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This paper develops a Factor-Copula methodology for constructing high-dimensional dynamic tail risk networks based on the conditional expected shortfall (CoES) in order to overcome the limitations of traditional quantile regression and copula models. We backtest the CoES using cumulative joint violations and conditional coverage tests. The proposed factor‑copula‑CoES model reduces the rejection rates in 10‑th order conditional backtests by 56.37%, 47.72%, and 1.96% relative to the static‑copula‑CoES, time‑varying‑copula‑CoES, and factor‑copula‑CoVaR models, respectively, with all reductions being statistically significant. The dynamic analysis of the tail risk network of Chinese listed financial institutions indicates that the network topology characteristics align with real market risk events. Finally, the quantitative analyses show different effects of institutional and market factors on network-measured risk spillovers and contagion among Chinese financial institutions.

Seismic interferometry using ambient noise provides an effective approach for subsurface imaging through reconstructing passive virtual source (PVS) responses. Traditional crosscorrelation (CC) seismic interferometry relies on a uniform dense distribution of passive sources in the subsurface, which is often challenging in practice. The multidimensional deconvolution method (MDD) alleviates reliance on passive-source distribution, but requires wavefield decomposition of the original data. This is difficult to accurately achieve for uncorrelated noise sources, leading to the existence of non-physical artifacts in the reconstructed PVS data. To address this issue, this study proposes a method to improve the accuracy of PVS data reconstruction using an enhanced U-Net. This data-driven approach circumvents the challenge of noise wavefield decomposition encountered in the traditional MDD. By integrating a feature fusion module into U-Net, multi-scale sampling information is leveraged to improve the network’s ability to capture detailed PVS data features. The combination of active-source data constraints and the modified MDD further optimizes PVS data retrieval during training. Numerical tests show that the proposed method effectively recovers waveform information in PVS retrieval records with non-ideally distributed sources, suppressing coherent noise and false events. The reconstructed recordings have a clear advantage in the reverse time migration (RTM) imaging results, with strong generalization performance across various velocity models.

Ground penetrating radar (GPR) is one of the most recommended tools for routine inspection of tunnel linings. However, the rebars in the reinforced concrete produce a strong shielding effect on the electromagnetic waves, which may hinder the interpretation of GPR data. In this work, we proposed a method to improve the identification of tunnel lining voids by designing a generative adversarial network-based rebar clutter elimination network (RCE-GAN). The designed network has two sets of generators and discriminators, and by introducing the cycle-consistency loss, the network is capable of learning high-level features between unpaired GPR images. In addition, an attention module and a dilation center part were designed in the network to improve the network performance. Validation of the proposed method was conducted on both synthetic and real-world GPR images, collected from the implementation of finite-difference time-domain (FDTD) simulations and a controlled physical model experiment, respectively. The results demonstrate that the proposed method is promising for its lower demand on the training dataset and the improvement in the identification of tunnel lining voids.

Tax incentives play a crucial role in enhancing firm dynamism and aiding a nation in becoming a significant trade power. Drawing on data from the Annual Survey of Industrial Firms Database and the Chinese Customs Database for the period 2010 to 2013, this study employs a difference-in-differences approach to assess the impact of China’s transition from a business tax to a value-added tax (RBTVAT) on the export diversification of manufacturing firms. The findings indicate that the tax reform significantly decreases the number of export categories, increases export value, and elevates the export unit price for manufacturing firms. Specifically, by promoting specialized production and encouraging the manufacture of products with higher export tax rebate rates, the reforms have led firms to narrow their range of export categories. This effect is particularly pronounced among firms experiencing higher financing constraints, lower profitability, weaker innovation capabilities, and larger size. Furthermore, a consistent negative impact is observed for both state-owned and non-state-owned enterprises. These results provide novel insights and empirical evidence for understanding the relationship between tax reform and export diversification.

The type of muscle fiber plays a crucial role in the growth, development, and dynamic plasticity of animals’ skeletal muscle. Additionally, it is a primary determinant of the quality of both fresh and processed meat. Therefore, understanding the regulatory factors that contribute to muscle fibers’ heterogeneity is of paramount importance. Recent advances in sequencing and omics technologies have enabled comprehensive cross-verification of research on the factors affecting the types of muscle fiber across multiple levels, including the genome, transcriptome, proteome, and metabolome. These advancements have facilitated deeper exploration into the related biological questions. This review focused on the impact of individual characteristics, feeding patterns, and genetic regulation on the proportion and interconversion of different muscle fibers. The findings indicated that individual characteristics and feeding patterns significantly influence the type of muscle fiber, which can effectively enhance the type and distribution of muscle fibers in livestock. Furthermore, non-coding RNA, genes and signaling pathways between complicated regulatory mechanisms and interactions have a certain degree of impact on muscle fibers’ heterogeneity. This, in turn, changes muscle fiber profile in living animals through genetic selection or environmental factors, and has the potential to modulate the quality of fresh meat. Collectively, we briefly reviewed the structure of skeletal muscle tissue and then attempted to review the inevitable connection between the quality of fresh meat and the type of muscle fiber, with particular attention to potential events involved in regulating muscle fibers’ heterogeneity.

