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The growing network intrusions have put companies and organizations at a much greater risk of loss. In this paper, we propose a new learning methodology towards developing a novel intrusion detection system (IDS) by backpropagation neural networks (BPN) with sample-query and attribute-query. We test the proposed method by a benchmark intrusion dataset to verify its feasibility and effectiveness. Results show that choosing good attributes and samples will not only have impact on the performance, but also on the overall execution efficiency. The proposed method can significantly reduce the training time required. Additionally, the training results are good. It provides a powerful tool to help supervisors analyze, model and understand the complex attack behavior of electronic crime.

With these observations, it is demonstrated that LIUS simulation could enhance cardiomyogenesis from embryonic stem cells and increase its selectivity toward cardiomyocytes by reducing spontaneous differentiation.

Using trifluoroacetyl sulfuric acid (TFAOSO3H), we discovered a new methane activation method and revealed its radical pathway under mild conditions. Upon the addition of a radical initiator with methane, the crude solution of TFAOSO3H developed the methyltrifluoroacetylsulfate radical ((TFAO)­CH3S­(OH)­O2•). The resulting (TFAO)­CH3S­(OH)­O2• behaved as a critical radical propagator for carbon–hydrogen bond activation, culminating in successful methane sulfonation. With 9.5 mol % of K2S2O8, TFAOSO3H and methane were selectively converted to methanesulfonic acid in 94 and 86% conversion yields, respectively.

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