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This paper presents an enhanced crayfish optimization algorithm (ECOA). The ECOA includes four improvement strategies. Firstly, the Halton sequence was used to improve the population initialization of the crayfish optimization algorithm. Furthermore, the quasi opposition-based learning strategy is introduced to generate the opposite solution of the population, increasing the algorithm's searching ability. Thirdly, the elite factor guides the predation stage to avoid blindness in this stage. Finally, the fish aggregation device effect is introduced to increase the ability of the algorithm to jump out of the local optimal. This paper performed tests on the widely used IEEE CEC2019 test function set to verify the validity of the proposed ECOA method. The experimental results show that the proposed ECOA has a faster convergence speed, greater performance stability, and a stronger ability to jump out of local optimal compared with other popular algorithms. Finally, the ECOA was applied to two real-world engineering optimization problems, verifying its ability to solve practical optimization problems and its superiority compared to other algorithms. [ABSTRACT FROM AUTHOR]

Mycelium-based green composites (MBCs) represent an eco-friendly material innovation with vast potential across diverse applications. This paper provides a thorough review of the factors influencing the production and properties of MBCs, with a particular focus on interdisciplinary collaboration and long-term sustainability goals. It delves into critical aspects such as fungal species selection, substrate type selection, substrate preparation, optimal conditions, dehydrating methods, post-processing techniques, mold design, sterilization processes, cost comparison, key recommendations, and other necessary factors. Regarding fungal species selection, the paper highlights the significance of considering factors like mycelium species, decay type, hyphal network systems, growth rate, and bonding properties in ensuring the safety and suitability of MBCs fabrication. Substrate type selection is discussed, emphasizing the importance of chemical characteristics such as cellulose, hemicellulose, lignin content, pH, organic carbon, total nitrogen, and the C: N ratio in determining mycelium growth and MBC properties. Substrate preparation methods, optimal growth conditions, and post-processing techniques are thoroughly examined, along with their impacts on MBCs quality and performance. Moreover, the paper discusses the importance of designing molds and implementing effective sterilization processes to ensure clean environments for mycelium growth. It also evaluates the costs associated with MBCs production compared to traditional materials, highlighting potential cost savings and economic advantages. Additionally, the paper provides key recommendations and precautions for improving MBC properties, including addressing fungal strain degeneration, encouraging research collaboration, establishing biosecurity protocols, ensuring regulatory compliance, optimizing storage conditions, implementing waste management practices, conducting life cycle assessments, and suggesting parameters for desirable MBC properties. Overall, this review offers valuable insights into the complex interplay of factors influencing MBCs production and provides guidance for optimizing processes to achieve sustainable, high-quality composites for diverse applications. [ABSTRACT FROM AUTHOR]

Dual humanoid robot collaborative control systems possess better flexibility and adaptability in complex environments due to their similar structures to humans. This paper adopts a distributed model predictive controller based on the leader–follower approach to address the collaborative transportation control issue of dual humanoid robots. In the dual-robot collaborative control system, network latency issues may arise due to unstable network conditions, affecting the consistency of dual-robot collaboration. To solve this issue, a communication protocol was constructed through socket communication for dual-robot collaborative consistency, thereby resolving the problem of consistency in dual humanoid robot collaboration. Additionally, due to the complex structure of humanoid robots, there are deficiencies in position tracking accuracy during movement. To address the poor accuracy in position tracking, this paper proposes a distributed model predictive control that considers historical cumulative error, thus enhancing the position tracking accuracy of dual-robot collaborative control. [ABSTRACT FROM AUTHOR]

In humanitarian aid scenarios, the model of cumulative capacitated vehicle routing problem can be used in vehicle scheduling, aiming at delivering materials to recipients as quickly as possible, thus minimizing their wait time. Traditional approaches focus on this metric, but practical implementations must also consider factors such as driver labor intensity and the capacity for on-site decision-making. To evaluate driver workload, the operation times of relief vehicles are typically used, and multi-objective modeling is employed to facilitate on-site decision-making. This paper introduces a multi-objective cumulative capacitated vehicle routing problem considering operation time (MO-CCVRP-OT). Our model is bi-objective, aiming to minimize both the cumulative wait time of disaster-affected areas and the extra expenditures incurred by the excess operation time of rescue vehicles. Based on the traditional grey wolf optimizer algorithm, this paper proposes a dynamic grey wolf optimizer algorithm with floating 2-opt (DGWO-F2OPT), which combines real number encoding with an equal-division random key and ROV rules for decoding; in addition, a dynamic non-dominated solution set update strategy is introduced. To solve MO-CCVRP-OT efficiently and increase the algorithm's convergence speed, a multi-objective improved floating 2-opt (F2OPT) local search strategy is proposed. The utopia optimum solution of DGWO-F2OPT has an average value of two fitness values that is 6.22% lower than that of DGWO-2OPT. DGWO-F2OPT's average fitness value in the algorithm comparison trials is 16.49% less than that of NS-2OPT. In the model comparison studies, MO-CCVRP-OT is 18.72% closer to the utopian point in Euclidean distance than CVRP-OT. [ABSTRACT FROM AUTHOR]

Variable camber wing technology stands out as the most promising morphing technology currently available in green aviation. Despite the ongoing advancements in smart materials and compliant structures, they still fall short in terms of driving force, power, and speed, rendering mechanical structures based on kinematics the preferred choice for large long-range civilian aircraft. In line with this principle, this paper introduces a linkage-based variable camber trailing edge design approach. Covering coordinated design, internal skeleton design, flexible skin design, and drive structure design, the method leverages a two-dimensional supercritical airfoil to craft a seamless, continuous two-dimensional wing full-size variable camber trailing edge structure, boasting a 2.7 m span and 4.3 m chord. Given the significant changes in aerodynamic load direction, ground tests under cruise load utilize a tracking-loading system based on tape and lever. Results indicate that the designed single-degree-of-freedom Watt I mechanism and Stephenson III drive mechanism adeptly accommodate the slender trailing edge of the supercritical airfoil. Under a maximum cruise vertical aerodynamic load of 17,072 N, the structure meets strength requirements when deflected to 5°. The research in this paper can provide some insights into the engineering design of variable camber wings. [ABSTRACT FROM AUTHOR]

In this paper, we describe a sheet-shaped throwable transforming robot. Sheet-type robots can change their shape to perform tasks according to the situation. Therefore, they are expected to be useful in places with many restrictions, such as disaster sites. However, most of them can only move slowly on the ground. Therefore, in order to actually deliver the robot to the disaster site, it must be carried manually. To solve this problem, we are developing a sheet-shaped robot that can be thrown from the sky. Previously developed prototypes could only move in the forward direction, and the transition from falling to walking was complicated and uncertain. In this paper, we report on a new prototype that improves on these shortcomings. [ABSTRACT FROM AUTHOR]

