Search Results (743)

A parallel bacterial foraging algorithm was developed for the multiple sequence alignment problem. Four sets of homologous genetic and protein sequences related to Alzheimer’s disease among various species were collected from the NCBI database for convergence analysis and performance comparison. The main question was the following: is the bacterial foraging algorithm suitable for the multiple sequence alignment problem? Three versions of the algorithm were contrasted by performing a t-test and Mann–Whitney test based on the results of a 30-run scheme, focusing on fitness, execution time, and the number of function evaluations as performance metrics. Additionally, we conducted a performance comparison of the developed algorithm with the well-known Genetic Algorithm. The results demonstrated the consistent efficiency of the bacterial foraging algorithm, while the version of the algorithm based on gap deletion presented an increased number of function evaluations and excessive execution time. Overall, the first version of the developed algorithm was found to outperform the second version, based on its efficiency. Finally, we found that the third bacterial foraging algorithm version outperformed the Genetic Algorithm in the third phase of the experiment. The sequence sets, the algorithm’s Python 3.12 code and pseudocode, the data collected from the executions, and a GIF animation of the convergence on various different sets are available for download. Full article

Shark skin exhibits a well-defined multilayered architecture, consisting of three-dimensional denticles and an underlying dermal layer, which contributes to its passive drag reduction. However, the active drag reduction mechanisms of this interface remain largely unexplored. In this study, the Marangoni effect potentially arising from the active secretion of mucus on shark skin is investigated. A 3D-printed swimming robot with a porous substrate and a biomimetic shark denticle structure is developed. By introducing surfactants into the porous substrate and adjusting denticle arrangements, on-demand propulsion and controlled swimming trajectories are achieved. A superhydrophobic surface is fabricated on the swimming robot, which reduces water resistance and enhances propulsion. Moreover, denticles with a 30° attack angle demonstrate optimal propulsion performance in both Marangoni-driven hydrodynamics and aerodynamics. This study suggests that the secretion of mucus on shark skin may facilitate active drag reduction via the Marangoni effect, offering novel insights into the biomimetic structural design of autonomous swimming robots. Full article

This paper presents the design and development of a new modular wall-climbing robot—Modular Wall Climbing-1 (MC-1)—for solving the problem of autonomous wall switching observed in wall-climbing robots. Each modular robot is capable of independently adhering to vertical surfaces and maneuvering, making it a fully autonomous robotic system. Multiple modules of MC-1 are connected by an electromagnet-based magnetic attachment method, and wall transitions are achieved using a servo motor mechanism. Moreover, an ultrasonic sensor is employed to measure the unknown wall-inclination angle. Mechanical analysis is conducted for MC-1 at rest individually and in combination to determine the required suction force. Experimental investigations are performed to assess the robot’s crawling ability, loading capacity, and wall-transition performance. The results demonstrate that the MC-1 robot is capable of multi-angle wall transitions for executing multiple tasks. It provides a new approach for wall-climbing robots to collaborate during wall transitions through a quick attachment-and-disassembly device and an efficient wall detection method. Full article

This paper presents FWStab, a specialized video stabilization dataset tailored for flapping-wing platforms. The dataset encompasses five typical flight scenarios, featuring 48 video clips with intense dynamic jitter. The corresponding Inertial Measurement Unit (IMU) sensor data are synchronously collected, which jointly provide reliable support for multimodal modeling. Based on this, to address the issue of poor image acquisition quality due to severe vibrations in aerial vehicles, this paper proposes a multi-modal signal fusion video stabilization framework. This framework effectively integrates image features and inertial sensor features to predict smooth and stable camera poses. During the video stabilization process, the true camera motion originally estimated based on sensors is warped to the smooth trajectory predicted by the network, thereby optimizing the inter-frame stability. This approach maintains the global rigidity of scene motion, avoids visual artifacts caused by traditional dense optical flow-based spatiotemporal warping, and rectifies rolling shutter-induced distortions. Furthermore, the network is trained in an unsupervised manner by leveraging a joint loss function that integrates camera pose smoothness and optical flow residuals. When coupled with a multi-stage training strategy, this framework demonstrates remarkable stabilization adaptability across a wide range of scenarios. The entire framework employs Long Short-Term Memory (LSTM) to model the temporal characteristics of camera trajectories, enabling high-precision prediction of smooth trajectories. Full article

