Årbok 1964

To date, only one study by Strick and Volbeda (2018), titled ‘When the valence of unconditioned stimuli evolves over time: Evaluative conditioning with good-ending and bad-ending stories’, investigated stories in the context of evaluative conditioning to change brand attitudes. To find additional support for stories as unconditioned stimuli, we performed a partial replication of this study. As an extension, we also investigated the role of the need for affect as a mediator in this conditioning process. Our study had a within-subject design, in which MTurk workers (N = 66) participated in both our good- and bad-ending story conditions. In line with the original study and our hypothesis, our results suggest that the valence of the story ending determines the direction of the conditioning effect. Brands presented after good-ending stories have a stronger brand liking than brands presented after bad-ending stories. In practice, this would imply that advertisements should always end positively to induce a positive brand evaluation. Furthermore, as we hypothesized, our results indicate that the need for affect mediates this conditioning effect as people with a high need for affect rate brands more emotionally and strongly according to the story-ending valence than people with a low need for affect. Future research may distinguish other characteristics that mediate this effect to identify separate groups for targeted advertisements. To conclude, the ending of dramatic stories is determinative in brand evaluation when the brand is presented directly after, and the effect of these story endings is mediated by the need for affect

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis