Biomimetics, Volume 8, Issue 4 (August 2023) – 52 articles

The Mayfly Optimization Algorithm (MOA), as a new biomimetic metaheuristic algorithm with superior algorithm framework and optimization methods, plays a remarkable role in solving optimization problems. However, there are still shortcomings of convergence speed and local optimization in this algorithm. This paper proposes a metaheuristic algorithm for continuous and constrained global optimization problems, which combines the MOA, the Aquila Optimizer (AO), and the opposition-based learning (OBL) strategy, called AOBLMOA, to overcome the shortcomings of the MOA. The proposed algorithm first fuses the high soar with vertical stoop method and the low flight with slow descent attack method in the AO into the position movement process of the male mayfly population in the MOA. Then, it incorporates the contour flight with short glide attack and the walk and grab prey methods in the AO into the positional movement of female mayfly populations in the MOA. Finally, it replaces the gene mutation behavior of offspring mayfly populations in the MOA with the OBL strategy. To verify the optimization ability of the new algorithm, we conduct three sets of experiments. In the first experiment, we apply AOBLMOA to 19 benchmark functions to test whether it is the optimal strategy among multiple combined strategies. In the second experiment, we test AOBLMOA by using 30 CEC2017 numerical optimization problems and compare it with state-of-the-art metaheuristic algorithms. In the third experiment, 10 CEC2020 real-world constrained optimization problems are used to demonstrate the applicability of AOBLMOA to engineering design problems. The experimental results show that the proposed AOBLMOA is effective and superior and is feasible in numerical optimization problems and engineering design problems. Full article

Locomotion control of synergistical interaction between fins has been one of the key problems in the field of robotic fish research owing to its contribution to improving and enhancing swimming performance. In this paper, the coordinated locomotion control of the boxfish-like robot with pectoral and caudal fins is studied, and the effects of different control parameters on the propulsion performance are quantitatively analyzed by using hydrodynamic experiments. First, an untethered boxfish-like robot with two pectoral fins and one caudal fin was designed. Second, a central pattern generator (CPG)-based controller is used to coordinate the motions of the pectoral and caudal fins to realize the bionic locomotion of the boxfish-like robot. Finally, extensive hydrodynamic experiments are conducted to explore the effects of different CPG parameters on the propulsion performance under the synergistic interaction of pectoral and caudal fins. Results show that the amplitude and frequency significantly affect the propulsion performance, and the propulsion ability is the best when the frequency is 1 Hz. Different phase lags and offset angles between twisting and flapping of the pectoral fin can generate positive and reverse forces, which realize the forward, backward, and pitching swimming by adjusting these parameters. This paper reveals for the first time the effects of different CPG parameters on the propulsion performance in the case of the synergistic interaction between the pectoral fins and the caudal fin using hydrodynamic experimental methods, which sheds light on the optimization of the design and control parameters of the robotic fish. Full article

Fruit fly larvae, which exist widely in nature, achieve peristaltic motion via the contraction and elongation of their bodies and the asymmetric friction generated by the front and rear parts of their bodies when they are in contact with the ground. Herein, we report the development of an untethered, magnetic, temperature-sensitive hydrogel-based soft robot that mimics the asymmetric micro-patterns of fruit-fly-larvae gastropods and utilizes cyclic deformation to achieve directional peristaltic locomotion. Due to Néel relaxation losses of nanomagnetic Fe₃O₄ particles, the hydrogel-based soft robot is capable of converting changes in external alternating magnetic stimuli into contracting and expanding deformation responses which can be remotely controlled via a high-frequency alternating magnetic field (AMF) to realize periodic actuation. Furthermore, the Fe₃O₄ particles included in the hydrogel-based soft robot cause it to follow a gradient magnetic field in confined liquid environments and can be coupled with AMFs for the targeted release of water-soluble drugs or targeted magnetic hyperthermia therapy (MHT). We believe that such a controlled motion can enable highly targeted drug delivery, as well as vascular disease detection and thrombus removal tasks, without the use of invasive procedures. Full article

