Search Results (850)

Time-resolved visualization of local structural dynamics driven by external fields is essential for understanding structure–property relationships in functional materials and devices. Conventional ultrafast methods primarily capture femtosecond-to-picosecond photoinduced dynamics, yet they lack real-space access to spatially inhomogeneous processes occurring at their intrinsic mesoscopic timescales that govern material and device performance—particularly electrically driven processes that closely mimic actual device operating conditions. Here, we report a multifunctional ultrafast transmission electron microscopy (UTEM) platform targeting reversible structural dynamics spanning nanoseconds to microseconds under stroboscopic multi-field excitation. Our system employs photoelectron pulses generated by nanosecond UV laser illumination as the probe, alongside optical and electric pulses as pump excitation. A unified electronic synchronization scheme based on a high-speed photodiode and a digital delay generator enables precise timing control among the optical pump, electrical pump, and photoelectron pulses across the nanosecond-to-microsecond range. Using vanadium dioxide (${\mathrm{VO}}_2$) as a model system, we demonstrate a combined spatiotemporal resolution with measurable signals on the order of 10 nm–10 ns, allowing real-space mapping of spatially inhomogeneous dynamics. Electrical-pump experiments further reveal Joule-heating-induced non-uniform structural phase transitions and thermal-shock-excited megahertz-range mechanical oscillations. These results establish the developed multi-field UTEM platform as a practical tool for probing local structural dynamics in functional materials under optical and electrical excitation. Full article

To address the critical challenge of 0–100 Hz low-frequency vibration control for marine hydraulic pipelines, this paper proposes a dedicated fiber-reinforced magnetorheological elastomer (MRE) isolator and a genetic algorithm-optimized fuzzy control strategy utilizing the magnetically tunable properties of MREs. An upper-lower split-type isolator is designed to suppress axial and radial vibrations through the shear and Compression Modes of MRE, respectively, and a two-degree-of-freedom (2-DOF) dynamic model is established to analyze the effects of mass ratio and natural frequency ratio on the system’s amplitude magnification factor. A Mamdani-type fuzzy controller, with acceleration error and its rate of change as inputs and control voltage as output, is optimized via a genetic algorithm. Simulation and experimental results show that 31–56.5% amplitude attenuation is achieved under 25–35 Hz single-frequency excitation; 12 dB isolation in the 5–23 Hz band at the input end and a maximum 15 dB isolation in multiple bands for the suspended pipeline section are obtained without external forced excitation; and efficient 0–100 Hz full-band isolation is realized at an applied current of 1.5 A. This work verifies the effectiveness of the proposed scheme for low-frequency vibration control of marine hydraulic pipelines. Full article

Single-molecular white circularly polarized luminescence emitters show promise for use in chiral displays and solid-state lighting, but their design remains challenging because broadband emission, exciton utilization, color balance, and chiroptical activity must be integrated within one molecule. Herein, we report a chiral single-molecular white emitter, DCz-PTZ, constructed through a π-interrupted strategy by combining a rigid spiro framework, an oxygen-bridged carbazole/cyanobenzene segment, and a phenothiazine donor. The interrupted conjugation suppresses excessive charge-transfer (CT) domination and enables dual emissive channels, including short-wavelength locally excited (LE) emission and long-wavelength CT emission. DCz-PTZ exhibits near-ideal white emission in dilute toluene solution with CIE coordinates of (0.33, 0.33), and maintains balanced dual emission in 5 wt% doped films with CIE coordinates of (0.32, 0.34). Photophysical studies support the assignment of the yellow emission to a thermally activated delayed fluorescence (TADF)-active CT state. The enantiomers show mirror-image circularly polarized signals with |g_lum| up to 2.9 × 10⁻³. Optimized white organic light-emitting diodes (WOLEDs) achieve color rendering index (CRI) up to 92 and a maximum external quantum efficiency (EQE_max) of 1.3%. This work demonstrates a π-interrupted molecular strategy for integrating dual emission, TADF exciton utilization, and circularly polarized electroluminescence (CPEL) in a single chiral emitter. Full article

In this work, we propose a hyperbolic metamaterials (HMMs)-based coreless fiber surface plasmon resonance (SPR) sensor. Leveraging the absence of a core in coreless fibers, the evanescent waves at the cladding–external solution interface couple more effectively into the solution, enabling surface plasmon resonance without any additional processing. To enhance sensitivity, we adopted a multimode–coreless–multimode (MCM) structure and grew layered hyperbolic metamaterials as the SPR-excitation-sensitive layer within the coreless region. Through finite element simulations, we optimized HMM parameters and fabricated high-performance HMM-SPR sensors. Test results demonstrate that the fabricated HMM-SPR sensor achieves an optimal refractive index sensitivity of 3703.33 nm/RIU, representing a 49.68% improvement over single-layer gold film SPR sensors. It successfully detects glucose solutions at varying concentrations with a sensitivity of 2671.25 nm/RIU. The high-sensitivity, structurally simple HMM-SPR sensor we proposed demonstrates broad application prospects in biosensing, environmental monitoring, food safety, and other fields. Full article

