Search Results (265)

The transient electromagnetic (TEM) method is a key detection and monitoring technology for safe coal-mine production. Surface TEM depth penetration is limited by real geological conditions and transmitter–receiver hardware performance. Compared with the surface TEM method, the tunnel TEM method can enhance the depth of exploration to some extent, but it is constrained by the limited working space of the roadway, which makes it difficult to perform the area-wide and multi-line data acquisition, and thus the lateral detection resolution is directly compromised. Consequently, either surface or tunnel TEM alone suffers inherent limitations. The multidimensional surface and surface-to-tunnel TEM method employs a single large-loop transmitter and records electromagnetic (EM) signals both on the surface and in the tunnel, enabling joint data interpretation. The joint TEM observation method effectively addresses the limitations by using a single observation mode, with the goal of achieving high-precision detection. To investigate the detection capabilities of the joint surface and surface-to-tunnel TEM method, we propose a three-dimensional (3D) joint surface and surface-to-tunnel TEM forward modeling method based on the spectral element method (SEM). The SEM, using high-order vector basis functions, enables high-precision modeling of TEM responses with complex geo-electric earth models. The accuracy of the SEM is validated through comparisons with one-dimensional (1D) TEM semi-analytical solutions. To further reveal TEM response characteristics and multi-dimensional resolution under joint surface and tunnel detection modes, we construct several typical 3D geo-electric earth models and apply the SEM algorithm to simulate the TEM responses. We systematically analyze the horizontal and vertical resolution of 3D earth model targets at different decay times. The numerical results demonstrate that surface multi-line TEM surveying can accurately delineate the lateral extent of the target body, while vertical in-tunnel measurements are crucial for identifying the top and bottom interfaces of geological targets adjacent to the tunnel. Finally, the theoretical modeling results demonstrate that compared to individual TEM methods, the multi-dimensional joint surface and tunnel TEM observation yields superior target spatial information and markedly improves TEM detection efficacy under complex conditions. The 3D TEM forward modeling based on the SEM provides the theoretical foundation for subsequent 3D inversion and interpretation of surface-to-surface and surface-to-tunnel joint TEM data. Full article

Reaction–diffusion equations provide a fundamental framework for modelling spatial population dynamics and invasion processes in mathematical biology. Among these, Fisher’s equation combines diffusion with logistic growth to describe the spread of an advantageous gene and the formation of travelling population fronts. In this work, we investigate the one-dimensional Fisher’s equation using Lie symmetry analysis to obtain a deeper analytical understanding of its wave propagation behaviour. The Lie point symmetries of the partial differential equation are derived and used to construct similarity variables that reduce Fisher’s equation to ordinary differential equations. These reduced equations are then solved by a combination of direct integration and the tanh method, yielding explicit invariant and travelling-wave solutions. Symbolic computations in MAPLE are employed to compute the symmetries, verify the reductions, and generate illustrative plots of the resulting wave profiles. The computed solutions capture sigmoidal fronts connecting stable and unstable steady states, providing clear information about wave speed and shape. Overall, this study demonstrates that Lie group methods, combined with hyperbolic-function techniques, offer a powerful and systematic approach for analysing Fisher-type reaction–diffusion models and interpreting their biologically relevant invasion dynamics. Full article

