Isolation and identification of an isoflavone reducing bacterium from feces from a pregnant horse.

The aim of this research was to isolate bacteria capable of biotransforming daidzein from fresh feces from pregnant horses. A Hungate anaerobic roller tube was used for anaerobic culture. Single colonies were picked at random and incubated with daidzein. High performance liquid chromatography was used to detect whether the isolated bacteria were able to biotransform the substrate. A strain capable of reducing daidzein was selected and characterized using sequence analysis of 16S rDNA, and a phylogenetic tree was constructed. The morphological physiological and biochemical characteristics of the strain were investigated. A facultative anaerobic, Gram-positive bacterium capable of converting daidzein to dihydrodaidzein was isolated and named HXBM408 (MF992210). A BLAST search of HXBM408's 16S rDNA sequence against the GenBank database suggested that the strain has 99% similarity with Pediococcus acidilactici strain DSM (NR042057). The morphological, physiological, and biochemical characteristics of HXBM408 are very similar to those of Pediococcus. Based on these characteristics, the strain was identified as Pediococcus acidilactici. The bacterial strain HXBM408 isolated from the feces of pregnant horses was able to reduce the isoflavone daidzein to dihydrodaidzein

WATER VAPOR PERMEABILITY OF LEATHERS BY GREY SYSTEM THEORY

373 Water vapor permeability of leathers by grey system theory q ) ( * 7 V h S U W V I f g V k 9W f W d 9a % B f V % Rev. Adv. Mater. Sci. 33 (2013) Abstract. In this paper, Grey System Theory (GST) is used to study the water vapor permeability of leathers. Grey relation analysis was employed to analyze the main affecting factors, and the contributions of each factor to the water vapor permeability of leathers were investigated and compared. The relation equations between time and the water vapor permeability of leathers were obtained by computer calculation. The results indicated that Grey System Theory can be used to investigate and calculate the water vapor permeability of leathers, and the technique is effective and convenient

The Traffic Flow Prediction Method Using the Incremental Learning-Based CNN-LTSM Model: The Solution of Mobile Application

With the acceleration of urbanization and the increase in the number of motor vehicles, more and more social problems such as traffic congestion have emerged. Accordingly, efficient and accurate traffic flow prediction has become a research hot spot in the field of intelligent transportation. However, traditional machine learning algorithms cannot further optimize the model with the increase of the data scale, and the deep learning algorithms perform poorly in mobile application or real-time application; how to train and update deep learning models efficiently and accurately is still an urgent problem since they require huge computation resources and time costs. Therefore, an incremental learning-based CNN-LTSM model, IL-TFNet, is proposed for traffic flow prediction in this study. The lightweight convolution neural network-based model architecture is designed to process spatiotemporal and external environment features simultaneously to improve the prediction performance and prediction efficiency of the model. Especially, the K-means clustering algorithm is applied as an uncertainty feature to extract unknown traffic accident information. During the model training, instead of the traditional batch learning algorithm, the incremental learning algorithm is applied to reduce the cost of updating the model and satisfy the requirements of high real-time performance and low computational overhead in short-term traffic prediction. Furthermore, the idea of combining incremental learning with active learning is proposed to fine-tune the prediction model to improve prediction accuracy in special situations. Experiments have proved that compared with other traffic flow prediction models, the IL-TFNet model performs well in short-term traffic flow prediction

Malicious Code Classification Method Based on Deep Residual Network and Hybrid Attention Mechanism for Edge Security

Edge computing is a feasible solution for effectively collecting and processing data in industrial Internet of Things (IIoT) systems, and edge security is an important guarantee for edge computing. Fast and accurate classification of malicious code in the whole lift cycle of edge computing is of great significance, which can effectively prevent malicious code from attacking wireless sensor networks and ensure the stable and secure transmission of data in smart devices. Considering that there is a large amount of code reuse in the same malicious code family, making their visual feature similar, many studies use visualization technology to assist malicious code classification. However, traditional malicious code visual classification schemes have the problems such as single image source, weak ability of deep-level feature extraction, and lack of attention to key image details. Therefore, an innovative malicious code visual classification method based on a deep residual network and hybrid attention mechanism for edge security is proposed in this study. Firstly, the malicious code visualization scheme integrates the bytecode file and assembly file of the malware and converts them into a four-channel RGBA image to fully represent malicious code feature information without increasing the computational complexity. Secondly, a hybrid attention mechanism is introduced into the deep residual network to construct an effective classification model, which extracts image texture features of malicious code from two dimensions of the channel and spatial to improve the classification performance. Finally, the experimental results on the BIG2015 and Malimg datasets show that the proposed scheme is feasible and effective and can be widely applied used in various malicious code classification issues, and the classification accuracy rate is relatively higher than the existing better-performing malicious code classification methods

Preparation of rutile titanium dioxide pigment from low-grade titanium slag pretreated by the NaOH molten salt method

Low-grade titanium slag with a high accumulation of Mg, Al, Si, and the refractory structure of spinel, was sodiumized-oxidized to make it usable in the sulfate process for TiO2 pigment production. In the sodiumized-oxidized process, the Ti3O5 and Mg2SiO4 transform into Na2TiO3 and Na2MgSiO4, respectively. From combined ICP-OES with XRD refinement of the molten NaOH intermediate, the ideal stoichiometric ratio of NaOH to slag was calculated to be 0.9:1. After acidolysis of the intermediate, the obtained TiOSO4 solution, with Mg2+ and Al3+ impurities, was hydrolyzed to prepare metatitanic acid (H2TiO3) with a suitable particle size D(0.5) of 1.0-1.5 pi m. The particle size D(0.5) of the hydrolyzed H2TiO3 decreased, while the concentration of Mg2+ and Al3+ increased. Finally, under optimal doping and calcination conditions, the rutile content of the prepared sample was 97.0%, and the achromic abilities of TCS and SCX were 1937 and 1.54, respectively. (c) 2015 Elsevier Ltd. All rights reserved

Preparation of High Pore Volume Pseudoboehmite Doped with Transition Metal Ions through Direct Precipitation Method

Mesoporous pseudoboehmite with novel pore properties was prepared via the direct precipitation method using aluminum nitrate nonahydrate as the inorganic alumina precursor and different surfactants containing bis (2-ethylhexyl) sulfosuccinate sodium salt (AOT), cetyltrimethylammonium bromide (CTAB), and tert-octylphenoxypolyethoxyethanol (Triton X-100) as the structure-directing agents. The as-synthesized mesoporous products were characterized by wide-angle X-ray diffraction (XRD) and transmission electron microscope (TEM) imaging. Pure pseudoboehmite could be obtained when the final pH was between 8 and 10.5, and the presence of different surfactant micelles played an important role in the morphology and growth of pseudoboehmite. In addition, the pore properties could be enhanced significantly by the presence of transition metal ions. Particularly, when nickel nitrate was added to the aluminum nitrate solution at the molar ratio of 0.0040, the specific surface area, the pore volume, and the average pore diameter of pseudoboehmite reached significantly large values of 381 m²/g, 1.18 cm³/g, and 9 nm, respectively