In recent years, limited works on EOG (electrooculography)-based biometric authentication systems have been carried out with eye movements or eye blinking activities in the current literature. EOGs have permanent and unique traits that can separate one individual from another. In this work, we have investigated FSST (Fourier Synchrosqueezing Transform)-ICA (Independent Component Analysis)-EMD (Empirical Mode Decomposition) robust framework-based EOG-biometric authentication (one-versus-others verification) performances using ensembled RNN (Recurrent Neural Network) deep models voluntary eye blinkings movements. FSST is implemented to provide accurate and dense temporal-spatial properties of EOGs on the state-of-the-art time-frequency matrix. ICA is a powerful statistical tool to decompose multiple recording electrodes. Finally, EMD is deployed to isolate EOG signals from the EEGs collected from the scalp. As our best knowledge, this is the first research attempt to explore the success of the FSST-ICA-EMD framework on EOG-biometric authentication generated via voluntary eye blinking activities in the limited EOG-related biometric literature. According to the promising results, improved and high recognition accuracies (ACC/Accuracy: ≥99.99% and AUC/Area under the Curve: 0.99) have been achieved in addition to the high TAR (true acceptance rate) scores (≥98%) and low FAR (false acceptance rate) scores (≤3.33%) in seven individuals. On the other hand, authentication and monitoring for online users/students are becoming essential and important tasks due to the increase of the digital world (e-learning, e-banking, or e-government systems) and the COVID-19 pandemic. Especially in order to ensure reliable access, a highly scalable and affordable approach for authenticating the examinee without cheating or monitoring high-data-size video streaming is required in e-learning platforms and online education strategies. Hence, this work may present an approach that offers a sustainable, continuous, and reliable EOG-biometric authentication of digital applications, including e-learning platforms for users/students. Full article

With the rapid development of the geometric modeling industry and computer technology, the design and shape optimization of complex curve shapes have now become a very important research topic in CAGD. In this paper, the Hybrid Artificial Hummingbird Algorithm (HAHA) is used to optimize complex composite shape-adjustable generalized cubic Ball (CSGC–Ball, for short) curves. Firstly, the Artificial Hummingbird algorithm (AHA), as a newly proposed meta-heuristic algorithm, has the advantages of simple structure and easy implementation and can quickly find the global optimal solution. However, there are still limitations, such as low convergence accuracy and the tendency to fall into local optimization. Therefore, this paper proposes the HAHA based on the original AHA, combined with the elite opposition-based learning strategy, PSO, and Cauchy mutation, to increase the population diversity of the original algorithm, avoid falling into local optimization, and thus improve the accuracy and rate of convergence of the original AHA. Twenty-five benchmark test functions and the CEC 2022 test suite are used to evaluate the overall performance of HAHA, and the experimental results are statistically analyzed using Friedman and Wilkerson rank sum tests. The experimental results show that, compared with other advanced algorithms, HAHA has good competitiveness and practicality. Secondly, in order to better realize the modeling of complex curves in engineering, the CSGC–Ball curves with global and local shape parameters are constructed based on SGC–Ball basis functions. By changing the shape parameters, the whole or local shape of the curves can be adjusted more flexibly. Finally, in order to make the constructed curve have a more ideal shape, the CSGC–Ball curve-shape optimization model is established based on the minimum curve energy value, and the proposed HAHA is used to solve the established shape optimization model. Two representative numerical examples comprehensively verify the effectiveness and superiority of HAHA in solving CSGC–Ball curve-shape optimization problems. Full article

The mechanisms underlying bone-implant integration, or osseointegration, are still incompletely understood, in particular how blood and proteins are recruited to implant surfaces. The objective of this study was to visualize and quantify the flow of blood and the model protein fibrinogen using a computational fluid dynamics (CFD) implant model. Implants with screws were designed with three different surface topographies: (1) amorphous, (2) nano-trabecular, and (3) hybrid meso-spikes and nano-trabeculae. The implant with nano-topography recruited more blood and fibrinogen to the implant interface than the amorphous implant. Implants with hybrid topography further increased recruitment, with particularly efficient recruitment from the thread area to the interface. Blood movement significantly slowed at the implant interface compared with the thread area for all implants. The blood velocity at the interface was 3- and 4-fold lower for the hybrid topography compared with the nano-topography and amorphous surfaces, respectively. Thus, this study for the first time provides insights into how different implant surfaces regulate blood dynamics and the potential advantages of surface texturization in blood and protein recruitment and retention. In particular, co-texturization with a hybrid meso- and nano-topography created the most favorable microenvironment. The established CFD model is simple, low-cost, and expected to be useful for a wide range of studies designing and optimizing implants at the macro and micro levels. Full article

