Search Results (800)

Bioclimatic design, rooted in vernacular architecture, aims to create buildings that harmonise with their local climate and context. Over the past five decades, continuous advancements have strengthened its foundation for climate-responsive architecture. However, the development of bioinspired thinking extends new opportunities to enhance ecological sustainability and innovation in bioclimatic design. This study introduces Bioinspired Climatic Design (BCD) as an advancement of bioclimatic design, integrating ecological processes, human behaviour, and high-resolution climate data to create sustainable, climate-responsive low-carbon architecture. Focusing on residential buildings in hot–humid climates, it categorises BCD strategies into primary and modifying adaptive approaches, examined through four case studies using observation and spatial analysis. Findings emphasise the importance of aligning design with climate, ecology, and occupant behaviour to achieve low-carbon, resilient architecture, especially in challenging conditions. The research calls for a paradigm shift from conventional climate-responsive design towards a holistic, ecologically integrated framework for future-oriented built environments. Full article

The origin of life initiated an evolutionary continuum yielding biologically optimized systems capable of operating under extreme environmental constraints. Biomimetics, defined as the systematic abstraction and transfer of biological principles into engineering domains, has become a strategic design paradigm for addressing the multifactorial challenges of space systems. This study introduces two core contributions to formally establish the discipline of Aerospace Bionic Robotics (ABR): First, it elucidates the relevance of biologically derived functionalities such as autonomy, adaptability, and multifunctionality to enhance the efficiency of space robotic platforms operating in microgravity environments. Second, it proposed the BEAM-D (Biomimetic Engineering and Aerospace Mechatronics Design), a standard for the development of Aerospace Bionic Robotics. By integrating biological abstraction levels (morphological, functional, and behavioral) with engineering protocols including ISO, VDI, and NASA’s TRL, BEAM-D enables a structured design pathway encompassing subsystem specification, cyber–physical integration, in situ testing, and full-scale mission deployment. It is implemented through a modular BEAM-DX framework and reinforced by iterative BIOX design steps. This study thus establishes formalized bio-inspired design tools for advanced orbital and planetary robotic systems capable of sustained autonomous operations in deep space exploration scenarios. Full article

This study presents the design, development, and experimental validation of a novel cable-driven shoulder exosuit (CDSE) for upper limb rehabilitation and assistance. Unlike existing exoskeletons, which are often bulky, limited in degrees of freedom (DOFs), or impractical for home use, the proposed DSE offers a lightweight (≈2 kg), portable, and wearable solution capable of supporting three shoulder movements: abduction, flexion, and horizontal adduction. The system employs a bioinspired tendon-driven mechanism using Bowden cables, transferring actuation forces from a backpack to the arm, thereby reducing user load and improving comfort. Mathematical models and inverse kinematics were derived to determine cable length variations for targeted motions, while control strategies were implemented using a PID-based approach in MATLAB Simscape-Multibody simulations. The prototype was fabricated in three iterations using PLA, aluminum, and carbon fiber—culminating in a durable and ergonomic final version. Experimental evaluations on a healthy subject demonstrated high accuracy in position tracking (<5% error) and torque profiles consistent with simulation outcomes, validating system robustness. The CDSE successfully supported loads up to 4 kg during rehabilitation tasks, highlighting its potential for clinical and at-home applications. This research contributes to advancing wearable robotics by addressing portability, biomechanical alignment, and multi-DOF functionality in upper limb exosuits. Full article

The paper presents an innovative theoretical concept of a bio-inspired soft gripper system with two parallel jaws, a fixed and a mobile one. It is conceived for gripping fragile or soft objects with complex, irregular shapes that are easily deformable. This novel gripper is designed for handling small objects of masses up to 0.5 kg. The maximum gripping stroke of the mobile jaw is 13.5 mm. The driving motor is a pneumatic muscle, an actuator with inherently compliant, spring-like behavior. Compliance is the feature responsible for the soft character of the gripper system, ensuring its passive adaptability to the nature of the object to be gripped. The paper presents the structural, kinematic, static, and dynamic models of the novel gripper system and describes the compliant behavior of the entire assembly. The results of the dynamic simulation of the gripper have confirmed the attaining of the imposed motion-related performance. Full article

