‎in this work we introduce and study discrete time periodically correlated stable‎ ‎processes and multivariate stationary stable processes related to periodic and cyclic‎ ‎flows‎. ‎our study involves producing a spectral representation and a‎ ‎spectral identification for such processes‎. ‎we show that the third‎ ‎component of a periodically correlated stable process has a component related to a...

The present study develops a physics-constrained neural network (PCNN) to predict sequential patterns and motions of multiphase flows (MPFs), which includes strong interactions among various fluid phases. To the order parameters, locate individual phases in future time, (NN) is applied quickly infer dynamics by encoding observations. consistent conservative boundedness mapping algorithm (MCBOM)...

Since 1950, information flows have been in the centre of scientific research. Up until internet penetration in the late 90s, these studies were based over traditional offline social networks. Several observations in “offline” information flows studies, such as two-step flow of communication and the importance of weak ties, were verified in several “online” studies, showing that the diffused inf...

Data flows in modern networks have exhibited increasingly complex prospect along with the evolving network infrastructures and emerging diverse applications. Performance evaluation must adequately consider this trend. The tractable queueing network as a traditional theoretical tool for performance evaluation can not satisfy the new requirement anymore. One reason is that the important assumptio...

In Mobile Ad hoc Networks (MANETs), achieving fairness and increasing channel utilization are the important design goals of scheduling. However, these two goals contradict with each other. In this paper, a fair-scheduling technique for inelastic traffic flows in MANET is proposed. The network traffic is differentiated into two categories as elastic and inelastic flows. In this technique, data p...