Search for heavy neutral resonances in the dielectron channel with the CMS detector at the LHC

A search for dielectron decays of heavy neutral resonances has been performed using proton-proton collision data collected at √s = 7 TeV by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) in 2011. The data sample corresponds to an integrated luminosity of 5 fb⁻¹. The dielectron mass distribution is consistent with Standard Model (SM) predictions. An upper limit on the ratio of the cross section times branching fraction of new bosons, normalized to the cross section times branching fraction of the Z boson, is set at the 95 % confidence level. This result is translated into limits on the mass of new neutral particles at the level of 2120 GeV for the Z′ in the Sequential Standard Model, 1810 GeV for the superstring-inspired Z′_ψ resonance, and 1940 (1640) GeV for Kaluza-Klein gravitons with the coupling parameter k/M_Pl of 0.10 (0.05).

Optimal design of building structures using genetic algorithms

A general framework for multi-criteria optimal design is presented which is well-suited for automated design of structural systems. A systematic computer-aided optimal design decision process is developed which allows the designer to rapidly evaluate and improve a proposed design by taking into account the major factors of interest related to different aspects such as design, construction, and operation.

The proposed optimal design process requires the selection of the most promising choice of design parameters taken from a large design space, based on an evaluation using specified criteria. The design parameters specify a particular design, and so they relate to member sizes, structural configuration, etc. The evaluation of the design uses performance parameters which may include structural response parameters, risks due to uncertain loads and modeling errors, construction and operating costs, etc. Preference functions are used to implement the design criteria in a "soft" form. These preference functions give a measure of the degree of satisfaction of each design criterion. The overall evaluation measure for a design is built up from the individual measures for each criterion through a preference combination rule. The goal of the optimal design process is to obtain a design that has the highest overall evaluation measure - an optimization problem.

Genetic algorithms are stochastic optimization methods that are based on evolutionary theory. They provide the exploration power necessary to explore high-dimensional search spaces to seek these optimal solutions. Two special genetic algorithms, hGA and vGA, are presented here for continuous and discrete optimization problems, respectively.

The methodology is demonstrated with several examples involving the design of truss and frame systems. These examples are solved by using the proposed hGA and vGA.