Flapping-wing rotor (FWR) is an innovative bio-inspired micro aerial vehicle capable of vertical take-off and landing. This
unique design combines active flapping motion and passive wing rotation around a vertical central shaft to enhance aerodynamic
performance. The research on FWR, though relatively new, has contributed to 6% of core journal publications in the
micro aerial vehicle field over the past two decades. This paper presents the first comprehensive review of FWR, analysing
the current state of the art, key advances, challenges, and future research directions. The review highlights FWR’s distinctive
kinematics and aerodynamic superiority compared to traditional flapping wings, fixed wings, and rotary wings, discussing
recent breakthroughs in efficient, passive wing pitching and asymmetric stroke amplitude for lift enhancement. Recent
experiments and remote-controlled take-off and hovering tests of single and dual-motor FWR models have showcased their
effectiveness. The review compares FWR flight performance with well-developed insect-like flapping-wing micro aerial
vehicles as the technology readiness level progresses from laboratory to outdoor flight testing, advancing from the initial
flight of a 2.6 g prototype to the current free flight of a 60-gram model. The review also presents ongoing research in bionic
flexible wing structures, flight stability and control, and transitioning between hovering and cruise flight modes for an FWR,
setting the stage for potential applications.

Most flapping-wing aircraft wings use a single degree of freedom to generate lift and thrust by flapping up and down, while
relying on the tail control surfaces to manage attitude. However, these aircraft have certain limitations, such as poor accuracy
in attitude control and inadequate roll control capabilities. This paper presents a design for an active torsional mechanism at
the wing's trailing edge, which enables differential variations in the pitch angle of the left and right wings during flapping.
This simple mechanical form significantly enhances the aircraft's roll control capacity. The experimental verification of this
mechanism was conducted in a wind tunnel using the RoboEagle flapping-wing aerial vehicle that we developed. The study
investigated the effects of the control strategy on lift, thrust, and roll moment during flapping flight. Additionally, the impact
of roll control on roll moment was examined under various wind speeds, flapping frequencies, angles of attack, and wing
flexibility. Furthermore, several rolling maneuver flight tests were performed to evaluate the agility of RoboEagle, utilizing
both the elevon control strategy and the new roll control strategy. The results demonstrated that the new roll control strategy
effectively enhances the roll control capability, thereby improving the attitude control capabilities of the flapping-wing
aircraft in complex wind field environments. This conclusion is supported by a comparison of the control time, maximum
roll angle, average roll angular velocity, and other relevant parameters between the two control strategies under identical
roll control input.

To better understand the aerodynamic reasons for highly organized movements of flying organisms, the three-flapping wing
system in tandem formation was studied numerically in this paper. Different from previous relevant studies on the multiple
flapping wings that are equally spaced, this study emphasizes the impact of unequal spacing between individuals on the
aerodynamics of each individual wing as well as the whole system. It is found that swapping the distance between the first
and second wing with the distance between the second wing and the rearmost wing does not affect the overall aerodynamic
performance, but significantly changes the distribution of aerodynamic benefits across each wing. During the whole flapping
cycle, three effects are at play. The narrow channel effect and the downwash effect can promote and weaken the wing
lift, respectively, while the wake capture effect can boost the thrust. It also shows that these effects could be manipulated
by changing the spacing between adjacent wings. These findings provide a novel way for flow control in tandem formation
flight and are also inspiring for designing the formation flight of bionic aircraft.

This paper aims to address the problem of multi-UAV cooperative search for multiple targets in a mountainous environment, considering the constraints of UAV dynamics and prior environmental information. Firstly, using the target probability distribution map, two strategies of information fusion and information diffusion are employed to solve the problem of environmental information inconsistency caused by different UAVs searching different areas, thereby improving the coordination of UAV groups. Secondly, the task region is decomposed into several high-value sub-regions by using data clustering method. Based on this, a hierarchical search strategy is proposed, which allows precise or rough search in different probability areas by adjusting the altitude of the aircraft, thereby improving the search efficiency. Third, the Elite Dung Beetle Optimization Algorithm (EDBOA) is proposed based on bionics by accurately simulating the social behavior of dung beetles to plan paths that satisfy the UAV dynamics constraints and adapt to the mountainous terrain, where the mountain is considered as an obstacle to be avoided. Finally, the objective function for path optimization is formulated by considering factors such as coverage within the task region, smoothness of the search path, and path length. The effectiveness and superiority of the proposed schemes are verified by the simulation.

