Biomimetics最新文献

Upper limb rehabilitation exoskeletons form a spatial closed kinematic chain with the human arm, where inevitable joint-center and axis misalignment can generate hyperstatic interaction forces and torques. Passive degrees of freedom (DOF) are widely introduced to improve kinematic compatibility, yet different compatible configurations may exhibit distinct wearable performance. This study experimentally compares three compatible four-degree-of-freedom exoskeleton configurations derived from the synthesis of Li et al. using a single reconfigurable rehabilitation robot. The platform is assembled into each configuration through modular passive units and instrumented with two six-axis force-torque sensors at the upper-arm and forearm interfaces. Interaction forces and torques are measured in passive training mode during eating and combing trajectories. For each configuration, tests are performed with passive joints released and with passive joints locked to quantify the effect of passive motion accommodation. Directional and resultant metrics are computed using mean and peak values over movement cycles. Results show that releasing passive joints consistently reduces interaction loading, and Category 2 achieves the lowest forces and torques with the strongest peak suppression, indicating the best practical compatibility.

Wearable robots can improve human walking economy; however, their effectiveness depends on user adaptation to assistance. This study introduces a framework for real-time estimation of user adaptation that relies only on wearable sensor data during operation. Metabolic measurements were used solely to establish the ground truth adaptation curves for model training and validation but are not required for real-time inference. Five healthy adults completed six days of treadmill walking while wearing a soft hip exosuit that provided hip extension assistance. Thigh-mounted inertial measurement units recorded step timing and hip-angle trajectories, from which three variability-based features (step-frequency variability, maximum hip-flexion variability, and maximum hip-extension variability) were extracted. A Long Short-Term Memory (LSTM) model used these gait-variability inputs to estimate each user's adaptation level relative to a metabolic cost benchmark obtained from respiratory gas analysis. Across sessions, the metabolic cost decreased by 9.0 ± 5.6% from Day 1 to Day 6 (p < 0.01) with a mean time constant of 202 ± 78 min, In contrast, the variability in step frequency, maximum hip flexion, and maximum hip extension decreased by 66.4 ± 6.8%, 37.9 ± 24.2%, and 42.8 ± 10.6%, respectively, indicating that these reductions were users' progressive adaptation to the exosuit's assistance. Under leave-one-subject-out (LOSO) evaluation across five participants, 59.2% of the model predictions fell within ±10 percentage points of the metabolic cost-based adaptation curve. These results suggest that simple kinematic variability measured with wearable sensors can track user adaptation and support practical approaches to real-time monitoring. Such capability can facilitate adaptive control and training protocols that personalize exosuit assistance.

Lung cancer is considered to be a significant cause of death in the world, and the timely identification of nodules in the lungs in CT scans is very important to enhance the prognosis of patients. Although the state of the art of nodule delineation using deep learning-based segmentation models was achieved, major problems, including high feature diversity, low spatial discrimination, and overfitting of the models, require stronger feature-processing approaches. This research explores an enhanced symmetric encoder-decoder segmentation network known as the Improved Group-Shuffle Module (IGSM) to overcome these shortcomings. The most important feature of the proposed method is the IGSM, which hierarchically divides feature maps into a few groups, then transforms them independently, and then randomly switches channels between groups to increase inter-group interaction of features and diversity. This IGSM method is inspired by human brain functions, which are processed in specialized cortex areas, which are mimicked in this work through small-group feature processing. Channel shuffling is designed based on inter-modular communication in the human brain through coherent information sharing among the small groups of cortices. Through this mechanism, the model is much better at capturing discriminative spatial and contextual patterns, especially on complex and subtle nodule structures. The IGSM configurations have been optimized, specifically, the placement of the modules, grouping size, and shuffle permutation strategies. The proposed model's performance is then compared with the benchmarked models, like U-Net and DeepLab, with various performance indicators such as mean Intersection over Union (mIoU), Dice Score, Accuracy, Sensitivity, and Specificity. The simulation results proved the superiority of the IGSM-enhanced model with the mIoU of 0.7735, the Dice Score of 0.9665, and the Accuracy of 0.9873. The addition of the group and shuffle module not only enhances the discrimination between the nodules and their background, but it also improves the ability to generalize over a variety of nodules' morphology, thus producing a reliable tool for automated detection of lung cancer.