Spiking neural networks (SNNs) are widely recognized for their biomimetic and efficient computing features. They utilize spikes to encode and transmit information. Despite the many advantages of SNNs, they suffer from the problems of low accuracy and large inference latency, which are, respectively, caused by the direct training and conversion from artificial neural network (ANN) training methods. Aiming to address these limitations, we propose a novel training pipeline (called IDSNN) based on parameter initialization and knowledge distillation, using ANN as a parameter source and teacher. IDSNN maximizes the knowledge extracted from ANNs and achieves competitive top-1 accuracy for CIFAR10 ($94.22\%$) and CIFAR100 ($75.41\%$) with low latency. More importantly, it can achieve $14\times$ faster convergence speed than directly training SNNs under limited training resources, which demonstrates its practical value in applications. Full article

When intelligent mobile robots perform global path planning in complex and narrow environments, several issues often arise, including low search efficiency, node redundancy, non-smooth paths, and high costs. This paper proposes an improved path planning algorithm based on the rapidly exploring random tree (RRT) approach. Firstly, the target bias sampling method is employed to screen and eliminate redundant sampling points. Secondly, the adaptive step size strategy is introduced to address the limitations of the traditional RRT algorithm. The mobile robot is then modeled and analyzed to ensure that the path adheres to angle and collision constraints during movement. Finally, the initial path is pruned, and the path is smoothed using a cubic B-spline curve, resulting in a smoother path with reduced costs. The evaluation metrics employed include search time, path length, and the number of sampling nodes. To evaluate the effectiveness of the proposed algorithm, simulations of the RRT algorithm, RRT-connect algorithm, RRT* algorithm, and the improved RRT algorithm are conducted in various environments. The results demonstrate that the improved RRT algorithm reduces the generated path length by 25.32% compared to the RRT algorithm, 26.42% compared to the RRT-connect algorithm, and 4.99% compared to the RRT* algorithm. Moreover, the improved RRT algorithm significantly improves the demand for reducing path costs. The planning time of the improved RRT algorithm is reduced by 64.96% compared to that of the RRT algorithm, 40.83% compared to that of the RRT-connect algorithm, and 27.34% compared to that of the RRT* algorithm, leading to improved speed. These findings indicate that the proposed method exhibits a notable improvement in the three crucial evaluation metrics: sampling time, number of nodes, and path length. Additionally, the algorithm performed well after undergoing physical verification with an insect-like mobile robot in a real environment featuring narrow elevator entrances. Full article

There is no doubt that the involvement of the Internet of Things (IoT) in our daily lives has changed the way we live and interact as a global community, as IoT enables intercommunication of digital objects around us, creating a pervasive environment. As of now, this IoT is found in almost every domain that is vital for human survival, such as agriculture, medical care, transportation, the military, and so on. Day by day, various IoT solutions are introduced to the market by manufacturers towards making our life easier and more comfortable. On the other hand, even though IoT now holds a key place in our lives, the IoT ecosystem has various limitations in efficiency, scalability, and adaptability. As such, biomimicry, which involves imitating the systems found in nature within human-made systems, appeared to be a potential remedy to overcome such challenges pertaining to IoT, which can also be referred to as bio-inspired IoT. In the simplest terms, bio-inspired IoT combines nature-inspired principles and IoT to create more efficient and adaptive IoT solutions, that can overcome most of the inherent challenges pertaining to traditional IoT. It is based on the idea that nature has already solved many challenging problems and that, by studying and mimicking biological systems, we might develop better IoT systems. As of now, this concept of bio-inspired IoT is applied to various fields such as medical care, transportation, cyber-security, agriculture, and so on. However, it is noted that only a few studies have been carried out on this new concept, explaining how these bio-inspired concepts are integrated with IoT. Thus, to fill in the gap, in this study, we provide a brief review of bio-inspired IoT, highlighting how it came into play, its ecosystem, its latest status, benefits, challenges, and future directions. Full article

Wearable touch sensors, which can convert force or pressure signals into quantitative electronic signals, have emerged as essential smart sensing devices and play an important role in various cutting-edge fields, including wearable health monitoring, soft robots, electronic skin, artificial prosthetics, AR/VR, and the Internet of Things. Flexible touch sensors have made significant advancements, while the construction of novel touch sensors by mimicking the unique properties of biological materials and biogenetic structures always remains a hot research topic and significant technological pathway. This review provides a comprehensive summary of the research status of wearable touch sensors constructed by imitating the material and structural characteristics in nature and summarizes the scientific challenges and development tendencies of this aspect. First, the research status for constructing flexible touch sensors based on biomimetic materials is summarized, including hydrogel materials, self-healing materials, and other bio-inspired or biomimetic materials with extraordinary properties. Then, the design and fabrication of flexible touch sensors based on bionic structures for performance enhancement are fully discussed. These bionic structures include special structures in plants, special structures in insects/animals, and special structures in the human body. Moreover, a summary of the current issues and future prospects for developing wearable sensors based on bio-inspired materials and structures is discussed. Full article

