Sunlight-driven photosynthesis by covalent organic frameworks (COFs) from water and air without using sacrificial reagents is a promising H2O2 fabrication approach, but is still restricted by the insufficient charge separation and sluggish 2e- water oxidation process. Herein, we provide a facile strategy to simultaneously improve charge separation and water oxidation in COFs via confining the charge transfer pathways from two diversion ones to a confluence one through regulating the site of nitrogen in bipyridine. Combining in-situ characterization with computational calculations, we reveal that compared to COF-BD1 containing two diversion charge transfer pathways, the charge transfer pathway in COF-BD2 is confined to a confluence one due to the electron-deficiency effect of nitrogen, which greatly accelerates the intermolecular and out-of-plane charge transfer. Via effectively reducing the energy barrier of rate-determining water oxidation reaction, the subsequent water oxidation process to produce key *OH intermediate in COF-BD2 is also greatly facilitated, boosting the yield of H2O2 (5211 μmol g-1 h-1) from water, oxygen, and light without sacrificial agents or additional energy consumption. We further demonstrate that H2O2 can be efficiently produced by COF-BD2 in broad pH range, in real water, and in enlarged reactor with using natural sunlight for water decontamination.
The advantage of Chinese-as-a-heritage-language (CHL) learners in acquiring Chinese has been widely recognized. However, it is still unclear whether the effect of CHL background on Chinese receptive vocabulary breadth varies across different countries. To address this gap, the present study recruited 232 Chinese language learners (half were CHL learners) from Indonesia and Thailand and administered a Chinese vocabulary proficiency test. The results of regression analysis revealed an interaction effect between country and CHL background on vocabulary breadth, with the contribution of CHL background to vocabulary breadth more robust in the Indonesian group than that in the Thai group. Interviews were then conducted to explore the factors that might influence such an interaction effect. Analysis of the interview data found that the influencing factors could be categorized into four themes, including individual differences, family background, Chinese language education and socio-cultural factors. The overall results were discussed within the framework of ecological system theory, and pedagogical implications for CHL learners were proposed.
As a judicious correspondence to the classical maxcut, the anti-Cheeger cut has more balanced structure, but few numerical results on it have been reported so far. In this paper, we propose a continuous iterative algorithm (CIA) for the anti-Cheeger cut problem through fully using an equivalent continuous formulation. It does not need rounding at all and has advantages that all subproblems have explicit analytic solutions, the objective function values are monotonically updated and the iteration points converge to a local optimum in finite steps via an appropriate subgradient selection. It can also be easily combined with the maxcut iterations for breaking out of local optima and improving the solution quality thanks to the similarity between the anti-Cheeger cut problem and the maxcut problem. The performance of CIAs is fully demonstrated through numerical experiments on G-set from two aspects: one is on the solution quality where we find that the approximate solutions obtained by CIAs are of comparable quality to those by the multiple search operator heuristic method; the other is on the computational cost where we show that CIAs always run faster than the often-used continuous iterative algorithm based on the rank-two relaxation.
ABSTRACT An increasing body of research has investigated the role of enjoyment in second language acquisition (SLA); however, few studies have explored whether learners of Chinese as a second/foreign language (CS/FL) experience enjoyment in learning Hanzi (Chinese characters) and how enjoyment impacts Hanzi recognition performance. To address this gap, a Hanzi Learning Enjoyment Scale was developed and administered to 446 Arabic CS/FL learners, 144 of whom also completed a Hanzi recognition test. Two key findings emerged. First, the results of factor analysis revealed four factors underlying Hanzi learning enjoyment: Hanzi culture, personal attitudes, teacher support, and personal fulfillment. Second, enjoyment did not emerge as a significant predictor of Hanzi recognition performance. Notably, the variance in Hanzi recognition scores explained by enjoyment ranked among the top three explanatory variables, comparable to the predictive power of years spent learning Chinese. This study concludes with theoretical insights into the construct of foreign language enjoyment (FLE) across different languages and language components, as well as practical recommendations for enhancing Hanzi instruction.
