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The decomposed plastic products in the natural environment evolve into tiny plastic particles with characteristics such as small size, lightweight, and difficulty in removal, resulting in a significant pollution issue in aquatic environments. Significant progress has been made in microplastic separation technology benefiting from microfluidic chips in recent years. Based on the mechanisms of microfluidic control technology, this study investigates the enrichment and separation mechanisms of polystyrene particles in an unbuffered solution. The Faraday reaction caused by the bipolar electrodes changes the electric field gradient and improves the separation efficiency. We also propose  an evaluation scheme to measure the separation efficiency. Finite element simulations are conducted to parametrically analyze the influence of applied voltages, channel geometry, and size of electrodes on plastic particle separation. The numerical cases indicate that the electrode-installed microfluidic channels separate microplastic particles effectively and precisely. The electrodes play an important role in local electric field distribution and trigger violent chemical reactions. By optimizing the microchannel structure, applied voltages, and separation channel angle, an optimal solution for separating microplastic particles can be found. This study could supply some references to control microplastic pollution in the future.

In recent years, numerous studies have explored fault diagnosis methods for rotating machinery based on deep learning. For deep learning-based algorithms, the performance of fault diagnosis will be significantly worse while the labeled training data is insufficient or in case existing an obvious difference between the training and the test data. To overcome this thorny research problem, a novel domain adaptation approach is outlined, capable of realizing fault diagnosis of rotating machinery under different loads. For the proposed approach, a deep sparse filtering model is established as an extractor of the fault features to learn the representative and discriminative features of the source domain data. A domain classifier is applied for making the shift across domains to be indiscriminate. Moreover, Z -score standardization and CORAL are employed as the preprocessing tools to help reduce the influence of features with large variance and reduce the offset between the two domains, respectively. The effectiveness of the outlined method is verified via the experimental vibration data from a bearing and a gear dataset.

The expression changes of baculovirus inhibitor of apoptosis repeat‐containing protein5 in brain glioma after administration of Scutellarin was detected. To explore the effort of scutellarin on anti‐glioma by downregulating BIRC5.The effect of scutellarin on tumour growth and animal survival was detected by administering scutellarin to nude mice subcutaneous tumour formation and SD rats in situ tumour formation models. A significantly different gene BIRC5 was found by using the combination of TCGA databases and network pharmacology. And then qPCR was performed to detect the expression of BIRC5 in glioma tissues, cells and normal brain tissues and glial cells. CCK‐8 was used to detect the IC50 of scutellarin on glioma cells. The wound healing assay, flow cytometry and MTT test were used to detect the effect of scutellarin on the apoptosis and proliferation of glioma cells. The expression of BIRC5 in glioma tissues was significantly higher than that in normal brain tissues. Scutellarin can significantly reduce tumour growth and improve animal's survival. After scutellarin was administered, the expression of BIRC5 in U251 cells was significantly reduced. And after same time, apoptosis increased and cell proliferation was inhibited. This original research showed that scutellarin can promote the apoptosis of glioma cells and inhibit the proliferation by downregulating the expression of BIRC5.

This study presents an experimental investigation into the dynamic response and blast resistance of aluminum foam-cored sandwich panels with varied face sheet thicknesses under impulsive loading conditions. The primary focus is on analyzing how the thickness of front and back face sheets affects the deformation behavior and energy absorption capabilities of the sandwich panels. By employing a 3D digital image correlation (3D-DIC) system coupled with post-test analyses, the dynamic responses and permanent deformations were quantitatively characterized. Failure modes of the core layers, front face sheets, and back face sheets were identified and discussed. The results demonstrated that sandwich panels with thick front face sheets exhibited superior blast resistance and energy absorption performance than their thin-front counterparts under high localized impulsive loading. The findings provide important comparative insights about face sheet thickness distribution effects, though further studies with broader thickness variations are needed to establish comprehensive design guidelines.

As the key component to transmit power and torque, the fault diagnosis of rotating machinery is crucial to guarantee the reliable operation of mechanical equipment. Regrettably, sample class imbalance is a common phenomenon in industrial applications, which causes large cross-domain distribution discrepancies for domain adaptation (DA) and results in performance degradation for most of the existing mechanical fault diagnosis approaches. To address this issue, a novel DA approach that simultaneously reduces the cross-domain distribution difference and the geometric difference is proposed, which is defined as MRMI. This work contains three parts to improve the sample class imbalance issue: (1) A novel distance metric method (MVD) is proposed and applied to improve the performance of marginal distribution adaptation. (2) Manifold regularization is combined with instance reweighting to simultaneously explore the intrinsic manifold structure and remove irrelevant source-domain samples adaptively. (3) The ℓ2-norm regularization is applied as the data preprocessing tool to improve the model generalization performance. The gear and rolling bearing datasets with class imbalanced samples are applied to validate the reliability of MRMI. According to the fault diagnosis results, MRMI can significantly outperform competitive approaches under the condition of sample class imbalance.