Addressing the urgent demand for high-precision pressure measurement in real-time barometric monitoring, aerospace, and industrial control, this paper presents a high-accuracy MEMS resonant pressure sensor based on electrostatic excitation and piezoresistive detection. The sensor incorporates a symmetric double-ended fixed-finger comb-drive resonator structure, driven into stable vibration at its natural frequency by an alternating electrostatic force. Piezoresistors integrated at the root of the resonant beams transduce the mechanical vibration into a frequency output, enabling precise external pressure measurement. Experimental results show that the developed sensor achieves an accuracy of 0.009% FS over a pressure range of 0–350 kPa across an operating temperature span from −30 °C to 50 °C, with a room-temperature repeatability error below 0.008% FS, demonstrating excellent measurement stability. Building on this performance, a real-time atmospheric pressure monitoring experiment was conducted, yielding a mean absolute percentage error of less than 0.05%, highlighting the sensor’s potential for engineering practicality. This work provides an effective technique for a high-precision, high-stability resonant pressure sensor, with clear potential for deployment in real-time barometric monitoring, aerospace, and industrial control applications. Full article

A fundamental aspect in the design of modern utility-scale wind turbines is predicting the vibrational response of their blades when excited by gust pulses of various amplitudes and frequencies in atmospheric flow. Improved designs based on accurate blade-response predictions can prevent extreme oscillations, reduce fatigue stress, and extend turbine’s operational life. In previously published works, the authors introduced and applied a novel technique that provided an energy-based Reduced-Order Characterization (ROC) for the oscillatory response of wind turbine rotors, when excited by wind gust pulses with different combinations of timespan and amplitude under various operational conditions. Those studies established the universal nature of the ROC by expressing the turbine aeroelastic response as a vibrational Stability Map, plotted in terms of non-dimensional quantities, which could be applied to turbines of any size that share a similar blade construction. In the present paper, the authors will expand the ROC technique beyond the scope of their previously published studies, to analyze the Multi-Modal Response observed in regions located at the external boundaries of the stable zones of the Stability Map. This will provide valuable information about rotor stability behavior in extreme turbine operational conditions which were previously unexplored. Full article

Graphene-reinforced aluminum-based materials perfectly combine the excellent properties of graphene and aluminum, achieving superior lightweight structural characteristics. This study focuses on 1:2 internal resonance, analyzing the amplitude–frequency and force–amplitude responses of a graphene-platelet-reinforced aluminum-based truncated conical shell under multiple external excitations. Considering three different graphene distributions, an improved Halpin–Tsai mechanical model is used to predict the effective Young’s modulus of the GPL-enhanced aluminum-based truncated conical shell. Under temperature effects, based on the Reissner–Mindlin theory and von-Karman geometric nonlinear strain–displacement relationships, Hamilton’s principle and the Galerkin method are employed to derive the motion equations of the GPL-enhanced aluminum-based truncated conical shell. Through multiscale perturbation analysis, the averaged equations in polar coordinates are further derived. Based on the combined averaged equations, the amplitude–frequency and force–amplitude response curves of the system are plotted, investigating the influence of graphene distribution, graphene content, external excitation amplitude, tuning parameters, and graphene plate geometrical dimensions on its vibration characteristics. The analysis results indicate that graphene content is one of the primary factors affecting the vibration characteristics of graphene-reinforced aluminum-based truncated cones. Full article

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs). Full article

Following the elucidation of the fungal bioluminescence pathway (FBP), it was quickly adopted as a reporter system in plants; however, no such application has been documented in fungi to date. In this study, we established for the first time a luminescent reporter in the commercially important mushroom Hypsizygus marmoreus by expressing the luciferase gene from the luminous fungus Neonothopanus nambi. Using an established Agrobacterium-mediated transformation method, we separately introduced the wild-type luciferase gene nnLuz and the previously reported optimized variant nnLuz-v4 that can enhance bioluminescence expression into H. marmoreus arthroconidia. Both genes were stably integrated into the genome and expressed under the control of the H. marmoreus Glycerol 3-phosphate dehydrogenase (GPD) gene promoter. Upon addition of exogenous luciferin, transformants carrying the wild-type nnLuz produced clear, readily detectable bioluminescence signals, whereas no luminescence was observed in untransformed controls. Unexpectedly, the wild-type luciferase consistently exhibited substantially higher luminescence intensity than the optimized nnLuz-v4 variant. This finding suggests that codon optimization may be unnecessary or even detrimental when the donor and host are phylogenetically close basidiomycetes. The successful deployment of the fungal luciferase gene in H. marmoreus provides a sensitive and non-invasive genetic tool that does not require external excitation. This system opens new avenues for promoter characterization, real-time gene expression monitoring during mushroom development, and molecular breeding efforts aimed at improving agronomically important traits. Full article