The efficient design of biohybrid materials requires controlling the interaction between the cell and the material for a wide range of possible combinations. Single cell force spectroscopy (SCFS), an atomic force microscopy (AFM) experimental procedure based on the binding of an individual cell to an AFM cantilever and the assessment of the adhesion force between the cell and a target substrate, represents one of the most promising alternatives to characterize the interaction between cell and material. However, SCFS relies on the efficient binding of the cell to the AFM in order to avoid drawbacks, such as the detachment of the cell. In this work, three different versatile and robust procedures are presented that allow for the binding of either non-adherent (CD4⁺ T-lymphocytes) or adherent (mesenchymal stem cells, MSC) cells to the AFM probe. The three crosslinking strategies comprise (1) the streptavidin/biotin system, (2) sulfhydryl group-based crosslinkers, and (3) "click" (bioorthogonal) chemistry. Additionally, three decoration schemes of the functionalized AFM probes are explored: a specific antibody, concanavalin A, and direct binding of the cell through azide-derivatized membrane proteins. Differences are observed between these alternatives and it is found that the strength of the interaction (in decreasing order) is as follows: specific antibody, concanavalin A, and binding through azide-derivatized proteins.

This study addresses discontinuity prediction in income reporting within the Chilean student loan program, a critical event for credit risk management. Although the literature has incorporated machine learning models to anticipate non-compliance behavior, a gap remains in the development of methodologically robust evaluations that integrate nonlinear imputation, imbalance correction, and repeated validation across multiple partitions. To address this need, a complete pipeline was implemented on a dataset of 22,303 records, including MissForest imputation, SMOTE-based balancing, and a comparative assessment of a biologically inspired Deep Neural Network (DNN) and a Random Forest (RF) classifier used as a classical baseline model, evaluated across 35 stratified partitions. The results show that the bioinspired DNN, as the primary focus of this study, consistently outperforms the RF in metrics such as AUC (0.9991 vs 0.9709), F1-score (0.9966 vs 0.9497), and agreement measures, while also exhibiting lower variability across partitions. The interpretability analysis indicates that financial variables account for the greatest influence on predictions, whereas demographic variables contribute minimally. The study provides a replicable and robust methodology aligned with risk analysis practices in student credit contexts.

The Superb Fairy-wren Optimization Algorithm (SFOA) is a meta-heuristic algorithm inspired by the behavior of the superb fairy-wren. However, the conventional SFOA tends to converge to local optima and exhibits limited convergence accuracy when addressing complex optimization problems. To overcome these drawbacks, this study proposes an Improved Superb Fairy-wren Optimization Algorithm (ISFOA). The ISFOA incorporates four strategies-Chebyshev chaotic mapping, an adaptive weighting factor, Cauchy-Gaussian mutation, and t-distribution perturbation-to enhance the algorithm's ability to balance global exploration and local exploitation. An ablation study using the CEC 2021 test suite was performed to evaluate the individual contribution of each strategy. Moreover, to comprehensively assess the performance of ISFOA, a comparative analysis was carried out against eight other meta-heuristic algorithms on both the CEC2005 and CEC2021 benchmark function sets. Additionally, the practical applicability of ISFOA was examined by comparing it with eight other optimization algorithms across seven engineering design problems. The comprehensive experimental results indicate that ISFOA outperforms the original SFOA and other compared algorithms in terms of robustness and convergence accuracy, thereby offering an efficient and reliable approach for solving complex optimization problems.

Objective: In vitro study compared the efficacy in remineralizing initial caries of dentifrice containing natural hydroxyapatite (natHAP), synthetic HAP (synHAP), and fluoride.

Methods: Initial carious lesions were created on 105 bovine enamel blocks by 4-day demineralization in a microbial caries model inoculated and fed 3× daily with 10% sucrose (6 min/episode). The caries-bearing blocks were stratified across seven treatment groups (N = 15/group); 20% nat-nHAP tooth powder, 20% nat-nHAP toothpaste, 30% nat-nHAP toothpaste, 20% nat-microHAP toothpaste, 15% syn-nHAP, fluoride (1100 ppm) toothpaste (NaF), and artificial saliva (AS) were used, and the groups were subjected to 28-day remineralization using a standardized pH cycling model with a daily regimen consisting of three 2 min toothpaste slurry treatments and one 2 h acidic challenge, and AS storage for the rest of the day. Surface microhardness of each block was measured after demineralization and after remineralization. Statistical analyses were performed using a paired t-test and Tukey's multiple comparison test.

