In this paper, we develop a new adaptive hyperbolic-cross-space mapped Jacobi (AHMJ) method for solving multidimensional spatiotemporal integrodifferential equations in unbounded domains. By devising adaptive techniques for sparse mapped Jacobi spectral expansions defined in a hyperbolic cross space, our proposed AHMJ method can efficiently solve various spatiotemporal integrodifferential equations such as the anomalous diffusion model with reduced numbers of basis functions. Our analysis of the AHMJ method gives a uniform upper error bound for solving a class of spatiotemporal integrodifferential equations, leading to effective error control.
Wetlands are major microplastic sinks with a large atmospheric input. However, many details of such deposited atmospheric microplastics entering into wetlands remain unclear, including temporal patterns of input and ecological effects. We monitored the aerial microplastics during four seasons in eleven economically developed cities along the lower reaches of the Yangtze River Basin, China. The average microplastic deposition rate was 512.31 items m−2 d−1, equivalent to an annual contribution of 17.46 metric tons of plastic to the surveyed wetlands with a total area of 1652 km2. These microplastics were predominantly composed of polyamide and polyethylene terephthalate with 61.85 ± 92.29 µm sized pellets, and we obtained similar results for microplastics intercepted on moss in wetlands. Microplastic input varied between wet and dry periods, primarily influenced by wind, rainfall and ozone concentration. Civilian vehicle density and textile industry were the primary socioeconomic factors driving microplastic deposition. Further indoor microcosm experiments revealed that moss phyllosphere bacterial community structure and function were influenced by microplastic abundance and size, exemplifying the unique ecological risks of aerially deposited microplastics to wetlands. These results indicate that mosses and their phyllosphere microbiota could serve as bio-indicators of aerial microplastic characteristics and impacts.
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.
The digital economy has become a driving force for global economic development, resulting in high demand for balanced regional development. Using surname distance as a proxy variable for cultural distance, this study examined the impact of cultural differences on the development of a regional digital economy. The results of the analysis of panel data from 31 Chinese provinces from 2011 to 2019 indicated that the development of a region's digital economy positively contributes to the development of the digital economy in areas of cultural proximity. Further analysis of the mechanisms of cultural differences in the digital economy showed that cultural distance affects the development of the digital economy in a province through three mechanisms: birth rate, divorce rate, and the share of small families. Moreover, the findings suggest regional, divorce, and demographic heterogeneity in the impact of cultural distance on the digital economy.
WeproposeanODEapproachtosolvingmultiplechoicepolynomialprogram- ming (MCPP) after assuming that the optimum point can be approximated by the ex- pected value of so-called thermal equilibrium as usually did in simulated annealing. The explicit form of the feasible region and the affine property of the objective function are both fully exploited in transforming an MCPP problem into an ODE system. We also show theoretically that a local optimum of the former can be obtained from an equilib- rium point of the latter. Numerical experiments on two typical combinatorial problems, MAX-k-CUT and the calculation of star discrepancy, demonstrate the validity of the ODE approach, and the resulting approximate solutions are of comparable quality to those obtained by the state-of-the-art heuristic algorithms but with much less cost. When compared with the numerical results obtained by using Gurobi to solve MCPP directly, our ODE approach is able to produce approximate solutions of better quality in most instances. This paper also serves as the first attempt to use a continuous algorithm for approximating the star discrepancy.