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The primary focus of this study is to evaluate the impact of various levels of education on CO2 emissions in China. Moreover, the study also tested the EKC hypothesis for different levels of education and economic development. The analysis employed disaggregate and aggregate data for education that included enrollment at primary, secondary, and tertiary levels and the average year of schooling. For empirical analysis, we employed an error correction model and bounds testing approach to cointegration. The results of the study provided some useful information both in the short and long run. All the proxies of education positively impact CO2 emissions at the initial level both in the short and long run; however, when we take the square of these variables, the effects of education on CO2 emissions become negative. Similarly, the impact of economic growth on CO2 emissions is positive in the short and long run, and the square of economic growth on CO2 emissions is negative, supporting the EKC hypothesis.

The estimated number of outpatients with skin diseases in China is ~200 million per year, while the dermatologists are insufficient and the doctor-patient ratio remains low, which causes fewer patients receive effective diagnosis. Compared with others, the diagnosis of skin diseases, which is less reliant on laboratory tests, imaging and pathology, needs the assistance of large hardware devices. By contrast, dermatologic diagnosis requires a combination of visual inspection and interrogation frequently which is exactly what Artificial Intelligence (AI) specialises in — Computer Vision (CV), Natural Language Processing (NLP) and Speech Recognition (SR). This allows a simple image capturing tool embedded with an AI model to perform dermatological diagnosis at the primary level. Hence, based on the dataset, which from Asian, with more than 200,000 images and 220,000 medical records, we explored an AI skin diseases diagnosis model---DIET-AI to diagnose 31 skin diseases, covering the majority of common skin diseases. Ranging from 1st September to 1st December 2021, we prospectively collected case information from 15 hospitals in 7 provinces in China, using mobile devices to collect images and medical records of 6043 cases. Then, we compared the performance of the DIET-AI with 6 doctors of different seniority in the prospective clinical dataset, concluding the average performance of the DIET-AI in 31 diseases is no less than that of all different seniority doctors. By comparing the area under curve (AUC), sensitivity and specificity, we demonstrate that DIET-AI model is effective under the clinical scenario. It is further validated under more complex clinical scenarios, providing references for exploring the feasibility and performance evaluation of DIET-AI in clinical use afterwards

The key to solving the problem of redundant financial indicators in addressing financial warning issues is to reduce the dimensionality of the original financial indicators. This paper proposes a model based on the whale optimization algorithm with mixed strategy (IWOA) combined with support vector machine (SVM), namely, the IWOA-SVM early warning model, which simultaneously performs index optimization and dimensionality reduction, and financial risk early warning identification. This paper takes a total of 302 enterprises specially treated in Shanghai and Shenzhen stock exchanges and normal enterprises of the same specification as the research samples to design the model. The results show that the improved whale optimization algorithm has better optimization speed and accuracy and improves the search ability of the original algorithm for the optimal solution. Compared with other dimensionality reduction methods, the IWOA-SVM model has the lowest index dimension after dimensionality reduction and has more excellent recognition effect. The dimensionality reduction results have certain universality for different classifiers, which provides a new idea for the selection of indicators for financial early warning.