Insight into mineralizer modified and tailored scorodite crystal characteristics and leachability for arsenic-rich smelter wastewater stabilization

Arsenic-rich non-ferrous smelter wastewater has the potential to cause harm to the environment and human health. The use of mineralizer modified and tailored scorodite crystals, a secondary As-bearing mineral, is considered to be the most promising strategy for arsenic stabilization. In this work, firstly, the mechanisms influencing the scorodite crystal characteristics for arsenic stabilization were investigated, and the results indicated that the scorodite stability was greatly influenced by the scorodite crystal shape and particle size. The crystal shape changes that the scorodite solids undergo during the aging period were observed, from a laminar structure to a polyhedron to a standard octahedral structure, and meanwhile, the As-concentration decreased from 10.2 mg L⁻¹ to 3.7 mg L⁻¹, with the relative particle size value (RPS) increasing from 1.50 to 2.64. Secondly, the addition of a mineralizer to further improve the scorodite crystal stability was investigated. It was meaningful to observe that the lowest As-concentration of 0.39 mg L⁻¹ could be attained when trace NaF was employed, and it was of great significance to apply this strategy for the disposal of As and F-containing wastewater due to the electrostatic interaction between scorodite and sodium fluoride. However, the scorodite crystal stability was weakened when other mineralizers (Na₂SiO₃·9H₂O and Al(NO₃)₃·9H₂O) were added. This indicated that these mineralizers play different roles in influencing the crystal phase, shapes and sizes of the solid precipitate (mainly scorodite). Finally, the mechanisms of the scorodite crystal evolution and the arsenic leachability characteristics were analyzed. In conclusion, the addition of appropriate mineralizers is a potentially effective strategy for the control of crystal growth, and could be used in the disposal and stabilization of arsenic-rich non-ferrous effluents.

The addition of appropriate mineralizers is a potentially effective strategy for the control of crystal growth, and could be used in the disposal and stabilization of arsenic-rich non-ferrous effluents.     

冬虫夏草中砷及其安全性评价研究进展

传统名贵中药冬虫夏草Cordyceps sinensis中砷超标及其安全性问题近年来备受关注,针对冬虫夏草中砷的研究逐渐增加并不断深入。在文献查阅的基础上,从冬虫夏草砷及砷形态的分析方法,砷的含量、分布、存在形态,安全性评价,以及野生和人工仿生繁育冬虫夏草中砷含量等方面进行归纳总结,并对冬虫夏草中砷的认知及研究进行展望,以期为冬虫夏草安全性评价的深入研究提供参考。     

Discriminant subspace learning constrained by locally statistical uncorrelation for face recognition

High-dimensionality of data and the small sample size problem are two significant limitations for applying subspace methods which are favored by face recognition. In this paper, a new linear dimension reduction method called locally uncorrelated discriminant projections (LUDP) is proposed, which addresses the two problems from a new aspect. More specifically, we propose a locally uncorrelated criterion, which aims to decorrelate learned discriminant factors over data locally rather than globally. It has been shown that the statistical uncorrelation criterion is an important property for reducing dimension and learning robust discriminant projection as well. However, data are always locally distributed, so it is more important to explore locally statistical uncorrelated discriminant information over data. We impose this new constraint into a graph-based maximum margin analysis, so that LUDP also characterizes the local scatter as well as nonlocal scatter, seeking to find a projection that maximizes the difference, rather than the ratio between the nonlocal scatter and the local scatter. Experiments on ORL, Yale, Extended Yale face database B and FERET face database demonstrate the effectiveness of our proposed method.     

A divide-and-combine method for large scale nonparallel support vector machines

Nonparallel Support Vector Machine (NPSVM) which is more flexible and has better generalization than typical SVM is widely used for classification. Although some methods and toolboxes like SMO and libsvm for NPSVM are used, NPSVM is hard to scale up when facing millions of samples. In this paper, we propose a divide-and-combine method for large scale nonparallel support vector machine (DCNPSVM). In the division step, DCNPSVM divide samples into smaller sub-samples aiming at solving smaller subproblems independently. We theoretically and experimentally prove that the objective function value, solutions, and support vectors solved by DCNPSVM are close to the objective function value, solutions, and support vectors of the whole NPSVM problem. In the combination step, the sub-solutions combined as initial iteration points are used to solve the whole problem by global coordinate descent which converges quickly. In order to balance the accuracy and efficiency, we adopt a multi-level structure which outperforms state-of-the-art methods. Moreover, our DCNPSVM can tackle unbalance problems efficiently by tuning the parameters. Experimental results on lots of large data sets show the effectiveness of our method in memory usage, classification accuracy and time consuming.     