The sand cat swarm optimization algorithm (SCSO) is a novel metaheuristic algorithm that has been proposed in recent years. The algorithm optimizes the search ability of individuals by mimicking the hunting behavior of sand cat groups in nature, thereby achieving robust optimization performance. It is characterized by few control parameters and simple operation. However, due to the lack of population diversity, SCSO is less efficient in solving complex problems and is prone to fall into local optimization. To address these shortcomings and refine the algorithm's efficacy, an improved multi-strategy sand cat optimization algorithm (IMSCSO) is proposed in this paper. In IMSCSO, a roulette fitness–distance balancing strategy is used to select codes to replace random agents in the exploration phase and enhance the convergence performance of the algorithm. To bolster population diversity, a novel population perturbation strategy is introduced, aiming to facilitate the algorithm's escape from local optima. Finally, a best–worst perturbation strategy is developed. The approach not only maintains diversity throughout the optimization process but also enhances the algorithm's exploitation capabilities. To evaluate the performance of the proposed IMSCSO, we conducted experiments in the CEC 2017 test suite and compared IMSCSO with seven other algorithms. The results show that the IMSCSO proposed in this paper has better optimization performance. [ABSTRACT FROM AUTHOR]

When humanoid robots work in human environments, they are prone to falling. However, when there are objects around that can be utilized, humanoid robots can leverage them to achieve balance. To address this issue, this paper established the state equation of a robot using a variable height-inverted pendulum model and implemented online trajectory optimization using model predictive control. For the arms' optimal joint angles during movement, this paper took the distributed polygon method to calculate the arm postures. To ensure that the robot reached the target position smoothly and rapidly during its motion, this paper adopts a whole-body motion control approach, establishing a cost function for multi-objective constraints on the robot's movement. These constraints include whole-body dynamics, center of mass constraints, arm's end effector constraints, friction constraints, and center of pressure constraints. In the simulation, four sets of methods were compared, and the experimental results indicate that compared to free fall motion, adopting the method proposed in this paper reduces the maximum acceleration of the robot when it touches the wall to 69.1 m/s2, effectively reducing the impact force upon landing. Finally, in the actual experiment, we positioned the robot 0.85 m away from the wall and applied a forward pushing force. We observed that the robot could stably land on the wall, and the impact force was within the range acceptable to the robot, confirming the practical effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

To combat climate change, one approach is to manufacture products from biomass-fungi composite materials instead of petroleum-based plastics. These products can be used in packaging, furniture, and construction industries. A 3D printing-based manufacturing method was developed for these biomass-fungi composite materials, eliminating the need for molds, and enabling customized product design. However, previous studies on the 3D printing-based method showed significant shrinkage of printed samples. In this paper, an approach is proposed to reduce the shrinkage by incorporating ionic crosslinking into biomass-fungi composite materials. This paper reports two sets of experiments regarding the effects of sodium alginate (SA) and calcium chloride (CaCl2) on fungal growth and fungal viability. The first set of experiments was conducted using Petri dishes with fungi isolated from colonized biomass-fungi material and different concentrations of SA and CaCl2. Fungal growth was measured by the circumference of fungal colonies. The results showed that concentrations of SA and CaCl2 had significant effects on fungal growth and no fungal growth was observed on Petri dishes with 15% CaCl2. Some of these Petri dishes were also observed under confocal microscopy. The results confirmed the differences obtained by measuring the circumference of fungal colonies. The second set of experiments was conducted using Petri dishes with biomass-fungi mixtures that were treated with different concentrations of SA and exposure times in a CaCl2 (crosslinking) solution. Fungal viability was measured by counting colony-forming units. The results showed that the addition of the SA solution and exposure times in the crosslinking solution had statistically significant effects on fungal viability. The 2SA solution was prepared by dissolving 2 g of SA in 100 mL of water, the 5SA solution was prepared by dissolving 5 g of SA in 100 mL of water, and the crosslinking solution was prepared by dissolving 5 g of CaCl2 in 100 mL of water. The results also showed that fungal viability was not too low in biomass-fungi mixtures that included 2SA solution and were exposed to the crosslinking solution for 1 min. [ABSTRACT FROM AUTHOR]

Digital health tracking is a source of valuable insights for public health research and consumer health technology. The brain is the most complex organ, containing information about psychophysical and physiological biomarkers that correlate with health. Specifically, recent developments in electroencephalogram (EEG), functional near-infra-red spectroscopy (fNIRS), and photoplethysmography (PPG) technologies have allowed the development of devices that can remotely monitor changes in brain activity. The inclusion criteria for the papers in this review encompassed studies on self-applied, remote, non-invasive neuroimaging techniques (EEG, fNIRS, or PPG) within healthcare applications. A total of 23 papers were reviewed, comprising 17 on using EEGs for remote monitoring and 6 on neurofeedback interventions, while no papers were found related to fNIRS and PPG. This review reveals that previous studies have leveraged mobile EEG devices for remote monitoring across the mental health, neurological, and sleep domains, as well as for delivering neurofeedback interventions. With headsets and ear-EEG devices being the most common, studies found mobile devices feasible for implementation in study protocols while providing reliable signal quality. Moderate to substantial agreement overall between remote and clinical-grade EEGs was found using statistical tests. The results highlight the promise of portable brain-imaging devices with regard to continuously evaluating patients in natural settings, though further validation and usability enhancements are needed as this technology develops. [ABSTRACT FROM AUTHOR]

In recent decades, the term "ecosystem" has garnered substantial attention in scholarly and managerial discourse, featuring prominently in academic and applied contexts. While individual scholars have made significant contributions to the study of various types of ecosystem, there appears to be a research gap marked by a lack of comprehensive synthesis and refinement of findings across diverse ecosystems. This paper systematically addresses this gap through a hybrid methodology, employing bibliometric and content analyses to systematically review the literature from 1993 to 2023. The primary research aim is to critically examine theoretical studies on different ecosystem types, specifically focusing on business, innovation, and platform ecosystems. The methodology of this study involves a content review of the identified literature, combining quantitative bibliometric analyses to differentiate patterns and content analysis for in-depth exploration. The core findings center on refining and summarizing the definitions of business, innovation, and platform ecosystems, shedding light on both commonalities and distinctions. Notably, the research unveils shared characteristics such as openness and diversity across these ecosystems while highlighting significant differences in terms of participants and objectives. Furthermore, the paper delves into the interconnections within these three ecosystem types, offering insights into their dynamics and paving the way for discussions on future research directions. This comprehensive examination not only advances our understanding of business, innovation, and platform ecosystems but also lays the groundwork for future scholarly inquiries in this dynamic and evolving field. [ABSTRACT FROM AUTHOR]