In order to capture temporal interactions among processes in manufacturing and assembly processes, an end-to-end unified product quality prediction framework called QTD is proposed in this paper. It consists of three modules: quality embedding model pool, temporal-interactive model, and decoding model. Besides, to handle the information transfer and integration problems in the time direction of parallel processes, a novel bidirectional serial–parallel LSTM (Bi-SP-LSTM) is devised as an instantiated model of temporal-interactive model. Bi-SP-LSTM is an extension of bidirectional long short-term memory. Moreover, an unsupervised task and a loss function named adversarial focal loss have been designed to give the framework the ability to assess heteroscedastic uncertainty in classification task due to intrinsic uncertainty in data. Furthermore, experiments are devised based on a subset of a public dataset from Kaggle competition to demonstrate the validity of the proposed framework. Compared with other latest methods, the proposed framework is verified to be more accurate and robust. Taking Matthews correlation coefficient as an example, the adversarial Bi-SP-LSTM-based QTD framework is superior to the best existing methods with 95% confidence interval in most cases, and its mean MCC is 4.88% higher than the best existing method. The results suggest that the proposed framework has a broad application prospect for quality prediction in manufacturing and assembly processes.

Inorganic semiconductor-based microscale light-emitting diodes (micro-LEDs) have been widely considered the key solution to next-generation, ubiquitous lighting and display systems, with their efficiency, brightness, contrast, stability, and dynamic response superior to liquid crystal or organic-based counterparts. However, the reduction of micro-LED sizes leads to the deteriorated device performance and increased difficulties in manufacturing. Here, we report a tandem device scheme based on stacked red, green, and blue (RGB) micro-LEDs, for the realization of full-color lighting and displays. Thin-film micro-LEDs (size ∼100 μm, thickness ∼5 μm) based on III–V compound semiconductors are vertically assembled via epitaxial liftoff and transfer printing. A thin-film dielectric-based optical filter serves as a wavelength-selective interface for performance enhancement. Furthermore, we prototype arrays of tandem RGB micro-LEDs and demonstrate display capabilities. These materials and device strategies provide a viable path to advanced lighting and display systems.

The crosshole ground-penetrating radar (GPR) technique is widely used to characterize subsurface structures, yet the interpretation of crosshole GPR data involves solving non-linear and ill-posed inverse problems. In this work, we developed a generative adversarial network (GAN)-based inversion framework to translate crosshole GPR images to their corresponding 2D defect reconstruction images automatically. This approach uses fully connected layers to extract global features from crosshole GPR images and employs a series of cascaded U-Net structures to produce high-resolution defect reconstruction results. The feasibility of the proposed framework was demonstrated on a synthetic crosshole GPR dataset created with the finite-difference time-domain (FDTD) method and real-world data from a field experiment. Our inversion network obtained recognition accuracy of 91.36%, structural similarity index measure (SSIM) of 0.93, and RAscore of 91.77 on the test dataset. Furthermore, comparisons with ray-based tomography and full-waveform inversion (FWI) suggest that the proposed method provides a good balance between inversion accuracy and efficiency and has the best generalization when inverting actual measured crosshole GPR data.

Crosshole ground penetrating radar (GPR) is an efficient method for ensuring the quality of retaining structures without the need for excavation. However, interpreting crosshole GPR data is time-consuming and prone to inaccuracies. To address this challenge, we proposed a novel three-dimensional (3D) reconstruction method based on a generative adversarial network (GAN) to recover 3D permittivity distributions from crosshole GPR images. The established framework, named CGPR2VOX, integrates a fully connected layer, a residual network, and a specialized 3D decoder in the generator to effectively translate crosshole GPR data into 3D permittivity voxels. The discriminator was designed to enhance the generator’s performance by ensuring the physical plausibility and accuracy of the reconstructed models. This adversarial training mechanism enables the network to learn non-linear relationships between crosshole GPR data and subsurface permittivity distributions. CGPR2VOX was trained using a dataset generated through finite-difference time-domain (FDTD) simulations, achieving precision, recall and F1-score of 91.43%, 96.97% and 94.12%, respectively. Model experiments validate that the relative errors of the estimated positions of the defects were 1.67%, 1.65%, and 1.30% in the X-, Y-, and Z-direction, respectively. Meanwhile, the method exhibits noteworthy generalization capabilities under complex conditions, including condition variations, heterogeneous materials and electromagnetic noise, highlighting its reliability and effectiveness for practical quality assurance of retaining structures.

Full waveform inversion (FWI) comprehensively utilizes phase and amplitude information of seismic waves to obtain high-resolution subsurface medium parameter models, applicable to both active-source and passive-source seismic data. Passive-source seismic exploration, using natural earthquakes or ambient noise, reduces costs and environmental impact, with growing marine applications in recent years. Its rich low-frequency content makes passive-source FWI (PSFWI) a key research focus. However, PSFWI inversion quality relies heavily on accurate virtual source reconstruction. While multi-dimensional deconvolution (MDD) can handle uneven source distributions, it struggles with irregular receiver sampling. We propose a robust MDD method based on multi-domain stepwise interpolation to improve reconstruction under non-ideal source and sampling conditions. This approach, validated via an adaptive PSFWI strategy, exploits MDD’s insensitivity to source distribution and incorporates normalized correlation objective functions to reduce amplitude errors. Numerical tests on marine and complex scattering models demonstrate stable and accurate velocity inversion, even in challenging acquisition environments.