The current research aimed to use non traditional methods to control some stored grain insects. The effects of 180 millitesla (mT) magnetic field (MF) for six different exposure periods (3 min, 30 min, 1 h, 12 h, 24 h and 48 h) on mortality (%) of two stored grain insects, Tribolium casteneum adults and Trogoderma granarium larvae, reduction in F1-progeny (%), seeds germination (%) and seed components (%) after 8 months storage period were studied under laboratory conditions. According to results, the mortality (%) of tested insects increased with increasing of MF time exposure. Trogoderma granarium was more resistant than T. casteneum in which mortality reached 56 and 75%, respectively 14 days after from exposure period. Without any negative effect on seeds germination (%) the MF was very effective in protecting stored wheat from insect infestation up to 8 months compared to non-magnetic seeds which became infested after 3 months of storage. Furthermore, the germination (%) was accelerated by 6 h compared to non-magnetic seeds. The MF level caused a slight increase in the percent of total carbohydrate, crude protein and ash while slightly decrease the percent of moisture, total fats and crude fiber.
In this paper the new synthesis method for reversible networks is proposed. The method is suitable to generate optimal circuits. The examples will be shown for three variables reversible functions but the method is scalable to larger number of variables. The algorithm could be easily implemented with high speed execution and without big consuming storage software. Section 1 contains general concepts about the reversible functions. In Section 2 there are presented various descriptions of reversible functions. One of them is the description using partitions. In Section 3 there are introduced the cascade of the reversible gates as the target of the synthesis algorithm. In order to achieve this target the definitions of the rest and remain functions will be helpful. Section 4 contains the proposed algorithm. There is introduced a classification of minterms distribution for a given function. To select the successive gates in the cascade the condition of the improvement the minterms distribution must be fulfilled. Section 4 describes the algorithm how to improve the minterms distributions in order to find the optimal cascade. Section 5 shows the one example of this algorithm.