This paper discusses about supervisory control under possible loss of observability. The final goal of this work is to design a safe (avoiding disaster states) supervisory control loop structure taking into consideration the uncertainty that stems from the fact that some event(s) may turn into unobservable at some point along the normal system operation. This kind of failure may correspond to the breakdown of some plant sensor. The supervisor finally obtained is in general: a) more permissive than the supervisor obtained assuming those events as unobservable from the beginning, since at some points it shall be better informed, and b) more restrictive than the supervisor obtained assuming that those events shall never fail, since it will have to prevent the system from following some undesirable trajectories that the system could take in presence of an observability failure. This paper presents results to obtain a safe controller that avoids disaster states in presence of the described uncertainty, and also ensures that the system behaviour will not run out from its specifications in absence of failure.

Ant Colony Optimisation (ACO) is a nature-inspired, population-based metaheuristic that has been used to solve a wide variety of computationally hard problems. In order to take full advantage of the inherently stochastic and distributed nature of the method, we describe a parallelization strategy that leverages these features on heterogeneous and large-scale, massively-parallel hardware systems. Our approach balances workload effectively, by dynamically assigning jobs to heterogeneous resources which then run ACO implementations using different search strategies. Our experimental results confirm that we can obtain significant improvements in terms of both solution quality and energy expenditure, thus opening up new possibilities for the development of metaheuristic-based solutions to ``real world" problems on high-performance, energyefficient contemporary heterogeneous computing platforms.

No abstract

The protein-folding problem has been extensively studied during the last fifty years. The understanding of the dynamics of global shape of a protein and the influence on its biological function can help us to discover new and more effective drugs to deal with diseases of pharmacological relevance. Different computational approaches have been developed by different researchers in order to foresee the threedimensional arrangement of atoms of proteins from their sequences. However, the computational complexity of this problem makes mandatory the search for new models, novel algorithmic strategies and hardware platforms that provide solutions in a reasonable time frame. We present in this revision work the past and last tendencies regarding protein folding simulations from both perspectives; hardware and software. Of particular interest to us are both the use of inexact solutions to this computationally hard problem as well as which hardware platforms have been used for running this kind of Soft Computing techniques.