Previous research on the jumping structures of insects with strong leaping abilities mainly focused on overall jumping mechanisms. Our study reveals that the unilateral jumping structures (UJSs) of L. delicatula has relative functional autonomy. The UJSs consist of three distinct but interconnected parts: (1) energy storage component: it comprises the pleural arch and trochanteral depressor muscles, with the deformation zone extending about two-thirds of the pleural arch from the V-notch to the U-notch; (2) coupling component: made up of the coxa and trochanter, it serves as a bridge between the energy and lever components, connecting them via protuberances and pivots; and (3) lever component: it encompasses the femur, tibia, and tarsus. A complete jumping action lasts from 2.4 ms to 4.6 ms. During a jump, the deformation length of the pleural arch is 0.96 ± 0.06 mm. The angles ∠ct (angle between coxa and trochanter), ∠fp (angle between femur and pleural arch), and ∠ft (angle between femur and tibia) change by 57.42 ± 1.60, 101.40 ± 1.59, and 36.06 ± 2.41 degrees, respectively. In this study, we abstracted the jumping structures of L. delicatula and identified its critical components. The insights obtained from this study are anticipated to provide valuable inspiration for the design and fabrication of biomimetic jumping mechanisms. Full article

Silk fibroin has recently gained considerable attention as a promising biomaterial for use in medical and bioengineering technologies due to its biocompatibility and favorable mechanical properties. In this study, composite gel based on silk fibroin microparticles and carboxymethyl cellulose was developed, characterized by a viscous, homogeneous white mass containing uniformly distributed fibroin microparticles ranging from 1 to 20 μm in size. The gel exhibited a kinematic viscosity of 36.5 × 10⁻⁶ St, allowing for convenient application to wounds using a syringe or spatula while preventing uncontrolled spreading. The cytocompatibility of the gel was confirmed using the methylthiazol tetrazolium (MTT) assay, which showed no cytotoxic effects on 3T3 fibroblast cells. Furthermore, the gel remained stable for over one year when stored at 10 °C, in contrast to conventional fibroin solutions, which typically lose stability within a month under similar conditions. In a full-thickness skin wound model in rats, the application of the gel significantly accelerated skin regeneration, with complete wound closure observed by day 15, compared with 30 days in the control group. Histological analysis confirmed the restoration of all skin layers. These findings demonstrate the high potential of the gel for applications in regenerative medicine and tissue engineering. Full article

Bioinspired morphing offers a powerful route to higher aerodynamic and hydrodynamic efficiency. Birds reposition feathers, bats extend compliant membrane wings, and fish modulate fin stiffness, tailoring lift, drag, and thrust in real time. To capture these advantages, engineers are developing airfoils, rotor blades, and hydrofoils that actively change shape, reducing drag, improving maneuverability, and harvesting energy from unsteady flows. This review surveys over 296 studies, with primary emphasis on literature published between 2015 and 2025, distilling four biological archetypes—avian wing morphing, bat-wing elasticity, fish-fin compliance, and tubercled marine flippers—and tracing their translation into morphing aircraft, ornithopters, rotorcraft, unmanned aerial vehicles, and tidal or wave-energy converters. We compare experimental demonstrations and numerical simulations, identify consensus performance gains (up to 30% increase in lift-to-drag ratio, 4 dB noise reduction, and 15% boost in propulsive or power-capture efficiency), and analyze materials, actuation, control strategies, certification, and durability as the main barriers to deployment. Advances in multifunctional composites, electroactive polymers, and model-based adaptive control have moved prototypes from laboratory proof-of-concept toward field testing. Continued collaboration among biology, materials science, control engineering, and fluid dynamics is essential to unlock robust, scalable morphing technologies that meet future efficiency and sustainability targets. Full article