The load-adaptive behavior of the muscles in the human musculoskeletal system offers great potential for minimizing resource and energy requirements in many technical systems, especially in drive technology and robotics. However, the lack of knowledge about suitable technical linear actuators that can reproduce the load-adaptive behavior of biological muscles in technology is a major reason for the lack of successful implementation of this biological principle. In this paper, therefore, the different types of linear actuators are investigated. The focus is particularly on artificial muscles and rope pulls. The study is based on literature, on the one hand, and on two physical demonstrators in the form of articulated robots, on the other hand. The studies show that ropes are currently the best way to imitate the load-adaptive behavior of the biological model in technology. This is especially illustrated in the context of this paper by the discussion of different advantages and disadvantages of the technical linear actuators, where ropes, among other things, have a good mechanical and control behavior, which is very advantageous for use in an adaptive system. Finally, the next steps for future research are outlined to conclude how ropes can be used as linear actuators to transfer load-adaptive lightweight design into technical applications. [ABSTRACT FROM AUTHOR]

This paper aims to propose an quantitative engineering approach to study and compare the jaw mechanisms of different marine species, considering essential mechanical parameters generally used to evaluate the performance of industrial linkage mechanisms. By leveraging these parameters, the paper demonstrates how the species' characteristics and behaviors align with the findings of biologists, enabling a meaningful comparison that was not previously possible. Seven fish species from various families are chosen to maintain a generic approach. [ABSTRACT FROM AUTHOR]

Precision control of multiple robotic fish visual navigation in complex underwater environments has long been a challenging issue in the field of underwater robotics. To address this problem, this paper proposes a multi-robot fish obstacle traversal technique based on the combination of cross-modal variational autoencoder (CM-VAE) and imitation learning. Firstly, the overall framework of the robotic fish control system is introduced, where the first-person view of the robotic fish is encoded into a low-dimensional latent space using CM-VAE, and then different latent features in the space are mapped to the velocity commands of the robotic fish through imitation learning. Finally, to validate the effectiveness of the proposed method, experiments are conducted on linear, S-shaped, and circular gate frame trajectories with both single and multiple robotic fish. Analysis reveals that the visual navigation method proposed in this paper can stably traverse various types of gate frame trajectories. Compared to end-to-end learning and purely unsupervised image reconstruction, the proposed control strategy demonstrates superior performance, offering a new solution for the intelligent navigation of robotic fish in complex environments. [ABSTRACT FROM AUTHOR]

Organisms in nature have evolved a variety of surfaces with different tribological properties to adapt to the environment. By studying, understanding, and summarizing the friction and lubrication regulation phenomena of typical surfaces in nature, researchers have proposed various biomimetic friction regulation theories and methods to guide the development of new lubrication materials and lubrication systems. The design strategies for biomimetic friction/lubrication materials and systems mainly include the chemistry, surface structure, and mechanics. With the deepening understanding of the mechanism of biomimetic lubrication and the increasing application requirements, the design strategy of multi-strategy coupling has gradually become the center of attention for researchers. This paper focuses on the interfacial chemistry, surface structure, and surface mechanics of a single regulatory strategy and multi-strategy coupling approach. Based on the common biological friction regulation mechanism in nature, this paper reviews the research progress on biomimetic friction/lubrication materials in recent years, discusses and analyzes the single and coupled design strategies as well as their advantages and disadvantages, and describes the design concepts, working mechanisms, application prospects, and current problems of such materials. Finally, the development direction of biomimetic friction lubrication materials is prospected. [ABSTRACT FROM AUTHOR]

Under water, on land, or in the air, creatures use a variety of grasping methods to hunt, avoid predators, or carry food. Numerous studies have been conducted to construct a bionic surface for grasping tasks. This paper reviews the typical biomimetic structures and surfaces (wedge-shaped surface, suction cup surface and thorn claw surface) for grasping scenarios. Initially, progress in gecko-inspired wedge-shaped adhesive surfaces is reviewed, encompassing the underlying mechanisms that involve tuning the contact area and peeling behavior. The applications of grippers utilizing this adhesive technology are also discussed. Subsequently, the suction force mechanisms and applications of surfaces inspired by octopus and remora suction cups are outlined. Moreover, this paper introduces the applications of robots incorporating the principles of beetle-inspired and bird-inspired thorn claw structures. Lastly, inspired by remoras' adhesive discs, a composite biomimetic adhesive surface is proposed. It integrates features from wedge-shaped, suction cup, and claw thorn surfaces, potentially surpassing the adaptability of basic bioinspired surfaces. This surface construction method offers a potential avenue to enhance adhesion capabilities with superior adaptability to surface roughness and curvature. [ABSTRACT FROM AUTHOR]

In order to cope with the problems of energy shortage and environmental pollution, carbon emissions need to be reduced and so the structure of the power grid is constantly being optimized. Traditional centralized power networks are not as capable of controlling and distributing non-renewable energy as distributed power grids. Therefore, the optimal dispatch of microgrids faces increasing challenges. This paper proposes a multi-strategy fusion slime mould algorithm (MFSMA) to tackle the microgrid optimal dispatching problem. Traditional swarm intelligence algorithms suffer from slow convergence, low efficiency, and the risk of falling into local optima. The MFSMA employs reverse learning to enlarge the search space and avoid local optima to overcome these challenges. Furthermore, adaptive parameters ensure a thorough search during the algorithm iterations. The focus is on exploring the solution space in the early stages of the algorithm, while convergence is accelerated during the later stages to ensure efficiency and accuracy. The salp swarm algorithm's search mode is also incorporated to expedite convergence. MFSMA and other algorithms are compared on the benchmark functions, and the test showed that the effect of MFSMA is better. Simulation results demonstrate the superior performance of the MFSMA for function optimization, particularly in solving the 24 h microgrid optimal scheduling problem. This problem considers multiple energy sources such as wind turbines, photovoltaics, and energy storage. A microgrid model based on the MFSMA is established in this paper. Simulation of the proposed algorithm reveals its ability to enhance energy utilization efficiency, reduce total network costs, and minimize environmental pollution. The contributions of this paper are as follows: (1) A comprehensive microgrid dispatch model is proposed. (2) Environmental costs, operation and maintenance costs are taken into consideration. (3) Two modes of grid-tied operation and island operation are considered. (4) This paper uses a multi-strategy optimized slime mould algorithm to optimize scheduling, and the algorithm has excellent results. [ABSTRACT FROM AUTHOR]

