Applied Bionics and Biomechanics最新文献

This paper investigates the impact of chemical and Soret reactions on magnetohydrodynamic (MHD) oscillatory flow in a porous arteriole. Using appropriate mathematical techniques, a model of a mathematical equation is developed and solved. The flow governing equations are formulated based on certain assumptions. Exact solutions are attained for the profiles of velocity, temperature, and concentration. To highlight the key features, the numerical computations of the physical parameters, Grashof number, Reynolds number, Magnetic number, and Soret number were presented graphically. The present study reveals the viscoelasticity of blood significantly reduces flow velocity. And also illustrates blood flow (BF) in the artery is affected by the Lorentz force, which causes the velocity of the BF to increase as the magnetic field parameter values increase. The obtained outcome may be very useful in controlling BF during the surgical procedure.

The method is applicable for solving the obstacle avoidance workspace of a snake-like robot working on high-voltage transmission cables, based on an improved Monte Carlo method, to address the issues of uneven distribution of scattered points, difficulty in extracting point cloud boundaries, and insufficient accuracy in traditional Monte Carlo methods. The proposed method first generates a seed workspace for the snake-like robot using traditional Monte Carlo method and then envelops the seed workspace with a cube and divides it into several smaller cubes that contain points in the workspace equally. Next, Gaussian distribution probability density function is used to extend and sample the seed workspace of the robot, generating the workspace of the snake-like robot. Finally, the α - shape algorithm is used to extract the point cloud boundaries of the snake-like robot workspace and calculate its volume, accurately determining the workspace. Simulation experiments comparing the reconstructed surface obtained from the α - shape algorithm with the point cloud of the snake-like robot workspace show high accuracy.

The deployable and foldable structure is a unique structural system that provides convenience in terms of transportation and expansion. Although the current deployable mast can transition between closed and extended structures, it lacks stability during movement and does not have the ability to recover on its own after contracting. Hence, improving the stability of deployable mechanisms while enabling them to self-restore has become an essential area for development. Spatial deployable mast expansion and contraction properties are essential for aerospace. In this paper, based on a two-bar and four-cable tensegrity structure, the research was conducted. A new spatially deployable mast structure with zero Poisson's ratio and self-stability, as well as self-adaptability, is proposed. In comparison to existing space deployable masts, the new deployable masts incorporate a tensegrity structure feature that enables them to recover autonomously to their initial state in a contracted state. Firstly, an innovative design based on a two-bar, four-cord tensegrity structure is conducted, which evolves into a two-bar and four-cable tensegrity structure with zero Poisson's ratio by increasing the longitudinal axis to restrict the structure's degrees of freedom and combines with the force density method to analyze the stability of the structure and derive the optimal dimensional parameters. The positive definiteness of the stiffness matrix Q (K) demonstrates the exceptional stability of the mechanism. Secondly, the load-bearing characteristics of the mechanism were verified utilizing ABAQUS (DS, France) software. Besides, the minimum compressive stiffness of the new space deployable mast for locking three rails compared to locking one rail is 12 × 10⁸ N/m, as derived from ABAQUS (DS, France) analysis. Ultimately, a prototype was developed through the application of three-dimensional (3D) printing technology. The force-strain experiment was conducted using both a pressure testing machine and a universal testing machine, and the resulting images demonstrated the findings. The experimental results demonstrate that the novel deployable mast structure has self-recovering and self-stabilizing capabilities. This research pushes the frontiers of deployable mast multifunctionality as well as significantly expands the application domain of tensegrity structures to the aerospace sector.

