Search Results (441)

Hybrid RF-FSO-THz communication systems are promising candidates for future Internet of Things (IoT) and 6G networks because they combine the robustness of radio frequency links, the high-capacity potential of Free-Space Optical communications, and the ultra-wideband capabilities of terahertz transmission. Adaptive channel selection in such systems depends on multiple correlated environmental and physical-layer variables, including distance, rain intensity, humidity, visibility, turbulence strength, signal-to-noise ratio, channel capacity, and energy-efficiency metrics. This paper presents a physics-supported benchmark framework for supervised adaptive channel selection in hybrid RF-FSO-THz systems and systematically investigates the impact of linear and nonlinear dimensionality-reduction techniques on predictive performance, statistical robustness, computational complexity, and physical interpretability. A multi-scenario dataset comprising 5000 samples was generated using calibrated RF, FSO, and THz propagation models under clear, rain, fog, and worst-case environmental conditions. Principal Component Analysis (PCA) and Kernel PCA were evaluated together with Random Forest, Support Vector Machines (SVMs), XGBoost, Gradient Boosting (GB), Multi-Layer Perceptron (MLP), Logistic Regression, and Decision Trees. The results demonstrate that PCA preserves nearly all predictive capabilities while reducing the original 33-dimensional feature space by approximately 81.8%, maintaining accuracies close to 97–98% with the best-performing classifiers. Statistical significance analysis confirms that PCA introduces only modest degradations, whereas Kernel PCA consistently reduces the predictive performance while increasing memory requirements and inference latency. Additional environmental-only validation experiments indicate that adaptive channel selection remains highly learnable even when only pre-selection environmental descriptors are available, partially mitigating concerns regarding self-consistency bias. Overall, the results suggest that PCA provides an advantageous compromise among predictive accuracy, computational efficiency, statistical robustness, and physical interpretability for supervised adaptive channel selection in physics-supported hybrid wireless communication systems. Full article

A novel modified generalized Riccati equation mapping neural network-based approach is the basic theme of this study by exploring the nonlinear dynamical characteristics of the the strain wave model’s soliton solutions, which govern wave propagation in micro structured solids. Strain waves are particularly intriguing, since they preserve their form and speed throughout transmission. The nonlinear dynamical behaviors of strain waves may be modeled by partial differential equations in micro structured materials. In the realm of micro structured solids, there exists a class of phenomena that are referred to as micro strain waves. These waves arise in solids possessing intricate internal architectures, including periodic lattices, precisely engineered metamaterials Understanding these waves is key to designing more complex materials and new acoustic technologies. The activation function and the weight function of the neural network are assigned to each input layer, hidden layer and output layer and the neural network itself is a multi-layer computational network. Using the structure of the neural network, every neuron in the first hidden layer is given solutions to the Riccati equation, and the new highly expressive trial functions are generated in a systematic way. In this way, a large variety of exact soliton solutions are obtained, such as bright, dark, kink, and combined solitons as well as periodic and hyperbolic wave profiles. The influence of the essential physical and mathematical parameters is explored systematically using three-dimensional, two-dimensional and contour visualizations, which illustrate how parameter variations lead to changes in the amplitude, shape and stability of the wave structures. The solutions presented reveal the dynamic properties of micro strain solitons which leads to new avenues of investigation in the study of related nonlinear phenomena in micro structured solids. In a broader context, our results highlight the great potential of analytical techniques using neural networks as a powerful and versatile toolset to study complex nonlinear wave models within the applied sciences from acoustics to photonics to smart materials engineering. Full article

