Advances Of Machine Learning In Clean Energy And The Transportation Industry

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Advances of Machine Learning in Clean Energy and the Transportation Industry

Author : Pandian Vasant
Publisher :
ISBN 13 : 9781685072117
Total Pages : pages
Book Rating : 4.0/5 (721 download)

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Book Synopsis Advances of Machine Learning in Clean Energy and the Transportation Industry by : Pandian Vasant

Download or read book Advances of Machine Learning in Clean Energy and the Transportation Industry written by Pandian Vasant and published by . This book was released on 2021-11-30 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest research in the field of machine learning, discussing the real-world application problems associated with new innovative renewable energy methodologies as well as cutting edge technologies in the transport industry. The requirements and demands of problem solving have been increasing exponentially, and new artificial intelligence and machine learning technologies have reduced the scope of data coverage worldwide. Recent advances in data technology (DT) have contributed to reducing the gaps in the coverage of domains around the globe.Attention to clean energy in recent decades has been growing exponentially. This is mainly due to a decrease in the cost of both installed capacity of converters and a decrease in the cost of generated energy. Such successes were achieved thanks to the improvement of modern technologies for the production of converters, an increase in the efficiency of using incoming energy, optimization of the operation of converters and analysis of data obtained during the operation of systems with the possibility of planning production. The use of clean energy plays an important role in the transportation industry, where technologies are also being improved from year to year - the transportation industry is growing, and machinery and systems are becoming more autonomous and robotic, where it is no longer possible to do without complex intelligent computing, machine learning optimization, planning and working with large amounts of data.The book is a valuable reference work for researchers in the fields of renewable energy, computer science and engineering with a particular focus on machine learning and intelligent optimization as well as for postgraduates, managers, economists and decision makers, policy makers, government officials, industrialists and practicing scientists and engineers as well compassionate global decision makers. Topics include: Machine learning, Quantum Optimization, Modern Technology in Transport Industry, Innovative Technologies in Transport Education, Systems Based on Renewable Energy Conversion, Business Process Models and Applications in Renewable Energy, Clean Energy, and Climate Change.

Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies

Author : Krishna Kumar
Publisher : Academic Press
ISBN 13 : 0323914284
Total Pages : 418 pages
Book Rating : 4.3/5 (239 download)

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Book Synopsis Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies by : Krishna Kumar

Download or read book Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies written by Krishna Kumar and published by Academic Press. This book was released on 2022-03-18 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies analyzes the changes in this energy generation shift, including issues of grid stability with variability in renewable energy vs. traditional baseload energy generation. Providing solutions to current critical environmental, economic and social issues, this book comprises various complex nonlinear interactions among different parameters to drive the integration of renewable energy into the grid. It considers how artificial intelligence and machine learning techniques are being developed to produce more reliable energy generation to optimize system performance and provide sustainable development. As the use of artificial intelligence to revolutionize the energy market and harness the potential of renewable energy is essential, this reference provides practical guidance on the application of renewable energy with AI, along with machine learning techniques and capabilities in design, modeling and for forecasting performance predictions for the optimization of renewable energy systems. It is targeted at researchers, academicians and industry professionals working in the field of renewable energy, AI, machine learning, grid Stability and energy generation. Covers the best-performing methods and approaches for designing renewable energy systems with AI integration in a real-time environment Gives advanced techniques for monitoring current technologies and how to efficiently utilize the energy grid spectrum Addresses the advanced field of renewable generation, from research, impact and idea development of new applications

Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems

Author : Yuekuan Zhou
Publisher : Elsevier
ISBN 13 : 0443131783
Total Pages : 302 pages
Book Rating : 4.4/5 (431 download)

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Book Synopsis Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems by : Yuekuan Zhou

