The prostheses sockets use normally composite materials which means that their applications may be related with the human body. Therefore, it was very necessary to improve the mechanical properties of these materials. The prosthetic sockets are subjected to varying stresses in gait cycle scenario which may cause a fatigue damage. Therefore, it is necessary or this work to modify the fatigue behavior of the materials used for manufacturing the prostheses sockets. In this work, different Nano particle materials are used to modify the mechanical properties of the composite materials, and increase the fatigue strength. By using an experimental technique, the effect of using different volume fractions for various types for Nano particle materials on the fatigue behavior for composite materials, and preparing the fatigue samples and tested using the fatigue apparatus. The Nano particles used were (Nano SiO2 and Nano Al2O3) materials with volume fraction as (0% to 2%), for each type of Nano material used. The artificial neural network technique was adopted to have a verification for the experimental results and calculating the fatigue life and strength for composite materials, with the addition of nanoparticles and then, a comparison of the results was achieved. The comparison of the results indicate a maximum error between results calculated by two technique did not exceeded about (1%). Then, the results calculated showed that the mechanical properties and fatigue life and strength increase with reinforcement with Nano particle. Also, the results showed that the modified for fatigue limits with materials by (Nano SiO2) Nano particle was more than the modified for fatigue limits for materials reinforcement with other materials. Finally, it can be concluded that the modified for fatigue strength, by reinforcement with (Nano SiO2), leads to 60% more than fatigue limit without Nano additive.
The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi
... Show MoreBacterial infections pose an ongoing challenge due to resistance developed by infectious bacteria. So much research targeting designing new antibacterials is published annually. Our goal is to synthesize compounds that have given antibacterial activity according to molecular docking against the chosen target protein and that have acceptable ADMET properties that can be synthesized and used in the future. New 2-(5-methoxy-1-(4-chlorobenzene)-2-methyl-1H-indol-3-yl)acetohydrazide derivatives’ antibacterial efficacy against two common strains of Gram-negative and Gram-positive microorganisms has been developed, produced, and investigated. Sophisticated, modern analytical methods, including ATR-FTIR and 1H NMR spectroscopy, were used
... Show MoreThis study discusses risk management strategies caused by pandemic-related (Covid-19) suspensions in thirty-six engineering projects of different types and sizes selected from countries in the middle east and especially Iraq. The primary data collection method was a survey and questionnaire completed by selected project crew and laborers. Data were processed using Microsoft Excel to construct models to help decision-makers find solutions to the scheduling problems that may be expected to occur during a pandemic. A theoretical and practical concept for project risk management that addresses a range of global and local issues that affect schedule and cost is presented and results indicate that the most significant delays are due to a
... Show MoreA novel welded demountable shear connector for sustainable steel-concrete composite structures is proposed. The proposed connector consists of a grout-filled steel tube bolted to a compatible partially threaded stud, which is welded on a steel section. This connector allows for an easy deconstruction at the end of the service life of a building, promoting the reuse of both the concrete slabs and the steel sections. This paper presents the experimental evaluation of the structural behavior of the proposed connector using a horizontal pushout test arrangement. The effects of various parameters, including the tube thickness, the presence of grout infill, and the concrete slab compressive strength, were assessed. A nonlinear finite element mode
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