Dynamic objects bring about a large number of error accumulations in pose estimation of mobile robots in dynamic scenes, and result in the failure to build a map that is consistent with the surrounding environment. Along these lines, this paper presents a robust semi-direct 3D simultaneous localization and mapping (SLAM) algorithm for mobile robots based on dense optical flow. First, a preliminary estimation of the robot’s pose is conducted using the sparse direct method and the homography matrix is utilized to compensate for the current frame image to reduce the image deformation caused by rotation during the robot’s motion. Then, by calculating the dense optical flow field of two adjacent frames and segmenting the dynamic region in the scene based on the dynamic threshold, the local map points projected within the dynamic regions are eliminated. On this basis, the robot’s pose is optimized by minimizing the reprojection error. Moreover, a high-performance keyframe selection strategy is developed, and keyframes are inserted when the robot’s pose is successfully tracked. Meanwhile, feature points are extracted and matched to the keyframes for subsequent optimization and mapping. Considering that the direct method is subject to tracking failure in practical application scenarios, the feature points and map points of keyframes are employed in robot relocation. Finally, all keyframes and map points are used as optimization variables for global bundle adjustment (BA) optimization, so as to construct a globally consistent 3D dense octree map. A series of simulations and experiments demonstrate the superior performance of the proposed algorithm. Full article

The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic characteristics. An accurate assessment of tumor nuclei presence is crucial for determining tumor aggressiveness and grading. This paper proposes a new method called ColonNet, a heteromorphous convolutional neural network (CNN) with a feature grafting methodology categorically configured for analyzing mitotic nuclei in colon and lung histopathology images. The ColonNet model consists of two stages: first, identifying potential mitotic patches within the histopathological imaging areas, and second, categorizing these patches into squamous cell carcinomas, adenocarcinomas (lung), benign (lung), benign (colon), and adenocarcinomas (colon) based on the model’s guidelines. We develop and employ our deep CNNs, each capturing distinct structural, textural, and morphological properties of tumor nuclei, to construct the heteromorphous deep CNN. The execution of the proposed ColonNet model is analyzed by its comparison with state-of-the-art CNNs. The results demonstrate that our model surpasses others on the test set, achieving an impressive F1 score of 0.96, sensitivity and specificity of 0.95, and an area under the accuracy curve of 0.95. These outcomes underscore our hybrid model’s superior performance, excellent generalization, and accuracy, highlighting its potential as a valuable tool to support pathologists in diagnostic activities. Full article

Objective: The objective of this study was to investigate the use of the nanocapsule sequential delivery of BMP-2 and SDF-1α through the peripheral circulatory system to promote the healing of osteoporotic fractures. Methods: Based on increased vascular permeability in the early hematoma environment around the fracture and the presence of a large number of matrix metalloproteinase MMPs in the inflammatory environment, we designed MMP-sensitive nanocapsules which were formed viain situ free-radical polymerization on the surface of grow factors with 2-(methacryloyloxy) ethyl phosphorylcholine (MPC) and the bisacryloylated VPLGVRTK peptide. The antiphagic effect and biological activity of the growth factors for the nanomicrocapsule delivery system were tested by cell experiments. The 36 SD rats with an osteoporotic fracture model were randomly divided into six groups (A, B, C, D, E, and F). In this paper, the nanocapsules loaded with BMP-2 and SDF-1 are represented as n (BMP-2) and n (SDF-1α). In the six groups, the following different combinations of growth factors were injected into the bone defect site on days 1 and 3 after bone defect surgery: in group A, n (SDF-1α) combined with n (SDF-1α); in group B, n (BMP-2) combined with n (BMP-2); in group C, n (SDF-1α) + n (BMP-2) combined with n (SDF-1α) + n (BMP-2); in group D, n (SDF-1α) combined with n (BMP-2); in group E, n (BMP-2) combined with n (SDF-1α); in group F, nanocapsules without growth factor were used as the control group. Micro-CT was used to observe the effect of n(BMP-2) and n(SDF-1α) sequential delivery inearly healing in osteoporotic fractures. Finally, in this study, we evaluated the safety of the nanocapsules delivery system by detecting ectopic osteogenesis and inflammatory responses in animals. Results: Nanocapsules have low toxicity and protect the integrity and biological activity of growth factors. The results confirmed that nanocapsules could still be effectively targeted to the fracture site on days 1, 3, and 7 after intravenous administration. Growth factors encapsulated in nanocapsules have better bone repair results than natural growth factors. In particular, groups C and D had the best bone repair results than other groups.In vivo experiments confirmed that nanocapsules did not cause significant ectopic osteogenesis and inflammation. Conclusion: The results confirmed that the special vascular permeability and inflammatory factor microenvironment of the fracture site could be used to deliver two growth factors with a synergistic effect through venous circulation, which could better promote the healing process of osteoporotic fracture. Full article