Download or read book Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems written by Yuekuan Zhou and published by Elsevier. This book was released on 2023-11-20 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems examines the combined impact of buildings and transportation systems on energy demand and use. With a strong focus on AI and machine learning approaches, the book comprehensively discusses each part of the energy life cycle, considering source, grid, demand, storage, and usage. Opening with an introduction to smart buildings and intelligent transportation systems, the book presents the fundamentals of AI and its application in renewable energy sources, alongside the latest technological advances. Other topics presented include building occupants’ behavior and vehicle driving schedule with demand prediction and analysis, hybrid energy storages in buildings with AI, smart grid with energy digitalization, and prosumer-based P2P energy trading. The book concludes with discussions on blockchain technologies, IoT in smart grid operation, and the application of big data and cloud computing in integrated smart building-transportation energy systems. A smart and flexible energy system is essential for reaching Net Zero whilst keeping energy bills affordable. This title provides critical information to students, researchers and engineers wanting to understand, design, and implement flexible energy systems to meet the rising demand in electricity. Introduces spatiotemporal energy sharing with new energy vehicles and human-machine interactions Discusses the potential for electrification and hydrogenation in integrated building-transportation systems for sustainable development Highlights key topics related to traditional energy consumers, including peer-to-peer energy trading and cost-benefit business models

Artificial Intelligence for Renewable Energy Systems

Author : S. Balamurugan
Publisher : John Wiley & Sons
ISBN 13 : 1119761719
Total Pages : 272 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Artificial Intelligence for Renewable Energy Systems by : S. Balamurugan

Download or read book Artificial Intelligence for Renewable Energy Systems written by S. Balamurugan and published by John Wiley & Sons. This book was released on 2022-01-28 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.

Transportation Energy and Dynamics

Author : Sunil Kumar Sharma
Publisher : Springer Nature
ISBN 13 : 9819921503
Total Pages : 516 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Transportation Energy and Dynamics by : Sunil Kumar Sharma

Download or read book Transportation Energy and Dynamics written by Sunil Kumar Sharma and published by Springer Nature. This book was released on 2023-07-15 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a macro-level understanding of transportation as an industry, through the lens of all the stakeholders that make up the ecosystem. It aids understanding about the transportation ecosystem, its components, challenges, contribution to economic growth, and the interplay between the stakeholders that govern the system. The contents also examine the background and history of transportation, emphasizing the fundamental role and importance the industry plays in companies, society, and the environment in which transportation service is provided. The book also provides an overview of carrier operations, management, technology, and the strategic principles for the successful management of different modes of transportation. This book is of interest to those working in academia, industry, and policy in the areas of transportation.

Advances in Artificial Intelligence for Renewable Energy Systems and Energy Autonomy

Author : Mukhdeep Singh Manshahia
Publisher : Springer Nature
ISBN 13 : 3031264967
Total Pages : 302 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Advances in Artificial Intelligence for Renewable Energy Systems and Energy Autonomy by : Mukhdeep Singh Manshahia

Download or read book Advances in Artificial Intelligence for Renewable Energy Systems and Energy Autonomy written by Mukhdeep Singh Manshahia and published by Springer Nature. This book was released on 2023-06-14 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with emerging research that explores the theoretical and practical aspects of implementing new and innovative artificial intelligence (AI) techniques for renewable energy systems. The contributions offer broad coverage on economic and promotion policies of renewable energy and energy-efficiency technologies, the emerging fields of neuro-computational models and simulations under uncertainty (such as fuzzy-based computational models and fuzzy trace theory), evolutionary computation, metaheuristics, machine learning applications, advanced optimization, and stochastic models. This book is a pivotal reference for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research in emerging perspectives in artificial intelligence, renewable energy systems, and energy autonomy.

Machine Learning and Computer Vision for Renewable Energy

Author : Acharjya, Pinaki Pratim
Publisher : IGI Global
ISBN 13 :
Total Pages : 351 pages
Book Rating : 4.3/5 (693 download)

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Book Synopsis Machine Learning and Computer Vision for Renewable Energy by : Acharjya, Pinaki Pratim