Aerial recovery and redeployment can effectively increase the operating radius and the endurance of unmanned aerial vehicles (UAVs). However, the challenge lies in the effect of the aerodynamic force on the recovery system, and the existing road-based and sea-based UAV recovery methods are no longer applicable. Inspired by the predatory behavior of net-casting spiders, this study introduces a cable-driven parallel robot (CDPR) for UAV aerial recovery, which utilizes an end-effector camera to detect the UAV's flight trajectory, and the CDPR dynamically adjusts its spatial position to intercept and recover the UAV. This paper establishes a comprehensive cable model, simultaneously considering the elasticity, mass, and aerodynamic force, and the static equilibrium equation for the CDPR is derived. The effects of the aerodynamic force and cable tension on the spatial configuration of the cable are analyzed. Numerical computations yield the CDPR's end-effector position error and cable-driven power consumption at discrete spatial points, and the results show that the position error decreases but the power consumption increases with the increase in the cable tension lower limit (CTLL). To improve the comprehensive performance of the recovery system, a multi-objective optimization method is proposed, considering the error distribution, power consumption distribution, and safety distance. The optimized CTLL and interception space position coordinates are determined through simulation, and comparative analysis with the initial condition indicates an 83% reduction in error, a 62.3% decrease in power consumption, and a 1.2 m increase in safety distance. This paper proposes a new design for a UAV aerial recovery system, and the analysis lays the groundwork for future research. [ABSTRACT FROM AUTHOR]

Three-dimensional bioprinting is a promising technology for bone tissue engineering. However, most hydrogel bioinks lack the mechanical and post-printing fidelity properties suitable for such hard tissue regeneration. To overcome these weak properties, calcium phosphates can be employed in a bioink to compensate for the lack of certain characteristics. Further, the extracellular matrix of natural bone contains this mineral, resulting in its structural robustness. Thus, calcium phosphates are necessary components of bioink for bone tissue engineering. This review paper examines different recently explored calcium phosphates, as a component of potential bioinks, for the biological, mechanical and structural properties required of 3D bioprinted scaffolds, exploring their distinctive properties that render them favorable biomaterials for bone tissue engineering. The discussion encompasses recent applications and adaptations of 3D-printed scaffolds built with calcium phosphates, delving into the scientific reasons behind the prevalence of certain types of calcium phosphates over others. Additionally, this paper elucidates their interactions with polymer hydrogels for 3D bioprinting applications. Overall, the current status of calcium phosphate/hydrogel bioinks for 3D bioprinting in bone tissue engineering has been investigated. [ABSTRACT FROM AUTHOR]

The sophisticated, elegant protein-polymers designed by nature can serve as inspiration to redesign and biomanufacture protein-based materials using synthetic biology. Historically, petro-based polymeric materials have dominated industrial activities, consequently transforming our way of living. While this benefits humans, the fabrication and disposal of these materials causes environmental sustainability challenges. Fortunately, protein-based biopolymers can compete with and potentially surpass the performance of petro-based polymers because they can be biologically produced and degraded in an environmentally friendly fashion. This paper reviews four groups of protein-based polymers, including fibrous proteins (collagen, silk fibroin, fibrillin, and keratin), elastomeric proteins (elastin, resilin, and wheat glutenin), adhesive/matrix proteins (spongin and conchiolin), and cyanophycin. We discuss the connection between protein sequence, structure, function, and biomimetic applications. Protein engineering techniques, such as directed evolution and rational design, can be used to improve the functionality of natural protein-based materials. For example, the inclusion of specific protein domains, particularly those observed in structural proteins, such as silk and collagen, enables the creation of novel biomimetic materials with exceptional mechanical properties and adaptability. This review also discusses recent advancements in the production and application of new protein-based materials through the approach of synthetic biology combined biomimetics, providing insight for future research and development of cutting-edge bio-inspired products. Protein-based polymers that utilize nature's designs as a base, then modified by advancements at the intersection of biology and engineering, may provide mankind with more sustainable products. [ABSTRACT FROM AUTHOR]

The compound eye is a natural multi-aperture optical imaging system. In this paper, a continuous optical zoom compound eye imaging system based on Alvarez lenses is proposed. The main optical imaging part of the proposed system consists of a curved Alvarez lens array (CALA) and two Alvarez lenses. The movement of the CALA and two Alvarez lenses perpendicular to the optical axis is realized by the actuation of the dielectric elastomers (DEs). By adjusting the focal length of the CALA and the two Alvarez lenses, the proposed system can realize continuous zoom imaging without any mechanical movement vertically to the optical axis. The experimental results show that the paraxial magnification of the target can range from ∼0.30× to ∼0.9×. The overall dimensions of the optical imaging part are 54 mm × 36 mm ×60 mm (L × W × H). The response time is 180 ms. The imaging resolution can reach up to 50 lp/mm during the optical zoom process. The proposed continuous optical zoom compound eye imaging system has potential applications in various fields, including large field of view imaging, medical diagnostics, machine vision, and distance detection. [ABSTRACT FROM AUTHOR]

Acute cardiovascular events result from clots caused by the rupture and erosion of atherosclerotic plaques. This paper aimed to produce a functional biomimetic hydrogel of the neointimal layer of the atherosclerotic plaque that can support thrombogenesis upon exposure to human blood. A biomimetic hydrogel of the neointima was produced by culturing THP-1-derived foam cells within 3D collagen hydrogels in the presence or absence of atorvastatin. Prothrombin time and platelet aggregation onset were measured after exposure of the neointimal models to platelet-poor plasma and washed platelet suspensions prepared from blood of healthy, medication-free volunteers. Activity of the extrinsic coagulation pathway was measured using the fluorogenic substrate SN-17. Foam cell formation was observed following preincubation of the neointimal biomimetic hydrogels with oxidized LDL, and this was inhibited by pretreatment with atorvastatin. The neointimal biomimetic hydrogel was able to trigger platelet aggregation and blood coagulation upon exposure to human blood products. Atorvastatin pretreatment of the neointimal biomimetic layer significantly reduced its pro-aggregatory and pro-coagulant properties. In the future, this 3D neointimal biomimetic hydrogel can be incorporated as an additional layer within our current thrombus-on-a-chip model to permit the study of atherosclerosis development and the screening of anti-thrombotic drugs as an alternative to current animal models. [ABSTRACT FROM AUTHOR]

This paper describes a novel bionic eye binocular vision system designed to mimic the natural movements of the human eye. The system provides a broader field of view and enhances visual perception in complex environments. Compared with similar bionic binocular cameras, the JEWXON BC200 bionic binocular camera developed in this study is more miniature. It consumes only 2.8 W of power, which makes it ideal for mobile robots. Combining axis and camera rotation enables more seamless panoramic image synthesis and is therefore suitable for self-rotating bionic binocular cameras. In addition, combined with the YOLO-V8 model, the camera can accurately recognize objects such as clocks and keyboards. This research provides new ideas for the development of robotic vision systems. [ABSTRACT FROM AUTHOR]