Existing diffusion models for low-light image enhancement typically incrementally remove noise introduced during the forward diffusion process using a denoising loss, with the process being conditioned on input low-light images. While these models demonstrate remarkable abilities in generating realistic high-frequency details, they often struggle to restore fine details that are faithful to the input. To address this, we present a novel detail-preserving diffusion model for realistic and faithful low-light image enhancement. Our approach integrates a size-agnostic diffusion process with a reverse process reconstruction loss, significantly enhancing the fidelity of enhanced images to their low-light counterparts and enabling more accurate recovery of fine details. To ensure the preservation of region- and content-aware details, we employ an efficient noise estimation network with a simplified channel-spatial attention mechanism. Additionally, we propose a multiscale ensemble scheme to maintain detail fidelity across diverse illumination regions. Comprehensive experiments on eight benchmark datasets demonstrate that our method achieves state-of-the-art results compared to over twenty existing methods in terms of both perceptual quality (LPIPS) and distortion metrics (PSNR and SSIM). The code is available at: https://github.com/CSYanH/DePDiff.
Surface ozone (O3) pollution affects air quality, human health, and the ecosystem. Understanding the complex non-linear relationship between ozone formation and its precursors, nitrogen oxides (NOx), and volatile organic compounds (VOCs) is critical for policymakers to mitigate the pollution. The Empirical Kinetic Modeling Approach (EKMA) based on classical observation-constrained zero-dimension box model provides the sensitivity of ozone production to precursor concentrations instead of emissions. This makes the box-model EKMA hard to apply in a real emission reduction scenario. Here, we developed an alternative box model approach driven by localized emissions, which are derived from the field-observed concentrations. This model approach reproduced the O3 variations well by capturing the short-term changes of NOx and AVOCs emissions among different phases of pollution control during the 31st World University Games in Chengdu in 2023. The EKMA analysis based on this model approach showed a different O3 response to precursor reductions from the concentration-constrained approach, which overestimated the baseline of O3 concentration. The result from the EKMA analysis demonstrated that the O3 level was most sensitive to NOx due to stringent control strategies during the event and rapidly rebounded to almost VOC-limited regime after the event. The effects of VOCs reduction on O3 control examined by concentration-constrained model approach were less pronounced than those by emission-driven approach due to the lack of consideration of the emission-to-reaction process. Our findings suggest that the emission-driven box model is applicable for developing O3 control strategy in the local scale.
In the context of supply chain digitization and green development in full swing, it is crucial to clarify the impact of the former on green energy innovation. Using exogenous shocks deriving from supply chain innovation and application pilot events, this study examines the impact of supply chain digitization on green energy innovation based on the data of Chinese listed companies from 2012 to 2021. The findings show that supply chain digitization significantly enhances corporate green energy innovation and that receivable asset management is a path mechanism for supply chain digitization to drive green energy innovation. Moreover, there is a significant positive intra-city spillover. Supply chain digitization contributes significantly to corporate green energy innovation in state-controlled manufacturing firms with effective internal controls in the eastern region. This study has important policy implications for promoting green energy innovation and accelerating the development of modern supply chain systems.
Numerical resolution of moderately high-dimensional nonlinear PDEs remains a huge challenge due to the curse of dimensionality for the classical numerical methods including finite difference, finite element and spectral methods. Starting from the weak formulation of the Lawson-Euler scheme, this paper proposes a stochastic particle method (SPM) by tracking the deterministic motion, random jump, resampling and reweighting of particles. Real-valued weighted particles are adopted by SPM to approximate the high-dimensional solution, which automatically adjusts the point distribution to intimate the relevant feature of the solution. A piecewise constant reconstruction with virtual uniform grid is employed to evaluate the nonlinear terms, which fully exploits the intrinsic adaptive characteristic of SPM. Combining both, SPM can achieve the goal of adaptive sampling in time. Numerical experiments on the 6-D Allen-Cahn equation and the 7- D Hamiltonian-Jacobi-Bellman equation demonstrate the potential of SPM in solving moderately high-dimensional nonlinear PDEs efficiently while maintaining an acceptable accuracy
