Volume 8 | Issue 8 | August-2020

 Abstract

RAMAYAN MAHAKAVYA MEIN PRADHAN-RAS KE ROOP MEIN NIRUPIT

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RAMAYAN MAHAKAVYA MEIN PRADHAN-RAS KE ROOP MEIN NIRUPIT

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 Abstract

Political defection in India refers to an elected official abandoning their political party for another, a practice addressed by the Tenth Schedule (Anti-Defection Law) of the Constitution. Under this law, a Member of Parliament (MP) or Legislative Assembly (MLA) can be disqualified for voluntarily giving up party membership or voting against the party's whip without permission. Although the 52nd Amendment introduced this law in 1985 to curb instability caused by unprincipled shifts in loyalty, it has been criticized for being ineffective in its original purpose.

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Political Defection, Political Party, Parliament, Assembly, and Constitution.

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The convergence of Artificial Intelligence (AI) and industrial automation has created transformative opportunities in modern manufacturing systems. AI-driven approaches enable flexibility, adaptability, and autonomy in scheduling, task planning, and robotic motion control, which are critical in Industry 4.0 environments. This paper synthesizes insights from three major contributions: AI-based combined scheduling and motion planning in flexible robotic assembly lines, task planning of cooperating low-cost mobile manipulators, and advanced AI-driven task and motion planning of robotic assembly operations. By analyzing these studies, we present a comprehensive overview of methodologies, applications, challenges, and future research directions. The study highlights how AI techniques such as reinforcement learning, deep learning, hybrid symbolic-neural models, and distributed cooperative planning are redefining automation. Applications across flexible manufacturing lines, cooperative multi-robot systems, and intelligent robotic assembly operations are examined. Finally, challenges such as computational complexity, safety, interoperability, and sustainability are addressed, alongside opportunities for future research in digital twins, edge AI, blockchain-enabled automation, and Industry 5.0 paradigms.

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Industrial Automation, Artificial Intelligence (AI), Scheduling, Task Planning, Motion Control, Flexible Assembly Lines, Cooperative Robots, Mobile Manipulators, Digital Twins, Edge AI, Industry 4.0, Industry 5.0, Smart Manufacturing, Cyber-Physical Systems (CPS), Human-Robot Collaboration.

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In the current landscape of wireless communication, Mobile Ad-Hoc Networks (MANETs) have emerged as a significant area of interest for researchers and scholars due to their versatility and expanding applications. These networks are widely used for monitoring, sensing, and analyzing vast environments, especially in defense operations and in detecting physical parameters. One of the critical concerns in real-time MANET applications is energy efficiency, as the devices typically operate with limited battery life and constrained energy supplies. To address these core challenges, this work introduces an integrated strategy aimed at optimizing energy usage during data transmission while also improving the overall network lifespan. The proposed approach unfolds in three phases, starting with a model that identifies energy-efficient mobile nodes using a node detection mechanism. This strategy enables only a necessary subset of nodes to remain active, allowing others to conserve energy in sleep mode. To maximize energy savings, a calculated quantitative factor is employed, resulting in significantly enhanced network durability. Node selection prioritizes devices that are crucial for maintaining communication within the MANET and ensures effective power utilization. Due to the dynamic nature of ad-hoc networks, the number of nodes involved in communication must be adaptable rather than fixed, prompting the development of a second phase that introduces an adaptive model for node selection based on performance needs. The final phase integrates a secure, energy-conscious routing scheme that leverages trusted path identification and optimal route configuration. This results in reliable communication, improved prediction accuracy, and an extended network lifetime in mobile multihop ad hoc environments.

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Mobile Ad-Hoc Network, MANET, Energy Efficiency, Secure Communication, Wireless Sensor Networks, Optimal Path Selection, Dynamic Topology.

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In the pursuit of improving safety and connectivity in modern applications, this journal presents an innovative smart helmet system designed to enhance rider protection and vehicle interaction. The project is divided into two primary components: the transmitter and the receiver. Part 1: Transmitter System The transmitter unit, embedded within the helmet, integrates an Arduino Mini, a 12V battery, an alcohol sensor, and a confirmation button. The alcohol sensor detects the presence of alcohol, ensuring that the rider's sobriety is assessed before starting the vehicle. The confirmation button allows the rider to acknowledge readiness, and the RF transmitter kit facilitates wireless communication. An integrated buzzer provides immediate auditory alerts in case of safety breaches or system notifications. Part 2: Receiver System The receiver unit, installed on the vehicle, utilizes an Arduino Uno and a relay module to control the ignition system. It is also equipped with a DC motor to simulate the vehicle's operational output. The RF receiver kit receives signals from the helmet's transmitter, enabling remote activation and deactivation of the ignition system based on the helmet's sensor data and rider's confirmation. This dual-part system showcases a seamless integration of wearable technology with vehicle control mechanisms, emphasizing enhanced safety features and user interactivity. The implementation of this smart helmet technology provides a significant advancement in wearable safety technology, offering real-time monitoring and automated responses to critical safety conditions. The results indicate potential improvements in rider safety and vehicle operation, underscoring the importance of technological innovation in personal protective equipment.

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Smart Helmets, Wearable Technology, Arduino Microcontroller, RF Communication, Alcohol Detection, Vehicle Safety Systems, Ignition Control, DC Motor Control, Safety and Connectivity, Embedded Systems.

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The paper provides the Bayes estimate of shape parameter of Generalized Compound Rayleigh distribution assuming rest two scale and location parameter as known under the Linex loss function. Then by using Lindley approximation procedure we have obtained the Approximate Bayes estimate of Location parameter of Generalized Compound Rayleigh distribution under the Linex loss functions. We have done the sensitivity analysis of the Approximate Bayes estimators of model when prior specifications deviate from the true values and presented a numerical study to illustrate the above technique on generated observations and numerical comparison is done by R-programming.

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MLE and Bayes Estimator, Lindley Approximation,Generalized Compound Rayleigh distribution, Linex loss function, Approximate Bayes estimate.

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Kamal Jeet Choudhary Ki Kavitaon Mein Stree Vimarsh

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Kamal Jeet Choudhary Ki Kavitaon Mein Stree Vimarsh

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Big Data: Introduction, Applications, Opportunities & Challenges

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Big Data: Introduction, Applications, Opportunities & Challenges

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 Abstract

SHIKSHAK PRASHIKSHAN MAHAVIDYALAYON KE CHHATRADHYAPAKON KE MANSIK SWAASTHYA KA ADHYAYAN

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SHIKSHAK PRASHIKSHAN MAHAVIDYALAYON KE CHHATRADHYAPAKON KE MANSIK SWAASTHYA KA ADHYAYAN

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black, purple, family, home

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