Modular continuum robots possess signifcant versatility across various scenarios; however, conventional assembling methods typically rely on linear connection between modules. This limitation can impede the robotic interaction capabilities, especially in specifc engineering applications. Herein, inspired by the assembling pattern between the femur and tibia in a human knee, we proposed a multidirectional assembling strategy. This strategy encompasses linear, oblique, and orthogonal connections, allowing a two-module continuum robot to undergo in-situ reconfguration into three distinct initial confgurations. To anticipate the fnal confguration resulting from diverse assembling patterns, we employed the positional formulation fnite element framework to establish a mechanical model, and the theoretical results reveal that our customizable strategy can ofer an efective route for robotic interactions. We showcased diverse assembling patterns for coping with interaction requirements. The experimental results indicate that our modular continuum robot not only reconfgures its initial profle in situ but also enables on-demand regulation of the fnal confguration. These capabilities provide a foundation for the future development of modular continuum robots, enabling them to be adaptable to diverse environments, particularly in unstructured surroundings.

The goal of this paper is to develop a unifed online motion generation scheme for quadruped lateral-sequence walk and trot gaits based on a linear model predictive control formulation. Specifcally, the dynamics of the linear pendulum model is formulated over a predictive horizon by dimensional analysis. Through gait pattern conversion, the lateral-sequence walk and trot gaits of the quadruped can be regarded as unifed biped gaits, allowing the dynamics of the linear inverted pendulum model to serve quadruped motion generation. In addition, a simple linearization of the center of pressure constraints for these quadruped gaits is developed for linear model predictive control problem. Furthermore, the motion generation problem can be solved online by quadratic programming with foothold adaptation. It is demonstrated that the proposed unifed scheme can generate stable locomotion online for quadruped lateral-sequence walk and trot gaits, both in simulation and on hardware. The results show signifcant performance improvements compared to previous work. Moreover, the results also suggest the linearly simplifed scheme has the ability to robustness against unexpected disturbances.

In order to strike a balance between achieving desired velocities and minimizing energy consumption, legged animals have the ability to adopt the appropriate gait pattern and seamlessly transition to another if needed. This ability makes them more versatile and efcient when traversing natural terrains, and more suitable for long treks. In the same way, it is meaningful and important for quadruped robots to master this ability. To achieve this goal, we propose an efective gait-heuristic reinforcement learning framework in which multiple gait locomotion and smooth gait transitions automatically emerge to reach target velocities while minimizing energy consumption. We incorporate a novel trajectory generator with explicit gait information as a memory mechanism into the deep reinforcement learning framework. This allows the quadruped robot to adopt reliable and distinct gait patterns while benefting from a warm start provided by the trajectory generator. Furthermore, we investigate the key factors contributing to the emergence of multiple gait locomotion. We tested our framework on a closedchain quadruped robot and demonstrated that the robot can change its gait patterns, such as standing, walking, and trotting, to adopt the most energy-efcient gait at a given speed. Lastly, we deploy our learned controller to a quadruped robot and demonstrate the energy efciency and robustness of our method.

Legged robots show great potential for high-dynamic motions in continuous interaction with the physical environment, yet achieving animal-like agility remains signifcant challenges. Legged animals usually predict and plan their next locomotion by combining high-dimensional information from proprioception and exteroception, and adjust the stifness of the body’s skeletal muscle system to adapt to the current environment. Traditional control methods have limitations in handling highdimensional state information or complex robot motion that are difcult to plan manually, and Deep Reinforcement Learning (DRL) algorithms provide new solutions to robot motioncontrol problems. Inspired by biomimetics theory, we propose a perception-driven high-dynamic jump adaptive learning algorithm by combining DRL algorithms with Virtual Model Control (VMC) method. The robot will be fully trained in simulation to explore its motion potential by learning the factors related to continuous jumping while knowing its real-time jumping height. The policy trained in simulation is successfully deployed on the bio-inspired single-legged robot testing platform without further adjustments. Experimental results show that the robot can achieve continuous and ideal vertical jumping motion through simple training

