Search Results (369)

Ultrasonic wave attenuation in biological tissues arises from complex interactions between mechanical, structural, and fluidic properties, making it essential to identify dominant mechanisms for accurate simulation and device design. This work introduces a novel integration of experimentally measured tissue parameters into time-explicit nonlinear acoustic wave simulations, in which the equations are directly solved in the time domain using an explicit solver. This approach captures the full transient waveform without relying on frequency-domain simplifications, offering a more realistic representation of ultrasound propagation in heterogeneous media. The study estimates both sound diffusivity and viscous damping parameters (dynamic and bulk viscosity) for a broad range of ex vivo tissues (skin, adipose tissue, skeletal muscle, trabecular/cortical bone, liver, myocardium, kidney, tendon, ligament, cartilage, and gray/white brain matter). Four regression models (power law, linear, exponential, logarithmic) were applied to characterize their frequency dependence between 0.5 and 5 MHz. Results show that attenuation is more strongly driven by bulk viscosity than dynamic viscosity, particularly in fluid-rich tissues such as liver and myocardium, where compressional damping dominates. The power-law model consistently provided the best fit for all attenuation metrics, revealing a scale-invariant frequency relationship. Tissues such as cartilage and brain showed weaker viscous responses, suggesting the need for alternative modeling approaches. These findings not only advance fundamental understanding of attenuation mechanisms but also provide validated parameters and modeling strategies to improve predictive accuracy in therapeutic ultrasound planning and the design of non-invasive, tissue-specific acoustic devices. Full article

In the context of reservoir monitoring, time-lapse (4D) full-waveform inversion (FWI) of seismic data can potentially estimate reservoir changes with high resolution. However, most existing field-data applications are carried out with isotropic, and often acoustic, FWI algorithms. Here, we apply a time-lapse FWI methodology for transversely isotropic (TI) media with a vertical symmetry axis (VTI) to offshore streamer data acquired at Pyrenees field in Australia. We explore different objective functions, including those based on global correlation (GC) and designed to mitigate errors in the source signature (SI, or source-independent). The GC objective function, which utilizes mostly phase information, produces the most accurate inversion results by mitigating the difficulties associated with amplitude matching of the synthetic and field data. The SI FWI algorithm is generally more robust in the presence of distortions in the source wavelet than the other two methods, but its application to field data is hampered by reliance on amplitude matching. Taking anisotropy into account provides a better fit to the recorded data, especially at far offsets. In addition, the application of the anisotropic FWI improves the flatness of the major reflection events in the common-image gathers (CIGs). The 4D response obtained by FWI reveals time-lapse parameter variations likely caused by the reservoir gas coming out of solution and by the replacement of gas with oil. Full article

Real-time monitoring of the pile foundation pouring status is the key to ensuring the quality and reliability of cast-in-place bored pile foundation structures. In response to the technical challenge of difficult real-time monitoring and accurate evaluation of pile top morphology during concrete pouring, this paper proposes a method for detecting the cast-in-place bored pile top surface based on full waveform inversion. Firstly, a coupling equation between concrete sound waves and viscoelastic waves inside the borehole is constructed, forming a full waveform inversion method that considers multiple parameters of the complex environment inside the borehole. Subsequently, a pile top flatness factor that simultaneously considers the elevation and undulation characteristics of the pile top is constructed to achieve a comprehensive evaluation of the elevation between the center position and the center peripheral position of the bored pile top. Finally, the feasibility and accuracy of the proposed method are verified through indoor experiments. The results indicate that the detection method proposed in this article can not only accurately reflect the actual elevation of the pile top, ensuring the accuracy of the measurement data, but also achieve a comprehensive evaluation of the quality of the pile top considering the differences in the center and edge positions of the pile top, which can provide a new analysis method for quality control of bored piles. Full article

