Search Results (13,758)

(1) Background: Peripheral nerve repair requires structural guidance and reliable functional assessment, yet current commercial conduits lack internal microarchitecture and do not integrate real-time monitoring capabilities. This study presents an engineering-focused feasibility assessment of a dual system combining a microstructured 3D-printed nerve guidance conduit with a portable neuromuscular monitoring device. (2) Methods: Two conduit variants were digitally designed based on median nerve anatomical dimensions and fabricated using FDM (PLA) for large-scale prototypes and SLA for high-resolution miniaturized models. Structural behavior was evaluated through simplified finite element analysis (FEA) under physiological pressure ranges (1000–5000 Pa). A portable monitoring system incorporating surface EMG electrodes, an AD620 instrumentation amplifier, and an ESP32 microcontroller was assembled and tested non-invasively on a healthy adult volunteer to verify signal acquisition functionality. Results: Both conduit designs were successfully fabricated with accurate reproduction of internal microchannels. FEA indicated negligible deformation (1.41 × 10⁻¹¹–1.69 × 10⁻¹⁰ mm) and low stress values (0.0129–0.155 N/m²), confirming structural stability under the simplified loading model. The monitoring system recorded stable EMG signals (3200–3500 ADC units), demonstrating correct operation of the acquisition chain during controlled stimulation. (3) Conclusions: This work provides a design-stage engineering feasibility demonstration of an integrated platform combining a microstructured 3D-printed conduit with a portable neuromuscular monitoring device. The study does not include biological validation; prototypes were evaluated solely for geometric and mechanical fidelity; and the monitoring system was tested only for functional signal acquisition. Future work will address biocompatibility, in vitro assays, and in vivo evaluation. Full article

Hydrostatic rotary tables are widely used in the machine tool industry for high-precision machining applications. The standard design approach relies on two-dimensional (2D) Reynolds finite element models that treat the bearing gap as a rigid, uniform scalar per ring. Nevertheless, at the scale of these machines, elastic deformations become comparable to the oil film thickness. In this study, a monolithic three-dimensional elasto-hydraulic model is compared with the standard 2D benchmark for a 5.5 m diameter cast iron table with two concentric bearing rings, over a load sweep (115–230 t) and six clamping-rectangle configurations. At the nominal load, the 2D model underestimates the inner-ring mean film by 21%, overestimates the minimum outer-ring film by 30%, and underestimates the pumping power by 24%, all percentages growing with load. Independently of load, clamping configuration alone produces an inner-ring film dispersion of up to 88% of the 2D value and a ring load-sharing ratio varying by a factor of 2.8. These effects are invisible to the 2D framework and, in the most compact configuration, sufficient to collapse the oil film. These results indicate that the 2D model, while adequate for preliminary sizing, cannot substitute for the coupled model when verifying guideway safety margins. Full article

Background: Cardiac MRI analysis typically treats segmentation, biomarker estimation, and disease classification as separate problems, even though clinical biomarkers are themselves derived from segmentation masks and diagnosis depends on both. This paper investigates whether a single jointly trained network can perform all three tasks without sacrificing accuracy on any of them. Methods: I propose CardioSynergyNet, which is a multi-task network with a weight-shared encoder for end-diastolic (ED) and end-systolic (ES) frames. Three modules link the tasks: Cross-Phase Deformation Attention (CPDA) models ED–ES deformation, a Differentiable Biomarker Extraction Layer (DBEL) computes eight clinical biomarkers directly from the soft segmentation mask, and Class-Conditional Prototype Feedback (CCPF) conditions segmentation on the predicted disease class. The model was trained and tested on 150 patients (30 per class across five diagnostic groups), who were split patient-wise into 110/20/20 train/validation/test, yielding 1489 paired ED/ES slices. Results: On the test set, the model achieved a mean ED Dice score of 0.905 (computed across all four classes, including background), with the right ventricle being the hardest structure, particularly at ES. Biomarker regression was accurate for area- and mass-based quantities (R > 0.92) but weaker for ratio-based biomarkers such as ejection fraction (R = 0.66). Disease classification reached 76.65% accuracy with most confusions occurring between clinically similar disease pairs. Conclusions: The joint training of segmentation, biomarker extraction, and classification achieves performance competitive with task-specific models while keeping outputs across tasks consistent with one another, supporting cross-task feedback as a viable direction for integrated cardiac MRI analysis. Full article

