Asia Pacific Journal of Energy and Environment
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Advances in Autonomous Robotics for Environmental Cleanup and Hazardous Waste Management
This research investigates the progress made in autonomous robots for environmental cleaning and hazardous waste management. The objective is to evaluate their efficacy, adaptability, and prospective influence on existing methods. The study used a secondary data review process to combine information from several case studies, technical breakthroughs, and upcoming robot trends. Autonomous robots improve cleaning efficiency and safety in soil cleanup, oil spill response, garbage sorting, and disaster recovery. AI, sensors, and multi-modal robots boost performance. However, sensor accuracy, navigation, and energy management issues persist. The paper emphasizes the policy implications, such as the need for uniform rules, more investment in research and development, and the significance of addressing ethical and social concerns. By focusing on these specific areas, the incorporation of autonomous robots may be enhanced, resulting in more efficient and environmentally friendly solutions for handling environmental risks and waste.
Copyright (c) 2025 RamMohan Reddy Kundavaram; Abhishake Reddy Onteddu; Krishna Devarapu; Deekshith Narsina; Md. Nizamuddin https://creativecommons.org/licenses/by-nc/4.0
AI-Driven Robotics in Solar and Wind Energy Maintenance: A Path toward Sustainability
According to this research, AI-driven robots may improve operational efficiency, save costs, and promote sustainability objectives in solar and wind energy system maintenance. The paper examines AI and robotics technology, analyzes their applications in renewable energy maintenance, and identifies difficulties and prospects for maximizing their utilization. Using secondary data, the research synthesizes significant findings and trends from peer-reviewed publications, case studies, and industry reports. Primary results show that AI-driven robots may transform maintenance processes by boosting inspection accuracy, safety, and downtime while meeting sustainability objectives via resource efficiency and waste reduction. High initial costs, technological constraints in severe settings, and regulatory complexity still prevent broad implementation. Policy implications involve focused research and development, consistent rules, and financial incentives to make these technologies more accessible to smaller operators to solve these difficulties. Governments, business leaders, and academics must work together to overcome these challenges and maximize AI-driven robots in renewable energy. This research stresses robots' crucial role in expediting sustainable energy infrastructure transformation.
Copyright (c) 2022 Arjun Kamisetty https://creativecommons.org/licenses/by-nc/4.0
Integrating Blockchain and AI to Enhance Supply Chain Transparency in Energy Sectors
This research examines how Blockchain and AI might improve energy supply chain transparency. These technologies are discussed to solve energy supply chain inefficiencies, fraud, and transparency while encouraging sustainability and operational optimization. The paper evaluates Blockchain and AI applications in energy systems using secondary data from literature, case studies, and industry sources. In summary, Blockchain provides an immutable and decentralized ledger for transparency and data integrity, while AI improves operational efficiency via predictive analytics, demand forecasting, and asset management. These technologies provide real-time tracking, cost reduction, and renewable energy integration. Scalability, data integrity, and regulatory ambiguity remain issues for Blockchain. The paper also stresses the need for clear legislative frameworks to govern energy industry blockchain and AI deployment. Policymakers should stimulate innovation, invest in digital infrastructure, and set safe and efficient technology integration standards. The study shows that Blockchain and AI can transform energy supply chains by improving transparency, efficiency, and sustainability and solving sector issues.
Copyright (c) 2022 Rajasekhar Reddy Talla https://creativecommons.org/licenses/by-nc/4.0
Data Analytics for Enhanced Business Intelligence in Energy-Saving Distributed Systems
This research examines how data analytics might improve Business Intelligence (BI) in energy-saving distributed systems to improve energy management and sustainability. Secondary data-based reviews synthesize literature on data analytics frameworks, data processing methods, and BI tactics in distributed energy scenarios. According to critical results, descriptive, diagnostic, predictive, and prescriptive analytics turn raw data into energy-efficient insights. Descriptive and diagnostic analytics highlight historical trends and inefficiencies, whereas predictive and prescriptive methods maximize resource allocation and real-time decision-making. Adaptive energy management requires robust BI frameworks with centralized data warehousing, visualization, and real-time analytics. However, enormous data volume, real-time processing limits, data security, and lack of standards limit these analytics' usefulness. Policy guidelines should include cybersecurity safeguards, AI and edge computing integration incentives, and standardized protocols to improve data processing and system interoperability. These findings demonstrate the importance of data-driven BI in improving energy efficiency and sustainability in distributed energy systems and meeting global energy targets.
