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Honeycomb plates, due to their multi-cavity structure, exhibit excellent mechanical properties and sound insulation. Previous studies have demonstrated that altering the cell size and arrangement of honeycomb structures impacts their acoustic performance. Based on these findings, this study developed a wallboard structure with enhanced sound insulation by filling the cavities with paper fiber/cement facesheets and designing a stacked core structure. Through the reverberation chamber–anechoic chamber sound insulation experiment under 100–6300 Hz excitation and conducting orthogonal experiments from three dimensions, it was found that: (1) Compared to no filling, the filling with straw and glazed hollow bead can increase the sound transmission loss (STL) by more than 50% in the frequency bandwidth above 2000 Hz. This indicates that both types of fillings can significantly enhance the sound insulation performance of the honeycomb structure without a significant increase in economic costs. (2) The increase in paper fiber/cement facesheets improves the STL across the entire experimental bandwidth, with a maximum improvement exceeding 70%. This structural design not only offers superior sound insulation performance but also better suits practical engineering applications. (3) Increasing the number of core stacking units (from one to three), taking straw-filled paper honeycomb-core wallboards as an example, effectively increased the STL bandwidth. (4) This test enriches the application of honeycomb plates in sound insulation. Introducing fiber paper fiber/cement facesheets and eco-friendly, low-cost straw improves sound insulation and enhances the strength of honeycomb, making them more suitable for construction, particularly as non-load-bearing structures.

To apply functional honeycomb panels (FHPs) in actual engineering projects, the heat transfer performance and intrinsic heat transfer mechanism of laminated honeycomb panels (LHPs, total thickness of 60 mm) with different structural parameters were investigated in this study by a heat flow meter. The results showed that (1) the equivalent thermal conductivity λequ of the LHP was almost independent of the cell size, when it consisted of a small single-layer thickness. Thus, LHP panels with a single-layer thickness of 15–20 mm are recommended. (2) A heat transfer model of LHPs was developed, and it was concluded that the heat transfer performance of LHPs depends greatly on the performance of their honeycomb core. Then, an equation was derived for the steady state temperature distribution of the honeycomb core. (3) The contribution of each heat transfer method to the total heat flux of the LHP was calculated using the theoretical equation. According to the theoretical results, the intrinsic heat transfer mechanism affecting the heat transfer performance of LHPs was revealed. The results of this study laid the foundation for the application of LHPs in building envelopes.

Biomimetic actuators are typically constructed as functional bi- or multilayers, where actuating and resistance layers together dictate bending responses upon triggering by environmental stimuli. Inspired by motile plant structures, like the stems of the false rose of Jericho (Selaginella lepidophylla), we introduce polymer-modified paper sheets that can act as soft robotic single-layer actuators capable of hygro-responsive bending reactions. A tailored gradient modification of the paper sheet along its thickness entails increased dry and wet tensile strength and allows at the same time for hygro-responsiveness. For the fabrication of such single-layer paper devices, the adsorption behavior of a cross-linkable polymer to cellulose fiber networks was first evaluated. By using different concentrations and drying procedures fine-tuned polymer gradients throughout the thickness can be achieved. Due to the covalent cross-linking of polymer with fibers, these paper samples possess significantly increased dry and wet tensile strength properties. We furthermore investigated these gradient papers with respect to a mechanical deflection during humidity cycling. The highest humidity sensitivity is achieved using eucalyptus paper with a grammage of 150 g m−2 modified with the polymer dissolved in IPA (~13 wt%) possessing a polymer gradient. Our study presents a straightforward approach for the design of novel hygroscopic, paper-based single-layer actuators, which have a high potential for diverse soft robotic and sensor applications.

Mycelium-based composites have the potential to replace petrochemical-based materials within architectural systems and can propose biodegradable alternatives to synthetic sound absorbing materials. Sound absorbing materials help improve acoustic comfort, which in turn benefit our health and productivity. Mycelium-based composites are novel materials that result when mycelium, the vegetative root of fungi, is grown on agricultural plant-based residues. This research presents a material study that explores how substrate variants and fabrication methods affect the sound absorption properties of mycelium-based composites grown on paper-based waste substrate materials. Samples were grown using Pleurotus ostreatus fungi species on waste cardboard, paper, and newsprint substrates of varying processing techniques. Measurements of the normal-incidence sound absorption coefficient were presented and analyzed. This paper outlines two consecutive acoustic tests: the first round of experimentation gathered broad comparative data, useful for selecting materials for sound absorption purposes. The second acoustic test built on the results of the first, collecting more specific performance data and assessing material variability. The results of this study display that cardboard-based mycelium materials perform well acoustically and structurally and could successfully be used in acoustic panels.

