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Biomimetics
Special Issues
Bioinspired Intelligence II
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Bioinspired Intelligence II

A special issue of Biomimetics (ISSN 2313-7673).

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 22956

Special Issue Editors

Dr. Juan Luis Crespo-Mariño

E-Mail Website
Guest Editor
LIANA Lab (IA Lab for Natural Sciences), Department of Mechatronics Engineering, Tecnológico de Costa Rica, Cartago 30101, Costa Rica
Interests: artificial neural networks; evolutionary computation; AI-assisted design and modelling
Special Issues, Collections and Topics in MDPI journals
Prof. Andrés Segura-Castillo

E-Mail Website
Co-Guest Editor
Technological Research and Innovation Laboratory (LIIT), Universidad Estatal a Distancia, Sabanilla, Costa Rica
Interests: systemic inquiry; complex modeling; technology management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, we find ourselves confidently accepting that bioinspired intelligence methods have become common tools for both the engineer and the scientist. The growing availability of high-performance computing platforms, massive and affordable storage capacities, and high-speed networks have contributed significantly to this. Besides, these technological advances, along with a vibrant practitioner community, have enabled the growth of innovative software developments towards the modeling of complex issues, which are otherwise impossible to treat by means of classical or analytical paradigms. Thus, the implementation of biologically inspired models makes sense and has been widely adopted as a viable alternative to reduce complexity.

It has also been recognized that bioinspired approaches stimulate synergy among scientific disciplines. Multi- and transdisciplinary work have become integral in this respect. Researchers, from different knowledge fields, contribute towards unified goals, in an environment of discussion, interaction and collaboration, which leads to knowledge discovery and dissemination. In the process, scientists enrich each other’s areas of interest and develop innovative approaches for their studies.

It is with this perspective in mind that we have given this Special Issue its purpose. The works to be published are expected to be the result of multi- and transdisciplinary efforts, which present innovative findings beyond each expert’s specific knowledge area. We look forward to contributions that not only propose new methods and technologies but are exemplary of an effective and rewarding collaboration among the bioinspired intelligence scientific and technological community.

Prof. Dr. Juan Luis Crespo-Mariño
Prof. Andrés Segura-Castillo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomimetics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bioinspired intelligence
  • bioinspired modelling
  • artificial intelligence
  • complex problem solving

Published Papers (6 papers)