In order to reduce the labor intensity of high-altitude workers and realize the cleaning and maintenance of high-rise building exteriors, this paper proposes a design for a 4-DOF bipedal wall-climbing bionic robot inspired by the inchworm’s movement. The robot utilizes vacuum adsorption for vertical wall attachment and legged movement for locomotion. To enhance the robot’s movement efciency and reduce wear on the adsorption device, a gait mimicking an inchworm’s movement is planned, and foot trajectory planning is performed using a quintic polynomial function. Under velocity constraints, foot trajectory optimization is achieved using an improved Particle Swarm Optimization (PSO) algorithm, determining the quintic polynomial function with the best ftness through simulation. Finally, through comparative experiments, the climbing time of the robot closely matches the simulation results, validating the trajectory planning method’s accuracy.

This paper presents a learning-based control framework for fast (< 1.5 s) and accurate manipulation of a fexible object, i.e., whip targeting. The framework consists of a motion planner learned or optimized by an algorithm, Online Impedance Adaptation Control (OIAC), a sim2real mechanism, and a visual feedback component. The experimental results show that a soft actor-critic algorithm outperforms three Deep Reinforcement Learning (DRL), a nonlinear optimization, and a genetic algorithm in learning generalization of motion planning. It can greatly reduce average learning trials (to < 20% of others) and maximize average rewards (to > 3 times of others). Besides, motion tracking errors are greatly reduced to 13.29% and 22.36% of constant impedance control by the OIAC of the proposed framework. In addition, the trajectory similarity between simulated and physical whips is 89.09%. The presented framework provides a new method integrating data-driven and physics-based algorithms for controlling fast and accurate arm manipulation of a fexible object.

Unilateral motor impairment can disrupt the coordination between the joints, impeding the patient’s normal gait. To assist such patients to walk normally and naturally, an adaptive control algorithm based on inter-joint coordination was proposed in this work for lower-limb exoskeletons. The control strategy can generate the reference trajectory of the afected leg in real time based on a motion coordination model between the joints, and adopt an adaptive controller with virtual windows to track the reference trajectory. Long Short-Term Memory (LSTM) network was also adopted to establish the coordination model between the joints of both lower limbs, which was optimized by preprocessing angle information and adding gait phase information. In the adaptive controller, the virtual windows were symmetrically distributed around the reference trajectory, and its width was adjusted according to the gait phase of the auxiliary leg. In addition, the impedance parameters of the controller were updated online to match the motion capacity of the afected leg based on the spatiotemporal symmetry factors between the bilateral gaits. The LSTM coordination model demonstrated good accuracy and generality in the gait database of seven individuals, with an average root mean square error of 3.5? and 4.1? for the hip and knee joint angle estimation, respectively. To further evaluate the control algorithm, four healthy subjects walked wearing the exoskeleton while additional weights were added around the ankle joint to simulate an asymmetric gait. From the experimental results, it was shown that the algorithm improved the gait symmetry of the subjects to a normal level while exhibiting great adaptability to diferent subjects.

This study aims to develop a magnetorheological (MR) damper for semi-active knee prostheses to restore the walking ability of transfemoral amputees. The core dimensions of the MR damper were determined via theoretical magnetic feld calculations, and the theoretical relationship between current and joint torque was derived through electromagnetic simulation. Then, a physical prototype of the semi-active prosthetic knee equipped with the MR damper was manufactured. Based on the data obtained from angle sensor, pressure sensor (loadcell), and inertial measurement unit (IMU) on the prosthesis, a matching control algorithm is developed. The joint torque of the MR damper can be adaptively adjusted according to the walking speed of the amputee, allowing the amputee to realize a natural gait. The efectiveness of the control program was verifed by the ADAMS and MATLAB co-simulation. The results of the test and simulation show that the MR damper can provide sufcient torque needed for normal human activities.

