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In this work, bio-hydrometallurgy technology was employed and a novel two-stage method based on successive chemical and bio treatments was proposed to collaboratively utilize manganese–silver ore and silver-bearing pyrite. In the optimization research of the chemical leaching stage, the sensitive factors for the Mn leaching efficiency were screened by Plackett–Burman design, and central compound design was performed to settle the optimized parameters. A mixed strain of bacteria containing Sulfobacillus thermosulfidooxidans, At. caldus and Leptospirillum ferrooxidans was applied in the bioleaching stage. A conventional cyanidation process carried out with the Mn leaching residuals suggested an efficient recovery of Ag. Applying a two-stage method with the optimum conditions, the leaching efficiency of 95.3% (Mn) and 96.3% (Ag) were obtained with 284.94 kg/t silver-bearing pyrite addition and 277.44 kg/t sulfuric acid consumption with a temperature at 77.73 °C and stirring speed at 287.76 rpm. Mineral behaviors were investigated with XRD and SEM/EDS analysis, and it was revealed that the oxidation of sulfur is the crux in reducing the usage of reagents, and the presence of leaching bacteria enhanced the oxidation efficiently. Through optimization and mechanism study, this paper provides an opportunity to co-leach the manganese–silver ore and silver-bearing pyrite process in a more economical and environmental way.

This work investigated the effects of Fe3+, H+ and adsorbed leaching bacteria on the bioleaching of pentlandite. Collectively, an integrated model for the oxidation and decomposition of pentlandite was built to describe the behaviors of different components in a bioleaching system. Proton ions and ferric ions could promote the break and oxidation of Ni-S and Fe-S bonds. The iron-oxidizing microorganisms could regenerate ferric ions and maintain a high Eh value. The sulfur-oxidizing microorganisms showed significant importance in the oxidation of polysulfide and elemental sulfur. The atoms in pentlandite show different modification pathways during the bioleaching process: iron transformed through a (Ni,Fe)9S8 → Fe2+ → Fe3+ → KFe3(SO4)2(OH)6 pathway; nickel experienced a transformation of (Ni,Fe)9S8 → NiS → Ni2+; sulfur modified through the pathway of S2−/S22− → Sn2− → S0 → SO32− → SO42−. During bioleaching, a sulfur-rich layer and jarosite layer formed on the mineral surface, and the rise of pH value accelerated the process. However, no evidence for the inhibition of the layers was shown in the bioleaching of pentlandite at pH 3.00. This study provides a novel method for the extraction of nickel from pentlandite by bioleaching at elevated pH values.

This paper puts forward an innovative theory and method to calculate the canonical labelings of graphs that are distinct to N a u t y ’s. It shows the correlation between the canonical labeling of a graph and the canonical labeling of its complement graph. It regularly examines the link between computing the canonical labeling of a graph and the canonical labeling of its o p e n k- n e i g h b o r h o o d s u b g r a p h . It defines d i f f u s i o n d e g r e e s e q u e n c e s and e n t i r e d i f f u s i o n d e g r e e s e q u e n c e . For each node of a graph G, it designs a characteristic m _ N e a r e s t N o d e to improve the precision for calculating canonical labeling. Two theorems established here display how to compute the first nodes of M a x Q ( G ) . Another theorem presents how to determine the second nodes of M a x Q ( G ) . When computing C m a x ( G ) , if M a x Q ( G ) already holds the first i nodes u 1 , u 2 , ⋯ , u i , Diffusion and Nearest Node theorems provide skill on how to pick the succeeding node of M a x Q ( G ) . Further, it also establishes two theorems to determine the C m a x ( G ) of disconnected graphs. Four algorithms implemented here demonstrate how to compute M a x Q ( G ) of a graph. From the results of the software experiment, the accuracy of our algorithms is preliminarily confirmed. Our method can be employed to mine the frequent subgraph. We also conjecture that if there is a node v ∈ S ( G ) meeting conditions C m a x ( G − v ) ⩽ C m a x ( G − w ) for each w ∈ S ( G ) ∧ w ≠ v , then u 1 = v for M a x Q ( G ) .

