This paper develops an algorithm for finding the Dynamic shortest path from the source node to the sink node in stochastic Dynamic networks, in which the arc lengths are independent random variables with exponential distributions. In each node there is an environmental variable, which evolves in accordance with a continuous time Markov process. The parameter of the exponential distribution of the transition time of each arc is also a function of the state of the environmental variable of its initiating node. It is also assumed that upon arriving at each node, we know the state of its environmental variable and also the states of the environmental variables of its adjacent nodes. Upon arriving at each node, we can move toward the sink node through the best outgoing arc or wait for encountering the better state of its environmental variable, which reduces the expected transition times of the outgoing arcs. In this paper, we apply the stochastic Dynamic programming for finding the Dynamic shortest path from the source node to the sink node by obtaining the optimal strategy of movement in each node of the network.

One of the obvious reasons for most disorders in network service provisioning is network path congestion. Congestion avoidance in today's networks is too costly and sometimes impossible. With the introduction of SDN, centralizing the equipment's control plane has become possible. This paper presents an enhanced method named ESV-DBRA to avoid congestion in multi-tenant SDN networks. At first, ESV-DBRA monitors the traffic load and delay of all network paths for each tenant individually. Then, by merging the parameters obtained from the monitoring, the Service Level Agreements (SLA), and a novel proposed cost function, it calculates the cost of the network paths per tenant. As a result, traffic for each tenant is routed through the path/paths at the lowest possible cost from the tenant's perspective. Next, the bandwidth quotas will be calculated and assigned to the tenants over their optimal routes. Afterward, whenever congestion is likely to occur in a path, ESV-DBRA automatically changes the route or bandwidth of the tenants' traffic related to this path to avoid congestion. Related algorithms are also proposed.Eventually, simulations show that the proposed method effectively increases bandwidth utilization by 10.76%.

In this paper, we apply the stochastic Dynamic programming to approximate the mean project completion time in Dynamic Markov PERT networks. It is assumed that the activity durations are independent random variables with exponential distributions, but some social and economical problems influence the mean of activity durations. It is also assumed that the social problems evolve in accordance with the independent semi-Markov processes over the planning horizon. By using the stochastic Dynamic programming, we find a Dynamic path with maximum expected length from the source node to the sink node of the stochastic Dynamic network. The expected value of such path can be considered as an approximation for the mean project completion time in the original Dynamic PERT network.

Background: social networks are Dynamic due to continuous increases in their members, communications, and links, while these links may be lost. This study was conducted with the aim of investigating the link and communication between social network users using the centrality criterion and decision tree. Methods: After checking the nodes in the network for each pair of unrelated nodes, some common nodes in the proximity list of these two groups were extracted as common neighbors. Analysis was performed based on common neighbors, association prediction process, and weighted common neighbors. Prediction accuracy improved. Centrality criteria were used to determine the weight of each group. New Big Data techniques were used to calculate centrality measures and store them as features of common neighbors. Personal characteristics of users were added to build complete data for training a data mining model. After modeling, the decision tree model was used to predict communication. Results: There was an increase in sensitivity, which indicated model power in identifying positive categories (i. e., communications) when users' characteristics were used. It means that the model could identify potential latent communications. It can be stated that users are more willing to make a relationship with users similar to them through common neighbors. Personal characteristics of users and centrality were effective in method efficiency, while removal of these properties in the learning process of the decision tree model caused a reduction in efficiency criteria. Conclusion: Prediction of latent communications through social networks was promising. Better results can be obtained from further studies.

In water distribution networks the initial and rehabilitation design are usually perform separately. However, it seems that the influence of the initial design upon the future condition of the network performance during operational years and rehabilitation activities are undeniable. Therefore, by combining the initial and rehabilitation designs, a new method is presented in this paper. This method called Dynamic Design of water distribution networks is capable of introducing cheaper and more reliable long term designs in comparison with normal initial design and rehabilitation design of networks. To assess this method, a fuzzy reliability index is introduced. Then by developing the multi objective version of the honey-bee mating optimization algorithm and applying it on two sample networks, final results of the multi objective Dynamic design method is presented. Finally, this paper showed the positive performance and influence of Dynamic design method on decreasing the design costs and increasing system reliability.

In this research connectionist modeling of decision making has been presented. Important areas for decision making in the brain are thalamus, prefrontal cortex and Amygdala. Connectionist modeling with 3 parts representative for these 3 areas is made based the result of Iowa Gambling Task. In many researches Iowa Gambling Task is used to study emotional decision making. In these kind of decision making the role of Amygdala is so important and we expect that a model with two parts (thalamus and Amygdala) can have the best result in modeling participants decisions without considering any part for cortex process. For this purpose 56 participants composed of 20 men and 36 women wanted to do Iowa Gambling Task. Results show that the networks related to two parts model predict 62. 57 Percent’ s of participant’ s decisions and the 3parts model has 68. 46 Percent’ s of that. In conclusion it can be said that three parts modeling has been more success than mathematical two parts model in predicting the performance of participants and the difference is significant. In other words cortex role in this kind of decision making is quite important.