Synthetic aperture radar (SAR) exhibits all-day and all-weather capabilities, granting it significant application in remote sensing. However, interpreting SAR images requires extensive expertise, making SAR-to-optical remote sensing image translation a crucial research direction. While conditional generative adversarial networks (CGANs) have demonstrated exceptional performance in image translation tasks, their massive number of parameters pose substantial challenges. Therefore, this paper proposes ILF-BDSNet, a compressed network for SAR-to-optical image translation. Specifically, first, standard convolutions in the feature-transformation module of the teacher network are replaced with depthwise separable convolutions to construct the student network, and a dual-resolution collaborative discriminator based on PatchGAN is proposed. Next, knowledge distillation based on intermediate-layer features and channel pruning via weight sharing are designed to train the student network. Then, the bio-inspired dynamic search of channel configuration (BDSCC) algorithm is proposed to efficiently select the optimal subnet. Meanwhile, the pixel-semantic dual-domain alignment loss function is designed. The feature-matching loss within this function establishes an alignment mechanism based on intermediate-layer features from the discriminator. Extensive experiments demonstrate the superiority of ILF-BDSNet, which significantly reduces number of parameters and computational complexity while still generating high-quality optical images, providing an efficient solution for SAR image translation in resource-constrained environments. Full article

In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization Algorithm (WOA), Puma Optimization Algorithm (POA), and Flying Foxes Algorithm (FFA), which are grounded in behavioral models observed in nature. We consider large-scale warehouse robotic systems, partitioned into clusters. To manage shared resources between clusters, the set of clusters is first formulated as a symbolic control design task within a discrete synthesis framework. Subsequently, the desired control goals are integrated into the model, encoded using parallel synchronous dataflow languages; the resulting controller, derived using our safety-focused and optimization-based synthesis approach, serves as the manager for the cluster. Safety objectives address the rigid system behaviors, while optimization objectives focus on minimizing the traveled path of the warehouse robots through the constructed cost function. The metaheuristic algorithms contribute at this stage, drawing inspiration from real-world animal behaviors, such as walruses’ cooperative movement and foraging, pumas’ territorial hunting strategies, and flying foxes’ echolocation-based navigation. These nature-inspired processes allow for effective solution space exploration and contribute to improving the quality of cluster-level path optimization. Our hybrid approach, integrating symbolic control and metaheuristic techniques, demonstrates significantly higher performance advantage over existing solutions, with experimental data verifying the practical effectiveness of our approach. Our proposed algorithm achieves up to 3.01% shorter intra-cluster paths compared to the metaheuristic algorithms, with an average improvement of 1.2%. For the entire warehouse, it provides up to 2.05% shorter paths on average, and even in the worst case, outperforms competing metaheuristic methods by 0.28%, demonstrating its consistent effectiveness in path optimization. Full article

This study aimed to evaluate how different insertion angles of titanium orthodontic miniscrews (30°, 45°, and 90°) influence stress distribution and displacement in surrounding alveolar bone using three-dimensional finite element analysis (FEA), with a focus on biomechanical outcomes at the titanium–bone interface. The 90° insertion angle generated the highest stress in cortical bone (58.2 MPa) but the lowest displacement (0.023 mm), while the 30° angle produced lower stress (36.4 MPa) but greater displacement (0.052 mm). The 45° angle represented a compromise, combining moderate stress (42.7 MPa) and displacement (0.035 mm). This simulation-based study was conducted between January and April 2025 at the Department of Orthodontics, Kocaeli Health and Technology University. A standardized 3D mandibular bone model (2 mm cortical and 13 mm cancellous layers) was constructed, and Ti-6Al-4V miniscrews (1.6 mm × 8 mm) were virtually inserted at 30°, 45°, and 90°. A horizontal orthodontic load of 2 N was applied, and von Mises stress and displacement values were calculated in ANSYS Workbench. Stress patterns were visualized using color-coded maps. The 90° insertion angle generated the highest von Mises stress in cortical bone (50.6 MPa), with a total maximum stress of 58.2 MPa, followed by 45° (42.7 MPa) and 30° (36.4 MPa) insertions (p < 0.001). Stress was predominantly concentrated at the cortical entry point, especially in the 90° model. In terms of displacement, the 90° group exhibited the lowest mean displacement (0.023 ± 0.002 mm), followed by 45° (0.035 ± 0.003 mm) and 30° (0.052 ± 0.004 mm), with statistically significant differences among all groups (p < 0.001). The 45° angle showed a balanced biomechanical profile, combining moderate stress and displacement values, as confirmed by post hoc analysis. From a biomimetics perspective, understanding how insertion angle affects bone response provides insights for designing bio-inspired anchorage systems. By simulating natural stress dissipation, this study demonstrates that insertion angle strongly modulates miniscrew performance. Vertical placement (90°) ensures rigidity but concentrates cortical stress, whereas oblique placement, particularly at 45°, offers a balanced compromise with adequate stability and reduced stress. These results emphasize that beyond material properties, surgical parameters such as insertion angle are critical for clinical success. Full article

