Natural Predictions for the Higgs Boson Mass and Supersymmetric Contributions to Rare Processes

In the context of No-Scale F-SU(5), a model defined by the convergence of the F-lipped SU(5) Grand Unified Theory, two pairs of hypothetical TeV scale vector-like supersymmetric multiplets with origins in F-theory, and the dynamically established boundary conditions of No-Scale Supergravity, we predict that the lightest CP-even Higgs boson mass lies within the range of 119.0 GeV to 123.5 GeV, exclusive of the vector-like particle contribution to the mass. With reports by the CMS, ATLAS, CDF, and D0 Collaborations detailing enticing statistical excesses in the vicinity of 120 GeV in searches for the Standard Model Higgs boson, all signs point to an imminent discovery. While basic supersymmetric constructions such as mSUGRA and the CMSSM have already suffered overwhelming reductions in viable parameterization during the LHC's initial year of operation, about 80% of the original No-Scale F-SU(5) model space remains viable after analysis of the first 1.1 fb^{-1} of integrated luminosity. This model is moreover capable of handily explaining the small excesses recently reported in the CMS multijet supersymmetry search, and also features a highly favorable "golden" subspace which may simultaneously account for the key rare process limits on the muon anomalous magnetic moment (g - 2) and the branching ratio of the flavor-changing neutral current decay b to s\gamma. In addition, the isolated mass parameter responsible for the global particle mass normalization, the gaugino boundary mass M_{1/2}, is dynamically determined at a secondary local minimization of the minimum of the Higgs potential V_{min}, in a manner which is deeply consistent with all precision measurements at the physical electroweak scale.Comment: Physics Letters B Version, 10 pages, 2 figures, 2 table

Three new synonyms of Pohlia (Bryaceae) from China

Three species of Webera and Pohlia described from China were reduced to new synonyms of other species of Pohlia. Webera ciliifera Broth. is a synonym of Pohlia elongata Hedw., W. pygmaea Broth. is a synonym of P. minor Schleich. ex Schwaegr. and P. subflexuosa Broth. is a synonym of P. flexuosa Hook

Finite-time and fixed-time sliding mode control for second-order nonlinear multiagent systems with external disturbances

In this paper, the leader-following consensus of second-order nonlinear multiagent systems (SONMASs) with external disturbances is studied. Firstly, based on terminal sliding model control method, a distributed control protocol is proposed over undirected networks, which can not only suppress the external disturbances, but also make the SONMASs achieve consensus in finite time. Secondly, to make the settling time independent of the initial values of systems, we improve the protocol and ensure that the SONMASs can reach the sliding surface and achieve consensus in fixed time if the control parameters satisfy some conditions. Moreover, for general directed networks, we design a new fixed-time control protocol and prove that both the sliding mode surface and consensus for SONMASs can be reached in fixed time. Finally, several numerical simulations are given to show the effectiveness of the proposed protocols

A stream processing framework based on linked data for information collaborating of regional energy networks

© 2005-2012 IEEE. Coordinating of energy networks to form a city-level multidimensional integrated energy system becomes a new trend in Energy Internet (EI). The collaborating in the information layer is a core issue to achieve smart integration. However, the heterogeneity of multiagent data, the volatility of components, and the real-time analysis requirement in EI bring significant challenges. To solve these problems, in this article we propose a stream processing framework based on linked data for information collaboration among multiple energy networks. The framework provides a universal data representation based on linked data and semantic relation discovery approach to model and semantically fuse heterogeneous data. Semantics-based information transmission contracts and channels are automatically generated to adapt to structural changes in EI. A multimodel-based dynamic adjusting stream processing is implemented using data semantics. A real-world case study is implemented to demonstrate the adaptability, feasibility, and flexibility of the proposed framework

Deep Learning-assisted Accurate Defect Reconstruction Using Ultrasonic Guided Waves:一种基于深度学习的超声导波缺陷重构方法

Ultrasonic guided wave technology has played a significant role in the field of nondestructive testing due to its advantages of high propagation efficiency and low energy consumption. At present, the existing methods for structural defect detection and quantitative reconstruction of defects by ultrasonic guided waves are mainly derived from the guided wave scattering theory. However, taking into account the high complexity in guided wave scattering problems, assumptions such as Born approximation used to derive theoretical solutions lead to poor quality of the reconstructed results. Other methods, for example, optimizing iteration, improve the accuracy of reconstruction, but the time cost in the process of detection has remarkably increased. To address these issues, a novel approach to quantitative reconstruction of defects based on the integration of convolutional neural network with guided wave scattering theory has been proposed in this paper. The neural network developed by this deep learning-assisted method has the ability to quantitatively predict the reconstruction of defects, reduce the theoretical model error and eliminate the impact of noise pollution in the process of inspection on the accuracy of results. To demonstrate the advantage of the developed method for defect reconstruction, the thinning defect reconstructions in plate have been examined. Results show that this approach has high levels of efficiency and accuracy for reconstruction of defects in structures. Especially, for the reconstruction of the rectangle defect, the result by the proposed method is nearly 200% more accurate than the solution by the method of wavenumber-space transform. For the signals polluted with Gaussian noise, i.e., 15 db, the proposed method can improve the accuracy of reconstruction of defects by 71% as compared with the quality of results by the tradional method of wavenumber-space transform. In practical applications, the integration of theoretical reconstruction models with the neural network technique can provide a useful insight into the high-precision reconstruction of defects in the field of non-destruction testing