We are pleased to announce that version 5.3 of the Mercury tool has been released to the industry and academic community.
This new version fixes a number of bugs found in previous versions.
If you are a Mercury user, we strongly recommend that you get the new version.
We hope you will enjoy it. If you have any suggestions or find any bugs, please report them to us.
If you do not know the tool yet and want to try it, please go to www.modcs.org and go to “Downloads” and “Mercury Tool”.
There you will get a form, sign it and download the tool.
What’s new in Mercury?
- Performance improvement when performing evaluations on RBDs and FTs.
- Usability improvements.
- The editor now highlights the name of the tab of the selected model.
- Mercury now supports drag-and-drop rearrangement of model tabs. This allows you to customize the layout of your workspace by simply clicking on a tab and moving it to the desired location. This feature is especially valuable for projects where users frequently work with multiple models and need a flexible way to keep everything well organized and easily accessible.
- The editor now provides visual assistance through vertical and horizontal alignment lines when adding, resizing, or moving components on the canvas, ensuring precise alignment.
- Users can fine-tune component positioning with the keyboard arrow keys for precise control over element placement.
- In addition, users can activate and deactivate the grid mode by pressing the CTRL + G keys. When enabled, this mode draws multiple lines on the canvas to facilitate component alignment.
- Mercury exports models to PDF files.
- An Expectation-Maximization Auto-fitting (EMA) tool. An EMA algorithm is a fundamental technique for parameter estimation in statistical models when handling clustered data. The EMA tool implements an algorithm that provides a robust approach to parameter estimation in complex models. It is suitable for scenarios where data points have unknown probabilities of belonging to different clusters and these clusters follow known distributions with unknown parameters. The main goal of the EMA tool is to iteratively estimate the parameters for each cluster such that they maximize the likelihood of the observed data.
- Mercury exports SPN/CTMC models to the Wolfram Mathematica language/SageMath format. When exporting to Mathematica, Mercury creates the nb file that can be opened in Mathematica.
- Some minor improvements and bug fixes.
What is the Mercury tool used for?
Mercury enables a range of models to be created and evaluated for supporting performance and dependability evaluations, such as reliability block diagrams (RBDs), dynamic RBDs (DRBDs), fault trees (FTs), energy flow models (EFMs), stochastic Petri nets (SPNs), and continuous and discrete-time Markov chains (CTMCs and DTMCs).