Search Results (349)

Deep neural networks have substantially improved the performance of hyperspectral image classification, yet they remain vulnerable to adversarial attacks. Existing attack methods usually manipulate pixel spectra directly, ignoring the physical mixing mechanism of remote sensing imaging and potentially generating adversarial samples with limited physical consistency and interpretability. Moreover, balancing attack effectiveness and perturbation imperceptibility remains a challenging multi-objective optimization problem. To address these issues, this paper proposes an evolutionary multi-task multi-objective adversarial attack framework based on inter-task knowledge transfer. Instead of perturbing raw pixel spectra, the proposed method introduces perturbations into abundance maps obtained through spectral unmixing, thereby improving the physical plausibility of the generated adversarial samples. The generation of class-specific universal perturbations is formulated as a collaborative multi-task optimization problem. To solve this problem, we develop a Self-Adaptive Multi-Objective Multi-Factorial Evolutionary Algorithm for Adversarial Attacks (SAMO-MFEA-AA). By modeling the attack generation processes for different land-cover classes as distinct yet correlated optimization tasks, SAMO-MFEA-AA dynamically captures synergistic relationships among tasks. An asymmetric adaptive cooperation matrix is employed to regulate the intensity of knowledge transfer, allowing beneficial perturbation patterns to be shared across related classes while reducing the risk of negative transfer. Extensive experiments on the Indian Pines and Salinas datasets demonstrate that the proposed framework achieves competitive hypervolume performance and favorable solution diversity compared with existing multi-objective optimization algorithms. In adversarial attack scenarios, the proposed method achieves effective attack success rates against representative classification networks while maintaining the physical plausibility of abundance-space perturbations. Full article

This study develops a nonlinear dynamic model of a human-inspired electromechanical arm system subjected to high loads. The proposed simplified representation preserves essential nonlinear dynamics using a reduced number of generalized coordinates. The model is represented by an electromechanical analog comprising a DC motor, a transmission system, and a multi-degree-of-freedom mechanical structure. The formulation is based on Lagrangian mechanics and accounts for inertia, damping, stiffness, and nonlinear kinematic coupling induced by joint misalignment. The numerical results were assessed using a consistency-based verification approach with several independent nonlinear analysis tools. The Lyapunov exponent was used in conjunction with bifurcation diagrams, Poincaré maps, and FFT spectra to identify the transition from stable operation to chaotic behavior as the external load increased. The results reveal a progressive transition from periodic motion to quasi-periodic oscillations and chaotic regimes, with fully developed chaotic behavior emerging for loads exceeding approximately 35 kg. Analysis of the Lyapunov exponent supports this interpretation, indicating stable, quasi-critical, or chaotic regimes depending on the sign of λ_max. The concordance among these independent indicators provides numerical verification of the observed stability transitions. The control gain significantly influences energy dissipation and system stability. The proposed model provides a reduced-order framework for studying nonlinear stability phenomena in human-inspired electromechanical systems. Potential applications involve rehabilitation devices and safety studies of human–robot interactions. Full article

A novel mesoporous XAD-based adsorbent (A336/XAD-7) was produced by impregnating the ionic liquid A336 into the pores of XAD-7 resin and used to separate tin(IV) and cadmium(II) from high-level liquid waste (HLLW). The as-produced material was characterized by SEM-EDS, TG-DSC, and N₂ adsorption–desorption isotherms, which revealed a well-developed open pore structure, high loading capacity, and large specific surface area. Adsorption performance analysis showed that in 4 M HCl solution, the experimental saturated adsorption capacity qexp of A336/XAD-7 for Sn(IV) and Cd(II) were 39.51 mg/g and 34.18 mg/g, respectively, with equilibrium reached within 120 min. Among ten coexisting metal ions (Sn⁴⁺, Cd²⁺, Co²⁺, Ni²⁺, Cu²⁺, Eu³⁺, Y³⁺, Ca²⁺, Mg²⁺, Al³⁺) in HLLW, A336/XAD-7 exhibited excellent selectivity for Sn(IV) under high acidity, with a separation factor (SF_Sn/others) of 13.13. Column experiments further evaluated the dynamic separation of Sn(IV) from simulated HLLW using A336/XAD-7, achieving an enrichment factor greater than 7. XPS spectra indicated that the adsorption mechanism involved anion exchange between A336/XAD-7 and the complex anions SnCl₆²⁻ and CdCl₄²⁻. This work demonstrates the application potential of A336/XAD-7 for HLLW treatment and provides valuable guidance for the efficient separation of other metal ions. Full article

