The main challenge facing the oil industry is to reduce development costs while accelerating recovery with maximizing reserves. One of the key enabling technologies in this area is Intelligent well completions. Intelligent well technology (IWT) is a relatively new technology that has been adopted by many operators in recent years to improve oil and gas recovery. Intelligent well completions employ Annular Flow Control Valves (AFCVs) to balance the production profile along the length of the well completion by splitting it into two (or more) sections. The aim of Intelligent wells is to optimize the production (Delaying the gas and water breakthrough and decreasing water production).The energy that moves crude oil and natural gas from the subsurface rock to the production well is called the reservoir drive [1]. These energies because of their different mechanisms, have different effects on reservoir production. In spite of advancement in Intelligent Well Technology, the effect of Intelligent well on reservoir drive mechanisms under different reservoir characterization have not been well addressed. In this paper, six conceptual models of oil reservoir have been built and different production scenarios have been discussed. Based on the objective function, scenarios will be selected and will compare with a conventional scenario and decide whether to use smart well in these models or not.

This paper investigates beamforming design for Simultaneous transmission and reflection reconfigurable Intelligent surface (Star RIS) assisted secure wireless Communication with three operating protocols (energy splitting (ES), mode switching (MS), and time switching (TS)) with the assumption of the availability of imperfect CSI of two eavesdroppers at BS. We maximize the sum rate of legitimate users by designing both active and passive beamforming at BS and RIS, respectively, under the constraint of the limited maximum tolerable rate of eavesdroppers and a limited transmission power budget. The non-convex problem is solved by alternating optimization (AO) for active and passive beamforming optimization, and also successive convex approximation (SCA), and the penalty concave-convex procedure (PCCP). According to the simulation results, Star-RIS outperforms conventional RIS, ES mode outperforms MS and TS, and the performance is higher with lower channel estimation error.

Today’ s, Intelligent agent system (IAS) are considered as an important part of people's lives. Therefore, many of organizations try to implement IAS in their mechanism. One of these organizations in Iran is teleCommunication Infrastructure Company. Because any implementation need a model which clarify the structural and contextual components, therefore, the current research is conducted to provide a model for developing the necessary infrastructure for implementation of Intelligent technologies in the Communication and teleCommunication mechanisms of Ilam Province. To achieve the goal, a qualitative approach and thematic analysis method were used. The research population consisted of all experts in the field of ICT in Ilam province Infrastructure Communications Company that using purposeful sampling method and relying on theoretical data saturation, 10 of them were selected as sample. Semi-structured interviews were used to collect the data. The data were analyzed through theme analysis. Based on the method, 4 themes, 10 main categories and 153 open codes were extracted. The findings of the study showed that to transform Communication mechanisms into Intelligent technologies, there must be technological, management, marketing and cultural infrastructure. Technological infrastructure consisted of Intelligent software and hardware; management infrastructure consisted of knowledge and belief; marketing infrastructure included attracting Intelligent technology to audiences, encouraging ideas, physical and virtual channels; and finally, cultural infrastructure, it was staff training and public awareness.

This paper is directed to the question of how to model and design an efficient tool for the Intelligent mapping which is based on both dynamic and efficient storage of data and soft computing. The former is performed by our method that learns how to store, search and delete the data. After pointing out the limitation of the crisp evaluation of the distance between two points, we argue in favor of soft computing which is based on the extension of metric space to interval one and then to the fuzzy metric. A-Star algorithm is used to illustrate our model along with the injection of competitive data structures.

Kidney transplantation is the treatment of choice for patients with end-stage renal disease (ESRD). Prediction of the transplant survival is of paramount importance. The objective of this study was to develop a model for predicting survival in kidney transplant recipients. In a cross-sectional study, 717 patients with ESRD admitted to Nemazee Hospital during 2008–2012 for renal transplantation were studied and the transplant survival was predicted for 5 years. The multilayer perceptron of artificial neural networks (MLP-ANN), logistic regression (LR), Support Vector Machine (SVM), and evaluation tools were used to verify the determinant models of the predictions and determine the independent predictors. The accuracy, area under curve (AUC), sensitivity, and specificity of SVM, MLP-ANN, and LR models were 90.4%, 86.5%, 98.2%, and 49.6%, 85.9%, 76.9%, 97.3%, and 26.1%, and 84.7%, 77.4%, 97.5%, and 17.4%, respectively. Meanwhile, the independent predictors were discharge time creatinine level, recipient age, donor age, donor blood group, cause of ESRD, recipient hypertension after transplantation, and duration of dialysis before transplantation. SVM and MLP-ANN models could efficiently be used for determining survival prediction in kidney transplant recipients.