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Gluon is a simple decision-tree daemon, written in Perl, that executes external programs (scripts) to determine the path-flow within the decision-tree.

Gluon project essentially operates as a basic IF-THEN-ELSE expert system that can be used to monitor and take action.

BuildAMon not an ordinary SuperKaramba theme. Its more. Its a collection of graphics and sensors to build a monitor for Your specific system, according to Your needs.
The theme You will put together contains a number of stripes that 10,20 or 40 pixel in hight (plus the top and the bottom). The 10 pixel size could contain a text sensor, the 20 pixel size is a sensorbar, the 40 pixel size is a graph. Just open the .theme file in a text editor and begin the build. Instructions and comments written in.
Main features:
- Top system information (username, hostname, kernel, uptime, etc)
- Users logged in
- CPU load graph with top processes
- CPU usage and CPU speed in MHz bars
- CPU temp graph
- GPU temp for Nvidia GPUs
- CPU,M/B,AIR temperature bars
- HDD temperature bars (hddtemp or smartmontools)
- FAN speed bars
- Memory and Swap usage bars
- Disk usage bars
- Network graphs with IP address
- Wlan signal level graph
- Gateway text
- Hard disk Smart information
Graphics: There are currently 4 different backgrouds and a couple of sensorbars, icons.
PS: There are 4 example themes for the 4 basic designs. If You finished build up Your own monitor, then its very easy to change the design, just have to replace image filenames in text editor to get other designs.

SVNKit is a pure Java Subversion (SVN) client library. This means that users of the library (i.e. Java applications) do not have to include svn native binaries or javahl bindings to work with subversion repositories.
SVNKit project is not only a 100% Java replacement for javahl bindings, but also a library that provides a high level of control over subversion repository operations.
Main features:
- No external binaries or libraries are needed to work with Subversion repository.
- SVNKit supports http, https, svn and svn+ssh connection protocols.
- Subversion 1.4.2 and file protocol support.
- Low level API allows effective direct Subversion repository access.
- SVNKit is compatible with applications that already use native javahl bindings.
Enhancements:
- An interactive password prompt was added to the command line client.
- System-wide configuration file support was added and bugs were fixed.

FileBunker is a file backup application which uses one or more GMail accounts as its backup repository.

Because each GMail account supplies 1000 megabytes of storage, it serves as an excellent facility for this purpose. Files are compressed and encrypted for security and efficiency, and can be restored on demand.

Configuring Filebunker:

Before FileBunker can be used to backup your files, you need to configure the application. This is done by choosing Configuration from the File menu. The following describes the information that must be provided to complete this configuration.

FileBunker Password:

First and foremost, FileBunker requires you to establish a password. This password is used to encrypt the data that you backup so that it is secure.

Longer passwords are better, and you should not use a password which coincides with the password to any of the GMail accounts that you are using for backups. Note in order to make sure your password is correctly recorded, you must type it again in the Confirm Password field.

SMTP Server:

FileBunker transfers files to GMail by sending them using the standard email transfer protocol SMTP. You must identify an SMTP server that will send your email. This is usually an address like smtp.mycompany.com, or mail.myisp.com.

If you are not sure what to specify, check the account configuration in your email application (such as Outlook or Eudora), or contact your ISP. Some SMTP servers require authentication in order to transfer messages.

This involves specifying a user name and password which is used to authenticate you to the SMTP server. If your server requires this authentication, check the Requires Authentication checkbox, and click the Configure button to specify the user name and password.

As with establishing your FileBunker password, you will need to confirm your password to make sure it is correctly recorded. Again, check the configuration of your email application, or contact your ISP, if you are not sure whether your SMTP server requires authentication.

Email Repositories:

Finally, and perhaps most importantly, in order to perform any backups you need to specify one or more GMail accounts which your files will be sent to.

Clicking the New button will prompt you to specify the email address and password for an existing GMail account. Note the @gmail.com is already specified, so you will only need to specify the user name (e.g. jsmith).

As with other password fields in this configuration, you will need to confirm the password to ensure it is recorded correctly. After pressing OK you will be returned to the main configuration dialog, and you will see the new account in the Email Repositories list.

You can select it, and click Edit to change the details of that account, or Delete to remove it. Each GMail account gives you 1000 megabytes of storage. You can register multiple accounts at any time as your storage requirements grow.

FileBunker will automatically use a new account as old ones are filled up.
NOTE If you delete an account that already has files backed up to it, those files will not be accessible from FileBunker.

Castore comes from CApitalization & STORagE and takes place in a user-centered design approach to build an open archive platform, planned to create institutional repositories, managed by librarians in their respective institutions.