Copyright (c) 2022 Srinikhita Kothapalli https://creativecommons.org/licenses/by-nc/4.0
Code Refactoring for Energy-Saving Distributed Systems: A Data Analytics Approach
This research uses data analytics and code refactoring to improve distributed system energy usage. The goal is to provide a framework for energy profiling, performance monitoring, and predictive analytics to discover inefficiencies and save energy. Secondary data analysis is used to analyze research and case studies on energy-aware refactoring and distributed computing data analytics. Energy profiling is essential for discovering inefficiencies, while algorithm improvement, intelligent job allocation, and redundancy reduction considerably cut energy use. Predictive analytics allows dynamic energy optimization, and real-time feedback loops optimize energy-saving measures. The report also notes data accuracy, computational overhead, and energy efficiency-system performance balance issues. The policy implications include industry standards, clear guidelines, and government incentives required to disseminate energy-efficient code. By promoting energy-aware refactoring, these rules might create more sustainable and cost-effective distributed systems. This study emphasizes data-driven energy efficiency in distributed systems and advances sustainable computing expertise.
Copyright (c) 2024 Aditya Manikyala https://creativecommons.org/licenses/by-nc/4.0
Optimizing Home Energy Usage: HEMS-IoT Integration with Big Data and Machine Learning
The goal of this project is to optimize household energy consumption by combining machine learning (ML), big data analytics, and the Internet of Things (IoT) with household Energy Management Systems (HEMS). The primary goals are to assess how well HEMS-IoT integration contributes to cost savings, environmental sustainability, and energy efficiency in residential contexts. The methodology includes a thorough analysis of current literature, real-world case studies, and experimental results to examine the advantages, restrictions, and policy implications of HEMS-IoT integration. Among the key findings are personalized energy management, cost savings, increased energy efficiency, and home behavioral changes. Policy implications emphasize how crucial it is to address issues with fairness, data privacy, accessibility, and interoperability through proactive regulatory frameworks and policy interventions. The study highlights how HEMS-IoT integration can revolutionize residential energy efficiency and move us closer to a more robust and sustainable energy ecosystem.
Copyright (c) 2022 Ravikiran Mahadasa https://creativecommons.org/licenses/by-nc/4.0
Innovative Additives for Rubber: Improving Performance and Reducing Carbon Footprint
This study investigates how to improve performance and lessen environmental effects by integrating novel compounds into rubber. The principal aims of this study are to evaluate the potential of bio-based additives, sophisticated nanomaterials, and intelligent features to enhance rubber's mechanical, thermal, and chemical properties while reducing carbon emissions. The research assesses recent developments and potential future directions through an extensive secondary data analysis. Important discoveries show that carbon nanotubes and graphene considerably improve durability and tensile strength, while bio-based additives lessen reliance on fossil fuels. The automotive sector benefits significantly from these additives' lightweight and increased energy efficiency. The report also emphasizes the necessity of sustainable end-of-life management and enhanced recyclability. The analysis highlights the significance of policy interventions despite the high costs and scaling issues associated with these materials. To encourage the use of sustainable additives, governments must fund R&D, set precise guidelines, and promote recycling. The rubber sector may make great strides and contribute to industrial performance and environmental sustainability by addressing these constraints through supporting legislation. This study highlights how cutting-edge additives can revolutionize rubber technology in the future.
Copyright (c) 2022 Deng Ying, Srinivas Addimulam https://creativecommons.org/licenses/by-nc/4.0
Power Electronics Innovations: Improving Efficiency and Sustainability in Energy Systems
This study examines how power electronics advancements might alter energy system efficiency and sustainability. The main goals were to study wide bandgap (WBG) semiconductor materials, control algorithms, renewable energy integration, and future trends and problems. Synthesizing current knowledge and trends from peer-reviewed literature, conference papers, and industry reports was done using secondary data. Significant discoveries show that WBG semiconductors like SiC and GaN have superior electrical characteristics, improving power electronic device efficiency and reliability. Model predictive control (MPC) and AI-based control algorithms optimize system performance and handle renewable energy source variability. Modern inverters and converters help integrate renewable energy into the grid, improving energy efficiency and the environment. Policy implications underline the need for supporting regulatory frameworks, research funding, and industry collaboration to reduce cost barriers, ensure interoperability, and optimize power electronics breakthroughs in global energy transitions.