In the natural world, leaf-cutting ants cause vibrations through their mutual scraping of fle-scraper organs. In this study, we designed a Biomimetic Ultrasonic Exciter (BUE) that imitates leaf-cutting ants. The operating characteristics of the BUE were studied through experimental testing and fnite element simulations. The results showed that the BUE could generate stable ultrasonic vibrations, and that the excitation frequency only needed to be half the Output Frequency (OF). This frequency-doubling phenomenon was conducive to achieving BUE miniaturization. To further explore the phenomenon of frequency-doubling vibration output, this study designed scrapers of fve diferent sizes, conducted excitation and frst-order natural frequency measurement tests, and the corresponding fnite element simulations. It was found that each scraper could operate in frequency-doubling mode, but the operating frequency and natural mode frequencies did not correspond with one another. To further explicate experimental and simulation results, a two-degrees-of-freedom vibration model was developed. It was evident that the contact relationship between the dentate disc and scraper introduced strong nonlinear factors into the system, accounting for the frequency-doubling phenomenon and the diference between the BUE’s operating and mode frequencies. The BUE could be expected to facilitate the production of high-power micro-ultrasonic generators and has potential application value in the felds of mechanical processing, industrial production, and medical health.

At present, the existing piezoelectric stick-slip actuators have an inherent back-slip problem, which greatly limits the development and application of stick-slip actuators. In order to inhibit the regression phenomenon, a new bionic lemongrass stickslip actuator was prepared by using polymer PDMS to replicate natural biological surface. The surface microstructure of the grass was copied by PDMS, and the PDMS film was prepared. The rigid and flexible bionic friction pair was further prepared, and the flexible anisotropic PDMS stick slip actuator was developed. It was found that the anisotropic friction characteristics of the surface microstructure of the grass inhibited the anti-sliding motion, and the elastic potential energy of the PDMS film improved the output characteristics of the driver. By adjusting the input voltage to control the contact between the drive foot and the rotor, the rigid and flexible hybrid drive can be realized and the backsliding phenomenon can be suppressed. The actuator is compact, lightweight and can achieve high speed and high resolution output without preloading force, which has important application value in the field of fast and accurate positioning with load limitation.

Flexible attachment actuators are popular in a wide range of applications, owing to their flexibility and highly reliable attachment. However, their reversible adhesion performance depends on the actual effective contact area and peel angle during operation. Therefore, a good actuator must ensure a uniform and reliable pre-pressure load on an adhesive surface, to increase the effective contact area of the attached surface, thereby maximizing adhesion. This study was inspired by fusion bionics for designing a hierarchical attachment structure with vacuum-adsorption and dry-adhesion mechanisms. The designed structure used the normal force under the negative pressure of a suction cup as a stable source of a pre-pressure load. By optimizing the rigid and flexible structural layers of the attachment structure, a load was applied uniformly to the adhesion area; thus, reliable attachment was achieved by self-preloading. The structure achieved detachment by exploiting the large deformation of a pneumatic structure under a positive pressure. The hierarchical attachment structure achieved up to 85% of the optimal performance of the adhesive surface. Owing to its self-preloading and reliable attachment characteristics, the designed structure can be used as an attachment unit in various complex scenarios, such as small, lightweight climbing platforms and the transport of objects in long, narrow pipelines.

When a human lands from a high drop, there is a high risk of serious injury to the lower limbs. On the other hand, cats can withstand jumps and falls from heights without being fatally wounded, largely due to their impact-resistant paw pads. The aim of the present study was to investigate the biomechanism of impact resistance in cat paw pads, propose an optimal hierarchical Voronoi structure inspired by the paw pads, and apply the structure to bionic cushioning shoes to reduce the impact force of landing for humans. The microstructure of cat paw pads was observed via tissue section staining, and a simulation model was reconstructed based on CT to verify and optimize the structural cushioning capacity. The distribution pattern, wall thickness of compartments, thickness ratio of epidermis and dermis, and number of compartments in the model were changed and simulated to achieve an optimal composed structure. A bionic sole was 3D-printed, and its performance was evaluated via compression test and a jumping-landing experiment. The results show that cat paw pads are a spherical cap structure, divided from the outside to the inside into the epidermis, dermis, and compartments, each with different cushioning capacities. A finite element simulation of different cushioning structures was conducted in a cylinder with a diameter of 20 mm and a height of 10 mm, featuring a three-layer structure. The optimal configuration of the three layers should have a uniform distribution with 0.3–0.5 mm wall thickness, a 1:1–2 thickness ratio of epidermis and dermis, and 100–150 compartments. A bionic sole with an optimized structure can reduce the peak impact force and delay the peak arrival time. Its energy absorption rate is about 4 times that of standard sole. When jumping 80, 100, and 120 cm, the normalized ground reaction force is also reduced by 8.7%, 12.6% and 15.1% compared with standard shoes. This study provides theoretical and technical support for effective protection against human lower limb landing injuries.

