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Biomimetics
Special Issues
Advances in Digital Biomimetics
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Advances in Digital Biomimetics

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Biological Optimisation and Management".

Deadline for manuscript submissions: 25 July 2026 | Viewed by 2498

Special Issue Editor

Prof. Dr. Ameersing Luximon

E-Mail Website
Guest Editor
Industrial Design, Georgia Tech Institute of Technology, Tianjin University, Shenzhen, China
Interests: design for humanity, smart design, integrated design, biomechanics; social robots

Special Issue Information

Dear Colleagues,

Digital biomimetics involves the application of principles and strategies derived from biological systems to address complex challenges in digital technology and design. This interdisciplinary field integrates insights from biology, computer science, engineering, and design to formulate innovative solutions that emulate natural processes and structures.

A notable aspect of this field is the use of artificial intelligence techniques, particularly neural networks, which are inspired by the neural processes of the human brain. Emerging research in digital biomimetics increasingly leverages advanced digital technologies to replicate biological systems and behaviors. This encompasses the development of sophisticated algorithms that mimic natural phenomena, thereby enhancing machine learning capabilities.

Furthermore, digital tools are being utilized to simulate and analyze biological structures, facilitating the creation of novel designs in materials science and engineering. The integration of digital fabrication techniques, such as 3D printing, is enabling the production of biomimetic structures that closely resemble the forms and functions found in nature. As researchers continue to explore the convergence of digital technology and biomimicry, they are unlocking new opportunities for developing smart, adaptive systems capable of responding to environmental stimuli in ways akin to living organisms.

The following topics will be covered:

  • Systematic reviews on digital biomimetics;
  • Algorithms;
  • Digital soft robotics;
  • Bio-inspired materials;
  • Machine learning;
  • Digital twin;
  • Three-dimensional printing;
  • Neural networks;
  • Adaptive systems;
  • Environmental monitoring;
  • Data mining;
  • Bioinformatics;
  • Data analysis and pattern recognition;
  • Simulation software;
  • Human–computer interaction (HCI);
  • Healthcare applications;
  • Smart sensors;
  • Virtual environment.

Prof. Dr. Ameersing Luximon
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • systematic reviews on digital biomimetics
  • algorithms
  • digital soft robotics
  • bio-inspired materials
  • machine learning
  • digital twin
  • three-dimensional printing
  • neural networks
  • adaptive systems
  • environmental monitoring
  • data mining
  • bioinformatics
  • data analysis and pattern recognition
  • simulation software
  • human–computer interaction (HCI)
  • healthcare applications
  • smart sensors
  • virtual environment

