Abstract-The wheel hub and spindle are critical components in the suspension and steering system of a Formula Student car, directly influencing its vehicle dynamics, handling, and safety. This project focuses on the design, analysis, and optimization of the wheel hub and spindle assembly, aiming to ensure structural integrity, reduce weight, and improve overall performance under dynamic loading conditions. Using SolidWorks, the wheel hub and spindle were meticulously designed to meet the requirements of a Formula Student car, emphasizing lightweight construction while maintaining sufficient strength. The design process involved careful consideration of materials, geometry, and manufacturing feasibility to create a durable and efficient assembly. The geometry was optimized to reduce unsprung mass, which is crucial for enhancing vehicle stability and handling. The designed components were analyzed using ANSYS Workbench to simulate real-world conditions and assess their structural performance. Static and dynamic load analyses were performed to evaluate stress distribution, deformation, and factor of safety under various scenarios, such as cornering, braking, and acceleration. The results of the analysis demonstrate that the design meets the functional and safety criteria for a Formula Student car, ensuring reliability during competitive racing conditions. This study provides valuable insights into the integration of design and analysis tools for optimizing vehicle dynamics, contributing to the advancement of motorsport engineering.

Abstract-Optimizing the orientation of a part in the Material Extrusion (MEX) process is crucial for reducing print time, energy consumption, and material waste while maintaining part quality. This study focuses on optimizing the printing orientation and layer height using the Taguchi method in Minitab to achieve an efficient and sustainable additive manufacturing process. The research employs the Taguchi Design of Experiments (DOE) approach to systematically evaluate the effects of different orientation angles and layer heights on print time and energy consumption. Experiments were conducted by printing samples at various orientations and layer thicknesses, and the response variables—total printing time and energy usage—were recorded. Signal-to-noise (S/N) ratios were analyzed in Minitab to determine the optimal parameter settings that minimize both print time and energy usage. The results indicate that the print orientation significantly affects deposition path efficiency, while layer height plays a key role in determining the number of layers and total energy required. The optimized orientation and layer height configuration led to a substantial reduction in energy consumption without compromising part accuracy and mechanical integrity. This study demonstrates that using the Taguchi method in Minitab for orientation optimization provides a structured and statistical approach to improving additive manufacturing efficiency. The findings can be applied to enhance the sustainability of FDM-based 3D printing, reducing material wastage and operational costs while improving process efficiency.

Abstract-This research delves into an innovative app that seamlessly integrates AI-driven traffic management with emergency ambulance services, presenting users with a holistic solution to address both transportation efficiency and urgent medical needs. By harnessing advanced algorithms and real-time data analysis, the app optimizes traffic flow, reduces congestion, and delivers personalized travel routes tailored to the prevailing conditions. Moreover, it offers a dedicated emergency ambulance service equipped with priority response times and specialized care for its subscribers. The subscription-based model of the app affords users a plethora of benefits, including access to enhanced features, exclusive discounts on additional healthcare services, and a flexible payment structure catering to diverse user preferences. In .essence, the app epitomizes the convergence of technology and human-centric design, empowering users to navigate urban landscapes safely and efficiently while ensuring immediate access to critical medical assistance when circumstances demand. Through its innovative approach and commitment to user-centricity, the app stands as a beacon of progress in shaping the future of mobility and healthcare accessibility in the communities.

Abstract-Brake pedals are critical components in automotive applications, requiring a balance between high stiffness, low weight, and manufacturability. Traditional design approaches often result in suboptimal structures with excessive material usage. This study explores the generative design optimization of a brake pedal using Fusion 360, targeting maximum stiffness and minimum mass while considering different manufacturing constraints for milling and additive manufacturing (AM). Generative design algorithms were employed to generate multiple optimized pedal designs by defining material properties, boundary conditions, and load cases. The milling-based design focused on constraints like tool access, machining orientations, and material removal feasibility, whereas the AM-based design leveraged organic lattice structures and topology optimization to achieve minimal material usage while maintaining structural integrity. The optimized models were analyzed using finite element analysis (FEA) to compare stress distribution, deformation, and weight reduction for both manufacturing methods. Results indicate that additive manufacturing allows for a more complex, lightweight design with internal lattice structures, resulting in a higher stiffness-to-weight ratio compared to the milling approach. However, the milled design exhibits superior fatigue resistance and is better suited for high-load conditions due to the absence of microstructural porosity. A comparative evaluation of material usage, manufacturing feasibility, and mechanical performance highlights the trade-offs between AM and milling-based designs. This research demonstrates how generative design tools can optimize brake pedal geometry for different manufacturing processes, leading to weight savings and enhanced performance while ensuring manufacturability. The findings provide valuable insights into process-dependent design optimizations and serve as a reference for future lightweight automotive component development.