To enhance the assessment accuracy of tunnel face instability risks of active collapse during shield tunneling, this study establishes a novel unified analytical framework that couples the effects of unsaturated transient seepage induced by excavation drainage with soil stratification and heterogeneity. Grounded in unsaturated effective stress theory, the framework explicitly incorporates matric suction into the Mohr–Coulomb failure criterion via suction stress and apparent cohesion. By employing a horizontal two-layer nonhomogeneous soil model and solving the one-dimensional vertical Richards’ equation, an analytical solution for the face drainage boundary is derived to quantify the spatiotemporal evolution of suction stress and apparent cohesion. Subsequently, the critical support pressure is evaluated using the upper bound theorem of limit analysis, incorporating a horizontal layer-discretized rotational failure mechanism and the power balance equation. The validity of the proposed framework is confirmed through comparative analyses. Parametric studies reveal that in the upper hard and lower soft strata, the critical support pressure decreases and converges over time, indicating that unsaturated transient seepage exerts a significant influence in the short term that stabilizes over the long term. Additionally, sand–silt stratum exhibits lower overall stability and higher sensitivity to groundwater levels and temporal factors compared to silt–clay stratum. Conversely, silt–clay stratum displays a non-monotonic evolution with increasing cover-to-diameter ratios (C/D), reaching a minimum critical support pressure at approximately C/D = 1.1. Regarding heterogeneity, the internal friction angle of the lower layer exerts dominant control over the critical support pressure compared to seepage velocity, while the influence of other strength parameters remains secondary. These findings provide a theoretical basis for the time-dependent design of tunnel face support pressure under excavation drainage conditions. Full article

Soil moisture sensors deployed for long-term monitoring often suffer from prolonged data gaps caused by battery depletion, communication dropouts, or hardware failures. When such gaps overlap with irrigation events, key transient phases are obscured and become difficult for conventional imputers to recover. This study proposes HyDSoil, a hybrid diffusion-based imputation model tailored for event-centered block missingness in multichannel soil moisture time series. HyDSoil is first pretrained on a physically interpretable synthetic generator that mimics the baseline-rise-decay response to irrigation and then fine-tuned on field observations from the Baltimore Ecosystem Study dataset. During reverse diffusion, a mask-guided correction keeps observed values fixed while iteratively denoising missing regions. The denoising backbone integrates one-dimensional convolutions, gated recurrent units, and Transformer components to capture high-frequency event spikes, mid-range temporal dynamics, and long-range cross-depth dependencies, respectively. Experiments on both synthetic and real datasets show that HyDSoil reconstructs irrigation-driven peaks with higher fidelity and achieves consistent improvements over strong baselines in global metrics (MAE and DTW) as well as event-focused metrics (PTE and PAE). Ablation studies further verify the complementary contributions of the convolutional, recurrent, and attention branches, and confirm the benefit of synthetic pretraining for long-duration gaps. Overall, HyDSoil enables more reliable continuous soil moisture monitoring and supports precision irrigation analytics. Full article

In fields such as rock and soil grouting and petroleum extraction, the flow of water driven by an immiscible fluid (or vice versa) within a porous medium is frequently encountered. Due to the presence of an interface between the two fluids, whose position changes over time and needs to be solved concurrently with the fluid pressure field, this issue represents a special two-phase moving boundary problem. In this paper, fundamental governing equations for this moving boundary problem in one-dimensional Cartesian, cylindrical, and spherical coordinate systems are developed. Analytical solutions for the pore pressure distribution and interface movement are obtained through the method of similarity transformation. By disregarding the pressure variation in the original underground water, this two-phase moving boundary problem can be reduced into a one-phase moving boundary problem. Consequently, analytical solutions for this one-phase problem are also obtained. The analytical solutions mainly address specific boundary conditions. For cases with general boundary conditions, numerical solutions are provided through a combination of finite volume method and moving node approach. By assuming the instantaneous establishment of a steady-state pore pressure distribution within the medium, the transient two-phase flow model is transformed into a quasi-steady model. Subsequently, an approximate solution for the quasi-steady model is also established. After verifying the model solutions, computational examples are presented to evaluate the effectiveness of the one-phase approximation and the quasi-steady approximation. The one-phase model tends to underestimate fluid pressure within the porous medium under pressure boundary conditions, thereby overestimating the movement speed of the two-phase interface. Additionally, under flow rate boundary conditions, the one-phase model tends to underestimate the pressure required to achieve the design flow rate. As the stiffness of the porous medium increases, the influence of the pressure variation rate term in the transient model equations gradually diminishes. Consequently, the interface movement and pore pressure distribution obtained from the quasi-steady solutions are essentially consistent with those obtained from the transient model, and the quasi-steady solutions are convenient to apply under these circumstances. Full article