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

16 pages, 2113 KB  
Article
Silent Signals: Correlating Plant Bioelectric Activity with Human Emotional States via Wearable Sensing
by Peter A. Gloor
Biomimetics 2026, 11(4), 236; https://doi.org/10.3390/biomimetics11040236 - 2 Apr 2026
Viewed by 661
Abstract
We present a bio-hybrid sensing system that uses a living plant (Tradescantia pallida) as an ambient biosensor for human stress states as a single-participant proof-of-concept study. An AD8232 biosignal amplifier captures plant bioelectric activity, while a Happimeter smartwatch simultaneously measures the [...] Read more.
We present a bio-hybrid sensing system that uses a living plant (Tradescantia pallida) as an ambient biosensor for human stress states as a single-participant proof-of-concept study. An AD8232 biosignal amplifier captures plant bioelectric activity, while a Happimeter smartwatch simultaneously measures the wearer’s mood via machine learning on wrist-worn sensor data. Over 129 paired observations across eleven days in a naturalistic desk-work setting, a within-day fixed-effects analysis reveals robust stress–plant coupling: seven correlations survive Benjamini–Hochberg false discovery rate correction (q = 0.05), with two also surviving Bonferroni correction. The strongest results are stress_rolling vs. plant mean (r = +0.36, p = 3.3 × 10−5) and RMS (r = +0.34, p = 7.8 × 10−5). An incidental electrode reattachment mid-experiment created a natural control: mean/RMS correlation signs flipped with electrode polarity, while the coefficient of variation remained consistently negative across both configurations (r = −0.32, p = 2.6 × 10−4). This electrode-invariant finding—higher stress associated with lower relative signal variability—provides the strongest evidence for genuine bio-hybrid sensing. The results position living plants as bio-inspired ambient sensing elements for workplace wellbeing monitoring. Full article
(This article belongs to the Special Issue Advances in Digital Biomimetics)
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26 pages, 3168 KB  
Article
Four-Bar Linkage Path Generation Problems Using a New TLBO and Optimum Path Repairing Technique
by Seksan Winyangkul, Mahmoud Alfouneh and Suwin Sleesongsom
Biomimetics 2026, 11(3), 160; https://doi.org/10.3390/biomimetics11030160 - 28 Feb 2026
Viewed by 609
Abstract
A self-adaptive variant of teaching–learning-based optimization, incorporating a diversity archive and referred to as ATLBO-DA, has been proposed. Combined with a new path repairing technique (PRT), it efficiently accomplishes the four-bar linkage path generation problem, but an upgraded version is needed. An update [...] Read more.
A self-adaptive variant of teaching–learning-based optimization, incorporating a diversity archive and referred to as ATLBO-DA, has been proposed. Combined with a new path repairing technique (PRT), it efficiently accomplishes the four-bar linkage path generation problem, but an upgraded version is needed. An update of ATLBO-DA to self-adaptive teaching–learning-based optimization with evenness factor archive (ATLBO-EFA) and a new path repairing technique are proposed at the present. The diversity archive idea of the original version is replaced with the evenness factor archive to increase the exploitation and exploration performance of the TLBO. An optimum path repairing technique (OPRT) is proposed. This novel approach is used to identify the optimum combination of four-bar mechanism types by employing the concept of Degree of Limiting (DL). Moreover, in this article, a comparative analysis of present update and the previous version use to solve four-bar linkage path generation problems is performed. Several path generation problems are solved using both techniques. The results demonstrate that the updated technique consistently outperforms the earlier version, giving superior values for both mean and minimum descriptive statistics. In addition, the results make it clear that ATLBO-EFA and OPRT are superior to the original version. The result of non-parametric statistic testing using Friedman test indicate that ATLBO-EFA ranks 1st at p-value (0.0455) < α (0.05). It can be concluded that ATLBO-EFA with OPRT offers the best solution for solving the four-bar path synthesis problems. Full article
(This article belongs to the Special Issue Advances in Digital Biomimetics)
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25 pages, 2291 KB  
Article
Enhancing Flight Connectivity via Synchronization of Arrivals and Departures in Hub Airports with Evolutionary and Swarm-Based Metaheuristics
by Halil Ibrahim Demir and Suraka Dervis
Biomimetics 2026, 11(1), 6; https://doi.org/10.3390/biomimetics11010006 - 23 Dec 2025
Viewed by 961
Abstract
Global air transport has become the dominant mode of long-distance travel, carrying more than four billion passengers in 2019 and projected to exceed 8 billion by 2040. Nevertheless, limited demand and economic inefficiencies often make direct connections unfeasible, forcing many passengers to rely [...] Read more.
Global air transport has become the dominant mode of long-distance travel, carrying more than four billion passengers in 2019 and projected to exceed 8 billion by 2040. Nevertheless, limited demand and economic inefficiencies often make direct connections unfeasible, forcing many passengers to rely on transfers. In such cases, synchronizing arrivals and departures at hub airports is crucial to minimizing transfer times and maximizing passenger retention. This study investigates the synchronization problem at Istanbul Airport, one of the world’s largest hubs, using metaheuristic optimization. Three algorithms—Genetic Algorithms (GA), Modified Discrete Particle Swarm Optimization (MDPSO), and Evolutionary Strategies (ES)—were applied in parallel to optimize arrival and departure schedules for a major airline. The proposed chromosome-based framework was tested through parameter tuning and validated with statistical analyses, including ANOVA and Games–Howell pairwise comparisons. The results show that MDPSO achieved strong improvements, while ES consistently outperformed both GA and MDPSO, increasing successful passenger transfers by more than 200% compared to the original schedule. These findings demonstrate the effectiveness of evolutionary metaheuristics for large-scale airline scheduling and highlight their potential for improving hub connectivity. This framework is generalizable to other hub airports and airlines, and future research could extend it by integrating hybrid metaheuristics or applying enhanced forecasting methods and more dynamic scheduling approaches. Full article
(This article belongs to the Special Issue Advances in Digital Biomimetics)
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