To reveal the nonlinear instability mechanism by which the three-degree-of-freedom rotor system of a magnetic-liquid double suspension bearing transforms from stable suspension to periodic vibration, a nonlinear dynamic model considering electromagnetic suspension force, hydrostatic supporting force, rotor unbalance force, and liquid film resistance is established. The equilibrium point of the system is linearized, and the Hopf bifurcation boundary is determined using the Routh–Hurwitz criterion. Numerical simulations are then carried out to investigate the effects of the initial current i₀, supply flow rate q₀, and different initial disturbances on the displacement time histories, phase trajectories, and spatial phase trajectories of the rotor. The results show that, under the given system parameters, the Hopf bifurcation boundary is 0.61 A for the initial current and 9.62 × 10⁻⁵ m³/s for the supply flow rate. Current variation mainly affects electromagnetic stiffness and nonlinear electromagnetic force, whereas flow rate variation primarily changes the hydrostatic load capacity and oil film damping characteristics. Under different initial disturbances, the system may exhibit amplitude attenuation, recovery to stable suspension, or finite amplitude periodic vibration. Experimental results show good agreement with numerical simulations in terms of frequency spectra, displacement time histories, and phase trajectories, thereby verifying the effectiveness of the proposed three-degree-of-freedom dynamic model and Hopf bifurcation analysis method. The results can provide theoretical guidance for parameter matching, stability evaluation, and self-excited vibration suppression of magnetic-liquid double suspension bearings. Full article

A thermoreversible dynamic covalent network was constructed in recycled polyethylene terephthalate (RPET) via Diels–Alder (DA) chemistry to enhance mechanical performance, reprocessability, and self-healing. Furan-functionalized RPET (RPET-3F) was first prepared from maleated RPET (RPET-MA), followed by crosslinking with bismaleimide (BMI) at different feed ratios. FTIR spectra confirmed the successful grafting of furan groups and the formation of DA adducts. With increasing BMI content, the gel fraction and crosslink density increased substantially, whereas the swelling ratio decreased, indicating the progressive development of a three-dimensional network. RPET-3F-2B showed the highest network integrity among all samples. DSC analysis revealed a distinct retro-DA dissociation peak at 143 °C and a recrosslinking peak near 124 °C, confirming the thermal reversibility of the DA network. Owing to the optimized network structure, RPET-3F-2B exhibited the best mechanical properties and excellent reprocessability, retaining stable performance after three hot-pressing cycles. After repeated reprocessing, its tensile strength remained 74% higher than that of RPET-MA, while the elongation at break was still improved by about 10%. Moreover, the sample showed efficient thermally induced self-healing at 150 °C, with surface cracks nearly disappearing after 4 h. These results demonstrate that DA chemistry offers a promising route to the high-value reutilization of RPET into recyclable, multifunctional polymer materials. Full article

This study comprehensively investigates the degradation performance of a Mn-doped Zn₂SnO₄ photocatalyst based on time-dependent UV-Vis absorption spectra. Before machine learning modelling, the effects of experimental parameters such as UV–Vis measurement wavelength, reaction time, and Mn doping ratio were statistically validated using One-Way Analysis of Variance (ANOVA) and Multiple Linear Regression (MLR) methods. To overcome the limitations of linear models in representing complex physical systems, an optimized Multi-Layer Perceptron (MLP) architecture was developed to capture the system’s nonlinear dynamics with high accuracy. To validate the model’s out-of-sample prediction capability and prevent data leakage potentially arising from spectral data correlation, the “Leave-One-Doping-Level-Out” (LODLO) cross-validation strategy was applied, during which performance metrics of R²$=0.8889$ and $MSE=0.00238$ were recorded. To make the neural network’s decision-making mechanism transparent, a dual-validation explainability framework comprising Shapley Additive Explanations (SHAP) and Permutation Feature Importance analyses was employed. By quantifying the relative contributions of the experimental parameters to the model predictions, this approach revealed that the UV–Vis measurement wavelength was the dominant predictive variable, followed by the Mn doping ratio and reaction time. This study presents a transparent methodology that offers both strong predictive capability and physically grounded data to shed light on the complex interactions in doped semiconductor photocatalysts. Full article

