Automated Determination of Stellar Parameters from Simulated Dispersed Images for DIVA

We have assessed how well stellar parameters (T_eff, logg and [Fe/H]) can be retrieved from low-resolution dispersed images to be obtained by the DIVA satellite. Although DIVA is primarily an all-sky astrometric mission, it will also obtain spectrophotometric information for about 13 million stars (operational limiting magnitude V ~ 13.5 mag). Constructional studies foresee a grating system yielding a dispersion of ~200nm/mm on the focal plane (first spectral order). For astrometric reasons there will be no cross dispersion which results in the overlapping of the first to third diffraction orders. The one-dimensional, position related intensity function is called a DISPI (DISPersed Intensity). We simulated DISPIS from synthetic spectra (...) for a limited range of metallicites i.e. our results are for [Fe/H] in the range -0.3 to 1 dex. We show that there is no need to deconvolve these low resolution signals in order to obtain basic stellar parameters. Using neural network methods and by including simulated data of DIVA's UV telescope, we can determine T_eff to an average accuracy of about 2% for DISPIS from stars with 2000 K < T_eff < 20000 K and visual magnitudes of V=13 mag (end of mission data). logg can be determined for all temperatures with an accuracy better than 0.25 dex for magnitudes brighter than V=12 mag. For low temperature stars with 2000 K < T_eff < 5000 K and for metallicities in the range -0.3 to +1 dex a determination of [Fe/H] is possible (to better than 0.2 dex) for these magnitudes. Additionally we examined the effects of extinction E(B-V) on DISPIS and found that it can be determined to better than 0.07 mag for magnitudes brighter than V=14 mag if the UV information is included.Comment: 12 pages, 8 figures, Accepted for publication in A&

New Evolutionary Synthesis code. An application to the irregular galaxy NGC 1560

We have developed a new evolutionary synthesis code, which incorporates the output from chemical evolution models. We compare results of this new code with other published codes, and we apply it to the irregular galaxy NGC 1560 using sophisticated chemical evolution models. The code makes important contributions in two areas: a) the building of synthetic populations with time-dependent star formation rates and stellar populations of different metallicities; b) the extension of the set of stellar tracks from the Geneva group by adding the AGB phases for $m_i/M_\odot \geq 0.8$ as well as the very low mass stars. Our code predicts spectra, broad band colors, and Lick indices by using a spectra library, which cover a more complete grid of stellar parameters. The application of the code with the chemical models to the galaxy NGC 1560 constrain the star formation age for its stellar population around 10.0 Gy.Comment: 10 pages, 15 figures, submited to A&

From Finnish AEC knowledge ecosystem to business ecosystem: lessons learned from the national deployment of BIM

Government actors, public agencies, industry and academics have struggled to change the rules of the existing business ecosystem to support the networked practices that were envisioned back in the 1980s with the introduction of building information modelling (BIM). Despite the industry’s far-reaching technological capabilities, BIM has primarily assumed productivity improvement by individual firms, which has not lead to a systemic change in the Finnish architecture, engineering and construction (AEC) business ecosystem. A field study of the Finnish AEC industry has resulted in a critical understanding of why successful and intensive R&D at a national level and wide adoption of BIM technology in Finland has not led to the expected systemic evolution of its AEC business ecosystem. Additionally, a methodology based on inductive grounded theory and historical analysis has been used to capture and identify the evolving and dynamic relationships between various events and actors between 1965 and 2015, which, in turn, has aided in the identification and characterisation of the knowledge and innovation ecosystems. The research findings provide insights for BIM researchers and governments in terms of establishing new policies that will better align BIM adoption with the systemic evolution of business practices in the AEC business ecosystem