A random-batch method for interacting particle systems is proposed, extending the of S. Jin, L. Li, and J.-G. Liu (2020) [16] to multicomponent with without multiplicative noise. The idea algorithm randomly divide, at each time step, ensemble particles into small batches then evolve interaction within until next step. This reduces computational cost by one order magnitude, while keeping a certa...

Batch processing becomes more important in the process industries, because of the increasing product variety and the decreasing demand volumes for individual products. In batch process industries it is difficult to predict the completion time, or makespan, of a set of jobs, because jobs interact at the shop floor. In this paper, we compare two different methods for predicting the makespan of jo...

Technical variation plays an important role in microarray-based gene expression studies, and batch effects explain a large proportion of this noise. It is therefore mandatory to eliminate technical variation while maintaining biological variability. Several strategies have been proposed for the removal of batch effects, although they have not been evaluated in large-scale longitudinal gene expr...

In batch processes, end-product qualities are cumulatively determined by variable dynamic trajectories throughout each batch. Meanwhile, batch processes are inherently time-varying, implying that process variables may have different impacts on end-qualities at different time intervals. To take both the cumulative and the time-varying effects into better consideration for quality prediction, a b...

The use of entropy as a cost function in the neural network learning phase usually implies that, in the back-propagation algorithm, the training is done in batch mode. Apart from the higher complexity of the algorithm in batch mode, we know that this approach has some limitations over the sequential mode. In this paper we present a way of combining both modes when using entropic criteria. We pr...