1.

Record Nr.

UNINA9910796555003321

Autore

Ballone Angela

Titolo

The 1624 Tumult of Mexico in perspective (c. 1620-1650) : authority and conflict resolution in the Iberian Atlantic / / by Angela Ballone

Pubbl/distr/stampa

Leiden, Netherlands ; ; Boston, [Massachusetts] : , : Brill, , 2018

©2018

ISBN

90-04-33548-X

Descrizione fisica

1 online resource (365 pages) : illustrations

Collana

European Expansion and Indigenous Response, , 1873-8974 ; ; Volume 24

Disciplina

972/.02

Soggetti

Riots - Mexico - Mexico City - History - 17th century

Church and state - Mexico - History - 17th century

Mexico History Spanish colony, 1540-1810

Mexico Politics and government 1540-1810

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

Front Matter -- Introduction -- Theatre of the Disturbances -- Pre-Dating the Tumult -- A Viceroy in an Age of Decline -- The Two Heads of the Viceroyalty -- Storming the Viceregal Palace -- Illustrations -- The Day After -- Tools of Control from the Metropolitan Court -- From the Inspection to the General Pardon -- Metropolitan Déjà Vu -- Conclusions -- A Fructibus Eorum Cognoscetis Eos -- Glossary -- Select Bibliography -- Index.

Sommario/riassunto

In The 1624 Tumult of Mexico in Perspective Angela Ballone offers, for the first time, a comprehensive study of an understudied period of Mexican early modern history. By looking at the mandates of three viceroys who, to varying degrees, participated in the events surrounding the Tumult, the book discusses royal authority from a transatlantic perspective that encompasses both sides of the Iberian Atlantic. Considering the similarities and tensions that coexisted in the Iberian Atlantic, Ballone offers a thorough reassessment of current historiography on the Tumult proving that, despite the conflicts and arguments underlying the disturbances, there was never any intention to do away with the king’s authority in New Spain.



2.

Record Nr.

UNINA9911021971103321

Autore

Huang Lin

Titolo

Proceedings of 17th International Conference on Machine Learning and Computing : ICMLC2025, Volume 2 / / edited by Lin Huang, David Greenhalgh

Pubbl/distr/stampa

Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025

ISBN

3-031-94898-X

Edizione

[1st ed. 2025.]

Descrizione fisica

1 online resource (1054 pages)

Collana

Lecture Notes in Networks and Systems, , 2367-3389 ; ; 1476

Altri autori (Persone)

GreenhalghDavid

Disciplina

006.3

Soggetti

Computational intelligence

Engineering - Data processing

Machine learning

Computational Intelligence

Data Engineering

Machine Learning

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

Image Feature Analysis and Processing Technology -- Intelligent Detection Models and Algorithms -- Multimodal Image Intelligent Recognition and Calculation -- Image Segmentation and Classification -- Signal Recognition and Key Technologies -- Information Security Detection and Analysis -- Text Analysis and Classification.

Sommario/riassunto

This book comprises original and peer reviewed research papers presented at 2025 17th International Conference on Machine Learning and Computing that was held in Guangzhou, China, from February 14 to 17, 2025. The focus of the conference is to establish an effective platform for institutions and industries to share ideas and to present the works of scientists, engineers, educators and students from all over the world. Topics discussed in this volume include Machine Learning Theory and Algorithms, High-performance Computing Models and Data Processing, Large-scale Language Models and Natural Language Processing, Data-oriented Information System Optimization and Intelligent Computing, AI-based Intelligent Control Systems and System Security, etc. The book will become a valuable resource for academics,



industry professionals, and engineers working in the related fields of machine learning and computing.