Abstract: 

This study delves into the influence of muon positioning on particle behavior within atoms, exploring scenarios where the muon resides inside, outside, or partially inside the nucleus of a hydrogen atom. The observed behavior of the differential cross section (DCS) in these contexts yields significant insights into atomic interactions. When the muon is within the nucleus, a marked decrease in the DCS is noted as the electron's momentum undergoes changes. This suggests a substantial impact of the muon on scattering behavior, akin to phenomena in laser-assisted electron-hole scattering and classical scattering. Conversely, when the muon is outside the nucleus, a sharp decrease in DCS is observed at low momentum changes, in contrast to the behavior observed when the muon is within the nucleus. In cases where the muon is partially internal, an intermediary pattern emerges: exponential decay at lower energies and a gradual decline beyond 1.5 MeV. This behavior bridges the scenarios depicted with the muon inside and outside the nucleus. The findings underscore the pivotal role of muon placement in shaping scattering dynamics within atoms. This enhanced understanding of atomic interactions carries profound implications for the advancement of nuclear physics and our comprehension of the fundamental constituents of matter. The development and computational analysis of corresponding equations were facilitated through the MATLAB student package, bolstering the robustness of the study's conclusions.  

Keywords:  Muon, Differential cross section, atomic interaction, scattering, muon positioning, Hydrogen atom. 

Abstract: 

  Composite materials have found extensive applications in the field of medicine, offering a unique blend of mechanical strength and biological compatibility. This comprehensive review examines the critical role of composite materials in various medical applications and explores the intricate interplay between mechanical and biological interventions. We delve into the design of composite materials with optimal mechanical properties, enhancement of biocompatibility through surface modifications and bioactive fillers, and tailoring degradation profiles to meet specific medical needs. Additionally, we discuss manufacturing challenges, regulatory compliance, clinical outcomes, long-term health implications, and the growing importance of environmental sustainability in this dynamic field. This review underscores the transformative potential of composite materials in advancing patient care, healthcare outcomes, and sustainable healthcare technologies.

Keywords: Composite Materials, Biocompatibility, Mechanical Properties, Degradation, Manufacturing Challenges, Regulatory Compliance, Clinical Outcomes.

Abstract: 

  The purpose of this research is to obtain the best response for the arm of robot by using different theories of control. In the development, the mathematical model of the robot is simulated by using Matlab software. The following section, utilizes the PID algorithm and fuzzy logic controller were designed and analyzed according to the desired requirements. Finally, compared between the responses of different control theories.

Keywords: Fuzzy Logic Controller, Robot, Matlab.

Abstract: 

 Particulate nanomaterials have been and still are the focus of attention for many researchers and industrialists in many applications because of their unique advantages compared to other types of particles.

The current work includes the manufacture of zinc oxide particles from two different types of materials (zinc nitrate and zinc chloride). The aim of using two different materials at a temperature of (550) °C is to know the change of the chemical compassion effect on the softness and composition of zinc oxide particles and to choose the best for strengthening polymeric composite materials, as well as for future manufacturing processes. The resulting powder was evaluated through tests (X-ray - SEM - particle size analysis), where the use of nitrate showed the best results.

The second part of the work includes adding the best resulting powders to the polymer (laminating resin) according to the following selected ratios (3, 6, 9, 12) % by weight. The resulting composite nanomaterial was evaluated through (tension, hardness, and bending) tests, where the best percentage was (9%), so this percentage will be the best in terms of its use in prosthetic limb applications (Socket).

Keywords: Relaxation Method, Nanoparticles, Composites ZnO .

Abstract: 

 The FPGA was identified as an embedded device design project with the goal of determining the technology's suitability for use in real-time, high-performance applications. The test program in this paper is a speech encryption method designed for use in military and high-security environments. This paper employed a Xilinx Virtex II Pro platform FPGA and was particularly interested in using the peripheral technology made available. We created a design to test the feasibility of using FPGAs in embedded systems with real-time specifications by using one as the foundation for a digital speech encryption and decryption framework as shown in Figure (1). Digital speech encryption is one of the most effective countermeasures against eavesdropping on telephonic messages, making it an ideal test for both the FPGA and the surrounding infrastructure technologies. We choose the Xilinx Virtex II Pro platform FPGA for the purpose of the test.

Keywords: FPGA, Speech, Digital, Encryption, Decryption, Matlab, VHDL .

Abstract: 

Hand gesture recognition is a process that aims to identify and interpret hand movements and positions to understand their intended meaning. This technology has a wide range of applications, including improving the means of communication for individuals who are deaf or hard of hearing. This paper focuses on developing a system that recognizes American Sign Language alphabet characters through a Convolutional Neural Network model. The MNIST dataset was used as a reference to train the model. The proposed system can provide real-time predictions on images captured through a webcam. Then, the predicted American Sign Language alphabet characters are translated into text and saved in a text file for further processing. This could help convert deaf language into written text to simplify people with disabilities' lives and help them communicate with the community efficiently. This research was implemented using Python. The proposed model was successfully implemented and achieved a high accuracy rate of 98%, making it an accurate system for facilitating communication.

Keywords: Hand Gesture Recognition, Deep Learning, Disability Communication, CNN.