Mountain glaciers are the most direct and sensitive indicators of climate change. In the context of global warming, monitoring changes in glacier elevation has become a crucial issue in modern cryosphere research. The Global Ecosystem Dynamics Investigation (GEDI) is a full-waveform laser altimeter with a multi-beam that provides unprecedented measurements of the Earth’s surface. Many studies have investigated its applications in assessing the vertical structure of various forests. However, few studies have assessed GEDI’s performance in detecting variations in glacier elevation in land ice in high-mountain Asia. To address this limitation, we selected the Southeastern Tibetan Plateau (SETP), one of the most sensitive areas to climate change, as a test area to assess the feasibility of using GEDI to monitor glacier elevation changes by comparing it with ICESat-2 ATL06 and the reference TanDEM-X DEM products. Moreover, this study further analyzes the influence of environmental factors (e.g., terrain slope and aspect, and altitude distribution) and glacier attributes (e.g., glacier area and debris cover) on changes in glacier elevation. The results show the following: (1) Compared to ICESat-2, in most cases, GEDI overestimated glacier thinning (i.e., elevation reduction) to some extent from 2019 to 2021, with an average overestimation value of about −0.29 m, while the annual average rate of elevation change was relatively close, at −0.70 ± 0.12 m/yr versus −0.62 ± 0.08 m/yr, respectively. (2) In terms of time, GEDI reflected glacier elevation changes at interannual and seasonal scales, and the trend of change was consistent with that found with ICESat-2. The results indicate that glacier accumulation mainly occurred in spring and winter, while the melting rate accelerated in summer and autumn. (3) GEDI effectively monitored and revealed the characteristics and patterns of glacier elevation changes with different terrain features, glacier area grades, etc.; however, as the slope increased, the accuracy of the reported changes in glacier elevation gradually decreased. Nonetheless, GEDI still provided reasonable estimates for changes in mountain glacier elevation. (4) The spatial distribution of GEDI footprints was uneven, directly affecting the accuracy of the monitoring results. Thus, to improve analyses of changes in glacier elevation, terrain factors should be comprehensively considered in further research. Overall, these promising results have the potential to be used as a basic dataset for further investigations of glacier mass and global climate change research. Full article

Objectives: This study investigates the biomechanics of the bench press and overhead press exercises by modeling the trunk and upper limbs as a kinematic chain of rigid links connected by revolute joints and actuated by single- and two-joint muscles, with motion constrained by the barbell. The aims were to (i) assess the different contributions of shoulder and elbow torques during lifting, (ii) identify the parameters influencing joint loads, (iii) explain the origin of the sticking region, and (iv) validate the model against experimental barbell kinematics. Methods: Equations of motion and joint reaction forces were derived analytically in closed form. Dynamic simulations produced vertical barbell velocity profiles under various conditions. A waveform similarity analysis was used to compare simulated profiles with experimental data from maximal bench press trials. Results: The sticking region occurred when shoulder torque dropped below a critical threshold, resulting in a local velocity minimum. Adding elbow torque reduced this dip and shifted the velocity minimum from 38 cm to 23 cm above the chest, although it prolonged the time needed to overcome it. Static analysis revealed that grip width and barbell constraint had a greater effect on shaping the sticking region than muscle architecture parameters. Elbow extensors contributed minimally during early lift phases but became dominant near full extension. Model predictions showed high similarity to experimental data in the pre-sticking (SI = 0.962, p = 0.028) and sticking (SI = 0.949, p = 0.014) phases, with reduced, non-significant similarity post-sticking (SI = 0.881, p > 0.05) due to the assumption of constant torques. Conclusions: The model offers biomechanical insight into how joint torques and barbell constraints shape movement. The findings support training strategies that target shoulder strength early in the lift and elbow strength near lockout to minimize sticking and improve performance. Full article

Despite the widespread adoption of high-frequency electromagnetic wave (HF-EMW) processing, deep neural networks (DNNs) remain primarily black boxes. Interpreting the semantics behind the high-dimensional representations of a DNN is quite crucial for getting insights into the network. This study has proposed an evidential representation fusion approach that interprets the high-dimensional representations of a DNN as HF-EMW semantics, such as time- and frequency-domain signal features and their physical interpretation. In this approach, an evidential discrete model based on Dempster–Shafer theory (DST) converts a subset of DNN representations to mass function reasoning on a class set, indicating whether the subset contains HF-EMW semantics information. An interpretable continuous DST-based model maps the subset into HF-EMW semantics via representation fusion. Finally, the two DST-based models are extended to interpret the learning processes of high-dimensional DNN representations. Experiments on the two datasets with 2680 and 4000 groups of HF-EMWs demonstrate that the approach can find and interpret representation subsets as HF-EMW semantics, achieving an absolute fractional output change of 39.84% with an 10% removed elements in most important features. The interpretations can be applied for visual learning evaluation, semantic-guided reinforcement learning with an improvement of 4.23% on classification accuracy, and even HF-EMW full-waveform inversion. Full article