Gliomas constitute the majority of primary brain tumors, and accurate diagnosis through MRI is essential for patient management. Existing computer-aided diagnosis approaches frequently rely on tumor segmentation frameworks. In this study, a segmentation-independent framework for volumetric low-grade versus high-grade glioma (LGG/HGG) classification is proposed using a Convolutional Neural Network (CNN) designed through task-oriented Neural Architecture Search (NAS). The proposed method was evaluated on a multi-center dataset comprising 1194 patients with pre-operative MRI scans, including T1-CE and FLAIR sequences from four publicly available cohorts. NAS was conducted within a controlled search space to optimize a 3D U-Net–based backbone using Tree-structured Parzen Estimator (TPE) combined with Hyperband pruning. The optimized backbone was enhanced with residual connections and Squeeze-and-Excitation (SE) attention mechanisms to improve feature representation and training stability. Internal validation employed repeated 5-fold cross-validation across all four multi-center datasets. An external experiment used REMBRANDT as a test cohort (49 LGG, 19 HGG). The proposed model achieved 88.25% internal accuracy and 75.51% external accuracy (macro-F1: 87.37% internal, 73.77% external), outperforming benchmark 3D CNNs. Explainable Artificial Intelligence (XAI) analysis based on Grad-CAM revealed robust tumor localization without segmentation supervision, validated against available ground-truth masks. Additional experiments demonstrated the model’s generalization capacity, achieving 89.51% accuracy for IDH mutation prediction and 78.74% for multi-grade classification. Full article

Rotor imbalance and abnormal vibration are classical operating conditions in rotating machinery and can often be identified by conventional vibration analysis. However, the development of low-power, self-powered, and distributed sensing nodes remains important for long-term condition monitoring, particularly in scenarios where external power supply, wiring, and maintenance are constrained. Existing vibration sensors, including piezoelectric and capacitive types, are constrained by power consumption and degraded performance under low-frequency and weak excitation. To address this issue, a magnetically repulsive cushion triboelectric nanogenerator (MRCT) is proposed to enable self-powered vibration sensing. The magnetic-repulsion cushion allows the upper friction layer to undergo stable contact–separation motion under a non-contact restoring force, while the microstructured strip electrode array (MSEA) enhances the triboelectric output and signal stability. A hybrid convolutional neural network–gated recurrent unit (CNN-GRU) deep-learning model is employed to extract time-domain and frequency-domain features from the collected signals, enabling real-time identification of rotor vibration amplitude, frequency, and imbalance weight. Experimental results show that the MRCT provides stable output, a high signal-to-noise ratio, and an identification accuracy above 98% for predefined rotor imbalance-weight states under laboratory conditions. In addition, a shaft-misalignment-related abnormal vibration condition was examined on the motor platform. The corresponding time-domain and frequency-domain analyses show that the MRCT voltage signal exhibits distinguishable signal variations under normal and misalignment-related conditions, including spectral changes around the 2× rotational frequency. A laboratory-scale AIoT-oriented demonstration further verifies the feasibility of integrating MRCT signal acquisition, CNN-GRU inference, wireless transmission, and GUI-based visualization. It should be noted that the present work mainly focuses on imbalance-state recognition, while the misalignment-related experiment provides an additional sensor-response verification. Broader validation involving mechanical looseness, bearing defects, variable-speed operation, cross-machine testing, and long-term industrial conditions remains necessary. Full article

Safe and intelligent collision avoidance technology is essential for the autonomous underwater vehicle (AUV) to navigate in underwater environments. Most existing spike methods are constrained by a fixed static threshold and are unable to dynamically adjust to threshold changes reasonably, leading to difficulties in robustly adapting to external dynamic interference and thus resulting in insufficient homeostasis and generalization. To address these limitations, inspired by the dynamic threshold changes in biological neural systems, a bioinspired dynamic adaptive threshold (BDAT) is proposed. Combining the spiking neural network with deep reinforcement learning, a novel bioinspired dynamic adaptive threshold planner (BDAT-Planner) framework is constructed for underwater dynamic collision avoidance tasks performed by AUVs in complex, unknown environments. The proposed BDAT-Planner consists of the spiking dynamic adaptive actor network (SDAAN) and the deep critic normal network (DCNN). The BDAT is deployed to each spiking neuron in the SDAAN, dynamically adjusting the spike firing rate through threshold changes and avoiding excessive excitation or inhibition, thus maintaining homeostasis. The spiking encoder and spiking decoder are designed to convert continuous information and spiking sequences. Experimental results from both the training process and evaluation process (ablation studies, comparison experiments, and homeostasis experiments) demonstrate that the proposed BDAT-Planner has achieved superior performance in dynamic collision avoidance and model homeostasis compared to static threshold methods and existing comparison methods. The novel idea of bioinspired dynamic adaptive threshold can maintain model homeostasis and effectively enhance its adaptability to external dynamic interference, which offers significant development potential for promoting the efficient and stable operation of AUVs in marine environments. Full article

