Abstract-The gravity model to the blood supply chain is a conceptual framework that seeks to explain and optimize the distribution of blood products within a healthcare system. It is useful when identifying suitable geographical location within arrange. It is also used to find location that minimizes the cost of transporting raw material from the supplier and finished goods to the markets served. This model also assumes that the transportation cost grows linearly with the quantity shipped. All distances are calculated as the geometric distance between two points on the plane. Using a numerical example, the applicability of the proposed network is analyzed.

Abstract-This paper presents a solution to the inefficiencies of traditional manual attendance systems by proposing a face recognition-based attendance system. project aims to enhance the manual attendance process by using a mobile platform and face recognition technology. The design consists of three main modules: inputting attendance information, signing in with facial recognition, and maintaining attendance records. It begins by explaining the principles of face detection and classification, followed by an analysis of how to build a face recognition classifier. The system is then implemented on an Android platform, allowing for practical use in workplaces.

Abstract-Manufacturing green nanoparticles is a safe, secure, and promising technique. In the current study, piper dravidii leaves extract was used to prepare iron oxide nanoparticles (Fe2O3-NPs) and cobalt oxide nanoparticles (CoO-NPs). UV-visible spectroscopy, scanning electron microscopy (SEM), dynamic light scattering (DLS), vibrating sample magnetometer (VSM), and differential scanning calorimetry (DSC) were used to evaluate the produced Fe2O3-NPs and CoO-NPs. The surface plasmon resonance effect was used to validate the synthesis of FeONPs. FeONPs have an average particle size of about 163.5 nm, a polydispersity index of 0.091, and a zeta potential of -13.8 mV, according to dynamic light scattering (DLS). At 176.91°C, differential scanning calorimetry (DSC) revealed an endothermic peak. With a magnetization value of 3.483 emu/g at ambient temperature, iron nanoparticles were shown to have superparamagnetic characteristics by the Vibrating Sample Magnetometer (VSM) examination, suggesting that they might be used in a magnetically targeted medication delivery system. It has been shown that this biosynthetic method is economical, environmentally benign, and has a lot of potential for use in biomedical research.

Abstract-The study focuses on the development and analysis of polyvinyl alcohol (PVA) nanocomposites incorporating silver (Ag) nanoparticles, aimed at improving the performance characteristics of footwear insoles. The thesis abstract presents a comprehensive overview of research dedicated to the formulation and evaluation of polyvinyl alcohol (PVA) nanocomposites infused with silver (Ag) nanoparticles, with the primary objective of enhancing the functional properties of insoles used in footwear. The introduction outlines the significance of integrating nanotechnology into material science, particularly in the context of footwear applications, where comfort and durability are paramount. The materials and methods section details the synthesis of PVA nanocomposites, the incorporation of Ag nanoparticles, and the various analytical techniques employed to assess their performance characteristics. The conclusion summarizes the findings, highlighting the potential of these nanocomposites to significantly improve the quality and longevity of footwear insoles, thereby contributing to advancements in the field of wearable technology.

Abstract-In this paper, a generalized matrix inverse-based unknown input observer (UIO) has been developed for a benchmark 3-DOF helicopter system to manage unknown, time-varying nonlinear dynamics and disturbances. The goal is to ensure the helicopter accurately follows the specified elevation and pitch references. To achieve this, we introduce a novel, simplified UIO to estimate the combined unknown dynamics, which are subsequently incorporated into the control design as a compensator. By introducing an auxiliary system, an invariant manifold is derived and utilized in the UIO design. The full-order observer, constructed using the g-inverse, is expanded and implemented to achieve this purpose. This new estimator requires setting only a single scalar and achieves exponential convergence. Consequently, the proposed control design utilizing the estimator can achieve precise output tracking. This control method is implemented on a benchmark 3-DOF helicopter, and its efficacy is validated through simulations and results.

