Robots have become an essential part of modern industries in welding departments to increase the accuracy and rate of production. The intelligent detection of welding line edges to start the weld in a proper position is very important. This work introduces a new approach using image processing to detect welding lines by tracking the edges of plates according to the required speed by three degrees of a freedom robotic arm. The two different algorithms achieved in the developed approach are the edge detection and top-hat transformation. An adaptive neuro-fuzzy inference system ANFIS was used to choose the best forward and inverse kinematics of the robot. MIG welding at the end-effector was applied as a tool in this system, and the wel

Begging is an ancient social phenomenon since the Man's creation. It was simple at its beginning to get a living and the family needed, but presently, this simple social phenomenon converted to a big phenomenon which controlled by competent groups away from the values and principles. Their doings are far from humanity representing by the cutting of a hand or a foot and facial distortion or removed eye as a way of attracting people's emotions to get material and incorporeal helps; these doings became a trade which is exchanged by slave traders. Furthermore, different illegal and greedy methods were followed as a way of blackmailing others; contrariwise other cases of begging are doing by a people who real poor and needy. In this case, the

Background: The aim of this study was to evaluate and compare the apical microleakage around retrograde cavities prepared with ultrasonic technique and filled with (Biodentineâ„¢) Materials and methods: 40 extracted single rooted human permanent maxillary teeth with mature apices were selected. The roots were prepared chemo-mechanically using k-files with crown-down technique and then obturated with lateral condensation gutta-percha technique. Teeth were divided into four main groups according to the cavity preparation method either manual or ultrasonic technique: Group A (n=10): A class I retrograde cavity at root end was prepared with traditional handpeice equipped and placement of Biodentine with manual condensation. Group B (n=10):

Background: The aim of this study was to evaluate and compare the apical microleakage around retrograde cavities prepared with ultrasonic technique and filled with (Biodentineâ„¢) Materials and methods: 40 extracted single rooted human permanent maxillary teeth with mature apices were selected. The roots were prepared chemo-mechanically using k-files with crown-down technique and then obturated with lateral condensation gutta-percha technique. Teeth were divided into four main groups according to the cavity preparation method either manual or ultrasonic technique: Group A (n=10): A class I retrograde cavity at root end was prepared with traditional handpeice equipped and placement of Biodentine with manual condensation. Group B (n=10):

The CuInSe2 (CIS) nanocrystals are synthesized by arrested precipitation from molecular precursors are added to a hot solvent with organic cap- ping ligands to control nanocrystal formation and growth. CIS thin films deposited onto glass substrate by spray - coating, then selenized in Ar- atmosphere to form CIS thin films. PVs were made with power conversion efficiencies of 0.631% as -deposited and 0.846% after selenization, for Mo coated, under AM 1.5 illumination. X-ray diffraction (XRD) and energy dispersive spectroscopy (EDS) analysis it is evident that CIS have the chalcopyrite structure as the major phase with a preferred orientation along (112) direction and the atomic ratio of Cu : In : Se in the nanocrystals is nearly 1 : 1 : 2

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an