Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River

Vegetation in the upper catchment of Yellow River is critical for the ecological stability of the whole watershed. The dominant vegetation cover types in this region are grassland and forest, which can strongly influence the eco-environmental status of the whole watershed. The normalized difference vegetation index (NDVI) for grassland and forest has been calculated and its daily correlation models were deduced by Moderate Resolution Imaging Spectroradiometer products on 12 dates in 2000, 2003, and 2006. The responses of the NDVI values with the inter-annual grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) were analyzed showing that, except for the lowest temperature, the yearly precipitation and highest temperature had close correlations with the NDVI values of the two vegetation communities. The value of correlation coefficients ranged from 0.815 to 0.951 (p <0.01). Furthermore, the interactions of NDVI values of vegetation with the climatic indicators at monthly interval were analyzed. The NDVI of vegetation and three climatic indices had strong positive correlations (larger than 0.733, p <0.01). The monthly correlations also provided the threshold values for the three climatic indictors, to be used for simulating vegetation growth grassland under different climate features, which is essential for the assessment of the vegetation growth and for regional environmental management

Stability of Strutinsky Shell Correction Energy in Relativistic Mean Field Theory

The single-particle spectrum obtained from the relativistic mean field (RMF) theory is used to extract the shell correction energy with the Strutinsky method. Considering the delicate balance between the plateau condition in the Strutinsky smoothing procedure and the convergence for the total binding energy, the proper space sizes used in solving the RMF equations are investigated in detail by taking 208Pb as an example. With the proper space sizes, almost the same shell correction energies are obtained by solving the RMF equations either on basis space or in coordinate space.Comment: 9 pages, 4 figure

Magnons in Ferromagnetic Metallic Manganites

Ferromagnetic (FM) manganites, a group of likely half-metallic oxides, are of special interest not only because they are a testing ground of the classical doubleexchange interaction mechanism for the colossal magnetoresistance, but also because they exhibit an extraordinary arena of emergent phenomena. These emergent phenomena are related to the complexity associated with strong interplay between charge, spin, orbital, and lattice. In this review, we focus on the use of inelastic neutron scattering to study the spin dynamics, mainly the magnon excitations in this class of FM metallic materials. In particular, we discussed the unusual magnon softening and damping near the Brillouin zone boundary in relatively narrow band compounds with strong Jahn-Teller lattice distortion and charge/orbital correlations. The anomalous behaviors of magnons in these compounds indicate the likelihood of cooperative excitations involving spin, lattice, as well as orbital degrees of freedom.Comment: published in J. Phys.: Cond. Matt. 20 figure

Photosynthetic characterization of a rolled leaf mutant of rice (Oryza sativa L.)

A new rolling leaf rice mutant was identified which showed an apparently straighter longitudinal shape normal transverse rolling characters at all developing stages. The chlorophyll contents per fresh weight of this mutant leaves were lower than those of wild-type. The electron transfer rate (ETR) and photochemical quenching (qP) were a little higher than those of wild-type. However, because of significant increase of non-photochemical quenching (NPQ), the maximal photosystem II (PSII) photochemistry (Fv/Fm) and the efficiency of excitation energy trapping by open PSII reaction centers in the light–adapted state (Fv’/Fm’) were lower than those of wild-type. Low temperature fluorescence analysis showed that rolling leaf mutant assigned more excited energy to photosystem I (PSI) than to PSII. The superoxide dismutase (SOD) content, soluble sugar content, proline content and malonaldehyde (MDA) content of the rolling leaf mutant were nearly 39.4, 91.2, 96.7 and 143.7% of those of wild-type, respectively. The great increase of MDA content suggests that membrane lipid system was damaged in rolling leaf mutant leaves. These results indicate that rolling leaf mutant decrease the efficiency of light utilization compared to the wild-type. This was because of the reduction of leaf area and chlorophyll contents, and the dissipation of more excitation energy as NPQ as a result of avoiding potential damage of membrane structure.Key words: Malonaldehyde (MDA), photosynthetic characterization, rice, rolling leaf mutant