Copyright (c) 2022 Niravkumar Dhameliya https://creativecommons.org/licenses/by-nc/4.0
Bioprocess Automation with Robotics: Streamlining Microbiology for Biotech Industry
One key tactic for optimizing microbiology in the biotech sector is the combination of robotics and bioprocess automation. This research aims to improve scalability, accuracy, and efficiency in microbial bioprocessing by investigating the effects of automated technologies. The study uses a secondary data-based review methodology to look at present trends, technological developments, and prospects in bioprocess automation with robotics. Important discoveries demonstrate notable scalability, accuracy, and efficiency gains fueled by higher throughput and sophisticated AI algorithms. However, obstacles to widespread adoption include expensive initial investment costs and the requirement for specialized knowledge. The policy implications emphasize the significance of focused investments, incentives, and teamwork in removing obstacles and realizing the full potential of robotics-assisted bioprocess automation in the biotech sector, spurring innovation and advancing sustainability.
Copyright (c) 2022 Mohamed Ali Shajahan https://creativecommons.org/licenses/by-nc/4.0
The Role of Artificial Intelligence in Optimizing Rubber Manufacturing Processes
This review article examines how Artificial Intelligence (AI) can be used to optimize rubber production processes. The main goals are to list rubber manufacturers' difficulties, investigate AI applications, highlight significant discoveries, and discuss the policy ramifications for effective AI integration. Using a secondary data-based methodology, the study gathers information about AI applications unique to the rubber manufacturing business by reviewing a large body of literature from conferences, peer-reviewed journals, and industry reports. The results show that artificial intelligence (AI) technologies in rubber manufacturing facilitate improved process optimization, predictive maintenance, quality control, and adaptive process control. Artificial intelligence (AI)-powered technologies enhance compounded formulations, automate shaping procedures, forecast equipment breakdowns, and maximize resource efficiency. The policy consequences encompass resolving data privacy issues, allocating resources toward workforce training, instituting moral AI governance structures, and offering monetary incentives to encourage the deployment of AI. In summary, artificial intelligence has revolutionary prospects for rubber producers to improve productivity, excellence, and environmental friendliness. Rubber manufacturing processes can be made more innovative and continuously enhanced by embracing AI-driven solutions and strategic plans.
Copyright (c) 2023 Arun Kumar Sandu https://creativecommons.org/licenses/by-nc/4.0
Real World Applications of Cloud Computing: Architecture, Reasons for Using, and Challenges
Cloud computing is a significant IT innovation. It is one of the most remarkable ways to manage and allocate internet-wide information and resources. Cloud computing involves accessing IT infrastructure over a computer network without installing anything. Cloud computing lets businesses adjust resource levels to meet operational needs. Cloud computing reduces infrastructure costs for businesses. With improved administration and less maintenance, organizations can test their applications faster. Cloud computing lets the IT team respond to unexpected needs. Applications in numerous contexts show that cloud computing is part of daily life. Cloud computing architecture, attributes, types, service models, advantages, and challenges will be covered in this essay. In this article, we have explored the fundamental ideas of cloud computing, its architecture, the reasons for employing cloud computing, and the obstacles that cloud computing faces.
Copyright (c) 2020 Samuel Koehler, Harshith Desamsetti, Venkata Koteswara Rao Ballamudi, Sreekanth Dekkati https://creativecommons.org/licenses/by-nc/4.0
An Overview of Environmental Scalability and Security in Hybrid Cloud Infrastructure Designs
The practice of using IT resources via the internet with pay-as-you-go pricing is known as cloud computing. The cloud computing market has grown dramatically in the last few years. Most companies are transmitting from on-premises data centers to cloud computing. You can use the cloud as an alternative to purchasing and maintaining computer hardware and software. You can spend time, energy, or money doing everything yourself. This paper presents an overview of the architecture, scalability, economics, consistency-availability-partition, transactions, and security of cloud-based system designs. This study assesses the current cloud development ecosystem using various criteria.
