Determining more realistic OWA weights

The concept of ordered weighted averaging (OWA) operator weights arises in uncertain decision making problems, however some weights may have a specific relationship with other. This information about the weights can be obtained from decision makers (DMs). This paper intends to introduce a theory of weight restrictions into the existing OWA operator weight models. Based on the DMs' value judgment the obtained OWA operator weights could be more realistic.

Placenta growth factor-1 exerts time-dependent stabilization of adherens junctions following VEGF-induced vascular permeability

Increased vascular permeability is an early event characteristic of tissue ischemia and angiogenesis. Although VEGF family members are potent promoters of endothelial permeability the role of placental growth factor (PlGF) is hotly debated. Here we investigated PlGF isoforms 1 and 2 and present in vitro and in vivo evidence that PlGF-1, but not PlGF-2, can inhibit VEGF-induced permeability but only during a critical window post-VEGF exposure. PlGF-1 promotes VE-cadherin expression via the trans-activating Sp1 and Sp3 interaction with the VE-cadherin promoter and subsequently stabilizes transendothelial junctions, but only after activation of endothelial cells by VEGF. PlGF-1 regulates vascular permeability associated with the rapid localization of VE-cadherin to the plasma membrane and dephosphorylation of tyrosine residues that precedes changes observed in claudin 5 tyrosine phosphorylation and membrane localization. The critical window during which PlGF-1 exerts its effect on VEGF-induced permeability highlights the importance of the translational significance of this work in that PLGF-1 likely serves as an endogenous anti-permeability factor whose effectiveness is limited to a precise time point following vascular injury. Clinical approaches that would pattern nature's approach would thus limit treatments to precise intervals following injury and bring attention to use of agents only during therapeutic windows.

Quantitative interpretation of diffusion-ordered NMR spectra:can we rationalize small molecule diffusion coefficients?

Appealingly simple: A new method is described that allows the diffusion coefficient of a small molecule to be estimated given only the molecular weight and the viscosity of the solvent used. This method makes possible the quantitative interpretation of the diffusion domain of diffusion-ordered NMR spectra (see picture). © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Document similarity:a new measure using OWA

In this paper, we propose a new similarity measure to compute the pair-wise similarity of text-based documents based on patterns of the words in the documents. First we develop a kappa measure for pair-wise comparison of documents then we use ordered weighting averaging operator to define a document similarity measure for a set of documents.

Advanced perturbation technique for digital backward propagation in WDM systems

An improved digital backward propagation (DBP) is proposed to compensate inter-nonlinear effects and dispersion jointly in WDM systems based on an advanced perturbation technique (APT). A non-iterative weighted concept is presented to replace the iterative in analytical recursion expression, which can dramatically simplify the complexity and improve accuracy compared to the traditional perturbation technique (TPT). Furthermore, an analytical recursion expression of the output after backward propagation is obtained initially. Numerical simulations are executed for various parameters of the transmission system. The results indicate that the advanced perturbation technique will relax the step size requirements and reduce the oversampling factor when launch power is higher than -2 dBm. We estimate this technique will reduce computational complexity by a factor of around seven with respect to the conventional DBP. © 2013 Optical Society of America.

Porous tin film synthesized by electrodeposition and the electrochemical performance for lithium-ion batteries

Porous tin films as anode for lithium-ion batteries are electrodeposited on graphite paper. Homogeneous tin films with significant void space accommodate the volume change during tin lithiation/delithiation. Through adjusting the electrodeposition currents and time, the morphologies and void space of tin films on graphite paper are controllable. At fixed electrodeposition current densities, the prolonged electrodeposition time plays the role in growing big tin particles and resulting the disappearance of void space among tin particles. The increased electrodeposition current plays the role to increase the quantity of tin seeds in thickness of tin film, and the void space among tin particles remains but the thick film limits its electrochemical performance. The tin films electrodeposited at an optimized current densities and for an optimized electrodeposition time, present the best electrochemical performance, because the tin nanoparticles are well dispersed on graphite substrate including void space. The tin film electrodeposited at 0.2 A cm-2 for 2 min shows the capacity of 1.0 mAh cm-2 after 50 charge/discharge cycles. The void space of tin film is very important for the best capacity and cyclic ability. The metallic tin film produced at 0.4 A cm-2 for 3 min remains the uniform and microporous structure after charge/discharge for 50 cycles.

Are school-of-thought words characterizable?

School of thought analysis is an important yet not-well-elaborated scientific knowledge discovery task. This paper makes the first attempt at this problem. We focus on one aspect of the problem: do characteristic school-of-thought words exist and whether they are characterizable? To answer these questions, we propose a probabilistic generative School-Of-Thought (SOT) model to simulate the scientific authoring process based on several assumptions. SOT defines a school of thought as a distribution of topics and assumes that authors determine the school of thought for each sentence before choosing words to deliver scientific ideas. SOT distinguishes between two types of school-of-thought words for either the general background of a school of thought or the original ideas each paper contributes to its school of thought. Narrative and quantitative experiments show positive and promising results to the questions raised above © 2013 Association for Computational Linguistics. © 2013 Association for Computational Linguistics.

Exploiting heterogeneous scientific literature networks to combat ranking bias:evidence from the computational linguistics area

It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph-based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less-biased ranking than previous methods. MutualRank provides a unified model that involves both intra- and inter-network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well-known universities and two well-known textbooks. The experimental results show that MutualRank greatly outperforms the state-of-the-art competitors, including PageRank, HITS, CoRank, Future Rank, and P-Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.

Semantics, knowledge and grids on big data

Semantics, knowledge and Grids represent three spaces where people interact, understand, learn and create. Grids represent the advanced cyber-infrastructures and evolution. Big data influence the evolution of semantics, knowledge and Grids. Exploring semantics, knowledge and Grids on big data helps accelerate the shift of scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies.