Multi-Criteria Decision-Making for Power Grid Construction Project Investment Ranking Based on the Prospect Theory Improved by Rewarding Good and Punishing Bad Linear Transformation

Using the improved prospect theory with the linear transformations of rewarding good and punishing bad(RGPBIT),a new investment ranking model for power grid construction projects(PGCPs)is proposed.Given the uncertainty of each index value under the market environment,fuzzy numbers are used to describe qualitative indicators and interval numbers are used to describe quantitative ones.Taking into account decision-maker’s subjective risk attitudes,a multi-criteria decision-making(MCDM)method based on improved prospect theory is proposed.First,the[−1,1]RGPBIT operator is proposed to normalize the original data,to obtain the best andworst schemes of PGCPs.Furthermore,the correlation coefficients between interval/fuzzy numbers and the best/worst schemes are defined and introduced to the prospect theory to improve its value function and loss function,and the positive and negative prospect value matrices of the project are obtained.Then,the optimization model with the maximum comprehensive prospect value is constructed,the optimal attribute weight is determined,and the PGCPs are ranked accordingly.Taking four PGCPs of the IEEERTS-79 node system as examples,an illustration of the feasibility and effectiveness of the proposed method is provided.

Precision Higgs physics at the CEPC

The discovery of the Higgs boson with its mass around 125 GeV by the ATLAS and CMS Collaborations marked the beginning of a new era in high energy physics.The Higgs boson will be the subject of extensive studies of the ongoing LHC program.At the same time,lepton collider based Higgs factories have been proposed as a possible next step beyond the LHC,with its main goal to precisely measure the properties of the Higgs boson and probe potential new physics associated with the Higgs boson.The Circular Electron Positron Collider(CEPC)is one of such proposed Higgs factories.The CEPC is an e^+e^- circular collider proposed by and to be hosted in China.Located in a tunnel of approximately 100 km in circumference,it will operate at a center-of-mass energy of 240 GeV as the Higgs factory.In this paper,we present the first estimates on the precision of the Higgs boson property measurements achievable at the CEPC and discuss implications of these measurements.

ArborTracking:a tree topology track pattern recognition algorithm

Purpose The purpose of this work is to develop a novel pattern tracking algorithm to be used on the detectors of the future electron-positron colliders.Method ArborTracking,a light-weighted tracking algorithm,has been developed based on the tree topology of track clusters and applied to the baseline detector of the circular electron-positron collider(CEPC).The algorithm collects all the hits in the tracker as a tree(forest),splits the tree branches to form the track segments,and merges the track segments to form the tracks.Results Compared with the general track following method,the algorithm has the advantages of low coding complicity and low CPU cost.The performances at different benchmarks are studied.The results are exhaustively listed showing that the method is approaching the limit of the detector.The tracking efficiencies on single muon sample and three-prong sample are both higher than 99%.The transverse momentum resolution reaches 0.1%level and the boson mass resolution reaches 4.7 GeV/c.Conclusions The performances are similar with those of the baseline tracking algorithm of CEPC,and the physics requirement of CEPC is satisfied.The new tree pattern recognition algorithm is a necessary part in the CEPC software.And it is also a competitive algorithm on the market,which can be chosen by the future experiments.