Search Results (811)

Quantum beams-including X-rays, synchrotron radiation, electrons, neutrons, ions, and ultrafast photon sources-are indispensable tools for probing the structure, dynamics, and electronic properties of matter. The excitation time scale ${\tau }_{exc}$ is defined operationally as the characteristic temporal interval governing externally imposed energy deposition events within the interaction volume, such as pulse duration, bunch spacing, or beam dwell time. Interpretation of beam–matter interactions has traditionally relied on steady-state or quasi-equilibrium assumptions, implicitly presuming that intrinsic material relaxation processes can accommodate externally imposed excitation. Recent advances in high-brightness synchrotron sources, X-ray free-electron lasers (XFELs), and pulsed electron beams increasingly operate in regimes where this assumption is strained, and systematic nonequilibrium effects, radiation damage, and irreversible transformations are reported even under routine experimental conditions. This work examines the role of time-scale mismatch between beam-driven energy deposition and intrinsic material relaxation as a governing constraint in beam–matter interactions. Analyzing the hierarchy of excitation, electronic relaxation, phonon coupling, and thermal diffusion time scales, the analysis introduces a dimensionless mismatch parameter $\mathsf{\Lambda }={\displaystyle \frac{{\tau }_{rel}}{{\tau }_{exc}}}$, which quantifies the competition between externally imposed excitation and intrinsic relaxation processes in beam–matter interactions. The resulting framework provides a unified physical interpretation of beam-induced damage, signal distortion, dose dependence, and nonlinear response across quantum beam modalities, framing these effects as consequences of forced nonequilibrium dynamics rather than technique-specific artifacts. Full article

The time-varying mesh stiffness excitation of helical gears impacts the vibration state of the rotor–bearing systems, while the existence of mechanical dynamic eccentricity makes the rotor–bearing dynamics equation a system of parametric excitation. To address this situation, the time-varying mesh stiffness of the helical gear is substituted into the coupled bending–torsion–axial dynamic equation of the rotor–bearing system. By considering dynamic eccentricity, the rotor’s vibration displacement response is calculated. The unified strength theory is introduced to compute the complex stress state. The study’s results indicate that time-varying stiffness significantly influences the system’s vibration characteristics, with the equivalent stress values exceeding those under twin-shear stress. This finding demonstrates the advantage of using the unified strength theory under high-load conditions, providing an essential reference for optimizing the dynamic performance of high-speed helical gear transmission systems. Full article

With the rapidly growing global demand for clean energy, offshore wind power has become an important renewable energy source. To clarify how the principal dimensions affect the performance of a 15 MW-class floating wind turbine platform in 100 m water depth, this paper proposes a steel-concrete hybrid semi-submersible platform and systematically performs a parametric sensitivity analysis. The platform adopts a three-column configuration with heave tanks. The upper columns and cross braces are made of steel, while the lower hexagonal columns, pontoons, and heave tanks are constructed from concrete, significantly reducing steel consumption while satisfying structural and stability requirements. Focusing on three key design variables—draft, column spacing, and column diameter—this study establishes a unified normalized sensitivity analysis framework. It quantitatively evaluates their influence on platform mass, intact stability, natural periods, and fully coupled dynamic responses (including surge, heave, pitch motions, and mooring line tensions) under both operational and extreme conditions. The results reveal distinct roles of the principal dimensions in governing the platform dynamics: column spacing is the most sensitive parameter for tuning pitch response, restoring stiffness, and stability; increasing draft effectively suppresses heave and pitch responses but has only a limited effect on low-frequency surge motions; and column diameter strongly affects the natural periods of heave and pitch. Notably, dynamic responses exhibit significant nonlinear characteristics with variations in column diameter. When the diameter exceeds 110–120% of the baseline value, the peak pitch response under extreme sea states shows a deteriorating inflection point, accompanied by an accelerated surge in peak mooring loads. This indicates that excessive increases in column diameter may cause wave excitation forces to become dominant, thereby compromising the overall dynamic safety of the system. This paper identifies the governing geometric parameters for different motion modes and their control boundaries, providing a quantifiable and generalizable basis for the multi-objective collaborative design and cost reduction optimization of 15 MW steel-concrete hybrid semi-submersible floating wind turbine platforms. Full article