Low-frequency sound insulation remains a major challenge for conventional passive materials, as improved attenuation is usually achieved at the expense of increased thickness and mass. In this work, a smooth fixed third-order ring-curve fractal-maze acoustic metamaterial is proposed for compact low-frequency sound insulation, and a physics-guided long short-term memory–physics prediction surrogate network (LSTM–PPS-Net) tandem framework is developed for its full-spectrum inverse design. Different from conventional Hilbert-type, right-angled, or sharply folded labyrinthine structures, the proposed topology uses recursively arranged curved channels to extend the effective acoustic propagation path and enhance phase accumulation within a limited space. Based on this mechanism, four physically meaningful parameters, namely slit width d, characteristic radius R₃, wall thickness t_w, and inter-column spacing l_E, are selected to construct a low-dimensional design space. A COMSOL–MATLAB automated finite-element method (FEM) workflow is established to generate 1000 valid transmission-loss (TL) spectra over 100–1700 Hz with a 5 Hz interval. For forward prediction, PPS-Net is developed by integrating geometry encoding, frequency-conditioned spectral decoding, and peak-weighted learning. The proposed PPS-Net achieves the best prediction accuracy among the tested models, with a mean absolute error (MAE) of 0.75 dB, a root mean square error (RMSE) of 1.88 dB, and a coefficient of determination (R²) of 0.96, outperforming multi-layer perceptron (MLP), convolutional neural network (CNN) and Transformer models under the same dataset and training protocol. For inverse design, the LSTM encoder extracts frequency-ordered spectral features from the target TL curve, while the frozen PPS-Net decoder provides differentiable acoustic-response feedback, thereby addressing the non-unique mapping from acoustic response to structural parameters. Furthermore, a compactness-oriented optimization strategy is introduced to balance spectral consistency, peak alignment, bandwidth preservation, and occupied-area reduction. In two representative cases, the optimized designs reduce the occupied area by approximately 21% in both representative cases, while maintaining the target attenuation characteristics after FEM verification. These results demonstrate that the proposed framework provides an efficient and physically interpretable route for the full-spectrum inverse design and compact optimization of low-frequency acoustic metamaterials. Full article

With the emergence of mobile sixth-generation (6G) integrated sensing and communication (ISAC) scenarios, conventional multicarrier waveforms face challenges in maintaining reliable communication and robust physical-layer security. In this paper, we propose a multi-layer encryption multiple-input multiple-output (MIMO) affine frequency division multiplexing (AFDM) with index modulation (IM) scheme, which exploits the inherent flexibility of the AFDM modulation parameter $c_2$ and subcarrier IM to construct a multi-dimensional physical-layer security mechanism. To enable sensing and exploit MIMO spatial diversity, a unified downlink MIMO configuration is adopted, where sensing and communication share the same transmit waveform, receive array, and physical propagation environment. The proposed configuration enables multi-dimensional parameter estimation, including delay, Doppler, and angle. The obtained sensing information further assists beamforming design, channel reconstruction, and signal equalization. Furthermore, the base station and user equipment share synchronized secret keys, and a unified detection framework is developed to balance computational complexity and detection accuracy while remaining compatible with the multi-dimensional encryption structure of the MIMO-AFDM-IM system. Simulation results verify the effectiveness of the proposed scheme in mobile scenarios, demonstrating enhanced multi-dimensional sensing accuracy, improved resistance to eavesdropping, and superior communication reliability and energy efficiency (EE). Full article

This article studies the nonlinear wave dynamics of the recently introduced integrable combined Kairat-II-X (K-II-X) equation, which combines dynamical features of the Kairat-II and Kairat-X models. The considered model possesses relevance in nonlinear wave propagation, geometric curve dynamics, and localized optical pulse evolution, thereby providing a mathematical framework for describing curvature-driven nonlinear phenomena in higher-dimensional systems. To obtain exact analytical solutions, a symbolic neural analytical framework based on the improved Riccati neural networks (IRNNs) method is employed. The proposed framework integrates trial functions within multilayer neural network structures, where each neuron in the first hidden layer is constructed through solutions of the improved Riccati equation. The symbolic outputs obtained from the neural network computations are subsequently employed as trial functions for the integrable combined K-II-X equation. Using this framework, several classes of exact wave solutions are derived in the form of hyperbolic, trigonometric, rational, including localized solitary waves and interaction-type structures. In particular, the symbolic neural representation produces both single- and multisoliton wave profiles exhibiting nonlinear localization and interaction behavior. Furthermore, representative wave structures are illustrated through two-dimensional, three-dimensional, contour, and density visualizations to examine the qualitative influence of governing parameters on wave amplitude, localization, propagation behavior, and interaction patterns. The reported results demonstrate the capability of the IRNNs framework to generate diverse nonlinear wave structures in integrable higher-dimensional systems and provide a useful analytical reference for future investigations in nonlinear science and applied mathematical physics. Full article

Atmospheric turbulence originates from random fluctuations in the refractive index of the propagation medium that induce wavefront distortions and intensity scintillation. In application scenarios such as adaptive optics, rapid and accurate characterization of turbulence conditions is of critical importance. Existing turbulence-sensing approaches predominantly rely on intensity statistical analysis, wavefront measurements, and parameter estimation inferred from imaging degradation. However, these methods typically require complex reconstruction procedures, leading to increased system complexity and substantial computational overhead, which limits their applicability in scenarios demanding low-latency lightweight architectures, such as adaptive optics and ground-to-satellite laser communications. In this work, turbulence perception is reformulated from a conventional wavefront reconstruction problem into a measurement-operator design problem. We propose an all-optical turbulence perception framework based on a multilayer diffractive processor. The proposed approach maps the phase statistical characteristics induced by atmospheric turbulence into discriminative intensity-domain features, enabling direct perception of turbulence strength. The perception process is performed exclusively in the optical domain, without the need for numerical reconstruction. Numerical results demonstrate that the proposed diffractive processor can robustly distinguish different turbulence strength levels, with an overall classification accuracy of 79.50%, indicating its effectiveness as a new technological pathway for atmospheric turbulence perception. Full article

