Tibiofemoral contact forces during walking with and without peak vertical ground reaction force feedback.

Face stability of shallow shield tunnels in inclined sand-clay strata: effects of dip angle and interface elevation

Personalized musculoskeletal models show that gait biofeedback alters knee cartilage contact mechanics in ACL-reconstructed subjects.

To address the issue of suboptimal masonry quality in wall-building robots operating within the viscoelastic contact environment of cement mortar, a multi-objective trajectory optimization method is proposed based on Kriging surrogate modeling and the Fractal Evolutionary Particle Swarm Optimization (FEPSO) algorithm in this paper. First, orthogonal experimental design is employed to obtain design variable values, with corresponding objective function values derived experimentally. A Kriging surrogate model linking the objective function to design variables is established to overcome the difficulty in constructing a viscoelastic mechanical model for cement mortar. Subsequently, integrating the Kriging surrogate model, a multi-objective trajectory optimization model for bricklaying is developed. The FEPSO algorithm is employed to solve this model, simultaneously enhancing masonry quality while optimizing other performance metrics. The FEPSO algorithm is compared with NSGA-II and MOPSO optimization algorithms, demonstrating its superiority. Then, the TOPSIS algorithm is applied to derive a compromise solution from the Pareto solution set, which is adopted as the optimal masonry scheme. Finally, the optimal masonry scheme is contrasted with the standard door-shaped trajectory planning method. Results indicate that trajectory optimization increased the wall-building robot's efficiency by 23.66%, reduced energy consumption by 29.33%, and improved trajectory smoothness by 90.47%. Concurrently, environmental contact force decreased by 7.03%, and masonry error decreased from 2.57 to 0.14mm. The proposed method enhances bricklaying quality and provides theoretical references and practical guidance for trajectory planning and construction quality control of similar intelligent construction robots.

Anterior cervical discectomy and fusion (ACDF) is a well-established treatment for cervical degenerative disease (CDD). Nevertheless, its anterior approach carries inherent risks to critical neck structures, prompting the exploration of posterior fusion alternatives for selected patients. Traditional anterior cervical discectomy and fusion (ACDF) for cervical degenerative disease involves navigating complex anterior neck anatomy with potential risks to critical neurovascular structures, motivating the development of safer posterior fusion alternatives. Twenty-four fresh C5-C6 segments from adult goats were randomly assigned to the intact, ACDF, and CILC groups (n = 8 per group). After confirming the anatomical similarity between goat and human cervical spines via three-dimensional reconstruction, corresponding surgical procedures were performed at the C5-C6 level. Range of motions (ROMs) in flexion-extension, lateral bending, and axial rotation were measured under a pure moment of 1.5N·m using a material testing machine. A finite element model of C3-C7 was developed based on computed tomography data from healthy adults. ACDF and CILC procedures were simulated, with a 1N·m moment and 73.6N pre-load applied to analyse the stress distribution in adjacent segment discs, facet cartilage, and implants. Anatomical measurements confirmed a high similarity between goat and human C5-C6 segments (P > 0.05), validating its use as an experimental model. In biomechanical tests, both CILC and ACDF significantly reduced segmental motion (P < 0.001). Compared with ACDF, CILC demonstrated greater ROM in flexion (3.31° vs. 1.52°, P < 0.05), but superior stability in extension (1.86° vs. 3.86°) and axial rotation (left: 5.89° vs. 8.19°; right: 6.11° vs. 8.89°) (P < 0.05). Finite element analysis confirmed model reliability, demonstrating close alignment between predicted and reported ROM values. CILC generated lower or comparable stress in C3/4 and C4/5 discs compared with ACDF, but higher stress at C5/6. Moreover, CILC resulted in higher facet joint contact forces and implant stress than ACDF, particularly during flexion and extension. Although CILC demonstrates biomechanical performance comparable to ACDF with specific advantages in controlling extension and axial rotation without significant advantages, its posterior minimally invasive approach effectively avoids neurovascular risks associated with anterior surgery. The device provides a safer alternative for cervical fusion, particularly in cases with complex anterior anatomy or those requiring revision surgery. However, the observed higher facet and implant stresses underscore the need for careful patient selection, particularly regarding pre-existing facet joint condition and bone quality.