The evolution of super-resolution (SR) technology has seen significant advancements through the adoption of deep learning methods. However, the deployment of such models by resource-constrained devices necessitates models that not only perform efficiently, but also conserve computational resources. Binary neural networks (BNNs) offer a promising solution by minimizing the data precision to binary levels, thus reducing the computational complexity and memory requirements. However, for BNNs, an effective architecture is essential due to their inherent limitations in representing information. Designing such architectures traditionally requires extensive computational resources and time. With the advancement in neural architecture search (NAS), differentiable NAS has emerged as an attractive solution for efficiently crafting network structures. In this paper, we introduce a novel and efficient binary network search method tailored for image super-resolution tasks. We adapt the search space specifically for super resolution to ensure it is optimally suited for the requirements of such tasks. Furthermore, we incorporate Libra Parameter Binarization (Libra-PB) to maximize information retention during forward propagation. Our experimental results demonstrate that the network structures generated by our method require only a third of the parameters, compared to conventional methods, and yet deliver comparable performance. [ABSTRACT FROM AUTHOR]

Public transportation scheduling aims to optimize the allocation of resources, enhance efficiency, and increase passenger satisfaction, all of which are crucial for building a sustainable urban transportation system. As a complement to public transportation, bike-sharing systems provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, including peak usage periods in the morning and evening and significant demand fluctuations across different areas, optimizing shared bike dispatch can better meet user needs, reduce vehicle vacancy rates, and increase operating revenue. To address this issue, this article proposes a comprehensive decision-making approach for spatiotemporal demand prediction and bike dispatch optimization. For demand prediction, we design a T-GCN (Temporal Graph Convolutional Network)-based bike demand prediction model. In terms of dispatch optimization, we consider factors such as dispatch capacity, distance restrictions, and dispatch costs, and design an optimization solution based on genetic algorithms. Finally, we validate the approach using shared bike operating data and show that the T-GCN can effectively predict the short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution, verifying the model's effectiveness. The shared bike dispatch approach proposed in this paper combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch. [ABSTRACT FROM AUTHOR]

Skin aging is a complex physiological process, in which cells and the extracellular matrix (ECM) interreact, which leads to a change in the mechanical properties of skin, which in turn affects the cell secretion and ECM deposition. The natural skin microrelief that exists from birth has rarely been taken into account when evaluating skin aging, apart from the common knowledge that microreliefs might serve as the starting point or initialize micro-wrinkles. In fact, microrelief itself also changes with aging. Does the microrelief have other, better uses? In this paper, owing to the fast-developing 3D printing technology, skin wrinkles with microrelief of different age groups were successfully manufactured using the Digital light processing (DLP) technology. The mechanical properties of skin samples with and without microrelief were tested. It was found that microrelief has a big impact on the elastic modulus of skin samples. In order to explore the role of microrelief in skin aging, the wrinkle formation was numerically analyzed. The microrelief models of different age groups were created using the modified Voronoi algorithm for the first time, which offers fast and flexible mesh formation. We found that skin microrelief plays an important role in regulating the modulus of the epidermis, which is the dominant factor in wrinkle formation. The wrinkle length and depth were also analyzed numerically for the first time, owing to the additional dimension offered by microrelief. The results showed that wrinkles are mainly caused by the modulus change of the epidermis in the aging process, and compared with the dermis, the hypodermis is irrelevant to wrinkling. Hereby, we developed a hypothesis that microrelief makes the skin adaptive to the mechanical property changes from aging by adjusting its shape and size. The native-like skin samples with microrelief might shed a light on the mechanism of wrinkling and also help with understanding the complex physiological processes associated with human skin. [ABSTRACT FROM AUTHOR]

In the current scenario, liver abnormalities are one of the most serious public health concerns. Cirrhosis of the liver is one of the foremost causes of demise from liver diseases. To accurately predict the status of liver cirrhosis, physicians frequently use automated computer-aided approaches. In this paper, through clustering techniques like fuzzy c-means (FCM), possibilistic fuzzy c-means (PFCM), and possibilistic c means (PCM) and sample entropy features are extracted from normal and cirrhotic liver ultrasonic images. The extracted features are classified as normal and cirrhotic through the Gaussian mixture model (GMM), Softmax discriminant classifier (SDC), harmonic search algorithm (HSA), SVM (linear), SVM (RBF), SVM (polynomial), artificial algae optimization (AAO), and hybrid classifier artificial algae optimization (AAO) with Gaussian mixture mode (GMM). The classifiers' performances are compared based on accuracy, F1 Score, MCC, F measure, error rate, and Jaccard metric (JM). The hybrid classifier AAO–GMM, with the PFCM feature, outperforms the other classifiers and attained an accuracy of 99.03% with an MCC of 0.90. [ABSTRACT FROM AUTHOR]

Recurrent neural networks (RNNs) transmit information over time through recurrent connections. In contrast, biological neural networks use many other temporal processing mechanisms. One of these mechanisms is the inter-neuron delays caused by varying axon properties. Recently, this feature was implemented in echo state networks (ESNs), a type of RNN, by assigning spatial locations to neurons and introducing distance-dependent inter-neuron delays. These delays were shown to significantly improve ESN task performance. However, thus far, it is still unclear why distance-based delay networks (DDNs) perform better than ESNs. In this paper, we show that by optimizing inter-node delays, the memory capacity of the network matches the memory requirements of the task. As such, networks concentrate their memory capabilities to the points in the past which contain the most information for the task at hand. Moreover, we show that DDNs have a greater total linear memory capacity, with the same amount of non-linear processing power. [ABSTRACT FROM AUTHOR]

The crayfish optimization algorithm (COA), proposed in 2023, is a metaheuristic optimization algorithm that is based on crayfish's summer escape behavior, competitive behavior, and foraging behavior. COA has a good optimization performance, but it still suffers from the problems of slow convergence speed and sensitivity to the local optimum. To solve these problems, an improved multi-strategy crayfish optimization algorithm for solving numerical optimization problems, called IMCOA, is proposed to address the shortcomings of the original crayfish optimization algorithm for each behavioral strategy. Aiming at the imbalance between local exploitation and global exploration in the summer heat avoidance and competition phases, this paper proposes a cave candidacy strategy and a fitness–distance balanced competition strategy, respectively, so that these two behaviors can better coordinate the global and local optimization capabilities and escape from falling into the local optimum prematurely. The directly foraging formula is modified during the foraging phase. The food covariance learning strategy is utilized to enhance the population diversity and improve the convergence accuracy and convergence speed. Finally, the introduction of an optimal non-monopoly search strategy to perturb the optimal solution for updates improves the algorithm's ability to obtain a global best solution. We evaluated the effectiveness of IMCOA using the CEC2017 and CEC2022 test suites and compared it with eight algorithms. Experiments were conducted using different dimensions of CEC2017 and CEC2022 by performing numerical analyses, convergence analyses, stability analyses, Wilcoxon rank–sum tests and Friedman tests. Experiments on the CEC2017 and CEC2022 test suites show that IMCOA can strike a good balance between exploration and exploitation and outperforms the traditional COA and other optimization algorithms in terms of its convergence speed, optimization accuracy, and ability to avoid premature convergence. Statistical analysis shows that there is a significant difference between the performance of the IMCOA algorithm and other algorithms. Additionally, three engineering design optimization problems confirm the practicality of IMCOA and its potential to solve real-world problems. [ABSTRACT FROM AUTHOR]