Download or read book Machine Learning and Computer Vision for Renewable Energy written by Acharjya, Pinaki Pratim and published by IGI Global. This book was released on 2024-05-01 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the world grapples with the urgent need for sustainable energy solutions, the limitations of traditional approaches to renewable energy forecasting become increasingly evident. The demand for more accurate predictions in net load forecasting, line loss predictions, and the seamless integration of hybrid solar and battery storage systems is more critical than ever. In response to this challenge, advanced Artificial Intelligence (AI) techniques are emerging as a solution, promising to revolutionize the renewable energy landscape. Machine Learning and Computer Vision for Renewable Energy presents a deep exploration of AI modeling, analysis, performance prediction, and control approaches dedicated to overcoming the pressing issues in renewable energy systems. Transitioning from the complexities of energy prediction to the promise of advanced technology, the book sets its sights on the game-changing potential of computer vision (CV) in the realm of renewable energy. Amidst the struggle to enhance sustainability across industries, CV technology emerges as a powerful ally, collecting invaluable data from digital photos and videos. This data proves instrumental in achieving better energy management, predicting factors affecting renewable energy, and optimizing overall sustainability. Readers, including researchers, academicians, and students, will find themselves immersed in a comprehensive understanding of the AI approaches and CV methodologies that hold the key to resolving the challenges faced by renewable energy systems.

Machine Learning for Energy Systems

Author : Denis Sidorov
Publisher : MDPI
ISBN 13 : 3039433822
Total Pages : 272 pages
Book Rating : 4.0/5 (394 download)

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Book Synopsis Machine Learning for Energy Systems by : Denis Sidorov

Download or read book Machine Learning for Energy Systems written by Denis Sidorov and published by MDPI. This book was released on 2020-12-08 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume deals with recent advances in and applications of computational intelligence and advanced machine learning methods in power systems, heating and cooling systems, and gas transportation systems. The optimal coordinated dispatch of the multi-energy microgrids with renewable generation and storage control using advanced numerical methods is discussed. Forecasting models are designed for electrical insulator faults, the health of the battery, electrical insulator faults, wind speed and power, PV output power and transformer oil test parameters. The loads balance algorithm for an offshore wind farm is proposed. The information security problems in the energy internet are analyzed and attacked using information transmission contemporary models, based on blockchain technology. This book will be of interest, not only to electrical engineers, but also to applied mathematicians who are looking for novel challenging problems to focus on.

Intelligent Learning Approaches for Renewable and Sustainable Energy

Author : Josep M. Guerrero
Publisher : Elsevier
ISBN 13 : 044315807X
Total Pages : 315 pages
Book Rating : 4.4/5 (431 download)

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Book Synopsis Intelligent Learning Approaches for Renewable and Sustainable Energy by : Josep M. Guerrero

Download or read book Intelligent Learning Approaches for Renewable and Sustainable Energy written by Josep M. Guerrero and published by Elsevier. This book was released on 2024-02-21 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Learning Approaches for Renewable and Sustainable Energy provides a practical, systematic overview of the application of advanced intelligent control techniques, adaptive techniques, machine learning algorithms, and predictive control in renewable and sustainable energy.The book begins by introducing the intelligent learning approaches, and the roles of artificial intelligence and machine learning in terms of energy and sustainability, grid transformation, large-scale integration of renewable energy, and variability and flexibility of renewable sources. The second section of the book provides detailed coverage of intelligent learning techniques as applied to key areas of renewable and sustainable energy, including forecasting, supply and demand, integration, energy management, and optimization, supported by case studies, figures, schematics, and references.This is a useful resource for researchers, scientists, advanced students, energy engineers, R&D professionals, and other industrial personnel with an interest in sustainable energy and integration of renewable energy sources, energy systems, energy engineering, machine learning, and artificial intelligence. Explores cutting-edge intelligent techniques and their implications for future energy systems development Opens the door to a range of applications across forecasting, supply and demand, energy management, optimization, and more Includes a range of case studies that provide insights into the challenges and solutions in real-world applications

AI Techniques for Renewable Source Integration and Battery Charging Methods in Electric Vehicle Applications

Author : Angalaeswari, S.
Publisher : IGI Global
ISBN 13 : 1668488183
Total Pages : 308 pages
Book Rating : 4.6/5 (684 download)

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Book Synopsis AI Techniques for Renewable Source Integration and Battery Charging Methods in Electric Vehicle Applications by : Angalaeswari, S.