This study investigated a combined approach of a persulfate-based advanced oxidation process (AOP) followed by biological treatment of a textile industrial effluent. The effluent from the textile industry is primarily composed of various dyes in varying concentrations, resulting in high chemical oxygen demand (COD) and biological oxygen demand (BOD). The model pollutant rhodamine B (RhB) was used in the optimization studies. During the persulfate oxidation process (PSO), persulfate activation is required to generate sulfate radicals (SO₄ ^•-). Raw laterite soil was used as a catalyst for the treatment of RhB in batch studies, and it was able to reduce the dye concentration by about 20% in 60 min of operation, with initial RhB concentrations of 150 mg L^-1 and persulfate concentrations of 200 mg L^-1. Furthermore, alkali-treated laterite soil (ATLS) was used as a catalyst, achieving 57%-60% removal in 60 min at pH 3 and complete removal after 72 h of biological treatment. Furthermore, the optimized conditions were tested on real field waters to determine efficiency, and it was observed that the PSO removed approximately 45% of COD, with further biological treatment for 72 h increasing the removal efficiency to 64%. All other parameters of water quality were reduced by more than 60%.

Bone is one of the hardest tissues in the human body, but it can undergo microcracks under long-term and periodic mechanical loads. The Newton iterative method was used to calculate the steady state, and the effects of different inlet and outlet pressures, trabecular gap width and height, and microcrack's depth and width on the fluid shear stress (FSS) were studied, and the gradient of FSS inside the microcrack was analyzed. The results show that the pressure difference and trabecular gap heigh are positively correlated with the FSS (the linear correlation coefficients R ² were 0.9768 and 0.96542, respectively). When the trabecular gap width was 100 μm, the peak of FSS decreased by 28.57% compared with 800 and 400 μm, and the gradient of FSS inside the microcrack was 0.1-0.4 Pa/mm. This study can help people more intuitively understand the internal fluid distribution of trabecular bone and provide a reliable theoretical basis for the subsequent construction of gradient FSS devices in vitro.

The accident mortality rates are rapidly increasing due to driver inattention, and traffic accidents become a significant problem on a global scale. For this reason, advanced driver assistance systems (ADASs) are essential to enhance traffic safety measures. However, adverse environmental factors, weather, and light radiation affect the sensors' accuracy. Furthermore, potential risks may go unreported if they obstruct the sensor's line of sight or are outside its limited field of view. To overcome these problems, this research presents a vehicle collision estimate warning system that leverages a combined approach of a local linear wavelet neural network (LLWNN) and an unscented Kalman filter (UKF). The system integrates sensor data and vehicle-to-everything (V2X) communication to enhance the accuracy and reliability of vehicle state estimation and collision prediction. The LLWNN module is responsible for forecasting the future states of the vehicle based on historical sensor measurements. This powerful time-series modeling technique allows the system to anticipate the vehicle's trajectory and potential collision risks. The UKF then optimally fuses the LLWNN predictions with the real-time sensor data, including information received through V2X communication, to generate accurate, up-to-date estimates of the vehicle's state. The V2X technology enables the seamless exchange of critical safety information between the host vehicle, surrounding vehicles, infrastructure, and other road users. This includes data on vehicle position, speed, acceleration, and intended maneuvers. By incorporating this V2X-based situational awareness, the system can better perceive the dynamic traffic environment and identify potential collision threats that may be outside the line of sight or detection range of the vehicle's onboard sensors alone. The LLWNN-based UKF module then processes this rich, multimodal data to provide timely and pertinent collision alerts to the driver. These alerts can warn the driver of impending collisions with distant objects, enabling them to take appropriate evasive action. By implementing this integrated LLWNN-UKF approach leveraging sensor data and V2X communication, we aim to reduce the number of collisions caused by reckless driving, which will lead to a decrease in traffic-related fatalities and injuries.