State-selective single-electron capture in He⁺ + Ne collisions was studied at laboratory He⁺ projectile kinetic energies of 30–100 keV (corresponding to 7.5–25 keV/u) using a reaction microscope. Q-value spectra were obtained through recoil-ion momentum reconstruction, enabling the decomposition of the capture yield into three distinct contributions: (i) capture into excited states of the projectile without target excitation, (ii) capture into the projectile ground state accompanied by excitation of the residual Ne⁺ ion, and (iii) capture involving simultaneous excitation of both He and Ne⁺. Across the studied energy range, capture into the projectile ground state accompanied by target excitation is the dominant pathway. With increasing impact energy, the fraction of joint projectile–target excitation increases markedly, whereas the target-excitation-only contribution decreases; the projectile-excitation-only fraction remains at a low, nearly constant level. These findings underscore the significance of multi-electron dynamics in intermediate-energy collisions involving many-electron targets. Full article

Memristors, whose magnetic flux is inherently dependent on external excitation, have been widely employed to model electromagnetic induction effects in neural systems. However, when such induction mechanisms are incorporated into fractional-order neurons, the resulting nonlinear dynamics remain largely unexplored. This paper proposes a novel fractional-order memristive neural network (FO-MNN) by embedding two memristors into a single Hopfield-type neuron, both serving to characterize electromagnetic induction behavior. The complex nonlinear dynamics induced by the two memristive modules are systematically investigated. Numerical simulations reveal that, by tuning the parameters of the first memristive module, Lorenz-like double-wing butterfly attractors can be generated. When both memristive modules act simultaneously, the network exhibits highly complex multi-double-wing butterfly chaotic attractors, whose wing numbers can be flexibly adjusted via the control parameter of the second memristive module. Moreover, variations in the initial state of the second memristor lead to initial-condition-dependent coexistence of multiple double-wing butterfly attractors. These rich dynamical behaviors highlight the strong potential of the proposed FO-MNN for chaos-based engineering and security applications. Finally, a novel privacy-protection scheme for the Industrial Internet of Things (IIoT) is constructed based on the FO-MNN, and its effectiveness is validated through encryption experiments. Full article

Magnetorheological (MR) semi-active suspensions offer clear advantages in improving ride comfort and handling stability, yet their engineering applications are often hindered by strong nonlinear hysteresis of the damper, the randomness of road excitations, and the reliance on manual tuning of controller parameters. To address these issues, this paper proposes an integrated framework of “experimental modeling–semi-active implementation–adaptive control.” First, characteristic tests of the MR damper are conducted, based on which a current-dependent Bouc–Wen forward model is established. Tianji’s Horse Racing Optimization (THRO) is then employed for parameter identification to reproduce the hysteresis behavior accurately. Second, a back propagation (BP) neural network-based inverse current model is developed to achieve rapid mapping from “desired damping force” to “driving current,” enabling semi-active actuation. Furthermore, a radial basis function (RBF) neural network is embedded into the active disturbance rejection control (ADRC) structure to estimate the system Jacobian online and to tune key extended state observer (ESO) gains in real time, forming the proposed RBF-ADRC strategy and thereby enhancing disturbance observation and compensation capability. Simulation results under pulse-road and Class-C random-road excitations show that, compared with the passive suspension, the proposed method reduces the root mean square error values of sprung-mass acceleration, suspension dynamic deflection, and tire dynamic load by 25.14%, 18.71%, and 11.61%, respectively, while also outperforming skyhook control and fixed-gain ADRC. Frequency-domain results further show stronger attenuation in the low-frequency band relevant to body vibration. Under pulse excitation, RBF-ADRC yields smaller peak and trough body accelerations and faster post-impact recovery. Under ±30% sprung-mass variations, it achieves the best worst-case and fluctuation-range robustness among the compared strategies and remains close to offline retuning. These results demonstrate that the proposed method improves both control performance and robustness while reducing the need for repeated manual calibration. Full article

