Gap States Assisted MoO3 Nanobelt Photodetector with Wide Spectrum Response

10.1038/srep04891Scientific Reports

A deep learning model for network intrusion detection with imbalanced data

With an increase in the number and types of network attacks, traditional firewalls and data encryption methods can no longer meet the needs of current network security. As a result, intrusion detection systems have been proposed to deal with network threats. The current mainstream intrusion detection algorithms are aided with machine learning but have problems of low detection rates and the need for extensive feature engineering. To address the issue of low detection accuracy, this paper proposes a model for traffic anomaly detection named a deep learning model for network intrusion detection (DLNID), which combines an attention mechanism and the bidirectional long short-term memory (Bi-LSTM) network, first extracting sequence features of data traffic through a convolutional neural network (CNN) network, then reassigning the weights of each channel through the attention mechanism, and finally using Bi-LSTM to learn the network of sequence features. In intrusion detection public data sets, there are serious imbalance data generally. To address data imbalance issues, this paper employs the method of adaptive synthetic sampling (ADASYN) for sample expansion of minority class samples, to eventually form a relatively symmetric dataset, and uses a modified stacked autoencoder for data dimensionality reduction with the objective of enhancing information fusion. DLNID is an end-to-end model, so it does not need to undergo the process of manual feature extraction. After being tested on the public benchmark dataset on network intrusion detection NSL-KDD, experimental results show that the accuracy and F1 score of this model are better than those of other comparison methods, reaching 90.73% and 89.65%, respectively

Eliminating Plasmodium falciparum in Hainan, China: a study on the use of behavioural change communication intervention to promote malaria prevention in mountain worker populations

BACKGROUND: In the island of Hainan, the great majority of malaria cases occur in mountain worker populations. Using the behavioral change communication (BCC) strategy, an interventional study was conducted to promote mountain worker malaria prevention at a test site. This study found the methods and measures that are suitable for malaria prevention among mountain worker populations. METHODS: During the Plasmodium falciparum elimination stage in Hainan, a representative sampling method was used to establish testing and control sites in areas of Hainan that were both affected by malaria and had a relatively high density of mountain workers. Two different methods were used: a BCC strategy and a conventional strategy as a control. Before and after the intervention, house visits, core group discussions, and structural surveys were utilized to collect qualitative and quantitative data regarding mountain worker populations (including knowledge, attitudes, and practices [KAPs]; infection status; and serological data), and these data from the testing and control areas were compared to evaluate the effectiveness of BCC strategies in the prevention of malaria. RESULTS: In the BCC malaria prevention strategy testing areas, the accuracy rates of malaria-related KAP were significantly improved among mountain worker populations. The accuracy rates in the 3 aspects of malaria-related KAP increased from 37.73%, 37.00%, and 43.04% to 89.01%, 91.53%, and 92.25%, respectively. The changes in all 3 aspects of KAP were statistically significant (p < 0.01). In the control sites, the changes in the indices were not as marked as in the testing areas, and the change was not statistically significant (p > 0.05). Furthermore, in the testing areas, both the percentage testing positive in the serum malaria indirect fluorescent antibody test (IFAT) and the number of people inflicted decreased more significantly than in the control sites (p < 0.01). CONCLUSION: The use of the BCC strategy significantly improved the ability of mountain workers in Hainan to avoid malarial infection. Educational and promotional materials and measures were developed and selected in the process, and hands-on experience was gained that will help achieve the goal of total malaria elimination in Hainan