ئەز   Ahmed Loqman Alyousify

This paper introduces a novel Virtual Reality based counterposture training application designed to address the prevalent issue of workplace Repetitive Strain Injuries (RSIs), with a specific focus on the upper body. Traditional methods for RSI prevention through counter posture training often face challenges related to user engagement, personalization, and accessibility. To overcome these limitations, the proposed system leverages the immersive and interactive capabilities of VR technology, utilizing the Meta Quest 2 headsets and Unity 3D game engine. The application features a variety of exercise modules, each targeting different muscle groups and movement patterns to counteract harmful workplace postures. Core design principles emphasize RSI mitigation, active physical interaction, rich sensory feedback, and structured goals to enhance user experience and effectiveness. Potential benefits of this VR approach include increased user engagement and motivation, improved adherence to exercise routines, the ability to offer personalized and adaptive training programs, real-time feedback mechanisms, and long-term cost-effectiveness. While the technical implementation demonstrates feasibility, the paper underscores the need for further precise evaluation through user studies to validate clinical and occupational efficacy. Future research directions involve large-scale user studies, the integration of biofeedback sensors, expansion of exercise content, exploration of tele-rehabilitation models, and longitudinal studies to ascertain long term impact. This VR based counter-posture training holds significant promise as an innovative tool in occupational health, potentially improving worker well-being and reducing injury rates.

Internet proliferation and technological progress have made multimedia information quickly accessible, but they have also posed a threat to privacy and security. Researchers have been interested in digital images due to their capacity to store large amounts of data due to the possibility of protecting sensitive information through digital steganography. Despite their visual imperceptibility, robustness, and ability to embed information, existing image steganography techniques face several challenges. To overcome these challenges, a novel image steganography approach based on a blind model strategy has been proposed for hiding covert messages. The model consists of two stages: embedding and extracting. In the embedding stage, a suitable cover image is selected using the FAST feature point detector. A text message is then converted to a QR code and embedded in the feature points' neighbors using knight tour steps in a chess game. The result is a stego image that appears identical to the original cover image but contains the secret message in its feature points' neighbors. The extracting stage involves finding the feature points and extracting the QR code to obtain the original text message. Since the feature points were not altered during the embedding process, the proposed model is known as a blind model. This approach eliminates the need for the original cover image during the extracting stage. The proposed model was evaluated using several metrics, including the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The results demonstrate that the proposed algorithm can effectively embed and extract secret messages with high accuracy while maintaining the visual quality of the cover image with 100% of (SSIM), and 73.48 as an average of (PSNR).

Augmented reality (AR), virtual reality (VR), and remote-controlled devices are driving the need for a better 5G infrastructure to support faster data transmission. This paper emphasizes that mobile AR is a viable and widespread solution that can easily scale to millions of end-users and educators since it is lightweight, low-cost, and cross-platform. Low-efficiency smart devices and lengthy latency for real-time interactions via regular mobile networks are major barriers to using AR in education. The good news is that the upcoming 5G cellular networks can mitigate some of these issues via network slicing, device-to-device communication, and mobile edge computing. In this paper, we rely on technology to solve some of these problems. The proposed software monitors Image Targets on a printed book and renders 3D objects and alphabet models. In addition, the application considers phonetics. The sound (Phonetic) and 3D representation of the letter are played as soon as an image target is detected. The Turkish alphabet 3D models were created in Adobe Photoshop using Unity3D and the Vuforia SDK. The proposed application teaches Turkish alphabets and phonetics by using 3D object models, 3D letters, and 3D phrases including those letters and sounds.