Emptying pressurised pipelines with undulating profiles can generate subatmospheric pressure conditions that compromise structural integrity, particularly when no air admission valve is considered. One-dimensional models for this transient event have so far relied on the Rigid Water Column (RWC) assumption, which neglects the elasticity of the liquid phase and the deformability of the pipe walls. This study applies an Elastic Water Column Model (EWCM) to the emptying of pipelines with an undulating profile, in which the Method of Characteristics is applied on two independent moving grids coupled through a single entrapped air pocket governed by a polytropic law. The model is validated against nine configurations, each recorded twice, in a laboratory facility whose two branches rise at 30° to a common high point, forming an inverted V. The configurations cover symmetric air pockets under 6% and full valve opening, and asymmetric air pocket distributions under full opening. The EWCM predicts the minimum pressure with a mean absolute error of 0.21%, against 0.27% for the rigid formulation, and both remain below the scatter between experimental repetitions. The governing parameter depends on the valve opening level: under full opening, the shape of the valve manoeuvre dominates, whereas under a restrictive 6% opening, it leaves the minimum pressure unchanged and the polytropic exponent becomes the largest effect. The wave speed is negligible throughout. The ratio between the acoustic and mass-oscillation time scales, obtainable in closed form, remains between 0.014 and 0.028 across the configurations tested, with the divergence between the two formulations increasing with it. Full article

Tunnel excavation in clay-filled fractured rock masses is highly susceptible to sliding collapse due to the weak mechanical properties of clay-filled discontinuities and excavation-induced unloading. To investigate this problem, this study employed the Material Point Method (MPM) and developed an improved contact algorithm incorporating a strain-dependent dynamic friction coefficient to capture the nonlinear shear behavior of clay-filled fractures. A two-dimensional tunnel excavation model was established to investigate the possible deformation response and fracture-controlled sliding mechanism of the tunnel face, including stress release, base heaving, shear slip, and collapse. A sliding-block benchmark was used to verify the numerical implementation of the friction update, and field monitoring data provided a limited check of the small-deformation response during normal excavation. Numerical results suggest that persistent clay-filled fractures significantly alter stress redistribution and promote large-scale sliding deformation along the weak structural plane. Furthermore, a probabilistic evaluation framework based on Latin Hypercube Sampling and extensive numerical simulations was established to quantify the conditional probability distribution and exceedance characteristics of tunnel-face deformation under the adopted geological, mechanical, and numerical assumptions. The proposed framework provides a site-specific and model-conditioned method for probabilistic deformation analysis and scenario comparison in tunnels excavated through clay-filled fractured rock masses. Full article

In deep-water environments, the combined effects of hydrostatic pressure, explosion-induced shock waves, and bubble pulsation can produce complex nonlinear dynamic responses and instability in stiffened cylindrical shells. Clarifying these response mechanisms is critical for the safety assessment and blast-resistant design of deep-sea equipment. In this study, an acoustic–structural coupled numerical method was developed for stiffened cylindrical shells subjected to underwater explosion loading and validated using deep-water explosion tests conducted in a pressure vessel. The numerical results show that the relative error between the numerical and experimental wall-pressure impulses on the blast-facing surface is 3.6%, while the relative error in the maximum compressive strain at a representative measurement point on the blast-facing surface is 15.6%, indicating that the established numerical model can reasonably reproduce the pressure impulse and the primary dynamic response of the structure. Based on this validated model, a systematic investigation was conducted to evaluate the effects of hydrostatic pressure, stand-off distance, shell-plate thickness, and stiffener number on the deep-water explosion response of stiffened cylindrical shells. With increasing water depth, the structural deformation mode transitions from localized plastic indentation to global instability and crushing. The findings provide practical guidance for blast-resistant design and parameter optimization of deep-water stiffened cylindrical shells. Full article