Atractylodis Rhizoma is the dried rhizome of Atractylodes lancea (Thunb.) DC. or Atractylodes chinensis (DC.) Koidz and is often processed by stir-frying with wheat bran to reduce its dryness and increase its spleen tonifying activity. However, the mechanism by which the processing has this effect remains unknown. To explain the mechanism based on the pharmacokinetics of the active compounds, a rapid, sensitive ultra-performance liquid chromatography-tandem mass spectrometry method was developed to analyze atractylenolides I, II, and III, and atractyloside A simultaneously in rat plasma after oral administration of raw and processed Atractylodis Rhizoma. Acetaminophen was used as the internal standard and the plasma samples were pretreated with methanol. Positive ionization mode coupled with multiple reaction monitoring mode was used to analyze the four compounds. The method validation revealed that all the calibration curves displayed good linear regression over the concentration ranges of 3.2⁻350, 4⁻500, 4⁻500, and 3.44⁻430 ng/mL for atractylenolides I, II, and III, and atractyloside A, respectively. The relative standard deviations of the intra- and inter-day precisions of the four compounds were less than 6% with accuracies (relative error) below 2.38%, and the extraction recoveries were more than 71.90 ± 4.97%. The main pharmacokinetic parameters of the four compounds were estimated with Drug and Statistics 3.0 and the integral pharmacokinetics were determined based on an area under the curve weighting method. The results showed that the integral maximum plasma concentration and area under the curve increased after oral administration of processed Atractylodis Rhizoma.

This paper provides a full theoretical and experimental analysis of a serial algorithm for the point-in-polygon test, which requires less running time than previous algorithms and can handle all degenerate cases. The serial algorithm can quickly determine whether a point is inside or outside a polygon and accurately determine the contours of input polygon. It describes all degenerate cases and simultaneously provides a corresponding solution to each degenerate case to ensure the stability and reliability. This also creates the prerequisites and basis for our novel boolean operations algorithm that inherits all the benefits of the serial algorithm. Using geometric probability and straight-line equation F ( P ) = ( y i − y i + 1 ) ( x p − x i ) − ( y i − y p ) ( x i + 1 − x i ) , it optimizes our two algorithms that avoid the division operation and do not need to compute any intersection points. Our algorithms are applicable to any polygon that may be self-intersecting or with holes nested to any level of depth. They do not have to sort the vertices clockwise or counterclockwise beforehand. Consequently, they process all edges one by one in any order for input polygons. This allows a parallel implementation of each algorithm to be made very easily. We also prove several theorems guaranteeing the correctness of algorithms. To speed up the operations, we assign each vector a number code and derive two iterative formulas using differential calculus. However, the experimental results as well as the theoretical proof show that our serial algorithm for the point-in-polygon test is optimal and the time complexities of all algorithms are linear. Our methods can be extended to three-dimensional space, in particular, they can be applied to 3D printing to improve its performance.

This paper describes a simple, sensitive and environmentally benign method for the direct determination of aniline and its derivatives in environmental water samples by capillary zone electrophoresis (CZE) with field-enhanced sample injection. The parameters that influenced the enhancement and separation efficiencies were investigated. Surprisingly, under the optimized conditions, two linear ranges for the calibration plot, 1–50 ng/mL and 50–1000 ng/mL (R > 0.998), were obtained. The detection limit was in the range of 0.29–0.43 ng/mL. To eliminate the effect of the real sample matrix on the stacking efficiency, the standard addition method was applied to the analysis of water samples from local rivers.

This paper presents a novel theory and method to calculate the canonical labelings of digraphs whose definition is entirely different from the traditional definition of Nauty. It indicates the mutual relationships that exist between the canonical labeling of a digraph and the canonical labeling of its complement graph. It systematically examines the link between computing the canonical labeling of a digraph and the k-neighborhood and k-mix-neighborhood subdigraphs. To facilitate the presentation, it introduces several concepts including mix diffusion outdegree sequence and entire mix diffusion outdegree sequences. For each node in a digraph G, it assigns an attribute m_NearestNode to enhance the accuracy of calculating canonical labeling. The four theorems proved here demonstrate how to determine the first nodes added into M a x Q ( G ) . Further, the other two theorems stated below deal with identifying the second nodes added into M a x Q ( G ) . When computing C m a x ( G ) , if M a x Q ( G ) already contains the first i vertices u 1 , u 2 , ⋯ , u i , Diffusion Theorem provides a guideline on how to choose the subsequent node of M a x Q ( G ) . Besides, the Mix Diffusion Theorem shows that the selection of the ( i + 1 ) th vertex of M a x Q ( G ) for computing C m a x ( G ) is from the open mix-neighborhood subdigraph N + + ( Q ) of the nodes set Q = { u 1 , u 2 , ⋯ , u i } . It also offers two theorems to calculate the C m a x ( G ) of the disconnected digraphs. The four algorithms implemented in it illustrate how to calculate M a x Q ( G ) of a digraph. Through software testing, the correctness of our algorithms is preliminarily verified. Our method can be utilized to mine the frequent subdigraph. We also guess that if there exists a vertex v ∈ S + ( G ) satisfying conditions C m a x ( G − v ) ⩽ C m a x ( G − w ) for each w ∈ S + ( G ) ∧ w ≠ v , then u 1 = v for M a x Q ( G ) .