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1. |
Record Nr. |
UNINA9910890175503321 |
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Autore |
Kotnala R. K |
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Titolo |
Advanced Functional Materials for Sustainable Environments / / edited by R. K. Kotnala, Anjali Sharma Kaushik, S. Shankar Subramanian, Amit K. Vishwakarma |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (296 pages) |
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Altri autori (Persone) |
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Sharma KaushikAnjali |
SubramanianS. Shankar |
VishwakarmaAmit K |
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Disciplina |
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Soggetti |
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Energy harvesting |
Materials |
Catalysis |
Force and energy |
Energy Harvesting |
Materials for Energy and Catalysis |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Fundamental of Sustainable Materials -- Materials for Environmental Remediation -- Energy Generation from Water -- Advanced Materials for Energy Harvesting -- Carbon Based Structures for Energy and Environment Applications. |
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Sommario/riassunto |
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The book gives an insight into the latest research going on worldwide in the area of functional materials that specifically utilized for the energy harvesting, storage, and environmental monitoring. Since the technology is moving very fast day by day, it has become a need of hour to stay updated with recent advancements in materials which include electronic, magnetic, optical, adaptive, dielectric materials, etc., that are required to develop new functionalities with better performance that is beneficial for sustainable environment. The broad areas that are covered in the book include the knowledge of wide range of materials for energy harvesting, energy storage, and sensors for environmental monitoring. This book is a value additional reference for |
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beginners, researchers, and academicians regarding the new functional materials for device applications. This book covers a wide range of topics: multifunctional materials, 2D materials, sensing materials, materials for environmental studies, DFT and solar simulation of materials, perovskite and double perovskite materials, materials for energy conversion and storage, smart materials, advanced functional materials, polymeric materials, composites, materials for sustainable development, nanomaterials, and thin films. |
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2. |
Record Nr. |
UNINA9910557359003321 |
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Autore |
Gocheva-Ilieva Snezhana |
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Titolo |
Statistical Data Modeling and Machine Learning with Applications |
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Pubbl/distr/stampa |
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Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
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Descrizione fisica |
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1 online resource (184 p.) |
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Soggetti |
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Information technology industries |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as |
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straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section "Mathematics and Computer Science". Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties. |
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