An Investigation of estimated Mechanical Properties of AL-Alloys 2024-T3, which is the most commonly used in industrial applications, been established experimentally. A new novel Plasma Peening techniques had applied for the whole surfaces of the material by CNC-Plasma machine for 48 specimen, and then a new investigation were toke over to figure the amount of change in mechanical properties and estimated fatigue life. It found that improvement was showing a nonlinear behavior according to peening duration time, speed, peening distance, peening number, and amount of effected power on the depth of the material thickness. The major improvement was at medium speed long duration time normal peening distance. Which shows up t

Estimation of mechanical and physical rock properties is an essential issue in applications related to reservoir geomechanics. Carbonate rocks have complex depositional environments and digenetic processes which alter the rock mechanical properties to varying degrees even at a small distance. This study has been conducted on seventeen core plug samples that have been taken from different formations of carbonate reservoirs in the Fauqi oil field (Jeribe, Khasib, and Mishrif formations). While the rock mechanical and petrophysical properties have been measured in the laboratory including the unconfined compressive strength, Young's modulus, bulk density, porosity, compressional and shear -waves, well logs have been used to do a compar

Dental implants can be made of various materials, and amongst them, titanium and titanium alloy were the materials of choice for dental implants for many years because of their biocompatibility. The two alloys have a high level of biocompatibility, a lower modulus of elasticity, and better corrosion resistance than other alloys. Thus, they are frequently utilized in biomedical applications and mostly replace stiff fabrics. The latest advances in a new strontium oxide–cp titanium composite alloy are the main topic of this research. With regard to biomedical applications, additions of strontium oxide were synthesized at three distinct weight percentages (2%, 4%, and 6% by wt%). Powder metallurgy was used to create the alloys, which

Every year, millions of tons of waste glass are created across the globe. It is disposed of in landfills, which is unsustainable since it does not disintegrate into the environment. This study aims to produce reactive powder concrete by using recycled glass powder and determine the influence on the mechanical properties. This study investigated the effect of partial replacement of cement with recycled glass powder at two percentages (0, 20) % by weight of cement on some mechanical properties (Fresh density, Splitting tensile strength, Impact Strength, and voids%) of reactive powder concrete containing 1 % micro steel (MSRPC). Furthermore, using steam curing for (5 hours) at 90 degrees celsius after hardening the sample directly, RPC was

Image quality plays a vital role in improving and assessing image compression performance. Image compression represents big image data to a new image with a smaller size suitable for storage and transmission. This paper aims to evaluate the implementation of the hybrid techniques-based tensor product mixed transform. Compression and quality metrics such as compression-ratio (CR), rate-distortion (RD), peak signal-to-noise ratio (PSNR), and Structural Content (SC) are utilized for evaluating the hybrid techniques. Then, a comparison between techniques is achieved according to these metrics to estimate the best technique. The main contribution is to improve the hybrid techniques. The proposed hybrid techniques are consisting of discrete wavel

Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential a

     This paper presents an efficient system using a deep learning algorithm that recognizes daily activities and investigates the worst falling cases to save elders during daily life. This system is a physical activity recognition system based on the Internet of Medical Things (IoMT) and uses convolutional neural networks (CNNets) that learn features and classifiers automatically. The test data include the elderly who live alone. The performance of CNNets is compared against that of state-of-the-art methods, such as activity windowing, fixed sample windowing, time-weighted windowing, mutual information windowing, dynamic windowing, fixed time windowing, sequence prediction algorithm, and conditional random fields. Th

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