Speech emotion recognition based on gender holds great importance for achieving more accurate, personalized, and empathetic interactions in technology, healthcare, psychology, and social sciences. In this paper, we present a novel gender–emotion model. First, gender and emotion features were extracted from voice signals to lay the foundation for our recognition model. Second, a genetic algorithm (GA) processed high-dimensional features, and the Fisher score was used for evaluation. Third, features were ranked by their importance, and the GA was improved through novel crossover and mutation methods based on feature importance, to improve the recognition accuracy. Finally, the proposed algorithm was compared with state-of-the-art algorithms on four common English datasets using support vector machines (SVM), and it demonstrated superior performance in accuracy, precision, recall, F1-score, the number of selected features, and running time. The proposed algorithm faced challenges in distinguishing between neutral, sad, and fearful emotions, due to subtle vocal differences, overlapping pitch and tone variability, and similar prosodic features. Notably, the primary features for gender-based differentiation mainly involved mel frequency cepstral coefficients (MFCC) and log MFCC. [ABSTRACT FROM AUTHOR]

The protection of plants in mountainous and hilly areas differs from that in plain areas due to the complex terrain, which divides the work plot into many narrow plots. When designing the path planning method for plant protection UAVs, it is important to consider the generality in different working environments. To address issues such as poor path optimization, long operation time, and excessive iterations required by traditional swarm intelligence algorithms, this paper proposes a bionic three-dimensional path planning algorithm for plant protection UAVs. This algorithm aims to plan safe and optimal flight paths between work plots obstructed by multiple obstacle areas. Inspired by krill group behavior and based on group intelligence algorithm theory, the bionic three-dimensional path planning algorithm consists of three states: "foraging behavior", "avoiding enemy behavior", and "cruising behavior". The current position information of the UAV in the working environment is used to switch between these states, and the optimal path is found after several iterations, which realizes the adaptive global and local convergence of the track planning, and improves the convergence speed and accuracy of the algorithm. The optimal flight path is obtained by smoothing using a third-order B-spline curve. Three sets of comparative simulation experiments are designed to verify the performance of this proposed algorithm. The results show that the bionic swarm intelligence algorithm based on krill swarm behavior reduces the path length by 1.1~17.5%, the operation time by 27.56~75.15%, the path energy consumption by 13.91~27.35%, and the number of iterations by 46~75% compared with the existing algorithms. The proposed algorithm can shorten the distance of the planned path more effectively, improve the real-time performance, and reduce the energy consumption. [ABSTRACT FROM AUTHOR]

Path planning is an important research direction in the field of robotics; however, with the advancement of modern science and technology, the study of efficient, stable, and safe path-planning technology has become a realistic need in the field of robotics research. This paper introduces an improved sparrow search algorithm (ISSA) with a fusion strategy to further improve the ability to solve challenging tasks. First, the sparrow population is initialized using circle chaotic mapping to enhance diversity. Second, the location update formula of the northern goshawk is used in the exploration phase to replace the sparrow search algorithm's location update formula in the security situation. This improves the discoverer model's search breadth in the solution space and optimizes the problem-solving efficiency. Third, the algorithm adopts the Lévy flight strategy to improve the global optimization ability, so that the sparrow jumps out of the local optimum in the later stage of iteration. Finally, the adaptive T-distribution mutation strategy enhances the local exploration ability in late iterations, thus improving the sparrow search algorithm's convergence speed. This was applied to the CEC2021 function set and compared with other standard intelligent optimization algorithms to test its performance. In addition, the ISSA was implemented in the path-planning problem of mobile robots. The comparative study shows that the proposed algorithm is superior to the SSA in terms of path length, running time, path optimality, and stability. The results show that the proposed method is more effective, robust, and feasible in mobile robot path planning. [ABSTRACT FROM AUTHOR]

Causal discovery is central to human cognition, and learning directed acyclic graphs (DAGs) is its foundation. Recently, many nature-inspired meta-heuristic optimization algorithms have been proposed to serve as the basis for DAG learning. However, a single meta-heuristic algorithm requires specific domain knowledge and empirical parameter tuning and cannot guarantee good performance in all cases. Hyper-heuristics provide an alternative methodology to meta-heuristics, enabling multiple heuristic algorithms to be combined and optimized to achieve better generalization ability. In this paper, we propose a multi-population choice function hyper-heuristic to discover the causal relationships encoded in a DAG. This algorithm provides a reasonable solution for combining structural priors or possible expert knowledge with swarm intelligence. Under a linear structural equation model (SEM), we first identify the partial v-structures through partial correlation analysis as the structural priors of the next nature-inspired swarm intelligence approach. Then, through partial correlation analysis, we can limit the search space. Experimental results demonstrate the effectiveness of the proposed methods compared to the earlier state-of-the-art methods on six standard networks. [ABSTRACT FROM AUTHOR]

This paper presents a biomimetic fish robot featuring a flexible spine driven by cables, which integrates the cable-driven mechanism with a flexible spine. The drive system separates the body and tail fin drives for control, offering enhanced flexibility and ease in achieving phase difference control between the body and tail fin movements compared to the conventional servo motor cascaded structure. A prototype of the biomimetic fish robot was developed, accompanied by the establishment of a kinematic model. Based on this model, a control method for the biomimetic fish is proposed. Additionally, we introduce the concept of prestress to establish a numerical model for the biomimetic fish. Using multi-physical field simulation software, we simulate the two-dimensional autonomous swimming process of the biomimetic fish under different flapping frequencies and solve for its swimming characteristics as well as hydrodynamic properties. Both the simulation and experimental results validate the accuracy of our kinematic model. [ABSTRACT FROM AUTHOR]

In this paper, we introduce SonoNERFs, a novel approach that adapts Neural Radiance Fields (NeRFs) to model and understand the echolocation process in bats, focusing on the challenges posed by acoustic data interpretation without phase information. Leveraging insights from the field of optical NeRFs, our model, termed SonoNERF, represents the acoustic environment through Neural Reflectivity Fields. This model allows us to reconstruct three-dimensional scenes from echolocation data, obtained by simulating how bats perceive their surroundings through sound. By integrating concepts from biological echolocation and modern computational models, we demonstrate the SonoNERF's ability to predict echo spectrograms for unseen echolocation poses and effectively reconstruct a mesh-based and energy-based representation of complex scenes. Our work bridges a gap in understanding biological echolocation and proposes a methodological framework that provides a first-order model of how scene understanding might arise in echolocating animals. We demonstrate the efficacy of the SonoNERF model on three scenes of increasing complexity, including some biologically relevant prey–predator interactions. [ABSTRACT FROM AUTHOR]

