Search Results (1,425)

Finite-difference approaches are widely employed to solve partial differential equations in numerous practical applications. However, their computational efficiency is often limited by the need to solve linear systems through matrix inversion or iterative solvers, a challenge that is particularly acute in high-dimensional problems. Consequently, there is a growing demand for methods that ensure both high accuracy and computational efficiency. To address the two-dimensional (2D) heat conduction problem, we propose a novel hybrid technique that integrates a fourth-order implicit compact finite-difference approach with the discrete sine transform (DST). The incorporation of the DST significantly reduces the computational burden associated with solving the heat conduction equation on large grids. Detailed numerical experiments were conducted to evaluate this solver for 2D heat conduction equations subject to homogeneous Dirichlet boundary conditions (DBCs). The results demonstrate that the proposed method not only achieves substantial reductions in computational cost but also maintains a high level of numerical accuracy. All numerical experiments were performed on a computer running MATLAB R2024b. Full article

Unmanned aerial vehicle Synthetic Aperture Radar (UAV SAR) is inevitably affected by hardware performance and complex electromagnetic environments, resulting in noise in the radar echo signal. This causes image blurring and loss of detail, severely limiting the detection performance and imaging quality of UAV SAR. High-repetition-rate UAV SAR can achieve high signal-to-noise ratio (SNR), but the SAR data volume grows exponentially, posing a challenge for large-scale data processing. Furthermore, in the case of high repetition rate, downsampling methods are needed to reduce the amount of raw data, which leads to a decrease in the echo SNR, thus significantly affecting SAR image details. Existing SAR signal processing methods typically involve a series of processing steps on the raw echo data, such as azimuth and range direction processing. However, these traditional methods still have limitations in improving the SNR, especially in complex environments or when the target signal is weak, where their effectiveness is often unsatisfactory. To address these issues, this paper first analyzes the SNR gain in SAR echo data processing and proposes a phase-compensated parameter-adjusted Chebyshev filtering algorithm to improve the SNR of SAR echoes. The algorithm first utilizes azimuth Chebyshev filtering to avoid spectral aliasing during downsampling and fully leverages navigation information provided by the airborne platform to accurately compensate for phase changes between pulses. Then, it employs parameter-adjusted Chebyshev filtering and coherent superposition techniques to combine multiple adjacent pulses into a single pulse with a higher SNR. Finally, the enhanced pulses are combined into a new two-dimensional matrix for subsequent pulse compression and imaging processing. This method can improve the echo SNR while reducing the amount of echo data, minimizing the loss of the original echo SNR and reducing the memory footprint of subsequent imaging processing, thus effectively improving data processing efficiency. The effectiveness of the algorithm is verified through simulation and actual measurement data. Full article

To address the challenges of fault identification in renewable energy plant outgoing lines within “double-high” power systems, this paper proposes a novel parallel dual-channel method that fuses time-series signals and images. On one hand, the fault current signals from the renewable energy plant outgoing lines are acquired and fed into a constructed Multi-scale Adaptive Residual Shrinkage Network (MARS-Net) for one-dimensional temporal feature extraction. On the other hand, one-dimensional fault data is transformed into two-dimensional images via a Relative Angle Matrix (RAM). The generated 2D image data is then input into a network incorporating Dynamic Convolution (D-Conv) and a Transformer-enhanced MobileNetV3 (DT-MobileNetV3) for spatial feature extraction. Finally, feature fusion of the one-dimensional and two-dimensional information is performed to achieve fault type identification. To comprehensively evaluate the method’s performance, this paper designs experiments including noise interference tests, multi-network comparative experiments, ablation studies, comparisons of different 2D transformation methods and data loss. The results demonstrate that the proposed method possesses significant advantages in terms of identification accuracy, noise immunity, data loss tolerance, and generalization capability. Full article