The abstraction and implementation of plant movement principles into biomimetic compliant systems are of increasing interest for technical applications, e.g., in architecture, medicine, and soft robotics. Within the respective research and development approaches, advanced methods such as 4D printing or 3D-braiding pultrusion are typically used to generate proof-of-concept demonstrators at the laboratory or demonstrator scale. However, such techniques are generally time-consuming, complicated, and cost-intensive, which often impede the rapid realization of a sufficient number of demonstrators for testing or teaching. Therefore, we have produced comparable simple handcrafted compliant systems based on paper, wood, plastic foil, and/or glue as construction materials. A variety of complex plant movement principles have been transferred into these low-cost physical demonstrators, which are self-actuated by shrinking processes induced by the anisotropic hygroscopic properties of wood or paper. The developed systems have a high potential for fast, precise, and low-cost abstraction and transfer processes in biomimetic approaches and for the “hands-on understanding” of plant movements in applied university and school courses.

The construction industry makes a significant contribution to global CO2 emissions. Material extraction, processing, and demolition account for most of its environmental impact. As a response, there is an increasing interest in developing and implementing innovative biomaterials that support a circular economy, such as mycelium-based composites. The mycelium is the network of hyphae of fungi. Mycelium-based composites are renewable and biodegradable biomaterials obtained by ceasing mycelial growth on organic substrates, including agricultural waste. Cultivating mycelium-based composites within molds, however, is often wasteful, especially if molds are not reusable or recyclable. Shaping mycelium-based composites using 3D printing can minimize mold waste while allowing intricate forms to be fabricated. In this research, we explore the use of waste cardboard as a substrate for cultivating mycelium-based composites and the development of extrudable mixtures and workflows for 3D-printing mycelium-based components. In this paper, existing research on the use of mycelium-based material in recent 3D printing efforts was reviewed. This review is followed by the MycoPrint experiments that we conducted, and we focus on the main challenges that we faced (i.e., contamination) and the ways in which we addressed them. The results of this research demonstrate the feasibility of using waste cardboard as a substrate for cultivating mycelia and the potential for developing extrudable mixtures and workflows for 3D-printing mycelium-based components.

Multirotor drones are widely used in fields such as environmental monitoring, agricultural inspection, and package delivery, but they still face numerous challenges in durability and aerial operation capabilities. To address these issues, this paper presents a biomimetic leg-claw mechanism (LCM) inspired by the biomechanics of birds. The claw of the LCM adopts a bistable gripper design that can rapidly close through external impact or actively close via the coordination of internal mechanisms. Additionally, its foldable, parallelogram-shaped legs bend under external forces, stretching the main tendon. A ratchet and pawl mechanism at the knee joint locks the leg in the bent position, thereby enhancing the gripping force of the claw. This paper calculates and experimentally verifies the degrees of freedom in different states, the forces required to open and close the gripper, the application scenarios of active and passive grasping, and the maximum load capacity of the mechanism. Furthermore, perching experiments demonstrate that the LCM enables the drone to perch stably on objects of varying diameters.

Three-dimensional (3D) path planning is a crucial technology for ensuring the efficient and safe flight of UAVs in complex environments. Traditional path planning algorithms often find it challenging to navigate complex obstacle environments, making it challenging to quickly identify the optimal path. To address these challenges, this paper introduces a Nutcracker Optimizer integrated with Hyperbolic Sine–Cosine (ISCHNOA). First, the exploitation process of the sinh cosh optimizer is incorporated into the foraging strategy to enhance the efficiency of nutcracker in locating high-quality food sources within the search area. Secondly, a nonlinear function is designed to improve the algorithm’s convergence speed. Finally, a sinh cosh optimizer that incorporates historical positions and dynamic factors is introduced to enhance the influence of the optimal position on the search process, thereby improving the accuracy of the nutcracker in retrieving stored food. In this paper, the performance of the ISCHNOA algorithm is tested using 14 classical benchmark test functions as well as the CEC2014 and CEC2020 suites and applied to UAV path planning models. The experimental results demonstrate that the ISCHNOA algorithm outperforms the other algorithms across the three test suites, with the total cost of the planned UAV paths being lower.

