Volume 7, Issue 3

2026 : Volume 7 Issue 3

Title Green Chemistry Approaches for Sustainable Development: A Comprehensive Review
Authors 1.Tamarapalli Ranga Babu, 2.Radhika Mendu
Affiliation 1,2. Assistant Professor in Chemistry, Department of Basic Sciences and Humanities, Akkineni Nageswara Rao College of Engineering and Technology, Gudivada.
DOI 10.5281/zenodo.20082360
Abstract Green chemistry has emerged as an essential scientific discipline for achieving sustainable de velopment by minimizing environmental pollution, reducing hazardous chemical usage, and improving industrial efficiency. Traditional chemical processes often generate toxic byprod ucts, consume excessive energy, and contribute to environmental degradation. Green chemistry addresses these challenges through eco-friendly principles, sustainable materials, renewable feed stocks, and energy-efficient technologies.
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Title Generative Artificial Intelligence for Software Development and Automation: A Compre hensive Review
Authors 1.Khagga Dhanikonda,2. Dr. A. Rama Rao
Affiliation 1. Assistant Professor, Computer Science and Engineering Department, Akkineni Nageswara Rao College of Engineering and Technology, Gudivada.
2. Professor, Mechanical Engineering Department, Akkineni Nageswara Rao College of Engi neering and Technology, Gudivada.
DOI 10.5281/zenodo.20082441
Abstract Generative Artificial Intelligence (GenAI) has emerged as a transformative paradigm in soft ware engineering, fundamentally altering how software is designed, developed, tested, and main tained. Leveraging large language models (LLMs), deep learning, and transformer-based archi tectures, generative AI enables automation of coding tasks, enhances developer productivity, and introduces new workflows such as AI-assisted programming and autonomous agents.
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Title A Comprehensive Review of ESP32-Based IoT Architectures for Lean and Sustainable Smart Agriculture
Authors 1. Bhushana Kumar Kurumatla,2. Naga Latha Jarugu
Affiliation 1. Associate Professor, Electrical and Electronics Engineering, Akkineni Nageswara Rao College of Engineering and Technology, Gudivada.
2. Assistant Professor, Electronics and Communication Engineering, Akkineni Nageswara Rao College of Engineering and Technology, Gudivada.
DOI 10.5281/zenodo.20082499
Abstract The integration of Internet of Things (IoT) technologies with Lean Manufacturing principles has emerged as a promising approach to optimize agricultural productivity, sustainability, and resource utilization. Smart agriculture systems leveraging low-cost microcontrollers such as ESP32 enable real-time monitoring, automation, and data-driven decision-making. This review paper explores the convergence of IoT-based precision agriculture and Lean principles such as waste reduction, continuous improvement, and efficiency optimization. The study examines system architectures, applications, benefits, and challenges associated with ESP32-based smart farming systems.
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Title AI-Driven Predictive Maintenance for Smart Manufacturing Systems: A Comprehensive Review
Authors 1.Sirisha Chandu , 2.Dr. A. Rama Rao
Affiliation 1. Assistant Professor, Department of Mechanical Engineering, Akkineni Nageswara Rao Col lege of Engineering and Technology, Gudivada.
2. Professor, Department of Mechanical Engineering, Akkineni Nageswara Rao College of En gineering and Technology, Gudivada.
DOI 10.5281/zenodo.20082549
Abstract Predictive maintenance (PdM) has emerged as a critical application of Artificial Intelligence (AI) in modern manufacturing systems, enabling organizations to anticipate equipment failures and optimize maintenance schedules. Unlike traditional reactive or preventive maintenance strategies, AI-based predictive maintenance leverages machine learning, deep learning, and data analytics to predict equipment health in real time.
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Title Quantum Computing Applications in Theo retical and Applied Physics: A Systematic Review
Authors Dr. R. N. A. Prasad
Affiliation Associate Professor, Department of Basic Sciences and Humanities, Akkineni Nageswara Rao College of Engineering and Technology, Gudivada.
DOI 10.5281/zenodo.20082583
Abstract Quantum computing has emerged as a transformative computational paradigm capable of solv ing complex scientific problems beyond the capability of classical computers. Modern develop ments in quantum mechanics, quantum algorithms, and quantum hardware have significantly accelerated research in theoretical and applied physics. This review paper presents a system atic analysis of quantum computing applications in modern physics, including quantum simu lation, condensed matter physics, high-energy physics, computational chemistry, astrophysics, and material science. The study discusses the principles of quantum computation, qubits, super position, entanglement, and quantum algorithms that enable efficient processing of large-scale physical models.
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Title Artificial Intelligence Techniques for Solving Differential Equations: A Comprehensive Re view
Authors 1.V. Satya Sailaja Kommoju, 2.Kanulla Bindhu Madhavi
Affiliation 1. Assistant Professor in Mathematics, Department of Basic Sciences and Humanities, Akkineni Nageswara Rao College of Engineering and Technology, Gudivada.
2. Assistant Professor in Mathematics, Department of Basic Sciences and Humanities, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Krishna District, Andhra Pradesh, India.
DOI 10.5281/zenodo.20082643
Abstract Differential equations are fundamental to modeling physical, biological, and engineering sys tems. Traditional numerical methods such as finite difference, finite element, and Runge-Kutta methods have been widely used to solve differential equations but often suffer from computa tional complexity and scalability limitations. Recently, Artificial Intelligence (AI), particularly machine learning and deep learning techniques, has emerged as a powerful alternative for solving differential equations. This review paper presents a comprehensive overview of AI-based meth ods for solving ordinary and partial differential equations, including Physics-Informed Neural Networks (PINNs), Deep Neural Networks (DNNs), Gaussian Processes, and hybrid approaches.
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