Three-dimensional printing techniques can prepare complex bioceramic parts and scaffolds with high precision and accuracy, low cost, and customized geometry, which greatly broadens their application of 3D-printed bioceramics and bioceramic matrix composites in the clinical field. Nevertheless, the inadequate mechanical properties of 3D-printed bioceramic scaffolds, such as compressive strength, wear resistance, flexural strength, fracture toughness, and other properties, are a bottleneck problem and severely limit their application, which are overcome by introducing reinforcements. Three-dimensional printing techniques and the mechanical property of bioceramics and bioceramic matrix composites with different reinforcements, as well as their potential applications for bone tissue engineering, are discussed. In addition, the biological performance of 3D-printed bioceramics and scaffolds and their applications are presented. To address the challenges of insufficient mechanical strength and mismatched biological performance in bioceramic scaffolds, we summarize current solutions, including the advantages and strengthening effects of fiber, particle, whisker, and ion doping. The effectiveness of these methods is analyzed. Finally, the limitations and challenges in 3D printing of bioceramics and bioceramic matrix composites are discussed to encourage future research in this field. Our work offers a helpful guide to research and medical applications, especially application in the tissue engineering fields of bioceramics and bioceramic matrix composites. Full article

Neural Architecture Search (NAS) has made significant advancements in autonomously constructing high-performance network architectures, capturing extensive attention. However, a key challenge of existing NAS approaches is the intensive performance evaluation, leading to significant time and computational resource consumption. In this paper, we propose an efficient Evolutionary Neural Architecture Search (ENAS) method to address this issue. Specifically, in order to accelerate the convergence speed of the algorithm and shorten the search time, thereby avoiding blind searching in the early stages of the algorithm, we drew on the principles of biometrics to redesign the interaction between individuals in the evolutionary algorithm. By making full use of the information carried by individuals, we promoted information exchange and optimization between individuals and their neighbors, thereby improving local search capabilities while maintaining global search capabilities. Furthermore, to accelerate the evaluation process and minimize computational resource consumption, a multi-metric training-free evaluator is introduced to assess network performance, bypassing the resource-intensive training phase, and the adopted multi-metric combination method further solves the ranking offset problem. To evaluate the performance of the proposed method, we conduct experiments on two widely adopted benchmarks, NAS-Bench-101 and NAS-Bench-201. Comparative analysis with state-of-the-art algorithms shows that our proposed method identifies network architectures with comparable or better performance while requiring significantly less time. Full article

Brain-like models are commonly used for pattern recognition, but they face significant performance degradation in neuromorphic hardware when exposed to complex electromagnetic environments. The human brain has adaptability to the exterior attack, and we expect that incorporating bio-plausibility into a brain-like model will enhance its robustness. However, brain-like models currently lack bio-plausibility. Therefore, we construct a spiking neural network (SNN) whose topology is constrained by human brain functional Magnetic Resonance Imaging (fMRI), called fMRI-SNN. To certify its damage resistance, we investigate speech recognition accuracy against stochastic attack. To reveal its damage-resistant mechanism, we explore the neural electrical features, adaptive modulation of synaptic plasticity, and topological features against stochastic attack. Research shows that fMRI-SNN surpasses SNNs with distinct topologies in recognition accuracy against stochastic attack, notably maintaining similar accuracy levels before and after stochastic attacks when the damage proportion is below 30%, demonstrating that our method improves the damage resistance of brain-like models. In addition, the change in neural electrical activity serves as interior manifestation, corresponding to the damage resistance of SNNs for recognition tasks, while the synaptic plasticity serves as the inherent determinant of the damage resistance, and the topology serves as a determinant impacting the damage resistance. Full article

Affordance understanding means identifying possible operable parts of objects, which is crucial in achieving accurate robotic manipulation. Although homogeneous objects for grasping have various shapes, they always share a similar affordance distribution. Based on this fact, we propose AFF-NeRF to address the problem of affordance generation for homogeneous objects inspired by human cognitive processes. Our method employs deep residual networks to extract the shape and appearance features of various objects, enabling it to adapt to various homogeneous objects. These features are then integrated into our extended neural radiance fields, named AFF-NeRF, to generate 3D affordance models for unseen objects using a single image. Our experimental results demonstrate that our approach outperforms baseline methods in the affordance generation of unseen views on novel objects without additional training. Additionally, more stable grasps can be obtained by employing 3D affordance models generated by our method in the grasp generation algorithm. Full article

