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Global and local land-cover mapping products provide important data on land surface. However, the accuracy of land-cover products is the key issue for their further scientific application. There has been neglect of the relationship between inclusion probability and spatial heterogeneity in traditional spatially balanced sampling. The aim of this paper was to propose an improved spatially balanced sampling method using landscape pattern-based inclusion probability. Compared with other global land-cover datasets, Globeland30 has the advantages of high resolution and high classification accuracy. A two-stage stratified spatially balanced sampling scheme was designed and applied to the regional validation of GlobeLand30 in China. In this paper, the whole area was divided into three parts: the Tibetan Plateau region, the Northwest China region, and the East China region. The results show that 7242 sample points were selected, and the overall accuracy of GlobeLand30-2010 in China was found to be 80.46%, which is close to the third-party assessment accuracy of GlobeLand30. This method improves the representativeness of samples, reduces the classification error of remote sensing, and provides better guidance for biodiversity and sustainable development of environment.

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The rapid development of material science is increasing the demand for the multiscale design of materials. The concurrent multiscale topology optimization based on the Direct FE2 method can greatly improve computational efficiency, but it may lead to the checkerboard problem. In order to solve the checkerboard problem and reconstruct the results of the Direct FE2 model, this paper proposes a filtering-based reconstruction method. This solution is of great significance for the practical application of multiscale topology optimization, as it not only solves the checkerboard problem but also provides the optimized full model based on interpolation. The filtering method effectively eliminates the checkerboard pattern in the results by smoothing the element densities. The reconstruction method restores the smoothness of the optimized structure by interpolating between the filtered densities. This method is highly effective in solving the checkerboard problem, as demonstrated in our numerical examples. The results show that the proposed algorithm produces feasible and stable results.

Pulmonary fibrosis, a sequela of lung injury resulting from severe infection such as severe acute respiratory syndrome‐like coronavirus (SARS‐CoV‐2) infection, is a kind of life‐threatening lung disease with limited therapeutic options. Herein, inhalable liposomes encapsulating metformin, a first‐line antidiabetic drug that has been reported to effectively reverse pulmonary fibrosis by modulating multiple metabolic pathways, and nintedanib, a well‐known antifibrotic drug that has been widely used in the clinic, are developed for pulmonary fibrosis treatment. The composition of liposomes made of neutral, cationic or anionic lipids, and poly(ethylene glycol) (PEG) is optimized by evaluating their retention in the lung after inhalation. Neutral liposomes with suitable PEG shielding are found to be ideal delivery carriers for metformin and nintedanib with significantly prolonged retention in the lung. Moreover, repeated noninvasive aerosol inhalation delivery of metformin and nintedanib loaded liposomes can effectively diminish the development of fibrosis and improve pulmonary function in bleomycin‐induced pulmonary fibrosis by promoting myofibroblast deactivation and apoptosis, inhibiting transforming growth factor 1 (TGFβ1) action, suppressing collagen formation, and inducing lipogenic differentiation. Therefore, this work presents a versatile platform with promising clinical translation potential for the noninvasive inhalation delivery of drugs for respiratory disease treatment.