The rapid progress of artificial neural networks (ANN) is largely attributed to the development of the rectified linear unit (ReLU) activation function. However, the implementation of software-based ANNs, such as convolutional neural networks (CNN), within the von Neumann architecture faces limitations due to its sequential processing mechanism. To overcome this challenge, research on hardware neuromorphic systems based on spiking neural networks (SNN) has gained significant interest. Artificial synapse, a crucial building block in these systems, has predominantly utilized resistive memory-based memristors. However, the two-terminal structure of memristors presents difficulties in processing feedback signals from the post-synaptic neuron, and without an additional rectifying device it is challenging to prevent sneak current paths. In this paper, we propose a four-terminal synaptic transistor with an asymmetric dual-gate structure as a solution to the limitations of two-terminal memristors. Similar to biological synapses, the proposed device multiplies the presynaptic input signal with stored synaptic weight information and transmits the result to the postsynaptic neuron. Weight modulation is explored through both hot carrier injection (HCI) and Fowler–Nordheim (FN) tunneling. Moreover, we investigate the incorporation of short-term memory properties by adopting polysilicon grain boundaries as temporary storage. It is anticipated that the devised synaptic devices, possessing both short-term and long-term memory characteristics, will enable the implementation of various novel ANN algorithms. Full article

The lack of intuitive controllability remains a primary challenge in enabling transhumeral amputees to control a prosthesis for arm reaching with residual limb kinematics. Recent advancements in prosthetic arm control have focused on leveraging the predictive capabilities of artificial neural networks (ANNs) to automate elbow joint motion and wrist pronation–supination during target reaching tasks. However, large quantities of human motion data collected from different subjects for various activities of daily living (ADL) tasks are required to train these ANNs. For example, the reaching motion can be altered when the height of the desk is changed; however, it is cumbersome to conduct human experiments for all conditions. This paper proposes a framework for cloning motion datasets using deep reinforcement learning (DRL) to cater to training data requirements. DRL algorithms have been demonstrated to create human-like synergistic motion in humanoid agents to handle redundancy and optimize movements. In our study, we collected real motion data from six individuals performing multi-directional arm reaching tasks in the horizontal plane. We generated synthetic motion data that mimicked similar arm reaching tasks by utilizing a physics simulation and DRL-based arm manipulation. We then trained a CNN-LSTM network with different configurations of training motion data, including DRL, real, and hybrid datasets, to test the efficacy of the cloned motion data. The results of our evaluation showcase the effectiveness of the cloned motion data in training the ANN to predict natural elbow motion accurately across multiple subjects. Furthermore, motion data augmentation through combining real and cloned motion datasets has demonstrated the enhanced robustness of the ANN by supplementing and diversifying the limited training data. These findings have significant implications for creating synthetic dataset resources for various arm movements and fostering strategies for automatized prosthetic elbow motion. Full article

Sound reception was investigated in the Yangtze finless porpoise (Neophocaena phocaenoides asiaeorientalis) at its most sensitive frequency. The computed tomography scanning, sound speed, and density results were used to develop a three-dimensional numerical model of the porpoise sound-reception system. The acoustic fields showed that sounds can reach the ear complexes from various pathways, with distinct receptivity peaks on the forward, left, and right sides. Reception peaks were identified on the ipsilateral sides of the respective ears and found on the opposite side of the ear complexes. These opposite maxima corresponded to subsidiary hearing pathways in the whole head, especially the lower head, suggesting the complexity of the sound-reception mechanism in the porpoise. The main and subsidiary sound-reception pathways likely render the whole head a spatial receptor. The low-speed and -density mandibular fats, compared to other acoustic structures, are significant energy enhancers for strengthening forward sound reception. Based on the porpoise reception model, a biomimetic receptor was developed to achieve directional reception, and in parallel to the mandibular fats, the silicon material of low speed and density can significantly improve forward reception. This bioinspired and biomimetic model can bridge the gap between animal sonar and artificial sound control systems, which presents potential to be exploited in manmade sonar. Full article