Download or read book AI Techniques for Renewable Source Integration and Battery Charging Methods in Electric Vehicle Applications written by Angalaeswari, S. and published by IGI Global. This book was released on 2023-02-03 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence techniques applied in the power system sector make the prediction of renewable power source generation and demand more efficient and effective. Additionally, since renewable sources are intermittent in nature, it is necessary to predict and analyze the data of input sources. Hence, further study on the prediction and data analysis of renewable energy sources for sustainable development is required. AI Techniques for Renewable Source Integration and Battery Charging Methods in Electric Vehicle Applications focuses on artificial intelligence techniques for the evolving power system field, electric vehicle market, energy storage elements, and renewable energy source integration as distributed generators. Covering key topics such as deep learning, artificial intelligence, and smart solar energy, this premier reference source is ideal for environmentalists, computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

Machine Learning, Advances in Computing, Renewable Energy and Communication

Author : Anuradha Tomar
Publisher : Springer Nature
ISBN 13 : 9811623546
Total Pages : 651 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Machine Learning, Advances in Computing, Renewable Energy and Communication by : Anuradha Tomar

Download or read book Machine Learning, Advances in Computing, Renewable Energy and Communication written by Anuradha Tomar and published by Springer Nature. This book was released on 2021-08-19 with total page 651 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected papers presented at International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication (MARC 2020), held in Krishna Engineering College, Ghaziabad, India, during December 17–18, 2020. This book discusses key concepts, challenges, and potential solutions in connection with established and emerging topics in advanced computing, renewable energy, and network communications.

AI and IOT in Renewable Energy

Author : Rabindra Nath Shaw
Publisher : Springer Nature
ISBN 13 : 9811610118
Total Pages : 109 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis AI and IOT in Renewable Energy by : Rabindra Nath Shaw

Download or read book AI and IOT in Renewable Energy written by Rabindra Nath Shaw and published by Springer Nature. This book was released on 2021-05-12 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest research on applications of artificial intelligence and the Internet of Things in renewable energy systems. Advanced renewable energy systems must necessarily involve the latest technology like artificial intelligence and Internet of Things to develop low cost, smart and efficient solutions. Intelligence allows the system to optimize the power, thereby making it a power efficient system; whereas, Internet of Things makes the system independent of wire and flexibility in operation. As a result, intelligent and IOT paradigms are finding increasing applications in the study of renewable energy systems. This book presents advanced applications of artificial intelligence and the internet of things in renewable energy systems development. It covers such topics as solar energy systems, electric vehicles etc. In all these areas applications of artificial intelligence methods such as artificial neural networks, genetic algorithms, fuzzy logic and a combination of the above, called hybrid systems, are included. The book is intended for a wide audience ranging from the undergraduate level up to the research academic and industrial communities engaged in the study and performance prediction of renewable energy systems.

Green Internet of Things and Machine Learning

Author : Roshani Raut
Publisher : John Wiley & Sons
ISBN 13 : 1119793122
Total Pages : 279 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Green Internet of Things and Machine Learning by : Roshani Raut

Download or read book Green Internet of Things and Machine Learning written by Roshani Raut and published by John Wiley & Sons. This book was released on 2022-01-10 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Health Economics and Financing Encapsulates different case studies where green-IOT and machine learning can be used for making significant progress towards improvising the quality of life and sustainable environment. The Internet of Things (IoT) is an evolving idea which is responsible for connecting billions of devices that acquire, perceive, and communicate data from their surroundings. Because this transmission of data uses significant energy, improving energy efficiency in IOT devices is a significant topic for research. The green internet of things (G-IoT) makes it possible for IoT devices to use less energy since intelligent processing and analysis are fundamental to constructing smart IOT applications with large data sets. Machine learning (ML) algorithms that can predict sustainable energy consumption can be used to prepare guidelines to make IoT device implementation easier. Green Internet of Things and Machine Learning lays the foundation of in-depth analysis of principles of Green-Internet of Things (G-IoT) using machine learning. It outlines various green ICT technologies, explores the potential towards diverse real-time areas, as well as highlighting various challenges and obstacles towards the implementation of G-IoT in the real world. Also, this book provides insights on how the machine learning and green IOT will impact various applications: It covers the Green-IOT and ML-based smart computing, ML techniques for reducing energy consumption in IOT devices, case studies of G-IOT and ML in the agricultural field, smart farming, smart transportation, banking industry and healthcare. Audience The book will be helpful for research scholars and researchers in the fields of computer science and engineering, information technology, electronics and electrical engineering. Industry experts, particularly in R&D divisions, can use this book as their problem-solving guide.

