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.
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.
Salt crystallization within micro-fractures poses a significant challenge in shale gas production by impeding gas diffusion. This study investigates the real-time behavior of gas flow-induced salt crystallization within a visualized micro-fracture network. Observations reveal that salt crystals initially propagate along the fracture surface before exhibiting perpendicular growth. Crystal nucleation during the saturation stage occurs within a few seconds, while subsequent growth in the supersaturated stage takes approximately 15–20 s. Gas flow drives the evaporation of immobile water, leading to salt precipitation. Furthermore, increasing gas flow rate and decreasing solution salinity are found to accelerate crystal growth. To mitigate plugging damage caused by salt crystallization, controlling pressure differences and solution salinity is crucial.
This study aims to identify the associations between teacher mental health and student mental health. Cross-sectional data were collected from 127,877 students aged 9–20 years and 2,759 teachers across 31 provinces in China. The mental health of students and teachers were assessed by well-being (life satisfaction and positive mental health), and psychological distress (depression and anxiety). Controlling for demographic variables, multilevel regression analyses suggest that higher teacher positive mental health was linked to higher student positive mental health and lower student depression; higher teacher depression were correlated with higher student depression; and teacher life satisfaction and anxiety were not correlated with any indicators of student mental health. The study highlights the significant association between teacher mental health and student mental health.