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Research

24 pages, 2738 KiB  
Article
Low-Cost Algorithms for Metabolic Pathway Pairwise Comparison
by Esteban Arias-Méndez, Diego Barquero-Morera and Francisco J. Torres-Rojas
Biomimetics 2022, 7(1), 27; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics7010027 - 21 Feb 2022
Cited by 1 | Viewed by 2587
Abstract
Metabolic pathways provide key information for achieving a better understanding of life and all its processes; this is useful information for the improvement of medicine, agronomy, pharmacy, and other similar areas. The main analysis tool used to study these pathways is based on [...] Read more.
Metabolic pathways provide key information for achieving a better understanding of life and all its processes; this is useful information for the improvement of medicine, agronomy, pharmacy, and other similar areas. The main analysis tool used to study these pathways is based on pathway comparison, using graph data structures. Metabolic pathway comparison has been defined as a computationally complex task. In a previous work, two new algorithms were introduced to treat the problem of metabolic pathway pairwise comparison. Here we provide an extended analysis with more data and a deeper analysis of metabolic pathway comparison as listed in the discussion and results section. Full article
(This article belongs to the Special Issue Bioinspired Intelligence II)
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23 pages, 396 KiB  
Article
Biomimetic Leadership for 21st Century Companies
by Edita Olaizola, Rafael Morales-Sánchez and Marcos Eguiguren Huerta
Biomimetics 2021, 6(3), 47; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics6030047 - 14 Jul 2021
Cited by 5 | Viewed by 6556
Abstract
Biomimicry is a scientific discipline that aims to model the behavior or properties of biological systems so as to adapt them to other scientific areas. Recently, this approach has been adopted in order to develop an organizational model called “Organizational Biomimicry”. It proposes [...] Read more.
Biomimicry is a scientific discipline that aims to model the behavior or properties of biological systems so as to adapt them to other scientific areas. Recently, this approach has been adopted in order to develop an organizational model called “Organizational Biomimicry”. It proposes a systemic approach, a worldview that places the organization and the people related to it as an integral part of nature, and an R&D system based on continuous learning from nature. The effective management of this business model depends on leaders who can make dynamic decisions, generate commitment to the views of the company, define specific goals, actively learn on multiple levels and tackle conflicts. This type of leadership may actually be being exercised in business practice; however, no leadership style inspired by biomimicry has been theorized to date. Thus, the aim of this research was to present a biomimetic leadership model that considers nature as a model, measure and mentor. To this end, we proposed, firstly, a definition of a biomimetic leader from the point of view of the characteristics of biomimetic organizations. Then, we determined the characteristics of this leadership type. Secondly, we conducted a review of the main leadership styles analyzed in the recent literature about management; then, for each leadership type, we extracted the characteristics that will adapt to the biomimetic leadership model. From this process, we obtained the traits of a biomimetic leader. This characterization (definition plus characteristics) was subjected to an expert panel, which determined its validity. Full article
(This article belongs to the Special Issue Bioinspired Intelligence II)
17 pages, 12556 KiB  
Article
EvoSeg: Automated Electron Microscopy Segmentation through Random Forests and Evolutionary Optimization
by Manuel Zumbado-Corrales and Juan Esquivel-Rodríguez
Biomimetics 2021, 6(2), 37; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics6020037 - 01 Jun 2021
Cited by 4 | Viewed by 3427
Abstract
Electron Microscopy Maps are key in the study of bio-molecular structures, ranging from borderline atomic level to the sub-cellular range. These maps describe the envelopes that cover possibly a very large number of proteins that form molecular machines within the cell. Within those [...] Read more.
Electron Microscopy Maps are key in the study of bio-molecular structures, ranging from borderline atomic level to the sub-cellular range. These maps describe the envelopes that cover possibly a very large number of proteins that form molecular machines within the cell. Within those envelopes, we are interested to find what regions correspond to specific proteins so that we can understand how they function, and design drugs that can enhance or suppress a process that they are involved in, along with other experimental purposes. A classic approach by which we can begin the exploration of map regions is to apply a segmentation algorithm. This yields a mask where each voxel in 3D space is assigned an identifier that maps it to a segment; an ideal segmentation would map each segment to one protein unit, which is rarely the case. In this work, we present a method that uses bio-inspired optimization, through an Evolutionary-Optimized Segmentation algorithm, to iteratively improve upon baseline segments obtained from a classical approach, called watershed segmentation. The cost function used by the evolutionary optimization is based on an ideal segmentation classifier trained as part of this development, which uses basic structural information available to scientists, such as the number of expected units, volume and topology. We show that a basic initial segmentation with the additional information allows our evolutionary method to find better segmentation results, compared to the baseline generated by the watershed. Full article
(This article belongs to the Special Issue Bioinspired Intelligence II)
