Data visualization in yield component analysis: an expert study

Even though data visualization is a common analytical tool in numerous disciplines, it has rarely been used in agricultural sciences, particularly in agronomy. In this paper, we discuss a study on employing data visualization to analyze a multiplicative model. This model is often used by agronomists, for example in the so-called yield component analysis. The multiplicative model in agronomy is normally analyzed by statistical or related methods. In practice, unfortunately, usefulness of these methods is limited since they help to answer only a few questions, not allowing for a complex view of the phenomena studied. We believe that data visualization could be used for such complex analysis and presentation of the multiplicative model. To that end, we conducted an expert survey. It showed that visualization methods could indeed be useful for analysis and presentation of the multiplicative model

MODIS time series contribution for the estimation of nutritional properties of alpine grassland

This is an Accepted Manuscript of an article published by Taylor & Francis in European Journal of Remote Sensing on 17th February 2017, available online: https://doi.org/10.5721/EuJRS20164936Despite the Normalised Difference Vegetation Index (NDVI) has been used to make predictions on forage quality, its relationship with bromatological field data has not been widely tested. This relationship was investigated in alpine grasslands of the Gran Paradiso National Park (Italian Alps). Predictive models were built using remotely sensed derived variables (NDVI and phenological information computed from MODIS) in combination with geo-morphometric data as predictors of measured biomass, crude protein, fibre and fibre digestibility, obtained from 142 grass samples collected within 19 experimental plots every two weeks during the whole 2012 growing season. The models were both cross-validated and validated on an independent dataset (112 samples collected during 2013). A good predictability ability was found for the estimation of most of the bromatological measures, with a considerable relative importance of remotely sensed derived predictors; instead, a direct use of NDVI values as a proxy of bromatological variables appeared not to be supported

Nonlinear Lattice Waves in Random Potentials

Localization of waves by disorder is a fundamental physical problem encompassing a diverse spectrum of theoretical, experimental and numerical studies in the context of metal-insulator transition, quantum Hall effect, light propagation in photonic crystals, and dynamics of ultra-cold atoms in optical arrays. Large intensity light can induce nonlinear response, ultracold atomic gases can be tuned into an interacting regime, which leads again to nonlinear wave equations on a mean field level. The interplay between disorder and nonlinearity, their localizing and delocalizing effects is currently an intriguing and challenging issue in the field. We will discuss recent advances in the dynamics of nonlinear lattice waves in random potentials. In the absence of nonlinear terms in the wave equations, Anderson localization is leading to a halt of wave packet spreading. Nonlinearity couples localized eigenstates and, potentially, enables spreading and destruction of Anderson localization due to nonintegrability, chaos and decoherence. The spreading process is characterized by universal subdiffusive laws due to nonlinear diffusion. We review extensive computational studies for one- and two-dimensional systems with tunable nonlinearity power. We also briefly discuss extensions to other cases where the linear wave equation features localization: Aubry-Andre localization with quasiperiodic potentials, Wannier-Stark localization with dc fields, and dynamical localization in momentum space with kicked rotors.Comment: 45 pages, 19 figure

Local linear regression with adaptive orthogonal fitting for the wind power application

Short-term forecasting of wind generation requires a model of the function for the conversion of me-teorological variables (mainly wind speed) to power production. Such a power curve is nonlinear and bounded, in addition to being nonstationary. Local linear regression is an appealing nonparametric ap-proach for power curve estimation, for which the model coefficients can be tracked with recursive Least Squares (LS) methods. This may lead to an inaccurate estimate of the true power curve, owing to the assumption that a noise component is present on the response variable axis only. Therefore, this assump-tion is relaxed here, by describing a local linear regression with orthogonal fit. Local linear coefficients are defined as those which minimize a weighted Total Least Squares (TLS) criterion. An adaptive es-timation method is introduced in order to accommodate nonstationarity. This has the additional benefit of lowering the computational costs of updating local coefficients every time new observations become available. The estimation method is based on tracking the left-most eigenvector of the augmented covari-ance matrix. A robustification of the estimation method is also proposed. Simulations on semi-artificial datasets (for which the true power curve is available) underline the properties of the proposed regression and related estimation methods. An important result is the significantly higher ability of local polynomia

Cost calculation and prediction in adult intensive care: A ground-up utilization study

Publisher's copy made available with the permission of the publisherThe ability of various proxy cost measures, including therapeutic activity scores (TISS and Omega) and cumulative daily severity of illness scores, to predict individual ICU patient costs was assessed in a prospective “ground-up” utilization costing study over a six month period in 1991. Daily activity (TISS and Omega scores) and utilization in consecutive admissions to three adult university associated ICUs was recorded by dedicated data collectors. Cost prediction used linear regression with determination (80%) and validation (20%) data sets. The cohort, 1333 patients, had a mean (SD) age 57.5 (19.4) years, (41% female) and admission APACHE III score of 58 (27). ICU length of stay and mortality were 3.9 (6.1) days and 17.6% respectively. Mean total TISS and Omega scores were 117 (157) and 72 (113) respectively. Mean patient costs per ICU episode (1991 $AUS) were$6801 ($10311), with median costs of$2534, range $106 to$95,602. Dominant cost fractions were nursing 43.3% and overheads 16.9%. Inflation adjusted year 2002 (mean) costs were $9343 ($ AUS). Total costs in survivors were predicted by Omega score, summed APACHE III score and ICU length of stay; determination R2, 0.91; validation 0.88. Omega was the preferred activity score. Without the Omega score, predictors were age, summed APACHE III score and ICU length of stay; determination R2, 0.73; validation 0.73. In non-survivors, predictors were age and ICU length of stay (plus interaction), and Omega score (determination R2, 0.97; validation 0.91). Patient costs may be predicted by a combination of ICU activity indices and severity scores.J. L. Moran, A. R. Peisach, P. J. Solomon, J. Martinhttp://www.aaic.net.au/Article.asp?D=200403