Bacterial community composition in reclaimed and unreclaimed tailings of Dexing copper mine, China

An investigation was conducted to compare the microbial community structures between contaminated and reclaimed mine tailings of Dexing copper mine, Jiangxi province, China. Sample T1 was obtained from tailings site, where reclamation of phytostabilization has been conducted for 20 years successfully whereas, the other one (sample T4) was obtained from the unreclaimed tailings site. Physico-chemical characteristics, chemical speciation of heavy metals Cu, Cd and Zn were revealed to compare the effects of reclamation. A polymerase chain reaction (PCR)-based cloning approach was employed to investigate the bacterial community composition in these two samples. Results indicate the improved physico-chemical conditions and decreased bioavailability of heavy metals Cd and Zn significantly in the sample collected from reclaimed site compared with the other unreclaimed mine tailings site. Phylogenetic analysis indicated that bacteria in these two samples fell into 12 putative phylogenetic divisions. Sample T1 had 12 phyla were present, and the community was predominated by Planctomycetes (20%), α-proteobacteria (16%), Chloroflexi (12%) and Acidobacteria (10%). On the other hand, sample T4 had only 4 phyla and was predominated by y-proteobacteria (86%). The diversity of microbes in sample T1 significantly increased when compared with sample T4. The structure of microbial community composition in the first sample also was optimized to sustain more phyla associated with a healthier soil after reclamation. From the results, it could be deduced that the inventories of bacterial populations such as Planctomycetes, Chloroflexi, α-proteobacteria, Acidobacteria and Actinobacteria predominated in sample T1, but were essentially absent in sample T4. This may serve as potential bioindicator for the reclamation evaluation of tailings.Keywords: Mine tailings, reclamation, bacterial community composition, bioindicatorsAfrican Journal of Biotechnology Vol. 12(30), pp. 4841-484

Nonlinear dynamics of full-range CNNs with time-varying delays and variable coefficients

In the article, the dynamical behaviours of the full-range cellular neural networks (FRCNNs) with variable coefficients and time-varying delays are considered. Firstly, the improved model of the FRCNNs is proposed, and the existence and uniqueness of the solution are studied by means of differential inclusions and set-valued analysis. Secondly, by using the Hardy inequality, the matrix analysis, and the Lyapunov functional method, we get some criteria for achieving the globally exponential stability (GES). Finally, some examples are provided to verify the correctness of the theoretical results

A framework for characterising energy consumption of machining manufacturing systems

Energy consumption in machining manufacturing systems is increasingly of interest due to concern for global climate change and manufacturing sustainability. To utilise energy more effectively, it is paramount to understand and characterise the energy consumption of machining manufacturing systems. To this end, a framework to analyse energy consumption characteristics in machining manufacturing systems from a holistic point of view is proposed in this paper. Taking into account the complexity of energy consumption in machining manufacturing systems, energy flow is described in terms of three layers of machining manufacturing systems including machine tool layer, task layer and auxiliary production layer. Furthermore, the energy consumption of machining manufacturing systems is modelled in the spatial and temporal dimensions, respectively, in order to quantitatively characterise the energy flow. The application of the proposed modelling framework is demonstrated by employing a comprehensive analysis of energy consumption for a real-world machining workshop. The characteristics of energy consumption for machine tool layer, task layer and auxiliary production layer are, respectively, obtained using quantitative models in the spatial and temporal dimensions, which provides a valuable insight into energy consumption to support the exploration of energy-saving potentials for the machining manufacturing systems

Spectrums of Black Hole in de Sitter Spacetime with Highly Damped Quasinormal Modes: High Overtone Case

Motivated by recent physical interpretation on quasinormal modes presented by Maggiore, the adiabatic quantity method given by Kunstatter is used to calculate the spectrums of a non-extremal Schwarzschild de Sitter black hole in this paper, as well as electrically charged case. According to highly damped Konoplya and Zhidenko's numerical observational results for high overtone modes\cite{Konoplya}, we found that the asymptotic non-flat spacetime structure leads two interesting facts as followings: (i) near inner event horizon, the area and entropy spectrums, which are given by $A_{en} = 8 n_1 \pi \hbar$, $S_{en} = 2\pi n_1\hbar$, are equally spaced accurately. (ii) However, near outer cosmological horizon the spectrums, which are in the form of $A_{cn} = 16 n_2 \pi \hbar - \sqrt{\frac{48\pi}{\Lambda}A_{cn} - 3 A_{cn}^2}$, $S_{cn} = 4 \pi n_2 \hbar - \sqrt{\frac{3\pi}{\Lambda}A_{cn} - 3/16 A_{cn}^2}$, are not markedly equidistant. Finally, we also discuss the electrically charged case and find the black holes in de Sitter spacetime have similar quantization behavior no matter with or without charge.Comment: 12 pages, 2 firures, published versio

Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks

Effect of Eucommia ulmoides extract on osteoblast proliferation

Purpose: To evaluate the effect of Eucommia ulmoides extract (EUE) on osteoblast proliferation as well as investigate its probable mechanisms of action.Methods: EUE was pharmacologically evaluated at three doses. Osteoblast cells were divided as follows: Group I: negative control; groups II–IV: received EUE (180, 360 and 540 μg/ml, respectively). We performed 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay to determine osteoblast viability following treatment. Alkaline phosphatase (ALP), osteocalcin, and collagen I levels in osteoblasts were quantified using commercially available kits. Thereafter, mRNA and protein expression of ALP, collagen I, osteocalcin, transforming growth factor-β1 (TGF-β1) were measured using real-time quantitative PCR (qPCR) and western blot, respectively.Results: EUE significantly (p < 0.01) promoted osteoblast proliferation at three treatment doses (180, 360, and 540 μg/mL). Furthermore, ALP, osteocalcin, collagen I and TGF-β1 expression at both mRNA and protein levels increased significantly (all p < 0.05) following EUE treatment.Conclusion: The results suggest that EUE may promote osteoblast cell proliferation and that ALP, osteocalcin, collagen I and TGF-β1 gene expression may be involved in the mechanism of action.Keywords: qPCR, collagen I, Bone, Liver, Eucommia ulmoides extrac

