ocean | A experimental module loader with the same APIs of SeaJS | Reactive Programming library

why my code does not read my specified keys from my enum.

The code itself compiles fine and the program runs without any runtime errors.

Header file with the enum:

I am trying to apply an example from R "particles" package to ocean velocity data (kinetic energy). Example data is as follows:

I'm trying to deploy a MERN app to a digital ocean droplet. I have used nodemon and concurrently to run my app through "npm run dev". Everything works fine when running in my local machine, but when I git clone the project to the droplet and try to npm run dev to run the app on the remote server, I get:

I am trying to deploy Django project in digital ocean. My site’s CSS is working perfectly but my admin panel CSS is not working good. The dashboard of the database window overrides my entry page always.

Before few days it seems to be working good but now the CSS makes the whole page messy.

Before:

After:

I have this script which works great. It created 3 instances with the sepcified tags to identify them easily. But issue is i want to add a remote-exec provisioner (currently commented) to the code to install some packages. If i was using count, i could have looped over it to do the remote-exec over all the instances. I could not use count because i had to use for_each to loop over a local list. Since count and for_each cannot be used together, how do i loop over the instances to retrieve their IP addresses for using in the remote-exec provisioner.

On digital ocean and AWS, i was able to get it work using host = "${self.public_ip}" But it does not work on vultr and gives the Unsupported attribute error

instance.tf

I'm trying to manage large datasets or files (e.g., GeoTIFF) on plain R (via terminal) or RStudio (1.4.1106), but both apps crash every time on Linux (Manjaro, x64, core i7, and 8 GB RAM) for some scripts (especially when a raster data is plotted using ggplot2 to produce a high-quality map, as well as a lmer function with random factors using a csv file with ~3000 rows and 6 columns). Probably the issue refers to memory management since all the memory is consumed. To overcome, I tried two packages to limit/increase the memory size, such as "unix" and "RAppArmor". However, if the memory size is limited, all your available RAM was exhausted and the famous message "cannot allocate a vector..." is shown. On the other hand, if the memory size is increased to high levels, R/RStudio simply crashes. On Windows, the following code works like a charm to increase memory size (only needed to plot a raster into ggplot2):

I would like the last field to have 50px border radius on the right. Why is this not working?

I have a Spring Boot app which uses Hibernate to generate the below query to a postgresql database. On a fast, local system (6 fast cores with HT, lots of ram, fast ssd) the query to the database runs at a reasonable 12-65 ms.

To my surprise, once I deployed to one of Digital Ocean's virtual servers, the response of the same query dropped to an unacceptable 150-250 ms or higher. Since this is still a test environment, with almost no data on or traffic to the server, I wasn't really expecting even the cheapest servers, with only 1 shared CPU and 2gb of ram, to have any problems with this query. Am I simply in the wrong here? Upgrading the CPU to something heavier increased the performance back to what I was expecting.

In any case, is there anything wrong with the below query? Can it be optimized further? Since it's an important part of the app, I want it to be as performant as possible. Since the joins are likely taking the majority of the execution time, I'm considering smashing all the tables together into one table, at which point I'd only need a large where clause to achieve the same but without the joins. Would that be faster?

I am trying to set up a Hyperledger Fabric Network with Hyperledger Explorer. I spin up a VM on the digital ocean cloud with ubuntu OS. From there, I spin up 3 orderers node, and 2 peers node. Which result in total of 5 nodes. (I am using RAFT setup).

However, I encounter the error as below when trying to start the hyperledger fabric explorer docker-container images.