Microbial functional traits play a central role in regulating carbon mineralization efficiency (CME) in peatlands, yet how they respond to concurrent warming and atmospheric nitrogen deposition remains unclear. In this study, peat soils from three vegetation types (sedge, reed, and shrub) were subjected to controlled microcosm incubations simulating warming and nitrogen addition gradients. Microbial community composition and functional profiles were characterized using 16S rRNA high-throughput sequencing and Functional Annotation of Prokaryotic Taxa (FAPROTAX) functional prediction, while dissolved organic matter (DOM) composition was analyzed via excitation–emission matrix fluorescence spectroscopy with parallel factor analysis (EEM-PARAFAC) and fluorescence indices. Integrating correlation analysis, Random Forest, and partial least squares path modeling (PLS-PM) modeling, we identified microbial functional traits as key factors linking environmental changes to soil CME, with DOM serving as a substrate-mediated pathway. External nitrogen input primarily drove shifts in microbial functional composition, whereas warming modulated substrate utilization preferences and DOM turnover. The interaction between warming and nitrogen selectively reshaped microbial functional profiles, thereby jointly determining CME. Functional traits explained more variation in CME than taxonomic composition, indicating a “structure–function decoupling” under environmental change. These findings highlight the central role of microbial functional traits in peatland carbon transformation and suggest that the net response of peatland carbon emissions to future environmental change will depend critically on the balance between warming magnitude and nitrogen deposition levels. Full article

The growing diversity of wireless standards and complex real-world channel effects render automatic modulation classification (AMC) increasingly challenging for spectrum monitoring and edge intelligence. However, most competitive deep-learning-based AMC networks still require ${10}^5$–${10}^6$ parameters, exceeding the memory available on resource-constrained edge platforms. We propose LGP-Net, a lightweight gated-fusion network that pairs a physics-informed expert branch with a compact temporal encoder built from depthwise separable convolution (DSConv), squeeze-and-excitation (SE) attention, and a single-layer gated recurrent unit (GRU). Specifically, unlike other dual-branch structures that directly concatenate the outputs of both pathways, this work designs a lightweight gating unit that requires no external signal-to-noise ratio (SNR) labels and adaptively reweights the two pathways according to signal-quality degradation. With fewer than 40 K parameters, a peak activation footprint of 26.00 KB and an amortised inference latency of 9.7 $\mathsf{\mu }$s per sample under GPU acceleration, LGP-Net attains 65.00% overall accuracy on RadioML 2016.10B (91.48% at 0 dB) and 62.76% on RadioML 2016.10A, placing it in a competitive accuracy–efficiency regime relative to architectures consuming 5× to 500× more parameters. These characteristics support deployment-oriented feasibility under memory-constrained edge settings and high-throughput spectrum-monitoring pipelines. Full article

Controller performance is strongly influenced by its parameters. Estimating these parameters requires an effective estimation approach for obtaining the best possible response. This study proposes a novel methodology for the estimation of controller parameters, utilizing the dream optimization algorithm (DOA) and a new objective function. The proposed method is employed to determine the optimal parameters of various PID controllers used in the automatic voltage regulator (AVR) system. Thus, the suggested objective function consists of transient response metrics and the stability index “integral of time-weighted absolute error ($ITAE$)”. Three different PID controllers are used, which are cascaded PIPD with filter (CPIPDF), cascaded fractional-order PI fractional-order PDF (CFOPIFOPDF), and PIDF. The DOA’s performance is compared with famous and recent optimizers and shows more reliable performance. For example, based on the statistical analysis, the DOA obtained a standard deviation of 0.0042, while the closest competitor obtained 0.0089. Furthermore, the CPIPDF, CFOPIFOPDF, and PIDF controllers are compared under a wide variety of operating conditions. Based on ITAE, the CPIPDF controller achieved lower values than the CFOPIFOPDF and PIDF controllers. Also, the results show that the CPIPDF controller achieves better performance than other published controllers. For instance, the CPIPDF controller improves AVR performance by approximately 45.3% compared to the fireworks whale optimization algorithm-based PIDD² controller in the case of varying load condition impact. Moreover, scenarios that remain insufficiently addressed in the literature, such as communication delays, restricted excitation voltages, and external disturbances, are considered. Full article