Photocatalytic nitrogen fixation under ambient conditions offers a sustainable alternative to the energy-intensive Haber–Bosch process, yet remains limited by the inertness of N≡N bonds and sluggish multi-electron/proton transfer kinetics. Nature’s nitrogenase enzymes, featuring the FeMo cofactor and ATP-driven electron cascades, inspire a new generation of artificial systems capable of mimicking their catalytic precision and selectivity. This review systematically summarizes recent advances in bio-inspired photocatalytic nitrogen reduction, focusing on six key strategies derived from enzymatic mechanisms: Fe–Mo–S active site reconstruction, hierarchical electron relay pathways, ATP-mimicking energy modules, defect-induced microenvironments, interfacial charge modulation, and spatial confinement engineering. While notable progress has been made in enhancing activity and selectivity, challenges remain in dynamic regulation, mechanistic elucidation, and system-level integration. Future efforts should prioritize operando characterization, adaptive interface design, and device-compatible catalyst platforms. By abstracting nature’s catalytic logic into synthetic architectures, biomimetic photocatalysis holds great promise for scalable, green ammonia production aligned with global decarbonization goals. Full article

This review summarizes recent advances in multifunctional metamaterials (MF-MMs) for electromagnetic (EM) wave absorption. MF-MMs overcome the key limitations of conventional absorbers—such as narrow bandwidth, limited functionality, and poor environmental adaptability—offering enhanced protection against EM security threats in radar, aerospace, and defense applications. This review focuses on an integrated structure-material-function co-design strategy, highlighting advances in three-dimensional (3D) lattice architectures, composite laminates, conformal geometries, bio-inspired topologies, and metasurfaces. When synergized with multicomponent composites, these structural innovations enable the co-regulation of impedance matching and EM loss mechanisms (dielectric, magnetic, and resistive dissipation), thereby achieving broadband absorption and enhanced multifunctionality. Key findings demonstrate that 3D lattice structures enhance mechanical load-bearing capacity by up to 935% while enabling low-frequency broadband absorption. Composite laminates achieve breakthroughs in ultra-broadband coverage (1.26–40 GHz), subwavelength thickness (<5 mm), and high flexural strength (>23 MPa). Bio-inspired topologies provide wide-incident-angle absorption with bandwidths up to 31.64 GHz. Metasurfaces facilitate multiphysics functional integration. Despite the significant potential of MF-MMs in resolving broadband stealth and multifunctional synergy challenges via EM wave absorption, their practical application is constrained by several limitations: limited dynamic tunability, incomplete multiphysics coupling mechanisms, insufficient adaptability to extreme environments, and difficulties in scalable manufacturing and reliability assurance. Future research should prioritize intelligent dynamic response, deeper integration of multiphysics functionalities, and performance optimization under extreme conditions. Full article

This review explores recent advancements in morphing structures for Unmanned Ariel Vehicles (UAVs), focusing on mechanical designs and control strategies of quadrotors that enable real-time geometric reconfiguration. Morphing mechanisms, ranging from closed-loop linkages to bioinspired and compliant structures, are evaluated in terms of adaptability, actuation simplicity, and flight stability. Control approaches, including model predictive control, reinforcement learning, and sliding mode control, are analyzed for their effectiveness in handling dynamic morphology. The review also highlights key morphing wing concepts such as GNATSpar and Zigzag Wingbox, which enhance aerodynamic efficiency and structural flexibility. A novel concept featuring an inverted slider-crank mechanism (ISCM) is introduced, enabling dual-mode UAV operation for both aerial and terrestrial missions, which is particularly useful in scenarios like wildfire suppression where stability and operation longevity are crucial. This study emphasizes the importance of integrated design approaches that align mechanical transformation with adaptive control. Critical gaps in real-world testing, swarm coordination, and scalable morphing architectures are identified, suggesting future research directions for developing robust, mission-adaptive UAV systems. Full article

This study presents a comparative analysis of horizontal-axis wind turbines (HAWTs) equipped with and without bio-inspired active oscillating vortex generators (VGs). The experimental investigation examines key aspects of mechanical integration and the resulting variations in aerodynamic behavior, demonstrating measurable improvements in electrical power output. The VGs were designed and implemented using servomechanisms and embedded control systems to enable oscillatory motion during operation. Experimental findings were validated against CFD simulations, indicating that the use of VGs increases annual energy production efficiency by 16.7%, primarily due to the stabilization of wake turbulence. While a reduction in output voltage was observed at wind speeds below 5 m/s, the VGs exhibited enhanced performance under variable wind conditions. These results highlight the potential of combining biomimetic design principles with electronically actuated flow-control devices to advance HAWT technology, improving energy efficiency and contributing to operational sustainability. Full article

