A reconnaissance of the possible donor stars to the Kepler supernova

The identity of Type Ia supernova progenitors remains a mystery, with various lines of evidence pointing towards either accretion from a non-degenerate companion, or the rapid merger of two degenerate stars leading to the thermonuclear destruction of a white dwarf. In this paper we spectroscopically scrutinize 24 of the brightest stars residing in the central 38" x 38" of the SN 1604 (Kepler) supernova remnant to search for a possible surviving companion star. We can rule out, with high certainty, a red giant companion star - a progenitor indicated by some models of the supernova remnant. Furthermore, we find no star that exhibits properties uniquely consistent with those expected of a donor star down to L>10Lsun. While the distribution of star properties towards the remnant are consistent with unrelated stars, we identify the most promising candidates for further astrometric and spectroscopic follow-up. Such a program would either discover the donor star, or place strong limits on progenitor systems to luminosities with L<<Lsun.Comment: accepted by Ap

MONICA in Hamburg: Towards Large-Scale IoT Deployments in a Smart City

Modern cities and metropolitan areas all over the world face new management challenges in the 21st century primarily due to increasing demands on living standards by the urban population. These challenges range from climate change, pollution, transportation, and citizen engagement, to urban planning, and security threats. The primary goal of a Smart City is to counteract these problems and mitigate their effects by means of modern ICT to improve urban administration and infrastructure. Key ideas are to utilise network communication to inter-connect public authorities; but also to deploy and integrate numerous sensors and actuators throughout the city infrastructure - which is also widely known as the Internet of Things (IoT). Thus, IoT technologies will be an integral part and key enabler to achieve many objectives of the Smart City vision. The contributions of this paper are as follows. We first examine a number of IoT platforms, technologies and network standards that can help to foster a Smart City environment. Second, we introduce the EU project MONICA which aims for demonstration of large-scale IoT deployments at public, inner-city events and give an overview on its IoT platform architecture. And third, we provide a case-study report on SmartCity activities by the City of Hamburg and provide insights on recent (on-going) field tests of a vertically integrated, end-to-end IoT sensor application.Comment: 6 page

Leading innovative endeavours:The role of leadership for learning and interpersonal justice

To remain competitive, organizations have to increasingly rely on employees doing more work than is required by their formal job description. Therefore, it is important to understand the conditions under which employees are likely to go beyond their formal job description and engage in innovative work behaviour (IWB). Innovative work behaviour implies that employees voluntarily generate, promote and implement new ideas aimed at increasing organizational success. In this research, we investigated the interactive effect of leadership for learning and interpersonal justice on IWB in a sample of 209 employed participants by means of an online survey. As predicted, we found a positive association between leadership for learning and IWB. Importantly, we found that leadership for learning was more strongly related to IWB at higher levels of interpersonal justice than at lower levels of interpersonal justice. In practical terms, workplaces can be designed for innovation to take place. To achieve this, managers should focus on creating an environment that is supportive of learning and live up to their responsibilities of treating employees with dignity and respect

Is there a European solidarity?: Attitudes towards fiscal assistance for debt-ridden European Union member states

This paper analyses if European citizens are willing to show solidarity with debt-ridden EU member states during the recent crisis. Based on a theoretical concept comprehending four di-mensions of solidarity - generalised willingness to support, existence of social cleavages, rea-sons of supporting others, acceptance of conditions a crisis country has to meet to receive as-sistance - we derived hypotheses stating that the existence of a European wide solidarity is rather unlikely. We analysed data from two Eurobarometer surveys 2010 and 2011 and a unique survey conducted in Germany and Portugal in 2012. Descriptive and multilevel analyses indi-cated that in 2010 and 2011, a narrow majority of all EU citizens supported fiscal assistance for crisis countries, and socio-economic and cultural cleavages in attitudes regarding financial as-sistance for crisis countries were rather low. Findings from the two country comparison showed that the willingness to show solidarity was predominantly guided by moral reasoning instead of the respondent’s self-interest. However, German and Portuguese respondents disagree on austerity measures, with the exception of social spending cuts. Taken all together, we come to the conclusion that recent years have brought a new legitimacy to the use of EU bailout measures which are now a given European practice

Cosmology inference at the field level from biased tracers in redshift-space

Cosmology inference of galaxy clustering at the field level with the EFT likelihood in principle allows for extracting all non-Gaussian information from quasi-linear scales, while robustly marginalizing over any astrophysical uncertainties. A pipeline in this spirit is implemented in the \texttt{LEFTfield} code, which we extend in this work to describe the clustering of galaxies in redshift space. Our main additions are: the computation of the velocity field in the LPT gravity model, the fully nonlinear displacement of the evolved, biased density field to redshift space, and a systematic expansion of velocity bias. We test the resulting analysis pipeline by applying it to synthetic data sets with a known ground truth at increasing complexity: mock data generated from the perturbative forward model itself, sub-sampled matter particles, and dark matter halos in N-body simulations. By fixing the initial-time density contrast to the ground truth, while varying the growth rate $f$, bias coefficients and noise amplitudes, we perform a stringent set of checks. These show that indeed a systematic higher-order expansion of the velocity bias is required to infer a growth rate consistent with the ground truth within errors. Applied to dark matter halos, our analysis yields unbiased constraints on $f$ at the level of a few percent for a variety of halo masses at redshifts $z=0,\,0.5,\,1$ and for a broad range of cutoff scales $0.08\,h/\mathrm{Mpc} \leq \Lambda \leq 0.20\,h/\mathrm{Mpc}$. Importantly, deviations between true and inferred growth rate exhibit the scaling with halo mass, redshift and cutoff that one expects based on the EFT of Large Scale Structure. Further, we obtain a robust detection of velocity bias through its effect on the redshift-space density field and are able to disentangle it from higher-derivative bias contributions

Workforce Rostering for Tomorrow's Industry: The Workforce Scheduling Dilemma in Decentrally Controlled Production Systems

The workforce rostering for tomorrow’s industry needs to be reconsidered. The development of new types of production control mechanisms, like decentralized production control, impacts the effectivity and efficiency of workforce rostering methods, too. Simultaneously, social trends, like the growing demand of flexible working time models and labor shortages, take their influence on the rostering process. We are facing these requirements by developing a new rostering method which is appropriate for decentrally controlled production systems, the consideration of individual preferred working times independent of rigid shift systems and the simultaneous targeting of production-related performance variables. Therefore, we apply a simulation-based optimization approach which is based on a genetic algorithm