This paper presents an analytical model for one-dimensional consolidation analysis of multi-layered unsaturated soils under depth-dependent initial conditions. The general solutions are derived explicitly using the Laplace transform. By combining these general solutions with interfacial continuity conditions between layers and the boundary conditions, the reduced-order system is solved via the Euler method to obtain analytical solutions in the Laplace domain. Numerical inversion of the Laplace transform is then performed using Crump’s method to yield the final analytical solutions in the time domain. The model incorporates initial conditions that account for both uniform and linear distributions of initial excess pore pressure within the soil stratum. The proposed solution is verified by reducing it to degenerated cases (e.g., uniform initial pressure) and comparing it with existing analytical solutions, showing excellent agreement. This confirms the model’s correctness and demonstrates its generalization to multi-layered systems with depth-dependent initial conditions. Focusing on a double-layered unsaturated soil system, the one-dimensional consolidation characteristics under depth-dependent initial conditions are investigated by varying the physical parameters of individual layers. The proposed solution can serve as a theoretical reference for the consolidation analysis of multi-layered unsaturated soils with depth-dependent initial conditions. Full article

Automatic sleep-stage classification is a key component of long-term sleep monitoring and digital health applications. Although deep learning models trained on centralized datasets have achieved strong performance, their deployment in real-world healthcare settings is constrained by privacy, data-governance, and regulatory requirements. Federated learning (FL) addresses these issues by enabling decentralized training in which raw data remain local and only model parameters are exchanged; however, its effectiveness under realistic physiological heterogeneity remains insufficiently understood. In this work, we investigate a subject-level federated deep learning framework for sleep-stage classification using polysomnography data from the ISRUC-Sleep dataset. We adopt a realistic one subject = one client setting spanning three clinically distinct subgroups and evaluate a lightweight one-dimensional convolutional neural network (1D-CNN) under four training regimes: a centralized baseline and three federated strategies (FedAvg, FedProx, and FedBN), all sharing identical architecture and preprocessing. The centralized model, trained on a cohort with regular sleep architecture, achieves stable performance (accuracy 69.65%, macro-F1 0.6537). In contrast, naive FedAvg fails to converge under subject-level non-IID data (accuracy 14.21%, macro-F1 0.0601), with minority stages such as N1 and REM largely lost. FedProx yields only marginal improvement, while FedBN—by preserving client-specific batch-normalization statistics—achieves the best federated performance (accuracy 26.04%, macro-F1 0.1732) and greater stability across clients. These findings indicate that the main limitation of FL for sleep staging lies in physiological heterogeneity rather than model capacity, highlighting the need for heterogeneity-aware strategies in privacy-preserving sleep analytics. Full article

A one-dimensional analytical model for a proton exchange membrane fuel cell (PEMFC) engine is presented. The model is structured into three main subsystems: the fuel cell stack, the intake and exhaust system, and the thermal management system. The modeling of the thermal management system specifically encompasses key components such as the expansion tank, thermostat, pump, fan, and radiator. The heat transfer and fluid flow within key thermal management components—primarily fans and radiators—are analyzed via three-dimensional modeling. A porous media model represents the unit parallel-flow radiator, where the complex fin structures are replaced by a homogenized medium. This allows for the efficient calculation of 3D thermal and flow fields once the necessary constitutive parameters are identified. Ultimately, the one-dimensional (1D) thermal management system is coupled with the three-dimensional (3D) flow field analysis. This integrated 1D-3D co-simulation framework is implemented to enhance the computational fidelity of the PEMFC engine’s thermal management model. Full article

