To alleviate the social contradiction between limited medical resources and increasing medical needs, the medical image-assisted diagnosis based on deep learning has become the research focus in Wise Information Technology of med. Most of the existing medical segmentation models based on Convolution or Transformer have achieved relatively sound effects. However, the Convolution-based model with a limited receptive field cannot establish long-distance dependencies between features as the Network deepens. The Transformer-based model produces large computation overhead and cannot generalize the bias of local features and perceive the position feature of medical images, which are essential in medical image segmentation. To address those issues, we present Triple Gate MultiLayer Perceptron U-Net (TGMLP U-Net), a medical image segmentation model based on MLP, in which we design the Triple Gate MultiLayer Perceptron (TGMLP), composed of three parts. Firstly, considering encoding the position information of features, we propose the Triple MLP module based on MultiLayer Perceptron in this model. It uses linear projection to encode features from the high, wide, and channel dimensions, enabling the model to capture the long-distance dependence of features along the spatial dimension and the precise position information of features in three dimensions with less computational overhead. Then, we design the Local Priors and Global Perceptron module. The Global Perceptron divides the feature map into different partitions and conducts correlation modelling for each partition to establish the global dependency between partitions. The Local Priors uses multi-scale Convolution with high local feature extraction ability to explore further the relationship of context feature information within the structure. At last, we suggest a Gate-controlled Mechanism to effectively solves the problem that the dependence of position embeddings between Patches and within Patches in medical images cannot be well learned due to the relatively small number of samples in medical images segmentation data. Experimental results indicate that the proposed model outperforms other state-of-the-art models in most evaluation indicators, demonstrating its excellent performance in segmenting medical images.

Green funds play pivotal roles in driving corporate sustainable development. Utilizing data from Chinese publicly listed companies from 2010 to 2021, we examine the impact of green funds on corporate environmental, social, and governance (ESG) performance and the underlying mechanisms. The research findings claim that green funds positively affect corporate ESG performance. Mechanism analysis systematically demonstrates that green funds contribute to elevated corporate ESG performance by alleviating financial constraints, enhancing managerial efficiency, and fostering green innovation. Heterogeneity analysis further underscores that the effect of green funds is particularly potent in companies with high external attention. Furthermore, green funds also play significant roles in production capabilities and economic value. This research enriches the micro-level evidence on the development of green funds and furnishes substantial implications for sustainable development.

Air pollution has become a major danger to human health and a hindrance to social and economic growth. This study endeavours to examine the impact of air pollution on entrepreneurial activities in China. Using registration data from 2000 to 2018, we find that severe air pollution can decrease the number of newly registered firms by about 36%, suggesting a negative effect on entrepreneurship. To strengthen the causal inference, we adopt the two-stage least square method to address the potential endogenous problems. The result is robust to different model settings and alternative measurements of air pollution. The study further delves into the mechanisms through which air pollution affects entrepreneurial activities, including brain drain, social capital, startup costs, and financial constraints. This study recommends effective environmental regulation policies to improve air quality as well as increase the attraction of mobile factors to alleviate the adverse impacts of air pollution on entrepreneurial activities.