Serotonergic projections innervate both the dorsal and ventral cochlear nuclei; however, the electrophysiological consequences of serotonergic input in the ventral cochlear nucleus (VCN) remain incompletely understood. This study aimed to identify the serotonin receptor subtypes involved in serotonergic modulation of stellate cells in the mouse anteroventral cochlear nucleus (AVCN) and to determine the underlying ion channel mechanisms. Whole-cell patch-clamp recordings were performed in acute brain slices obtained from postnatal day 12–17 mice. Bath application of serotonin (25 µM) induced membrane depolarization (~5 mV) and increased action potential firing. Pharmacological experiments demonstrated that antagonists of 5-HT1A, 5-HT2A, and 5-HT2C receptors partially reversed the depolarization and reduced serotonin-induced inward currents, indicating that multiple receptor subtypes contribute to serotonergic excitation. Blockade of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels with extracellular Cs⁺ suppressed approximately 95% of the serotonin-induced depolarization and inward current, implicating HCN channel-mediated Ih as a principal ionic mechanism. Serotonin significantly increased Ih amplitude. Analysis of steady-state activation revealed no statistically significant shift in V0.5; however, under near-resting membrane potential conditions, serotonin significantly reduced the slope factor of the activation curve, consistent with altered voltage sensitivity of Ih gating. Immunohistochemical analysis confirmed the presence of 5-HT1A, 5-HT2A, and 5-HT2C receptors in the AVCN. Together, these findings indicate that serotonergic excitation of AVCN stellate cells is mediated by coordinated activation of multiple 5-HT receptor subtypes and primarily involves modulation of HCN-dependent subthreshold membrane dynamics. Full article

As modern power systems grow increasingly complex, there is a pressing need for stability analysis methods capable of handling nonlinear dynamics while providing physically meaningful and reliable stability indices. Port-Hamiltonian (PH) frameworks have emerged as strong candidates in this regard, offering inherently stable formulations, energy-consistent representations, and modular plug-and-play scalability. However, the practical deployment of PH-based stability analysis remains hindered by the absence of reliable, high-fidelity parameter identification methods that rely on sensor measurements to capture system dynamics while remaining compatible with PH model structures. This paper addresses that gap by proposing a comprehensive three-stage data-driven identification framework for PH modeling of synchronous generators—the central dynamic component of any power system. While the IEEE Standard 115 provides established procedures for transient parameter identification, it exhibits fundamental limitations when applied to PH modeling, including single-scenario identifiability constraints, noise-sensitive derivative-based formulations that amplify sensor measurement errors, and the inability to decouple generator-internal damping from grid contributions. The proposed framework resolves these limitations through multi-scenario excitation using sensor-acquired voltage and current signals, derivative-free state consistency optimization, and physics-based regularization that enforces PH structure preservation. Complete identification of eight key parameters (H, D, $X_d$, $X_q$, $X_d^{\prime }$, $X_q^{\prime }$, $T_{do}^{\prime }$, $T_{qo}^{\prime }$) is achieved with errors ranging from 1.26% to 9.10%, and validation confirms RMS rotor angle errors below 1.2° and speed errors below 0.15%, demonstrating suitability for transient stability analysis, passivity-based control design, and oscillation damping assessment. Full article

Vehicle–infrastructure cooperative perception (VICP) extends the sensing capability of single-vehicle systems by integrating multi-source information from onboard and roadside sensors, thereby alleviating limitations in sensing range and field-of-view coverage. However, in complex urban environments, the robustness of such systems—particularly in terms of blind-spot coverage and feature representation—is severely affected by both static and dynamic occlusions, as well as distance-induced sparsity in point cloud data. To address these challenges, a 3D object detection framework incorporating point cloud feature enhancement and spatially adaptive fusion is proposed. First, to mitigate feature degradation under sparse and occluded conditions, a Redefined Squeeze-and-Excitation Network (R-SENet) attention module is integrated into the feature encoding stage. This module employs a dual-dimensional squeeze-and-excitation mechanism operating across pillars and intra-pillar points, enabling adaptive recalibration of critical geometric features. In addition, a Feature Pyramid Backbone Network (FPB-Net) is designed to improve target representation across varying distances through multi-scale feature extraction and cross-layer aggregation. Second, to address feature heterogeneity and spatial misalignment between heterogeneous sensing agents, a Spatial Adaptive Feature Fusion (SAFF) module is introduced. By explicitly encoding the origin of features and leveraging spatial attention mechanisms, the SAFF module enables dynamic weighting and complementary fusion between fine-grained vehicle-side features and globally informative roadside semantics. Extensive experiments conducted on the DAIR-V2X benchmark and a custom dataset demonstrate that the proposed approach outperforms several state-of-the-art methods. Specifically, Average Precision (AP) scores of 0.762 and 0.694 are achieved at an IoU threshold of 0.5, while AP scores of 0.617 and 0.563 are obtained at an IoU threshold of 0.7 on the two datasets, respectively. Furthermore, the proposed framework maintains real-time inference performance, highlighting its effectiveness and practical potential for real-world deployment. Full article

