China has experienced an upsurge in child abandonment since the late 1970s in parallel with its one-child policy (OCP) and market reforms. Due to the scarcity of individual-level data, the literature focuses on informal adoption and child trafficking. This study first demonstrates the spatial-temporal trends of child abandonment across over 100,000 self-reported cases spanning 40 years in China collected from an internet platform. We then examine how the OCP and the long-established clan culture influence the incidence of child abandonment at the provincial level. We further compare whether the influences vary across genders. The results indicate that a tougher OCP penalty increases child abandonment, particularly the abandonment of girls. The influence of the OCP on girl abandonment is weaker in provinces with a strong clan culture, where sex ratios at birth are more unbalanced due to an increased incidence of gender-selective abortions.
Accurately controlling catalytic activity and mechanism as well as identifying structure–activity–selectivity correlations in Fenton-like chemistry is essential for designing high-performance catalysts for sustainable water decontamination. Herein, active center size-dependent catalysts with single cobalt atoms (CoSA), atomic clusters (CoAC), and nanoparticles (CoNP) were fabricated to realize the changeover of catalytic activity and mechanism in peroxymonosulfate (PMS)-based Fenton-like chemistry. Catalytic activity and durability vary with the change in metal active center sizes. Besides, reducing the metal size from nanoparticles to single atoms significantly modulates contributions of radical and nonradical mechanisms, thus achieving selective/nonselective degradation. Density functional theory calculations reveal evolutions in catalytic mechanisms of size-dependent catalytic systems over different Gibbs free energies for reactive oxygen species generation. Single-atom site contact with PMS is preferred to induce nonradical mechanisms, while PMS dissociates and generates radicals on clusters and nanoparticles. Differences originating from reaction mechanisms endow developed systems with size-dependent selectivity and mineralization for treating actual hospital wastewater in column reactors. This work brings an in-depth understanding of metal size effects in Fenton-like chemistry and guides the design of intelligent catalysts to fulfill the demand of specific scenes for water purification.
A novel adaptive spectral method has been recently developed to numerically solve partial differential equations (PDEs) in unbounded domains. To achieve accuracy and improve efficiency, the method relies on the dynamic adjustment of three key tunable parameters: the scaling factor, a displacement of the basis functions, and the spectral expansion order. In this paper, we perform the first numerical analysis of the adaptive spectral method using generalized Hermite functions in both one- and multi-dimensional problems. Our analysis reveals why adaptive spectral methods work well when a “frequency indicator” of the numerical solution is controlled. We then investigate how the implementation of the adaptive spectral methods affects numerical results, thereby providing guidelines for the proper tuning of parameters. Finally, we further improve performance by extending the adaptive methods to allow bidirectional basis function translation, and the prospect of carrying out similar numerical analysis to solving PDEs arising from realistic difficult-to- solve unbounded models with adaptive spectral methods is also briefly discussed.
In the boundary element method (BEM), the sinh transformation method is an effective method for evaluating nearly singular integrals, but a relationship between the integration accuracy and the number of Gaussian points is needed to achieve adaptive computation. Based on deep learning, we propose a novel integration scheme, adaptive sinh transformation Gaussian quadrature (ASTGQ), which can determine the number of Gaussian points according to the required accuracy. First, a large number of integration data samples of the sinh transformation method are generated in different cases, and the neural network is trained to establish the relationship between the number of Gaussian points and the integration accuracy. Then, based on the improved loss function and evaluation index, a better network model is obtained to ensure that the actual integration accuracy is slightly higher than the requirement of using the minimum Gaussian points. In this way, when the trained neural network is used in the sinh transformation method, the higher accuracy requirement can be met at a lower cost. Numerical examples demonstrate that, compared to the adaptive Gaussian quadrature (AGQ) method, the proposed scheme can significantly improve the computational efficiency when evaluating the nearly singular integrals for very thin coatings and other structures.