Machine Learning for Advanced Functional Materials

Author : Nirav Joshi
Publisher : Springer Nature
ISBN 13 : 9819903939
Total Pages : 306 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Machine Learning for Advanced Functional Materials by : Nirav Joshi

Download or read book Machine Learning for Advanced Functional Materials written by Nirav Joshi and published by Springer Nature. This book was released on 2023-05-22 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material’s electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.

Artificial Intelligence Techniques for a Scalable Energy Transition

Author : Moamar Sayed-Mouchaweh
Publisher : Springer Nature
ISBN 13 : 3030427269
Total Pages : 383 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Artificial Intelligence Techniques for a Scalable Energy Transition by : Moamar Sayed-Mouchaweh

Download or read book Artificial Intelligence Techniques for a Scalable Energy Transition written by Moamar Sayed-Mouchaweh and published by Springer Nature. This book was released on 2020-06-19 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents research in artificial techniques using intelligence for energy transition, outlining several applications including production systems, energy production, energy distribution, energy management, renewable energy production, cyber security, industry 4.0 and internet of things etc. The book goes beyond standard application by placing a specific focus on the use of AI techniques to address the challenges related to the different applications and topics of energy transition. The contributions are classified according to the market and actor interactions (service providers, manufacturers, customers, integrators, utilities etc.), to the SG architecture model (physical layer, infrastructure layer, and business layer), to the digital twin of SG (business model, operational model, fault/transient model, and asset model), and to the application domain (demand side management, load monitoring, micro grids, energy consulting (residents, utilities), energy saving, dynamic pricing revenue management and smart meters, etc.).

Advances of Artificial Intelligence in a Green Energy Environment

Author : Pandian Vasant
Publisher : Academic Press
ISBN 13 : 0323885748
Total Pages : 416 pages
Book Rating : 4.3/5 (238 download)

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Book Synopsis Advances of Artificial Intelligence in a Green Energy Environment by : Pandian Vasant

Download or read book Advances of Artificial Intelligence in a Green Energy Environment written by Pandian Vasant and published by Academic Press. This book was released on 2022-05-20 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances of Artificial Intelligence in a Green Energy Environment reviews the new technologies in intelligent computing and AI that are reducing the dimension of data coverage worldwide. This handbook describes intelligent optimization algorithms that can be applied in various branches of energy engineering where uncertainty is a major concern. Including AI methodologies and applying advanced evolutionary algorithms to real-world application problems for everyday life applications, this book considers distributed energy systems, hybrid renewable energy systems using AI methods, and new opportunities in blockchain technology in smart energy. Covering state-of-the-art developments in a fast-moving technology, this reference is useful for engineering students and researchers interested and working in the AI industry. Looks at new techniques in artificial intelligence (AI) reducing the dimension of data coverage worldwide Chapters include AI methodologies using enhanced hybrid swarm-based optimization algorithms Includes flowchart diagrams for exampling optimizing techniques

Deep Learning and Big Data for Intelligent Transportation

Author : Khaled R. Ahmed
Publisher : Springer Nature
ISBN 13 : 3030656616
Total Pages : 264 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Deep Learning and Big Data for Intelligent Transportation by : Khaled R. Ahmed

Download or read book Deep Learning and Big Data for Intelligent Transportation written by Khaled R. Ahmed and published by Springer Nature. This book was released on 2021-04-10 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contributes to the progress towards intelligent transportation. It emphasizes new data management and machine learning approaches such as big data, deep learning and reinforcement learning. Deep learning and big data are very energetic and vital research topics of today’s technology. Road sensors, UAVs, GPS, CCTV and incident reports are sources of massive amount of data which are crucial to make serious traffic decisions. Herewith this substantial volume and velocity of data, it is challenging to build reliable prediction models based on machine learning methods and traditional relational database. Therefore, this book includes recent research works on big data, deep convolution networks and IoT-based smart solutions to limit the vehicle’s speed in a particular region, to support autonomous safe driving and to detect animals on roads for mitigating animal-vehicle accidents. This book serves broad readers including researchers, academicians, students and working professional in vehicles manufacturing, health and transportation departments and networking companies.