In this paper, we developed and constructed the Haar wavelet (HW) for the two-dimensional (2D) time-fractional neuronal dynamics model (TFNDM) with the dynamical electro-diffusion behaviour of ions in nerve cells. The Haar wavelet method is considered in space and the difference method in time for the time-fractional Riemann–Liouville (TFRL) derivative. The calculation CPU time of this proposed method is very short because the Haar matrix and Haar integral matrix are stored only once and used for each iteration. Moreover, the results show that the solution of the Haar wavelet method is good even when there are fewer grid points. Full article

Long-term data loss resulting from sensor malfunctions, communication interruptions, and other factors in Structural Health Monitoring (SHM) significantly undermines the reliability of damage identification and safety assessment. Existing methods—ranging from statistical approaches and low-rank matrix completion to traditional machine learning and deep learning imputation techniques—often suffer from either limited accuracy or excessive model size and slow inference, making deployment in resource-constrained scenarios difficult. To address these challenges, this paper proposes TEFN–Imputation, a lightweight and efficient time-series imputation model. This model utilizes observation-driven non-stationary normalization to mitigate the impact of time-varying characteristics and dimensional discrepancies. It employs linear projection for temporal length alignment and constructs BPA-style mass representations from dual perspectives of time and channel. Furthermore, it replaces strict Dempster–Shafer belief combination with an expectation-based evidential aggregation (readout), thereby significantly reducing computational overhead while enabling uncertainty-aware evidential indicators for interpretation rather than claiming a direct accuracy gain from uncertainty modeling. The observed accuracy and robustness improvements are primarily attributed to the normalization and dual temporal–channel modeling design under the same lightweight readout. Systematic experiments on two real-world bridge monitoring datasets, Z24 and Hell Bridge, demonstrate that TEFN consistently maintains low Mean Absolute Error (MAE) and minimal volatility across various combinations of training and testing missing rates, exhibiting high robustness against variations in missing rates and train–test mismatches. Concurrently, compared to RNN and large-scale Transformer baselines, TEFN reduces parameter count and CPU inference time by one to two orders of magnitude. Thus, it achieves a superior trade-off among accuracy, efficiency, and model scale, making it highly suitable for online SHM and imputation tasks in practical engineering applications. Across the settings on Z24, TEFN achieves a mean MAE of 0.218 with a standard deviation of 0.002, while using only 0.02 MB parameters and 2.73 ms per batch CPU inference. Full article

Herpes simplex virus type 1 (HSV-1) is a neurotropic alphaherpesvirus that interacts dynamically with host cells within structured tissue environments. Conventional two-dimensional (2D) cultures do not fully recapitulate these spatial and microenvironmental features. In this study, we established a three-dimensional (3D) culture system using the self-assembling peptide RADA16-I to generate an extracellular matrix–mimetic hydrogel scaffold. This platform supported the formation of stable Vero cell spheroids that remained viable for more than 30 days. Following HSV-1 infection, viral spread initiated at the spheroid periphery and progressively extended toward the core. Sustained viral replication was detected for up to 22 days, indicating long-term maintenance of infection within the 3D structure. Ultrastructural examination identified viral particles and vesicular compartments consistent with autophagy-related organelles. Comparative analysis of autophagy-associated markers revealed distinct temporal patterns between 2D monolayer cultures and 3D spheroids. In the 3D system, LC3B-II levels progressively increased, accompanied by a reduction in p62, suggesting altered regulation of autophagic flux relative to conventional 2D conditions. These findings demonstrate that the RADA16-I-based 3D culture model supports prolonged HSV-1 infection and reproduces key spatial features of viral dissemination. The differential autophagic responses observed between 2D and 3D systems highlight the influence of cellular architecture on host–virus interactions and support the application of 3D culture platforms for mechanistic studies of HSV-1 pathogenesis. Full article