Very high-power short-duration (vHPSD) radiofrequency ablation (RFA) is an alternative strategy for pulmonary vein isolation. However, rapid temperature rises may cause complications. The QDOT Micro is designed to detect temperature rises for automatic power and irrigation flow adjustments. We compared how differences in ablation electrode design between three RFA catheters impact vHPSD lesions. vHPSD RFA was performed within a validated gel tank model. Four-second ablations were delivered with stable contact force using the QDOT, SmartTouch, and TactiFlex SE at 60, 70, 80, and 90 watts, positioned perpendicular (90°), oblique (45°), and parallel (0°) to the ablation target, utilizing 0.9% and 0.45% "half-normal" saline (HNS) irrigation. The SmartTouch and TactiFlex were operated in power-controlled mode, and each configuration was repeated three times, with images captured every second to characterize lesions. In total, 216 vHPSD lesions were delivered. At 90 W, 90°, and 0.9% saline, the QDOT produced lesions with a mean depth of 1.93 mm. The TactiFlex produced shallower lesions, with a mean depth of 1.88 mm (p = 0.02), and the SmartTouch produced the deepest, with a mean depth of 2.37 mm (p < 0.001). The mean diameter of QDOT lesions, at 4.63 mm, was comparable to TactiFlex lesions (4.58 mm, p = 0.8), whilst the SmartTouch lesions were significantly wider than both the QDOT and TactiFlex (6.04 mm, p < 0.001). Steam-pop risk, based on lesion temperature, was lowest for the TactiFlex. vHPSD lesions produced with the QDOT achieved greater dimensions and temperatures than those with the TactiFlex under controlled conditions, suggesting a difference in the cooling profile of the ablation electrodes. vHPSD with the SmartTouch or 0.45% HNS are likely to increase the risk of steam pops.

Based on vision and prior experience, humans can make rough physical predictions and adjust their manipulation strategies. This paper aims to endow robots with a similar ability. To collect paired data of vision and forces, we use a rigid-body simulator commonly adopted in robotics. However, unlike simulators that output noisy point forces, humans are able to make consistent predictions even in unfamiliar situations. Based on this observation, we hypothesize that predicting smooth force distributions rather than raw point forces can improve both force prediction itself and downstream task performance. To validate this hypothesis, we construct a model that predicts three-dimensional force distributions from a single RGB image of piled daily objects. The target distribution is generated by applying statistical smoothing to point forces obtained from the simulator. Moreover, by incorporating object geometry into the smoothing process, we aim to account for variations in contact states and achieve more consistent vision-based predictions. We conduct extensive evaluations in both simulation and real environments. Results show that our approach improves prediction accuracy, enhances downstream task performance through smoothing, and further benefits from geometry-guided smoothing. Remarkably, the trained model generalizes effectively to real-world scenes despite being trained solely in simulation.

Utility of a novel non-steerable 3 French decapolar catheter for mapping of distal coronary sinus during ablation of premature ventricular contractions originating from the left ventricular summit

Optimizing pace-and-ablate: structural determinants of transmural lesion formation in atrial ablation

The origins of left ventricular outflow tract premature ventricular contractions (PVCs) differ in depth and may involve preferential pathways, potentially requiring complex ablation. However, a noninvasive method to preprocedurally estimate ablation complexity has not been established. Sixteen patients with idiopathic left ventricular outflow tract PVCs (V2 transition ratio ≥0.6) underwent 2-dimensional speckle-tracking echocardiography during monomorphic PVCs. Endocardial peak systolic strain timing in 18 left ventricular segments was displayed on a bull's-eye map using 8 color-coded intervals (0-800 ms). Patients were classified as localized (n=6) when the earliest interval appeared in 1 segment and nonlocalized (n=10) when it involved ≥2 segments. Ablation outcomes were compared according to whether a simple ablation approach (PVC elimination within 30 s at a single site) was achieved. Baseline electrocardiographic characteristics were comparable between the groups. Ablation-related parameters, including contact force, power output, and impedance drop at the initial ablation site, were also similar. However, the nonlocalized group required statistically significantly greater total radiofrequency energy to eliminate the targeted PVCs (median, 22 206 versus 10 409 J; P=0.031) and demonstrated a statistically significantly lower rate of successful simple ablation approach compared with the localized group (20.0% versus 83.3%; P=0.035). Nonlocalized patterns may reflect conduction from deeper origins with preferential pathways, thereby requiring more complex ablation strategies. A localized earliest-strain pattern was associated with successful PVC elimination using a simple ablation approach, whereas a nonlocalized pattern indicated the need for more complex ablation. This simple, noninvasive metric may aid preprocedural planning for left ventricular outflow tract PVC ablation.

Biomechanical analysis in hip joints according to sagittal pelvic tilt in non-ambulatory flaccid neuromuscular scoliosis: a finite element study.

