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The wheel–rail contact force is a crucial indicator for ensuring the secure operation of a heavy-load train. However, obtaining the real-time wheel–rail contact force of a heavy-load train is a challenging task as, due to safety considerations, it is not possible to install instrumented wheelsets on heavy-load trains. This work presents a novel approach to quantify the wheel–rail contact force of a heavy-load train, utilizing a cooperative calibration methodology. First, a ground measurement platform for the wheel–rail contact force of a heavy-load train is constructed on a selected rail section. The railway inspection car’s wheel–rail contact force measurement system is fine-tuned using a multilayer perceptron calibration approach, and the ground platform then uses the results to fine-tune the railway inspection car’s examined wheelset. Second, based on actual measured data on the wheel–rail contact force of a heavy-load train, and using the golden jackal optimization algorithm, the multilayer perceptron correction approach is employed to create a data relationship mapping model. This model correlates the corrected data on the wheel–rail contact force obtained from the railway inspection car with the wheel–rail contact force of a heavy-haul train with an axle load of 25 tons, and the precision of the mapping is guaranteed. Finally, by combining the wheel–rail contact force correction method for the railway inspection car and the contact force mapping model between the railway inspection car and the heavy-load train, collaborative calibration of the wheel–rail contact force of the heavy-load train is realized. The experimental results under two working conditions show that this method can realize high-precision, real-time measurement of the wheel–rail contact force of a heavy-load train. For the working condition of a straight-line section, the calibration error was within 1.593 kN, and the MAPE was 0.105%; for the working condition of a curved-line section, the calibration error was 2.344 kN, and the RMSE was 184.72 N.

Chiral phononic crystals (PnCs) provide unique properties not offered by conventional metamaterial based on classic Bragg scattering and local resonance. However, it is insufficient to only consider the inertial amplification effect to describe its bandgap mechanism due to the absence of the bandgap caused by the chirality in some specific chiral structures. Here, we theoretically and experimentally introduce an analogy with Thomson scattering in electromagnetic waves to characterize the bandgap phenomena in chiral PnCs with translation–rotation coupling. Another phononic structures with translation–translation coupling are proposed to illustrate the universality of the analogy. We evidence that the coupling motion in chiral unit cells is similar to the result of Thomson scattering, which quantitatively formulizing as inertial amplification in theory and, twice elastic Thomson scattering allows the waves in the same polarization mode to superpose in antiphase, which is essence of the bandgap formation. This finding sheds a new light on the physics of the elastodynamic wave manipulation in chiral PnCs, thus opening a definite route for the pragmatic exploitation of chiral PnCs as well as other structures with motion coupling in achieving low-frequency and broad bandgaps.

In order to address the problem of coordinated flood forecasting in the urbanized watershed, this study proposes a framework for discriminating easily occurring runoff component, which considers vertical spatial heterogeneity based on soil type, land use type and topographic slope, and integrates a Grid-based Runoff Generation Model (GRGM). Taking the control watershed of Jialu River at Zhongmou station (including the central city of Zhengzhou) as the study area, on the basis of GRGM model tests based on 11 observed rainfall-runoff events, the spatial and temporal evolution of runoff components in the study area from 1980 to 2020 and their correlation with the underlying surface changes are explored. The study reveals that: (a) the average relative error of the runoff generation calculation by GRGM model in the study area is reduced by 27.76% and the average coefficient of determination is increased by 0.11 compared with Horton Infiltration (HI) model, which means GRGM model are more accurate. (b) The percentage of excess surface runoff ( R s ) in the central city increased significantly from 22 to 67%, and showed a trend of expansion from the central city to the suburbs. (c) The land use types have changed significantly, mainly manifested as a substantial reduction of cropland and a sharp expansion of construction land. R s is significantly positively correlated with construction land, and the Pearson correlation coefficient exceeds 0.93. The study findings can serve as a scientific basis for coordinated management of flood prevention and disaster reduction in the urbanized watershed.

