Burnishing improves fatigue strength, surface hardness and decrease surface roughness of metal because this process transforms tensile residual stresses into compressive residual stresses. Roller burnishing tool is used in the present work on low carbon steel (AISI 1008) specimens. In this work, different experiments were used to study the influence of feed parameter and speed parameter in burnishing process on fatigue strength, surface roughness and surface hardness of low carbon steel (AISI 1008) specimens. The first parameter used is feed values which were (0.6, 0.8, and 1) mm at constant speed (370) rpm, while the second parameter used is speed at values (540, 800 and 1200) rpm and at constant feed (1) mm. The results of the fatigue

This contribution investigates the effect of the addition of the Hubbard U parameter on the electronic structural and mechanical properties of cubic (C-type) lanthanide sesquioxides (Ln2O3). Calculated Bader's charges confirm the ionic character of Lnsingle bondO bonds in the C-type Ln2O3. Estimated structural parameters (i.e., lattice constants) coincide with analogous experimental values. The calculated band gaps energies at the Ueff of 5 eV for these compounds exhibit a non-metallic character and Ueff of 6.5 eV reproduces the analogous experimental band gap of cerium sesquioxide Ce2O3. We have thoroughly investigated the effect of the O/Ce ratios and the effect of hafnium (Hf) and zirconium (Zr) dopants on the reduction energies of C

Image retrieval is used in searching for images from images database. In this paper, content – based image retrieval (CBIR) using four feature extraction techniques has been achieved. The four techniques are colored histogram features technique, properties features technique, gray level co- occurrence matrix (GLCM) statistical features technique and hybrid technique. The features are extracted from the data base images and query (test) images in order to find the similarity measure. The similarity-based matching is very important in CBIR, so, three types of similarity measure are used, normalized Mahalanobis distance, Euclidean distance and Manhattan distance. A comparison between them has been implemented. From the results, it is conclud

Hydrocarbon production might cause changes in dynamic reservoir properties. Thus the consideration of the mechanical stability of a formation under different conditions of drilling or production is a very important issue, and basic mechanical properties of the formation should be determined.
There is considerable evidence, gathered from laboratory measurements in the field of Rock Mechanics, showing a good correlation between intrinsic rock strength and the dynamic elastic constant determined from sonic-velocity and density measurements.
The values of the mechanical properties determined from log data, such as the dynamic elastic constants derived from the measurement of the elastic wave velocities in the material, should be more a

The objective of this work is to design and implement a cryptography system that enables the sender to send message through any channel (even if this channel is insecure) and the receiver to decrypt the received message without allowing any intruder to break the system and extracting the secret information. In this work, we implement an interaction between the feedforward neural network and the stream cipher, so the secret message will be encrypted by unsupervised neural network method in addition to the first encryption process which is performed by the stream cipher method. The security of any cipher system depends on the security of the related keys (that are used by the encryption and the decryption processes) and their corresponding le

In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc

. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a

Cloud computing provides huge amount of area for storage of the data, but with an increase of number of users and size of their data, cloud storage environment faces earnest problem such as saving storage space, managing this large data, security and privacy of data. To save space in cloud storage one of the important methods is data deduplication, it is one of the compression technique that allows only one copy of the data to be saved and eliminate the extra copies. To offer security and privacy of the sensitive data while supporting the deduplication, In this work attacks that exploit the hybrid cloud deduplication have been identified, allowing an attacker to gain access to the files of other users based on very small hash signatures of