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Based on 368.5 pb −1 of e + e − collision data collected at center-of-mass energies 4.914 and 4.946 GeV by the BESIII detector, the e + e − → φχc1(3872) process is searched for the first time. No significant signal is observed and the upper limits at the 90% confidence level on the product of the Born cross section σ(e + e − → φχc1(3872)) and the branching fraction B[χc1(3872) → π + π − J/ψ] at 4.914 and 4.946 GeV are set to be 0.85 and 0.96 pb, respectively. These measurements provide useful information for the production of the χc1( 3872) at e + e − collider and deepen our understanding about the nature of this particle.

This paper analyzes the existing credit card anomaly detection classification algorithms, summarizes the parts that can be improved, and proposes an outlier detection classification algorithm based on the unsupervised algorithm and active learning decision trees. Since the 1990s, machine learning technology has been widely used in the field of credit card fraud detection. Among them, the supervised learning expert system used for classification tends to perform better than the unsupervised learning model. The good performance of supervised learning methods requires high learning costs. The training process is usually serial or partially parallel. Therefore, the computing power, time, and manual labeling cost for learning need to be considered as algorithm selection factors. However, manual labeling usually involves the counter or ATM sending information to the back-end large-value transaction authorization department for manual review, and then the employee judges whether it is an abnormal transaction based on experience. This rigorous process will produce a very large time delay. Combining this feature, this article selects the appropriate unsupervised outlier detection method, selects part of the training data, and uses a small part of the more valuable data for annotation learning. Experiments show that this method can improve the accuracy while saving the time cost of training. Compared with the training time, the added time of classification is negligible.

For the sake of the technology of multimedia network classroom under cross-platforms is becoming popular, this paper propose a analytic selection on system architecture and the development solution on the multimedia network classroom for the experence and requirement from teaching activity. We chooses Java for system design and implementation to achieve the goal of crossing platform, using the C/S communicating paradigm, MVC model, and giving the detailed description on the development to some key modulars such as connection with maintenance, screen broadcast, file transfer and remote control.