Search Results (19,055)

A hybrid graphene-VO₂ reconfigurable terahertz metamaterial absorber is proposed for broadband radar cross-section (RCS) reduction and high-performance sensing. The designed structure leverages the phase transition property of VO₂ and the electrostatic tunability of graphene to achieve dynamic switching between ultra-broadband and narrowband absorption states. When VO₂ is in the metallic state and graphene is unbiased, the absorber exhibits over 90% absorption across 0.82~3.50 THz, corresponding to a relative bandwidth of 124%. In the narrowband mode, with VO₂ in the insulating state and graphene biased (E_f = 1 eV), a sharp absorption peak exceeding 60% is achieved at 1.48 THz. The symmetrical design ensures polarization insensitivity and wide-angle stability. Applications in broadband RCS reduction higher than 10 dB and refractive index sensing with a sensitivity of 24.86 GHz/RIU are demonstrated, surpassing conventional terahertz sensors. This work provides a promising platform for adaptive terahertz stealth and sensing systems. Full article

The Electro-Hydraulic Servo Actuator for Active Suspensions (ASEHSA) plays a decisive role in shaping the holistic performance of vehicle suspension systems through its dynamic response speed and control precision. However, achieving high-performance control of ASEHSA still faces challenges. On one hand, existing model-based control methods are highly sensitive to parameter uncertainties and unmodeled nonlinear hydraulic dynamics, which can easily lead to reduced robustness in practical applications. On the other hand, traditional model-free strategies have limited time-delay compensation capabilities and often struggle to balance overshoot and settling time under delayed and disturbed conditions. To resolve this challenge, this study proposes an improved model-free adaptive control method that incorporates the differentiation of the tracking error (DE-IMFAC). Within the framework of traditional model-free adaptive control (MFAC), this approach reconfigures the time-delay term from an explicit form in the control law to implicit management, substantially mitigating the influence of time delays on system control performance. At the same time, by refining the performance criterion function and integrating a tracking error differentiation term together with dynamic weighting factors, the dynamic performance and adjustment flexibility of the controller are significantly enhanced. Additionally, by leveraging the characteristic equation of discrete autonomous systems and compression mapping theory, the BIBO stability of the DE-IMFAC control system and the monotonic convergence of the tracking error are rigorously established through theoretical analysis. Simulation and experimental results demonstrate that, compared with PID and traditional MFAC methods, DE-IMFAC significantly reduces integral absolute error, overshoot, settling time, and maximum position tracking error, while improving disturbance rejection capability. This approach does not depend on an accurate mathematical model of the ASEHSA system and maintains robust dynamic performance under complex operating environments characterized by time delays and data disturbances, providing a practical solution for ASEHSA and related industrial control systems. Full article

Hydrogen gas sensors are essential for ensuring safety and efficient operation in the expanding hydrogen energy economy and its infrastructure. However, real-world sensor performance is frequently compromised by interference from coexisting gases, environmental fluctuations, and physical disturbances, leading to false alarms, missed detections, and increased recalibration or maintenance burden. This comprehensive review systematically summarizes recent advances in anti-interference technologies for hydrogen gas sensors across material-, signal-processing-, and system-level domains. At the material level, strategies such as noble-metal doping, nanostructure engineering, and selective membrane coatings improve selectivity and long-term stability. At the signal level, advanced noise reduction, dynamic calibration, drift compensation, and machine-learning-based pattern recognition enhance detection accuracy and robustness under varying conditions. At the system level, sensor arrays, optimized packaging, structural isolation, and adaptive redundancy mitigate interference in realistic deployments. By critically evaluating these multi-scale strategies, this review highlights progress, identifies key performance trade-offs, and outlines research directions toward interference-resilient sensing that supports scalable, low-maintenance, and energy-efficient hydrogen infrastructure. Full article