Shoulder periarthritis, a prevalent musculoskeletal disorder, causes significant pain and functional impairment, severely affecting patients' quality of life. With the increasing incidence of shoulder periarthritis linked to modern lifestyle changes, effective prevention and treatment strategies remain elusive. This study explores two areas: first, identifying risk factors for shoulder periarthritis through Mendelian randomization (MR) analysis, and second, designing a motion intervention system incorporating MediaPipe and virtual reality (VR) technology. The MR analysis revealed positive causal relationships between shoulder periarthritis and body mass index (BMI), cigarettes per day, insomnia, and sedentary behavior, while higher educational attainment was inversely associated with the condition. Based on these findings, we developed a "VR Harvest" exercise system aimed at alleviating shoulder stiffness due to prolonged sitting and reducing shoulder pain. By adjusting task difficulty to increase physical activity, the system also facilitates BMI reduction, potentially lowering the risk of shoulder periarthritis. The experimental results indicate that the "VR Harvest" therapy effectively alleviates pain and, compared to traditional exercise therapy, offers greater enjoyment, thereby enhancing participant engagement. Moreover, participants across different BMI categories experienced reductions in BMI after completing the intervention. This study offers a novel approach for shoulder periarthritis prevention and treatment, leveraging VR technology to improve both symptom relief and underlying risk factors.

This pilot study investigated the potential of a newly developed shoe design to improve gait parameters without altering muscle activity in healthy women. The shoe design features a V-shaped heel and a high-elasticity midsole, which are intended to enhance stability during heel contact and promote efficient load transfer throughout the gait cycle. Ten study participants underwent a randomized crossover design, wearing developed and general shoes during the trials. Spatiotemporal gait data and muscle activity were measured to assess the impact of the shoe design developed on gait efficiency. Significant improvements in gait speed, step and stride length, and swing time were observed with the developed shoes, indicating improved gait efficiency. Importantly, these improvements were achieved without significant changes in muscle activity, suggesting that the developed shoe design improves gait efficiency without increasing muscle workload. Considering the limitations of the small sample size and the exploratory nature of this pilot study, further research with a larger cohort is necessary to validate these preliminary findings. Trial Registration: Clinical Trial Registry identifier: UMIN000054260.

Real-time gait estimation is important for the synchrony control of robotic exoskeleton to provide walking assistance. However, for stroke patients with hemiplegic paralysis, the gait pattern is very complex. Accurate and timely gait intention recognition is therefore difficult. To achieve human-robot synchrony control for an unilateral knee exoskeleton, a gait intention recognizer coupling the adaptive frequency oscillator (AFO) and back propagation neural networks (BPNN) is proposed in this paper. The BPNN is trained with gait data of healthy subjects and stroke patients to improve the accuracy of recognized gait pattern, which is then imported into flexible interaction module to provide appropriate assistance. To evaluate the performance of gait intention recognition, three stroke patients were recruited to conduct level ground walking tests. The kinematic and biomechanical data were captured in each test and processed for the evaluation. Experimental results demonstrate the effectiveness of gait intention recognition and movement assistance.

Upper limb exoskeleton rehabilitation devices can improve the quality of rehabilitation and relieve the pressure of rehabilitation medical treatment, which is a research hotspot in the field of medical robots. Aiming at the problems such as large volume, high cost, low comfort, and difficulty in promotion of traditional exoskeleton rehabilitation devices, and considering the lightweight, discontinuous, high flexibility, and high biomimetic characteristics of tensegrity structure, we designed an upper limb bionic exoskeleton rehabilitation device based on tensegrity structure. First, this article uses mapping methods to establish a mapping model for upper limb exoskeletons based on the tensegrity structure and designs the overall structure of upper limb exoskeletons based on the mapping model. Second, a bionic elbow joint device based on gear and rack was designed, and the stability of the bionic elbow joint was proved using the positive definite matrix method. This device can simulate the micro displacement between bones of the human elbow joint, improve the axial matching ability between the upper limbs and the rehabilitation device, and enhance the comfort of rehabilitation. Third, an impedance control scheme based on back propagation (BP) neural network was designed to address the low control accuracy of flexible structures and patient spasms. Finally, we designed the impedance control scheme of the PSO-BP neural network based on a fuzzy rehabilitation state evaluator. The experimental results show that the exoskeleton rehabilitation device has good flexion motion stability and assist ability and has significant advantages in volume and mobility. The control strategy proposed in this paper has high control precision and adaptive ability and has potential application value in the field of medical rehabilitation.