Abstract-The Material Extrusion (MEX) process, commonly known as Fused Deposition Modeling (FDM), is widely used for manufacturing complex geometries with minimal material wastage. However, optimizing key printing parameters is crucial for improving efficiency while maintaining print quality. This study focuses on enhancing the MEX process using Cura 5.9.0 and the B to optimize layer height, line width, and wall count, aiming to reduce print time and material consumption. The Taguchi DOE method was employed to systematically analyze the effects of these parameters on printing performance. A set of experiments was conducted by varying layer height, line width, and wall count within practical limits. The primary objectives were to minimize printing time and optimize material usage while maintaining structural integrity. Print time and material consumption were recorded for each experiment, and statistical analysis was performed using Minitab to determine the optimal parameter combination. The results show that layer height significantly influences printing time, as higher layer heights reduce the number of layers but may impact surface quality. Line width affects material flow and print strength, while wall count directly impacts material consumption. The optimized parameter settings achieved a significant reduction in print time and material usage, ensuring an efficient balance between speed, material economy, and part durability. This study demonstrates that using Cura 5.9.0 in combination with DOE techniques provides a structured methodology for enhancing the efficiency of the MEX process. The findings are beneficial for industries and researchers seeking to optimize print settings, reduce operational costs, and improve sustainability in additive manufacturing.

Abstract-The integration of neural networks and machine learning technologies in allied healthcare has the potential to revolutionize diagnostic accuracy, treatment personalization, and patient care. This study focuses on practical applications and strategies for implementing these advanced technologies to optimize healthcare processes in real-world scenarios. By leveraging artificial intelligence, this study seeks to enhance diagnostic imaging, predictive analytics, personalized treatment plans, and remote patient monitoring. A key innovation explored is the Geo Health Sync ID, a centralized health record system designed to improve diagnosis accuracy, streamline medical histories, and enhance treatment outcomes by enabling global access to healthcare data. Additionally, this is a proposal of the development of an AI-powered chatbot and wearable device that utilizes neural networks to monitor patient vitals in real-time, detect anomalies, and provide early alerts to healthcare providers. Addressing challenges such as data privacy, AI model fairness, and seamless clinical integration, this study aims to bridge existing gaps and establish a more efficient, patient-centric healthcare ecosystem. This initiative holds the potential to transform allied healthcare by improving patient outcomes, reducing healthcare costs, and driving innovation through AI-driven decision-making and automation. With the rapid advancements in artificial intelligence (AI), neural networks and machine learning have become integral to allied healthcare. These technologies offer predictive analytics, disease diagnosis, treatment recommendations, and administrative efficiency. Machine learning algorithms, particularly deep learning models, process vast amounts of healthcare data, enhancing accuracy in medical imaging, patient monitoring, and personalized medicine. This paper explores the applications, benefits, challenges, and future scope of neural networks in allied healthcare, including real-world case studies and implementation strategies.

Abstract-The Automobile Industry has shown keen interest for replacement of steel leaf spring with that of glass fiber composite leaf spring, since the composite material has high strength to weight ratio, good corrosion resistance properties. The present study searches the new material for leaf spring. In present study the material selected was glass fiber reinforced plastic (GFRP) and the epoxy resin is used against conventional steel. A spring with constant width and thickness was fabricated by hand lay-up technique which was very simple and economical. The numerical analysis is carried via finite element analysis using ANSYS software. Stresses, deflection and strain energy results for both steel and composite leaf spring material were obtained. Result shows that, the composite spring has maximum strain energy than steel leaf spring and weight of composite spring was nearly reduced up to 85% compared with steel material. This paper describes design and FEA analysis of composite leaf spring made of glass fiber reinforced polymer. The dimensions of an existing conventional steel leaf spring of a light commercial vehicle are taken for evaluation of results.

Abstract-Block chain is an emerging technology and cryptocurrency is one of the backbone of blockchain technology in the financial sector. Even though other cryptocurrencies have been around for a few years, Bitcoin has kept its position as the most valuable cryptocurrency. On the other hand, Bitcoin’s price has been very unpredictable, making it almost impossible to guess where it might go in the future. The goal of this research is to find out which model is the most accurate and best at making predictions. Several different machine learning and time series approaches are used to determine Bitcoin prices. The goal of this article is to see how well it is possible to predict where the price of Bitcoin in US Dollars will go in the future. US Dollars are used to figure out how much Bitcoin costs. The price information came from a website called Coin market cap. We used machine learning techniques like Random Forest, XG Boost, kNN and time series models like AR, MA, ARIMA, Prophet. It has been found in our experiments that time series models give better results than machine learning approaches.

Abstract-Oracle Cloud is well-suited for large corporations in industries such as finance, healthcare, telecommunications, retail, and manufacturing. These organizations require robust, secure, and scalable cloud solutions to manage complex workloads, global operations, and sensitive data

Abstract-File storage and transfer have become increasingly important in a data-driven world where efficiency and reliability are critical. Traditional file compression techniques often fail when subjected to lossy compression algorithms used in video formats, leading to data corruption. This research introduces a novel approach to encoding file data into video frames using two distinct modes: RGB and binary. RGB encoding maps data bytes to the colour channels of pixels, offering high efficiency, while binary encoding represents each bit as black-and-white pixels, ensuring resilience to compression artifacts. To further enhance user-friendliness, the system embeds encoding settings in the first video frame, automating the decoding process and reducing manual intervention. This hybrid system provides a practical balance between efficiency and robustness, making it suitable for real-world applications such as secure data transfer, archival storage, and multimedia-based information systems. This paper evaluates both encoding modes and proposes solutions to minimize data corruption caused by video compression. The research concludes with potential future enhancements, including hybrid systems that combine RGB and binary encoding, integration with AI-based algorithms for adaptive encoding, and scalability for 4K and 8K video resolutions. These advancements aim to optimize both storage efficiency and data integrity, addressing the growing demand for reliable file storage solutions.