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Identification of Bio- and Eco-Materials Using Advanced Computational Methods
Topic Information
Dear colleagues,
In the modern world, ‘eco’ and ‘bio’ have become two of the most used prefixes. They identify a given product with a clear trend related to both ecology, closed circuit, and sustainable production, as well as re-use and recycling, or the recently very popular upcycling. On the other hand, tools based on advanced computational methods, i.e., numerical simulations, inverse analysis, artificial intelligence, and machine learning, are increasingly used to assess quality, and there is a search for the trends, recognition, and identification of these products. In addition, new, powerful numerical algorithms and metamodels based on deep learning and stochastic processes allow us to quickly and effectively achieve desired goals. In this topic, we want to collect works related to bio-products and eco-materials, but also bio-materials widely used in orthopedics and more broadly in medicine. The collection of bio- and eco-materials is not only limited to biologically compatible medical implants or modern ecological building materials. They belong to a much wider space, also including all kinds of food, textile, and wood or paper products, as well as waste and their use for the production of green energy and much more.
There are no particular restrictions on the thematic areas of this Special Issue, as long as submissions are related to these kinds of materials, with particular emphasis on the appropriate measurements and experimental techniques used for their identification and characterization. The readers and authors of this SI are encouraged to send their latest research studies in these areas, with an emphasis on experimental validation and empirical evidence as well as metamodels and artificial intelligence in the identification of eco- and bio-materials.
Dr. Tomasz Garbowski
Prof. Dr. Maciej Zaborowicz
Topic Editors
Keywords
- computational methods
- inverse analysis
- artificial intelligence
- artificial neural networks
- deep learning
- Gaussian processes
- bio-products
- eco-materials
- biomaterials
- identification
- measurements
- experimental data
Participating Journals
| Journal Name | Impact Factor | CiteScore | Launched Year | First Decision (median) | APC | |
|---|---|---|---|---|---|---|
Applied Sciences
|
2.7 | 4.5 | 2011 | 16.9 Days | CHF 2400 | Submit |
Energies
|
3.2 | 5.5 | 2008 | 16.1 Days | CHF 2600 | Submit |
Materials
|
3.4 | 5.2 | 2008 | 13.9 Days | CHF 2600 | Submit |
Remote Sensing
|
5.0 | 7.9 | 2009 | 23 Days | CHF 2700 | Submit |
Sensors
|
3.9 | 6.8 | 2001 | 17 Days | CHF 2600 | Submit |
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