Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.

Guangzhou’s time-honored brands are the image of Guangzhou City, the historical legacy of the cultural emblems of the millennium commercial capital, the bearer of the memory of “old Guangdong” over generations, and the legends inscribed in the vast era of history. It not only carries the rise and fall of the city, but also documents the awakening, rejuvenation, and development of these “gold-lettered signboards.” In this age of big data, it is not only the expectation of the older generation, but also the responsibility of the new generation to “polish” the gold-lettered signboards of time-honored brands. “Time-honored brands” have accumulated rich and priceless knowledge from their ancestors in the vast era of business history and have profound cultural legacy. The time-honored brands that have been handed down not only allow the new generation to experience and identify with the unbounded knowledge of ancient people, but also provide economic value that can ensure the survival of future generations. However, in the market tide, the development of time-honored brands is not easy; some have even vanished. Therefore, it is imperative to take action to safeguard the development of time-honored brands, helping them to revitalize and shine once again. This paper discusses the difficulties in the development of Guangzhou’s time-honored brands and provides relevant suggestions for digital intelligence marketing of these “brands.”

The impact of global greenhouse gas emissions is increasingly serious, and the development of green low-carbon circular economy has become an inevitable trend for the development of all countries in the world. To achieve emission peak and carbon neutrality is the primary goal of energy conservation and emission reduction. As the core province in central China, Hubei Province is under prominent pressure of carbon emission reduction. In this paper, the future development trend of carbon emissions is analyzed, and the emission peak value and carbon peak time in Hubei Province is predicted. Firstly, the generalized Divisia index method (GDIM) model is proposed to determine the main influencing factors of carbon emissions in Hubei Province. Secondly, based on the main influencing factors identified, a novel STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) extended model with ridge regression is established to predict carbon emissions. Thirdly, the scenario analysis method is used to set the variables of the STIRPAT extended model and to predict the emission peak value and carbon peak time in Hubei Province. The results show that Hubei Province’s carbon emissions peaked first in 2025, with a peak value of 361.81 million tons. Finally, according to the prediction results, the corresponding suggestions on carbon emission reduction are provided in three aspects of industrial structure, energy structure, and urbanization, so as to help government establish a green, low-carbon, and circular development economic system and achieve the industry’s cleaner production and sustainable development of society.

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The goal of this research is to apply diagrams to environmental color studies. The experience of environmental color is not only visual experience, but also the relationship of the color and the form of the object, the material, the place, the human perception experience. Diagrams records these factors along with the human perception experiences. These diagrams make it possible to study and analyze environmental color more deeply and more easily. In this paper, we first reviewed the "case studies of environmental color workshops" and "theory of information processing by rearranging matrix of Jacques Bertin" which are related to this research. Next, we conducted an environmental color workshop and evaluated the application of diagrams at workshop by students and experts (design teachers and designers). As a result, we confirmed that the diagrams proposed by us are effective for the environmental color workshop. In particular, it was confirmed that these diagrams are effective for group discussion (analysis).

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Because electric vehicles have many advantages such as low carbon, environmental protection, and low cost compared with fuel vehicles, electric vehicles have developed rapidly in recent years, which will lead to large-scale impact load. In this paper, the electric vehicle is regarded as an energy storage device, a multi-energy VPP electric thermal scheduling model including electric vehicles is established, and a scheduling strategy for electric vehicles to participate in the power system scheduling is proposed. With the minimum cost of VPP and the minimum carbon dioxide emissions as the optimization objectives, the relevant objective functions are described, and equality constraints and inequality constraints are applied to them. Then, the improved algorithm is applied to solve the model. The results of the example analysis show that the virtual power plant system model with electric vehicles established in this paper can reduce the operating cost of the VPP system, reduce carbon emissions, and be conducive to the safe, low-carbon, and economic operation of the power system.