During the long-term service and construction of bridges, various types of surface defects frequently occur in the anti-corrosion coating on the surface of prestressed metallic corrugated ducts, severely threatening the overall durability of the bridge structure. To address the challenges of uneven optical imaging, high difficulty in coating defect detection, and low manual interpretation efficiency caused by the periodic linear groove structure of the corrugated ducts, this paper proposes the BCDC-YOLO (Bridge Corrugated Duct Coating defect detection–YOLO) method, which is tailored for inspecting surface defects in corrugated duct coatings. First, the captured images of coating surface defects are augmented and processed to construct a standardized dataset suitable for training deep learning models. Second, aiming at the limitations of the original YOLO network in detecting tiny and low-contrast defects within complex corrugated groove backgrounds, four improvement strategies are introduced to optimize the network model from both microscopic and macroscopic dimensions: deformable convolution, the contrast-enhanced adaptive SiLU (CE-ASiLU) activation function, the efficient channel attention (ECA) module, and an improved dual-index Gaussian Wasserstein distance loss function. Finally, the optimized network model is utilized to train and predict the surface coating defect dataset, and the identification outcomes are thoroughly compared and discussed with existing mainstream detection methods. The results demonstrate that the proposed method achieves a mean average precision (mAP) of 95.42 across all coating damages, with an inference speed of 66.8 FPS, which far surpasses the industrial real-time detection threshold. Notably, for hidden and extremely elusive tiny pitting defects, the single-class average precision (AP) yields an improvement of 12.46 compared with the baseline network. Ablation experiments confirm the effectiveness of the four introduced optimization strategies. Compared with other mainstream detection frameworks, the proposed BCDC-YOLO achieves the optimal balance between detection precision and real-time computational efficiency, thereby providing solid methodological and technical support for the intelligent inspection of surface coating defects. Full article

In prestressed concrete cable-stayed truss bridges, the prestress losses in the upper chords, tensile web members, lower chords, and closure segment of the mid-span region have a significant influence on the stress distribution and node displacements of the truss structure. To rapidly and accurately reveal the structural response laws induced by multi-source prestress loss coupling, an improved genetic algorithm (GA) and backpropagation (BP) neural network hybrid model, referred to as the improved GA-BP algorithm, was developed based on orthogonal experimental design. The model was validated by comparing its performance with those of BP, GA-BP, PSO-BP, and SABO-BP models under the same data partitioning. The improved GA-BP model achieved R² values of 0.9727, 0.9861, 0.9720, and 0.9524 for the maximum tensile stress, maximum compressive stress, maximum X-displacement, and maximum Z-displacement, respectively, with corresponding MAPE values of 2.28%, 0.86%, 0.43%, and 1.11%. Sensitivity analyses based on 800 sets of global stochastic predicted samples revealed that the prestress loss of the upper chord plays a dominant role in controlling node displacement and compressive stress, while that of the tensile web members serves as the core sensitive component inducing mid-span tensile stress exceedance. These findings provide practical guidance for structural health monitoring: the upper chord prestress loss should be prioritized for deformation control, while the tensile web members warrant close inspection to prevent tensile stress exceedance. The study demonstrates that the improved GA-BP neural network is suitable for predicting prestress loss effects and quantitatively evaluating structural responses for such complex bridges. Full article

At the microscale, the interplay between size effects and boundary constraints strongly influences the mechanical behavior of quasicrystal structures. To elucidate this interplay, we investigate the static and dynamic responses of one-dimensional (1D) hexagonal piezoelectric quasicrystal Timoshenko microbeams. Our model integrates the modified couple stress theory with a Winkler–Pasternak two-parameter elastic foundation. Under open-circuit conditions, the electric potential is generated solely by mechanical deformation through piezoelectric coupling. Using Hamilton’s principle, we establish a non-classical beam theory framework for piezoelectric quasicrystals that simultaneously incorporates size effects, piezoelectric coupling, and foundation constraints. A normalized system contact stiffness is introduced to quantify the coupling between size effects and the foundation, providing a possible reference for evaluating foundation parameters. Fourier series solutions reveal that increasing the material length scale parameter markedly enhances both bending stiffness and the free vibration frequencies, particularly at small scales. The elastic foundation further suppresses static deformation and elevates all natural frequencies. Notably, the phason field exhibits a non-classical, non-monotonic response under foundation constraints. Increasing the Pasternak shear stiffness not only reduces the overall phason deflection but also triggers a qualitative reversal of the size sensitivity. The apparent critical Pasternak stiffness required for this transition decreases systematically as the Winkler spring stiffness increases, indicating a synergistic effect between the two foundation parameters. Detailed scanning around the critical interval further reveals a transitional regime in which the phason deflection first rises and then declines, signifying a competitive handover between two driving mechanisms within a narrow stiffness band. Unlike existing size-dependent quasicrystal beam models, the present formulation simultaneously incorporates a two-parameter elastic foundation and piezoelectric coupling in a Timoshenko microbeam, and further reveals a non-monotonic phason response governed by a critical energy partition ratio. These findings provide theoretical insight into the complex multi-field coupling in quasicrystals, and the open-circuit formulation may be particularly relevant for high-frequency or electrically insulated configurations. Full article

