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The state prediction of resources in large scale distributed systems represents an important aspect for resources allocations, systems evaluation, and autonomic control. The paper presents advanced techniques for resources state prediction in Large Scale Distributed Systems, which include techniques based on bio-inspired algorithms like neural network improved with genetic algorithms. The approach adopted in this paper consists of a new fitness function, having prediction error minimization as the main scope. The proposed prediction techniques are based on monitoring data, aggregated in a history database. The experimental scenarios consider the ALICE experiment, active at the CERN institute. Compared with classical predicted algorithms based on average or random methods, the authors obtain an improved prediction error of 73%. This improvement is important for functionalities and performance of resource management systems in large scale distributed systems in the case of remote control ore advance reservation and allocation.

The paper presents the main theoretical elements underlying the separation of seeds after their surface. Usually in the seed cleaning and sorting process by surface are used machines with electromagnetic or magnetic separation systems, which represent the main working elements. Based on the characteristic element which may rely make a difference in the seed surface separation process, it was developed a mathematical model of the smooth seed motion on the drum surface on an rotary cylinder in order to determine the seed detachment place and speed. Also in this paper, is made a theoretic analyze of smooth seed trajectory in free flight detached from the separation drum and it is assessed the collection area of loose seeds. On this basis were carried out case studies and numerical applications for three different working machines

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