text: (1) Launch Steam and log into your Steam account. (2) Click the Games Menu. (3) Choose Activate a Product on Steam. (4) Follow the onscreen instructions to activate Untitled Rhythm Puzzle Game key. After successful code verification go to the "MY GAMES" tab and start downloading.
  
  Scheduling method have been investigated for Just-In-Time (JIT) production systems with painting process that has longer lead time than the planning period of assemble line, followed by the painting prosess. In such JIT production systems, it is difficult to keep stable production between painting process and assemble line.Therefore, it is necessary that painting process has independent production schedule forecasted on the processing requirement jobs of assemble line. However, when multi-volume production is done or defective jobs occur in painting process, production balance between painting process and assemble line is destroyed, and then delays of due dates often occurred. To cope with this difficulty, we propose a scheduling method based on heuristic algorithm for painting process in the JIT production system in order to prevent delays of due date and to keep the smallest inventory in buffer of painting process. We have evaluated the scheduling method by computer simulation.
  For the problem of estimating unknown parameters of the transfer function model from input and output (I/O) data contaminated by colored measurement noise, a three-step estimation procedure has been previously proposed to exploit the I/O correlation information with respect to the correlation time. This paper analyzes this procedure from the compatibility between the parameter transformation and the bias-compensated least-squares (BCLS) estimation used in the third step. The transformation is made from the over-parameterized model in the first and second steps to the final model with the true order in the final step, and is based on the minimization of the mean square error (MSE) between the outputs of the two models. The BCLS estimation is based on the minimization of MSE between the final model output and the process output. It is verified that the condition for compatibility, i.e. the equivalence of two minimizations, is a consequence of the two over-parameterized estimation equations in the first two steps, and that the over-parameterization assures an inclusive expansion of the I/O correlation information. Simulation results are presented to demonstrate the equivalence.