The detection and degradation analysis of subsurface microcracks in mural paintings remain challenging due to their inhomogeneous multilayered structure and complex deterioration mechanisms. In this study, we propose a multimodal stepwise method for three-dimensional characterization and cross-scale degradation analysis by integrating digital holography (DH), infrared thermography (IRT), acoustic excitation (AE), and molecular dynamics (MD) simulations. In the first step, an adjustable field-of-view (FOV) digital holographic system is developed to capture subsurface deformation under acoustic excitation, enabling high-resolution planar characterization of subsurface microcracks. Infrared thermography is then employed to estimate crack depth through an inverse thermal model, achieving full three-dimensional reconstruction of crack geometry. Based on the reconstructed structures, MD simulations are conducted to investigate the evolution of stress, bond breaking, and crack propagation under varying temperature and humidity conditions, with particular emphasis on water molecule migration and chemically induced degradation. The results demonstrate that environmental factors promote stress concentration and material embrittlement at crack tips, leading to secondary microcrack formation and progressive deterioration. Experimental aging tests show strong agreement with simulation results, validating the proposed methodology. This work establishes a unified “characterization–simulation–validation” paradigm, providing new insights into the mechanisms of mural degradation and offering a robust framework for non-destructive evaluation and preventive conservation of multilayer cultural heritage materials. Full article

Accurate identification of weak nodes is a prerequisite for online security assessment, preventive control, and resilience enhancement in modern power systems. However, conventional single-layer graph-learning models mainly emphasize local neighborhood aggregation and are insufficient for characterizing vulnerability propagation from equipment-level disturbance to regional congestion and system-level transfer constraints. This paper proposes a mechanism-aware hierarchical graph-learning framework for weak-node identification in complex interconnected power grids. We emphasize that attention, fusion, and gating operations are standard neural-network mechanisms and are not claimed as new generic deep-learning blocks. The contribution of this paper is the power-system-specific formulation: constructing an electrically meaningful local-supernode hierarchy, defining reproducible mechanism-based node and branch-vulnerability proxies, and interpreting weak-node rankings through node–line–corridor coupling evidence. In the validated implementation, a local graph convolutional encoder and a supernode/global graph convolutional encoder generate 32-dimensional local embeddings and 16-dimensional global embeddings, which are concatenated and decoded by a 48 → 24 → 1 multilayer perceptron to obtain node vulnerability scores. Experiments are conducted on reproducible IEEE benchmark data generated from pandapower standard systems, with representative comparisons on the IEEE 57-bus, 145-bus, and 300-bus systems and a detailed structural interpretation on the IEEE 145-bus case. The present results validate the ability of the implemented local–global hierarchical model to reproduce the proposed mechanism-based vulnerability proxy on representative small- and medium-scale benchmarks. Full article

Rapid, accurate, and scalable ion sensing technologies are highly desirable for future flexible healthcare and lab-on-a-chip applications. Here, we present a fully roll-to-roll (R2R) gravure-printed single-walled carbon nanotube complementary ring oscillator (SWCNT-cRO)-based microfluidic ion sensing platform fabricated on a flexible substrate. The proposed platform combines scalable printed complementary electronics with frequency-based ion sensing via electrostatically induced top-gating in aqueous microfluidic environments. The fabricated SWCNT-cRO devices exhibited stable oscillation characteristics, with a high device yield (>80%) and continuous manufacturing capability at a web speed of 5.4 m/min. Printable ethanolamine/zirconium acetylacetonate-based n-doping technology enabled complementary SWCNT transistor operation, while multilayer CYTOP/FG-3650 encapsulation ensured stable electrical operation under ionic aqueous conditions. After integration into a polydimethylsiloxane-based microfluidic channel, the oscillation frequency of the SWCNT-cRO was systematically modulated by Na⁺ concentration and pH. The sensing mechanism was based on electrostatically induced carrier modulation in n-type SWCNT transistors, resulting in variations in propagation delay and corresponding shifts in oscillation frequency. Compared with conventional ion-sensitive transistor platforms, the proposed approach offers scalable manufacturing, non-contact ion sensing, elimination of external reference electrodes, and direct compatibility with digital frequency-signal processing systems. This work establishes a promising strategy for future low-cost, disposable, and flexible microfluidic sensing platforms for wearable healthcare and lab-on-a-chip applications, ion sensing, and thin-film transistors. Full article

