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[null,"1.","Record Nr.",null,"UNINA9911031667003321"]
[null,null,"Autore",null,"Ripoll Thierry"]
[null,null,"Titolo",null,"Human Psychology and the Destruction of the Planet / / by Thierry Ripoll"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"3-032-02080-8"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (320 pages)"]
[null,null,"Collana",null,"Anthropocene – Humanities and Social Sciences, , 2731-6351"]
[null,null,"Disciplina",null,"170"]
[null,null,"Soggetti",null,"Ethics"]
[{"$nulls":4},"Evolutionary psychology"]
[{"$nulls":4},"Anthropology"]
[{"$nulls":4},"Economics - Psychological aspects"]
[{"$nulls":4},"Neurosciences"]
[{"$nulls":4},"Ecology"]
[{"$nulls":4},"Moral Philosophy and Applied Ethics"]
[{"$nulls":4},"Evolutionary Psychology"]
[{"$nulls":4},"Economic Psychology"]
[{"$nulls":4},"Neuroscience"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Chapter 1. A Brief Overview -- Chapter 2. A Magnificent and Irresistible Decoy: The Pursuit of Happiness -- Chapter 3. Why Can't We Stop Consuming? -- Chapter 4. The economy as a destructive force on the planet -- Chapter 5. The Strength of the Group and the Weakness of the Individual: Insights from Anthropology and Evolutionary Psychology -- Chapter 6. The Boredom and Horror of Meaninglessness: The Ultimate Obstacle -- Chapter 7.The Pursuit of Personal Interest and the Tragedy of the Commons: The Strength of the System -- Chapter 8. But What Kind of Ecology Are We Talking About? -- Chapter 9. The human trajectory: as extraordinary as it is despairing but contingent -- Chapter 10. What room for manoeuver? -- Chapter 11. Beyond reason, the beauty of the world."]
[null,null,null,"Sommario/riassunto","This book sheds light on the reasons for our inaction in matters of the environmental crisis and presents solutions to address the crisis. The work draws on numerous disciplinary fields, such as psychology, neuroscience, anthropology, biology, philosophy, sociology, "]
[{"$nulls":4},"economics, ethology. The author argues that it is not possible to correctly understand the current environmental crisis in the light of a single discipline and emphasizes the importance of cognitive sciences to address the environmental crisis. He shows that the reasons for human inaction relate as much to the characteristics of our psyche/brain as to those of the consumption society that we invented and have lived in at least since the Neolithic. The analysis presented stresses the need to regulate individual behavior and to call into question our consumption patterns. Without questioning the characteristics of both modern society, and the way our brains work, will make it impossible to resolve the environmental crisis. This book proposes a revolutionary change of the way we live and act to preserve the planet."]
[null,"2.","Record Nr.",null,"UNINA9911049085303321"]
[null,null,"Autore",null,"Stanimirović Predrag S"]
[null,null,"Titolo",null,"Generalized Matrix Inversion: A Machine Learning Approach / / by Predrag S. Stanimirović, Yimin Wei, Shuai Li, Dimitrios Gerontitis, Xinwei Cao"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"3-032-01493-X"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (492 pages)"]
[null,null,"Collana",null,"Artificial Intelligence (R0) Series"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Machine learning"]
[{"$nulls":4},"Dynamics"]
[{"$nulls":4},"Nonlinear theories"]
[{"$nulls":4},"Algebras, Linear"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Mathematics - Data processing"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Applied Dynamical Systems"]
[{"$nulls":4},"Linear Algebra"]
[{"$nulls":4},"Dynamical Systems"]
[{"$nulls":4},"Design and Analysis of Algorithms"]
[{"$nulls":4},"Computational Mathematics and Numerical Analysis"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"1. Background Information -- 2 Gradient Neural Network (GNN) and their Modifications -- 3 Zeroing Neural Network (ZNN) -- 4 From iterations to ZNNs and vice versa, 5 Modified ZNN dynamical systems."]
[null,null,null,"Sommario/riassunto","This book presents a comprehensive exploration of the dynamical system approach in numerical linear algebra, with a special focus on computing generalized inverses, solving systems of linear equations, and addressing linear matrix equations. Bridging four major scientific domains—numerical linear algebra, recurrent neural networks (RNNs), dynamical systems, and unconstrained nonlinear optimization—this book offers a unique, interdisciplinary perspective. Generalized Matrix Inversion: A Machine Learning Approach explores the theory and application of recurrent neural networks, particularly continuous-time recurrent neural networks (CTRNNs), which use systems of ordinary "]
[{"$nulls":4},"differential equations to model the influence of inputs on neurons. Special attention is given to CTRNNs designed for finding zeros of equations or minimizing nonlinear functions, with detailed coverage of two important classes: Gradient Neural Networks (GNN) and Zhang (Zeroing) Neural Networks (ZNN). Both time-varying and time-invariant models are examined across scalar, vector, and matrix cases. Based on the authors’ research that has been published in leading scientific journals, the book spans a variety of disciplines, including linear and multilinear algebra, generalized inverses, recurrent neural networks, dynamical systems, time-varying problem solving, and unconstrained nonlinear optimization. Readers will find a global overview of activation functions, rigorous convergence analysis, and innovative improvements in the definition of error functions for GNN and ZNN dynamic systems. Generalized Matrix Inversion: A Machine Learning Approach is an essential resource for researchers and practitioners seeking advanced methods at the intersection of machine learning, optimization, and matrix computation."]