Copyright (c) 2021 Sandesh Achar https://creativecommons.org/licenses/by-nc/4.0
An Optimal Cloud Based Electric Vehicle Charging System
With the evolution of the internet-of-things and the emergence of cloud computing, the charging dynamics of vehicles have changed. This work discusses cloud-based monitoring and management used in charging electric vehicles and their impact on the smart charging system. Charging management plays a key role in assessing the charging infrastructure because of the automakers and charging service providers. As the market evolves, this system looks at the present public and private sectors that provide charging stations and contrasts them with modern cloud-based charging in electric vehicles. The cloud module developed contains layers, with the top layer of the robust calculating ability, which is globally optimized using machine learning technology. The bottom layer counters the real-time issues with the controller. The system also analyzes the current demands in the market and forms strategies to maximize profits through smart charging systems.
Copyright (c) 2021 Venkata Naga Satya Surendra Chimakurthi https://creativecommons.org/licenses/by-nc/4.0
Reinforcement Learning for Robotic Grasping and Manipulation: A Review
A century of robots is the 21st century. The robots have long been able to cross the divide between the virtual universe and the real world. Robotics, as the most successful contender in the upcoming great technological revolution, will play an ever more important role in society because of the impact it has in every field of life, including medicine, healthcare, architecture, manufacturing and food supplies, logistics and transport. This document introduces a modern approach to the grasp of robots, which draws grasp techniques from the human demonstration and combines these strategies into a grasp-planning framework, in order to produce a viable insight into the objective geometry and manipulation tasks of the object. Our study findings show that grasping strategies of the form of grasp and thumbs positioning are not only necessary for human grasp but also significant restrictions on posture and wrist posture which greatly reduce both the robot hand's workplace and the search space for grasp planning. In the simulation and with a true robotic system this method has been extensively tested for several everyday living representative objects. In the experiment with varying degrees of perceiving in certainties, we have demonstrated the power of our method.
Copyright (c) 2020 Praveen Kumar Donepudi https://creativecommons.org/licenses/by-nc/4.0
Enhancing Energy Efficiency in Distributed Systems through Code Refactoring and Data Analytics
This research examines code restructuring and data analytics to improve distributed system energy efficiency. The main goal is to optimize software design and use data-driven insights to decrease energy usage without compromising performance. The secondary data-based assessment examines code refactoring methods like algorithm optimization and memory management and data analytics tools like predictive models and real-time monitoring. Key findings show that code refactoring streamlines algorithms, reduces redundant processes, and improves task distribution. At the same time, data analytics enables adaptive energy management through predictive forecasting, anomaly detection, and dynamic resource allocation. Combining these methods yields a scalable distributed energy efficiency solution. However, ongoing data processing energy costs and integration complexity persist. The report emphasizes the need for incentives for technology investments, training, and established best practices to promote energy-efficient distributed systems. These results indicate that a balanced strategy combining code optimization and powerful data analytics may maintain and improve energy efficiency in the continually changing distributed computing ecosystem.
Copyright (c) 2023 Takudzwa Fadziso; Aditya Manikyala; Hari Priya Kommineni; Satya Surya MKLG Gudimetla Naga Venkata https://creativecommons.org/licenses/by-nc/4.0
Thermoplastic Polymer Compounding with ZnS Nanostructures: Innovations and Applications
This study investigates the compounding of thermoplastic polymers with ZnS nanostructures, focusing on innovative synthesis methods and applications. The principal objective was to enhance the mechanical, thermal, and electrical properties of thermoplastic polymers by incorporating well-characterized ZnS nanoparticles. Utilizing a chemical precipitation method, ZnS nanoparticles with controlled size and high purity were synthesized. Characterization techniques, including XRD, SEM, TEM, EDS, and FTIR, confirmed the crystalline structure and elemental composition of the nanoparticles. Three compounding techniques—melt blending, solution casting, and in-situ polymerization—were explored for integrating ZnS nanoparticles into thermoplastic matrices. Each method was optimized for uniform nanoparticle dispersion and strong interfacial bonding. The findings revealed that nanocomposites produced via these techniques exhibited significantly enhanced tensile strength, impact resistance, thermal stability, and electrical conductivity compared to pure thermoplastics. The study's implications extend to industrial applications in electronics, automotive components, aerospace parts, and advanced packaging materials. The environmental benefits, such as reduced material consumption and improved recyclability, along with potential economic savings, underscore the broader impact of these advanced materials. This research provides a solid foundation for future development of high-performance ZnS-thermoplastic nanocomposites, promoting innovation in material science and industrial applications.