Hydraulic fracture network initiation and propagation in tight sandstone are strongly controlled by mineral heterogeneity and fluid–solid interaction. However, existing numerical models still have limited capability in simultaneously representing multi-mineral distributions and dynamically coupled fracture-fluid processes. In this study, a two-dimensional polygonal discrete element fluid–solid coupled model was established based on mineralogical images of tight sandstone. Compared with conventional continuum-based approaches, the proposed model is better suited to describing fracture initiation, branching, and network evolution in multi-mineral granular media. Under dimensionless operating conditions calibrated against field data, coupled and uncoupled formulations were systematically compared to evaluate the role of hydro-mechanical interaction during hydraulic fracturing. The coupled simulations generated consistently more fractures than the uncoupled simulations over the investigated injection-rate range, with an average increase of 28.7% and a maximum increase of 67.2%. Compared with the uncoupled model, the coupled model also predicted higher breakdown pressures and stronger fracture-tip pressure concentrations, and the breakdown pressure increased with injection rate. Under low injection rates, the coupled formulation reproduced pressure-buildup-driven fracture-tip advance, whereas the uncoupled formulation failed to sustain fracture propagation. Under higher injection rates, the coupled formulation produced multilayered and highly branched fracture networks, while the uncoupled formulation mainly generated simple first-order branching. These results demonstrate that hydro-mechanical coupling is a controlling mechanism for fluid-energy dissipation, fracture-tip pressure evolution, and complex fracture network formation in tight sandstone. This study provides an image-based polygonal DEM framework for evaluating hydro-mechanical fracture network evolution in multi-mineral tight sandstone. Full article

Composite hydrogen storage vessels exhibit pronounced anisotropy, multilayered winding architectures, and strong ultrasonic attenuation, which severely degrade the focusing accuracy and defect visibility of the conventional isotropic total focusing method (TFM). To address these challenges, this study proposes an enhanced TFM framework for defect inspection in composite hydrogen storage vessels by integrating anisotropic delay correction, Gray-code coded excitation, and coherence-weighted reconstruction. First, an anisotropic propagation delay model is established using forward ray tracing to compensate for beam deviation and focusing mismatch induced by the anisotropic winding structure. Then, Gray-code excitation and pulse compression are introduced to improve signal energy and echo detectability under high-attenuation conditions. Finally, coherence-weighted imaging is applied to suppress incoherent background noise and structural artifacts, thereby enhancing defect contrast and image readability. The proposed method is validated on hydrogen storage vessel specimens containing artificial defects, with CT results used as references. Experimental results show that, compared with conventional isotropic TFM, the proposed collaborative approach significantly improves defect imaging quality for defects of different sizes and depths. The signal-to-noise ratio is increased from 7.2, 12.8, 14.8, and 7.4 dB for isotropic TFM to 32.5, 29.9, 52.6, and 42.7 dB, respectively, for the combined anisotropic, coded-excitation, and coherence-weighted TFM. In addition, the defect depth estimation remains stable and agrees well with the CT references, yielding approximately 9.0–9.6 mm for shallow defects and 18.7–19.3 mm for deeper defects. These results demonstrate that the proposed method can effectively improve defect detectability, image contrast, and depth characterization for embedded delamination-like artificial defects in composite hydrogen storage vessels, providing a promising ultrasonic imaging strategy for thick-walled anisotropic composite pressure structures. Full article

