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Molecular property prediction is a hot issue in the field of material chemistry. Although the calculation method based on the first principle can clearly describe the electron distribution in the system, the calculation process is too complex, and the calculation complexity increases exponentially with the increase of atoms in the molecule. In recent years, with the deepening of related research, a variety of deep learning algorithms have emerged. The algorithms were divided into two categories based on multi-layer perceptron ( MLP) and graph neural network ( GNN) and six sub categories, and the characteristics of different algorithms were studied. The analysis shows that MLP algorithm has simple structure, limited expansibility and low correlation with the internal structure of molecules. On the contrary, GNN class algorithms integrate the message passing mechanism, and transform the interaction between molecules into the feature transfer between nodes and edges, which is superior in the evaluation indexes of all directions. At present, the molecular property prediction algorithms based on deep learning is developing from MLP algorithm to GNN algorithm. Based on the above analysis, this paper puts forward the development direction of molecular property prediction algorithm based on deep learning in data set, anisotropic feature transfer, guiding the practical application in material science and Life Science in the future. [ABSTRACT FROM AUTHOR]

The digitization of meteorological historical data is one of the important construction contents of the “Climate Change Response Project” of the China Meteorological Administration. The main content of the digitization of meteorological historical data is to carry out the digital processing of characters and curves information in precious paper meteorological data, and to establish historical long-sequence meteorological data datasets. The curves information recorded by the meteorological self-recording paper is an important content of the meteorological historical data. The accurate tracking and extraction of the meteorological curves information is an important part of realizing the digitization of the meteorological historical data. However, due to the long storage time of paper meteorological data, the identification of meteorological curves has problems such as curves blurring or curves smearing. Traditional image segmentation and curves extraction algorithms rely on local or single image features, and cannot effectively identify meteorological curves information in complex backgrounds, especially changeable, fuzzy, and hazy curves. To solve the above problems, meteorological curves identification method based on deep learning was proposed. Traditional algorithm and the U-net semantic segmentation network were combined to automatically extract spectral and spatial features of different scales, and high-precision meteorological curves recognition was achieved. Taking dyne wind self-recording paper as an example, based on the Ground Meteorological Observation Specification, more than 3 200 dynes wind self-recording papers from different stations and in different years were verified. The comparison with manual reference results show that the average wind direction and wind speed recognition accuracy rate reaches 95% and 95. 5%, respectively. The results indicate that the method proposed can accurately identify the curves and highly restore the meteorological curves information. And the method is suitable for automatic identification and business processing of meteorological data curves such as wind, air pressure, temperature, and humidity, which can greatly reduce the workload and reduce labor costs, providing basic data support for meteorological forecasting business and scientific research [ABSTRACT FROM AUTHOR]

Breast cancer is a serious threat to human life and health. Tamoxifen is a common treatment for breast cancer patients after surgery. However, patients still face the risk of recurrence or metastasis after treatment, so effective prognostic methods are needed to predict efficacy. In order to explore a method for prognostic analysis of postoperative tamoxifen treatment for breast cancer based on molybdenum target X-ray image. In this paper, the model for mammographic density extraction from molybdenum target X-ray image is studied by using the Squeeze-and- Excitation Convolutional Neural Network (SE-CNN) method. A prognostic imaging marker, mammographic density change rate (MDCR), was proposed and survival analysis was performed to study its prognostic ability for postoperative tamoxifen treatment of breast cancer. The results show that the threshold absolute error of SE-CNN is 9.92±4.78, and the determination coefficient is 0.77. The results show that this method can accurately extract the threshold. The progression-free survival was HR 2.654(95%CI,1.102-6.395), P =0.030.Patients with high MDCR had a better prognosis, while those with low MDCR had a worse prognosis. It is concluded that the rate of mammographic density change can be used as a prognostic imaging marker of postoperative tamoxifen treatment for breast cancer. [ABSTRACT FROM AUTHOR]

Based on the importance of helmet detection in construction site management and the cost control of hardware facilities in engineering projects, this paper proposes a helmet detection approach based on Lighter and Tiny-YOLO ( LT-YOLO), a lightweight and improved version of the deep learning network Tiny-YOLO v3. The number of prediction layer is increased and an innovative R-DSC feature extraction module is introduced in LT-YOLO. The complexity of the model can be greatly reduced by R-DSC module without changing the size of the network inputs and outputs. The experimental results showed that LT-YOLO achieved an excellent balance between light weight and detection performance, reaching 59.3 mAP (mean average precision) and 59.4% Recall with only 3.5 M parameters. Because of very few parameters and very low computation, LT-YOLO has low dependence on high computing hardware, and is suitable for actual construction site safety management. LT-YOLO can greatly reduce the cost of enterprises and improve the level of construction safety management. [ABSTRACT FROM AUTHOR]