| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910557780403321 |
|
|
Autore |
Tzanakakis Vasileios |
|
|
Titolo |
Water Supply and Water Scarcity |
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
|
|
|
|
|
|
|
|
|
Descrizione fisica |
|
1 online resource (290 p.) |
|
|
|
|
|
|
Soggetti |
|
Environmental economics |
Research & information: general |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Sommario/riassunto |
|
This Book includes selected papers that has been published in the Water journal Special Issue (SI) on Water Supply and Water Scarcity. Moreover, an overview of the SI is included. The papers selected for publication in the SI include review and research papers on water history, on water management issues under water scarcity regimes, on rainwater harvesting, on water quality and degradation, and on climatic variability impacts on water resources. Overall, the issue identify and highlight the main challenges in water sector, and particularly in management and protection of water resources and in use of alternative (non-conventional) water resources, especially in areas with demographic change and climate vulnerability in order to achieve sustainable and secure water supply. Furthermore, general guidelines and possible solutions for an improved and sophisticated water management system are proposed and discussed, such as the adoption of advanced technological solutions and practices that improve water-use efficiency and the use of alternative water resources, to address the growing environmental and health issues and to reduce the emerging conflicts among water users. |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
2. |
Record Nr. |
UNINA9910520099003321 |
|
|
Autore |
Wang Jing <1974 April 21-> |
|
|
Titolo |
Data-Driven Fault Detection and Reasoning for Industrial Monitoring |
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Springer Nature, 2022 |
|
Singapore : , : Springer Singapore Pte. Limited, , 2022 |
|
©2022 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Descrizione fisica |
|
1 online resource (277 pages) |
|
|
|
|
|
|
Collana |
|
Intelligent Control and Learning Systems ; ; v.3 |
|
|
|
|
|
|
Classificazione |
|
|
|
|
|
|
Altri autori (Persone) |
|
|
|
|
|
|
|
|
Soggetti |
|
Robotics |
Artificial intelligence |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Sommario/riassunto |
|
This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book. |
|
|
|
|
|
|
|
| |