The experimental verification for the computational method sometimes varies due to numerous factors such as the manufacturing process and the materials' property change due to environmental aspects. In this work, we performed verification of experimental and computational evaluation of a hybrid composite moderate thick plate. The experiment was performed with simplistic approaches and without the advanced tools of preparing composite materials. This is due to the fact that most of the students in many developing countries around the world cannot have access to such equipment. As such, in this research, we are presenting cheap and easy preparation methods, with some details, for even equipment calibration and some tricks to attain a reliable composite structure for educational purposes. Moreover, the software and solvers used in this study are freely provided by the supplier for educational purposes. This study examined two methods for producing carbon and glass/polyester composite plates and discussed which one was best based on mechanical properties for different volume fractions, random stacking sequences, and ply angles (using OCTAVE's random estimation program). It also determined the three natural frequencies experimentally and with the aid of ANSYS. Less than 6% separated the experimentally determined natural frequencies from the calculated results.

Rubber-based composites are widely used especially in transportation. The goal of this paper is to study the mechanical properties of rubber-based composites of carbon black and Nano Aluminum trioxide additive (50 nm). Various percentage of carbon black was used (20,40, and 60 phr). The increase in Carbon black percentage shows an increase in mechanical properties of the composites (for 60 phr properties tensile test improve by 49%, for hardness resistance the improve was 21%, and for the wear test the composite improve by 22%). Various wetting percentage of nano Aluminum trioxide was used (1,1.5,2, and 2.5 %). Increasing the wetting percentage increase tensile strength (27%, hardness resistance increase by 28%, and wear resistance increase nonlinearly with a percentage reaching 70%). Selecting the optimal composition of the two fillers, then study different irradiances for it with the ultraviolet laser of moderately low energy after the vulcanization process. Post ultraviolet laser of (345 nm). Furthermore, laser vulcanization shows improvement in mechanical properties.

Computer-aided image diagnostics (CAD) have been used in many fields of diagnostic medicine. It relies heavily on classical computer vision and artificial intelligence. Quantum neural network (QNN) has been introduced by many researchers around the world and presented recently by research corporations such as Microsoft, Google, and IBM. In this paper, the investigation of the validity of using the QNN algorithm for machine-based breast cancer detection was performed. To validate the learnability of the QNN, a series of learnability tests were performed alongside with classical convolutional neural network (CCNN). QNN is built using the Cirq library to perform the assimilation of quantum computation on classical computers. Series of investigations were performed to study the learnability characteristics of QNN and CCNN under the same computational conditions. The comparison was performed for real Mammogram data sets. The investigations showed success in terms of recognizing the data and training. Our work shows better performance of QNN in terms of successfully training and producing a valid model for smaller data set compared to CCNN.