Dialysis membranes are not hemocompatible with human blood, as the patients are suffering from the blood–membrane interactions' side effects. Zwitterionic structures have shown improved hemocompatibility; however, their complicated synthesis hinders their commercialization. The goal of the study is to achieve fast functionalization for carboxybetaine and sulfobetaine zwitterionic immobilization on PES membranes while comparing the stability and the targeted hemocompatibility. The chemical modification approach is based on an aminolysis reaction. Characterization, computational simulations, and clinical analysis were conducted to study the modified membranes. Atomic force microscopy (AFM) patterns showed a lower mean roughness for carboxybetaine-modified (6.3 nm) and sulfobetaine-modified (7.7 nm) membranes compared to the neat membrane (52.61 nm). The pore size of the membranes was reduced from values above 50 nm for the neat PES to values between 2 and 50 nm for zwitterionized membranes, using Brunauer–Emmett–Teller (BET) analysis. More hydrophilic surfaces led to a growth equilibrium water content (EWC) of nearly 6% for carboxybetaine and 10% for sulfobetaine-modified membranes. Differential scanning calorimetry (DSC) measurements were 12% and 16% stable water for carboxybetaine- and sulfobetaine-modified membranes, respectively. Sulfobetaine membranes showed better compatibility with blood with respect to C5a, IL-1a, and IL-6 biomarkers. Aminolysis-based zwitterionization was found to be suitable for the improvement of hemodialysis membranes. The approach introduced in this paper could be used to modify the current dialysis membranes with minimal change in the production facilities. [ABSTRACT FROM AUTHOR]

Robotic control is a fundamental part of autonomous robots. Modular legged and climbing robots are complex machines made up of a variety of subsystems, ranging from a single robot with simple legs to a complex system composed of multiple legs (or modules) with computing power and sensitivity. Their complexity, which is increased by the fact of needing elements for climbing, makes a correct structure crucial to achieve a complete, robust, and versatile system during its operation. Control architectures for legged robots are distinguished from other software architectures because of the special needs of these systems. In this paper, we present an original classification of modular legged and climbing robots, a comprehensive review of the most important control architectures in robotics, focusing on the control of modular legged and climbing robots, and a comparison of their features. The control architecture comparison aims to provide the analytical tools necessary to make informed decisions tailored to the specific needs of your robotic applications. This article includes a review and classification of modular legged and climbing robots, breaking down each category separately. [ABSTRACT FROM AUTHOR]

In this paper, a nonlinear simulation block for a fish robot was designed using MATLAB Simulink. The simulation block incorporated added masses, hydrodynamic damping forces, restoring forces, and forces and moments due to dorsal fins, pectoral fins, and caudal fins into six-degree-of-freedom equations of motion. To obtain a linearized model, we used three different nominal surge velocities (i.e., 0.2 m/s, 0.4 m/s, and 0.6 m/s). After obtaining output responses by applying pseudo-random binary signal inputs to a nonlinear model, an identification tool was used to obtain approximated linear models between inputs and outputs. Utilizing the obtained linearized models, two-degree-of-freedom proportional, integral, and derivative controllers were designed, and their characteristics were analyzed. For the 0.4 m/s nominal surge velocity models, the gain margins and phase margins of the surge, pitch, and yaw controllers were infinity and 69 degrees, 26.3 dB and 85 degrees, and infinity and 69 degrees, respectively. The bandwidths of surge, pitch, and yaw control loops were determined to be 2.3 rad/s, 0.17 rad/s, and 2.0 rad/s, respectively. Similar characteristics were observed when controllers designed for linear models were applied to the nonlinear model. When step inputs were applied to the nonlinear model, the maximum overshoot and steady-state errors were very small. It was also found that the nonlinear plant with three different nominal surge velocities could be controlled by a single controller designed for a linear model with a nominal surge velocity of 0.4 m/s. Therefore, controllers designed using linear approximation models are expected to work well with an actual nonlinear model. [ABSTRACT FROM AUTHOR]

The besiege and conquer algorithm has shown excellent performance in single-objective optimization problems. However, there is no literature on the research of the BCA algorithm on multi-objective optimization problems. Therefore, this paper proposes a new multi-objective besiege and conquer algorithm to solve multi-objective optimization problems. The grid mechanism, archiving mechanism, and leader selection mechanism are integrated into the BCA to estimate the Pareto optimal solution and approach the Pareto optimal frontier. The proposed algorithm is tested with MOPSO, MOEA/D, and NSGAIII on the benchmark function IMOP and ZDT. The experiment results show that the proposed algorithm can obtain competitive results in terms of the accuracy of the Pareto optimal solution. [ABSTRACT FROM AUTHOR]

Deployable hind wings of beetles led to a bio-inspired idea to design deployable micro aerial vehicles (MAVs) to meet the requirement of miniaturization. In this paper, a bionic deployable wing (BD-W) model is designed based on the folding mechanism and elliptical wing vein structure of the Protaetia brevitarsis hindwing, and its structural static and aerodynamic characteristics are analyzed by using ANSYS Workbench. Finally, the 3D-printed bionic deployable wing was tested in a wind tunnel and compared with simulation experiments to explore the effects of different incoming velocity, flapping frequency, and angle of attack on its aerodynamic characteristics, which resulted in the optimal combination of the tested parameters, among which, the incoming velocity is 3 m/s, the flapping frequency is 10 Hz, the angle of attack is 15°, and the lift-to-drag ratio of this parameter combination is 4.91. The results provide a theoretical basis and technical reference for the further development of bionic flapping wing for MAV applications. [ABSTRACT FROM AUTHOR]

Standard alternating leg motions serve as the foundation for simple bipedal gaits, and the effectiveness of the fixed stimulus signal has been proved in recent studies. However, in order to address perturbations and imbalances, robots require more dynamic gaits. In this paper, we introduce dynamic stimulus signals together with a bipedal locomotion policy into reinforcement learning (RL). Through the learned stimulus frequency policy, we induce the bipedal robot to obtain both three-dimensional (3D) locomotion and an adaptive gait under disturbance without relying on an explicit and model-based gait in both the training stage and deployment. In addition, a set of specialized reward functions focusing on reliable frequency reflections is used in our framework to ensure correspondence between locomotion features and the dynamic stimulus. Moreover, we demonstrate efficient sim-to-real transfer, making a bipedal robot called BITeno achieve robust locomotion and disturbance resistance, even in extreme situations of foot sliding in the real world. In detail, under a sudden change in torso velocity of − 1.2 m/s in 0.65 s, the recovery time is within 1.5–2.0 s. [ABSTRACT FROM AUTHOR]