While it is a global imperative that firms should achieve superior environmental, social, and governance (ESG) performance, the specific impact of ESG on export product quality remains under-explored. Based on stakeholder theory and principal–agent theory, this paper utilizes a sample of Chinese listed companies and the High-Dimensional Fixed Effects (HDFE) Model to empirically examine the impact and underlying mechanisms of ESG performance on export product quality. The results indicate a U-shaped relationship between ESG performance and export product quality, a non-linear correlation that has received limited attention in the previous literature. This U-shaped relationship is more pronounced among state-owned enterprises (SOEs), firms producing non-high-tech products, and those in heavy-polluting industries. Mechanism analysis reveals that ESG performance influences export product quality primarily through three channels: innovation levels, total factor productivity (TFP), and supply chain stability. By unveiling these non-linear dynamics and their underlying pathways, this study provides a novel theoretical framework and critical empirical evidence that reconcile conflicting views on ESG effects. These findings offer important insights for policymakers and exporters seeking to align ESG practices with export objectives, thereby contributing to more sustainable and high-quality development of foreign trade in China and beyond. Full article

To overcome the limitations of conventional control strategies in simultaneously suppressing external sway disturbances and internal parameter variations—induced by strong eddy current effects in marine canned magnetic bearings (MBs)—this paper introduces an improved model reference adaptive control (MRAC) method. First, electromagnetic force and dynamic models of the marine canned MBs are developed, taking into account eddy current effects and oscillatory motion. On this basis, a state observer is designed to estimate the system’s unknown dynamics. A predictive error term is formulated to capture the combined influence of model uncertainties and external disturbances. An adaptive law is then applied to compensate for these unknown dynamics and external disturbances. Moreover, the stability of the marine canned MBs system under the proposed improved MRAC scheme is rigorously analyzed using Lyapunov stability theory. Simulation results confirm the effectiveness of the algorithm, showing that, compared with conventional PID control, the improved MRAC approach reduces rotor vibration by more than 53%, significantly strengthening the disturbance rejection performance of marine canned MBs. Full article

This paper investigates the mechanical stability and critical thickness of free-standing, ultrathin molten polyethylene films using Molecular Dynamics simulations. By comparing the “interfacial drying” and “film stretching” methodologies, this research establishes that both methods consistently identify a stability threshold where continuous films transition into fibrillar and void structures known as “crazes”. A key finding is that films at extremely reduced thicknesses exhibit an anisotropic pressure profile in their core—characterized by a positive normal pressure—which serves as a manifestation of positive disjoining pressure and a precursor to film transformation. Consequently, the study proposes a more rigorous stability criterion based on mechanical isotropy, which yields higher critical thickness values (approximately 6.5 nm at 373.15 K and 9.3 nm at 673.15 K) than those previously estimated from short-term (100 ns) visual observations. Ultimately, the work concludes that maintaining a negative disjoining pressure is fundamental to the structural integrity of these polymeric nanomaterials. Full article

As robotic manipulators evolve toward lightweight and long-link structures, flexibility increasingly affects dynamic response and trajectory tracking accuracy. However, existing studies often lack a consistent coupling mechanism between finite element structural models and control models, and flexible effects are typically treated as disturbances, limiting the direct use of structural parameters for control prediction and optimization. This paper proposes a structure–control collaborative co-simulation framework for a six-degree-of-freedom (6-DOF) flexible-joint manipulator. ANSYS-based finite element analysis (FEA) is integrated with the MATLAB/Simulink control environment to extract joint-level equivalent stiffness, inertia, modal frequencies, and damping parameters, which are embedded into a rigid–flexible coupled dynamic model. A regression-based representation is introduced to capture unmodeled flexible residual dynamics, and a regression-compensated adaptive PID torque controller with σ-modification and a dead-zone mechanism is developed to ensure bounded adaptation and closed-loop stability. Simulation results under no-load and payload conditions demonstrate improved oscillation suppression and tracking accuracy. By establishing a unified coupling mechanism from structural parameters to the control model, the proposed method achieves consistent co-modeling of the structural and control domains and provides an engineering-feasible co-simulation approach for dynamic prediction and control optimization of multi-DOF flexible manipulators under varying operating conditions. Full article