Copyright (c) 2020 Charlotte Roberts https://creativecommons.org/licenses/by-nc/4.0
Quantum Dot Sensitized Solar Cells: A Promising Avenue for Next-Generation Energy Conversion
By taking advantage of the unique qualities of colloidal quantum dots, quantum dot-sensitized solar cells (QDSSCs) provide a viable route for next-generation energy conversion while increasing the device's adaptability and light harvesting efficiency. This research aims to thoroughly examine the possibilities and difficulties associated with QDSSCs and offer information on their applications, methods for performance optimization, and policy ramifications. To assess the state-of-the-art research on QDSSCs, the technique systematically evaluates existing literature, including peer-reviewed articles, conference proceedings, and patents. Significant discoveries highlight developments in materials design, methods for fabricating devices, and potential integrations in consumer electronics, building-integrated photovoltaics, and off-grid applications. The policy implications underscore the necessity of regulatory frameworks to tackle environmental issues, set up guidelines and certification procedures, and foster global cooperation. In summary, this research highlights the importance of QDSSCs as a viable choice for sustainable energy conversion. It advocates for cooperative endeavors to surmount obstacles and expedite their integration into the renewable energy terrain.
Copyright (c) 2020 Pavani Surarapu, Janaki Rama Phanendra Kumar Ande, Aleena Varghese, Suman Reddy Mallipeddi, Dileep Reddy Goda, Sridhar Reddy Yerram, Swathi Kaluvakuri https://creativecommons.org/licenses/by-nc/4.0
Sunlight to Sustainability: A Comprehensive Analysis of Solar Energy's Environmental Impact and Potential
The study navigates the intricate landscape of solar energy, examining its historical foundations, environmental implications, economic viability, and transformative innovations. The study begins by elucidating the fundamentals of solar energy, exploring the principles of photovoltaic and solar thermal technologies. Subsequent chapters delve into the environmental footprint of photovoltaic technologies, revealing life cycle assessments and considerations for sustainability. The economic dynamics of solar energy are scrutinized, assessing market forces, government policies, and financial metrics. Innovation takes center stage in the final chapter, exploring advancements shaping the solar landscape. Major findings underscore the promising trajectory of solar energy, positioning it as a dynamic force in the global pursuit of sustainability. The study concludes by emphasizing the need for ongoing research, technological innovation, and strategic planning to fully unlock solar energy's potential in the transition towards a sustainable future.
Copyright (c) 2020 Vishal Reddy Vadiyala https://creativecommons.org/licenses/by-nc/4.0
Real-Time Scheduling for Energy Optimization: Smart Grid Integration with Renewable Energy
This research investigates the scheduling of tasks in real-time to optimize energy use in the context of integrating renewable energy sources into the smart grid. The primary goals are to analyze the influence of fluctuations in renewable energy on grid synchronization, evaluate the efficiency of different optimization methods, and identify significant obstacles and corresponding remedies. Secondary data studies advanced forecasting methods, energy storage systems, and optimization techniques, including Linear Programming (LP), Dynamic Programming (DP), and metaheuristics. The significant findings show that renewable energy fluctuations affect power system stability. Advanced prediction methods and energy storage are essential in reducing these impacts. Optimization approaches enhance the scheduling efficiency, but the computational complexity and practical application constraints limit their effectiveness. Challenges such as frequency regulation, voltage management, and integrating Distributed Energy Resources (DERs) need specific solutions such as dynamic voltage support and grid modernization. The policy implications include supporting advanced technologies, encouraging real-time scheduling system research, and enhancing grid infrastructure to increase resilience. These measures are essential for integrating renewable energy, ensuring a reliable smart grid, and achieving a sustainable future.
