Convert exe files to mac. See the original video of MindCuber in action at See for links to build instructions for this and for the latest version of MindCuber.rxe for NXT 2.0 Find useful tips and share your experiences building MindCuber on facebook at Other LEGO solvers including MicroCuber, Android Speedcuber and CubeStormer II can be seen on and on. This video shows how to download the MindCuber.rxe executable program to the NXT using the original LEGO MINDSTORMS NXT software v1.0 The MindCuber.rxe shown here is for MindCuber build from the original MINDSTORMS NXT kit (part number 8527 orange box with the addition of a LEGO color sensor).

NeuroShell 2 combines powerful neural network architectures, a Microsoft® Windows icon driven user interface, sophisticated utilities, and popular options to give users the ultimate neural network experimental environment. NeuroShell 2. NeuroShell 2 is our legacy neural network product targeted towards computer science instructors and students. It contains classic algorithms and architectures popular with graduate school professors. NeuroShell 2 combines powerful neural network architectures.

A back‐propagation neural network was applied to predicting the K IC values using tensile material data and investigating the effects of crack plane orientation and temperature. The 595 K IC data of structural steels were used for training and testing the neural network model. In the trained neural network model, yield stress has relatively the most effect on K IC value among tensile material properties and K IC value was more sensitive to K IC test temperature than to crack plane orientation valid in the range of material data covered in this study. The performance of the trained artificial neural network (ANN) was evaluated by comparing output of the ANN with results of a conventional least squares fit to an assumed shape.

The conventional linear or nonlinear least squares fitting methods gave very poor fitting results but the results predicted by the trained neural network were considerably satisfactory. This study shows that the neural network can be a good tool to predict K IC values according to the variation of the temperature and the crack plane orientation using tensile test results.