Maintaining the integrity of impermeable strata between mine workings and overlying aquifers is critical, because seepage pathways may cause mine flooding and surface subsidence. In the Upper Kama potash deposit, the impermeable sequence is a 50–140 m thick layered sequence of evaporites and clays overlying mined-out chambers. Under long-term loading, salt rocks tend to creep, soften, and localize damage, which can cause failure in the impermeable strata. In this paper, the Concrete damage-plasticity model, supplemented by the N2PC-MCT viscoplastic creep model, is applied to simulate the initiation and evolution of seepage pathways in the Upper Kama impermeable strata. Model parameters are obtained from published laboratory tests (uniaxial and triaxial compression and tension) and validated using observed ground-surface subsidence. A plane-strain finite-element model incorporates the stratified lithology, interface elements between layers, and sequential excavation. Long-term simulations up to 50 years investigate two operational scenarios: with and without backfilling. The calibrated model reproduces the main stages of surface subsidence and chamber closure. Without backfilling, simulations indicate that tensile damage localizes mainly in a stiff central salt layer of the impermeable strata, with most cracks appearing approximately between 33 and 37 years after the start of mining. With backfill, tensile crack propagation stops and damage remains stable. A hypothetical homogeneous impermeable strata case confirms that the observed central-layer cracking is associated with stiffness contrasts and composite bending in the stratified system. An approximate analytical multilayer beam solution, based on energy minimization, predicts bending stress concentration in stiff intermediate layers and is consistent with the numerical stress distribution. The combined numerical and analytical results provide insight into the mechanisms of long-term conductive fracture initiation in stratified impermeable strata and may serve as a basis for preliminary hazard indication and for planning mitigation measures, including backfilling and focused monitoring of stiff central layers. Because the study is based on a 2D plane-strain model, the quantitative estimates should be regarded as preliminary and require verification by 3D modelling and further field observations. Full article

To address the escalating vulnerability of metro systems under multiple perturbations—including extreme rainfall, equipment failures, and passenger surges—this study tackles several limitations in existing research: the predominant focus on single-layer topology, the neglect of cross-layer coupling effects between physical facilities and functional systems, the lack of dynamic global information in critical node identification, and the insufficient consideration of network clustering characteristics in cascading failure analysis. Drawing on complex systems theory, this study constructs a physical–functional bilayer coupled network model, proposes three improved Deep Q-Network algorithms for identifying cross-layer critical nodes, and introduces a cluster-augmented sandpile model to simulate the differentiated propagation of cascading failures. An empirical case study of the Zhengzhou Metro network demonstrates that the constructed bilayer network exhibits scale-free properties, that the improved DQN algorithms significantly outperform classical benchmarks—including degree centrality, betweenness centrality, closeness centrality, and the greedy algorithm—in sequential disruption efficiency, and that the safety tolerance coefficient and limit coefficient exert substantial regulatory effects on network vulnerability. The methodological framework developed herein—integrating bilayer coupled modeling, deep reinforcement learning-based critical node identification, and cluster-augmented sandpile cascading failure analysis—provides a transferable technical pathway for vulnerability assessment of multilayer coupled networks, with its applicability validated through the Zhengzhou case. Full article

This paper addresses the challenge of accurately measuring liquid level interfaces in multiphase mixed media by proposing a detection method based on ultrasonic transmission. First, a mathematical model of the ultrasonic measurement system was established, and the acoustic field characteristics of transducers with different frequencies and diameters in slurry were simulated and analyzed to determine the optimal excitation frequency and probe diameter. On this basis, an echo sound pressure calculation model based on the side-incidence method was constructed, and a formula for calculating the liquid level interface height was derived. Finally, an experimental test platform with a multi-layer steel container was built to measure the propagation velocity, attenuation coefficient, and acoustic impedance coefficient of ultrasound in the slurry, verifying the feasibility of the liquid level interface measurement method. Full article

In marine service environments, material surfaces inevitably suffer from wear damage, which can compromise the integrity of protective coatings and further affect their corrosion resistance. Therefore, investigating the post-wear corrosion resistance of coatings is of great significance. In this work, single-layer CrN coatings, CrAlN coatings, and CrN/CrAlN multilayer coatings were deposited on stainless-steel substrates by arc ion plating, and the microstructure, tribological properties, and corrosion behavior before and after wear were systematically investigated. Wear tests were performed under applied loads of 2.5 N and 5 N. The corrosion behavior in the unworn condition and the post-wear corrosion resistance condition was evaluated in a 3.5 wt.% NaCl solution. The results showed that all coatings exhibited a face-centered cubic (FCC) structure, while the CrN/CrAlN multilayer coating possessed the smallest average grain size (13.47 nm). Under applied loads of 2.5 N and 5 N, the CrN/CrAlN multilayer coating exhibited the lowest wear rate, indicating the best wear resistance. In the unworn condition, the CrN/CrAlN multilayer coating showed the lowest corrosion current density (2.74 × 10⁻¹⁰ A/cm²) and the most positive corrosion potential (0.025 V), demonstrating the best corrosion resistance. After wear under a load of 5 N, the CrN/CrAlN multilayer coating retained a low corrosion current density (3.35 × 10⁻¹⁰ A/cm²), in contrast to the marked increases observed for the single-layer coatings. The enhanced performance is considered to be mainly associated with the periodic heterogeneous interfaces in the multilayer structure, which help suppress crack propagation and prolong the penetration path of corrosive media. Full article