Copyright (c) 2021 Chunhua Deming; Prasanna Pasam; Abhishekar Reddy Allam; Rahimoddin Mohammed; SSMLG Gudimetla Naga Venkata; Kanaka Rakesh Varma Kothapalli https://creativecommons.org/licenses/by-nc/4.0
Green Chemistry Approaches to Vulcanization: Reducing Environmental Impact in Rubber Manufacturing
To lessen its environmental influence, this study explores using green chemical techniques in rubber manufacturing vulcanization procedures. The primary goals are investigating bio-based additives, innovative catalysts, ionic liquids, renewable energy sources, circular economy strategies, and life cycle assessment techniques. From a methodological standpoint, the study uses an extensive literature review to investigate the present status of industry practices and research in green chemistry for vulcanization. Important discoveries demonstrate how innovative catalysts, ionic liquids, and bio-based additives can reduce environmental damage while boosting process effectiveness. Furthermore, combining the concepts of the circular economy with renewable energy sources is a potential approach to sustainability. The policy implications highlight the necessity of creating regulatory frameworks, encouraging cooperation, and providing incentives for research and development to facilitate the shift to more environmentally friendly vulcanization procedures. This study emphasizes how critical it is to adopt green chemistry ideas to accomplish environmental responsibility and long-term sustainability in the rubber industry.
Copyright (c) 2021 Charlotte Roberts, Rajani Pydipalli, Jayadip GhanshyamBhai Tejani, Md. Nizamuddin https://creativecommons.org/licenses/by-nc/4.0
Driving the Shift to Sustainable Industry 5.0 with Green Manufacturing Innovations
With an emphasis on the factors driving the shift towards sustainability in industrial sectors—such as drivers, obstacles, impacts, constraints, and policy implications—this study examines the shift towards Sustainable Industry 5.0 with Green Manufacturing Innovations. The research aims to investigate the effects of sustainable industrial development on the economy, environment, and society, identify important forces and obstacles, and evaluate the significance of the findings for policymakers and regulatory agencies. The study's methodology entails a thorough analysis of the body of research on green manufacturing techniques, the transition to a sustainable industrial sector, and legislative frameworks. The main conclusions emphasize Sustainable Industry 5.0's enormous economic potential, favorable environmental effects, and social ramifications. The study also points out restrictions on the generalizability and availability of data, and it emphasizes how crucial it is to fortify regulatory frameworks, fund R&D, increase stakeholder engagement, and support capacity building to propel the industrial sectors' transition to sustainability. These findings' policy implications are significant in propelling sustainable industrial development and expediting the shift towards Sustainable Industry 5.0.
Copyright (c) 2021 Sridhar Reddy Yerram https://creativecommons.org/licenses/by-nc/4.0
AI-Driven Solutions for Energy Optimization and Environmental Conservation in Digital Business Environments
The potential of AI-driven solutions for environmental preservation and energy optimization in digital business settings is examined in this paper. The main goals were to investigate how AI technologies may support sustainability, identify major obstacles and opportunities, and evaluate the policy implications for implementation. The approach thoroughly examined the literature, including research articles and case studies, to assess AI's uses in energy optimization and environmental preservation. The main conclusions show how AI technologies can revolutionize energy optimization by enabling intelligent control systems, integrating renewable energy sources, and enabling precision energy optimization. To guarantee successful implementation, constraints, including data quality problems, technological complexity, and ethical issues, need to be resolved. To encourage the ethical and responsible usage of AI-driven solutions for sustainability in digital business environments, regulators and enterprises must work together and establish clear legislative frameworks and incentives for technology adoption. This work generally advances knowledge of the potential and difficulties of utilizing AI technology for energy optimization and environmental preservation in the digital age.
Copyright (c) 2022 Aleena Varghese https://creativecommons.org/licenses/by-nc/4.0
Harnessing AI and IoT Technologies for Sustainable Business Operations in the Energy Sector
The potential for improving sustainable business operations in the energy industry through the combination of artificial intelligence (AI) and Internet of Things (IoT) technology is considerable. This research investigates the potential benefits, obstacles, and policy ramifications of utilizing AI and IoT technology for sustainable commercial activities within the energy industry. A thorough analysis of current literature, including government publications, industry reports, and peer-reviewed journal papers, is part of the methodology used. Important discoveries demonstrate how AI and IoT technology can revolutionize resource efficiency, improve grid stability, encourage the integration of renewable energy sources, and lessen environmental effects. To guarantee successful acceptance and deployment, however, obstacles must be addressed, including worries about data privacy and security, unpredictability in regulations, interoperability problems, and the need for workforce development, Clear regulatory frameworks, workforce development programs, interoperability standards, and cybersecurity measures are among the policy implications that must be addressed to enable the appropriate and successful integration of AI and IoT technologies in the energy sector. In summary, this research highlights the significance of deliberate investments, cooperation, and legislative measures when utilizing AI and IoT technology to propel sustainable business practices within the energy industry.
