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Self-assembly of sphere-forming solution-state amphiphilic diblock copolymers under spherical nanopore confinement is investigated using a simulated annealing technique. For two types of cases of different pore-surface/copolymer interactions, sequences of self-assembled patchy nanospheres are obtained, and phase diagrams are constructed. Self-assembled patchy nanospheres with 1-21 solvophobic domains are observed. The outermost solvophobic domains (patches) are packed into various polyhedrons when their number is larger than 3, where three Platonic solids of a regular tetrahedron, an octahedron, and an icosahedron and seven Johnson solids of J12, J13, J17, J50, J51, J86, and J87 are identified. In addition, another Johnson solid of J84 is identified in a structure with two categories of B-domains. These polyhedrons have all or most of their faces in a triangular shape, and hence, they are closer to spherical in shape, which may relieve the chain stretching. Nanospheres with 1, 4, 6, 9, and 12 numbers of patches occur in relatively large windows in the phase diagrams of both types of cases. In one of the two types of cases, all nanospheres with any number of 1-14 patches occur in the phase diagram, whereas in the other type of cases, nanospheres with 2, 3, 5, 11, and 13 numbers of patches are absent in the phase diagram. Furthermore, at a given pore size, the number of patches changes nonmonotonically or is unchanged with an increase in the strength of the pore-surface/copolymer interactions for one type or the other type of case, respectively. Quantitative calculations are performed to elucidate mechanisms of the window size in the phase diagrams of nanospheres with different numbers of patches and structure details.

Machine learning algorithms have been widely used in intrusion detection systems, including Multi-layer Perceptron (MLP). In this study, we proposed a two-stage model that combines the Birch clustering algorithm and MLP classifier to improve the performance of network anomaly multi-classification. In our proposed method, we first apply Birch or Kmeans as an unsupervised clustering algorithm to the CICIDS-2017 dataset to pre-group the data. The generated pseudo-label is then added as an additional feature to the training of the MLP-based classifier. The experimental results show that using Birch and K-Means clustering for data pre-grouping can improve intrusion detection system performance. Our method can achieve 99.73% accuracy in multi-classification using Birch clustering, which is better than similar researches using a stand-alone MLP model.

The effectiveness of machine learning models is significantly affected by the size of the dataset and the quality of features as redundant and irrelevant features can radically degrade the performance. This paper proposes IGRF-RFE: a hybrid feature selection method tasked for multi-class network anomalies using a Multilayer perceptron (MLP) network. IGRF-RFE can be considered as a feature reduction technique based on both the filter feature selection method and the wrapper feature selection method. In our proposed method, we use the filter feature selection method, which is the combination of Information Gain and Random Forest Importance, to reduce the feature subset search space. Then, we apply recursive feature elimination(RFE) as a wrapper feature selection method to further eliminate redundant features recursively on the reduced feature subsets. Our experimental results obtained based on the UNSW-NB15 dataset confirm that our proposed method can improve the accuracy of anomaly detection while reducing the feature dimension. The results show that the feature dimension is reduced from 42 to 23 while the multi-classification accuracy of MLP is improved from 82.25% to 84.24%., Comment: 26 pages

Morphological transformations of amphiphilic AB diblock copolymers in mixtures of a common solvent (S1) and a selective solvent (S2) for the B block are studied using the simulated annealing method. We focus on the morphological transformation depending on the fraction of the selective solvent CS2, the concentration of the polymer CP, and the polymer-solvent interactions {\epsilon}ij (i = A, B; j = S1, S2). Morphology diagrams are constructed as functions of CP, CS2, and/or {\epsilon}AS2. The copolymer morphological sequence from dissolved -> sphere -> rod-> ring/cage-> vesicle is obtained upon increasing CS2 at a fixed CP. This morphology sequence is consistent with previous experimental observations. It is found that the selectivity of the selective solvent affects the self-assembled microstructure significantly. In particular, when the interaction {\epsilon}BS2 is negative, aggregates of stacked lamellae dominate the diagram. The mechanisms of aggregate transformation and the formation of stacked lamellar aggregates are discussed by analyzing variations of the average contact numbers of the A or B monomers with monomers and with molecules of the two types of solvent, as well as the mean square end-to-end distances of chains. It is found that the basic morphological sequence of spheres to rods to vesicles and the stacked lamellar aggregates result from competition between the interfacial energy and the chain conformational entropy. Analysis of the vesicle structure reveals that the vesicle size increases with increasing CP or with decreasing CS2, but remains almost unchanged with variations in {\epsilon}AS2.