Combination optimization of green energy supply in data center based on simulated annealing particle swarm optimization algorithm

At present, the high energy consumption of data centers based on grid power supply not only brings huge direct cost of electricity, but also indirectly produces a lot of greenhouse gases, which affects the natural environment. Academia and industry are beginning to introduce clean renewable energy sources such as wind and solar power into data centers to reduce operating costs and environmental damage by building new green data centers. To solve this problem, this study considers the use of waste heat for refrigeration while taking natural gas power generation into account, and introduces wind energy as a green energy source. On the premise of considering the response level of data centers, the two resources are combined and deployed to improve resource utilization and reduce energy consumption costs. Aiming at the instability of wind power generation, a particle swarm energy scheduling optimization algorithm based on simulated annealing algorithm was proposed by combining simulated annealing algorithm and particle swarm optimization algorithm. The research shows that, considering the response level of data centers, the use of natural gas and wind energy as the main energy supply can effectively reduce the overall energy consumption of data centers

Isolation and Characterization of 89K Pathogenicity Island-Positive ST-7 Strains of Streptococcus suis Serotype 2 from Healthy Pigs, Northeast China

Streptococcus suis is a swine pathogen which can also cause severe infection, such as meningitis, and streptococcal-like toxic shock syndrome (STSS), in humans. In China, most of the S. suis infections in humans were reported in the southern areas with warm and humid climates, but little attention had been paid to the northern areas. Data presented here showed that the virulent serotypes 1, 2, 7, and 9 of S. suis could be steadily isolated from the healthy pigs in the pig farms in all the three provinces of Northeast China. Notably, a majority of the serotype 2 isolates belonged to the 89K pathogenicity island-positive ST-7 clone that had historically caused the human STSS outbreaks in the Sichuan and Jiangsu provinces of China, although the human STSS case caused by S. suis had never been reported in northern areas of China. Data presented here indicated that the survey of S. suis should be expanded to or reinforced in the northern areas of China

SVCNet: Scribble-based Video Colorization Network with Temporal Aggregation

In this paper, we propose a scribble-based video colorization network with temporal aggregation called SVCNet. It can colorize monochrome videos based on different user-given color scribbles. It addresses three common issues in the scribble-based video colorization area: colorization vividness, temporal consistency, and color bleeding. To improve the colorization quality and strengthen the temporal consistency, we adopt two sequential sub-networks in SVCNet for precise colorization and temporal smoothing, respectively. The first stage includes a pyramid feature encoder to incorporate color scribbles with a grayscale frame, and a semantic feature encoder to extract semantics. The second stage finetunes the output from the first stage by aggregating the information of neighboring colorized frames (as short-range connections) and the first colorized frame (as a long-range connection). To alleviate the color bleeding artifacts, we learn video colorization and segmentation simultaneously. Furthermore, we set the majority of operations on a fixed small image resolution and use a Super-resolution Module at the tail of SVCNet to recover original sizes. It allows the SVCNet to fit different image resolutions at the inference. Finally, we evaluate the proposed SVCNet on DAVIS and Videvo benchmarks. The experimental results demonstrate that SVCNet produces both higher-quality and more temporally consistent videos than other well-known video colorization approaches. The codes and models can be found at https://github.com/zhaoyuzhi/SVCNet.Comment: accepted by IEEE Transactions on Image Processing (TIP

Carbon, Nitrogen and Phosphorus nutrients and stoichiometric characteristics of reclaimed soil aggregate in coal mining subsidence land

In order to clarify the nutrients and stoichiometric changes of soil aggregates in different forest types, reveal the soil nutrient retention and limitation characteristics of coal mining subsidence land, and then screen out the best reclaimed-forest types. In this study, three types of reclaimed forests, namely Amygdalus sylvestris, Prunus sylvestris, and Xanthomonas serrata, were set up on the Shenfu-Dongsheng mining area in the coal mining subsidence area. At the same time, the nearby unsettled and unplanted abandoned farmland was used as a control, based on the dry sieve method to explore the soil mechanical stability aggregate composition, soil SOC (organic carbon), TN (total nitrogen), TP (total phosphorus) content and its stoichiometric characteristics in response to long-term vegetation reclamation in 0-40 cm soil law. The results showed that the content of soil micro-aggregates of different reclaimed-forest types decreased while the content of large aggregates increased, and the total amount of large aggregates >0.25 mm in each forest type exceeded 70%. The content of SOC, TN and TP in soils of different forest types 0.25 mm large aggregates to soil SOC, TN and TP were 72%-83%,66%-74%, 65%-82% respectively. The C/N, C/P and N/P of soil aggregates of different forest types were all lower than the average of national and global. Soil TN was positively correlated with SOC and TP, and negatively correlated with C/N (P<0.05); TP was negatively correlated with C/P and N/P significantly(P<0.05). In summary, vegetation reclamation increased the content of soil large aggregates, and large aggregates contributed the most to soil Carbon, Nitrogen and Phosphorus. The contribution rate of Chinese canopy to soil Carbon, Nitrogen and Phosphorus was greater than that of other forest types. The variation of C/P of soil aggregates in different forest types ranged from 5.44 to 20.83, which was lower than the levels of national and global average, indicated that the soil in the coal mining subsidence area lacked Nitrogen. So, the soil Nitrogen was the nutrient limiting factor