Solving the N-Queens problem with a binary Hopfield-type network

The application of a discrete Hopfield-type neural network to solving the NP-Hard optimization problem — the N-Queens Problem (NQP) — is presented. The applied network is binary, and at every moment each neuron potential is equal to either 0 or 1. The network can be implemented in the asynchronous mode as well as in the synchronous one with n parallel running processors. In both cases the convergence rate is up to 100%, and the experimental estimate of the average computational complexity is polynomial. Based on the computer simulation results and the theoretical analysis, the proper network parameters are established. The behaviour of the network is explained.     

Clinical significance of platelet alloantibodies detected in immunofluorescence test (neonatal alloimmune thrombocytopenia and post-transfusion purpura)

Three cases of neonatal alloimmune thrombocytopenia and one patient with post-transfusion purpura could be diagnosed only by introducing the platelet immunofluorescence test. Thrombocytopenia was caused by anti-PlA1 platelet alloantibodies detected neither in the agglutination nor by the complement fixation test.