To address the challenges of signal recognition and prediction in intelligent sliding sleeve downlink communication systems, this paper proposes a dual-model framework based on Long Short-Term Memory (LSTM) networks. The system comprises a classifier for identifying pressure wave edge types and a generator for predicting pressure waveforms. High-quality training data are generated by simulating pressure wave propagation caused by throttle valve modulations. A sliding window strategy and Z-score normalization are applied to enhance temporal modeling. The classifier achieves a high accuracy in identifying rising and falling edges under noise-free conditions. The generator, trained on down-sampled waveform segments, accurately reconstructs pressure dynamics using a dual-input strategy based on historical segments and hypothetical labels. A residual-based decision mechanism is employed to complete the full sequence label prediction. To evaluate robustness, noise intensities of 30 dB and 40 dB are introduced. The proposed system maintains high performance under both conditions, achieving label prediction accuracies of 100%. Error metrics such as MAE and RMSE remain within acceptable bounds, even in noisy environments. The results demonstrate that the proposed LSTM-based method has been validated on simulated data, showing its potential to approximate performance in real-world conditions. It provides a promising solution for cable-free measurement-while-drilling (MWD) and remote control of intelligent sliding sleeves in complex downhole environments. Full article

Forest ecosystems act as major carbon sinks, highlighting the need for the accurate estimation of aboveground biomass (AGB). The Global Ecosystem Dynamic Investigation (GEDI), a full-waveform spaceborne LiDAR system developed by NASA, provides detailed global observations of three-dimensional forest structures, playing a critical role in quantifying biomass and carbon storage. However, its performance has not yet been assessed in the Mediterranean forest ecosystems of Southern Italy. Therefore, the objectives of this study were to (i) evaluate the utility of the GEDI L4A gridded aboveground biomass density (AGBD) product in the Apulia region by comparing it with the Apulia AGBD map, and (ii) develop GEDI-derived AGBD models using multiple GEDI metrics. The results indicated that the GEDI L4A gridded product significantly underestimated AGBD, showing large discrepancies from the reference data (RMSE = 40.756 Mg/ha, bias = −30.075 Mg/ha). In contrast, GEDI-derived AGBD models using random forest (RF), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR) demonstrated improved accuracy. Among them, the MGWR model emerged as the optimal choice for AGBD estimation, achieving the lowest RMSE (14.059 Mg/ha), near-zero bias (0.032 Mg/ha), and the highest R² (0.714). Additionally, the MGWR model consistently outperformed other models across four different plant functional types. These findings underscore the importance of local calibration for GEDI data and demonstrate the capability of the MGWR model to capture scale-dependent relationships in heterogeneous landscapes. Overall, this research highlights the potential of the GEDI to estimate AGBD in the Apulia region and its contribution to enhanced forest management strategies. Full article

This paper investigates the suppression of the cohesive cracking mode (CCM) in the sintered silver (s-Ag) die layer by intentionally introducing anisotropic porosity through two press sintering methods. Full press (FP) and local press (LP) bonding represent the s-Ag formed by pressing the die-attached assemblies (DAAs) on either the entire top surface or only on the silicon carbide (SiC) top surface, respectively. The fabricated DAAs were encapsulated with epoxy molding compounds. Degradation was evaluated using a nine-point bending test (NBT) under cyclic force between 0 and 270 N with a triangle waveform for 3 min per cycle at 150 °C. Scanning tomography images after 500 NBT cycles showed that the LP reduced the inner degradation ratio by up to 21.1% compared to the FP. Cross-sectional scanning electron microscopy revealed that the FP progressed cracking in the s-Ag die layer, whereas the LP showed no evidence of cracking. A finite element analysis revealed that in the FP, the accumulated plastic strain (APS) was concentrated in the s-Ag layer within the inner SiC chip. In contrast, the APS of the LP was preferentially concentrated outside the SiC chip. This preferential localization of damage outside the chip presents a promising approach for enhancing the reliability of packaging products. Full article