The enhancement of adhesive perception is crucial to maintaining a stable and comfortable grip of the skin-touch products. To study the tactile perception of adhesive surfaces, subjective evaluation, skin friction and vibrations, and neurophysiological response of the brain activity were investigated systematically. Silicone materials, which are commonly used for bionic materials and skin-touch products, were chosen for the tactile stimulus. The results showed that with the increasing of surface adhesion, the dominant friction transferred from a combination of adhesive friction and deformation friction to adhesive friction. The friction coefcient and vibration amplitude had strong correlations with the perceived adhesion of surfaces. The parietal lobe and occipital lobe were involved in adhesive perceptions, and the area and intensity of brain activation increased with the increasing surface adhesion. Surfaces with larger adhesion tended to excite a high P300 amplitude and short latency, indicating that the judgment was faster and that more attentional resources were involved in adhesive perception. Furthermore, the electroencephalograph signals of the adhesive perception were simulated by the neural mass model. It demonstrated that the excitability and intensity of brain activity, and the connectivity strength between two neural masses increased with the increasing surface adhesion. This study is meaningful to understand the role of surface adhesion in tactile friction and the cognitive mechanism in adhesive perception to improve the tactile experience of adhesive materials.

Surface electromyography (sEMG)-based gesture recognition is a key technology in the feld of human–computer interaction. However, existing gesture recognition methods face challenges in efectively integrating discriminative temporal feature representations from sEMG signals. In this paper, we propose a deep learning framework named TFN-FICFM comprises a Temporal Fusion Network (TFN) and Fuzzy Integral-Based Classifer Fusion method (FICFM) to improve the accuracy and robustness of gesture recognition. Firstly, we design a TFN module, which utilizes an attention-based recurrent multi-scale convolutional module to acquire multi-level temporal feature representations and achieves deep fusion of temporal features through a feature pyramid module. Secondly, the deep-fused temporal features are utilized to generate multiple sets of gesture category prediction confdences through a feedback loop. Finally, we employ FICFM to perform fuzzy fusion on prediction confdences, resulting in the ultimate decision. This study conducts extensive comparisons and ablation studies using the publicly available datasets Ninapro DB2 and DB5. Results demonstrate that the TFN-FICFM model outperforms state-ofthe-art methods in classifcation performance. This research can serve as a benchmark for sEMG-based gesture recognition and related deep learning modeling.

This study investigates the friction and deformation behavior of the skin in contact with a rigid massage ball and its infuencing factors. Pressing and stretching experiments were conducted using a collaborative robot experimental platform. The experiments encompassed a loading normal force range of 2 N to 18 N and a sliding speed range of 10 mm/s to 20 mm/s. The friction response curve exhibits two diferent stages: static stick state and dynamic stick-slip stage, both of which have been mathematically modeled. By analyzing the experimental data, we analyzed the efects of elastic modulus, sliding speed and normal loading force on skin tangential friction and tensile deformation. The results indicate that as the normal load increases, both friction and deformation exhibit an increase. Conversely, they decrease with an increase in elastic modulus. Notably, while deformation diminishes with higher sliding speed, friction force remains relatively unafected by velocity. This observation can be attributed to the strain rate sensitivity resulting from the viscoelastic characteristics of the skin under substantial deformation. This study advances the understanding of friction and deformation behavior during skin friction, ofering valuable insights to enhance the operational comfort of massage robots.

