Litter decomposition in a subtropical plantation in Qianyanzhou, China

A long-term (20 months) bulk litter decomposition experiment was conducted in a subtropical plantation in southern China in order to test the hypothesis that stable isotope discrimination occurs during litter decomposition and that litter decomposition increases concentrations of nutrients and organic matter in soil. This was achieved by a litter bag technique. Carbon (C), nitrogen (N) and phosphorus (P) concentrations in the remaining litter as well as delta(13)C and delta(15)N during the experimental period were measured. Meanwhile, organic C, alkali-soluble N and available P concentrations were determined in the soils beneath litter bags and in the soils at the control plots. The dry mass remaining (as % of the initial mass) during litter decomposition exponentially declined (y = 0.9362 e(-0.0365x) , R (2) = 0.93, P < 0.0001), but total C in the remaining litter did not decrease significantly with decomposition process during a 20-month period. By comparison, total N in the remaining litter significantly increased from 5.8 +/- A 1.7 g kg(-1) dw litter in the first month to 10.1 +/- A 1.4 g kg(-1) dw litter in the 20th month. During the decomposition, delta(13)C values of the remaining litter showed an insignificant enrichment, while delta(15)N signatures exhibited a different pattern. It significantly depleted (15)N (y = -0.66x + 0.82, R (2) = 0.57, P < 0.0001) during the initial 7 months while showing (15)N enrichments in the remaining 13 months (y = 0.10x - 4.23, R (2) = 0.32, P < 0.0001). Statistically, litter decomposition has little impact on concentrations of soil organic C and alkali-soluble N and available P in the top soil. This indicates that nutrient return to the topsoil through litter decomposition is limited and that C cycling decoupled from N cycling during decomposition in this subtropical plantation in southern China

Constitutive relationship of TC4 titanium alloy based on back propagating (BP) neural network (NN)

Using Gleeble-3800 thermal simulation testing machine, the TC4 titanium alloy was subjected to hot compression experiments under the conditions of deformation temperature of 810 – 950 °C, strain rate of 0.001 - 1s-1. The research shows that the flow stress of TC4 titanium alloy is more sensitive to the deformation temperature and strain rate during thermal deformation, and it increases with the decrease of the deformation temperature and the increase of the strain rate. Based on BP neural network, a constitutive model of TC4 titanium alloy α+β two-phase region is established. The correlation coefficient reaches 0,996, which proves that the model can predict the high temperature flow stress of TC4 titanium alloy

Constitutive relationship of 7075 aluminum alloy based on modified Zerilli-Armstrong (M - ZA) model

The Gleeble – 3500 thermal simulation testing machine was used to perform isothermal tensile test on 7075 aluminum alloy at a deformation temperature of 300 – 450 °C and a strain rate of 0,01 – 1 s-1, and the true stress-strain curve of the alloy was obtained. Based on the true stress-strain data, the modified Zerilli-Armstrong (M-ZA) model was used to construct the constitutive model of the alloy, and the fitting accuracy of the model was analyzed

Learning Concept Interestingness for Identifying Key Structures from Social Networks

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordIdentifying key structures from social networks that aims to discover hidden patterns and extract valuable information is an essential task in the network analysis realm. These different structure detection tasks can be integrated naturally owing to the topological nature of key structures. However, identifying key network structures in most studies has been performed independently, leading to huge computational overheads. To address this challenge, this paper proposes a novel approach for handling key structures identification tasks simultaneously under the unified Formal Concept Analysis (FCA) framework. Specifically, we first implement the FCA-based representation of a social network and then generate the fine-grained knowledge representation, namely concept. Then, an efficient concept interestingness calculation algorithm suitable for social network scenarios is proposed. Next, we then leverage concept interestingness to quantify the hidden relations between concepts and network structures. Finally, an efficient algorithm for jointly key structures detection is developed based on constructed mapping relations. Extensive experiments conducted on real-world networks demonstrate that the efficiency and effectiveness of our proposed approach.Fundamental Research Funds for the Central Universitie

A general model for collaboration networks

In this paper, we propose a general model for collaboration networks. Depending on a single free parameter "{\bf preferential exponent}", this model interpolates between networks with a scale-free and an exponential degree distribution. The degree distribution in the present networks can be roughly classified into four patterns, all of which are observed in empirical data. And this model exhibits small-world effect, which means the corresponding networks are of very short average distance and highly large clustering coefficient. More interesting, we find a peak distribution of act-size from empirical data which has not been emphasized before of some collaboration networks. Our model can produce the peak act-size distribution naturally that agrees with the empirical data well.Comment: 10 pages, 10 figure

Widely adaptable oil-in-water gel emulsions stabilized by an amphiphilic hydrogelator derived from dehydroabietic acid

A surfactant, R-6-AO, derived from dehydroabietic acid has been synthesized. It behaves as a highly efficient low-molecular-weight hydrogelator with an extremely low critical gelation concentration (CGC) of 0.18 wt % (4 mm). R-6-AO not only stabilizes oil-in-water (O/W) emulsions at concentrations above its critical micelle concentration (cmc) of 0.6 mm, but also forms gel emulsions at concentrations beyond the CGC with the oil volume fraction freely adjustable between 2 % and 95 %. Cryo-TEM images reveal that R-6-AO molecules self-assemble into left-handed helical fibers with cross-sectional diameters of about 10 nm in pure water, which can be turned to very stable hydrogels at concentrations above the CGC. The gel emulsions stabilized by R-6-AO can be prepared with different oils (n-dodecane, n-decane, n-octane, soybean oil, olive oil, tricaprylin) owing to the tricyclic diterpene hydrophobic structure in their molecules that enables them to adopt a unique arrangement in the fibers

Exact Histogram Specification Optimized for Structural Similarity

An exact histogram specification (EHS) method modifies its input image to have a specified histogram. Applications of EHS include image (contrast) enhancement (e.g., by histogram equalization) and histogram watermarking. Performing EHS on an image, however, reduces its visual quality. Starting from the output of a generic EHS method, we maximize the structural similarity index (SSIM) between the original image (before EHS) and the result of EHS iteratively. Essential in this process is the computationally simple and accurate formula we derive for SSIM gradient. As it is based on gradient ascent, the proposed EHS always converges. Experimental results confirm that while obtaining the histogram exactly as specified, the proposed method invariably outperforms the existing methods in terms of visual quality of the result. The computational complexity of the proposed method is shown to be of the same order as that of the existing methods. Index terms: histogram modification, histogram equalization, optimization for perceptual visual quality, structural similarity gradient ascent, histogram watermarking, contrast enhancement