The reaction condition for purifying carbonic anhydrase from Sulfurihydrogenibium yellowstonense (SspCA) by direct heating without prior cell lysis was optimized; heating at 70 °C for 5 min resulted in the highest total activity of 23,460 WAU (Wilbur–Anderson unit) from a 50 mL culture. Heat-purified SspCA was examined for its capability to increase the rate of the mineralization of CO₂; compared with an uncatalyzed control, the onset time of CaCO₃ formation was shortened by up to 71%. Cyanase can be used to degrade toxic cyanate; however, one of the limitations of this biomimetic process is that the reaction needs HCO₃⁻ as a substrate. Heat-purified SspCA was combined with heat-purified cyanase from Thermomyces lanuginosus to alleviate the HCO₃⁻ dependence; in industrial wastewater, the HCO₃⁻ required was reduced by 50% when 0.75 WAU of SspCA was added. Heat-purified SspCA is stable at 4 °C; 88% of the initial activity was retained for up to five weeks. Partially purified SspCA can be obtained with ease and applied to a variety of applications. Full article

Locomotion and manipulation are two essential skills in robotics but are often divided or decoupled into two separate problems. It is widely accepted that the topological duality between multi-legged locomotion and multi-fingered manipulation shares an intrinsic model. However, a lack of research remains to identify the data-driven evidence for further research. This paper explores a unified formulation of the loco-manipulation problem using reinforcement learning (RL) by reconfiguring robotic limbs with an overconstrained design into multi-legged and multi-fingered robots. Such design reconfiguration allows for adopting a co-training architecture for reinforcement learning towards a unified loco-manipulation policy. As a result, we find data-driven evidence to support the transferability between locomotion and manipulation skills using a single RL policy with a multilayer perceptron or graph neural network. We also demonstrate the Sim2Real transfer of the learned loco-manipulation skills in a robotic prototype. This work expands the knowledge frontiers on loco-manipulation transferability with learning-based evidence applied in a novel platform with overconstrained robotic limbs. Full article

Biomaterials such as chitosan and simvastatin (Sim) have been introduced to accelerate the extensive and multicellular biological process of bone healing. The aim of this study was to evaluate the bone healing potential of chitosan and Sim, alone or combined. Forty-two male New Zealand rabbits were divided into three groups: chitosan nanoparticles (ChN), Sim and chitosan simvastatin nanoparticles (ChSimN). Two bony defects were created in the maxillary bone. The hole on the right side received one of the experimental materials, while the other side was assigned as the control and left to heal without any intervention. Bone specimens were collected at 2 and 4 weeks and then taken for histological and histomorphometrical analyses. The histological findings revealed that ChN possessed the highest number of osteoblasts and osteoclasts at weeks 2 and osteocytes after 4 weeks. There was a significant difference between the two healing periods regarding all bone parameters across all groups. ChN stood out as the only group that had a significant difference in the count of all bone cells between the two periods, thus having the best potential in promoting bone healing. Full article

Effective bioinspiration requires dialogue between designers and biologists, and this dialogue must be rooted in a shared scientific understanding of living systems. To support learning from “nature’s overarching design lessons” the Biomimicry Institute has produced ten “Unifying Patterns of Nature”. These patterns have been developed to engage with those interested in finding biologically inspired solutions to human challenges. Yet, although well-intentioned and appealing, they are likely to dishearten biologists. The aim of this paper is to identify why and propose alternative principles based on evolutionary theory. Full article

To address the problem of insufficient real-world data on planetary gearboxes, which makes it difficult to diagnose faults using deep learning methods, it is possible to obtain sufficient simulation fault data through dynamic simulation models and then reduce the difference between simulation data and real data using transfer learning methods, thereby applying diagnostic knowledge from simulation data to real planetary gearboxes. However, the label space of real data may be a subset of the label space of simulation data. In this case, existing transfer learning methods are susceptible to interference from outlier label spaces in simulation data, resulting in mismatching. To address this issue, this paper introduces multiple domain classifiers and a weighted learning scheme on the basis of existing domain adversarial transfer learning methods to evaluate the transferability of simulation data and adaptively measure their contribution to label predictor and domain classifiers, filter the interference of unrelated categories of simulation data, and achieve accurate matching of real data. Finally, partial transfer experiments are conducted to verify the effectiveness of the proposed method, and the experimental results show that the diagnostic accuracy of this method is higher than existing transfer learning methods. Full article