In this study, a preparation method of superhydrophobic composite coating based on a titanium alloy (Ti-6Al-4V) substrate is proposed. The micro-scale pit array structure was fabricated via laser etching technology. Utilizing the synergistic effects of epoxy resin (EP), polydimethylsiloxane (PDMS), and fluorinated nanosilica (F-SiO₂), we successfully prepared an EP@PDMS@F-SiO₂ composite coating. The effects of the contents of EP, PDMS, and F-SiO₂ on the surface wettability, mechanical stability, and UV durability were studied by optimizing the coating ratio through orthogonal experiments. The results show that the micro–nano composite structure formed by laser etching can effectively fix the coating particles and provide excellent superhydrophobicity on the surface. The coating retains high hydrophobicity after paper abrasion (1000 cm under a 200 g load), demonstrating the mechanical stability of the armor-like structure, High-content F-SiO₂ coatings exhibit greater UV durability. In addition, the coating surface has low droplet adhesion and self-cleaning capabilities for efficient contaminant removal. The research provides theoretical and technical support for the design and engineering application of a non-fluorinated, environmentally friendly superhydrophobic coating. Full article

Sericin, a natural glycoprotein constituting 20–30% of the silk cocoon, has emerged as a promising biomaterial due to its excellent biological properties, including biocompatibility, antioxidant properties and potential applications in regenerative medicine. The quality and the features of sericin are strongly dependent on the extraction and purification methods, which can employ mild conditions to preserve the molecular integrity of the protein or recovery techniques from waste streams produced during the industrial degumming processes. The silk industry prioritizes fiber yield over protein preservation, so often harsh alkaline conditions at high temperatures are adopted. These divergent approaches result in fundamentally different products with distinct molecular characteristics and functional capabilities. This review comprehensively examines the current technological approaches for sericin extraction techniques and for its recovery from textile industry waste, focusing on how these aspects affect the biological properties of the protein and the potential applications. Full article

This study proposes a novel effective improved whale optimization algorithm via angle penalized distance (IWOA-APD) for automatic train operation (ATO) to effectively improve the ATO quality. Specifically, aiming at the high-quality target speed curve of urban rail trains, a target speed curve multi-objective optimization model for ATO is established with energy saving, punctuality, accurate stopping, and comfort as the indexes; and the comprehensive evaluation strategy utilizing angle-penalized distance as the evaluation index is proposed to enhance the assessment’s rationality and applicability. On this basis, the IWOA-APD is proposed using strategies of non-linear decreasing convergence factor, solutions of out-of-bounds eliminating via combination of reflection and refraction, mechanisms of genetic evolution with variable probability, and elite maintenance based on fusion distance and crowding degree distance. In addition, the detailed design scheme of IWOA-APD is given. The test results show that the proposed IWOA-APD achieves significant performance improvements compared to traditional MOWOA. In the optimization scenario from Lvshun New Port Station to Tieshan Town Station of Dalian urban rail transit line No.12, the IGD value shows a remarkable 69.1% reduction, while energy consumption decreases by 12.5%. The system achieves a 64.6% improvement in punctuality and a 76.5% enhancement in parking accuracy. Additionally, comfort level improves by 15.9%. Full article

In this work, we propose the integration of a mechanism to enable smooth transitions between different locomotion patterns in a hexapod robot. Specifically, we utilize a spiking neural network (SNN) functioning as a Central Pattern Generator (CPG) to generate three distinct locomotion patterns, or gaits: walk, jog, and run. This network produces coordinated spike trains, mimicking those generated in the brain, which are translated into synchronized robot movements via PWM signals. Subsequently, these spike trains are compared using a similarity metric known as SPIKE-synchronization to identify the optimal point for transitioning from one gait to another. This approach aims to achieve three main objectives: first, to maintain the robot’s balance during transitions; second, to ensure that gait transitions are almost imperceptible; and third, to improve energy efficiency by reducing abrupt changes in the robot’s actuators (servomotors). To validate our proposal, we incorporated FSR sensors on the robot’s legs to detect the rigidity of the terrain it navigates. Based on the terrain’s rigidity, the robot dynamically transitions between gaits. The system was tested in real time on a physical hexapod robot across four different types of terrain. Although the method was validated exclusively on a hexapod robot, it can be extended to any legged robot. Full article