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.

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.

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.