Daily Archives: April 4, 2025

Abstract-We have Developed a Pneumatic Sheet Bending Machine, it is a cost-effective, efficient, and automated solution for bending thin metal sheets using pneumatic power, addressing the challenges of laborintensive manual methods and expensive hydraulic systems. It utilizes a pneumatic cylinder that applies controlled pressure to bend metal sheets with precision, speed, and consistency, reducing manual effort and the need for multiple operators. The machine is designed to be compact, portable, and easy to operate, making it ideal for small to medium-scale workshops, metal fabrication units, and the automotive industry. Its high-speed operation enhances productivity while maintaining accuracy, making it a valuable tool for producing brackets, enclosures, and sheet metal components. However, the machine is limited to bending thin sheets and requires an air compressor, which may not always be available in smaller setups. Future advancements could improve its capacity for thicker sheets, integrate automated controls for enhanced precision, and utilize advanced pneumatic technologies for greater efficiency. Overall, this innovation provides a practical and scalable alternative to traditional bending methods, significantly improving productivity, reducing labor costs, and making metal fabrication more accessible to small industries.

Abstract-Image inpainting is a crucial task in computer vision that focuses on reconstructing missing or corrupted parts of an image while maintaining structural consistency and visual realism. Traditional inpainting methods, such as diffusion-based and exemplar-based techniques, often struggle to restore fine textures and complex structures, leading to blurry and unrealistic results. The advent of Generative Adversarial Networks (GANs) has significantly enhanced image inpainting by learning to generate plausible image content. However, conventional GAN-based models emphasize global image coherence while neglecting finer local details, causing inconsistencies in high-texture regions. To overcome these limitations, PatchGAN-based inpainting evaluates image realism at the patch level rather than analyzing the entire image as a whole. This technique employs multi-scale discriminators that ensure improved texture synthesis and structural continuity at different spatial resolutions. Experimental studies reveal that PatchGAN-based models outperform conventional GAN-based methods in terms of perceptual quality, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), producing sharper and more realistic image restorations. This review explores the advancements in PatchGAN-based inpainting, highlighting its benefits, architectural components, and future research directions to further enhance image reconstruction quality.

Abstract-The rapid growth of e-commerce has brought convenience to consumers but has also led to increasing cyber threats and data breaches. Traditional security measures, primarily reliant on centralized identity management, pose critical vulnerabilities that can be exploited by malicious actors. To address these concerns, this research proposes an advanced security model utilizing Zero-Trust Architecture (ZTA) combined with Decentralized Identity (DID) within a MERN stack-based e-commerce platform. This approach ensures that every access request is verified, significantly reducing unauthorized access risks. Furthermore, blockchain-backed DID solutions offer a tamper-proof identity verification system, empowering users with greater control over their credentials while eliminating the dependency on third-party identity providers. This paper explores the implementation, benefits, and real-world applicability of this security model, highlighting its ability to enhance trust and improve cybersecurity in modern e-commerce platforms.

Abstract-Promptify represents a novel approach to AI-driven content creation by offering a centralized online mar- ketplace for buying and selling creative and technical prompts. The platform bridges the gap between prompt creators and consumers, streamlining the process of ac- cessing high-quality prompts across diverse categories such as creative writing, technical tasks, and artistic projects. Existing platforms lack centralized resources, quality moderation, and secure payment mechanisms, leaving users with fragmented solutions. Promptify addresses these challenges by providing secure trans- actions via Stripe, user shop management systems, and community engagement features. The platform uses modern technologies like Next.js, MongoDB, and Prisma ORM to ensure scalability, security, and perfor- mance optimization. The paper details Promptify’s ar- chitecture, implementation, and testing methodologies, emphasizing its potential to democratize access to qual- ity prompt resources. Future work explores AI-based prompt recommendations, blockchain-based ownership verification, and multilingual support.