Frustrated Total Internal Reflection FTIR phenomenon is manifested employing Newton‟s rings setup generated via a coherent light beam of a laser diode ( . All concentric bright and dark rings, except the central bright spot, were noticed to recede (disappear) when the incident angle exceeded the critical angle of 41o.
It was also shown that the current setup has proven its applicability for other tests and can give convenient results that conform with theory. Neither the concept nor the design is beyond what can be realized in an undergraduate laboratory. However, technical improvements in mounting the prism - lens may be advisable. As an extension of the experiments, the effect can be studied using hollow prism filled with liquids

Background: As the development of zirconia crown using CAD/CAM technology, the usage of full zirconia crown is gradually increased. The aim of this in-vitro study was to evaluate and contrast the vertical marginal fit of single all-ceramic translucent zirconia crowns constructed from different brands translucent zirconia blanks. Materials and Methods: An acrylic resin model of a left maxillary premolar was prepared all around the tooth with (1 mm) depth and 3D scanning to get fifteen STL files, then distributed into three groups (Imes-icor, Whitepeaks and Dental direct), 3D scanning and milling machine by Imes-icor CAD/CAM devise. Marginal gaps along vertical planes were measured at four indentations at the (mid mesial, mid distal, mid bucc

It is well-known that the existence of outliers in the data will adversely affect the efficiency of estimation and results of the current study. In this paper four methods will be studied to detect outliers for the multiple linear regression model in two cases :  first, in real data; and secondly,  after adding the outliers to data and the attempt to detect it. The study is conducted for samples with different sizes, and uses three measures for  comparing between these methods . These three measures are : the mask, dumping and standard error of the estimate.

RNA Sequencing (RNA-Seq) is the sequencing and analysis of transcriptomes. The main purpose of RNA-Seq analysis is to find out the presence and quantity of RNA in an experimental sample under a specific condition. Essentially, RNA raw sequence data was massive. It can be as big as hundreds of Gigabytes (GB). This massive data always makes the processing time become longer and take several days. A multicore processor can speed up a program by separating the tasks and running the tasks’ errands concurrently. Hence, a multicore processor will be a suitable choice to overcome this problem. Therefore, this study aims to use an Intel multicore processor to improve the RNA-Seq speed and analyze RNA-Seq analysis's performance with a multiproce

This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.

The rapid development of Internet of Things (IoT) devices and their increasing numbers have caused a tremendous increase in network traffic and a wider range of cyber-attacks. This growing trend has complicated the detection process for traditional intrusion detection systems and heightened the challenges faced by these devices, such as imbalanced and large training data. This study presents a cohesive methodology of a series of intelligent techniques to prepare clean and balanced data for training the first (core) layer of a robust hierarchical intrusion detection system. The methodology was built by cleaning and compressing the data using an Autoencoder and preparing a strong latent space for balancing using a hybrid method that combines