Finite-time stabilization control for discontinuous time-delayed networks: New switching design

This paper discusses the finite-time stabilization problem for time-varying delayed neural networks (DNNs) with discontinuous activation functions. By using fixed point theory and set-valued analysis, we establish the existence theorem of equilibrium point. In order to stabilize the states of this class of discontinuous DNNs in finite time, we design two different kinds of switching controllers which are described by discontinuous functions. Under the framework of Filippov solutions, several new and effective criteria are derived to realize finite-time stabilization of discontinuous DNNs based on the famous finite-time stability theory. Besides, the upper bounds of the settling time of stabilization are estimated. Numerical examples are finally provided to illustrate the correctness of the proposed design method and theoretical results.     

Complete genome sequence analysis of novel human bocavirus reveals genetic recombination between human bocavirus 2 and human bocavirus 4

Epidemiological surveillance of human bocavirus (HBoV) was conducted on fecal specimens collected from hospitalized children with diarrhea in Chiang Mai, Thailand in 2011. By partial sequence analysis of VP1 gene, an unusual strain of HBoV (CMH-S011-11), was initially identified as HBoV4. The complete genome sequence of CMH-S011-11 was performed and analyzed further to clarify whether it was a recombinant strain or a new HBoV variant. Analysis of complete genome sequence revealed that the coding sequence starting from NS1, NP1 to VP1/VP2 was 4795 nucleotides long. Interestingly, the nucleotide sequence of NS1 gene of CMH-S011-11 was most closely related to the HBoV2 reference strains detected in Pakistan, which contradicted to the initial genotyping result of the partial VP1 region in the previous study. In addition, comparison of NP1 nucleotide sequence of CMH-S011-11 with those of other HBoV1–4 reference strains also revealed a high level of sequence identity with HBoV2. On the other hand, nucleotide sequence of VP1/VP2 gene of CMH-S011–11 was most closely related to those of HBoV4 reference strains detected in Nigeria. The overall full-length sequence analysis revealed that this CMH-S011-11 was grouped within HBoV4 species, but located in a separate branch from other HBoV4 prototype strains. Recombination analysis revealed that CMH-S011-11 was the result of recombination between HBoV2 and HBoV4 strains with the break point located near the start codon of VP2.     

Global dynamics of bidirectional associative memory neural networks involving transmission delays and dead zones

In this article, a model describing the activation dynamics of bidirectional associative memory (BAM) neural networks involving transmission delays was considered. The concept of BAM networks employed in this work is improved and it includes the earlier notions known in the literature and is applied to a wider class of networks. Further, we introduced a new notion, as a measure of restoring stability and termed it as a dead zone. In this article, the influence of the presence of dead zones on the global asymptotic stability of the equilibrium pattern was investigated. Existence and uniqueness of an equilibrium pattern under fairly general and easily verifiable conditions were also established.     

DETECTOR: structural information guided artifact detection for super-resolution fluorescence microscopy image

Super-resolution fluorescence microscopy, with a spatial resolution beyond the diffraction limit of light, has become an indispensable tool to observe subcellular structures at a nanoscale level. To verify that the super-resolution images reflect the underlying structures of samples, the development of robust and reliable artifact detection methods has received widespread attention. However, the existing artifact detection methods are prone to report false alert artifacts because it relies on absolute intensity mismatch between the wide-field image and resolution rescaled super-resolution image. To solve this problem, we proposed DETECTOR, a structural information-guided artifact detection method for super-resolution images. It detects artifacts by computing the structural dissimilarity between the wide-field image and the resolution rescaled super-resolution image. To focus on structural similarity, we introduce a weight mask to weaken the influence of strong autofluorescence background and proposed a structural similarity index for super-resolution images, named MASK-SSIM. Simulations and experimental results demonstrated that compared with the state-of-the-art methods, DETECTOR has advantages in detecting structural artifacts in super-resolution images. It is especially suitable for wide-field images with strong autofluorescence background and super-resolution images of single molecule localization microscopy (SMLM). DETECTOR has extreme sensitivity to the weak signal region. Moreover, DETECTOR can guide data collection and parameter tuning during image reconstruction.