This paper presents a blind source separation (BSS)-based framework for joint communication and sensing (JCAS) in in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) systems operating under time-varying channel conditions. Conventionally, self-interference (SI) in IBFD systems is a major obstacle to recovering the signal of interest (SOI). Under the JCAS paradigm, however, this high-power SI signal presents an opportunity for efficient sensing. Since each transceiver node has access to the original SI signal, its environmental reflections can be exploited to estimate channel conditions and detect changes, without requiring dedicated radar waveforms. We propose a blind source separation (BSS)-based framework to simultaneously perform self-interference cancellation (SIC) and extract sensing information in IBFD MIMO settings. The approach applies the Fast Independent Component Analysis (FastICA) algorithm in dynamic scenarios to separate the SI and SOI signals while enabling simultaneous signal recovery and channel estimation. Simulation results quantify the trade-off between estimation accuracy and channel dynamics, demonstrating that while FastICA is effective, its performance is fundamentally limited by a frame size optimized for the rate of channel variation. Specifically, in static channels, the signal-to-residual-error ratio (SRER) exceeds 22 dB with 500-symbol frames, whereas for moderately time-varying channels, performance degrades significantly for frames longer than 150 symbols, with SRER dropping below 4 dB. Full article

Subway train operators face the risk of cumulative cognitive stress due to factors such as visual fatigue from prolonged high-speed tunnel driving, irregular shift patterns, and the monotony of automated operations. This can lead to cognitive decline and human error accidents. Current monitoring of cognitive stress risk predominantly relies on single-modal methods, which are susceptible to environmental interference and offer limited accuracy. This study proposes an intelligent multimodal framework for cognitive stress monitoring by leveraging the symmetry principles in physiological and behavioral manifestations. The symmetry of photoplethysmography (PPG) waveforms and the bilateral symmetry of head movements serve as critical indicators reflecting autonomic nervous system homeostasis and cognitive load. By integrating these symmetry-based features, this study constructs a spatiotemporal dynamic feature set through fusing physiological signals such as PPG and galvanic skin response (GSR) with head and facial behavioral features. Furthermore, leveraging deep learning techniques, a hybrid PSO-CNN-GRU-Attention model is developed. Within this model, the Particle Swarm Optimization (PSO) algorithm dynamically adjusts hyperparameters, and an attention mechanism is introduced to weight multimodal features, enabling precise assessment of cognitive stress states. Experiments were conducted using a full-scale subway driving simulator, collecting data from 50 operators to validate the model’s feasibility. Results demonstrate that the complementary nature of multimodal physiological signals and behavioral features effectively overcomes the limitations of single-modal data, yielding significantly superior model performance. The PSO-CNN-GRU-Attention model achieved a predictive coefficient of determination (R²) of 0.89029 and a mean squared error (MSE) of 0.00461, outperforming the traditional BiLSTM model by approximately 22%. This research provides a high-accuracy, non-invasive solution for detecting cognitive stress in subway operators, offering a scientific basis for occupational health management and the formulation of safe driving intervention strategies. Full article

A single-station lightning location has the obvious advantages of low cost and convenience in lightning monitoring and warning. To address the critical challenge of distance estimation accuracy in this technology, we propose a novel physical time-reversal (PTR) method to utilize the full wave information of both the ground wave and the sky wave in the detected signal. First, we improved the numerical model for accurately calculating the lightning sferics signals in the complex propagation environment of the Earth–ionosphere waveguide using the measured International Reference Ionosphere 2020. Subsequently, the sferics signal with multipath effect is transformed by time reversal and back propagated in the numerical model. Furthermore, a broadening factor reflecting the waveform dispersion in the back propagation is defined as the single-station focusing criterion to determine the optimal lightning propagation distance, considering the multipath effect and the focus of the PTR process. The experimental results demonstrate that the average root mean square error (RMSE) and the mean relative error (MRE) of the PTR method for the lightning distance estimation in the numerical simulation within the range of 100–1200 km are 5.517 km and 1.21%, respectively, and the average RMSE and the MRE for the natural lightning strikes to the Canton Tower from the measured data in the range of 181.643–1152.834 km are 9.251 km and 2.07%, respectively. Moreover, the correlation coefficients of the detection results are all as high as 0.999. These results indicate that the PTR method significantly outperforms the traditional ionospheric reflection method, demonstrating that it is able to perform a more accurate single-station lightning distance estimation by utilizing the compensation mechanism of the multipath effect on the sferics. The implementation of the proposed method has significant application value for improving the accuracy of single-station lightning location. Full article