The strengthening of reinforced concrete (RC) beams requires repair systems that can enhance strength, stiffness, and energy dissipation without significantly increasing self-weight or compromising durability. This study explores the structural response of RC beams strengthened using an integrated shear–flexure system combining near-surface-mounted carbon fiber-reinforced polymer (NSM-CFRP) ropes and steel-reinforced geopolymer overlays in the compression zone. Monotonic three-point bending tests were performed on two RC beam specimens, one unstrengthened control and one strengthened beam, to obtain preliminary observations of load–deflection behavior, stiffness, ductility, and energy absorption. The strengthened specimen exhibited increases in ultimate load (28.6%), stiffness (13.6%), and energy absorption (7.65%) relative to the control beam, suggesting the potential for effective composite action between the CFRP ropes and geopolymer material. A three-dimensional nonlinear finite element model was developed using ATENA to support interpretation of the experimental response, incorporating detailed constitutive models for concrete, steel reinforcement, and CFRP ropes. The numerical predictions showed reasonable agreement with the experimental results. Within the limitations of the test matrix, the results indicate that the proposed dual strengthening system may offer a viable and sustainable approach for enhancing the shear–flexural performance of RC beams. Full article

We investigate the dynamical stability of circular orbits around a Kerr black hole embedded in a Dehnen-type dark matter halo. The effective spacetime metric of the combined system is constructed using the Newman–Janis algorithm, and the effective potential for test-particle motion in the equatorial plane is derived. The stability of circular orbits is analyzed through the Hessian matrix of the effective potential, while the stability strength and restoring-force distribution are employed to quantify the orbital response to small perturbations. Our results show that the presence of the dark matter halo significantly alters the spatial structure of stable circular orbits, leading to non-continuous stable regions whose location and extent depend sensitively on the halo’s characteristic density, scale radius, and the black hole spin. The innermost stable circular orbit (ISCO) is shifted relative to the vacuum Kerr case, with its position determined by the combined effects of the spin and halo parameters. Two-dimensional heatmaps, parameter scans, and three-dimensional visualizations systematically illustrate how the black hole spin and dark matter halo properties influence the ISCO and the distribution of stable orbits. Finally, we analyze the influence of the dark matter halo on the structure of the black hole event horizon. These results provide a detailed theoretical investigation of orbital dynamics around rotating black holes in dark-matter-rich environments. Full article

Epithelial ovarian cancer (EOC) is a highly lethal malignancy characterized by significant heterogeneity and poor prognosis due to late-stage diagnosis and chemotherapy resistance. Traditional two-dimensional (2D) models fail to replicate the complexity of the tumor microenvironment (TME), necessitating the development of advanced in vitro systems. Here, we present a novel microfluidic tumor-on-a-chip (ToC) system that accurately models key features of EOC, including heterogeneity and vascularization. The developed cellular model was evaluated for functionality. It was demonstrated that endothelial cells of blood vessels within a collagen matrix successfully migrated toward the cancerous tissue, while the multicellular and multilayered tumor construct secreted pro-angiogenic factors. Additionally, long-term culture conditions induced inflammatory responses, mimicking in vivo tumor progression. This innovative platform enables precise investigations into EOC biology, angiogenesis, and TME interactions. Furthermore, it holds significant potential for drug screening, assessing therapeutic efficacy, and advancing personalized oncology approaches. Full article

We revisit the black-hole information problem from the viewpoint of a population–coherence decomposition of density-matrix purity. Building on a previously developed formalism for n-dimensional density matrices, we characterize each state by a normalized global purity index and two complementary indices, which quantify the contributions of level populations and coherences. This yields a simple quadratic relation and a geometric representation in a “population–coherence plane”, where different routes to purity can be distinguished. In the two-level case, we construct explicit families of states with identical spectra and global purity but opposite internal structure, realizing population-dominated and coherence-dominated routes. We then apply this framework to a standard Page-type evaporation model without an explicit Hamiltonian, in which a black hole and its Hawking radiation form a bipartite pure state with varying Hilbert-space dimensions. Using known results for typical reduced states in large dimensions, we analyze the behavior of population and coherence components of purity along the evaporation process. Under the physically motivated requirement that, in this energy-free setting, the radiation populations remain nearly uniform in the chosen basis, we show that the late-time recovery of purity must be coherence-dominated: the global purity of the radiation approaches unity while the population index stays small and the coherence index carries essentially all the purity. Full article

