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The reverse osmosis (RO) water desalination system powered by the photovoltaic (PV) unit is commonly used to produce drinking water due to their reasonable price. This review focused to display different designs of small-scale PV powered brackish water RO (PV-BWRO) systems that were installed to improve the water productivity at remote areas. The configuration of BWRO units, which determined the operating cost, permeate flow and amount of rejected water, is of great concerns in this work. The specific energy consumption (SEC), total capital cost and water production cost are also analysed to display the feasibility of application of PV unit for small-scale BWRO desalination system. The single-stage configuration was suitable for most of small-scale BWRO systems owning high recovery rates (i.e., higher than 50%). The PV solar system pairing with batteries was not recommended at production capacities below 5 m3 for their high cost and short lifetime. Independently operated PV-BWRO systems required the highest capital cost while produced the lowest operating and overall production costs compared to hybrid systems.

—Recently, many technological improvement apply in the discovery of various designs of haptic devices. Several mechanism structures including serial, parallel, and hybrid-kinematic manipulators can be considered for making a haptics device. The most successful haptic mechanisms are parallel-type, because of low moving inertia, large force reflection, and high stiffness. This research shows the 6-DOF parallel haptics device based on the parallel mechanism using a translation driver motor mounted on each leg. Firstly, we introduce a 6-DOF parallel mechanism using a translation driver motor haptics device model. Due to the unsure parameters, we focus on solving the mathematics model with the nonlinear parameters of the 6-DOF parallel mechanism. Secondly, to fix the kinematics and dynamics nonlinear uncertainties parameters, the SMCNN controller for 6-DOF parallel mechanism application using a translation driver motor is designed. The Sliding model control base on artificial intelligence neural network is used to calculate the unsure factors. In this technique, to prove the stability of the system the Lyapunop theory is used. Finally, the authors the simulation results of two control algorithms with different uncertain components are presented and comparing them to demonstrate the effectiveness of the new control method. The control method is demonstrated by way of implementing the set of rules in artificial surroundings with realistic parameters, in which the received consequences are fairly promising. The obtained from SMCNN algorithm results are highly promising and accurate.