Carbon fiber-reinforced resin matrix composites (CFRC) are extensively used in aerospace, automotive manufacturing, and sports equipment. However, the brittle nature of the resin matrix causes CFRC to exhibit severe vibrations and noise under dry friction conditions. Enhancing the intrinsic damping properties of the resin matrix serves as a fundamental and effective strategy to mitigate vibration and noise radiation in composite components. This study systematically investigates high-temperature co-curing damping composites using co-curing technology, aiming to improve the mechanical performance and damping characteristics of traditional fiber-reinforced epoxy resin composites. A novel carbon fiber-reinforced terminal carboxyl nitrile epoxy pre-polymer composite material demonstrates both stable chemical properties and excellent high-temperature resistance. Through formulation adjustments, the curing temperature and time of epoxy resin are matched with those of the terminal carboxyl nitrile epoxy pre-polymer. The performance of epoxy carbon fiber composites was evaluated through tensile tests, flexural tests, impact tests, infrared spectroscopy, thermogravimetric analysis, dynamic mechanical analysis, scanning electron microscopy, and X-ray diffraction. Results show that blending epoxy resin with terminal carboxyl nitrile liquid rubber enhances energy dissipation by increasing intermolecular friction and hydrogen bonding interactions. The damping ratio of epoxy resin-based carbon fiber composites reaches as high as 1.67%. Tensile strength, flexural strength, and impact strength reach 1968 MPa, 1343 MPa, and 127 kJ/m², respectively. The addition of terminal carboxylated nitrile liquid rubber facilitates the formation of continuous friction membranes, enhancing friction stability. Tensile tests demonstrate that carbon fiber composites containing 25% terminal carboxylated nitrile liquid rubber outperforms other formulations. As evidenced by impact tests, the performance of the prepared composites is superior to that of other configurations. Dynamic mechanical analysis indicates that the 25% rubber-containing composites exhibit enhanced damping characteristics and higher loss modulus. Experimental results confirm that this study advances the development of functional composites for vibration reduction and noise control applications. Full article

Electrical Resistivity Tomography (ERT) has proven to be a highly sensitive geophysical method for characterizing the dynamics of seawater intrusion. This study uses tank experiments to simulate seawater intrusion, utilizing electrical resistivity tomography to monitor real-time changes in groundwater resistivity during the intrusion process. The objective is to quantitatively reveal the development and evolution mechanisms of seawater intrusion wedges in sandy aquifers, thereby establishing a real-time resistivity monitoring method for groundwater distribution and migration characteristics. This study improves resistivity imaging data processing methods, enhancing both efficiency and accuracy. The refined cross-hole ERT technique is applicable not only to meter-scale indoor experiments; its optimized forward and inverse algorithms can also be directly transferred to regional-scale field monitoring. Experimental results show that the average resistivity in the study area continuously decreases from 57 Ω·m in the initial freshwater state to 1.1 Ω·m at the intrusion stabilization point. Areas with resistivity values below 20 Ω·m corresponded exactly to the brine intrusion zone. The evolution of the freshwater-saltwater interface unfolded in three stages: Initially, the density difference (0.025 g/cm³) dominated, with the saltwater intrusion depth at the aquifer base reaching 0.45 m, significantly exceeding the 0.04 m penetration at the upper section. During the intermediate stage, the interface morphology differentiated into an “upper triangular, lower arc-shaped” configuration. The bottom intrusion distance increased to 1.65 m, and the thickness of the brackish-freshwater mixing zone expanded from 0.1 m to 0.3 m. In the final stage, the interface stabilized and began intruding toward the surface, establishing a new hydrodynamic equilibrium. In addition, the migration rate of saline water at the aquifer base gradually decreased from 6.25 × 10⁻⁴ cm/s initially to 1.16 × 10⁻⁵ cm/s at steady state. These results reflect the dynamic coupling process between seepage and dispersion and demonstrate that this method enables effective real-time monitoring of seawater intrusion development and conditions, as well as early warning capabilities. Full article

