A comparison of a genetic algorithm with an experimental design technique in the optimization of a production process

This paper describes the optimization of a textile process by means of two differing approaches: the use of a genetic algorithm and the use of an experimental design technique. It considers the efficiency of each method in reaching the optimum and the nature of the information obtained regarding the process. Consideration is given to the success of the optimization and the transferability of the approaches to other industrial processes.     

Manifold preprocessing for laser‐induced breakdown spectroscopy under Mars conditions

Laser‐induced breakdown spectroscopy (LIBS) is currently being used onboard the Mars Science Laboratory rover Curiosity to predict elemental abundances in dust, rocks, and soils using a partial least squares regression model developed by the ChemCam team. Accuracy of that model is constrained by the number of samples needed in the calibration, which grows exponentially with the dimensionality of the data, a phenomenon known as the curse of dimensionality. LIBS data are very high dimensional, and the number of ground‐truth samples (i.e., standards) recorded with the ChemCam before departing for Mars was small compared with the dimensionality, so strategies to optimize prediction accuracy are needed. In this study, we first use an existing machine learning algorithm, locally linear embedding (LLE), to combat the curse of dimensionality by embedding the data into a low‐dimensional manifold subspace before regressing. LLE constructs its embedding by maintaining local neighborhood distances and discarding large global geodesic distances between samples, in an attempt to preserve the underlying geometric structure of the data. We also introduce a novel supervised version, LLE for regression (LLER), which takes into account the known chemical composition of the training data when embedding. LLER is shown to outperform traditional LLE when predicting most major elements. We show the effectiveness of both algorithms using three different LIBS datasets recorded under Mars‐like conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

Improved search for Muon-neutrino to electron-neutrino oscillations in MINOS

We report the results of a search for ν(e) appearance in a ν(μ) beam in the MINOS long-baseline neutrino experiment. With an improved analysis and an increased exposure of 8.2 × 10(20) protons on the NuMI target at Fermilab, we find that 2 sin(2) (θ(23))sin(2)(2θ(13))<0.12(0.20) at 90% confidence level for δ = 0 and the normal (inverted) neutrino mass hierarchy, with a best-fit of 2sin(2) (θ(23))sin(2)(2θ(13)) = 0.041(-0.031)(+0.047) (0.079(-0.053) (+0.071)). The θ(13) = 0 hypothesis is disfavored by the MINOS data at the 89% confidence level.     

Do columnar defects produce bulk pinning?

From magneto-optical imaging performed on heavy-ion-irradiated YBa(2)Cu(3)O(7-delta) single crystals, it is found that at fields and temperatures where strong single vortex pinning by individual irradiation-induced amorphous columnar defects is to be expected, vortex motion is limited by the nucleation of vortex kinks at the specimen surface. In the material bulk, vortex motion occurs through (easy) kink sliding. Depinning in the bulk determines the screening current only at fields comparable to or larger than the matching field, at which the majority of vortices is not trapped by an ion track.     

First direct observation of muon antineutrino disappearance

This Letter reports the first direct observation of muon antineutrino disappearance. The MINOS experiment has taken data with an accelerator beam optimized for ν(μ) production, accumulating an exposure of 1.71 × 102? protons on target. In the Far Detector, 97 charged current ν(μ) events are observed. The no-oscillation hypothesis predicts 156 events and is excluded at 6.3σ. The best fit to oscillation yields |Δm2| = [3.36(-0.40)(+0.46)(stat) ± 0.06(syst)] × 10?3 eV2, sin2(2θ) = 0.86(-0.12)(+0.11)(stat) ± 0.01(syst). The MINOS ν(μ) and ν(μ) measurements are consistent at the 2.0% confidence level, assuming identical underlying oscillation parameters.