The development of efficient and reliable trajectory-planning strategies for swarms of unmanned aerial vehicles (UAVs) is an increasingly important area of research, with applications in surveillance, search and rescue, smart agriculture, defence operations, and communication networks. This article provides a comprehensive and critical review of the various techniques available for UAV swarm trajectory planning, which can be broadly categorised into three main groups: traditional algorithms, biologically inspired metaheuristics, and modern artificial intelligence (AI)-based methods. The study examines cutting-edge research, comparing key aspects of trajectory planning, including computational efficiency, scalability, inter-UAV coordination, energy consumption, and robustness in uncertain environments. The strengths and weaknesses of these algorithms are discussed in detail, particularly in the context of collision avoidance, adaptive decision making, and the balance between centralised and decentralised control. Additionally, the review highlights hybrid frameworks that combine the global optimisation power of bio-inspired algorithms with the real-time adaptability of AI-based approaches, aiming to achieve an effective exploration–exploitation trade-off in multi-agent environments. Lastly, the article addresses the major challenges in UAV swarm trajectory planning, including multidimensional trajectory spaces, nonlinear dynamics, and real-time adaptation. It also identifies promising directions for future research. This study serves as a valuable resource for researchers, engineers, and system designers working to develop UAV swarms for real-world, integrated, intelligent, and autonomous missions. Full article

Nanoparticle-based transdermal drug delivery systems (TDDS) have emerged as a revolutionary approach for antiparasitic therapy, addressing key challenges such as poor bioavailability, systemic toxicity, and drug resistance. This review highlights the advancements in nanotechnology-driven TDDS for combating zoonotic parasitic diseases, including leishmaniasis, malaria, and infections treated by broad-spectrum drugs like ivermectin and albendazole. By leveraging nanocarriers such as liposomes, nanoemulsions, and microneedles, which enhance skin permeation, enable controlled drug release, and improve targeting specificity. For instance, deformable transfersomes and ethosomes achieve high transdermal efficiency without chemical adjuvants, while microneedle arrays physically bypass the stratum corneum for precise delivery. Furthermore, sustained-release hydrogels and stimuli-responsive nanoparticles optimize therapeutic efficacy and reduce adverse effects. Despite promising results, clinical translation faces challenges in manufacturing scalability, long-term safety, and accessibility in resource-limited settings. Future directions include bioinspired nanocarriers, artificial intelligence (AI)-driven design, and integration with global health initiatives like “One Health”, all aimed at ensuring equitable implementation. This review highlights the transformative potential of nanotechnology in achieving sustainable antiparasitic solutions for zoonotic diseases. Full article

Enhanced lubrication is critical for improving gear wear resistance. Current research on surface textures has overlooked the fundamental role of structural connectivity. Inspired by biological scales, a biomimetic hexagonal texture (BHT) was innovatively designed for tooth flanks, featuring dual-orientation grooves (perpendicular and inclined to the rolling-sliding direction) with bidirectional interconnectivity. This design synergistically combines hydrodynamic effects and directional lubrication to achieve tribological breakthroughs. A lubrication model for line contact conditions was established. Subsequently, the texture parameters were then optimized using response surface methodology and numerical simulations. FZG gear tests demonstrated the superior performance of the optimized BHT, which achieved a substantial 82.83% reduction in the average wear area ratio and a 25.35% decrease in tooth profile deviation variation. This indicated that the biomimetic texture can effectively mitigate tooth surface wear, thereby extending the service life of gears. Furthermore, it significantly improves thermal management by enhancing convective heat transfer and lubricant distribution, as evidenced by a 7–11 °C rise in bulk lubricant temperature. This work elucidates the dual-mechanism coupling effect of bio-inspired textures in tribological enhancement, thus establishing a new paradigm for gear surface engineering. Full article

Oceans, which cover more than 70% of Earth’s surface, are home to vast biological and mineral resources. Deep-sea exploration encounters significant challenges due to harsh environmental factors, including low temperatures, high pressure, and complex hydrodynamic forces. These constraints have led to the widespread use of underwater robots as essential tools for deep-sea resource exploration and exploitation. Conventional underwater robots, whether rigid with fixed stiffness or fully flexible, fail to achieve the propulsion efficiency observed in biological fish. To overcome this limitation, researchers have developed adjustable stiffness mechanisms for robotic fish designs. This innovation strikes a balance between structural rigidity for stability and flexible adaptability to dynamic environments. By dynamically adjusting localized stiffness, these bio-inspired robots can alter their mechanical properties in real time. This capability improves propulsion efficiency, energy utilization, and resilience to external disturbances during operation. This paper begins by reviewing the evolution of underwater robots, from fixed-stiffness systems to adjustable-stiffness designs. Next, existing methods for stiffness adjustment are categorized into two approaches: offline component replacement and online real-time adaptation. The principles, implementation strategies, and comparative advantages of each approach are then analyzed. Finally, we identify the current challenges in adjustable-stiffness underwater robotics and propose future directions, such as advancements in intelligent sensing, autonomous stiffness adaptation, and enhanced performance in extreme environments. Full article