Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset

The network intrusion threats are increasingly severe with the application of computer supported coorperative work. 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 K-means 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

Supplemental Material - Implication of microglia in ketamine-induced long-term cognitive impairment in murine pups

Supplemental Material for Implication of microglia in ketamine-induced long-term cognitive impairment in murine pups by Y Yin, H Li, J Wang, Y Kong, J Chang and G Chu in Human & Experimental Toxicology</p

Evaluating different extraction solvents for GC-MS based metabolomic analysis of the fecal metabolome of adult and baby giant pandas.

The gut microbiome plays a fundamental role in host health and the fecal metabolome can be analysed to assess microbial activity and can be used as an intermediate phenotype monitoring the host-microbiome relationship. However, there is no established extraction protocol to study the fecal metabolome of giant pandas. The aim of this research is to optimize extraction of the fecal metabolome from adult and baby pandas for high throughput metabolomics analysis using gas chromatography-mass spectrometry (GC-MS). Fecal samples were collected from eight adult pandas and a pair of twin baby pandas. Six different extraction solvents were investigated and evaluated for their reproducibility, metabolite coverage, and extraction efficiency, particularly in relation to the biochemical compound classes such as amino acids, tricarboxylic acid (TCA) cycle intermediates, fatty acids, secondary metabolites, and vitamin and cofactors. Our GC-MS results demonstrated that the extraction solvents with isopropanol: acetonitrile: water (3:2:2 ratio) and 80% methanol were the most appropriate for studying the fecal metabolome of adult and baby giant pandas respectively. These extraction solvents can be used in future study protocols for the analysis of the fecal metabolome in giant pandas

Mixed Surface Chemistry on Carbon Fibers to Promote Adhesion in Epoxy and PMMA Polymers

Carbon fibers were surface modified using mixed grafting solutions of methyl methacrylate and glycidyl (epoxy) methacrylate in ratios of 0:100; 25:75; 50:50; 75:25; and 100:0, respectively. When evaluated in an epoxy resin, all modified fibers showed significant improvement in fiber-to-matrix adhesion. Notably, the surface-grafted polymer with blends of methyl methacrylate:glycidyl methacrylate of 0:100 and 25:75, respectively, showed a >200% improvement in adhesion relative to control fibers. When evaluated in PMMA, again significant adhesion improvements were observed, though fibers grafted with ≥25% methyl methacrylate were statistically indistinguishable. This shows that by correctly tuning the surface chemistry an optimal covalent sizing can be developed for thermoplastic and thermoset resins. As an additional benefit, a significant improvement in the treated fiber's tensile strength and modulus was also observed

Using optical spectroscopy to probe the impact of atomic disorder on the Heusler alloy Co2MnGa

The exceptional electronic and spintronic properties of magnetic Heusler alloys, which include half-metals and Weyl semimetals, are strongly sensitive to deviations from the ideal atomic structure. To ensure that these materials have been produced with the desired properties, it is necessary to determine both the structural ordering and the electronic structure, which can be challenging. Here, we present the results of a far-infrared-to-visible optical spectroscopy study of films of room-temperature ferromagnetic Weyl semimetal Co2MnGa. Combined with a determination of the level of ordering from x-ray diffraction, we have investigated near Fermi energy valence and conduction band intra- and interband transitions and their dependence on the atomic order. Motivated by band structure calculations, we have modeled our optical spectra with two Drude terms and two Lorentz oscillators, where the latter are assigned to interband transitions. The scattering rate of the itinerant carriers, determined from the width of the Drude term, increases threefold with increasing disorder, while the carrier density to effective mass ratio is unchanged. Based on our band structure and the joint density of states calculations, we have assigned the oscillator that dominates the interband spectral region near 1 eV to transitions across the minority spin gap along the Γ-X direction. It is found that the energy of this transition is strongly sensitive to the degree of order and decreases rapidly with increasing disorder as states fill a decreasing minority spin gap. Our results demonstrate optical spectroscopy is a sensitive way to fingerprint structural order in the technologically relevant near Fermi level electronic states in Heusler alloys