Almost all living organisms exhibit autonomic oscillatory activities, which are primarily generated by the rhythmic activities of their neural systems. Several nonlinear oscillator models have been proposed to elucidate these neural behaviors and subsequently applied to the domain of robot control. However, the oscillation patterns generated by these models are often unpredictable and need to be obtained through parameter search. This study introduces a mathematical model that can be used to analyze multiple neurons connected through fast inhibitory synapses. The characteristic of this oscillator is that its stationary point is stable, but the location of the stationary point changes with the system state. Only through reasonable topology and threshold parameter selection can the oscillation be sustained. This study analyzed the conditions for stable oscillation in two-neuron networks and three-neuron networks, and obtained the basic rules of the phase relationship of the oscillator network established by this model. In addition, this study also introduces synchronization mechanisms into the model to enable it to be synchronized with the sensing pulse. Finally, this study used these theories to establish a robot single leg joint angle generation system. The experimental results showed that the simulated robot could achieve synchronization with human motion, and had better control efects compared to traditional oscillators.

Within the consistent daily rhythm of human life, intervertebral discs endure a variety of complex loads beyond the infuences of gravity and muscle forces, leading to signifcant morphological changes (in terms of volume, area, and height) as well as biomechanical alterations, including an increase in disc stifness and a decrease in intradiscal pressure. Remarkably, the discs demonstrate an ability to regain their original morphological and biomechanical characteristics after a period of nocturnal rest. The preservation of normal disc function is critically dependent on this recovery phase, which serves to forestall premature disc degeneration. This phenomenon of disc recovery has been extensively documented through numerous in vivo studies employing advanced clinical techniques such as Magnetic Resonance Imaging (MRI), stadiometry, and intradiscal pressure measurement. However, the fndings from in vitro studies present a more complex picture, with reports varying between full recovery and only partial recuperation of the disc properties. Moreover, research focusing on degenerated discs in vitro has shed light on the quantifable impact of degeneration on the disc ability to recover. Fluid dynamics within the disc are considered a primary factor in recovery, yet the disc intricate multiscale structure and its viscoelastic properties also play key roles. These elements interact in complex ways to infuence the recovery mechanism, particularly in relation to the overall health of the disc. The objective of this review is to collate, analyze, and critically evaluate the existing body of in vivo and in vitro research on this topic, providing a comprehensive understanding of disc recovery processes. Such understanding ofers a blueprint for future advancements in medical treatments and bionic engineering solutions designed to mimic, support, and enhance the natural recovery processes of intervertebral discs.

Corneal diseases, the second leading cause of global vision loss affecting over 10.5 million people, underscores the unmet demand for corneal tissue replacements. Given the scarcity of fresh donor corneas and the associated risks of immune rejection, corneal tissue engineering becomes imperative. Developing nanofibrous scaffolds that mimic the natural corneal structure is crucial for creating transparent and mechanically robust corneal equivalents in tissue engineering. Herein, Aloe Vera Extract (AVE)/Polycaprolactone (PCL) nanofibrous scaffolds were primed using electrospinning. The electrospun AVE/PCL fibers exhibit a smooth, bead-free morphology with a mean diameter of approximately 340±95 nm and appropriate light transparency. Mechanical measurements reveal Young’s modulus and ultimate tensile strength values of around 3.34 MPa and 4.58 MPa, respectively, within the range of stromal tissue. In addition, cell viability of AVE/PCL fibers was measured against Human Stromal Keratocyte Cells (HSKCs), and improved cell viability was observed. The cell-fiber interactions were investigated using scanning electron microscopy. In conclusion, the incorporation of Aloe Vera Extract enhances the mechanical, optical, hydrophilic, and biological properties of PCL fibers, positioning PCL/AVE fiber scaffolds as promising candidates for corneal stromal regeneration.

With an elemental composition similar to bone mineral, and the ability to release phosphorus and calcium that beneft bone regeneration, Calcium Phosphate Glass (CPG) serves as a promising component of bone tissue engineering scafolds. However, the degradation of CPG composites typically results in increased acidity, and its impact on bone-forming activity is less studied. In this work, we prepared 3D-printed composite scafolds comprising CPG, Poly-ε-caprolactone (PCL), and various Magnesium Oxide (MgO) contents. Increasing the MgO content efectively suppressed the degradation of CPG, maintaining a physiological pH of the degradation media. While the degradation of CPG/PCL scafolds resulted in upregulated apoptosis of Rat Bone Marrow-derived Stem Cells (rBMSC), scafolds containing MgO were free from these negative impacts, and an optimal MgO content of 1 wt% led to the most pronounced osteogenic diferentiation of rBMSCs. This work demonstrated that the rapid degradation of CPG impaired the renewability of stem cells through the increased acidity of the surrounding media, and MgO efectively modulated the degradation rate of CPG, thus preventing the negative efects of rapid degradation and supporting the proliferation and osteogenic diferentiation of the stem cells.

