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Advances in Technical Sciences and Architecture: Selected Contributions of CCIA 2024 [Kõva köide]

  • Formaat: Hardback, 885 pages, kõrgus x laius: 235x155 mm, 20 Illustrations, black and white; XV, 885 p. 20 illus., 1 Hardback
  • Sari: Lecture Notes in Networks and Systems 1499
  • Ilmumisaeg: 20-Oct-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031961560
  • ISBN-13: 9783031961564
Teised raamatud teemal:
  • Kõva köide
  • Hind: 308,12 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 362,49 €
  • Säästad 15%
  • See raamat ei ole veel ilmunud. Raamatu kohalejõudmiseks kulub orienteeruvalt 2-4 nädalat peale raamatu väljaandmist.
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
  • Formaat: Hardback, 885 pages, kõrgus x laius: 235x155 mm, 20 Illustrations, black and white; XV, 885 p. 20 illus., 1 Hardback
  • Sari: Lecture Notes in Networks and Systems 1499
  • Ilmumisaeg: 20-Oct-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031961560
  • ISBN-13: 9783031961564
Teised raamatud teemal:

This book showcases the latest innovations in architecture and engineering, featuring research and case studies presented at the 21st International Scientific Conference on Engineering and Architecture (CCIA 2024) in Havana, Cuba. It covers a wide range of topics, including control systems, communications, computer technologies, industrial applications, business management, construction advancements, and sustainable energy solutions. Combining theoretical insights and practical studies, this volume offers valuable perspectives for both academics and professionals looking to tackle the challenges of today's rapidly evolving fields.

1. Type A Gelatin Electrospun Scaffolds: comparison between
non-crosslinked and cross.-
2. Effect of gelatine crosslinking over scaffolds
of composite polymers for tissue engineering.-
3. Power Transformer Health
Index Calculation Web page.-
4. Data augmentation strategies for
machine learning modelling of compressive strength of biomedical scaffolds.-
5. Estimation of working temperature in distribution transformers using the
finite element method.