Partial Extinction, Permanence, and Global Attractivity in Nonautonomous n-Species Gilpin-Ayala Competitive Systems with Impulses

The qualitative properties of general nonautonomous n-species Gilpin-Ayala competitive systems with impulsive effects are studied. Some new criteria on the permanence, extinction, and global attractivity of partial species are established by using the methods of inequalities estimate and Liapunov functions

NLO QCD corrections to Single Top and W associated production at the LHC with forward detector acceptances

In this paper we study the Single Top and W boson associated photoproduction via the main reaction $\rm pp\rightarrow p\gamma p\rightarrow pW^{\pm}t+Y$ at the 14 TeV Large Hadron Collider (LHC) up to next-to-leading order (NLO) QCD level assuming a typical LHC multipurpose forward detector. We use the Five-Flavor-Number Schemes (5FNS) with massless bottom quark assumption in the whole calculation. Our results show that the QCD NLO corrections can reduce the scale uncertainty. The typical K-factors are in the range of 1.15 to 1.2 which lead to the QCD NLO corrections of 15$$ to 20$$ correspond to the leading-order (LO) predictions with our chosen parameters.Comment: 41pages, 12figures. arXiv admin note: text overlap with arXiv:1106.2890 by other author

5-(4-Methyl­phenyl­sulfon­yl)-1,3-dithiolo[4,5-c]pyrrole-2-thione

The asymmetric unit of the title compound, C12H9NO2S4, contains one half-mol­ecule with the N, two S amd four C atoms lying on a mirror plane. The mol­ecule exhibits a V-shaped conformation, with a dihedral angle of 87.00 (7)° between the benzene and dithiol­opyrrole rings. The methyl group was treated as rotationally disordered between two orientations in a 1:1 ratio. In the crystal, weak C—H⋯O hydrogen bonds link the mol­ecules into chains in [010]

Generalized multiscale finite element methods (GMsFEM)

In this paper, we propose a general approach called Generalized Multiscale Finite Element Method (GMsFEM) for performing multiscale simulations for problems without scale separation over a complex input space. As in multiscale finite element methods (MsFEMs), the main idea of the proposed approach is to construct a small dimensional local solution space that can be used to generate an efficient and accurate approximation to the multiscale solution with a potentially high dimensional input parameter space. In the proposed approach, we present a general procedure to construct the offline space that is used for a systematic enrichment of the coarse solution space in the online stage. The enrichment in the online stage is performed based on a spectral decomposition of the offline space. In the online stage, for any input parameter, a multiscale space is constructed to solve the global problem on a coarse grid. The online space is constructed via a spectral decomposition of the offline space and by choosing the eigenvectors corresponding to the largest eigenvalues. The computational saving is due to the fact that the construction of the online multiscale space for any input parameter is fast and this space can be re-used for solving the forward problem with any forcing and boundary condition. Compared with the other approaches where global snapshots are used, the local approach that we present in this paper allows us to eliminate unnecessary degrees of freedom on a coarse-grid level. We present various examples in the paper and some numerical results to demonstrate the effectiveness of our method

Optimizing Ontology Alignments through NSGA-II without Using Reference Alignment

Ontology is widely used to solve the data heterogeneity problems on the semantic web, but the available ontologies could themselves introduce heterogeneity. In order to reconcile these ontologies to implement the semantic interoperability, we need to find the relationships among the entities in various ontologies, and the process of identifying them is called ontology alignment. In all the existing matching systems that use evolutionary approaches to optimize their parameters, a reference alignment between two ontologies to be aligned should be given in advance which could be very expensive to obtain especially when the scale of ontologies is considerably large. To address this issue, in this paper we propose a novel approach to utilize the NSGA-II to optimize the ontology alignments without using the reference alignment. In our approach, an adaptive aggregation strategy is presented to improve the efficiency of optimizing process and two approximate evaluation measures, namely match coverage and match ratio, are introduced to replace the classic recall and precision on reference alignment to evaluate the quality of the alignments. Experimental results show that our approach is effective and can find the solutions that are very close to those obtained by the approaches using reference alignment, and the quality of alignments is in general better than that of state of the art ontology matching systems such as GOAL and SAMBO