The neural network (NN) method is applied to mechanical properties estimation and alloy development of single crystal superalloys. Databases have been constructed from previous publications and the Rolls-Royce materials database. The Bayesian neural network technique was used for the modeling of mechanical properties of single crystal superalloys in terms of alloy compositions and test conditions. Creep lives, yield strengths, and ultimate tensile strengths of various superalloys as a function of contents of alloying elements are estimated and analyzed. New alloys were designed by calculations of various properties as well as creep rupture lives for millions of compositions, followed by selection of optimized alloys. The developed alloys are made in single crystal form by directional solidification and tested. They exhibit excellent phase stability and creep rupture lives which are better than or equivalent to those of CMSX-4.

Abstract. Many physics-based numerical models produce a gridded, spatial field of forecasts, e.g., a temperature "map". The field for some quantities generally consists of spatially coherent and disconnected "objects". Such objects arise in many problems, including precipitation forecasts in atmospheric models, eddy currents in ocean models, and models of forest fires. Certain features of these objects (e.g., location, size, intensity, and shape) are generally of interest. Here, a methodology is developed for assessing the impact of model parameters on the features of forecast objects. The main ingredients of the methodology include the use of (1) Latin hypercube sampling for varying the values of the model parameters, (2) statistical clustering algorithms for identifying objects, (3) multivariate multiple regression for assessing the impact of multiple model parameters on the distribution (across the forecast domain) of object features, and (4) methods for reducing the number of hypothesis tests and controlling the resulting errors. The final "output" of the methodology is a series of box plots and confidence intervals that visually display the sensitivities. The methodology is demonstrated on precipitation forecasts from a mesoscale numerical weather prediction model.

1. Automated, ship-board flow cytometers provide high-resolution maps of phytoplankton composition over large swaths of the world's oceans. They therefore pave the way for understanding how environmental conditions shape community structure. Identification of community changes along a cruise transect commonly segments the data into distinct regions. However, existing segmentation methods are generally not applicable to flow cytometry data, as this data is recorded as "point cloud" data, with hundreds or thousands of particles measured during each time interval. Moreover, nonparametric segmentation methods that do not rely on prior knowledge of the number of species are desirable to map community shifts.2. We present CytoSegmenter, a kernel-based change-point estimation method for segmenting point cloud data. Our method allows us to represent and summarize a point cloud of data points by a single element in a Hilbert space. The change-point locations can be found using a fast dynamic programming algorithm. 3.Through an analysis of 12 cruises, we demonstrate that CytoSegmenter allows us to locate abrupt changes in phytoplankton community structure. We show that the changes in community structure generally coincide with changes in the temperature and salinity of the ocean. We also illustrate how the main parameter of CytoSegmenter can be easily calibrated using limited auxiliary annotated data. 1 Accepted Article Methods in Ecology and EvolutionThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

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