In this work, we present a theoretical investigation of electron–surface optical phonon (SOP) interactions and Auger recombination processes in monolayer PtSe₂ and PtS₂ supported on polar dielectric substrates such as SiO₂ and hBN. The analysis is based on a low-energy effective Hamiltonian describing the electronic structure near the K and K′ valleys of the Brillouin zone, combined with the Fröhlich interaction model to account for the coupling between charge carriers and substrate-induced optical phonons. The comparison between Auger recombination and SOP scattering is performed at a representative carrier density of ${n=10}^{12} {\mathrm{c}\mathrm{m}}^{-2}$ within the investigated temperature range. We analyze the formation of polaronic states arising from the hybridization between electronic excitations and SOPs and evaluate the associated Rabi splitting energies and oscillator strengths. The temperature dependence of the SOP-induced scattering rates and the influence of the monolayer–substrate separation on carrier–phonon interactions are also examined. Our results show that electron–phonon coupling strongly depends on the dielectric properties of the supporting substrate, with larger anticrossing gaps predicted for hBN-supported structures compared with SiO₂-supported systems. Auger recombination constitutes the dominant carrier relaxation channel within the investigated temperature range, whereas SOP scattering becomes increasingly significant at elevated temperatures, where both mechanisms approach a comparable inelastic phonon-limited regime. These findings highlight the role of dielectric engineering in controlling carrier relaxation dynamics in Pt-based TMDC heterostructures. Full article

We report an optical method to estimate local microviscosity in thermotropic liquid crystals using viscosity-responsive aggregation-induced emission luminogens. Pendant-type luminogens were designed by covalently attaching 4-cyano-4′-n-octyloxybiphenyl mesogens (n = 8, 10) to a bis(N,N-dialkylamino)anthracene emissive core. When introduced at 1.0 wt% into 8OCB and 10OCB, thermal and optical analyses showed that the intrinsic liquid crystal properties were essentially unchanged, indicating good structural compatibility. Temperature-dependent fluorescence and polarization measurements revealed that emission changes are governed mainly by microviscosity rather than macroscopic phase disruption. Effective microviscosity was evaluated from absolute fluorescence quantum yields using the Förster–Hoffmann relation. On this basis, the microviscosity in the nematic phase is 21 mPa·s for 8OCB upon cooling, which correlates with the enhancement in fluorescence. In the smectic phase, although the director distribution parameter remains nearly constant, the effective microviscosity is ca. 21 mPa·s for 10OCB and ca. 54 mPa·s for 8OCB, and the fluorescence varies smoothly with temperature, reflecting changes in local segmental mobility within the layered structure. These values are broadly consistent with reported viscosity ranges/trends for cyanobiphenyl-type liquid crystals. Full article

The design and implementation of a dynamic frequency sweeping algorithm for a 3 kW RF power source are underpinned by theoretical principles aimed at optimizing impedance matching under pulse-mode operation. The algorithm dynamically adjusts the output frequency within a predefined range to align the source impedance Z_source with the conjugate of the load impedance Z*_load, maximizing the power transfer efficiency and minimizing the reflection coefficient Γ. This is achieved by leveraging the maximum power transfer theorem and adapting to dynamic load variations, such as those induced by the plasma state transitions. The algorithm incorporates adaptive step size adjustments based on the rate of change of Γ, predictive frequency initialization using historical data, and real-time impedance monitoring to ensure efficient convergence within the constrained pulse “ON” time (TON). Integration with pulse mode requires synchronization with the pulse signal, fast convergence, and optimized search strategies. Experimental validation on a 13.56 MHz, 3 kW Automatic Sweep Generator testbed operating at 20 kHz pulse modulation with a 50% duty cycle demonstrates a linear and stable sweep, achieving impedance matching and low reflected power within 5.0172 ms. These findings highlight the algorithm’s potential for high-precision applications, such as RF plasma excitation, and underscore the importance of adaptive techniques in dynamic RF systems. Full article