The realization of hand function reengineering using a manipulator is a research hotspot in the field of robotics. In this paper, we propose a multimodal perception and control method for a robotic hand to assist the disabled. The movement of the human hand can be divided into two parts: the coordination of the posture of the fingers, and the coordination of the timing of grasping and releasing objects. Therefore, we first used a pinhole camera to construct a visual device suitable for finger mounting, and preclassified the shape of the object based on YOLOv8; then, a filtering process using multi-frame synthesized point cloud data from miniature 2D Lidar, and DBSCAN algorithm clustering objects and the DTW algorithm, was proposed to further identify the cross-sectional shape and size of the grasped part of the object and realize control of the robot’s grasping gesture; finally, a multimodal perception and control method for prosthetic hands was proposed. To control the grasping attitude, a fusion algorithm based on information of upper limb motion state, hand position, and lesser toe haptics was proposed to realize control of the robotic grasping process with a human in the ring. The device designed in this paper does not contact the human skin, does not produce discomfort, and the completion rate of the grasping process experiment reached 91.63%, which indicates that the proposed control method has feasibility and applicability.

With the increasing number of space debris, the demand for telescopes to observe space debris is also constantly increasing. The telescope observation scheduling problem requires algorithms to schedule telescopes to maximize observation value within the visible time constraints of space debris, especially when dealing with large-scale problems. This paper proposes a practical heuristic algorithm to solve the telescope observation of space debris scheduling problem. In order to accelerate the solving speed of algorithms on large-scale problems, this paper combines the characteristics of the problem and partitions the large-scale problem into multiple sub-problems according to the observation time. In each sub-problem, a coding method based on the priority of the target going into the queue is proposed in combination with the actual observation data, and a decoding method matching the coding method is designed. In the solution process for each sub-problem, an adaptive variable neighborhood search is used to solve the space debris observation plan. When solving all sub-problems is completed, the observation plans obtained on all sub-problems are combined to obtain the observation plan of the original problem.

Nacre has excellent balanced strength and toughness. In this paper, the mechanical performance of the typical “brick-and-mortar” structure, including the stress–strain and strain at the interface as well as the stress in the bricks, was calculated by a simplified analytical model of the nacre. This paper proposes a new method to control the crack deflection based on the toughening mechanism of the nacre. The crack extension of the “brick-and-mortar” structure was simulated using cohesive elements based on the traction–separation law with elastic and softening stiffness as variables, and it was found that both stiffness could effectively control the crack extension. The strength and toughness of the models with different stiffness combinations were calculated and plotted as a function of elastic stiffness and softening stiffness, showing that elastic stiffness significantly affects strength and softening stiffness is a determinant of toughness.

Snake robots require autonomous localization and mapping capabilities for field applications. However, the characteristics of their motion, such as large turning angles and fast rotation speeds, can lead to issues like drift or even failure in positioning and map building. In response to this situation, this paper starts from the gait motion characteristics of the snake robot itself, proposing an improved gait motion method and a tightly coupled method based on IMU and visual information to solve the problem of poor algorithm convergence caused by head-shaking in snake robot SLAM. Firstly, the adaptability of several typical gaits of the snake robot to SLAM methods was evaluated. Secondly, the serpentine gait was selected as the object of gait improvement, and a head stability control method for the snake robot was proposed, thereby reducing the interference of the snake robot’s motion on the sensors. Thirdly, a visual–inertial tightly coupled SLAM method for the snake robot’s serpentine gait and Arc-Rolling gait was proposed, and the method was verified to enhance the robustness of the visual SLAM algorithm and improve the positioning and mapping accuracy of the snake robot. Finally, experiments proved that the methods proposed in this paper can effectively improve the accuracy of positioning and map building for snake robots.

