Enhancing corporate green innovation has become a critical question in the context of sustainable development. Prior studies have predominantly examined the macro-level effects of regional integration while largely overlooking its micro-level impacts on enterprises. This study aims to examine the institutional effect of regional integration on corporate green innovation. Taking the Yangtze River Delta integration as a quasi-natural experiment, we utilize panel data from A-share listed companies between 2003 and 2022 and apply a multi-period difference-in-differences method. The empirical results reveal that regional integration significantly enhances corporate green innovation, with a more pronounced effect for non-state-owned firms, large firms, and those located in non-corridor cities. Mechanism analyses further reveal that regional integration promotes corporate green innovation by alleviating financing constraints and attracting foreign direct investment. By identifying regional integration as a critical driver of corporate green innovation, this study broadens the research perspective on corporate green innovation and provides policy implications for promoting sustainability through coordinated regional development strategies.

Gravitational wave detection remains challenging due to low signal amplitudes often obscured by instrumental noise, particularly in low signal-to-noise ratio scenarios where conventional Convolutional Neural Networks (CNNs) exhibit limited effectiveness due to their restricted receptive fields. This paper presents an enhanced EfficientNet-based architecture for continuous gravitational wave classification that addresses these limitations through several key innovations. We transform input data into spectrograms using constant Q transformation and augment the baseline EfficientNet architecture with an additional convolutional layer featuring enlarged kernel sizes to expand the receptive field. This modification enables improved detection of weak gravitational wave signals embedded in noisy datasets while enhancing texture feature learning capabilities. Through K-fold cross-validation on a private dataset, our proposed model achieves Area Under the Curve (AUC) scores ranging from 0.878 to 0.978, demonstrating significantly enhanced sensitivity to gravitational wave signals. These results indicate substantial potential for advancing next-generation detection methodologies in astrophysical applications and improving the reliability of gravitational wave detection systems.