The paper explores the potential of the biomorphic approach to context-based design with a focus on special-purpose mobility in the Arctic. The study seeks to contribute to the analytical and conceptual basis for developing the transport component of the Arctic life-support system, i.e., a set of objects and technologies, and knowledge and skills for handling them, allowing a person to survive and comfortably exist in severe environmental conditions. The central argument is that the system should incorporate structural components that possess not only technical but also artistic and emotional characteristics that align with the geographic (environmental and climatic), socio-cultural, and psychological peculiarities of use. This can be achieved by drawing inspiration from local nature. We probe the visual image of “soft military presence” using two case studies in different parts of the Russian Arctic: the Yamal and Chukchi peninsulas. Full article

Digital twins are computer programs that use real-world data to create simulations that predict the performance of processes, products, and systems. Digital twins may integrate artificial intelligence to improve their outputs. Models for dealing with uncertainties and noise are used to improve the accuracy of digital twins. Most currently used systems aim to reduce noise to improve their outputs. Nevertheless, biological systems are characterized by inherent variability, which is necessary for their proper function. The constrained-disorder principle defines living systems as having a disorder as part of their existence and proper operation while kept within dynamic boundaries. In the present paper, we review the role of noise in complex systems and its use in bioengineering. We describe the use of digital twins for medical applications and current methods for dealing with noise and uncertainties in modeling. The paper presents methods to improve the accuracy and effectiveness of digital twin systems by continuously implementing variability signatures while simultaneously reducing unwanted noise in their inputs and outputs. Accounting for the noisy internal and external environments of complex biological systems is necessary for the future design of improved, more accurate digital twins. Full article

With the use of collaborative robots in intelligent manufacturing, human–robot interaction has become more important in human–robot collaborations. Human–robot handover has a huge impact on human–robot interaction. For current research on human–robot handover, special attention is paid to robot path planning and motion control during the handover process; seldom is research focused on human handover intentions. However, enabling robots to predict human handover intentions is important for improving the efficiency of object handover. To enable robots to predict human handover intentions, a novel human handover intention prediction approach was proposed in this study. In the proposed approach, a wearable data glove and fuzzy rules are firstly used to achieve faster and accurate human handover intention sensing (HIS) and human handover intention prediction (HIP). This approach mainly includes human handover intention sensing (HIS) and human handover intention prediction (HIP). For human HIS, we employ wearable data gloves to sense human handover intention information. Compared with vision-based and physical contact-based sensing, wearable data glove-based sensing cannot be affected by visual occlusion and does not pose threats to human safety. For human HIP, we propose a fast handover intention prediction method based on fuzzy rules. Using this method, the robot can efficiently predict human handover intentions based on the sensing data obtained by the data glove. The experimental results demonstrate the advantages and efficacy of the proposed method in human intention prediction during human–robot handover. Full article

Pathological aseptic calcification is the most common form of structural valvular degeneration (SVD), leading to premature failure of heart valve bioprostheses (BHVs). The processing methods used to obtain GA-fixed pericardium-based biomaterials determine the hemodynamic characteristics and durability of BHVs. This article presents a comparative study of the effects of several processing methods on the degree of damage to the ECM of GA-fixed pericardium-based biomaterials as well as on their biostability, biocompatibility, and resistance to calcification. Based on the assumption that preservation of the native ECM structure will enable the creation of calcinosis-resistant materials, this study provides a soft biomimetic approach for the manufacture of GA-fixed biomaterials using gentle decellularization and washing methods. It has been shown that the use of soft methods for preimplantation processing of materials, ensuring maximum preservation of the intactness of the pericardial ECM, radically increases the resistance of biomaterials to calcification. These obtained data are of interest for the development of new calcinosis-resistant biomaterials for the manufacture of BHVs. Full article

Semantic segmentation predicts dense pixel-wise semantic labels, which is crucial for autonomous environment perception systems. For applications on mobile devices, current research focuses on energy-efficient segmenters for both frame and event-based cameras. However, there is currently no artificial neural network (ANN) that can perform efficient segmentation on both types of images. This paper introduces spiking neural network (SNN, a bionic model that is energy-efficient when implemented on neuromorphic hardware) and develops a Spiking Context Guided Network (Spiking CGNet) with substantially lower energy consumption and comparable performance for both frame and event-based images. First, this paper proposes a spiking context guided block that can extract local features and context information with spike computations. On this basis, the directly-trained SCGNet-S and SCGNet-L are established for both frame and event-based images. Our method is verified on the frame-based dataset Cityscapes and the event-based dataset DDD17. On the Cityscapes dataset, SCGNet-S achieves comparable results to ANN CGNet with 4.85 × energy efficiency. On the DDD17 dataset, Spiking CGNet outperforms other spiking segmenters by a large margin. Full article

