic question on Box-Jenkins MA models. As I understand, an MA model is basically a linear regression of time-series values $Y$ against previous error terms $e_t,..., e_{t-n}$. That is, the observation $Y$ is first regressed against its previous values $Y_{t-1}, ..., Y_{t-n}$ and then one or more $Y - \hat{Y}$ values are used as the error terms for the MA model.  But how are the error terms calculated in an ARIMA(0, 0, 2) model? If the MA model is used without an autoregressive part and thus no estimated value, how can I possibly have an error term? SQL Delete clears the table instead of erroring I have two tables in my database:  NEWS table with columns:   id - the news id  user - the user id of the author)   USERS table with columns:   id - the user id   I want to execute this SQL:   SELECT * FROM news JOIN users ON news.user = user.id    When I get the results in PHP I would like to get associative array and get column names by $row['column-name']. How do I get the news ID and the user ID, having the same column name? How do I get from   $$B(\alpha,\beta)=\int_{0}^{1}{ t^{ \alpha -1}  (1-t)^{ \beta -1} \,dt}$$ to:   $$2^{2- \alpha - \beta } \int_{0}^{ \infty}{ \frac{ \cosh (( \alpha - \beta )x)}{( \cosh(x))^{ \alpha + \beta }} \,dx}$$  ? I have tried different change of variables such as $t=1- \frac{1}{ \cosh (x)}$ and got $\int_{0}^{ \infty}{ \frac{ \sinh (x)  ( \cosh (x)-1)^{ \alpha -1} }{( \cosh(x))^{ \alpha + \beta }} \,dx}$ but without success. Class imbalance strategies When dealing with the class imbalance problem in a binary classifier, there are three ways I know of to address it: over-sampling, under-sampling and using cost-sensitive methods.   Are there any guidelines, rules of thumb or general strategies to choose among these methods? A possible answer would be: over-sample when the positive class has more than 100 instances (I just made it up). How can I prevent Overflow for the Gamma function? I have a Dirichlet distribution from which I'm maximizing $\alpha$ by calculating the log likelihood with the following equation  $p\left(s|\alpha\right) = \sum\limits_{i=1}^O\log\left(\frac{n_i!}{\prod\limits_{j=1}^kn_{ij}!}\frac{\Gamma\left(\alpha_0\right)}{\Gamma\left(\sum\limits_{j=1}^k\left(n_{ij}+a_j\right)\right)} \prod\limits_{j=1}^k \frac{\Gamma\left(n_{ij}+\alpha_j\right)}{\Gamma\left(\alpha_j\right)}\right)$  I have a python script to calculate the log likelihood. It works for small values, but when $n_{ij}$ gets high $\Gamma\left(\sum\limits_{j=1}^k\left(n_{ij}+a_j\right)\right)$ and $\Gamma\left(n_{ij}+\alpha_j\right)$ python runs into overflow error (the max for math.gamma is between 171 and 172).   Is there a way to change the likelihood function so that $\Gamma\left(\sum\limits_{j=1}^k\left(n_{ij}+a_j\right)\right)$ and $\Gamma\left(n_{ij}+\alpha_j\right)$ don't run into overflow errors. Can I just divide the values in $\Gamma$ by for example 100, if I then compare the likelihood between $p(s|\alpha)$ with different $\alpha$ values do the best $\alpha$ values still have the highest likelihood? How do I connect multiple devices to an ISP that gives web-based portal for logging in? Integral representation of Euler's constant Prove that : $$ \gamma=-\int_0^{1}\ln \ln \left ( \frac{1}{x} \right) \ \mathrm{d}x.$$  where $\gamma$ is Euler's constant ($\gamma \approx 0.57721$).    This integral was mentioned in  as in  , but the solutions I've got uses corollaries from . Can you give me a simple solution (not using much advanced theorems) or at least some hints. Prove that inequality $\sum _{cyc}\frac{a^2+b^2}{a+b}\le \frac{3\left(a^2+b^2+c^2\right)}{a+b+c}$ Let $a&gt;0$,$b&gt;0$ and $c&gt;0$. Prove that:    $$\dfrac{a^2+b^2}{a+b}+\dfrac{b^2+c^2}{b+c}+\dfrac{c^2+a^2}{c+a}\le \dfrac{3\left(a^2+b^2+c^2\right)}{a+b+c}.