The friction performance and compression properties of geotextile bags under different degrees of consolidation are researched through the slope sliding test, direct shear test, and unconfined compression test in this study. The geotextile bags are made up of geotextile and tailings sands obtained from a construction site of a prototype tailings dam in Yunnan Province. The slope sliding test is considered as an easy method to study the friction performance of geotextile bags with different degrees of consolidation. Through the test, the friction coefficients between the bags were obtained. Given the geotextile bags can withstand the vertical load in practical engineering, the direct shear test is also proposed and conducted. The ultimate bearing capacity of the geotextile bags with different consolidation degrees is studied in the unconfined compression test. The test reveals that the ultimate bearing capacity of geotextile bags, although featured with different moisture contents before the test, stays the same under the condition of slow loading. Finally, a stability analysis method based on the limit equilibrium theory is developed to simulate the stability of the tailings dam using geotextile bags (TDGB). According to the test results, the TDGB has demonstrated an improved stability compared with the general tailings dam.

Data clustering is an important field in pattern recognition and machine learning. Fuzzy c-means is considered as a useful tool in data clustering. The neutrosophic set, which is an extension of the fuzzy set, has received extensive attention in solving many real-life problems of inaccuracy, incompleteness, inconsistency and uncertainty. In this paper, we propose a new clustering algorithm, the single-valued neutrosophic clustering algorithm, which is inspired by fuzzy c-means, picture fuzzy clustering and the single-valued neutrosophic set. A novel suitable objective function, which is depicted as a constrained minimization problem based on a single-valued neutrosophic set, is built, and the Lagrange multiplier method is used to solve the objective function. We do several experiments with some benchmark datasets, and we also apply the method to image segmentation using the Lena image. The experimental results show that the given algorithm can be considered as a promising tool for data clustering and image processing.

With the rapid development of the Internet, there are a large number of high-dimensional multi-label data to be processed in real life. To save resources and time, semi-supervised multi-label feature selection, as a dimension reduction method, has been widely used in many machine learning and data mining. In this paper, we design a new semi-supervised multi-label feature selection algorithm. First, we construct an initial similarity matrix with supervised information by considering the similarity between labels, so as to learn a more ideal similarity matrix, which can better guide feature selection. By combining latent representation with semi-supervised information, a more ideal pseudo-label matrix is learned. Second, the local manifold structure of the original data space is preserved by the manifold regularization term based on the graph. Finally, an effective alternating iterative updating algorithm is applied to optimize the proposed model, and the experimental results on several datasets prove the effectiveness of the approach.

The hydrogen production efficiency and microbial community of ethanol-type fermentation were investigated in this experiment. An internal circulation (IC) reactor was used to produce hydrogen with molasses wastewater as a substrate. The IC system was started up at the organic load rate of 8 kg COD/m³ day and the hydraulic retention time of 6 h. The stable ethanol-type fermentation was achieved on Day 32; a maximum hydrogen production rate of 1.72 l/l day was obtained on Day 39 with the hydrogen content of 65.42%. The microbial diversity of the microorganisms in the enrichment phase (L1) and the ethanol-type fermentation phase (L5) was analyzed based on the high-throughput sequencing of the V3 region of the 16S rRNA gene. A total of 14 009 effective sequences and 426 operational taxonomic units (OTUs) were obtained from L1, which were classified into 226 genera from 19 phyla. The dominant genus was Propionibacterium with the relative abundance of 50.62%, and the dominant microorganism was Propionibacterium_unclassified with the relative abundance of 44.5%. In the L5, a total of 12 206 effective sequences and 416 OTUs were obtained, including 20 phyla, of which Firmicutes accounted for 55%; 230 genera, of which Propionibacterium accounted for 50.26% of the total abundance; and 338 species, of which Lactobacillus_unclassified accounted for 20.32% of the total abundance.

Assume that the stock prices driven by fractional Brownian motions, we establish the pricing model in fractional Brownian motion environment. Using the physical probability measure of price process and the principle of fair premium, we obtain the explicit pricing formula for Maximum or Minimum Option.