In this study, the stability, electronic, structural, and fracture toughness, and mechanical properties of the Half-Heusler(HH) alloys MnCoSb, MnCoAs, MnCoP, and MnNiSb were comprehensively investigated using first-principles calculations based on density functional theory (DFT). The calculated results reveal that all four alloys exhibit half-metallic characteristics, characterized by the presence of a substantial band gap in the spin-down channel. The phonon spectra and negative formation energies confirm that these alloys possess both dynamic and thermodynamic stability. The Born criteria further validate the structural stability in terms of mechanical properties. Three-dimensional representations of the Young’s modulus, bulk modulus, and shear modulus for the four alloys indicate that MnCoP exhibits the most pronounced anisotropy. The overall fracture toughness of the alloys ranges from 1.58 MPa·m^1/2 to 2.63 MPa·m^1/2, which falls within the typical range for half-metallic materials, albeit at the lower end, attributable to the relatively ductile nature of the four alloys. Although the two methods yield different absolute values, the explicit crack model (Method I) is considered more reliable for anisotropic systems because it directly simulates crack propagation and accounts for local relaxations, while the empirical formula (Method II) provides a useful reference for high-throughput screening. Among the alloys, MnCoSb demonstrates a superior mechanical performance, with K_IC values of 2.63 MPa·m^1/2 and 1.58 MPa·m^1/2 and brittleness indices M of 8.97 and 14.94, indicating excellent damage tolerance compared to the other three alloys. In contrast, MnCoP exhibits higher brittleness and lower mechanical reliability, with K_IC values of 2.00 MPa·m^1/2 and 1.63 MPa·m^1/2 and higher M values of 13.83 and 16.99. This study provides quantitative predictions of fracture toughness and establishes a relationship between microscopic and mechanical properties. These findings offer a theoretical foundation for the application of damage-tolerant HH alloys in fields such as spintronics and magnetism. Full article

In the theoretical understanding of nuclear structure, different models play a crucial role due to the complications of the quantum mechanical many-body problem. One of the fundamental models is the cluster model, based on the molecular picture. We investigate here the possible phases of nuclear clusterization in a quantitative way. In particular, the semimicroscopic algebraic cluster model is applied, in which the phases are defined by the dynamical symmetry limits. We construct detailed spectra corresponding to the shell-like, rigid-molecule-like and weak-coupled phases, and compare them. Special attention is focused on the role of the Pauli principle. The spectra of the limiting cases show dramatic differences, and only one of them harmonizes with this basic natural law. The experimentally observed ¹²C + $\alpha$ spectrum is considered for the discussion. Full article

Gravity energy storage systems (GESS) have emerged as a promising long-duration energy storage technology capable of supporting large-scale renewable integration and enhancing grid resilience. However, the modeling framework for the hoisting electromechanical subsystem in wire-rope-based GESS remains underdeveloped, thereby limiting the accurate characterization of its transient grid-connected behavior, dynamic operating response, and cross-domain coupling effects. Existing studies commonly simplify wire ropes and related transmission components as rigid bodies or low-dimensional mechanical elements, failing to adequately account for their flexibility and the resulting high-dimensional nonlinear dynamics. Although related studies in mine hoisting and elevator systems have addressed mechanical vibration phenomena, they primarily focus on mechanical-side effects, such as shock loading and guide-structure response, whereas the mechanism by which flexible mechanical vibrations propagate through electromechanical coupling and influence electrical dynamic performance remains inadequately understood. To address this gap, this study establishes a distributed-parameter model for the wire-rope hoisting mechanism based on Hamilton’s principle and solves the corresponding vibration governing equations using the Galerkin method to capture nonlinear multi-modal dynamics. An electromechanical coupling model is then developed to elucidate how rope-vibration-induced tension fluctuations propagate through the drive chain, resulting in torque ripple, electrical interharmonics, and low-frequency grid-side oscillations. A Bessel-function-based analytical representation is further introduced to explain the formation of interharmonic clusters and beat-frequency phenomena under converter modulation. An experimental prototype is constructed to validate the proposed modeling framework. The measured vibration spectra, beat-frequency characteristics, and torque ripple align closely with analytical predictions, confirming the model’s capability to capture key propagation paths from rope vibration to electromechanical oscillation and grid-side dynamic response. The results provide a solid theoretical foundation for vibration mitigation, dynamic analysis, and control design of hoisting electromechanical subsystems in gravity energy storage applications. Full article