Origami, the art of paper folding, has long fascinated researchers and designers in its potential to replicate and tap the complexity of nature. In this paper, we pursue the crossing of origami engineering structures and biology, the realm of biologically-inspired origami structures categorized by the two biggest taxonomy kingdoms and DNA origami. Given the diversity of life forms that Earth comprises, we pursue an analysis of biomimetic designs that resemble intricate patterns and functionalities occurring in nature. Our research begins by setting out a taxonomic framework for the classification of origami structures based on biologically important kingdoms. From each of these, we explore the engineering structures inspired by morphological features, behaviors, and ecological adaptations of organisms. We also discuss implications in realms such as sustainability, biomaterials development, and bioinspired robotics. Thus, by parlaying the principles found in nature’s design playbook through the art of folding, biologically inspired origami becomes fertile ground for interdisciplinary collaboration and creativity. Through this approach, we aim to inspire readers, researchers, and designers to embark on a journey of discovery in which the boundaries between art, science, and nature are blurred, providing a foundation for innovation to thrive.

This paper reviews how multifunctioning joints produce highly agile limbs in animals with lessons for robotics. One of the key reasons why animals are so fast and agile is that they have multifunctioning joints in their limbs. The multifunctioning joints lead to a high degree of compactness which then leads to a host of benefits such as low mass, low moment of inertia and low drag. This paper presents three case studies of multifunctioning joints—the human wrist joint, knee joint and foot joints—in order to identify how multifunctioning is achieved and what lessons can be learned for robotics. It also reviews the multifunctioning nature of muscle which plays an important role in joint actuation. A key finding is that multifunctioning is achieved through various means: multiple degrees of freedom, multifunctioning parts, over-actuation and reconfiguration. In addition, multifunctioning is achieved through highly sophisticated layouts with high levels of integration and fine-tuning. Muscle also makes an important contribution to animal agility by performing multiple functions including providing shape, protection and heat. The paper reviews progress in achieving multifunctioning in robot joints particularly for the wrist, knee and foot. Whilst there has been some progress in creating multifunctioning robotic joints, there is still a large gap between the performance of animal and robotic joints. There is an opportunity to improve the agility of robots by using multifunctioning to reduce the size and mass of robotic joints.

As the capacity of individual offshore wind turbines increases, prolonged downtime (due to maintenance or faults) will result in significant economic losses. This necessitates enhancing the efficiency of vessel operation and maintenance (O&M) to reduce O&M costs. Existing research mostly focuses on planning O&M schemes for individual vessels. However, there exists a research gap in the scientific scheduling for state-of-the-art O&M vessels. To bridge this gap, this paper considers the use of an advanced O&M vessel in the O&M process, taking into account the downtime costs associated with wind turbine maintenance and repair incidents. A mathematical model is constructed with the objective of minimizing overall O&M expenditure. Building upon this formulation, this paper introduces a novel restructuring particle swarm optimization which is tailed with a bespoke encoding and decoding strategy, designed to yield an optimized solution that aligns with the intricate demands of the problem at hand. The simulation results indicate that the proposed method can achieve significant savings of 28.85% in O&M costs. The outcomes demonstrate the algorithm’s proficiency in tackling the model efficiently and effectively.

Biological structures optimized through natural selection provide valuable insights for engineering load-bearing components. This paper reviews six key strategies evolved in nature for efficient mechanical load handling: hierarchically structured composites, cellular structures, functional gradients, hard shell–soft core architectures, form follows function, and robust geometric shapes. The paper also discusses recent research that applies these strategies to engineering design, demonstrating their effectiveness in advancing technical solutions. The challenges of translating nature’s designs into engineering applications are addressed, with a focus on how advancements in computational methods, particularly artificial intelligence, are accelerating this process. The need for further development in innovative material characterization techniques, efficient modeling approaches for heterogeneous media, multi-criteria structural optimization methods, and advanced manufacturing techniques capable of achieving enhanced control across multiple scales is underscored. By highlighting nature’s holistic approach to designing functional components, this paper advocates for adopting a similarly comprehensive methodology in engineering practices to shape the next generation of load-bearing technical components.