Reliable robotic manipulation in aquatic environments requires flexible triaxis force sensors capable of precise contact force detection, while well-sealed force sensors generally suffer from high hydrostatic pressure preloading that occupies the sensing range and compromises the sensitivity. Herein, we develop a novel and highly sensitive iontronic-based aquatic triaxis force sensor. It features an open-architecture design to compensate for hydraulic pressure changes caused by water depth. Additionally, it incorporates hybrid sensitive microstructures with distinct elastic moduli to improve sensitivity for force sensing while maintaining a sufficient sensing range. To achieve precise shear and normal force sensing, a mathematical model for triaxis force sensing is established by using the differential capacitance changes. Characterization tests demonstrated our aquatic triaxis force sensor has high normal force sensitivity of 0.32 N-1 at normal force sensing range of 0-18.5 N, along with a high shear force sensitivity of 0.761 N-1 for the x-axis and 0.758 N-1 for the y-axis within the range of 0-4.2 N. Notably, the sensor achieves a high normal force resolution of 0.02 N and a shear force resolution of 0.01 N. Moreover, the sensor generally maintains consistent sensing performance across varying aquatic environments. Finally, successful demonstrations in different object grasping tasks and underwater pipeline docking applications for three-axis force sensing validate the promising potential of our developed triaxis force sensor for delicate three-axis force measurement in aquatic environments.

This study aimed to quantify ontogenetic changes in femoral morphology - specifically femoral head and condyle sizes - and joint loads in children. MRI and 3D movement data were recorded from nine children without any known pathologies at two timepoints, two years apart. Femoral geometry was segmented from the MRI to quantify femoral head and medial and lateral condyle volumes. Personalized MRI-informed musculoskeletal models were created and used to estimate hip and knee joint contact forces (JCFs) based on the 3D movement data. Multiple linear regression models were used to assess whether changes in JCF and femoral morphology can be predicted from simple measures such as age, body weight, and height. Both femoral joint contact geometry and absolute JCF significantly increase with age. However, when normalized to body weight, JCF remained stable - or even decreased in the case of medial condyle JCF - over time. Predicting changes in JCF based on the simple measures of age, weight, and height worked reasonably well for hip JCF (R2 = 0.67) and lateral condyle JCF (R2 = 0.78), but less well for medial condyle JCF (R2 = 0.41). Changes in femoral morphology could be predicted with moderate accuracy, with R2 values ranging from 0.40 for lateral condyle volume to 0.59 for femoral head volume. Our findings demonstrate ontogenetic increases in both absolute JCF and joint contact surface areas, which may help maintain relatively stable cartilage pressures during growth. These data provide valuable reference values for differentiating normal developmental patterns from pathological alterations in femoral morphology and joint loading. While simple regression models effectively captured population-level trends, they were not suitable for subject-specific predictions, underscoring the need for more advanced and individualized modelling approaches in paediatric biomechanics.

Scaling contact force parameters across body size, limb count, and number of contact spheres.

The purpose of this study was to investigate how powder fill weight, particle size, and particle size distribution influence force transmission and compaction behavior of powders during compaction within narrow die cavities. Discrete element method simulations were used to model confined powder compaction. Monodisperse and polydisperse systems were analyzed by varying particle size, size distribution breadth, and fill weight. Compaction force-displacement behavior was examined together with particle-level normal, tangential, and cohesive forces, including their axial and radial spatial distributions. Particle size had a stronger influence on force heterogeneity than fill weight within the range examined, with larger particles generating higher and more heterogeneous contact forces. Force distributions were positively skewed, indicating load localization within force chains. Normal and tangential forces were highest near the die wall, reflecting strong confinement effects. While individual particle forces followed similar size-dependent trends across different size distributions, narrow distributions required higher compaction forces due to localized load-bearing structures, whereas wider distributions promoted more uniform force sharing and lower resistance to compaction. The results demonstrated that macroscopic compaction behavior was governed by the organization of force networks rather than particle-scale force magnitudes alone, highlighting the critical roles of particle size, polydispersity, and confinement effects in powder compaction.

Low back disorders are highly prevalent and disproportionately affect women and older adults, yet the biomechanical mechanisms underlying these demographic disparities remain unclear. This study aimed to investigate how sex- and age-related anatomical differences in vertebral geometry influence lumbar spine loading patterns. Three hundred sixty lumbar motion segments were derived from CT scans of 60 asymptomatic adults (30 males, 30 females; ages 20-69, evenly distributed by decade). Subject-specific multibody dynamic models were developed for each segment featuring anatomically accurate vertebrae and facet joints with simplified discs and loading conditions to isolate geometric effects. Segments were tested under seven physiologic load cases, and disc and facet measures were examined to assess the effects of sex and age on spinal loading. Age significantly influenced facet contact forces and the magnitude of intervertebral disc loading with older subjects exhibiting increased facet forces and elevated shear loads but reduced compression. Sex-based differences were pronounced when loads were normalized by endplate cross-sectional areas. Females exhibited up to 31% higher normalized compression and shear loads than males. Significant asymmetry and inter-subject variability were also observed, particularly in the younger and older age groups. Sex- and age-related anatomical variation in vertebral geometry significantly impacts lumbar spine biomechanics. These findings highlight the need for more inclusive and anatomically realistic modeling approaches that reflect the diversity of the population for accurate risk assessment and personalized clinical strategies.