$$     $$\Leftrightarrow \frac{(a^2+b^2)(a+b+c)}{a+b}+\frac{(b^2+c^2)(a+b+c)}{b+c}+\frac{(c^2+a^2)(a+b+c)}{c+a}\leq 3(a^2+b^2+c^2)$$  $\Leftrightarrow 2(a^2+b^2+c^2)+\frac{c(a^2+b^2)}{a+b}+\frac{a(b^2+c^2)}{b+c}+\frac{b(a^2+c^2)}{a+c}\leq 3(a^2+b^2+c^2)$  $\Leftrightarrow \frac{c(a^2+b^2)}{a+b}+\frac{a(b^2+c^2)}{b+c}+\frac{b(a^2+c^2)}{a+c}\leq a^2+b^2+c^2$  $\Leftrightarrow \frac{c(a+b)^2-2abc}{a+b}+\frac{a(b+c)^2-2abc}{b+c}+\frac{b(a+c)^2-2abc}{a+c}\leq a^2+b^2+c^2$  $\Leftrightarrow 2(ab+bc+ac)\leq a^2+b^2+c^2+2abc\left(\frac{1}{a+b}+\frac{1}{b+c}+\frac{1}{a+c}\right)$  I can't continue. Help me How to validate an email address in JavaScript In Java, how do I call a base class's method from the overriding method in a derived class? I have two Java classes: B, which extends another class A, as follows :  class A {     public void myMethod() { /* ... */ } }  class B extends A {     public void myMethod() { /* Another code */ } }   I would like to call the A.myMethod() from B.myMethod(). I am coming from the , and I don't know how to do this basic thing in Java. I read in  in this site that the KE a free-falling ball acquires is not originated by the attracting body but that energy was actually stored in the ball when it had been lifted to the height it dropped from.  In this way, it was said, gravity is subject to the conservation of energy principle and cannot change the total energy of an object.  Consider now the maneuver known as a  (also gravity assist) used by space probes such as the . A space probe approaches a planet with velocity $v$, slingshots around, and ends up with velocity $v+2U$, where $U$ is the velocity of the planet.  Consider the energy of the probe. Before, it was $E_i=\frac{1}{2} mv^2$ and after it is $E_f=\frac{1}{2} m(v+2U)^2$. It looks like $E_f$ is much bigger than $E_i$ - but where did the additional energy come from?   Is this not a violation of the conservation of energy principle? Do I need a US visa to transit (or layover) through an American airport? XeLaTeX update [20201004] and item ball in Beamer Sequence of triangles with exponentiated sides Let $a,b,c\in\Bbb R^+$. For any given $n\in\Bbb N$ there exists a triangle with sides $a^n,b^n,c^n$. Show that at least two of $a,b,c$ are equal.   I know that you can create inequalities for $a^n,b^n,c^n$, but what's next? Model selection with low N? I've got a study which is kinda messed up by the design...Turns out I ended up with about 50 patients of which 80% have the outcome and 20% don't (binary outcome).  I've been turning in my bed for the last month trying to figure out what to do with this. The only real answer is "don't" but I have to deliver something in the next days as it's part of a master's thesis.  I have about 10 predictors of interest for the outcome (various variable types) and the research question is whether any of these predictors can predict the outcome. Some already have an established correlation in the literature while some are original hypothesis. Several are significant with a univariate logistic regression, but it doesn't look good in any other way than the actual p-value.  Now obviously I'm not going to be able to answer this research question sufficiently but if you were in my shoes, what sort of statistical analysis would you perform to relay to your supervisors?  EDIT: Link to my (anonymized) data (CSV): Are the decimal places in a CSS width respected?