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AAPP Atti della Accademia Peloritana dei Pericolanti, Classe di Scienze Fisiche, Matematiche e Naturali

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JOURNAL INFORMATION
Publisher Accademia Peloritana dei Pericolanti
Open Access YES
Quartile Q3
Citation Count 77
P-ISSN 0365-0359
E-ISSN 1825-1242
Categories History and Philosophy of Science, Agricultural and Biological Sciences (miscellaneous), Chemistry (miscellaneous), Computer Science (miscellaneous), Earth and Planetary Sciences (miscellaneous)
Area Agricultural and Biological Sciences, Arts and Humanities, Chemistry, Computer Science, Earth and Planetary Sciences
Region Western Europe
Country Italy
Summary

  1. AAPP Atti della Accademia Peloritana dei Pericolanti, Classe di Scienze Fisiche, Matematiche e Naturali is published by Accademia Peloritana dei Pericolanti
  2. It is an oppen access journal
  3. The Subject Area : Agricultural and Biological Sciences; Arts and Humanities; Chemistry; Computer Science; Earth and Planetary Sciences; Mathematics; Physics and Astronomy

Latest Articles published

Last updated: 29/08/2024

Implicit highly discontinuous boundary value problems involving the p-Laplacian


Last updated: 29/08/2024

The Scarabaeoidea (Coleoptera) of the Nebrodi Mountains (North-Eastern Sicily, Italy


Last updated: 25/03/2024

Assessment of the 222Rn radioactivity content in drinking water by using Liquid Scintillation Counting (LSC)


Last updated: 25/03/2024

The Bruchinae (Coleoptera, Chrysomelidae) of Sicily: recent records and updated checklist


Last updated: 13/02/2024

Modelling river flows as jump-diffusion processes


Last updated: 10/02/2024

On the algebraic structure of Pythagorean triples


Last updated: 26/01/2024

On the electromagnetic energies and forces


Last updated: 02/01/2024

Karush-Kuhn-Tucker conditions and Lagrangian approach for improving machine learning techniques: A survey and new developments


Last updated: 30/11/2023

The space and time dimensions in the criminal behaviour of lust murderers


Last updated: 30/11/2023

GIS-based RAG-coded search priority scenarios for predictive maps to prevent future serial serious crimes: The case study of the Florence Monster