during the past few years, modeling in agriculture has attracted considerable attention. new modeling methods including neural networks are employed in various industries, and it is necessary that their use in agriculture be also considered. this research addressed the trend of energy use in broiler farms in alborz province and sought to model the trend of energy consumption and production in t...

Human conflict, geopolitical crises, terrorist attacks, and natural disasters can turn large parts of energy distribution networks offline. Europe's current gas supply network is largely dependent on deliveries from Russia and North Africa, creating vulnerabilities to social and political instabilities. During crises, less delivery may mean greater congestion, as the pipeline network is used in...

State of the art in the use of gas quality tracking systems for billing purposes are systems for transmission grids, where all inlets and rather all offtakes are measured. Such a complete measurement infrastructure is for most gas distribution networks very expensive and not feasible in practice. One promising approach for solving this challenge is the Nodal Point Load Observer, developed at Cl...

After extreme events such as floods, thunderstorms, blizzards and hurricanes there will be devastating effects in the distribution networks which may cause a partial or complete blackout. Then, the major concern for the system operators is to restore the maximum critical loads as soon as possible by available generation units.  In order to solve this problem, this paper provides a restoration s...

Considering the network structure is one of the new approaches in studying stochastic PERT networks (SPN). In this paper, planar networks are studied as a special class of networks. Two structural reducible mechanisms titled arc contraction and deletion are developed to convert any planar network to a series-parallel network structure. In series-parallel SPN, the completion time distribution f...

Generative adversarial networks (GANs) [7] are a recent approach to train generative models of data, which have been shown to work particularly well on image data. In the current paper we introduce a new model for texture synthesis based on GAN learning. By extending the input noise distribution space from a single vector to a whole spatial tensor, we create an architecture with properties well...