Copyright (c) 2022 Suman Reddy Mallipeddi https://creativecommons.org/licenses/by-nc/4.0
Heat Waves in Bangladesh: Understanding the Threats and Finding Solutions
Bangladesh has seen a significant rise in the number and strength of heat waves in recent years. These heat waves are hazardous for health, farming, and the economy. This paper investigates why these heat waves are happening, how they affect different areas, and what can be done to lessen their harmful effects. By carefully studying weather data, climate models, and economic and social factors, this study aims to give valuable ideas on how to adapt and reduce the impact of heat waves. The results show that it’s essential to work together to deal with heat waves' many effects and help communities become stronger. With the right actions and policies, we can lower the risks of extreme heat, protect people’s health, and ensure the economy and environment stay strong.
Copyright (c) 2022 Md. Shelim Miah, Safayet Ullah, Mehedi Hasan Siddique https://creativecommons.org/licenses/by-nc/4.0
Data Analytics for Energy-Efficient Code Refactoring in Large-Scale Distributed Systems
It examines how data analytics improves energy efficiency in large-scale distributed systems via code reworking. The primary goal is to study how data-driven techniques maximize resource allocation, energy usage, and system performance. Secondary data-based reviews of energy-efficient data analytics case studies from Google, Facebook, AWS, and Microsoft are used in the process. Significant results show that performance profiling, real-time monitoring, predictive modeling, and energy-aware resource management reduce energy use and ensure system scalability and performance. Energy savings were realized utilizing dynamic resource allocation, job scheduling, load balancing, and predictive analytics using machine learning. Energy consumption is also reduced by managing network traffic and data storage. However, integrating contemporary analytics tools into older systems and handling their massive data sets remain substantial obstacles. The paper recommends uniform legislation to promote energy-efficient practices, incentives for sustainable computing research, and industry best practices. This work emphasizes energy efficiency in large-scale distributed systems and advances sustainable computing research.
Copyright (c) 2021 Narayana Reddy Bommu Sridharlakshmi https://creativecommons.org/licenses/by-nc/4.0
MLOps Pipelines for GenAI in Renewable Energy: Enhancing Environmental Efficiency and Innovation
This article explores the integration of MLOps pipelines with Generative Artificial Intelligence (GenAI) in renewable energy systems, aiming to enhance environmental efficiency and foster innovation. The objectives are to evaluate advancements in energy harvesting technologies for wireless sensor networks (WSNs), analyze the potential of GenAI for optimizing renewable energy operations, and address challenges in deploying MLOps frameworks in dynamic energy environments. The principal findings reveal that MLOps pipelines enable continuous model refinement, scalability, and efficient management of GenAI models, significantly improving renewable energy applications such as resource optimization, predictive maintenance, and energy storage. Energy harvesting technologies, coupled with GenAI, promise autonomous and sustainable solutions, reducing dependency on traditional power sources. Policy implications emphasize the need for standardized regulations, investments in computational infrastructure, and ethical guidelines for AI deployment in energy systems. By addressing current challenges, policymakers and researchers can unlock GenAI's full potential, advancing global sustainability goals.
Generative Artificial Intelligence (GenAI)
Copyright (c) 2019 Pavan Kumar Gade https://creativecommons.org/licenses/by-nc/4.0
Bioinformatics Algorithms for Molecular Docking: IT and Chemistry Synergy
Drug discovery and molecular biology can be advanced through the synergistic combination of bioinformatics techniques and molecular docking. This research attempts to investigate the most recent developments in this multidisciplinary subject, emphasizing enhancing the efficiency and accuracy of predictions. The process entails a thorough literature review and an analysis of significant advancements in search algorithms, machine learning integration, and scoring systems. Notable discoveries include improved search and scoring algorithms powered by machine learning methods that enhance protein flexibility and binding affinity predictions. The report highlights issues like data availability and computational complexity and suggests policy solutions, such as data-sharing programs, computational infrastructure investments, and regulatory guidelines for AI-driven drug discovery. This study highlights the revolutionary potential of bioinformatics docking synergy, opening the door for faster therapeutic advancements in the biomedical sciences and personalized medicine.