Insufficient three-dimensional (3D) geolocation accuracy of interferometric synthetic aperture radar (InSAR) scatterers on dikes often hampers the distinction between the dike crest and slopes. This limitation hinders effective monitoring and interpretation of deformation associated with different dike structures using the multi-temporal InSAR (MT-InSAR) technique. To address this, we propose a geolocation improvement method by integrating light detection and ranging (LiDAR) point cloud. We first construct and transform a 3D error ellipsoid of each InSAR scatterer by quantitatively estimating its geolocation uncertainty. Next, we design and employ a rotation matrix and projection model to extract LiDAR points located within each error ellipsoid. Then we use the mean position and height of the extracted LiDAR counterparts to improve the geolocation accuracy of the InSAR scatterers. The Houtribdijk, as our test site, is a 26.5 km long dike in the Netherlands, where we used both ascending and descending Sentinel-1 tracks acquired from 2018 to 2022. Results show that the original geocoded InSAR scatterers exhibit positional and height discrepancies between the two datasets, and their height variations fail to reflect the actual topographic features of individual slopes. After improvement, the heights of the InSAR scatterers agree well with LiDAR measurements, with root mean square errors reduced by up to 97%. Coefficients of determination with the AHN4-derived digital surface model increase from 0.22 and 0.24 to 0.82 and 0.78 for ascending and descending tracks, respectively, and reach 0.96 along the Houtribdijk. The percentage of boundary scatterers located within the dike extent also increases from 42% and 20% to 85% and 84% for the ascending and descending tracks, respectively. This test demonstrates that our method effectively improves InSAR geolocation and provides the spatial and geometric basis for deformation analysis of different dike structures. Full article

Accurate plant height estimation is important for monitoring crop growth and supporting precision agricultural management. Manual measurements are labor-intensive, while LiDAR-based methods are expensive and require complex processing. UAV photogrammetry provides a lower-cost alternative but remains challenging in flooded rice paddies because of canopy deformation and difficulties in terrain extraction. This study proposes Rice-STNet, a spatiotemporal deep learning framework for end-to-end rice plant height estimation using multi-temporal UAV RGB imagery. Rice-STNet integrates a convolutional neural network for spatial feature extraction, Time2Vec for temporal encoding, and a gated recurrent unit network for modeling temporal dependencies across observation dates. The framework was evaluated using field data collected from rice paddies over two growing seasons. Rice-STNet achieved an R² of 0.97, a root mean squared error of 1.97 cm, and a mean absolute error of 1.14 cm. It outperformed random forest, support vector regression, a CNN-only baseline, and a UAV photogrammetry-based point-cloud approach. In addition, the framework generated high-resolution plant height maps for field-scale analysis of spatial growth variability. These results underscore the importance of jointly modeling spatial and temporal characteristics for continuously evolving crop traits. The proposed framework offers an accurate, scalable, and non-destructive solution for large-scale crop phenotyping and precision agriculture. Full article

