Research on Chest Disease Recognition Based on Deep Hierarchical Learning Algorithm.

Chest X-ray has become one of the most common ways in diagnostic radiology exams, and this technology assists expert radiologists with finding the patients at potential risk of cardiopathy and lung diseases. However, it is still a challenge for expert radiologists to assess thousands of cases in a short period so that deep learning methods are introduced to tackle this problem. Since the diseases have correlations with each other and have hierarchical features, the traditional classification scheme could not achieve a good performance. In order to extract the correlation features among the diseases, some GCN-based models are introduced to combine the features extracted from the images to make prediction. This scheme can work well with the high quality of image features, so backbone with high computation cost plays a vital role in this scheme. However, a fast prediction in diagnostic radiology is also needed especially in case of emergency or region with low computation facilities, so we proposed an efficient convolutional neural network with GCN, which is named SGGCN, to meet the need of efficient computation and considerable accuracy. SGGCN used SGNet-101 as backbone, which is built by ShuffleGhost Block (Huang et al., 2021) to extract features with a low computation cost. In order to make sufficient usage of the information in GCN, a new GCN architecture is designed to combine information from different layers together in GCNM module so that we can utilize various hierarchical features and meanwhile make the GCN scheme faster. The experiment on CheXPert datasets illustrated that SGGCN achieves a considerable performance. Compared with GCN and ResNet-101 (He et al., 2015) backbone (test AUC 0.8080, parameters 4.7M and FLOPs 16.0B), the SGGCN achieves 0.7831 (-3.08%) test AUC with parameters 1.2M (-73.73%) and FLOPs 3.1B (-80.82%), where GCN with MobileNet (Sandler and Howard, 2018) backbone achieves 0.7531 (-6.79%) test AUC with parameters 0.5M (-88.46%) and FLOPs 0.66B (-95.88%).

Research on the optimized route of cold chain logistics transportation of fresh products in context of energy-saving and emission reduction.

With the level of science and technology, the logistics industry is paying more and more attention to sustainable development. However, the current cold chain logistics industry generally has problems such as backward refrigeration technology, irrational distribution management, and outdated equipment, which make its energy consumption serious and cause severe carbon emission effects on the environment. Optimizing the carbon emission structure of the cold chain logistics industry is the top priority for ensuring the sustainable development of the logistics industry. The circulation of fresh products is mainly through cold chain logistics. However, there are some problems of excessively high cost of the cold chain and easy disconnection during the cold transportation process in Chinese cold chain logistics, which severely restricts the development of Chinese cold chain logistics. Because of the perishability and strong timeliness of distribution of fresh products, inventory costs, penalties, and damage costs need to be considered. Because fresh products should utilize refrigerated transportation, it is also necessary to consider refrigerated costs during transportation and cold storage cost in distribution centers. Based on the goal of the lowest total cost, this paper constructs an integer programming model and analyzes cases. By comparing the simulation results of the genetic algorithm and the hybrid algorithm, it is concluded that the hybrid algorithm is more effective.