Amidst the dual challenges of escalating ecological environmental pressures and economic transformation globally, green innovation emerges as a pivotal pathway toward achieving high-quality sustainable development. To elucidate how digitalization and novel productive forces synergistically drive the green transition, the research utilizes panel data from 30 provincial-level administrative regions in China spanning 2009 to 2022, constructing a green innovation efficiency measurement frame-work grounded in the Super Slack-Based Measure (Super-SBM)model, alongside a novel productive forces evaluation system based on the triad of laborers, labor objects, and means of production. Employing spatial difference-in-differences and double machine learning methodologies within a quasi-natural experimental design, the research investigates the causal mechanisms through which digital empowerment and novel productive forces influence regional green innovation efficiency. The findings reveal that both digital empowerment and novel productive forces significantly enhance regional green innovation efficiency, exhibiting pronounced positive spatial spillover effects on neighboring regions. Heterogeneity analyses demonstrate that the promotive impacts are more pronounced in eastern provinces compared to central and western counterparts, in provinces participating in carbon trading relative to those that do not, and in innovation-driven provinces versus non-innovative ones. Mediation analysis indicates that digital empowerment operates by fostering the aggregation of innovative talent and elevating governmental ecological attentiveness, whereas new-type productivity exerts its influence primarily through intellectual property protection and the clustering of high-technology industries. The results offer empirical foundations for policymakers to devise coordinated regional green development strategies, refine digital transformation policies, and promote industrial structural optimization. Furthermore, this research provides valuable data-driven insights and theoretical guidance for local governments and enterprises in cultivating green innovation and new-type productivity.

Accurate monitoring of the depth of anesthesia (DoA) is essential for ensuring patient safety and effective anesthesia management. Existing methods, such as the Bispectral Index (BIS), are limited in real-time accuracy and robustness. Current methods have problems in generalizability across diverse patient datasets and are sensitive to artifacts, making it difficult to provide reliable DoA assessments in real time. This study proposes a novel method for DoA monitoring using EEG signals, focusing on accuracy, robustness, and real-time application. EEG signals were pre-processed using wavelet denoising and discrete wavelet transform (DWT). Features such as Permutation Lempel–Ziv Complexity (PLZC) and Power Spectral Density (PSD) were extracted. A random forest regression model was employed to estimate anesthetic states, and an unsupervised learning method using the Hurst exponent algorithm and hierarchical clustering was introduced to detect transitions between anesthesia states. The method was tested on two independent datasets (UniSQ and VitalDB), achieving an average Pearson correlation coefficient of 0.86 and 0.82, respectively. For the combined dataset, the model demonstrated an R-squared value of 0.70, a RMSE of 6.31, a MAE of 8.38, and a Pearson correlation of 0.84, showcasing its robustness and generalizability. This approach offers a more accurate and reliable real-time DoA monitoring tool that could significantly improve patient safety and anesthesia management, especially in diverse clinical environments.

This paper presents a new method to apply timing characteristics of electroencephalograph (EEG) beta frequency bands to assess the depth of anaesthesia (DoA). Firstly, the measured EEG signals are denoised and decomposed into 20 different frequency bands. The Mobility (M), permutation entropy (PE) and Lempel–Ziv complexity (LCZ) of each frequency band are calculated. The M, PE and LCZ values of beta frequency bands (21.5–30 Hz) are selected to derive a new index. The new index is evaluated and compared with measured bispectral (BIS). The results show that there is a very close correlation between the proposed index and the BIS during different anaesthetic states. The new index also shows a 25–264 s earlier time response than BIS during the transient period of anaesthetic states. In addition, the proposed index is able to continuously assess the DoA when the quality of signal is poor and the BIS does not have any valid outputs.

This paper applies the nonlocal mean (NLM) method to denoise the simulated and real electroencephalograph signals. As a patch-based method, the NLM method calculates the weighted sum of a patch. The weight of each point is determined by the similarity between the points of the own patch and its neighbor. Based on the weighted sum, the noise is filtered out. In this study, the NLM denoising method is applied to signals with additive Gaussian white noise, spiking noise and specific frequency noise and the results are compared with that of the popular sym8 and db16 Wavelet threshold denoising (WTD) methods. The outcomes show that the NLM on average achieves 2.70 dB increase in improved signal to noise ratio (SNRimp) and 0.37 % drop in improved percentage distortion ratio compared with WTD. The moving adaptive shape patches-NLM performs better than the original NLM when the signals change dramatically. In addition, the performance of combined NLMWTD denoising method is also better than original WTD method (0.50–4.89 dB higher in SNRimp), especially, when the signal quality is poor.