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10 pages, 1622 KiB  
Article
Approaches for the Prediction of Leaf Wetness Duration with Machine Learning
by Martín Solís and Vanessa Rojas-Herrera
Biomimetics 2021, 6(2), 29; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics6020029 - 14 May 2021
Cited by 1 | Viewed by 2809
Abstract
The prediction of leaf wetness duration (LWD) is an issue of interest for disease prevention in coffee plantations, forests, and other crops. This study analyzed different LWD prediction approaches using machine learning and meteorological and temporal variables as the models’ input. The information [...] Read more.
The prediction of leaf wetness duration (LWD) is an issue of interest for disease prevention in coffee plantations, forests, and other crops. This study analyzed different LWD prediction approaches using machine learning and meteorological and temporal variables as the models’ input. The information was collected through meteorological stations placed in coffee plantations in six different regions of Costa Rica, and the leaf wetness duration was measured by sensors installed in the same regions. The best prediction models had a mean absolute error of around 60 min per day. Our results demonstrate that for LWD modeling, it is not convenient to aggregate records at a daily level. The model performance was better when the records were collected at intervals of 15 min instead of 30 min. Full article
(This article belongs to the Special Issue Bioinspired Intelligence II)
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20 pages, 6257 KiB  
Article
Bio-Inspired Design of a Porous Resorbable Scaffold for Bone Reconstruction: A Preliminary Study
by Daria Scerrato, Alberto Maria Bersani and Ivan Giorgio
Biomimetics 2021, 6(1), 18; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics6010018 - 10 Mar 2021
Cited by 26 | Viewed by 3856
Abstract
The study and imitation of the biological and mechanical systems present in nature and living beings always have been sources of inspiration for improving existent technologies and establishing new ones. Pursuing this line of thought, we consider an artificial graft typical in the [...] Read more.
The study and imitation of the biological and mechanical systems present in nature and living beings always have been sources of inspiration for improving existent technologies and establishing new ones. Pursuing this line of thought, we consider an artificial graft typical in the bone reconstruction surgery with the same microstructure of the bone living tissue and examine the interaction between these two phases, namely bone and the graft material. Specifically, a visco-poroelastic second gradient model is adopted for the bone-graft composite system to describe it at a macroscopic level of observation. The second gradient formulation is employed to consider possibly size effects and as a macroscopic source of interstitial fluid flow, which is usually regarded as a key factor in bone remodeling. With the help of the proposed formulation and via a simple example, we show that the model can be used as a graft design tool. As a matter of fact, an optimization of the characteristics of the implant can be carried out by numerical investigations. In this paper, we observe that the size of the graft considerably influences the interaction between bone tissue and artificial bio-resorbable material and the possibility that the bone tissue might substitute more or less partially the foreign graft for better bone healing. Full article
(This article belongs to the Special Issue Bioinspired Intelligence II)
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15 pages, 1518 KiB  
Article
Discriminative Multi-Stream Postfilters Based on Deep Learning for Enhancing Statistical Parametric Speech Synthesis
by Marvin Coto-Jiménez
Biomimetics 2021, 6(1), 12; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics6010012 - 07 Feb 2021
Cited by 3 | Viewed by 2560
Abstract
Statistical parametric speech synthesis based on Hidden Markov Models has been an important technique for the production of artificial voices, due to its ability to produce results with high intelligibility and sophisticated features such as voice conversion and accent modification with a small [...] Read more.
Statistical parametric speech synthesis based on Hidden Markov Models has been an important technique for the production of artificial voices, due to its ability to produce results with high intelligibility and sophisticated features such as voice conversion and accent modification with a small footprint, particularly for low-resource languages where deep learning-based techniques remain unexplored. Despite the progress, the quality of the results, mainly based on Hidden Markov Models (HMM) does not reach those of the predominant approaches, based on unit selection of speech segments of deep learning. One of the proposals to improve the quality of HMM-based speech has been incorporating postfiltering stages, which pretend to increase the quality while preserving the advantages of the process. In this paper, we present a new approach to postfiltering synthesized voices with the application of discriminative postfilters, with several long short-term memory (LSTM) deep neural networks. Our motivation stems from modeling specific mapping from synthesized to natural speech on those segments corresponding to voiced or unvoiced sounds, due to the different qualities of those sounds and how HMM-based voices can present distinct degradation on each one. The paper analyses the discriminative postfilters obtained using five voices, evaluated using three objective measures, Mel cepstral distance and subjective tests. The results indicate the advantages of the discriminative postilters in comparison with the HTS voice and the non-discriminative postfilters. Full article
(This article belongs to the Special Issue Bioinspired Intelligence II)
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