α-Synuclein (α-syn) aggregation underlies synucleinopathies, yet the physicochemical determinants that govern which assembly states form under defined solution conditions remain incompletely resolved. Here, we examine how pH and metal ions reshape α-syn self-assembly. Across acidic and physiological pH conditions, α-syn populates monomeric, nanoscale oligomeric, and mesoscale aggregate states whose relative abundances evolve over time. Fluorescence microscopy reveals robust mesoscale assembly at pH 5, minimal aggregation at pH 7, and transient assemblies at pH 3, highlighting the limitations of imaging-based detection alone. Therefore, we use dynamic light scattering (DLS) to resolve oligomeric populations and quantify pH-dependent redistribution of assembly mass. Toxicity-mitigating modulators altered α-syn assembly in a strongly pH-dependent manner. Anle138b increased the abundance of oligomeric species at low pH, whereas EGCG produced divergent effects at pH 5 and pH 3. We further examined the effects of metal ions, finding that Fe³⁺ stabilized higher-order assemblies under acidic conditions, Cu²⁺ delayed assembly at pH 5 while enhancing aggregation at pH 3, and Zn²⁺ increased oligomerization primarily at low pH. Overall, these results demonstrate that α-syn assembly is highly sensitive to coupled effects of pH, metal chemistry, and time. Full article

Unmanned Aerial Vehicles (UAVs) have made significant advancements in communication stability and security through techniques such as frequency hopping, signal spreading, and adaptive interference suppression. However, challenges remain in modeling spectrum competition, integrating expert knowledge, and predicting opponent behavior. To address these issues, we propose UAV-FPG (Unmanned Aerial Vehicle–Frequency Point Game), a game-theoretic environment model that simulates the dynamic interaction between interference and anti-interference strategies of opponent and ally UAVs in communication frequency bands. The model incorporates a prior expert knowledge base to optimize frequency selection and employs large language models for episode-level opponent trajectory generation and planning within UAV-FPG, serving as an operationally more challenging simulator adversary for stress-testing anti-jamming policies under our evaluation protocol. Experimental results highlight the effectiveness of integrating the expert knowledge base and the large language model: relative to fixed-path baselines, iterative feedback-conditioned LLM planning tends to generate more adaptive trajectories and achieve higher opponent rewards in UAV-FPG. These findings are confined to the proposed simulation environment and are not intended as general claims about real-world jamming capability or onboard planning performance. UAV-FPG provides a robust platform for advancing anti-jamming strategies and intelligent decision-making in UAV communication systems. Full article

Rural shrinkage is increasingly expressed through changing residential mobility, housing under occupancy, and intermittent dwelling use, rather than a simple linear process of permanent outmigration and abandonment. Yet empirical measurement of occupancy dynamics and the service-mediated mechanisms shaping residence stability remains limited. This study proposes an evidence-driven and explainable assessment framework that links energy-informed occupancy dynamics with settlement building area and mechanism identification, using Fuyuan City as a case study. Daily electricity consumption time series from 2021 to 2024 are used to infer occupancy dynamics and detect behavioral signatures of long term residence, seasonal residence, return visits, and vacancy. Shape-based temporal clustering identifies six occupancy trajectories, revealing pronounced heterogeneity in mobility rhythms within the rural settlement system. Settlement vacancy-related built-environment changes are characterized from 2 m remote sensing imagery, using a trained YOLO-based building detection workflow, producing settlement-level total building area as a physical indicator of the development intensity. Integrating these behavioral measures with multi-source spatial factors, the mechanism model shows that development, governance, and environmental conditions influence residence stability primarily through service provision. Among service domains, education services exhibit the strongest direct association with long-term residence stability, while transport and daily life services show modest positive effects and healthcare presents a smaller positive effect. Development conditions positively promote all service types, whereas governance and environmental context display differentiated and, in some pathways, opposing effects across services. Overall, the framework enables interpretable monitoring of rural shrinkage dynamics by jointly quantifying occupancy trajectories, settlement morphology, and service-mediated pathways shaping residential outcomes. Full article