The structural vibration of industrial droplet dispensers can be modeled by telegraph-like equations to a good approximation. We reinterpret the telegraph equation from the standpoint of an electric–circuit system consisting of an inductor and a resistor, which is in interaction with an environment, say, a substrate. This interaction takes place through a capacitor and a shunt resistor. Such interactions serve as leakage. We have performed an analytical investigation of the frequency dispersion of telegraph equations over an unbounded one-dimensional domain. By varying newly identified key parameters, we have not only recovered the well-known characteristics but also discovered crossover phenomena regarding phase and group velocities. We have examined frequency responses of the electric circuit underlying telegraph equations, thereby confirming the role as low-pass filters. By identifying a set of physically meaningful reduced cases, we have laid the foundations on which we could further explore wave propagations over a finite domain with appropriate side conditions. Full article

The increasing complexity of large-scale thin-walled structures can be effectively addressed using hierarchical modelling approaches. However, in structural engineering, true preliminary design phases are often skipped due to the unavailability or insufficient accuracy of reduced models capable of handling the required complexity. Historically, the shear panel theory provided a computationally efficient method for analysing thin-walled structures before the adoption of the finite element method (FEM). While the shear panel theory includes a formulation for curved panels, it remains limited to one-dimensional representations, making it unsuitable for three-dimensional curved shell structures such as fuselages and wing boxes. Consequently, these structures are typically approximated using plane panels, introducing inaccuracies. This paper presents a novel formulation for rectangular simply curved panels in three-dimensional space that extends shear panel theory to simply curved structures by explicitly incorporating the shear centre into the derivation, enabling its use in preliminary design. The new formulation is validated through comparisons with numerical solutions of a wing box featuring a curved leading edge and analytical solutions of stiffened cylindrical shells. The results demonstrate that the curved panel formulation reproduces the analytical solution and significantly improves accuracy over state-of-the-art planar panel models, providing a more robust tool for the preliminary design of simply curved thin-walled structures. Full article

This expository paper provides a unified and pedagogical introduction to optimal quantization for probability measures supported on spherical curves and discrete subsets of the sphere, emphasizing both continuous and discrete settings. We first present a detailed geometric and analytical foundation for intrinsic quantization on the unit sphere, including definitions of great and small circles, spherical triangles, geodesic distance, Slerp interpolation, the Fréchet mean, spherical Voronoi regions, centroid conditions, and quantization dimensions. Building upon this framework, we develop explicit continuous and discrete quantization models on spherical curves, namely great circles, small circles, and great circular arcs—supported by rigorous derivations and pedagogical exposition. For uniform continuous distributions, we compute optimal sets of n-means and the associated quantization errors on these curves; for discrete distributions, we analyze antipodal, equatorial, tetrahedral, and finite uniform configurations, illustrating convergence to the continuous model. The central conclusion is that for a uniform probability distribution supported on a one-dimensional geodesic subset of total length L, the optimal n-means form a uniform partition and the quantization error satisfies $V_n=L^2/\left(12n^2\right)$.The exposition emphasizes geometric intuition, detailed derivations, and clear step-by-step reasoning, making it accessible to beginning graduate students and researchers entering the study of quantization on manifolds. This article is intended as an expository and tutorial contribution, with the main emphasis on geometric reformulation and pedagogical clarity of intrinsic quantization on spherical curves, rather than on the development of new asymptotic quantization theory. Full article

This work presents the simulation and design of 3C-SiC power MOSFETs, focusing on critical parameters including avalanche impact ionization, breakdown voltage, bulk and channel mobilities, and the trade-off between on-resistance and breakdown voltage. The device design is carried out by evaluating the blocking voltage of scaled structures as a function of the blocking layer’s doping concentration. To mitigate edge-effect breakdown at the p-well/n-drift interface, a step-profile doping strategy is employed. Multiple transistor layouts with varying pitches are developed using a commercially available device simulator. Results are benchmarked against a one-dimensional analytical model, validating the on-state resistance, current–voltage behavior, and overall accuracy of the simulation approach. For the selected material properties, simulations predict that a 600 V 3C-SiC MOSFET achieves an on-state resistance of 0.8 mΩ·cm², corresponding to a 7 μm drift layer with a doping concentration of 1 × 10¹⁶ cm⁻³. Full article

