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Identification of Bio- and Eco-Materials Using Advanced Computational Methods
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Identification of Bio- and Eco-Materials Using Advanced Computational Methods (Closed)

A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Biosensors".

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Editors

Dr. Tomasz Garbowski

E-Mail Website
Guest Editor
Faculty of Environmental and Mechanical Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-637 Poznan, Poland
Interests: computational mechanics; structural optimization; mathematical programming; inverse problems; mechanics of materials; paper physics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maciej Zaborowicz

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Poznań University of Life Sciences, Poznan, Poland
Interests: computer image analysis; artificial neural networks; neural modeling; machine learning; deep learning; computer science in agriculture
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

In the modern world, EKO or BIO become two of the most used prefixes. They identify a given product with a clear trend related to both ecology, closed circuit, 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 search for trends, recognition and identification of those products. In addition, new, powerful numerical algorithms and metamodels based on deep learning or stochastic processes allow us to quickly and effectively achieve the desired goals. In this Special Issue, we want to collect works related to bio-products and eco-materials, but also biomaterials widely used in orthopedics and more broadly in medicine. The collection of bio- and eco-materials is not limited only 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 the submissions are related to these kind of materials, with particular emphasis on appropriate measurements and experimental techniques used for their identification and characterization. The readers and authors of Sensors are encouraged to send their latest research studies in these areas, with an emphasis on experimental validation and empirical evidence using and metamodels artificial intelligence in the identification of eco- and bio-materials.

Prof. Dr. Tomasz Garbowski
Prof. Dr. Maciej Zaborowicz
Guest Editors

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Keywords

  • computational methods
  • inverse analysis
  • artificial intelligence, artificial neural networks
  • deep learning
  • Gaussian processes
  • bio-products
  • eco-materials
  • biomaterials
  • identification
  • measurements
  • experimental data

Published Papers (3 papers)

Download All Papers

2022

14 pages, 5071 KiB  
Article
Mechanical Property Test of Grass Carp Skin Material Based on the Digital Image Correlation Method
by Mei Zhang, Pengxiang Ge, Zhongnan Fu, Xizuo Dan and Guihua Li
Sensors 2022, 22(21), 8364; https://doi.org/10.3390/s22218364 - 31 Oct 2022
Cited by 2 | Viewed by 1094
Abstract
Fish is a common and widely distributed creature. Its skin has a unique physiological structure and plays an important role in many fields. Fish skin also has important potential value for bionics research. This study aims to provide a method and a reliable [...] Read more.
Fish is a common and widely distributed creature. Its skin has a unique physiological structure and plays an important role in many fields. Fish skin also has important potential value for bionics research. This study aims to provide a method and a reliable data for the study of bionics. A method of measuring the mechanical properties of fish skin samples using a binocular stereo digital image correlation (DIC) system combined with a synchronous tensile testing machine was proposed. The mechanical properties (e.g., elastic modulus E and strain) of grass fish skin samples (GFSA) were tested in hydrophilic and dry states. A dual-frequency laser interferometer was used to calibrate the tensile testing machine synchronously, and the feasibility and strain accuracy of DIC in GFSA measurement were verified by finite element method (FEM). The results show differences in the mechanical properties of GFSA between different individuals, different parts, and different states. Under the same stress, the head was easy to deform, and the strain was the largest, and E was the smallest. The tail result was the opposite of the head result. Full article
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19 pages, 4058 KiB  
Article
Influence of Analog and Digital Crease Lines on Mechanical Parameters of Corrugated Board and Packaging
by Tomasz Garbowski, Tomasz Gajewski and Anna Knitter-Piątkowska
Sensors 2022, 22(13), 4800; https://doi.org/10.3390/s22134800 - 25 Jun 2022
Cited by 6 | Viewed by 2662
Abstract
When producing packaging from corrugated board, material weakening often occurs both during the die-cutting process and during printing. While the analog lamination and/or printing processes that degrade material can be easily replaced with a digital approach, the die-cutting process remains overwhelmingly analog. Recently, [...] Read more.
When producing packaging from corrugated board, material weakening often occurs both during the die-cutting process and during printing. While the analog lamination and/or printing processes that degrade material can be easily replaced with a digital approach, the die-cutting process remains overwhelmingly analog. Recently, new innovative technologies have emerged that have begun to replace or at least supplement old techniques. This paper presents the results of laboratory tests on corrugated board and packaging made using both analog and digital technologies. Cardboard samples with digital and analog creases are subject to various mechanical tests, which allows for an assessment of the impact of creases on the mechanical properties of the cardboard itself, as well as on the behavior of the packaging. It is proven that digital technology is not only more repeatable, but also weakens the structure of corrugated board to a much lesser extent than analog. An updated numerical model of boxes in compression tests is also discussed. The effect of the crushing of the material in the vicinity of the crease lines in the packaging arising during the analog and digital finishing processes is taken into account. The obtained enhanced computer simulation results closely reflect the experimental observations, which prove that the correct numerical analysis of corrugated cardboard packaging should be performed with the model taking into account the crushing. Full article
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16 pages, 1042 KiB  
Article
Identification of Factors Affecting Environmental Contamination Represented by Post-Hatching Eggshells of a Common Colonial Waterbird with Usage of Artificial Neural Networks
by Agnieszka Sujak, Dariusz Jakubas, Ignacy Kitowski and Piotr Boniecki
Sensors 2022, 22(10), 3723; https://doi.org/10.3390/s22103723 - 13 May 2022
Cited by 2 | Viewed by 1604
Abstract
Artificial Neural Networks are used to find the influence of habitat types on the quality of the environment expressed by the concentrations of toxic and harmful elements in avian tissue. The main habitat types were described according to the Corine Land Cover CLC2012 [...] Read more.
Artificial Neural Networks are used to find the influence of habitat types on the quality of the environment expressed by the concentrations of toxic and harmful elements in avian tissue. The main habitat types were described according to the Corine Land Cover CLC2012 model. Eggs of free-living species of a colonial waterbird, the grey heron Ardea cinerea, were used as a biological data storing media for biomonitoring. For modeling purposes, pollution indices expressing the sum of the concentration of harmful and toxic elements (multi-contamination rank index) and indices for single elements were created. In the case of all the examined indices apart from Cd, the generated topologies were a multi-layer perceptron (MLP) with 1 hidden layer. Interestingly, in the case of Cd, the generated optimal topology was a network with a radial basis function (RBF). The data analysis showed that the increase in environmental pollution was mainly influenced by human industrial activity. The increase in Hg, Cd, and Pb content correlated mainly with the increase in the areas characterized by human activity (industrial, commercial, and transport units) in the vicinity of a grey heron breeding colony. The decrease in the above elements was conditioned by relative areas of farmland and inland waters. Pollution with Fe, Mn, Zn, and As was associated mainly with areas affected by industrial activities. As the location variable did not affect the quality of the obtained networks, it was removed from the models making them more universal. Full article
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