In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm optimization hybrid dandelion optimization algorithm (PSODO) is proposed, which is based on the problems of slow optimization speed and being easily susceptible to falling into local extremum in the optimization ability of the dandelion optimization algorithm. This hybrid algorithm makes the whole algorithm more diverse by introducing the strong global search ability of particle swarm optimization and the unique individual update rules of the dandelion algorithm (i.e., rising, falling and landing). The ascending and descending stages of dandelion also help to introduce more changes and explorations into the search space, thus better balancing the global and local search. The experimental results show that compared with other algorithms, the proposed PSODO algorithm greatly improves the global optimal value search ability, convergence speed and optimization speed. The effectiveness and feasibility of the PSODO algorithm are verified by solving 22 benchmark functions and three engineering design problems with different complexities in CEC 2005 and comparing it with other optimization algorithms. [ABSTRACT FROM AUTHOR]

As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services' computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time. [ABSTRACT FROM AUTHOR]

This paper explores if plants are capable of responding to human movement by changes in their electrical signals. Toward that goal, we conducted a series of experiments, where humans over a period of 6 months were performing different types of eurythmic gestures in the proximity of garden plants, namely salad, basil, and tomatoes. To measure plant perception, we used the plant SpikerBox, which is a device that measures changes in the voltage differentials of plants between roots and leaves. Using machine learning, we found that the voltage differentials over time of the plant predict if (a) eurythmy has been performed, and (b) which kind of eurythmy gestures has been performed. We also find that the signals are different based on the species of the plant. In other words, the perception of a salad, tomato, or basil might differ just as perception of different species of animals differ. This opens new ways of studying plant ecosystems while also paving the way to use plants as biosensors for analyzing human movement. [ABSTRACT FROM AUTHOR]

The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust optimization capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, and unsatisfactory convergence speed when facing complex optimization problems. In response, this paper proposes the multi-strategy improved dung beetle optimization algorithm (MDBO). The core improvements include using Latin hypercube sampling for better population initialization and the introduction of a novel differential variation strategy, termed "Mean Differential Variation", to enhance the algorithm's ability to evade local optima. Moreover, a strategy combining lens imaging reverse learning and dimension-by-dimension optimization was proposed and applied to the current optimal solution. Through comprehensive performance testing on standard benchmark functions from CEC2017 and CEC2020, MDBO demonstrates superior performance in terms of optimization accuracy, stability, and convergence speed compared with other classical metaheuristic optimization algorithms. Additionally, the efficacy of MDBO in addressing complex real-world engineering problems is validated through three representative engineering application scenarios namely extension/compression spring design problems, reducer design problems, and welded beam design problems. [ABSTRACT FROM AUTHOR]

This paper presents an improved pelican optimization algorithm (IPOA) to solve the economic load dispatch problem. The vertical crossover operator in the crisscross optimization algorithm is integrated to expand the diversity of the population in the local search phase. The optimal individual is also introduced to enhance its ability to guide the whole population and add disturbance factors to enhance its ability to jump out of the local optimal. The dimensional variation strategy is adopted to improve the optimal individual and speed up the algorithm's convergence. The results of the IPOA showed that coal consumption was reduced by 0.0292%, 2.7273%, and 3.6739%, respectively, when tested on 10, 40, and 80-dimensional thermal power plant units compared to POA. The IPOA can significantly reduce the fuel cost of power plants. [ABSTRACT FROM AUTHOR]

This document provides an introduction to biomimetic scaffolds for hard tissue surgery, specifically focusing on bones and teeth. It explains that three-dimensional biomaterial scaffolds are being used to repair damaged hard tissues by providing mechanical stability and a porous network for cell growth and tissue regeneration. The document also introduces a special issue that discusses the latest advances in this field, including design strategies, fabrication techniques, biomaterials, and clinical applications. The special issue includes contributions from scholars in the field, with articles covering bone and joint surgery, maxillofacial and oral surgery, and general surgery. The document provides an overview of the published papers in the special issue, highlighting review articles on biomimetic silk-based materials and scaffolds, as well as the evolution of designs in arthroplasty endoprostheses. It also summarizes research articles on personalized biomimetic scaffolds, drug-releasing scaffolds, composite materials, ceramic biocomposites, and highly porous scaffolds. Overall, the contributions demonstrate advancements in the field and the potential for biomimetic scaffolds to assist in tissue regeneration. [Extracted from the article]

In this paper, we address the challenge of ensuring stability in bipedal walking robots and exoskeletons. We explore the feasibility of real-time implementation for the Predicted Step Viability algorithm (PSV), a complex multi-step optimization criterion for planning future steps in bipedal gait. To overcome the high computational cost of the PSV algorithm, we performed an analysis using 11 classification algorithms and a stacking strategy to predict if a step will be stable or not. We generated three datasets of increasing complexity through PSV simulations to evaluate the classification performance. Among the classifiers, k Nearest Neighbors, Support Vector Machine with Radial Basis Function Kernel, Decision Tree, and Random Forest exhibited superior performance. Multi-Layer Perceptron also consistently performed well, while linear-based algorithms showed lower performance. Importantly, the use of stacking did not significantly improve performance. Our results suggest that the feature vector applied with this approach is applicable across various robotic models and datasets, provided that training data is balanced and sufficient points are used. Notably, by leveraging classifiers, we achieved rapid computation of results in less than 1 ms, with minimal computational cost. [ABSTRACT FROM AUTHOR]

In addition to water repellency, superhydrophobic leaves of plants such as Salvinia molesta adsorb oil and separate it from water surfaces. This phenomenon has been the inspiration for a new method of oil–water separation, the bionic oil adsorber (BOA). In this paper, we show how the biological effect can be abstracted and transferred to technical textiles, in this case knitted spacer textiles hydrophobized with a layered silicate, oriented at the biology push approach. Subsequently, the transport of the oil within the bio-inspired textile is analyzed by a three-dimensional fluid simulation. This fluid simulation shows that the textile can be optimized by reducing the pile yarn length, increasing the pile yarn spacing, and increasing the pile yarn diameter. For the first time, it has been possible with this simulation to optimize the bio-inspired textile with regard to oil transport with little effort and thus enable the successful implementation of a self-driven and sustainable oil removal method. [ABSTRACT FROM AUTHOR]

The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, a novel golden jackal optimization algorithm (SCMGJO) combining sine–cosine and Cauchy mutation is proposed. On one hand, tent mapping reverse learning is introduced in population initialization, and sine and cosine strategies are introduced in the update of prey positions, which enhances the global exploration ability of the algorithm. On the other hand, the introduction of Cauchy mutation for perturbation and update of the optimal solution effectively improves the algorithm's ability to obtain the optimal solution. Through the optimization experiment of 23 benchmark test functions, the results show that the SCMGJO algorithm performs well in convergence speed and accuracy. In addition, the stretching/compression spring design problem, three-bar truss design problem, and unmanned aerial vehicle path planning problem are introduced for verification. The experimental results prove that the SCMGJO algorithm has superior performance compared with other intelligent optimization algorithms and verify its application ability in engineering applications. [ABSTRACT FROM AUTHOR]