To solve the problems of low convergence accuracy, slow speed, and common falls into local optima of the Chicken Swarm Optimization Algorithm (CSO), a performance enhancement strategy of the CSO algorithm (PECSO) is proposed with the aim of overcoming its deficiencies. Firstly, the hierarchy is established by the free grouping mechanism, which enhances the diversity of individuals in the hierarchy and expands the exploration range of the search space. Secondly, the number of niches is divided, with the hen as the center. By introducing synchronous updating and spiral learning strategies among the individuals in the niche, the balance between exploration and exploitation can be maintained more effectively. Finally, the performance of the PECSO algorithm is verified by the CEC2017 benchmark function. Experiments show that, compared with other algorithms, the proposed algorithm has the advantages of fast convergence, high precision and strong stability. Meanwhile, in order to investigate the potential of the PECSO algorithm in dealing with practical problems, three engineering optimization cases and the inverse kinematic solution of the robot are considered. The simulation results indicate that the PECSO algorithm can obtain a good solution to engineering optimization problems and has a better competitive effect on solving the inverse kinematics of robots. Full article

The wireless sensor network (WSN) is an essential technology of the Internet of Things (IoT) but has the problem of low coverage due to the uneven distribution of sensor nodes. This paper proposes a novel enhanced whale optimization algorithm (WOA), incorporating Lévy flight and a genetic algorithm optimization mechanism (WOA-LFGA). The Lévy flight technique bolsters the global search ability and convergence speed of the WOA, while the genetic optimization mechanism enhances its local search and random search capabilities. WOA-LFGA is tested with 29 mathematical optimization problems and a WSN coverage optimization model. Simulation results demonstrate that the improved algorithm is highly competitive compared with mainstream algorithms. Moreover, the practicality and the effectiveness of the improved algorithm in optimizing wireless sensor network coverage are confirmed. Full article

The utilization of lower extremity exoskeletons has witnessed a growing presence across diverse domains such as the military, medical treatment, and rehabilitation. This paper introduces a novel design of a lower extremity exoskeleton specifically tailored for individuals engaged in heavy object carrying tasks. The exoskeleton incorporates an impressive 12 degrees of freedom (DOF), with four of them being effectively controlled through hydraulic cylinders. To achieve optimal control of this intricate lower extremity exoskeleton system, the authors propose an adaptive dynamic programming (ADP) algorithm. Several crucial components are established to implement this control scheme. These include the formulation of the state equation for the lower extremity exoskeleton system, which is well-suited for the ADP algorithm. Additionally, a corresponding performance index function based on the tracking error is devised, along with the game algebraic Riccati equation. By employing the value iteration ADP scheme, the lower extremity exoskeleton demonstrates highly effective tracking control. This research not only highlights the potential of the proposed control approach but also showcases its ability to enhance the overall performance and functionality of lower extremity exoskeletons, particularly in scenarios involving heavy object carrying. Overall, this study contributes to the advancement of lower extremity exoskeleton technology and offers valuable insights into the application of ADP algorithms for achieving precise and efficient control in demanding tasks. Full article

The long-range migration of monarch butterflies, extended over 4000 km, is not well understood. Monarchs experience varying density conditions during migration, ranging as high as 3000 m, where the air density is much lower than at sea level. In this study, we test the hypothesis that the aerodynamic performance of monarchs improves at reduced density conditions by considering the fluid–structure interaction of chordwise flexible wings. A well-validated, fully coupled Navier–Stokes/structural dynamics solver was used to illustrate the interplay between wing motion, aerodynamics, and structural flexibility in forward flight. The wing density and elastic modulus were measured from real monarch wings and prescribed as inputs to the aeroelastic framework. Our results show that sufficient lift is generated to offset the butterfly weight at higher altitudes, aided by the wake-capture mechanism, which is a nonlinear wing–wake interaction mechanism, commonly seen for hovering animals. The mean total power, defined as the sum of the aerodynamic and inertial power, decreased by 36% from the sea level to the condition at 3000 m. Decreasing power with altitude, while maintaining the same equilibrium lift, suggests that the butterflies generate lift more efficiently at higher altitudes. Full article