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1. |
Record Nr. |
UNINA9910703617603321 |
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Titolo |
Climate change adaptation : DOD can improve infrastructure planning and processes to better account for potential impacts : report to congressional requesters |
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Pubbl/distr/stampa |
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[Washington, D.C.] : , : United States Government Accountability Office, , 2014 |
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Descrizione fisica |
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1 online resource (ii, 63 pages) : color illustrations |
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Soggetti |
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Military bases - Risk assessment - United States |
Government property - Risk assessment - United States |
Infrastructure (Economics) - Risk assessment - United States |
Climatic changes - Risk assessment - United States |
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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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Note generali |
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Title from title screen (viewed Apr. 14, 2015). |
"May 2014." |
"GAO-14-446." |
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Nota di bibliografia |
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Includes bibliographical references. |
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2. |
Record Nr. |
UNISA990000113010203316 |
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Autore |
Huppert, Bertram |
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Titolo |
Finite groups II / B. Huppert, N. Blackburn |
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Pubbl/distr/stampa |
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Berlin [etc.], : Springer-Verlag, copyr. 1982 |
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ISBN |
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Descrizione fisica |
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XIII, 531 p. : ill. ; 24 cm |
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Collana |
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Die Grundlehren der Mathematischen Wissenschaften ; 242 |
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Disciplina |
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Collocazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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3. |
Record Nr. |
UNINA9910637793403321 |
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Autore |
Nastasi Benedetto |
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Titolo |
Energy Consumption in a Smart City |
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Pubbl/distr/stampa |
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Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
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ISBN |
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Descrizione fisica |
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1 online resource (270 p.) |
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Soggetti |
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Physics |
Research & information: general |
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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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A Smart City is the perfect environment to study and exploit the interactions between actors because its architecture already integrates |
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vaious elements to collect data and connect to its citizens. Furthermore, the proliferation of web platforms (e.g., social media and web fora) and the increased affordability of sensors and IoT devices (e.g., smart meters) make data related to a large and diverse set of users accessible, as their activities in the digital world reflect their real-life actions. These new technologies can be of great use for the stakeholders as, on the one hand, they provide them with semantically rich inputs and frequent updates at a relatively cheap cost and, on the other, form a direct channel of communication with the citizens. To fully exploit these new data sources, we need both novel computational methods (e.g., AI, data mining algorithms, knowledge representation) that are suitable for analyzing and understanding the dynamics behind energy consumption and also a deeper understanding of how these methods can be integrated into the existing design and decision processes (e.g., human-in-the-loop processes).Therefore, this Special Issue welcomed original multidisciplinary research works about AI, data science methods, and their integration in existing design/decision-making processes in the domain of energy consumption in Smart Cities. |
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