Background: The purpose of this study was to evaluate and compare centering ability and canal transportation of simulated S-shaped canals instrumented with four different types of rotary nickel-titanium systems. Materials and Methods: Forty simulated S-shaped canals in resin blocks were divided into four groups of ten each and were instrumented to an apical size 25 by different instrumentation technique using ProTaper Universal files (group A), ProTaperNext (group B), Reciproc (group C) and WaveOne (group D).Centering ability and canal transportation was measured at (11) measuring points from D0 to D10 bysuperimposion of the pre- and post-operative images obtained by using digital camera in standardized manner. An assessment of the canals

In all applications and specially in real time applications, image processing and compression plays in modern life a very important part in both storage and transmission over internet for example, but finding orthogonal matrices as a filter or transform in different sizes is very complex and importance to using in different applications like image processing and communications systems, at present, new method to find orthogonal matrices as transform filter then used for Mixed Transforms Generated by using a technique so-called Tensor Product based for Data Processing, these techniques are developed and utilized. Our aims at this paper are to evaluate and analyze this new mixed technique in Image Compression using the Discrete Wavelet Transfo

Correct grading of apple slices can help ensure quality and improve the marketability of the final product, which can impact the overall development of the apple slice industry post-harvest. The study intends to employ the convolutional neural network (CNN) architectures of ResNet-18 and DenseNet-201 and classical machine learning (ML) classifiers such as Wide Neural Networks (WNN), Naïve Bayes (NB), and two kernels of support vector machines (SVM) to classify apple slices into different hardness classes based on their RGB values. Our research data showed that the DenseNet-201 features classified by the SVM-Cubic kernel had the highest accuracy and lowest standard deviation (SD) among all the methods we tested, at 89.51 %  1.66 %. This