This paper examines the existence and stability of an m-cyclic coupled system of higher-order fractional differential equations with non-singular kernels. Sufficient conditions for the existence and stability of solutions are obtained using fixed-point techniques. Two numerical examples involving coupled and triply coupled systems are presented to validate the theoretical results, and simulations of the triply coupled case illustrate the influence of different fractional orders on the system dynamics. Full article

Reliable prediction of the Remaining Useful Life (RUL) of lithium-ion batteries (LIBs) plays a pivotal role in maintaining safe operation, enhancing system dependability, and supporting economically sustainable lifecycle planning in electric mobility and stationary energy storage applications. However, battery aging is governed by highly nonlinear, interacting, and chemistry-dependent processes, which pose significant challenges for conventional data-driven prognostic models. In this study, a unified RUL prediction framework is proposed by integrating multi-domain feature engineering, a Multi-Criteria Adaptive Selection (MCAS) strategy, and a Bidirectional Long Short-Term Memory (Bi-LSTM) network enhanced with dual multi-head attention. Degradation-relevant descriptors extracted from time, frequency, and chaotic domains are employed to capture complementary aging dynamics across battery cycling. In addition, a novel degradation-consistency indicator, termed the M-score, is introduced to characterize the regularity and stability of degradation behavior using observable electrical, thermal, and statistical signals. The MCAS mechanism systematically identifies informative and temporally stable features while suppressing redundancy, thereby improving both predictive robustness and interpretability. The resulting architecture jointly exploits adaptive feature refinement and attention-based temporal modeling to enhance the RUL estimation accuracy. The proposed framework is validated using two widely adopted benchmark datasets: the Toyota Research Institute (TRI) dataset, representing fast-charging lithium iron phosphate (LFP) cells, and the Sandia National Laboratories (SNL) dataset, which includes multiple chemistries, such as LFP, NMC, and NCA. Experimental results demonstrate substantial improvements in the RUL prediction accuracy compared with baseline Bi-LSTM and single-attention models, while systematic ablation studies confirm the individual contributions of the M-score and MCAS components. Within the evaluated datasets and operating conditions, the results suggest that the proposed framework offers a robust and interpretable data-driven solution for battery RUL estimation. However, extending its generalizability and validating its performance on unseen datasets and in real-world scenarios remain important areas for future research. Full article

The stabilization of rock mass vibrations in underground excavations presents a critical engineering challenge due to the interplay of viscoelastic dynamics, impulsive shocks from blasting or rock bursts, and time-varying delays induced by wave propagation and sensor–actuator networks. In this paper, an integral sliding mode control scheme is developed for a Kelvin–Voigt type hyperbolic system subject to such impulsive effects and time-varying delays. To preserve sliding surface continuity under impulsive disturbances, the impulse information is explicitly incorporated into the design of the integral sliding function. The resulting sliding mode dynamics, which include discrete state jumps, are analyzed using a piecewise Lyapunov functional combined with inequality techniques; sufficient conditions are derived to guarantee asymptotic stability. Moreover, a sliding mode control law is synthesized to ensure that the system trajectories reach and remain on the sliding manifold from the initial time onward, despite parameter uncertainties and external disturbances. Numerical simulations with parameters reflecting realistic mining scenarios verify the effectiveness of the proposed control strategy, demonstrating its potential for practical rock mass vibration stabilization in geotechnical engineering. Full article