Accurate modeling of multiphase flow in porous media remains challenging due to the nonlinear transport and sharp displacement fronts described by the Buckley–Leverett (B-L) equation. Although Fourier Neural Operators (FNOs) have recently emerged as powerful surrogates for parametric partial differential equations, they exhibit limited robustness when extrapolating beyond the training regime, particularly for shock-dominated fractional flows. This study aims to enhance the extrapolative performance of FNOs for one-dimensional B-L displacement. Analytical solutions were generated using Welge’s graphical method, and datasets were constructed across a range of mobility ratios. A baseline FNO was trained to predict water saturation profiles and evaluated under both interpolation and extrapolation conditions. While the standard FNO accurately reconstructs saturation profiles within the training window, it misestimates shock positions and saturation jumps when extended to longer times or higher mobility ratios. To address these limitations, we develop Physics-Informed FNOs (PI-FNOs), which embed PDE residuals and boundary constraints, and Transfer-Learned FNOs (TL-FNOs), which adapt pretrained operators to new regimes using limited data. Comparative analyses show that both approaches markedly improve extrapolation accuracy, with PI-FNOs achieving the most consistent and physically reliable performance. These findings demonstrate the potential of combining physics constraints and knowledge transfer for robust operator learning in multiphase flow systems. Full article

As a foundational analytical tool, the discernibility matrix plays a pivotal role in the systematic reduction of knowledge in rough set-based systems. Recent advancements in rough set theory have witnessed the proliferation of discernibility matrix-based knowledge reduction algorithms, with notable applications in classical, neighborhood, covering, and fuzzy rough set models. However, the quadratic growth of the discernibility matrix’s complexity (relative to domain size) imposes fundamental scalability limits, rendering it inefficient for real-world applications with massive datasets. To address this issue, we introduced a discernibility hashing strategy to limit the growth scale of the discernibility attributes and proposed a feature selection algorithm via discernibility hash based on rough set theory. First, on the premise of keeping the information of the original discernibility matrix unchanged, the method maps the discernibility attribute set of all objects to the storage unit through a hash function and records the number of collisions to construct a discernibility hash. By using this mapping, the two-dimensional matrix space can be reduced to a one-dimensional hash space, which greatly removes invalid and redundant elements. Secondly, based on the discernibility hash, an efficient knowledge reduction algorithm is proposed. The algorithm avoids invalid and redundant element attribute sets to participate in the knowledge reduction process and improves the efficiency of the algorithm. Finally, the experimental results show that the method is superior to the discernibility matrix method in terms of storage space and running time. Full article

Addressing the challenges of scarce measured data for Class 3–4 bridges and strong subjectivity in manual assessments in bridge technical-condition evaluation, this study innovatively proposes a FastText+CNN evaluation model that integrates semantic features with spatial pattern recognition. By constructing a hierarchical data structure of bridge scale matrices using the analytic hierarchy process (AHP) and generating a balanced training set encompassing Class 1–5 bridges through computational code, the model overcomes the bottleneck of training under small-sample conditions. It employs N-Gram embeddings to achieve semantic representation of defect feature combinations, combines one-dimensional convolutional neural networks to capture cross-component spatial correlation patterns, and utilizes hierarchical Softmax to optimize multi-classification efficiency. Experiments show that the model achieves 92.4% accuracy on the test set, outperforming random forest and multi-layer CNN models by 15.9% and 3.7%, respectively, with recognition rates for Class 3–5 bridges rising to 85% and cross-entropy loss reduced to 0.36. Validated with data from 30 actual bridges, the model maintains 92.3% accuracy and demonstrates the ability to discover implicit patterns in cross-component defect chains, providing an intelligent solution for bridge technical condition evaluation that combines semantic understanding with spatial feature extraction. Full article