Abstract-Developing a GenAI-enabled AR platform for museums to offer personalized, interactive experiences, enhancing visitor engagement and educational value, thereby preserving and promoting the heritage and culture of the nation. The system proposes the development of a novel GenAI-enabled Augmented Reality (AR) platform tailored for museums, aimed at delivering personalized and interactive experiences to visitors. Leveraging Unity Vuforia Area Target/Image Target technology, C# API, GenAI, langchain, and the OpenAI API, the platform seeks to revolutionize traditional museum visits by offering enhanced engagement and educational value. While existing solutions such as audio guides, mobile apps, and interactive displays have improved visitor experiences, they often lack interactivity and personalization. The proposed platform addresses these limitations by employing Generative AI to power a virtual assistant that delivers detailed information about exhibits and aids in navigation. Accessible via a cross- platform AR application on web, Android, and iOS devices, the solution promises to create a more immersive and enriching museum experience, ultimately contributing to the preservation and promotion of cultural heritage.

Abstract-In order to provide a structured and interactive learning environment, a Learning Management System (LMS) is essential to modern education and training. This project entails designing and developing a feature-rich LMS using Django for backend development and Tailwind CSS for a responsive and user-friendly interface. The system offers a centralized dashboard for administrators, instructors, and students, integrating essential functionalities like secure user authentication, dynamic course enrollment, and real-time attendance tracking. The platform enhances the learning experience by enabling personalized learning pathways, robust assessment tools, automated certification, and role- based access control for administrators, instructors, and students. The LMS is built with scalability and seamless third- party integrations, including payment gateways and video conferencing solutions, supporting both self-paced and instructor-led learning models. This LMS solution is intended to transform online learning by creating an efficient and captivating digital learning ecosystem. By emphasizing usability, security, and efficiency, this project seeks to improve educational outcomes, automate administrative workflows, and increase learner engagement. Django integration guarantees a stable and scalable backend, while Tailwind CSS offers an aesthetically pleasing and highly responsive design.

Abstract-In modern society, electronic and online voting systems are emerging as significant advancements in electoral technology, offering the potential to reduce organizational costs and increase voter turnout. Electronic Voting Machines (EVMs) have already revolutionized the electoral process by improving voter participation and enhancing the speed and accuracy of elections compared to traditional methods like paper ballots, punch card voting, and optical scan systems. These conventional approaches often face challenges such as fraud, voter manipulation, inaccuracies, and inefficiencies. Similarly, online voting systems promise to further streamline elections by eliminating the need for physical infrastructure, enabling voters to cast their votes from any location with internet access. However, despite their advantages, online voting solutions are met with caution due to vulnerabilities to cybersecurity threats. Risks such as Man-in-the-Middle (MitM) attacks, Denial-of-Service (DoS) attacks, and malware injection jeopardize the integrity and reliability of elections, highlighting the need for more secure and robust solutions. Blockchain technology offers a transformative approach to modernizing voting systems by providing a decentralized, transparent, and tamper-proof framework. Its decentralized architecture eliminates single points of failure, ensuring higher levels of security and reliability. This paper explores how blockchain technology addresses the limitations of conventional voting systems, including EVMs and online voting systems, by leveraging its inherent characteristics—speed, accuracy, immutability, and transparency. By distributing control across a network of nodes, blockchain-based voting systems enhance the integrity, accessibility, and trustworthiness of elections. Furthermore, the paper examines the potential of blockchain to modernize the existing voting framework, significantly improving the efficiency and trust in electoral processes while safeguarding democratic values in the digital age.

Abstract-Lunar mineral mapping is crucial for understanding the Moon’s formation, geological evolution, and resource potential. This review paper examines the significant contributions of the Chandrayaan-2 mission to this field. Prior to Chandrayaan-2, missions like Clementine and Chandrayaan-1 provided foundational mineralogical data, revealing the Moon’s diverse composition dominated by minerals such as plagioclase feldspar, pyroxenes, and olivine, with regional variations reflecting magmatic differentiation and impact processes. Chandrayaan-2, equipped with advanced instruments including the Imaging Infrared Spectrometer (IIRS), significantly enhanced lunar mineral mapping capabilities. This review synthesizes key findings from Chandrayaan-2, highlighting its high-resolution spectral and spatial data that have refined our understanding of mineral distribution across the lunar surface. Methodologies employed encompass sophisticated spectral unmixing and analysis techniques applied to IIRS data, enabling the identification and mapping of subtle mineralogical variations, including hydration features and the composition of lunar geological units. The improved mineral maps generated by Chandrayaan-2 have profound implications for future lunar exploration, resource utilization strategies, and a more nuanced comprehension of planetary formation processes within our solar system. This paper underscores the enduring legacy of Chandrayaan- 2 in advancing lunar science.