Solar radio bursts at very low frequencies are key phenomena in the Sun–Earth space environment, providing crucial diagnostics of the acceleration and propagation of solar wind, coronal mass ejection (CME), and non-thermal energetic particles and serving as important indicators for space weather forecasting. To meet the demand for rapid screening of burst events in large-scale observational datasets, we present an end-to-end automatic detection and evaluation framework tailored for Type III bursts, built upon long-term radio dynamic spectra from STEREO-A/SWAVES. We formulate radio burst detection as a one-dimensional interval localization task along the time axis and, in view of the scarcity of annotated samples, cast it as a few-shot object detection task. Building upon the Faster R-CNN architecture with a ResNet50-FPN backbone, we propose the Meta-FSOD framework, which adopts an episodic training paradigm to construct support–query episode pairs. The framework incorporates a metric-guided prototype learning branch to semantically align and calibrate region-of-interest (RoI) features via class prototypes, and integrates a dynamic Beta-Gating mechanism coupled with Soft-NMS to effectively suppress false positives while preserving high-recall performance. Experimental results demonstrate that, despite being trained on a significantly smaller dataset than comparable studies, Meta-FSOD achieves competitive performance, closely matching that of conventional supervised model. The proposed framework exhibits strong cross-temporal generalization capabilities and holds considerable potential for engineering applications in deep space exploration missions. Full article

Background: Menthol is a naturally occurring volatile terpene alcohol, widely used in food, pharmaceutical, and tobacco products; however, its high volatility leads to significant flavor loss during storage and handling. Methods: Herein, bimodal mesoporous silica materials (BMMs) were employed as carriers to encapsulate menthol, the loading and release behaviors were systematically compared using normal-temperature and alcohol-thermal loading methods. Results: Comprehensive characterizations (XRD and SAXS patterns, FT-IR spectra, SEM images, and N₂-sorption isotherms) confirmed that menthol incorporation did not disrupt the hierarchical mesoporous channels of BMMs. The alcohol-thermal loading method achieved a superior menthol loading capacity of 87%, significantly outperforming the normal-temperature loading (58%). Release performances revealed a transition in the dominant release mechanism, from diffusion-controlled behavior at low loading levels to concentration gradient-driven desorption at high loadings. Molecular dynamics simulations further demonstrated that alcohol-thermal loading enabled faster molecular diffusion and a more uniform distribution of menthol within the mesopores due to weaker interfacial interactions, whereas normal-temperature loading induced localized multilayer adsorption, resulting in mesopore blockage and hindered diffusion. In addition, long-term atmospheric release tests assessed sustained menthol retention over 30 days. Conclusions: Overall, this work establishes alcohol-thermal loading as an effective approach for regulating adsorption and release in mesoporous carriers, providing a foundation for developing volatile compound encapsulation strategies. Full article

The membrane fusion process, mediated by the entry fusion complex (EFC) of the monkeypox virus (MPXV), is crucial for host cell invasion. Apolipoprotein B mRNA Editing Catalytic Polypeptide-like 3 (APOBEC3)-driven mutation bias is a key factor in MPXV’s adaptive evolution during its global spread. However, how these mutations affect the structure and function of EFC proteins remains poorly understood. To address this, we performed genomic mutation analysis on globally circulating MPXV clades Ib and IIb, combined with protein monomer, binary, and quaternary complex structure modeling based on AlphaFold 3 and experimental validation by ELISA. We first delineated the mutational spectra of all 11 EFC proteins, revealing that although EFC proteins in clade Ib are highly conserved, lineage IIb B exhibits extensive APOBEC3-driven mutations and the G9 M142I mutation is identified as a lineage-associated APOBEC3-type mutation of lineage IIb B. Structural predictions revealed that while the M142I mutation does not alter G9 monomer folding, it induces a conformational shift in the G9/A16 subcomplex. Furthermore, within the predicted G9/A16/A56/K2 quaternary complex, this mutation enlarges the interfacial gap and reduces docking stability between the G9/A16 subcomplex and A56/K2. Experimental validation demonstrated that the M142I mutation significantly reduces the binding affinity of G9 for A16 and impairs the recruitment of A56/K2 to the quaternary complex, confirming the computationally predicted mechanism of interface destabilization. These findings highlight a dynamic interplay between APOBEC3-driven evolution and EFC protein structure, demonstrating that the M142I mutation alters EFC complex assembly dynamics and may shift the regulatory balance of the membrane fusion system. These structural changes provide molecular insights into MPXV lineage differentiation, though direct functional assays are required to determine the net effect on viral entry efficiency. Full article

