An asynchronous discrete model of nonsynaptic chemical interactions between neurons is proposed. The model significantly extends the previous work [5,6] by novel concepts that make it more biologically plausible. In the model, neurons interact by emitting neurotransmitters to the shared extracellular space (ECS). We introduce dynamics of membrane potentials that comprises two factors: the endogenous currents depending on the neurons of firing type, and the exogenous current, depending on the concentrations of neurotransmitters that the neuron is sensitive to. The firing type of a neuron is determined by the individual composition of endogenous currents. We consider three basic firing types: oscillatory, tonic and reactive. Each of them is essential for modeling central pattern generators, i.e. neural ensembles generating rhythmic activity in the absence of external stimuli. Variability of endogenous currents of different neurons leads to asynchronous neural interactions and significant fluctuations of phase durations in the activity patterns present in simple neural systems. An algorithm computing the behavior of the proposed model is provided.

Considered system consists of two renewable channels that connected in parallel. The system operates in a random environment having k states. The functioning of both components are described by two continuous time alternating processes. The sojourn time in the state 0 (work state) of both channels has exponential distribution with parameters μ1,i and μ2,i if the random environment has state i. The sojourn times in the state 1 (failed state) have general absolute continuous distributions. These sojourn times are independent and doesn’t depend on the random environment state too. The system is working at time t if at least one channel is working. The system reliability on given time interval is calculated for the known initial states of the components.

In the paper, we consider a polling system with a cyclic adaptive polling order. The server polls the queues in a cyclic way but skips (does not visit) those that were empty when polling them in the previous cycle. We apply the generating function method to derive first and second order moments of the stationary state distribution of the queue length at the polling moments that allows calculating the mean delay in queues. Also we provide numerical results.

Mobile broadband (MBB) and ubiquitous machine type communications (MTC) appear essential in future 5G networks, however their requirements in terms of data rate, expected number of users, latency and reliability are largely contradictory, which constitutes a major challenge for 5G architecture design. MTC data aggregation via device clustering may prevent signaling congestion due to a massive number of MTC devices in the cell. Resource sharing between MBB and MTC sessions with MTC data aggregation can be modeled using a multi-service loss system, however its numerical analysis is impeded due to the considerable difference in scales of MBB and MTC parameters. In the paper, we use simulation to assess the performance measures of the loss system and its more realistic modification. Cases of light, moderate and heavy MTC loading and various parameters of the MMPP of MTC arrivals are considered.

In this paper we assume that the customers arrive at a single server queueing system according to Markovian Arrival process. When the system is empty, the server goes for vacation and produces inventory for future use during this period. The maximum processed inventory at a stretch isL. The inventory processing time follows phase type distribution. These are required for the service of customers-one for each customer. The server returns from vacation when there are N customers in the system. The service time follows two distinct phase type distributions depending on whether there is processed items or no processed item available at service commencement epoch. We analyse the distributions of time till the number of customers hit N or the inventory level reaches L, idle time, the distribution of time until the number of customers hit N and also the distribution of the number of inventory processed before the arrival of first customer. Also we provide the distribution of a busy cycle, LSTs of busy cycles in which no item is left in the inventory and at least one item is left in the inventory. We perform some numerical experiments to evaluate the expected idle time, standard deviation and coefficient of variation of idle time of the server.