Ceramic Matrix Composites (CMC) are widely used in critical applications such as leading edges of aircraft wings and thermal insulation layers of thermal protection systems due to their advantages of being lightweight, high-temperature resistant, and impact-resistant. However, influenced by manufacturing processes and service environments, internal defects such as pores and delamination are prone to occur, significantly compromising the mechanical properties and service reliability of the material. This paper primarily evaluates the feasibility and applicability of using Terahertz Frequency Modulated Continuous Wave (FMCW) technology for the non-contact detection of CMC. First, the measurement principle of FMCW is introduced, and the structure of the detection system, including a two-dimensional mechanical scanning platform, optical lenses, a control platform, and a data acquisition unit, is outlined. Subsequently, scanning imaging was performed on CMC specimens and their bonded thermal protection structure (TPS) specimens, demonstrating the feasibility of Terahertz FMCW technology as an advanced non-destructive testing tool for CMC inspection. The issues of diffraction and the Rayleigh limit inherent in real-aperture terahertz imaging were analyzed and discussed. A multi-scale fusion defect detection method incorporating background estimation is proposed to enable precise delineation of defect regions. Experimental results show that, after processing with the proposed algorithm, the minimum detectable pore diameter at the focal plane is 1 mm, with a regional error of approximately 3%. The detection error for pores and debonding areas in CMC is maintained within 6.44%. Analysis indicates that combining terahertz imaging technology with image processing algorithms enables the quantitative analysis of internal defects in composite materials, offering a new technical approach for defect detection in composite materials. Full article

Delamination is a major failure Mode in laminated composites, typically triggered by premature interlaminar matrix cracking and leading to severe structural degradation. To address this, various through-thickness reinforcement strategies have been explored, including three-dimensional woven architecture. Although these designs significantly improve delamination resistance, their industrial adoption stays limited due to reproducibility challenges and the high cost and operational complexity of advanced manufacturing systems needed for controlled through-thickness reinforcement. This study investigates an alternative interlaminar reinforcement method, through-thickness stitching, aimed at enhancing Mode-I delamination resistance of a commercial fiberglass laminate without changing its native architecture. Composites were manufactured using a low-viscosity epoxy infusion system (MAX 1618 A/B) and a [0/90] biaxial fiberglass fabric. An eight-filament polyethylene thread (Ø = 0.12 mm) was introduced in predefined stitch architectures consisting of three longitudinal patterns having two, three, and five continuous stitch lines, referred to as AV, BV and CV samples, respectively. Results show that stitching highly increases Mode-I interlaminar fracture toughness G_IC by 0.3808, 0.4152 and 0.5192 kJ/m² for AV, BV and CV respectively, compared to 0.0265 kJ/m² for the unstitched composite O, highlighting the strong influence of stitch orientation and spacing on interlaminar performance. But scanning electron microscopy revealed added failure mechanisms in stitched specimens, including localized fiber misalignment of up to 33° and resin-rich regions approximately 0.6 mm in length, suggesting that while stitching enhances delamination resistance, it may also influence other mechanical properties. Full article

