This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO) for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In ord

This paper deals with load-deflection behavior the jointed plain concrete pavement system using steel dowel bars as a mechanism to transmit load across the expansion joints. Experimentally, four models of the jointed plain concrete pavement system were made, each model consists of two slabs of plain concrete that connected together across expansion by two dowel bars and the concrete slab were supported by the subgrade soil. Two variables were dealt with, the first is diameter of dowel bar (12, 16 and 20 mm) and the second is type of the subgrade soil, two types of soil were used which classified according to the (AASHTO): Type I (A-6) and type II (A-7-6). Experimental results showed that increasing dowel bar diameter from 12 mm to 20 mm

The pure ZnS and ZnS-Gr nanocomposite have been prepared
successfully by a novel method using chemical co-precipitation. Also
conductive polymer PPy nanotubes and ZnS-PPy nanocomposite
have been synthesized successfully by chemical route. The effect of
graphene on the characterization of ZnS has been investigated. X-ray
diffraction (XRD) study confirmed the formation of cubic and
hexagonal structure of ZnS-Gr. Dc-conductivity proves that ZnS and
ZnS-Gr have semiconductor behavior. The SEM proved that
formation of PPy nanotubes and the Gr nanosheet. The sensing
properties of ZnS-PPy/ZnS-Gr for NO2 gas was investigated as a
function of operating temperature and time under optimal condition.
The sensitivity,

This paper deals with, Bayesian estimation of the parameters of Gamma distribution under Generalized Weighted loss function, based on Gamma and Exponential priors for the shape and scale parameters, respectively. Moment, Maximum likelihood estimators and Lindley’s approximation have been used effectively in Bayesian estimation. Based on Monte Carlo simulation method, those estimators are compared in terms of the mean squared errors (MSE’s).

Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To