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1. |
Record Nr. |
UNINA9910860852303321 |
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Titolo |
Pathos - Affektformationen in Kunst, Literatur und Philosophie : Festschrift zu Ehren von Gerhard Poppenberg / Christina Pasedag, Franz Baumann, Martin Ziegler, Jürgen Paul Schwindt, Melanie Möller, Joséphine Jacquier, Florian Borchmeyer, Martin von Koppenfels, Ingrid Simson, Robert Folger, Anne Brüske, Dieter Borchmeyer, Sebastian Neumeister, Walter Marx, Stephan Leopold, Bernhard Teuber, Hanno Ehrlicher, Angela Calderón Villarino, Frank Witzel, Klaus Kempter, Friedhelm Schmidt-Welle, Xuan Jing, Wolfgang Matzat, Peter König, Einat Davidi, Jan-Ivar Lindén, Giulia Agostini, Herle Christin Jessen |
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Pubbl/distr/stampa |
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Paderborn, : Brill | Fink, 2020 |
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ISBN |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (609 pages) |
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Soggetti |
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Logos |
Ethos |
Affekte |
Leiden |
Leidenschaft |
Gefühl und ästhetische Gestaltung |
logos |
ethos |
affects |
suffering |
Criticism, interpretation, etc. |
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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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»Pathos« ist ein zentraler Begriff abendländischer Geistes-geschichte. Dieser umfasst eine emotionale Konfiguration, die in ihren etymologischen Ursprüngen das menschliche Leiden bedeutet und sich davon ausgehend semantisch als Gemütsbewegung schlechthin |
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entfaltet, als Leidenschaft, Gefühl, Affekt oder Begierde. In einem ersten Schritt widmet sich der Band in Teil I dem Pathos in Literatur und Literaturtheorie, wobei die Beiträge einen Bogen von der Antike, über das Mittelalter, die frühe Neuzeit bis in die Moderne des 20. Jahrhunderts schlagen. Teil II konturiert das Pathos je in einem über das Literarische hinausgehenden öffentlichen Raum in Religion und Politik, Malerei und Film. Teil III untersucht das Pathos als das Seelische, Affektive und Emotionale in philosophischen Diskursen mit einer Spanne von Platon bis Walter Benjamin und Martin Buber. |
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2. |
Record Nr. |
UNINA9910483580003321 |
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Autore |
Cai Baoping |
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Titolo |
Bayesian Networks for Reliability Engineering / / by Baoping Cai, Yonghong Liu, Zengkai Liu, Yuanjiang Chang, Lei Jiang |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (IX, 257 p. 153 illus., 125 illus. in color.) |
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Disciplina |
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Soggetti |
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Computational intelligence |
Security systems |
Electric power production |
Computer simulation |
Computational Intelligence |
Security Science and Technology |
Electrical Power Engineering |
Computer Modelling |
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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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Bayesian networks for reliability -- Using Bayesian networks in reliability evaluation for subsea blowout preventer control system -- Risk analysis of subsea blowout preventer by mapping GO models into Bayesian networks -- Reliability evaluation of auxiliary feedwater system by mapping GO-FLOW models into Bayesian networks -- |
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Dynamic Bayesian networks based performance evaluation of subsea blowout preventers in presence of imperfect repair -- Performance evaluation of subsea BOP control systems using dynamic Bayesian networks with imperfect repair and preventive maintenance -- Dynamic Bayesian network modelling of reliability of subsea blowout preventer stack in presence of common cause failures -- A framework for the reliability evaluation of grid-connected photovoltaic systems in the presence of intermittent faults -- Real-time reliability evaluation methodology based on dynamic Bayesian networks -- Reliability evaluation methodology of complex systems based on dynamic object-oriented Bayesian networks -- Bayesian network-based risk analysis methodology, a case of atmospheric and vacuum distillation unit -- A multiphase dynamic Bayesian networks methodology for the determination of safety integrity levels -- Availability-based engineering resilience metric and its corresponding evaluation methodology. |
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Sommario/riassunto |
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This book presents a bibliographical review of the use of Bayesian networks in reliability over the last decade. Bayesian network (BN) is considered to be one of the most powerful models in probabilistic knowledge representation and inference, and it is increasingly used in the field of reliability. After focusing on the engineering systems, the book subsequently discusses twelve important issues in the BN-based reliability methodologies, such as BN structure modeling, BN parameter modeling, BN inference, validation, and verification. As such, it is a valuable resource for researchers and practitioners in the field of reliability engineering. |
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