The Properties of H{\alpha} Emission-Line Galaxies at z = 2.24

Using deep narrow-band $H_2S1$ and $K_{s}$-band imaging data obtained with CFHT/WIRCam, we identify a sample of 56 H$\alpha$ emission-line galaxies (ELGs) at $z=2.24$ with the 5$\sigma$ depths of $H_2S1=22.8$ and $K_{s}=24.8$ (AB) over 383 arcmin$^{2}$ area in the ECDFS. A detailed analysis is carried out with existing multi-wavelength data in this field. Three of the 56 H$\alpha$ ELGs are detected in Chandra 4 Ms X-ray observation and two of them are classified as AGNs. The rest-frame UV and optical morphologies revealed by HST/ACS and WFC3 deep images show that nearly half of the H$\alpha$ ELGs are either merging systems or with a close companion, indicating that the merging/interacting processes play a key role in regulating star formation at cosmic epoch z=2-3; About 14% are too faint to be resolved in the rest-frame UV morphology due to high dust extinction. We estimate dust extinction from SEDs. We find that dust extinction is generally correlated with H$\alpha$ luminosity and stellar mass (SM). Our results suggest that H$\alpha$ ELGs are representative of star-forming galaxies (SFGs). Applying extinction correction for individual objects, we examine the intrinsic H$\alpha$ luminosity function (LF) at $z=2.24$, obtaining a best-fit Schechter function characterized by a faint-end slope of $\alpha=-1.3$. This is shallower than the typical slope of $\alpha \sim -1.6$ in previous works based on constant extinction correction. We demonstrate that this difference is mainly due to the different extinction corrections. The proper extinction correction is thus key to recovering the intrinsic LF as the extinction globally increases with H$\alpha$ luminosity. Moreover, we find that our H$\alpha$ LF mirrors the SM function of SFGs at the same cosmic epoch. This finding indeed reflects the tight correlation between SFR and SM for the SFGs, i.e., the so-called main sequence.Comment: 15 pages, 12 figures, 2 tables, Received 2013 October 11; accepted 2014 February 13; published 2014 March 18 by Ap

Key exchange with the help of a public ledger

Blockchains and other public ledger structures promise a new way to create globally consistent event logs and other records. We make use of this consistency property to detect and prevent man-in-the-middle attacks in a key exchange such as Diffie-Hellman or ECDH. Essentially, the MitM attack creates an inconsistency in the world views of the two honest parties, and they can detect it with the help of the ledger. Thus, there is no need for prior knowledge or trusted third parties apart from the distributed ledger. To prevent impersonation attacks, we require user interaction. It appears that, in some applications, the required user interaction is reduced in comparison to other user-assisted key-exchange protocols

TUMK-ELM: A fast unsupervised heterogeneous data learning approach

© 2013 IEEE. Advanced unsupervised learning techniques are an emerging challenge in the big data era due to the increasing requirements of extracting knowledge from a large amount of unlabeled heterogeneous data. Recently, many efforts of unsupervised learning have been done to effectively capture information from heterogeneous data. However, most of them are with huge time consumption, which obstructs their further application in the big data analytics scenarios, where an enormous amount of heterogeneous data are provided but real-time learning are strongly demanded. In this paper, we address this problem by proposing a fast unsupervised heterogeneous data learning algorithm, namely two-stage unsupervised multiple kernel extreme learning machine (TUMK-ELM). TUMK-ELM alternatively extracts information from multiple sources and learns the heterogeneous data representation with closed-form solutions, which enables its extremely fast speed. As justified by theoretical evidence, TUMK-ELM has low computational complexity at each stage, and the iteration of its two stages can be converged within finite steps. As experimentally demonstrated on 13 real-life data sets, TUMK-ELM gains a large efficiency improvement compared with three state-of-the-art unsupervised heterogeneous data learning methods (up to 140 000 times) while it achieves a comparable performance in terms of effectiveness