Surface topography plays a critical role in determining the tribological performance of engineering surfaces. This study systematically investigates the lubrication film characteristics of bump array surfaces (isotropic and anisotropic), groove surfaces, and herringbone surfaces through point-contact elastohydrodynamic lubrication (EHL) analyses. Numerical simulations were conducted to evaluate the influences of surface topographical parameters on the lubrication performance, which is quantified by average film thickness and contact load ratio. The results indicate that transverse textures lead to thicker average film as compared with longitudinal textures. This is mainly because the transverse textures can generate more effective hydrodynamic pressures from the oil film behind the ridges due to micro-EHL. By analyzing the topographical parameters and their impacts on the average film thickness and contact load ratio, this study provides practical guidance for designing surface topographies that optimize average film thickness, applicable to a wide range of tribological systems.

Frequency stabilization can overcome the dependence of resonance frequency on amplitude in nonlinear microelectromechanical systems, which is potentially useful in nonlinear mass sensor. In this paper, the physical conditions for frequency stabilization are presented theoretically, and the influence of system parameters on frequency stabilization is analyzed. Firstly, a nonlinear mechanically coupled resonant structure is designed with a nonlinear force composed of a pair of bias voltages and an alternating current (AC) harmonic load. We study coupled-mode vibration and derive the expression of resonance frequency in the nonlinear regime by utilizing perturbation and bifurcation analysis. It is found that improving the quality factor of the system is crucial to realize the frequency stabilization. Typically, stochastic dynamic equation is introduced to prove that the coupled resonant structure can overcome the influence of voltage fluctuation on resonance frequency and improve the robustness of the sensor. In addition, a novel parameter identification method is proposed by using frequency stabilization and bifurcation jumping, which effectively avoids resonance frequency shifts caused by driving voltage. Finally, numerical studies are introduced to verify the mass detection method. The results in this paper can be used to guide the design of a nonlinear sensor.

This study explored the applicability of TRMM, TRMM nonlinear downscaling, and ANUSPLIN (ANU) interpolation of three different types of precipitation data to define regional-scale rainfall-triggered landslide thresholds. The spatial resolution of TRMM precipitation data was downscaled from 0.25° to 500 m by the downscaling model considering the relationship between humidity, NDVI, and numerous topographic factors and precipitation. The rainfall threshold was calculated using the rainfall intensity–duration threshold model. The calculation showed that TRMM downscaled precipitation data have better detection capability for extreme precipitation events than the other two, the TRMM downscaling threshold was better than the ANU interpolation, and the cumulative effective rainfall of TRMM downscaling was preferred as the macroscopic critical rainfall-triggered landslide threshold for the early warning of the Wudu. The predictive performance of the rainfall threshold of 50% was better than the other two (10% and 90%). When the probability of landslide occurrence was 50%, the TRMM downscaled threshold curve was given by I50=21.03×D−1.004. The authors also analyzed the influence of factors such as topography landform and soil type on the rainfall threshold of landslides in the study area. The rainfall intensity of small undulating mountains was higher than that of medium and large undulating mountains, and the rainfall intensity of landslides peaks at high altitude mountains of 3500–5000 m.

This paper examines how the worship of ancient wisdom affects economic progress in historical China, where the learned class embraced classical wisdom for millennia but encountered the shock of Western industrial influence in the mid-nineteenth century. Using the number of sage temples to measure the strength of classical worship in 269 prefectures, I find that classical worship discouraged intellectuals from appreciating modern learning and thus inhibited industrialization between 1858 and 1927. By contrast, industrialization grew faster in regions less constrained by classicism. This finding implies the importance of cultural entrepreneurship, or the lack thereof, in shaping modern economic growth. “The humor of blaming the present, and admiring the past, is strongly rooted in human nature, and has an influence even on persons endued with the profoundest judgment and most extensive learning.” —David Hume (1754, p. 464).

From 1580, the Jesuits introduced European sciences to China―an autarkic civilization whose intelligentsia was dominated by Confucian literati. Drawing upon prefectural distributions of the Jesuits and Chinese scientific works, this paper demonstrates that the Jesuits stimulated Confucian literati to study science. On average, the literati’s scientific works increased four times in prefectures with Jesuit scientists after 1580. But this effect shrank after the Jesuits were expelled by the emperor of China in 1723. Since China’s scholar-official system remained unchanged, the literati’s scientific research aimed to serve the needs of statecraft rather than translating into economic progress.