Cystic Fibrosis (CF) is a life-shortening, genetic disease that affects approximately 145,000 people worldwide. CF causes a dehydrated mucus layer in the lungs, leading to damaging infection and inflammation that eventually result in death. Nanoparticles (NPs), drug delivery vehicles intended for inhalation, have become a recent source of interest for treating CF and CF-related conditions, and many formulations have been created thus far. This paper is intended to provide an overview of CF and the effect it has on the lungs, the barriers in using NP drug delivery vehicles for treatment, and three common material class choices for these NP formulations: metals, polymers, and lipids. The materials to be discussed include gold, silver, and iron oxide metallic NPs; polyethylene glycol, chitosan, poly lactic-co-glycolic acid, and alginate polymeric NPs; and lipid-based NPs. The novelty of this review comes from a less specific focus on nanoparticle examples, with the focus instead being on the general theory behind material function, why or how a material might be used, and how it may be preferable to other materials used in treating CF. Finally, this paper ends with a short discussion of the two FDA-approved NPs for treatment of CF-related conditions and a recommendation for the future usage of NPs in people with Cystic Fibrosis (pwCF).

This paper introduces a novel approach to robotic assistance in bottle opening using the dual-arm robot TIAGo++. The solution enhances accessibility by addressing the needs of individuals with injuries or disabilities who may require help with common manipulation tasks. The aim of this paper is to propose a method involving vision, manipulation, and learning techniques to effectively address the task of bottle opening. The process begins with the acquisition of bottle and cap positions using an RGB-D camera and computer vision. Subsequently, the robot picks the bottle with one gripper and grips the cap with the other, each by planning safe trajectories. Then, the opening procedure is executed via a position and force control scheme that ensures both grippers follow the unscrewing path defined by the cap thread. Within the control loop, force sensor information is employed to control the vertical axis movements, while gripper rotation control is achieved through a Deep Reinforcement Learning (DRL) algorithm trained to determine the optimal angle increments for rotation. The results demonstrate the successful training of the learning agent. The experiments confirm the effectiveness of the proposed method in bottle opening with the TIAGo++ robot, showcasing the practical viability of the approach.

Catch fish optimization algorithm (CFOA) is a newly proposed meta-heuristic algorithm based on human behaviors. CFOA shows better performance on multiple test functions and clustering problems. However, CFOA shows poor performance in some cases, and there is still room for improvement in convergence accuracy, getting rid of local traps, and so on. To further enhance the performance of CFOA, a multi-strategy improved catch fish optimization algorithm (MICFOA) is proposed in this paper. In the exploration phase, we propose a Lévy-based differential independent search strategy to enhance the global search capability of the algorithm while minimizing the impact on the convergence speed. Secondly, in the exploitation phase, a weight-balanced selection mechanism is used to maintain population diversity, enhance the algorithm’s ability to get rid of local optima during the search process, and effectively boost the convergence accuracy. Furthermore, the structure of CFOA is also modified in this paper. A fishermen position replacement strategy is added at the end of the algorithm as a way to strengthen the robustness of the algorithm. To evaluate the performance of MICFOA, a comprehensive comparison with nine other metaheuristic algorithms is performed on the 10/30/50/100 dimensions of the CEC 2017 test functions and the 10/20 dimensions of the CEC2022 test functions. Statistical experiments show that MICFOA has more significant dominance in numerical optimization problems, and its overall performance outperforms the CFOA, PEOA, TLBO, COA, ARO, EDO, YDSE, and other state-of-the-art algorithms such as LSHADE, JADE, IDE-EDA, and APSM-jSO.

Biomass–fungi composite materials primarily consist of biomass particles (sourced from agricultural residues) and a network of fungal hyphae that bind the biomass particles together. These materials have potential applications across diverse industries, such as packaging, furniture, and construction. 3D printing offers a new approach to manufacturing parts using biomass–fungi composite materials, as an alternative to traditional molding-based methods. However, there are challenges in producing parts with desired quality (for example, geometric accuracy after printing and height shrinkage several days after printing) by using 3D printing-based methods. This paper introduces an innovative approach to enhance part quality by incorporating ionic crosslinking into the 3D printing-based methods. While ionic crosslinking has been explored in hydrogel-based bioprinting, its application in biomass–fungi composite materials has not been reported. Using sodium alginate (SA) as the hydrogel and calcium chloride as the crosslinking agent, this paper investigates their effects on quality (geometric accuracy and height shrinkage) of 3D printed samples and physiochemical characteristics (rheological, chemical, and texture properties) of biomass–fungi composite materials. Results show that increasing SA concentration led to significant improvements in both geometric accuracy and height shrinkage of 3D printed samples. Moreover, crosslinking exposure significantly enhanced hardness of the biomass–fungi mixture samples prepared for texture profile analysis, while the inclusion of SA notably improved cohesiveness and springiness of the biomass–fungi mixture samples. Furthermore, Fourier transform infrared spectroscopy confirms the occurrence of ionic crosslinking within 3D printed samples. Results from this study can be used as a reference for developing new biomass–fungi mixtures for 3D printing in the future.