In the oil and gas industry, fiber-optic telemetry is hindered by transmission degradation from high-temperature macro-bend loss. In this study, to address the lack of a unified model, we develop a numerical framework incorporating both bending-dominated effects and thermo-optic modulation. We systematically analyze the coupled responses of multimode (MMF) and single-mode (SMF) fibers at 1.55 μm across varying temperatures (303.15~483.15 K) and bending radii (1~12 mm). Power spectral density (PSD) and phase spectra are utilized to characterize the loss response and explore its modulation mechanisms. Our results indicate that the MMF temperature response is relatively smooth, with a peak magnitude of 10³. In the frequency domain, increased bending raises the MMF PSD main peak by over an order of magnitude, enhancing structural features. While the MMF phase response exhibits a wide dynamic range under tight bending, it becomes unstable in weak modulation regions. Conversely, SMF exhibits more pronounced structural fluctuations (order of 10⁴) but maintains a continuous, smooth phase gradient, demonstrating superior stability. Furthermore, MMF frequency-domain characteristics are highly wavelength-dependent (1.2~2.0 μm), whereas SMF fluctuations remain below 10%, indicating a higher parameter robustness. These findings provide a theoretical foundation for optimizing downhole fiber-optic telemetry selection and signal processing strategies. Full article

Low-Earth orbit satellites are gradually becoming the core infrastructure of integrated aerospace communication networks, with their significant advantages of high communication rates, small transmission delay, and wide coverage. Interference with military communications in response to their security and protection needs is a current research challenge. Consequently, this paper introduces an interference technique optimized for low-Earth orbit satellite signals using a multimodal learning transformer model (OI-MLT). The proposed method incorporates symmetry-aware design by exploiting the inherent time–frequency structural characteristics of LEO satellite signals and the spatially distributed topology of interference sources. An optimized model for distributed interference sources is developed, and multimodal information of spectra and numerical values is processed in parallel through the self-attention mechanism. This approach effectively addresses the problem of dynamic matching between the interference signal and target signal in high-speed LEO scenarios, as well as high-precision interference synchronization under time-varying channels. Experimental results demonstrate that this technique enhances the precision of frequency tracking, reduces the time required for synchronization establishment, and improves the interference success rate by 27.52% on average compared with existing methods. Full article

This study introduces a switchable and tunable multimodal, multi-peak, perfect terahertz absorber, utilizing a composite structure of graphene and double concentric metal rings. From bottom to top, the absorber consists of a gold substrate, a SiO₂ dielectric layer, a patterned graphene layer, another SiO₂ dielectric layer, and double concentric metal rings on the top. The structure achieves three high-absorption resonance peaks in the far-infrared band: a relatively broad peak with 99.05% absorptance at 38.128 THz, and two extremely narrow peaks with 99.56% and 97.23% absorptance at 47.909 THz and 49.873 THz, respectively. Analysis of the absorption spectra and electric field distributions reveals that the generation mechanism of Peak I is Fabry–Pérot cavity resonance, while Peaks II and III result from the coupling between the high-order localized surface plasmons in the outer ring and the graphene surface plasmon polaritons. Benefiting from graphene’s excellent electrical tunability, the absorption peaks’ positions and intensities can be dynamically tuned by varying the Fermi level. The core innovation of this work lies in the high-level integration of multiple functionalities. By leveraging the sensitive response of Peak III to variations in the Fermi level, a four-input AND logic gate is embedded within the metamaterial absorber in this frequency band. The Fermi levels of four independent graphene regions serve as the binary inputs, while the absorption state of Peak III is defined as the logical output. Additionally, the two narrow peaks display high sensitivity to the surrounding refractive index, with sensitivities of 30.1 THz/RIU and 62.5 THz/RIU, demonstrating significant potential for sensing. This multifunctional integrated device combines tunable absorption, a logic gate, and sensing capabilities, making it promising for terahertz communication systems, intelligent sensing networks, and reconfigurable platforms. Full article