Results: All groups induced significant (p < 0.001) remineralization. The %Rem was significantly lower for AS (9.61 ± 6.17%) than 30% nat-nHAP (29.21 ± 16.47%) and fluoride (27.05 ± 10.9%) toothpastes. There were no significant differences in %Rem among the natHAP formulations or between natHap and synHAP formulations.

Conclusions: Within the limitations of the present in vitro study, dentifrices based on natHAP or synHAP have comparable efficacy to standard fluoride toothpaste in remineralizing initial caries.

Prolonged tooth loss causes alveolar ridge atrophy, complicating implantation, especially in patients with impaired mineral metabolism. This study aimed to develop a personalized titanium mesh for guided bone regeneration and qualitatively evaluate its local tissue response in a vitamin D3-deficient rabbit model. A titanium mesh design has been developed in the form of a plate-shaped profile frame of a truncated pyramid with a solid upper base and perforated side faces. For testing in a rabbit model with vitamin D3 deficiency, a bone defect was created and repaired in the mandible using hydroxyapatite, an individual titanium mesh and a collagen membrane. Histological analysis was performed in the Laboratory of Digital Microscopic Analysis. The optimized geometry and parameters of the mesh openings contributed to effective vascularization and osteogenesis. In the postoperative period (3, 5 and 7 days), moderate edema and hyperemia were noted with their complete leveling by the 7th day (p < 0.05). According to the histological examination, 3 months after the installation of the titanium mesh, the formation of dense connective tissue with signs of active osteogenesis was observed in the defect area, including zones of mineralized bone trabeculae, osteocytes and osteon elements. The findings of this study indicate acceptable biocompatibility of the developed titanium structure and suggest osteoconductive potential, which, however, needs to be confirmed in controlled, quantitatively powered studies.

Corrugated insect wings inspire biomimetic aerodynamic design, yet their behaviour at low and transitional Reynolds numbers remains not fully understood. This study presents a three-dimensional computational analysis of flow over an infinite corrugated wing across Reynolds numbers from 10 to 10,000 and angles of attack from -5 to 20°, with emphasis on spanwise effects. An expanded verification and validation procedure ensured numerical reliability. At the lowest Reynolds numbers, the flow is steady and largely two-dimensional, with localised recirculation zones. As Reynolds numbers or angles of attack increase, the flow transitions to periodic vortex shedding, and three-dimensional structures appear. At a Reynolds number of ten thousand, periodic shedding occurs at zero degrees incidence, indicating a shift toward turbulent or bluff body-like behaviour. The examined corrugated profile does not exhibit a lift-to-drag benefit over smooth aerofoils in steady gliding, although root section corrugation helps delay separation in transitional regimes. This behaviour reflects mechanisms used by dragonflies to maintain stable gliding despite textured wings. By extending flow regime classification, the study identifies conditions where two-dimensional assumptions fail and highlights the influence of spanwise flow structures. These findings deepen understanding of insect wing aerodynamics and support biomimetic design of future wings.

This finite element study compared the effects of prosthetic superstructure material and supporting implant number on stresses in implants, multiunit abutments, and restorations, and on peri-implant bone strains under bruxism-like loading. Two posterior mandibular models representing missing left FDI 34-36 were generated: a 2-implant configuration (implants at 34 and 36) and a 3-implant configuration (implants at 34, 35, and 36), each restored with a three-unit implant-supported fixed bridge. For each configuration, three superstructure materials were simulated: cobalt-chromium (Co-Cr), polyetheretherketone (PEEK), and monolithic zirconia (MZ). Static parafunctional loads were applied as a 500 N oblique load (30° to the implant long axis; 125 N to each buccal cusp) and a 1000 N vertical load applied to the central fossae. Cortical bone generally exhibited higher strain than trabecular bone, and the maximum cortical principal strain under vertical loading averaged approximately 5800 μɛ. The highest implant von Mises stress occurred in the first molar implant of the 2-implant MZ model under oblique loading, while the maximum under vertical loading was 236 MPa (also 2-implant MZ). Prosthetic peak stresses reached 184 MPa under vertical loading (3-implant PEEK composite-veneered model) and 233 MPa under oblique loading (2-implant MZ), with a minimum of 51 MPa in the 3-implant PEEK framework under vertical loading. Overall, increasing implant number reduced the stress/strain values, and MZ showed comparatively higher stress and strain levels.