Crypto currency mining is pivotal in blockchain ecosystems, serving as the backbone for decentralized consensus and token generation.This process enables secure and transparent validation of transactions while incentivizing participants through token rewards.However, the inherent complexity of mining operations and the environmental concerns associated with energy-intensive algorithms present significant challenges.This paper introduces a comprehensive crypto token mining website to bridge the gap between technical complexity and user accessibility.The platform provides various tools, including real-time data dashboards for monitoring mining activity, profitability calculators for informed decision-making, and educational resources tailored to varying levels of expertise.By integrating blockchain technology, gamification elements, and secure wallet features, the platform simplifies the mining process and enhances user engagement.In addition to improving accessibility, the platform addresses critical challenges such as environmental impact, regulatory uncertainties, and technological complexities through innovative methodologies.The research elaborates on the system's architecture, emphasizing the seamless integration of front-end and back-end technologies, block chain-based rewards systems, and sustainable mining strategies.It also explores the potential of renewable energy and Energy-efficient algorithms to mitigate the environmental footprint of mining activities.This paper evaluates the platform's contribution to fostering a more sustainable, user-friendly, and secure cryptocurrency mining ecosystem while outlining future directions for scaling and enhancing its features.By offering a holistic approach, the project aims to democratize cryptocurrency mining and make it accessible to a broader audience without compromising efficiency or sustainability.

Low power has emerged as a principal theme in today's world of electronics industries.Power dissipation has become an important consideration as performance and area for VLSI Chip design.With shrinking technology reducing power consumption and over all power management on chip are the key challenges below 100nm due to increased complexity.For many designs, optimization of power is important as timing due to the need to reduce package cost and extended battery life.For power management leakage current also plays an important role in low power VLSI designs.Leakage current is becoming an increasingly important fraction of the total power dissipation of integrated circuits.This paper describes about the various strategies, methodologies and power management techniques for low power circuits and systems.Future challenges that must be met to designs low power high performance circuits are also discussed.

Economic Load Dispatch (ELD) is a key optimization problem used in power systems to schedule the generation such that network constraints drawn from transmission capability, generation strengths and load demands among other aspects are met efficiently and production costs incurred are minimal.As has been mentioned before, data on generator input-output curves are non-linear and nonconvex; therefore, ELD is not easy for traditional optimization.For these challenges, the present work proposes advanced techniques among which are Dynamic Programming (DP), Teaching-Learning Based Optimization (TLBO), and Particle Swarm Optimization (PSO).In this research, MATLAB environment was used to perform a number of evaluations to determine the performance of these algorithms.The results also state the potential of these approaches for practical power system dispatch by demonstrating their applicability of providing approximated optimal solutions for complex ELD.

IoT (Internet of Things) technologies are essential to supplying the world's expanding food demand, and the change in farming methods has brought along both benefits and challenges.IoT sensors monitor essential parameters like soil pH, moisture, and water levels.For instance, water volume sensors, integrated with Arduino UNO modules, ensure precise irrigation by supplying the required amount of water and preventing overflow.Networking technologies enable remote operation, improving efficiency.IoT's ability to connect agricultural tools, sensors, and experts has enhanced productivity, reduced costs, and improved service accessibility, particularly in off-grid areas.This research explores current and future IoT applications in agriculture, focusing on security, trends, and technology implementation.IoT devices support smart farming by enabling precision farming, livestock monitoring, crop health assessment and smart irrigation systems.The study highlights the potential of IoT and wireless sensors in agriculture and discusses the challenges of integrating these technologies into traditional farming methods.AI technologies also assist in reducing crop diseases and pest infestations with autonomous farming equipment and improved forecasts based on data analysis.IoT and AI are revolutionizing agriculture by enhancing efficiency, reducing costs and providing actionable insights with the potential to transform global farming practices.

The current reality is very fast-paced edge computing that shortly will be demanding robust security and scaling solutions for its necessarily distributed and resource-constrained nature.Addressing this need, this paper critically assesses machine learning and deep learning models tailored to enhance both security and scalability in edge computing and points at improvement areas so further work in these regards may be fruitful.On the other hand, most of the surveys about this topic usually suffer from the narrow scope, since most of them miss either a wide spectrum of ML and DL techniques or their concrete applications in the context of edge computing.This work gives a comprehensive review of a good number of ML and DL models, ranging from supervised learning algorithms like Support Vector Machines and Random Forests to unsupervised learning methods like K-means clustering, and advanced deep learning architectures such as Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks.Thus, these models are primarily rated based on their capability to enhance security measures like intrusion detection, data encryption, and anomaly detection, and scalability in handling edge device dynamics and heterogeneity.As noted in the review, among all the models, very few can take such complex patterns of data and realize high accuracy for security applications like the DL models, especially CNNs and GANs.In particular, SVM and RF are known to be robust and efficient for processing small-to medium-sized datasets typical in edge environments.However, some limitations include high computational costs and large training datasets required by the DL models are also discussed.Hybrid approaches combining multiple models have been reviewed to leverage their strengths and make up for the weaknesses of individual models.The comprehensive review has developed several insights that can support future research in strengthening more efficient, scalable, and more secure edge computing frameworks.In particular, gaps and potential improvements related to ML and DL applications stand as greatly important in the progress of edge computing technologies toward better reliability and enhanced performance in real-world scenarios.This thus has a very significant impact on the domain because it provides an in-depth understanding of the current state of capabilities and future directions for ML and DL models at the edge.

The swift advancement of lithium-ion batteries (LIBs) is central to the performance and sustainability of electric vehicles (EVs).The paper documents considerable improvements in cathode and anode materials, electrolyte design, and new manufacturing processes like nanotechnology and 3D printing.Intelligent battery management systems (BMS) and thermal management maximize longevity and efficiency, while wireless and fast charging solutions enhance convenience.The future of LIBs is solid-state batteries and AI-based diagnostics that meet the safety requirements and energy density.Recycling and sustainability are also significant to reduce the environmental impact further.All of these technologies are converging into the next-generation highperformance, long-duration, and sustainable EV battery.

The System-on-Chip (SoC) design paradigm has revolutionized modern electronics by integrating an entire system into a single chip.SoCs combine various components like processors (CPU/GPU), memory, I/O interfaces, and peripheral units into one compact module, significantly reducing the size, cost, and power consumption of electronic devices.This seminar will explore the fundamental principles behind SoC design, its architecture, and key components, focusing on both the theoretical concepts and practical implementation strategies.We will delve into the design methodology of SoCs, examining top-down and bottom-up approaches, system integration, and verification.

The role of humans within world is to acculturation, care for, innovate, and learn while understanding both the strengths and limitations of the biological world.Prioritizing these goals, humans can advantage technology effectively.The natural cycle is all-important for the survival of humanity and all living species.Among the most pressing issues touching nature is air pollution, and forestation stands as the only practicable solution.Purpose -Crowd-Sourced approach to enhancing forestation in Bharat via the "MGNREGA", to reduce atmospheric pollution.How crowd-source is helpful to enhance forestation.Design/methodology/approach -Crowd-source model designed and introduced for MGNREGA scheme.Findings -The effective use of technology by humans can greatly conduce to social causes, promoting healthier lives for all forms of aliveness in nature.Originality/Value -This paper introduced Crowd-source model and it\'s easiest implementation for MGNREGA scheme to increase forestation.