W Ϯ H ϯ Z i (iϭ1,2) vertices available exclusively in the extra U͑1͒ superstring-inspired model have been utilized to evaluate the W-W-␥ vertex contribution at the one loop level. The expressions so obtained have been used to estimate the anomalous magnetic dipole moment (⌬k WZ i H ) and electric quadrupole moment (⌬Q WZ i H ) of the W ϩ boson. The contribution of the Z 2 WW vertex is also added to these values. The resulting values in the unit of (Ϫ␣/) when the Z 2 mass varies from 555 to 620 GeV have the following ranges: for m t(CDF) ϭ175.6 GeV, ⌬k ͓U(1)͔ , from 25.402 to 41.559, ⌬Q ͓U(1)͔ , from 6.886 to 10.858; for m t(D0) ϭ169 GeV, ⌬k ͓U(1)͔ , from 20.821 to 34.121, ⌬Q ͓U(1)͔ , from 5.738 to 9.033. These are larger than the standard model radiative correction contributions but an order of magnitude smaller than those predicted by the composite model of Abbott and Farhi. ͓S0556-2821͑97͒03619-9͔ PACS number͑s͒: 12.60. Fr, 14.70.Fm In recent years precision electroweak measurements have started acquiring a sensitivity at which they are capable of testing the standard model ͑SM͒ having the gauge symmetry SU(3) C ϫSU(2) L ϫU(1) Y at the level of its electroweak radiative corrections and exploring the small ''new physics'' effects ͓1-3͔. Already the precision measurements of the weak interaction parameters have reached the level of 1% accuracy of the determination of the W mass and the parameters of the Z 0 resonance. As such, any deviation from standard model expectations could be a hint for additional tree level interactions involving physics beyond the SM, or a signal for further loop corrections. The prevailing framework for physics beyond the SM includes technicolor theories including composite models ͓4-7͔, supersymmetry, and superstring theories ͓8͔.An interesting outcome of the precision measurement of various electroweak parameters has been the prediction that the parameters ͓9͔ S ͑and possibly T͒ should have negative values ͓1,9,10͔. In fact the result has been used to constrain ͓1͔ the technicolor models which by and large give positive S values, with the exception of a recent observation by Luty and Sundrum ͓11͔. On the other hand, Marciano and Rosner ͓1͔ and Holdom ͓12͔ have shown that models having an extra U͑1͒ with an additional ZЈ do give a negative contribution to S. Recently we have been exploring the phenomenology of the extra U͑1͒ models emanating from the E 6 group in superstring theory ͓13͔. The fact that the extra U͑1͒ model may provide a viable alternative as a model for physics beyond the SM has further motivated us to explore some new aspects related to this model, in particular, our recent calculations for the S, T, U parameters in the extra U͑1͒ model do give reasonable negative values ͓14͔.The static properties of the W boson, such as the anomalous magnetic dipole moment (⌬k) and electric quadrupole moment (⌬Q) are two important quantities and may provide a crucial test for the SM. Bardeen et al. and others ͓15͔, have calculated these quantities in the SM in the massless fermion l...
In the era of Web 2.0, the data are growing immensely and is assisting E-commerce websites for better decision-making. Collaborative filtering, one of the prominent recommendation approaches, performs recommendation by finding similarity. However, this approach fails in managing large-scale datasets. To mitigate the same, an efficient map-reduce-based clustering recommendation system is presented. The proposed method uses a novel variant of the whale optimization algorithm, tournament selection empowered whale optimization algorithm, to attain the optimal clusters. The clustering efficiency of the proposed method is measured on four large-scale datasets in terms of F-measure and computation time. The experimental results are compared with state-of-the-art map-reduce-based clustering methods, namely map-reduce-based K-means, map-reduce-based bat algorithm, map-reduce-based Kmeans particle swarm optimization, map-reduce-based artificial bee colony, and map-reduce-based whale optimization algorithm. Furthermore, the proposed method is tested as a recommendation system on the publicly available movie-lens dataset. The performance validation is measured in terms of mean absolute error, precision and recall, over a different number of clusters. The experimental results assert that the proposed method is a permissive approach for the recommendation over large-scale datasets.
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