Spatio-Temporal Characteristics of Water Ecological Footprint and Countermeasures for Water Sustainability in Japan.

Water-related problems are mostly caused by water imbalances between supply and demand. This study adopts the ecological footprint method to conduct an empirical study on the sustainable utilization of water resources in Japan. According to the basic principles and calculation methods of water ecological footprint (WEF), the characteristics of Japan's water ecological footprint were investigated from the time and space dimensions, and a comparative analysis was made with the water ecological footprint of China. The results show that: from 1980 to 2020, the total water ecological footprint in Japan showed a downward trend in both the traditional account and pollutant account, and its spatial pattern was characterized by the relation that the higher the urbanization rate, the larger the water ecological footprint. In terms of water ecological footprint efficiency, Japan's agricultural water ecological footprint efficiency was the lowest, and the domestic water ecological footprint efficiency was the highest. The water resources policies and measures that Japan and other developing countries should take to ensure the sustainability of water resources were analyzed separately.

Image smoothing using regularized entropy minimization and self-similarity for the quantitative analysis of drug diffusion.

BACKGROUND: Targetable drug delivery is an important method for the treatment of liver tumors. For the quantitative analysis of drug diffusion, the establishment of a method for information collection and characterization of extracellular space is developed by imaging analysis of magnetic resonance imaging (MRI) sequences. In this paper, we smoothed out interferential part in scanned digital MRI images. MATERIALS AND METHODS: Making full use of priors of low rank, nonlocal self-similarity, and regularized sparsity-promoting entropy, a block-matching regularized entropy minimization algorithm is proposed. Sparsity-promoting entropy function produces much sparser representation of grouped nonlocal similar blocks of image by solving a nonconvex minimization problem. Moreover, an alternating direction method of multipliers algorithm is proposed to iteratively solve the problem above. RESULTS AND CONCLUSIONS: Experiments on simulated and real images reveal that the proposed method obtains better image restorations compared with some state-of-the-art methods, where most information is recovered and few artifacts are produced.