Zur Morphologie des Huehnerknochenmarkes unter normalen Bedingungen und bei verschiedenen anaemischen Zustaenden

Available from: Zentralstelle fuer Agrardokumentation und -information (ZADI), Villichgasse 17, D-53177 Bonn / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Analysing the SEDs of protoplanetary disks with machine learning

Context. The analysis of spectral energy distributions (SEDs) of protoplanetary disks to determine their physical properties is known to be highly degenerate. Hence, a full Bayesian analysis is required to obtain parameter uncertainties and degeneracies. The main challenge here is computational speed, as one proper full radiative transfer model requires at least a couple of CPU minutes to compute. Aims. We performed a full Bayesian analysis for 30 well-known protoplanetary disks to determine their physical disk properties, including uncertainties and degeneracies. To circumvent the computational cost problem, we created neural networks (NNs) to emulate the SED generation process. Methods. We created two sets of MCFOST Monte Carlo radiative transfer disk models to train and test two NNs that predict SEDs for continuous and discontinuous disks, with 18 and 26 free model parameters, respectively. A Bayesian analysis was then performed on 30 protoplanetary disks with SED data collected by the FP7-Space DIANA project to determine the posterior distributions of all parameters. We ran this analysis twice, (i) with old distances and additional parameter constraints as used in a previous study, to compare results, and (ii) with updated distances and free choice of parameters to obtain homogeneous and unbiased model parameters. We evaluated the uncertainties in the determination of physical disk parameters from SED analysis, and detected and quantified the strongest degeneracies. Results. The NNs are able to predict SEDs within ∼1 ms with uncertainties of about 5% compared to the true SEDs obtained by the radiative transfer code. We find parameter values and uncertainties that are significantly different from previous values obtained by χ 2 fitting. Comparing the global evidence for continuous and discontinuous disks, we find that 26 out of 30 objects are better described by disks that have two distinct radial zones. The analysed sample shows a significant trend for massive disks to have small scale heights, which is consistent with lower midplane temperatures in massive disks. We find that the frequently used analytic relationship between disk dust mass and millimetre-flux systematically underestimates the dust mass for high-mass disks (dust mass ≥10-4 M⊙). We determine how well the dust mass can be determined with our method for different numbers of flux measurements. As a byproduct, we created an interactive graphical tool that instantly returns the SED predicted by our NNs for any parameter combination.</p

Understanding Sustainability Innovations Through Positive Ethical Networks

In this paper, a positive organizational ethics (POE)-based framework is informed by the microfinance and socially responsible investing movements to capture the process of sustainable financial innovations. Both of these movements are uniquely characterized by the formation of positive ethical networks (PENs) to develop sustainability innovations in response to external crises. The crisis–PEN–innovation framework proposed makes four contributions to the POE literature: (1) positions corporate sustainability through a POE lens; (2) formalizes the PEN construction through POE theory; (3) proposes PENs are mobilized to respond to external crises; and (4) demonstrates how PENs facilitate sustainability innovations. The theoretical framework is tested using theory-guided process tracing in the sustainable banking sector to understand how sustainability innovations were realized. The findings are consistent with the crisis–PEN–innovation framework proposed