Accurate parameter estimation is essential for gravitational wave data analysis. In extreme mass-ratio inspiral binary systems, orbital eccentricity is a critical parameter for parameter estimation. However, the current software for the parameter estimation of the gravitational wave often neglects the direct estimation of orbital eccentricity. To fill this gap, we have developed the MatBYIB, a MATLAB-based software (Version 1.0) package for the parameter estimation of the gravitational wave with arbitrary eccentricity. The MatBYIB employs the Analytical Kludge waveform as a computationally efficient signal generator and computes parameter uncertainties via the Fisher Information Matrix and the Markov Chain Monte Carlo. For Bayesian inference, we implement the Metropolis–Hastings algorithm to derive posterior distributions. To guarantee convergence, the Gelman–Rubin convergence criterion (the Potential Scale Reduction Factor $\widehat{R}$) is used to determine sampling adequacy, with MatBYIB dynamically increasing the sample size until $\widehat{R}<1.05$ for all parameters. Our results demonstrate strong agreement between predictions based on the Fisher Information Matrix and full MCMC sampling. This program is user-friendly and allows for the estimation of the gravitational wave parameters with arbitrary eccentricity on standard personal computers. Full article

Civil engineering structures with damage, defects, or subsurface utilities create a high-contrast exploration environment. These anomalies of interest exhibit different electromagnetic properties from the surrounding medium, and ground-penetrating radar (GPR) has the potential to accurately locate and map their three-dimensional (3D) distributions. However, full-waveform inversion (FWI) for GPR data struggles to simultaneously reconstruct high-resolution 3D images of both permittivity and conductivity models. Considering the magnitude and sensitivity disparities of the model parameters in the inversion of GPR data, this study proposes a 3D dual-parameter FWI algorithm for GPR with a composite model constraint strategy. It balances the gradient updates of permittivity and conductivity models through performing total variation (TV) regularization and minimum support gradient (MSG) regularization on different parameters in the inversion process. Numerical experiments show that TV regularization can optimize permittivity reconstruction, while MSG regularization is more suitable for conductivity inversion. The TV+MSG composite model constraint strategy improves the accuracy and stability of dual-parameter inversion, providing a robust solution for the 3D imaging of subsurface anomalies with high-contrast features. These outcomes offer researchers theoretical insights and a valuable reference when investigating scenarios with high-contrast environments. Full article

In marine seismic surveys, towed streamers record only pressure data with limited offsets and insufficient low-frequency content, whereas Ocean Bottom Nodes (OBNs) acquire multi-component data with wider offset and sufficient low-frequency content, albeit with sparser spatial sampling. Elastic full waveform inversion (EFWI) is used to estimate subsurface elastic properties by matching observed and synthetic data. However, using only towed streamer data makes it impossible to reliably estimate shear-wave velocities due to the absence of direct S-wave recordings and limited illumination. Inversion using OBN data is prone to acquisition footprint artifacts. To overcome these challenges, we propose a hierarchical joint inversion method based on P- and S-wave separation (PS-JFWI). We first derive novel acoustic-elastic coupled equations based on wavefield separation. Then, we design a two-stage inversion framework. In Stage I, we use OBN data to jointly update the P- and S-wave velocity models. In Stage II, we apply a gradient decoupling algorithm: we construct the P-wave velocity gradient by combining the gradient using PP-waves from both towed streamer and OBN data and construct the S-wave velocity gradient using the gradient using PS-waves. Numerical experiments demonstrate that the proposed method enhances the inversion accuracy of both velocity models compared with single-source and conventional joint inversion methods. Full article