Disruption predictors are implemented as data-driven models obtained from machine learning techniques. These data-driven models are deduced from a training process with thousands of discharges (both disruptive and non-disruptive). ITER or DEMO, the next step devices cannot afford to wait for hundreds of disruptions to start predicting. A novelty approach for disruption prediction is to avoid the use of past discharges for learning purposes. The objective is to learn in every discharge how a safe plasma evolution is and to trigger an alarm when anomalies in the data flow appear. Of course, these anomalies have to be signatures of the phenomenon's precursors. By applying these ideas in JET to a dataset of more than 1700 non-disruptive shots and more than 550 that ended in a disruption, the success rate is about 90% and the false alarm rate is slightly above 7%. On average, the alarm is triggered with an anticipation time above 200 ms.

Using Internet-based networking technologies traditional control laboratories in engineering education can be replaced with a remote or simulated experimental session. Thus, the way of studying becomes more flexible: the assistance to the laboratories is minimized. Accessing to the application students can make experiments and obtain results with a real plant from different localizations far from the university. This paper presents a complete virtual and remote control laboratory for experimentation of an oscillatory system: the ball and hoop. Using this application students can understand in a practical way important topics such as non-minimum phase behaviors, zeros transmission of the system, resonance, or to demonstrate control of oscillatory systems. The client-side of the virtual laboratory has been developed using the programming support provided by easy Java simulations (Ejs). The server-side has been developed using Labview and a data acquisition card.

The standard DP (dynamic programming) algorithms are limited by the substantial computational demands they put on contemporary serial computers. In this work, the theory behind the solution to serial monadic dynamic programming problems highlights the theory and application of parallel dynamic programming on a general-purpose architecture (cluster or network of workstations). A simple and well-known technique, message passing, is considered. Several parallel serial monadic DP algorithms are proposed, based on the parallelization in the state variables and the parallelization in the decision variables. Algorithms with no interpolation are also proposed. It is demonstrated how constraints introduce load unbalance which affect scalability and how this problem is inherent to DP.