This study develops a conceptual framework for characterizing reservoir architecture in multi-component, discrete systems using pressure transient analysis (PTA), aimed at calibrating inflow geometry prior to full-field dynamic simulation for subsurface gas storage applications such as CO₂ and hydrogen. A secondary objective is to identify variations in permeability over time by analyzing flow capacity trends and evaluating the dynamic influence of faults and fractures. The analysis is based on a gas-condensate field comprising seven wells and four zones (A, B, C, D), using integrated dynamic datasets including extended well tests (EWTs), mud loss, production logs, and production data. Detailed interpretation of PX-1’s EWT indicated delayed re-pressurization and persistent under-pressure, suggesting a compartmentalized or transient system with limited gas-in-place connectivity. Four reservoir architecture concepts were developed: (1) lithology-dominated inflow, (2) structurally controlled inflow, (3) discrete, weakly connected compartments, and (4) transient-dominated systems with tight matrix GIIP. These concepts informed four reservoir models: matrix-only (M), areal heterogeneity (A), sparse bodies (B), and sparse networks (S). Application of these models across other wells revealed consistent localized KH (permeability–thickness product) behavior, with all models fitting short-duration data comparably. However, only sparse drainage models (B/S) adequately matched PX-1’s EWT response. PTA results confirm that well tests constrain KH locally but provide limited insight into large-scale reservoir architecture. EWTs may reach ~1 km, while shorter tests are confined to ~200–400 m, typically within one to two simulation grid blocks. This study demonstrates how integrating PTA with multi-scale data improves characterization of naturally fractured, tight carbonate reservoirs and supports reservoir simulation and history matching for hydrogen storage evaluation. Based on reservoir simulations, this study concluded that naturally fractured carbonate gas reservoirs can provide significant storage and injection capacities for underground hydrogen storage. This study exemplifies how to characterize the naturally fractured tight carbonate reservoirs by integrating multi-scale and multi-dimensional data such as PTA. Furthermore, this study assists in gridding for full-field reservoir models, for history matching and quantifying the potential of hydrogen storage in these complex reservoirs. The proposed workflow provides an uncertainty-bounded reservoir characterization framework and should not be interpreted as a complete field-design methodology for hydrogen storage. The modeling does not explicitly couple geomechanical fracture growth, hydrogen diffusion, long-term geochemical reactions, or caprock integrity degradation. Therefore, the presented storage scenarios represent technically feasible cases under defined assumptions. Comprehensive site-specific geomechanical and containment assessments are required prior to field-scale implementation. Full article

In the Non-Terrestrial Networks (NTNs) formed by the integration of fifth-generation mobile communication systems and Low Earth Orbit (LEO) satellites, Doppler frequency offsets can severely degrade the performance of OFDM signal detection. Particularly for the Physical Uplink Control Channel (PUCCH), conventional detection algorithms suffer significant performance degradation due to the difficulty of accurately estimating and compensating for Doppler frequency offsets at the receiver. Consequently, achieving robust signal detection under conditions with high Doppler frequency offsets becomes particularly critical. To address this challenge, we propose a maximum-likelihood detection algorithm robust to both Doppler frequency and time offsets. In the first step, we derive the frequency-offset matrix, which directly affects the detection peaks. Subsequently, we develop a novel two-dimensional search algorithm that jointly considers UCI and frequency offset. Finally, based on the sparse characteristics of the dominant elements in the frequency offset matrix, we simplify the implementation of the frequency offset matrix, reducing computational complexity to 9% of the original algorithm while achieving negligible performance loss. This approach satisfies the requirements for onboard implementation. Full article

Despite advances in the treatment of cutaneous melanoma, there is still a high percentage of patients who fail to respond or develop resistance to treatment. Establishing robust in vitro melanoma models will enable mechanism-based drug screening while reducing animal testing. In this work, a three-dimensional (3D) melanoma skin model (3DMSM) was developed on a porous scaffold. The culture of three melanoma cell lines (SKMEL-1, A375, and G361) in co-culture with human fibroblasts, melanocytes, and keratinocytes allowed the formation of the dermis, and stratified epidermis. Tumors were established in this model using two methodologies: adding previously formed melanoma cell aggregates (CA) or seeding melanoma cells directly into the dermis (CD). In this model, melanoma cells remain in their original microenvironment and, after proliferation, invade the basal layer. The model recapitulates correct melanocyte localization, epidermal disruption, extracellular matrix (ECM) remodeling, including collagen deposition, and epithelial-to-mesenchymal transition (EMT). Additionally, the cytokine profiles studied indicate that the model could mirror the inflammatory and immune-evasive traits of melanoma. Overall, 3DMSM provides a useful tool for understanding the mechanisms of melanoma progression and invasion, and for developing personalized medicine strategies through the implementation of a patient-derived model. Full article