The objective of this study is to determine the efficacy of class V Er:YAG laser (2940 nm) cavity preparation and conventional bur cavity preparation regarding Intrapulpal temperature rise during cavity preparation in extracted human premolar teeth. Twenty non carious premolar teeth extracted for orthodontic purposes were used and class V cavity preparation was applied both buccal and lingual sides for each tooth .Samples were equally grouped into two major groups according to cavity depth (1mm and 2mm). Each major group was further subdivided into two subgroupsof ten teeth for each (twenty cavities for each subgroup). TwinlightEr:YAG laser (2940 nm) with 500mJ pulse energy, P.R.R of 10 Hz and 63.69 J/cm2 energy density was used. The analysis of the data collected revealed that there was highly significant difference between subgroups of each group, i.e., (Er:YAG laser and conventional bur cavity preparation). Also there was a highly significant difference between both group1 and group 2 subgroups (with 1mm and 2mm cavity depth). Best results were obtained from subgroup A which represents class V cavities prepared using Er:YAG laser with energy density of 63.69 J/cm2 .Er:YAG laser cavity preparation with energy density of 63.69 J/cm2 was less temperature rise than conventional bur cavity preparation taking into account the invitro temperature rise of class V cavity preparation.
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Because of vulnerable threats and attacks against database during transmission from sender to receiver, which is one of the most global security concerns of network users, a lightweight cryptosystem using Rivest Cipher 4 (RC4) algorithm is proposed. This cryptosystem maintains data privacy by performing encryption of data in cipher form and transfers it over the network and again performing decryption to original data. Hens, ciphers represent encapsulating system for database tables
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Recently, Image enhancement techniques can be represented as one of the most significant topics in the field of digital image processing. The basic problem in the enhancement method is how to remove noise or improve digital image details. In the current research a method for digital image de-noising and its detail sharpening/highlighted was proposed. The proposed approach uses fuzzy logic technique to process each pixel inside entire image, and then take the decision if it is noisy or need more processing for highlighting. This issue is performed by examining the degree of association with neighboring elements based on fuzzy algorithm. The proposed de-noising approach was evaluated by some standard images after corrupting them with impulse
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Let be a ring. Given two positive integers and , an module is said to be -presented, if there is an exact sequence of -modules with is -generated. A submodule of a right -module is said to be -pure in , if for every -Presented left -module the canonical map is a monomorphism. An -module has the -pure intersection property if the intersection of any two -pure submodules is again -pure. In this paper we give some characterizations, theorems and properties of modules with the -pure intersection property.
Image fusion is one of the most important techniques in digital image processing, includes the development of software to make the integration of multiple sets of data for the same location; It is one of the new fields adopted in solve the problems of the digital image, and produce high-quality images contains on more information for the purposes of interpretation, classification, segmentation and compression, etc. In this research, there is a solution of problems faced by different digital images such as multi focus images through a simulation process using the camera to the work of the fuse of various digital images based on previously adopted fusion techniques such as arithmetic techniques (BT, CNT and MLT), statistical techniques (LMM,
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The main reason for the emergence of a deepfake (deep learning and fake) term is the evolution in artificial intelligence techniques, especially deep learning. Deep learning algorithms, which auto-solve problems when giving large sets of data, are used to swap faces in digital media to create fake media with a realistic appearance. To increase the accuracy of distinguishing a real video from fake one, a new model has been developed based on deep learning and noise residuals. By using Steganalysis Rich Model (SRM) filters, we can gather a low-level noise map that is used as input to a light Convolution neural network (CNN) to classify a real face from fake one. The results of our work show that the training accuracy of the CNN model
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