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This study examines the relationship between environmental, social, and corporate governance (ESG) and firm performance, with a focus on the impact of Buddhism. Our findings suggest the following: (1) The local Buddhism environment weakens the positive relationship between ESG and firm performance, indicating that ESG practices motivated by internal altruism may not contribute to firm performance. (2) The moderating effect of Buddhism is more pronounced in firms with stronger alignment or monitoring, in which ESG practices are more likely to be motivated by the desire for profitability, i.e., privately owned firms and those with higher institutional ownership and media attention. (3) The attenuating effect of Buddhism's moderating role is observed in two categories of firms: those with heightened exposure to ESG-related risks and those operating in recent eras with a greater focus on ESG, which are more likely to benefit from ESG practices with greater external utility.

In practical applications, the issue of data imbalance inevitably rises. In most studies, the predominant focus regarding long-tailed class imbalance pertains to a setting within a single domain in which the training and test samples are presumed to originate from the same feature space and possess identical data distributions. However, natural datasets can be derived from distinct domains in which minority classes in a specific domain can be majority classes in other domains. Multi-domain long-tailed learning is the process of acquiring knowledge from imbalanced datasets spanning numerous domains, ensuring that the learned model can generalize to all classes across all domains. This study offers a comprehensive review of existing multi-domain long-tailed learning methods that includes challenges, advances in research, and prospects. Our study first defines multi-domain long-tailed learning and its associated challenges. Then, an overall categorization of existing methods is introduced, and an overview of these research advancements is provided.

Soil-coated fabrics were fabricated by scrape-coating of soil slurry onto cotton fabrics. The raw materials, soil, and cotton fabrics were, respectively, obtained from farmland and waste bed sheets, making the method a zero-material cost way to produce superwetting membrane. The superhydrophilic/underwater superoleophobic soil-coated fabrics exhibit high efficiency (>99%), ultra-high flux (~45,000 L m−2 h−1), and excellent antifouling behavior for separating water from various oils driven by gravity. The simple fabrication and superior performance suggest that the soil-coated fabric could be a promising candidate as a filtration membrane for practical applications in industrial oily wastewater and oil spill treatments.