A phosphorescent material (Fppy)2Ir(dipba) possessing high PL efficiency in the solid state and superior hole/electron transporting property has acted successfully as an efficient neat phosphorescent emitter as well as an excellent host for high-performance PHOLEDs.
The increasing literature leads to formidable pressure for medical researchers. Most existing recommender approaches mainly depend on text-based information. How to extract and utilize the heterogeneous information, especially the graphic ones, to improve the recommender is worthy of further exploring. To this end, we establish a document-to-document recommender system for medical literature (D2D-MR). Specifically, we proposed HB-GED, the Half-branch GED algorithm, and the bipartite-graph-based algorithm for solving the molecule similarity and the paper similarity, respectively. Experimental results on real-world datasets demonstrate the effectiveness of the proposed recommender system.
Network security situation prediction is an important part of network security situation awareness. Traditional network security situation prediction methods are insensitive to the characteristics of input data, and there are also important degrees of variability and temporal correlation among data. To address these problems, a network security situation prediction model that combines attention mechanism (AM) with convolutional neural network (CNN) and long short-term memory (LSTM) is proposed. Using time-series data as input, CNN is used to extract the input features of the data to obtain a high-level feature representation; LSTM is used to capture the short-term mutations of time series for prediction. AM is introduced to assign different weights to the input features before LSTM prediction to improve the model accuracy and find more useful information for prediction. Through experimental comparison, the method has better prediction accuracy compared with the three models of CNN-LSTM, AM-LSTM and LSTM, indicating that the proposed method is more suitable for application in network security situation prediction and has great potential in network security situation prediction.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Objective:To assess the reliability level of existing field-based fitness in children and adolescents,and form the overall framework of field-based fitness tests according to levels of evidence.Method:Search related English full text document published from Jan.1990 to Jul.2011,according to quality assessment criteria including the description of the participants,the time interval between measurements,the description of the results and the appropriateness of statistics.Three levels of evidence were constructed according to the number of studies and the consistency of the findings.Result:31 high quality studies were finally analyzed,the most common tests to measure cardiorespiratory fitness were the 20m shuttle run,1 mile run/walk and Andersen test.Tests for musculoskeletal fitness were the handgrip strength,push-up,vertical jump,pull-up,bent arm hang,sit and reach,trunk lift,curl-up,sit-up and standing broad jump tests.Tests for assessing motor fitness were 4×10m shuttle run and 30m dash.Tests for assessing body composition were anthropometric measurements and percentage body fat estimated from skinfold thickness.Conclusion:Although some fitness components warrant further investigation,this research provides an evidence-based proposal for reliable framework of field-based fitness tests for use with children and adolescents.
Abstract In recent years, the Internet has shown rapid development, and network security issue has gradually become the focus of research by scholars and enterprises. Network security time series is a reliable source to obtain future network security situation, so as to develop network security defense strategy by exploring the correlation of time series. The network security time series is a reliable source to obtain the future network security situation, and it is the main direction of current network security defense by exploring the correlation of time series, and analyzing the future network security situation so as to formulate network security defense strategies. This is the main direction of network security defense. The existing research focuses on the short-term prediction of network attacks, and the robustness and accuracy of long-term prediction still have big problems. To fuse the information from different data sources and capture the correlation between sequences, we design a data source selection module based on the similarity of measurement curves. We then model the network security situation prediction based on deep learning models and propose a situation prediction model based on Temporal Convolutional Network (TCN)-combined Transformer, which focuses on the time series long-term prediction problem, combining the network condition and attack situation to obtain the future network security situation. Our proposed model is divided into three parts, which are the information encoding module, the information synthesis module, and situation value calculation and prediction accuracy evaluation module. The selected multi-dimensional situations element data are used as model input, and the TCN-combined Transformer is employed as the network security situational data processing unit to complete the information fusion and prediction tasks. Finally, the role of data source selection on prediction accuracy is evaluated using an ablation study. We experimented and evaluated the model at different prediction horizon lengths using five existing baseline models and three performance metrics. The experimental results show that our proposed prediction model has better robustness and accuracy in most of the metrics.
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