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In China, with the development of the era, the Architectural Design (AD) education has been given the requirement that students should master creative thinking mode and design method. The teaching target of integrating the Information-Based Teaching (IBT) into Creative Thinking (CT) mode is analyzed, and the Teaching Mode (TM) of integrating the information-based teaching into architectural design is constructed in order to achieve the goal of teaching reform to cultivate creative thinking mode and design method, moreover, the results of teaching reform is also carried out with analysis of teaching quality, hoping to provide reference for the education of architectural design.

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With the continuous advancement of urbanization, the socioeconomic development and environmental damage in Zhengdong New District of Zhengzhou City seriously threaten the development of green space. The paper focused on Zhengdong New District and selected four important nodes in 2007, 2011, 2015, and 2019 to interpret the remote sensing image, explore the local spatial evolution through the method of land use transfer model construction, extract the green space area conversion analysis information of the study area, and analyze the influence factors of its transformation. The research shows that from 2007 to 2019, the internal structure of green space in Zhengdong New District that were cultivated land, woodland, grassland, and water bodies, its showed different trends, and the dynamic changes were different. Among them, the area of cultivated land continued to decrease, the area of construction land continued to increase, the total area of woodland increased, the area of grassland and water bodies fluctuated, and the total green space was greatly reduced; the conversion of land types in Zhengdong New District mainly focused on the conversion of cultivated land to construction land, woodland, and grassland. The results shows that the green space of Zhengdong New District is affected by social, economic, natural, and other factors and various factors interact with each other. However, social and economic development factors are obviously stronger than natural and administrative factors on the evolution of green space area.

A model based on multidimensional features and GRNN was designed for electronic nose (eNose) in the paper. It can be applied to distinguish hepatocellular carcinoma from normal controls. Hepatocellular carcinoma patients have altered composition of exhaled gas due to abnormal metabolism. Thus, we can detect them by the exhaled gas. In the paper, the exhaled gas signals of hepatocellular carcinoma patients and health controls were first collected with eNose. And then the features were extracted and the multidimensional combined features were achieved. Furthermore, the PCA method was applied to optimize the features. Next, the classification model based on GRNN was constructed for training and generalization ability testing. Finally, the constructed model was adopted to predict the test and the performance was calculated. The result shows that, with the limited training set, the performance of the GRNN model is better than the BPNN model. The prediction accuracy could reach to 91.3%. Therefore, the proposed model is well suited for the classification detection with small training set and this will contribute to the study of the practical application of the eNose in the clinic.