Copyright (c) 2018 Marcus Rodriguez, Jayadip GhanshyamBhai Tejani, Rajani Pydipalli, Bhavik Patel https://creativecommons.org/licenses/by-nc/4.0
Innovations in Energy Harvesting Technologies for Wireless Sensor Networks: Towards Self-Powered Systems
This research looks into new developments in wireless sensor network (WSN) energy harvesting technologies and how they affect self-powered systems. The primary goals are to investigate various energy harvesting technologies, pinpoint integration tactics and obstacles, look at case studies and real-world applications, and suggest future lines of inquiry and research avenues. A thorough analysis of the body of research from credible internet sources, conference proceedings, and peer-reviewed publications is part of the technique. The importance of developments in materials science, their integration with AI and ML methods, the creation of multimodal energy harvesting systems, the investigation of novel energy sources and mechanisms, and the consideration of environmental and social impacts are all highlighted by critical findings. Policy implications include the need for financial incentives, legal frameworks, ecological assessments, social equality programs, and support for research and innovation to encourage the widespread acceptance and sustainable deployment of self-powered sensor networks. By expanding our knowledge and developing new energy harvesting methods for WSNs, this research helps to create sensor systems that are reliable, efficient, and self-sufficient.
Copyright (c) 2019 Ravikiran Mahadasa, Dileep Reddy Goda, Pavani Surarapu https://creativecommons.org/licenses/by-nc/4.0
Solid-State Electrolytes for High-Energy-Density Lithium-Ion Batteries: Challenges and Opportunities
For various applications, solid-state electrolytes (SSEs) present exciting possibilities for improving lithium-ion batteries' performance, stability, and safety (LIBs). To shed light on the significant variables influencing the direction of energy storage technology in the future, this paper examines the opportunities and problems related to SSEs for high-energy-density LIBs. The study's primary goals are to explore the characteristics and difficulties of SSEs, appraise manufacturing methods, appraise the effectiveness of SSE-based LIBs, and investigate potential future directions and policy ramifications. The study's methodology involves a thorough literature analysis, summarizing previous research findings and highlighting areas and chances for additional investigation. Significant discoveries emphasize how crucial multifunctional SSEs, interface engineering, improved materials design, scalable manufacturing techniques, and international cooperation are to the advancement of SSE-based LIBs. Policy implications: To expedite the development and deployment of SSE-based energy storage systems, investments in infrastructure, regulatory standards, environmental sustainability, and cooperative research projects are essential.
Copyright (c) 2018 Suman Reddy Mallipeddi, Dileep Reddy Goda https://creativecommons.org/licenses/by-nc/4.0
Modeling and Simulation of Electromagnetic Interference in Power Distribution Networks: Implications for Grid Stability
The subjects of this study are the modeling and simulation of electromagnetic interference (EMI) in power distribution networks and its consequences for grid stability. The key goals are to find the sources of EMI, assess how they affect grid performance, and create mitigation plans. A thorough study of research articles and literature on EMI modeling, simulation methods, and grid stability assessment is part of the methodology. Important discoveries emphasize the various origins and traits of electromagnetic interference (EMI), how it affects voltage control, frequency stability, and power quality, and how to mitigate and improve grid resilience. The policy implications emphasize the significance of standards, research projects, and regulatory frameworks in tackling EMI issues and guaranteeing the dependability of distribution networks. Stakeholders can ensure a consistent and adequate supply of energy to consumers by strengthening the resilience of power distribution networks and including electromagnetic interference (EMI) considerations in design, planning, and operational procedures.
Copyright (c) 2017 Janaki Rama Phanendra Kumar Ande, Aleena Varghese, Suman Reddy Mallipeddi, Dileep Reddy Goda, Sridhar Reddy Yerram https://creativecommons.org/licenses/by-nc/4.0