Uplift of shallow foundations from clay seabeds is governed by the generation and dissipation of negative excess pore pressure beneath the foundation. Previous centrifuge tests have revealed markedly different uplift responses between surface and skirted foundations, but the underlying mechanisms remain unclear. This study investigates these mechanisms using coupled finite-element analyses with a hydro-mechanical interface model, validated against published centrifuge data. The analyses examine uplift resistance, suction evolution, gap opening, soil deformation, and stress paths under different uplift rates. Results show that uplift resistance for both foundation types is primarily generated by suction beneath the top plate, with negligible tensile contribution from the soil skeleton. For the surface foundation, suction dissipates mainly through tangential flow along the plate–soil interface, accompanied by progressive gap opening from the edge towards the centre and eventual interface breakaway. In contrast, the skirted foundation mobilizes a deeper reverse bearing mechanism and a more uniform displacement field within the confined soil plug. Under partially drained conditions, suction dissipation in the skirted foundation is accompanied by marked stress redistribution within the confined soil plug. Although the present formulation does not explicitly simulate progressive failure within the plug, the numerical pre-breakaway response, together with the centrifuge observations, suggests that skirted foundation breakaway cannot be interpreted simply as detachment at the plate–soil interface. These findings clarify the fundamental differences in uplift and breakaway mechanisms between surface and skirted foundations. Full article

The conventional analysis of casing deformation relies on engineering logging, analytical models, or repeated finite element modeling and simulation for different wells or field restricts, which may involve high costs, simplifying assumptions, and low computational efficiency. To improve casing deformation and casing configuration analysis in creep formations, a surrogate model is proposed. A dataset is generated through the parametric finite element modeling of the casing–cement sheath–creep formation system, and input features are optimized by feature engineering. Light Gradient Boosting Machine (LightGBM) is adopted as the prediction model, while Comprehensive Learning Particle Swarm Optimization (CLPSO) is employed for hyperparameter optimization. The surrogate model is further used to generate casing deformation contour plots. Algorithm comparisons and finite element simulations are performed to evaluate the prediction accuracy and computational efficiency. The results show that the RMSE for casing nodes in the test set is 0.0825 mm, and the average overlap ratio between the predicted and finite element deformation intervals reaches 98.42%. The predictions show good agreement with the finite element results in terms of the casing deformation magnitude and casing configuration. Compared with conventional analytical models and repeated finite element model construction, the proposed model reduces the dependence on simplifying assumptions, supports the visualization of casing configurations, and improves the computational efficiency. Full article

The linear axis is a core motion module in precision equipment and directly affects assembly and machining accuracy. Inaccurate prediction of motion error may lead to failure in aerospace manufacturing and assembly. Most existing methods typically assume rigid components and neglect tolerance coupling, yielding inaccurate predictions. Therefore, a method considering both tolerance coupling and worktable elastic deformation is developed. First, the variation ranges of geometric errors under the coupled tolerances were characterized using Small Displacement Torsor theory. Second, a two-stage error propagation model is established: errors were initially propagated from the base to four sliders by Homogeneous Transformation Matrices, and subsequently mapped to the worktable utilizing transfer coefficients derived from finite element analysis. Finally, Monte Carlo Simulation was employed to obtain the error variation intervals and their statistical distributions. A case study demonstrated that neglecting worktable elastic deformation underestimates translational errors by up to 40%. Meanwhile, neglecting tolerance coupling would underestimate rotational and translational errors by up to 43% and 25%, respectively. Furthermore, applying the proposed model to tolerance allocation proved that the schemes guided by simplified models would cause critical design failures. Therefore, incorporating both factors is important for obtaining physically grounded error predictions and for improving the reliability of tolerance allocation. Full article

Compression packers are widely used in oil and gas well operations for zonal isolation, yet the large-deformation mechanical behavior of their rubber sealing elements lacks a closed-form analytical solution. This paper presents a complete theoretical analysis of the axisymmetric compression of an annular rubber cylinder based on the incompressible Mooney–Rivlin hyper-elastic model. The deformation process is divided into three successive stages: free expansion, casing-constrained deformation, and fully constrained deformation. Analytical expressions for the principal stretches, stress fields, and axial force are derived for each stage by integrating the radial equilibrium equation with proper treatment of the Lagrange multiplier. The frictionless analytical results are validated against axisymmetric finite element simulations, showing excellent agreement for all stress components. The applicability of the frictionless theory to frictional conditions is then examined. Results show that although friction introduces non-uniform axial deformation and end bulging, the average contact pressure on the rubber–mandrel interface agrees closely with the theoretical prediction, especially at higher axial forces (150–250 kN). A linear relationship between the average contact pressure and the axial force is confirmed, providing a simple design tool. The theoretical solution offers a computationally efficient alternative to finite element analysis for preliminary packer design and parametric studies. Full article