Probing Anomalous Quartic Couplings in e-gamma and gamma-gamma Colliders

We analyze the potential of the e+e- Linear Colliders, operating in the e-gamma and gamma-gamma modes, to probe anomalous quartic vector--boson interactions through the multiple production of W's and Z's. We examine all $SU(2)_L \otimes U(1)_Y$ chiral operators of order p^4 that lead to new four--gauge--boson interactions but do not alter trilinear vertices. We show that the e-gamma and gamma-gamma modes are able not only to establish the existence of a strongly interacting symmetry breaking sector but also to probe for anomalous quartic couplings of the order of 10^{-2} at 90% CL. Moreover, the information gathered in the e-gamma mode can be used to reduced the ambiguities of the e+e- mode.Comment: Revtex, 18 pages, 6 figure

Triple Electroweak Gauge-Boson Production at Fermilab Tevatron Energies

We calculate the three gauge-boson production in the Standard Model at Fermilab Tevatron energies. At $\sqrt s=2$ TeV in $p\bar p$ collisions, the cross sections for the triple gauge-boson production are typically of order 10 femtobarns (fb). For the pure leptonic final states from the gauge-boson decays and with some minimal cuts on final state photons, the cross sections for $p \bar p \rightarrow W^\pm \gamma\gamma, Z\gamma\gamma$ and $W^+W^- \gamma$ processes are of order a few fb, resulting in a few dozen clean leptonic events for an integrated luminosity of 10 fb$^{-1}$. The pure leptonic modes from other gauge-boson channels give significantly smaller rate. Especially, the trilepton modes from $W^+W^-W^\pm$ and $t \bar t W^\pm \rightarrow W^+W^-W^\pm$ yield a cross section of order 0.1 fb if there is no significant Higgs boson contribution. For a Higgs boson with $m_H^{} \simeq 2M_Z^{}$, the triple massive-gauge-boson production rate could be enhanced by a factor of $4-6$.Comment: RevTeX 3.0; 14 pages plus 7 figures; ps files available via anonymous ftp at ftp://ucdhep.ucdavis.edu/han/vvv/vvv.ps,fig*_vvv.p

The regulatory mechanisms of NG2/CSPG4 expression

Neuron-glial antigen 2 (NG2), also known as chondroitin sulphate proteoglycan 4 (CSPG4), is a surface type I transmembrane core proteoglycan that is crucially involved in cell survival, migration and angiogenesis. NG2 is frequently used as a marker for the identification and characterization of certain cell types, but little is known about the mechanisms regulating its expression. In this review, we provide evidence that the regulation of NG2 expression underlies inflammation and hypoxia and is mediated by methyltransferases, transcription factors, including Sp1, paired box (Pax) 3 and Egr-1, and the microRNA miR129-2. These regulatory factors crucially determine NG2-mediated cellular processes such as glial scar formation in the central nervous system (CNS) or tumor growth and metastasis. Therefore, they are potential targets for the establishment of novel NG2-based therapeutic strategies in the treatment of CNS injuries, cancer and other conditions of these types

Metal-Substituted Microporous Aluminophosphates

This chapter aims to present the zeotypes aluminophosphates (AlPOs) as a complementary alternative to zeolites in the isomorphic incorporation of metal ions within all-inorganic microporous frameworks as well as to discuss didactically the catalytic consequences derived from the distinctive features of both frameworks. It does not intend to be a compilation of either all or the most significant publications involving metal-substituted microporous aluminophosphates. Families of AlPOs and zeolites, which include metal ion-substituted variants, are the dominant microporous materials. Both these systems are widely used as catalysts, in particular through aliovalent metal ions substitution. Here, some general description of the synthesis procedures and characterization techniques of the MeAPOs (metal-contained aluminophosphates) is given along with catalytic properties. Next, some illustrative examples of the catalytic possibilities of MeAPOs as catalysts in the transformation of the organic molecules are given. The oxidation of the hardly activated hydrocarbons has probably been the most successful use of AlPOs doped with the divalent transition metal ions Co2+, Mn2+, and Fe2+, whose incorporation in zeolites is disfavoured. The catalytic role of these MeAPOs is rationalized based on the knowledge acquired from a combination of the most advanced characterization techniques. Finally, the importance of the high specificity of the structure-directing agents employed in the preparation of MeAPOs is discussed taking N,N-methyldicyclohexylamine in the synthesis of AFI-structured materials as a driving force. It is shown how such a high specificity could be predicted and how it can open great possibilities in the control of parameters as critical in catalysis as crystal size, inter-and intracrystalline mesoporosity, acidity, redox properties, incorporation of a great variety of heteroatom ions or final environment of the metal site (surrounding it by either P or Al)

Modeling and forecasting US presidential election using learning algorithms

Abstract The primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six independent variables such as GDP, unemployment rate, the president’s approval rate, and others are considered in a stepwise regression to identify significant variables. The president’s approval rate is identified as the most significant variable, based on which eight other variables are identified and considered in the model development. Preprocessing methods are applied to prepare the data for the learning algorithms. The proposed procedure significantly increases the accuracy of the model by 50%. The learning algorithms (ANN and SVR) proved to be superior to linear regression based on each method’s calculated performance measures. The SVR model is identified as the most accurate model among the other models as this model successfully predicted the outcome of the election in the last three elections (2004, 2008, and 2012). The proposed approach significantly increases the accuracy of the forecast

Importance analysis considering time-varying parameters and different perturbation occurrence times

Importance measures are integral parts of risk assessment for risk-informed decision making. Because the parameters of a risk model, such as the component failure rates, are functions of time and a perturbation (change) in their values can occur during the mission time, time dependence must be considered in the evaluation of the importance measures. In this paper, it is shown that the change in system performance at time t, and consequently the importance of the parameters at time t, depends on the parameters perturbation time and their value functions during the system mission time. We consider a nonhomogeneous continuous time Markov model of a series-parallel system to propose the mathematical proofs and simulations, while the ideas are also shown to be consistent with general models having nonexponential failure rates. Two new measures of importance and a simulation scheme for their computation are introduced to account for the effect of perturbation time and time-varying parameters

An advanced teaching-learning-based algorithm to solve unconstrained optimization problems

The Teaching-Learning-Based Optimization (TLBO) algorithm is being extended to a broader range of applied optimization problems in the literature, mimicking the teaching-learning process. This paper proposes an Advanced Teaching-Learning-Based Optimization (Ad-TLBO) algorithm to enhance the efficiency and performance of the original version of TLBO in terms of accuracy, convergence rate, and reliability characteristics. The advancement is obtained by modifying the initialization, search approach, and structure of the two main phases of this algorithm in four steps to improve exploration and exploitation capability. Efficiency comparisons are shown in four challenges with various benchmark functions with multimodal, separable, differentiable, and continuity characteristics. The results are compared with several intelligent optimization algorithms. It is also deduced that this algorithm outperforms all investigated optimization algorithms in terms of accuracy, convergence speed, and success to reach acceptable solutions for various benchmark functions