A TPMS-integrated paediatric proximal femoral osteotomy implant demonstrates structural feasibility and improved load sharing: An in silico proof-of-concept study.

The intelligent soft robotic gripper integrated with tactile sensors significantly enhances the robot’s execution capabilities in complex tasks, resolving critical shortcomings of traditional mechanical grippers—namely, fragile item breakage from rigid impacts, irregular object slippage, and inefficiency due to recognition errors. While electrical sensors (e.g., piezoresistive, capacitive) struggle with structural complexity, signal crosstalk, and environmental interference, optical waveguide tactile sensing offers superior sensitivity, rapid dynamics, and electromagnetic immunity. However, existing waveguide tactile systems face two key limitations: millimeter-scale waveguides cause beam divergence, limiting deformation sensitivity and complicating heterogeneous integration. Additionally, critical gaps remain in adaptive grasping control and contextual object recognition during manipulation. Herein, we present a soft robotic gripper integrated with slender elastic optical waveguide sensors (EOWS) and equipped with a closed-loop feedback control module to achieve intelligent grasping and object attribute recognition. The hand comprises three flexible silicone fingers, each finger seamlessly integrates three EOWS for multi-modal tactile sensing. These sensors exhibit high sensitivity to bending angle (0.273%/°), contact force (0.843%/N), and pressure (1.064%/N). Furthermore, a PID adaptive grasping control strategy and a long short-term memory (LSTM) deep learning algorithm are introduced to dynamically adjust the grasping force and intelligently recognize object attributes such as shape, size, and hardness, with accuracies exceeding 97% for each attribute. Ultimately, experimental validation via a smart fruit-sorting system highlights the platform’s potential for precision agriculture, intelligent logistics, and medical robotics, demonstrating robust, adaptive manipulation in real-world applications.We present a soft robotic gripper seamlessly integrated with slender multi-modal elastic optical waveguide sensors (EOWS) and equipped with an adaptive control module to achieve intelligent grasping and object attribute recognition. Experimental validation via a smart fruit-sorting system highlights the platform’s potential for precision agriculture, intelligent logistics, and medical robotics, demonstrating robust, adaptive manipulation in real-world applications

With the continuous increase in the operating speed of high-speed railways, the fluctuation of the contact force in pantograph-catenary systems becomes increasingly significant, posing higher requirements for current collection quality and operational reliability. Conventional passive control methods mainly rely on structural parameter optimization and lack online adaptability, which limits their effectiveness under complex operating conditions. Although the Linear Quadratic Regulator (LQR) provides good response performance and robustness, its weighting matrices are typically selected through empirical tuning, and the fixed-gain structure cannot adequately address the nonlinear and time-varying characteristics of pantograph-catenary systems. To overcome these limitations, this paper proposes an Adaptive Linear Quadratic Regulator based on the Multi-strategy Dandelion Optimization Algorithm (ALQR-MDO). First, the original Dandelion Optimization (DO) algorithm is enhanced by integrating several improvement strategies, including boundary reflection, sub-elite guidance, Gaussian-Cauchy hybrid mutation, and dynamic population adjustment, thereby improving global search capability and convergence efficiency. Second, an adaptive LQR controller based on pantograph head displacement-segmented gain scheduling is developed, in which smooth transitions of feedback gains under different operating conditions are achieved using linear interpolation. Subsequently, the weighting matrices of the ALQR controller are treated as optimization variables, and a fitness function considering both pantograph head displacement and contact force fluctuations is constructed. The optimal control parameters are obtained using the MDO algorithm. Finally, a pantograph-catenary coupled dynamic model is established and validated through simulations under different operating speeds. Additionally, control energy consumption analysis and closed-loop stability verification are conducted. Simulation results show that under 200 km/h operating conditions, the proposed ALQR-MDO method reduces the standard deviation and range of contact force as well as the standard deviation and range of pantograph head displacement by 49.72%, 41.92%, 59.69%, and 50.96%, respectively, compared with passive control. Under 300 km/h, the corresponding improvements reach 50.48%, 48.14%, 54.95%, and 48.60%. These results demonstrate that the proposed method significantly improves current collection performance while exhibiting strong adaptability to high-speed operating conditions and robust disturbance rejection capability.

A neural network for predicting knee contact forces from clinic-friendly data.