Responsiveness of the Early Childhood Oral Health Impact Scale (ECOHIS) is related to dental treatment complexity

Abstract\ud \ud Background\ud The responsiveness of the Early Childhood Oral Health Impact Scale (ECOHIS) has varied greatly across studies; hence, we hypothesized that this discrepancy could be related to the complexity of dental treatment received. Thus, we aimed to evaluate the responsiveness of the ECOHIS to changes in oral health-related quality of life (OHRQoL) following dental treatments of varying complexity in preschool children.\ud \ud \ud Methods\ud Preschool children aged 3 to 6 years were selected; their parents responded to the ECOHIS at baseline. The parents responded to the ECOHIS again and a global transition question 30 days after the children were treated. The type of treatment received by the children was categorized according to complexity, as follows: 1) non-operative treatment only, 2) restorative treatment, and 3) endodontic treatment and/or tooth extraction. Change scores and effect sizes (ES) were calculated for total scores, as well as considering the different treatment types and global transition question responses.\ud \ud \ud Results\ud Of the 152 children who completed the study, the ECOHIS yielded large ES for total scores (0.89). The children showed increasing ES values associated with better perception of improvement, assessed by the global transition question. The magnitude of ES after treatment was related to treatment complexity (0.53, 0.92 and 1.43, for children who received non-operative treatment only, restorative treatment, and endodontic treatment and/or tooth extraction, respectively).\ud \ud \ud Conclusions\ud Parents whose children required more complex dental treatment are more likely to perceive treatment-related changes to OHRQoL assessed with the ECOHIS.The study was funded by Conselho Nacional de Desenvolvimento Científico\ud e Tecnológico – CNPq (Grants # 471817/2012–0 and 306,304/2015–5),\ud Fundação de Amparo à Pesquisa do Estado de São Paulo – FAPESP (Grant #\ud 2012/24243–7), and Coordenação de Aperfeiçoamento de Pessoal de Nível\ud Superior (CAPES)

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