Immersion of scaffolds in Simulated Body Fluid (10SBF) is a standardized method for evaluating their bioactivity, simulating in vivo conditions where apatite deposits can be formed on the surface of scaffold, facilitating bone integration and ensuring their suitability for bone implant purposes, ultimately contributing to long-term implant success. The effect of apatite deposition on bioactivity and cell behavior of TiO2 scaffolds was studied. Scaffolds were soaked in 10SBF for different durations to form HAP layer on their surface. The results proved the development of a hydroxyapatite film resembling the mineral composition of bone Extracellular Matrix (ECM) on the TiO2 scaffolds. The XRD test findings showed the presence of hydroxyapatite layer similar to bone at the depth of 10 nm. A decrease in the specific surface area (18.913 m2 g?1 ), the total pore volume (0.045172 cm3 g?1 (at p/p0=0.990)), and the mean pore diameter (9.5537 nm), were observed by BET analysis which confirmed the formation of the apatite layer. It was found that titania scaffolds with HAP coating promoted human osteosarcoma bone cell (MG63) cell attachment and growth. It seems that immersing the scaffolds in 10SBF to form HAP coating before utilizing them for bone tissue engineering applications might be a good strategy to promote bioactivity, cell attachment, and implant fixation.

Sustainable cement-based concrete materials are primarily used for construction, among which vermiculite as lightweight fne aggregate gains more future development prospect. First, a bacterial solution was sprayed over vermiculite and wrapped using calcium sulphoaluminate (CSA) cement to replace with fne aggregate in concrete. Secondly, based on a preliminary test on compressive strength results, 10% of Ground Granulated Blast Furnace Slag (GGBS) and a healing solution proportion of 9:1 was selected for preparing self-healing concrete. The fne aggregate was replaced in concrete using vermiculite in 0%, 5%, 10% and 15% and the fndings suggest that bacterial vermiculite replacement should be at most 5% to achieve better results in strength and durable properties. The strength enhancement observed for compressive strength, strength regain, split tensile strength, fexural strength, and ultrasonic pulse velocity were 29.22%, 45.5%, 34.02%, 28.03% and 41.4% respectively. Surface crack healing at 7, 14 and 28 days of BIVC was 38.23%, 58.82% and 79.41%, which is 3–4% lower than internal crack healing. Microstructural analysis by Scanning Electron Microscopy (SEM), X-Ray Difractometer (XRD), and Energy Dispersive Spectroscopy (EDS) reveals the existence of calcite, and it was formed due to the bio-mineral action of bacteria with available nutrients in sustainable concrete.

Feature selection (FS) plays a crucial role in pre-processing machine learning datasets, as it eliminates redundant features to improve classifcation accuracy and reduce computational costs. This paper presents an enhanced approach to FS for software fault prediction, specifcally by enhancing the binary dwarf mongoose optimization (BDMO) algorithm with a crossover mechanism and a modifed positioning updating formula. The proposed approach, termed iBDMOcr, aims to fortify exploration capability, promote population diversity, and lastly improve the wrapper-based FS process for software fault prediction tasks. iBDMOcr gained superb performance compared to other well-esteemed optimization methods across 17 benchmark datasets. It ranked frst in 11 out of 17 datasets in terms of average classifcation accuracy. Moreover, iBDMOcr outperformed other methods in terms of average ftness values and number of selected features across all datasets. The fndings demonstrate the efectiveness of iBDMOcr in addressing FS problems in software fault prediction, leading to more accurate and efcient models.

Feature Selection (FS) is an important data management technique that aims to minimize redundant information in a dataset. 
This work proposes DENGO, an improved version of the Northern Goshawk Optimization (NGO), to address the FS problem. The NGO is an efcient swarm-based algorithm that takes its inspiration from the predatory actions of the northern 
goshawk. In order to overcome the disadvantages that NGO is prone to local optimum trap, slow convergence speed and 
low convergence accuracy, two strategies are introduced in the original NGO to boost the efectiveness of NGO. Firstly, a 
learning strategy is proposed where search members learn by learning from the information gaps of other members of the 
population to enhance the algorithm's global search ability while improving the population diversity. Secondly, a hybrid 
diferential strategy is proposed to improve the capability of the algorithm to escape from the trap of the local optimum by 
perturbing the individuals to improve convergence accuracy and speed. To prove the efectiveness of the suggested DENGO, 
it is measured against eleven advanced algorithms on the CEC2015 and CEC2017 benchmark functions, and the obtained 
results demonstrate that the DENGO has a stronger global exploration capability with higher convergence performance and 
stability. Subsequently, the proposed DENGO is used for FS, and the 29 benchmark datasets from the UCL database prove 
that the DENGO-based FS method equipped with higher classifcation accuracy and stability compared with eight other 
popular FS methods, and therefore, DENGO is considered to be one of the most prospective FS techniques. DENGO's code 
can be obtained at https://www.mathworks.com/matlabcentral/fleexchange/158811-project1.

Automatic identifcation and segmentation of lesions in medical images has become a focus area for researchers. Segmentation for medical image provides professionals with a clearer and more detailed view by accurately identifying and isolating specifc tissues, organs, or lesions from complex medical images, which is crucial for early diagnosis of diseases, treatment planning, and efcacy tracking. This paper introduces a deep network based on dendritic learning and missing region detection (DMNet), a new approach to medical image segmentation. DMNet combines a dendritic neuron model (DNM) with an improved SegNet framework to improve segmentation accuracy, especially in challenging tasks such as breast lesion and COVID-19 CT scan analysis. This work provides a new approach to medical image segmentation and confrms its efectiveness. Experiments have demonstrated that DMNet outperforms classic and latest methods in various performance metrics, proving its efectiveness and stability in medical image segmentation tasks.

Medical image segmentation is a powerful and evolving technology in medical diagnosis. In fact, it has been identifed as a very efective tool to support and accompany doctors in their fght against the spread of the coronavirus (COVID-19). Various techniques have been utilized for COVID-19 image segmentation, including Multilevel Thresholding (MLT)-based metaheuristics, which are considered crucial in addressing this issue. However, despite their importance, meta-heuristics have signifcant limitations. Specifcally, the imbalance between exploration and exploitation, as well as premature convergence, can cause the optimization process to become stuck in local optima, resulting in unsatisfactory segmentation results. In this paper, an enhanced War Strategy Chimp Optimization Algorithm (WSChOA) is proposed to address MLT problems. Two strategies are incorporated into the traditional Chimp Optimization Algorithm. Golden update mechanism that provides diversity in the population. Additionally, the attack and defense strategies are incorporated to improve the search space leading to avoiding local optima. The experimental results were conducted by comparing WSChoA with recent and well-known algorithms using various evaluation metrics such as Feature Similarity Index (FSIM), Structural Similarity Index (SSIM), Peak signal-to-Noise Ratio (PSNR), Standard deviation (STD), Freidman Test (FT), and Wilcoxon Sign Rank Test (WSRT). The results obtained by WSChoA surpassed those of other optimization techniques in terms of robustness and accuracy, indicating that it is a powerful tool for image segmentation.

In response to the shortcomings of Dwarf Mongoose Optimization (DMO) algorithm, such as insufcient exploitation capability and slow convergence speed, this paper proposes a multi-strategy enhanced DMO, referred to as GLSDMO. Firstly, we propose an improved solution search equation that utilizes the Gbest-guided strategy with diferent parameters to achieve a trade-of between exploration and exploitation (EE). Secondly, the Lévy fight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum. In addition, in order to address the problem of low convergence efciency of DMO, this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities, and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization, which enhances the search efciency of agents and accelerating the convergence of the algorithm to the global optimal solution (Gbest). Subsequently, the superiority of GLSDMO is verifed on CEC2017 and CEC2019, and the optimization efect of GLSDMO is analyzed in detail. The results show that GLSDMO is signifcantly superior to the compared algorithms in solution quality, robustness and global convergence rate on most test functions. Finally, the optimization performance of GLSDMO is verifed on three classic engineering examples and one truss topology optimization example. The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.