This article presents the design and detailed characterization of a new supersonic wind tunnel at the Aerospace Laboratory for Lasers, ElectroMagnetics, and Optics of Texas A&M University, tailored for optical diagnostic development and sub-scale fundamental compressible fluid dynamics research. A Ludwieg tube tunnel architecture was selected due to its robustness, versatility, and low operational costs. The tunnel consists of a 50-foot-long driver tube constructed from modular Tri-Clamp spools, a Mach 4 nozzle with 3 in. exit diameter configured as a free jet, and a fast-acting valve with 14 ms opening time for high-duty-cycle operation. Such construction proved to be a robust, compact, and affordable solution for academic applications. Characterization methods consisted of simultaneous high-speed dot-schlieren, total and static pressure measurements, and femtosecond laser electronic excitation tagging. Average flow velocity for the first steady-state test time was measured via FLEET at (668.0 ± 5.7) m/s. The Mach number was calculated based on the angles of the attached oblique shocks formed near the 30° cone model. Calculated Mach number was repeatable from run to run and had small oscillations near the average value of 3.96 ± 0.03. Based on the simultaneously measured velocity and Mach number, the static temperature was calculated to be between (68.6 ± 0.3) K and (66.3 ± 0.3) K throughout the 400 ms test time, completely defining the thermodynamic state of the generated freestream flow. Full article

This paper presents the design, modeling, and experimental validation of a capacitive tactile sensor specifically conceived to sense shear-driven contact dynamics in robotic manipulation. The proposed device is a layered flexible capacitive structure, in which controlled tangential interactions are induced. The electrode design maximizes sensitivity to shear motion and promotes an isotropic response with respect to slip direction, thereby addressing two key limitations that affect the majority of existing slip-sensing technologies. An analytical model was developed to describe the essential relationship between shear-induced displacements and the electrical response, providing insight into the design parameters and supporting the selection of geometry and materials. To test the sensor in real conditions, a dedicated capacitive readout circuit based on high-frequency excitation and synchronous demodulation was developed to robustly acquire capacitance variations while rejecting static offsets and parasitic effects. Several formulations for the interposed dielectric layer material were investigated, including viscous fluids and composite mixtures with high-permittivity nanoparticles, with the aim of improving electrical sensitivity while preserving mechanical stability. Experimental results obtained under controlled loading and sliding conditions demonstrate that the sensor is highly sensitive to changes in contact state and tangential interaction dynamics. The sensor responded consistently to both load-induced shear and slip-related phenomena, enabling the reliable monitoring of contact dynamics rather than binary slip detection. A proof-of-concept integration into a robotic finger confirms the suitability of the proposed approach for grasp monitoring. Full article

Predicting the motion of ships and floating structures is essential for ensuring economical and environmentally friendly operations in the ocean. In this study, we propose a hybrid encoder–decoder Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to predict motions of a moored Floating Production Storage and Offloading (FPSO) vessel under varying sea conditions. The model integrates a CNN for spatial wave-field feature extraction and an LSTM encoder–decoder to capture temporal dependencies in vessel motion. Synthetic datasets were generated using mid-fidelity dynamics simulations of a coupled FPSO–mooring–riser system subjected to wave excitations. Five sea states ranging from calm to severe were considered to evaluate the model’s robustness. A key preprocessing step involved determining the optimal spatial domain for wave field input, and a wave field size of 600 m × 600 m was identified as the most cost-effective configuration while maintaining accuracy. The model was validated using the Root Mean Square Error (RMSE) or relative RMSE (RRMSE). Despite low RRMSE values in low sea states, predictions were noisier due to high-frequency, low-amplitude responses. In contrast, higher sea states yielded more stable predictions despite higher RRMSE values. The proposed method offers high-resolution motion forecasting capability, which can enhance operational safety and energy efficiency of offshore platforms, particularly when integrated with stereo camera-based wave monitoring systems. Full article