The football team training algorithm (FTTA) is a new metaheuristic algorithm that was proposed in 2024. The FTTA has better performance but faces challenges such as poor convergence accuracy and ease of falling into local optimality due to limitations such as referring too much to the optimal individual for updating and insufficient perturbation of the optimal agent. To address these concerns, this paper presents an improved football team training algorithm called IFTTA. To enhance the exploration ability in the collective training phase, this paper proposes the fitness distance-balanced collective training strategy. This enables the players to train more rationally in the collective training phase and balances the exploration and exploitation capabilities of the algorithm. To further perturb the optimal agent in FTTA, a non-monopoly extra training strategy is designed to enhance the ability to get rid of the local optimum. In addition, a population restart strategy is then designed to boost the convergence accuracy and population diversity of the algorithm. In this paper, we validate the performance of IFTTA and FTTA as well as six comparison algorithms in CEC2017 test suites. The experimental results show that IFTTA has strong optimization performance. Moreover, several engineering-constrained optimization problems confirm the potential of IFTTA to solve real-world optimization problems.

The neural or mental simulation of actions is a powerful tool for allowing cognitive agents to develop Prospection Capabilities that are crucial for learning and memorizing key aspects of challenging skills. In previous studies, we developed an approach based on the animation of the redundant human body schema, based on the Passive Motion Paradigm (PMP). In this paper, we show that this approach can be easily extended to hyper-redundant serpentine robots as well as to hybrid configurations where the serpentine robot is functionally integrated with a traditional skeletal infrastructure. A simulation model is analyzed in detail, showing that it incorporates spatio-temporal features discovered in the biomechanical studies of biological hydrostats, such as the elephant trunk or octopus tentacles. It is proposed that such a generative internal model could be the basis for a cognitive architecture appropriate for serpentine robots, independent of the underlying design and control technologies. Although robotic hydrostats have received a lot of attention in recent decades, the great majority of research activities have been focused on the actuation/sensorial/material technologies that can support the design of hyper-redundant soft/serpentine robots, as well as the related control methodologies. The cognitive level of analysis has been limited to motion planning, without addressing synergy formation and mental time travel. This is what this paper is focused on.

Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through and create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras and structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements in event camera technology and neuromorphic processing offer promising opportunities to overcome these limitations. Event cameras inspired by biological vision systems capture the scenes asynchronously, consuming minimal power but with higher temporal resolution. Neuromorphic processors, which are designed to mimic the parallel processing capabilities of the human brain, offer efficient computation for real-time data processing of event-based data streams. This paper provides a comprehensive overview of recent research efforts in integrating event cameras and neuromorphic processors into VSLAM systems. It discusses the principles behind event cameras and neuromorphic processors, highlighting their advantages over traditional sensing and processing methods. Furthermore, an in-depth survey was conducted on state-of-the-art approaches in event-based SLAM, including feature extraction, motion estimation, and map reconstruction techniques. Additionally, the integration of event cameras with neuromorphic processors, focusing on their synergistic benefits in terms of energy efficiency, robustness, and real-time performance, was explored. The paper also discusses the challenges and open research questions in this emerging field, such as sensor calibration, data fusion, and algorithmic development. Finally, the potential applications and future directions for event-based SLAM systems are outlined, ranging from robotics and autonomous vehicles to augmented reality.

This article explores the growing prominence of nature-inspired design philosophies in the context of sustainability and human well-being within the built environment and focuses on their application within laboratory buildings. Biomimicry and biophilic design are highlighted as key nature-inspired design approaches, with biomimicry drawing inspiration from nature for innovations and biophilic design promoting human health through enhancing the connection with the surrounding natural elements. This paper further discusses living building strategy as an emerging method for creating dynamic and adaptable spaces by prioritizing user experience through co-creation and focusing on sustainable and regenerative structures. The potential of integrating these approaches is emphasized using laboratory buildings as an example, with nature-inspired and living laboratories serving as models for future built environments that promote both environmental responsibility and a positive human experience. Accordingly, this work aims to investigate the design and construction of laboratory buildings based on nature-inspired design strategies and the living building concept. Moreover, the paper discusses the application of biomimicry and living building concepts within laboratory buildings as a novel contribution to the body of knowledge, and concludes by proposing the Nature-inspired & Living Laboratory (NILL 1.0)TM Building Assessment index to serve as a guideline for the design and construction of laboratory buildings using nature as an inspiration and the analogy of human body systems.

