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In recent years, advanced threat attacks are increasing, but the traditional network intrusion detection system based on feature filtering has some drawbacks which make it difficult to find new attacks in time. This paper takes NSL-KDD data set as the research object, analyses the latest progress and existing problems in the field of intrusion detection technology, and proposes an adaptive ensemble learning model. By adjusting the proportion of training data and setting up multiple decision trees, we construct a MultiTree algorithm. In order to improve the overall detection effect, we choose several base classifiers, including decision tree, random forest, kNN, DNN, and design an ensemble adaptive voting algorithm. We use NSL-KDD Test+ to verify our approach, the accuracy of the MultiTree algorithm is 84.2%, while the final accuracy of the adaptive voting algorithm reaches 85.2%. Compared with other research papers, it is proved that our ensemble model effectively improves detection accuracy. In addition, through the analysis of data, it is found that the quality of data features is an important factor to determine the detection effect. In the future, we should optimize the feature selection and preprocessing of intrusion detection data to achieve better results.

Solid-state (13)C, (19)F, and (15)N magic angle spinning NMR studies of Form I of atorvastatin calcium are reported, including chemical shift tensors of all resolvable carbon sites and fluorine sites. The complete (13)C and (19)F chemical shift assignments are given based on an extensive analysis of (13)C-(1)H HETCOR and (13)C-(19)F HETCOR results. The solid-state NMR data indicate that the asymmetric unit of this material contains two atorvastatin molecules. A possible structure of Form I of atorvastatin calcium (ATC-I), derived from solid-state NMR data and density functional theory calculations of various structures, is proposed for this important active pharmaceutical ingredient (API).

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In this paper, we introduce and study differential graded (DG for short) polynomial algebras. In brief, a DG polynomial algebra A is a connected cochain DG algebra such that its underlying graded algebra A # is a polynomial algebra [x 1 , x 2 , · · · , xn] with |x i | = 1, for any i ∈ {1, 2, · · · , n}.We describe all possible differential structures on DG polynomial algebras; compute their DG automorphism groups; study their isomorphism problems; and show that they are all homologically smooth and Gorestein DG algebras. Furthermore, it is proved that the DG polynomial algebra A is a Calabi-Yau DG algebra when its differential ∂ A = 0 and the trivial DG polynomial algebra (A, 0) is Calabi-Yau if and only if n is an odd integer.2010 Mathematics Subject Classification. Primary 16E45, 16E65, 16W20,16W50.