With this system, the authors are able to store, convert (XML), fully index, manage, perpetuate, valorize, and distribute their digital documents.

It uses an assembly of components (Tomcat, OO, JDO, Saxon, Lucene, etc.) to build middleware applications, relying on XML and any relational database.

The system has been developed with a component architecture (J2EE) to be able to integrate the platform with any intranet environment with the minimum cost of development.

Channelflow is a direct numerical simulator for incompressible Navier-Stokes channel flow, written in C++.
Channelflow application simulates fluid flow in a rectangular box, with no-slip boundary conditions on the upper and lower surfaces of the box, and periodic boundary conditions in the stream and spanwise directions.
Channelflow uses a spectral discretization in spatial directions (Fourier x Chebyshev x Fourier) and finite-differencing in time, on primitive variables (3D velocity and pressure).
Main features:
Flexible object-oriented programming
- Channelflow is written as a C++ class library. The classes act as building blocks for expressing particular channel-flow simulations and associated data analysis, and underneath these, the mathematical structures needed to perform the calculations. Channelflow provides classes for representing Chebyshev expansions, Fourier x Chebyshev x Fourier expansions, DNS algorithms, and a number of differential equations. Each class has automatic memory management and a set of high-level elemental operations, so that auxiliary data fields and computations can be added to a program with a few lines of code.
- In channelflow, even the DNS algorithm is an object. This greatly increases the flexibility of DNS computations. For example, a DNS can be reparameterized and restarted multiple times within a single program, multiple independent DNS computations can run side-by-side within the same program, and DNS computations can run as small components within a larger, more complex computations. As a result, comparative calculations that formerly required coordination of several programs through shell scripts and saved data files can be done within single channelflow program.
Organized, readable library code
- Channelflow uses object-oriented programming and data abstraction to maximize the organization and readability of its library code. Channelflow defines about a dozen C++ classes that act as abstract data types for the major components of spectral channel-flow simulation (diagram of class libraries). Each class forms a level of abstraction in which a set of mathematical operations are performed in terms of lower-level abstractions, from time-stepping equations at the top to linear algebra at the bottom. The channelflow library code thus naturally reflects mathematical algorithm, both in overall structure and line-by-line. One can look at any part of the code and quickly understand what role it plays in the overall algorithm. One can learn the algorithm in stages, either top-down or bottom-up, by focusing on one level of abstraction at a time.
- Moderately general: Channelflow provides elemental algebraic and differential operators for its mathematical classes, so that most quantities of interest can be calculated with a few lines of code. However, Channelflow is not general regarding geometry: it works only with rectangular geometries with two periodic and one nonhomogeneous direction.
- Configurable: For example, channelflows DNS algorithms implement a variety of time-stepping schemes, external constraints, and methods of calculating nonlinear terms.
- Extendable: The library code is structured to take small-scale extensions such as additional time-stepping schemes. Channelflows object-oriented, modular structure allows channelflow simulations to be embedded as small components within larger, more complex computations.
- Verifiable: The source distribution contains a test suite that verifies the correct behavior of major classes.
- Documented: The Channelflow Users Manual contains annotated program examples, discussion of design, an overview of the main classes from a users perspective, and a review of the mathematical algorithm.
- Supported: Channelflow has a support website. with public CVS access, support-request and bug-tracking systems, etc.
- Fast: Channelflow is as fast as comparable Fortran codes

pmem software displays memory information of running processes. To do this, pmem reads the memory information that are provided by the /proc file systems. Therefore, pmem does not work on operating systems that do not maintain this files system.

To install pmem just untar the source package and run make. This will create the binary pmem-1.1.2. Run configure, make and finally make install to install the binary in /urs/local. You can also set another installation directory by calling configure with the option --prefix=/foo/bar.

Summary of the installation process:

1. tar xzf pmem-version.tar.gz
2. cd pmem-version
3. ./configure [--prefix=...]
3. make
4. make install

Usage:

pmem must be called with one or more process ids of the processes for which the memory usage should be display. By default pmem displays the resident memory of the process in bytes. But the behaviour of pmem can be influenced by the following arguments:

-z displays the size
-s displays the shared memory
-k displays the memory usage in kilobytes
-m displays the memory usage in megabytes
-d displays all memory information available
-l log memory usage of given pids
-i log interval in milliseconds [default: 100]
-g write a gnuplot command file into plot.gnu
-h displays the help
-v displays the version
On success pmem returns with the exit code 0, otherwise 1.

Example:

To log the memory usage of a process and create a gnuplot file out of the data:

./pmem -l pids -i 100 -g data >data
gnuplot plot.gnu (creates the graph plot).