This paper considers the design, manufacture, and testing of a prototype “soft–stiff” worm-inspired robot referred to herein, as the PneumaticallyActuated PeristaLtic Advancing Modular (PALAM) robot. The robot has a modular structure, mimicking the segmented nature of earthworms, and each segment is individually actuated by a set of three pneumatic artificial muscles (PAMs). The PAMs contract when inflated by pressurised air, generating a pulling force and fulfilling the role of biological muscles in the robot. The PAMs are made from the elastomer silicone rubber, which affords the robot flexibility and enables a wide range of real-life applications. A control-system is designed which can inflate any PAM on demand, and hence replicate the peristaltic motion of earthworms in the PALAM robot. Finally, this paper discusses a successful, low-cost, and widely accessible approach for the manufacture of the PAMs utilised herein. The PAMs can be scaled dimensionally and made from different materials with varying mechanical properties and behaviours, meaning that they are suitable for use in a wide range of robotics applications.

The question “How does it work” has motivated many scientists. Through the study of natural phenomena and behaviors, many intelligence algorithms have been proposed to solve various optimization problems. This paper aims to offer an informative guide for researchers who are interested in tackling optimization problems with intelligence algorithms. First, a special neural network was comprehensively discussed, and it was called a zeroing neural network (ZNN). It is especially intended for solving time-varying optimization problems, including origin, basic principles, operation mechanism, model variants, and applications. This paper presents a new classification method based on the performance index of ZNNs. Then, two classic bio-inspired algorithms, a genetic algorithm and a particle swarm algorithm, are outlined as representatives, including their origin, design process, basic principles, and applications. Finally, to emphasize the applicability of intelligence algorithms, three practical domains are introduced, including gene feature extraction, intelligence communication, and the image process.

One of the researchers’ concerns in structural engineering is to control the dynamic behavior of structures efficiently. The TMD (tuned mass damper) is one of the effective methods of controlling the vibration of structures, and various numerical techniques have been proposed to find the optimal parameters of the TMD. This paper develops a new explicit formula to derive the optimal parameters of the TMD of a single degree of freedom (SDOF) structure under seismic load using a genetic algorithm (GA). In addition, the state-space model and the H2 norm function are used to identify the optimal frequency ratio and damping ratio of the TMD that minimize the overall vibration energy of the structure. The MATLAB curve fitting toolbox is used for the explicit formula proposal, and the validity of the proposed formula is verified through multidimensional performance verification technique. Finally, the TMD parameters of the SDOF structure are applied to the multi-degrees of freedom (MDOF) structure to compare and analyze with the existing research results, and the results of the explicit formula proposed in this paper are confirmed to be excellent. This paper can suggest a new direction for determining the optimal TMD parameters using a GA and can be effectively applied to vibration control problems of various structures.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The increasing specialization in architecture has clearly left its marks not only on the general profession but also on architectural education. Many universities around the world react to this development by offering primarily conventional and overly discipline-specific courses that often lack bold new concepts. To remedy this situation, the authors propose an alternative teaching model called Studio One, which seeks to facilitate new dynamic links between architecture and other disciplines based on the interplay between fundamental research, design exploration, and practical application. The goal is to develop an interdisciplinary, collaborative design training that encompasses the best that nature has to teach us, realized through the technology that humans have achieved. At the core of this class is the study of biological structures and the development of bio-inspired construction principles for architectural design. Both aspects are rich sources of innovation and can play an important role in the training of future architects and engineers. This paper seeks to provide a coherent progress report. After a brief introduction to the general objectives of Studio One, the authors will specify the methods and 21st century skills that students learned during this class. Relying on four student capstone projects as examples, the paper will then go into more detail on how natural structures can inspire a new design process, in which students abstract basic biomimetic principles and transfer them into the construction of architectural prototypes and pavilions. Finally, the authors conclude by discussing the particular successes and challenges facing this teaching model and identify the key improvements that may give this program an even bigger impact in the future.

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.