Reservoir permeability is a critical parameter for the evaluation of hydrocarbon reservoirs. There are a lot of well log data related with this parameter. In this study, permeability is predicted using them and a supervised committee Machine neural network (SCMNN) which is combined of 30 estimators. All of data were divided in two low and high permeability populations using statistical study. Each estimator of SCMNN was combined of two simple networks to predict permeability in both low and high classes and one gating network, considered as a classifier, classified data to these two classes. Thus, each low and/or high input data would predict in related network. This SCMNN was used to predict permeability on the data of one of petroleum reservoirs of south-west of Iran. 210 samples of this reservoir were available. Because of the fewness of data 80% of them were used as training data and 20% of them were used as validation and testing data. The overall fitting between predicted permeability versus measured ones was qualified through R2 (R=correlation coefficient) to be 97.72% which is considered appropriate. Whereas, R2 in the simple network in the best stat was 84.14%. The high power and efficiency of SCMNN are indicated by lower bias and better R2 in results.

Design and optimization of high-power electric Machines for the use of electric vehicles is one of the important issues today for the development of green technologies. Engines required for electric vehicles must have a power of more than 50 kW and must also produce torques of more than 200 Nm. The motors currently most commonly used in electric vehicle propulsion are permanent magnet synchronous Machines. Due to the many problems of using permanent magnets in electric Machines, the use of non-magnet electric Machines such as switched reluctance Machines have received much attention. Double stator switched reluctance Machine is one of the newest types of these Machines. Recently, another new type of electric Machine called induction switched reluctance Machine has been introduced for the use of electric vehicles. In this Machine, the rotor conductors act like a magnetic shield by deflecting the magnetic flux, preventing magnetic field lines from passing through the rotor body. In this paper, a double-stator switched reluctance Machine and an induction switched reluctance Machine are considered and their properties are extracted by finite element method. The simulation results including torque profile, torque density and efficiency are presented and compared. Finally, the best topology for electric propulsion is proposed.

This paper passes under review the history of WordNets and introduces some of the most prominent projects of Word Net construction in order to illustrate how these tools can be employed as lexicon in finding equivalents in Machine translation projects. Then the process of designing and developing the Persian noun WordNet-as a part of the Persian WordNet which is an important tool for processing Persian language-is discussed.At the end, the applications of Word Nets in general and more specifically their application in Machine translation are put forward.

Predicting unexpected incidents and energy consumption decline is one of the current problems in the industry. The extant study addressed parallel Machine scheduling by consideration of failures and energy consumption decline. Moreover, the present paper aimed at minimizing early and late delivery penalties, and enhancing tasks. This research designed a mathematical model for this problem that considered processing times, delivery time, rotation speed and torque, failure time, and Machine availability after repair and maintenance. Failure times have been predicated on using Machine learning algorithms. The results indicated that the proposed model can be suitably solved for the size of 10 jobs or tasks and five Machines. This research addresses the problem in two parts: the first part predicts failures, and the second part includes the sequence of parallel Machine scheduling operations. After the previous data were received in the first step, Machine failure was predicted by using Machine learning algorithms, and a set of rules were obtained to correct the process. The obtained rules were used in the model to improve the machining process. In the second step, scheduling mode was used to determine operations sequence by consideration of these failures and Machinery unavailability to achieve the optimal sequence. Moreover, it is supposed to reduce energy consumption and failures. This study used the Light GBM algorithm and achieved 85% precision in failure prediction. The rules obtained from this algorithm contributed to cost reduction.

Nursing care during dialysis involves managing symptoms and preventing complications among patients undergoing hemodialysis or peritoneal dialysis. In this regard, to improve the quality of nursing care during dialysis, several approaches were developed to enhance hemodialysis adequacy and prevent complications,however, Machine learning (ML) emerged as a methodological approach for eval-uating hemodialysis adequacy and complications. The current study aims to analyze ML approach in predicting and managing hemo-dialysis by R programming language analysis to provide a therapeutic concept for hemodialysis management in critical nursing care. An R programming language was used to perform the logical analysis of the data. ML algorithms based on usage rate included logistic regression (LR), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Complement Naive Bayes (CNB), Takagi-Sugeno-Kang fuzzy system (G-TSK-FS), k-nearest neighbors' classifier (KNN), Stochastic gradient descent (SGD), Linear Discriminant Analysis (LDA), and Multi-adaptive neural-fuzzy system (MANFIS). Also, the use of ML in nursing care during hemodialysis is categorized into three indications for predicting hemodialysis adequacy, complications, and vascular access performance. Using ML in hemodialysis nursing care is a growing research interest. The main application areas are the prediction of hemodialysis adequacy, complications, and vascular access performance. LR and SVM are practical ML algorithms for constructing AI tools to improve hemodialysis management.