Asteroids' physical models from combined dense and sparse photometry and scaling of the YORP effect by the observed obliquity distribution

The larger number of models of asteroid shapes and their rotational states derived by the lightcurve inversion give us better insight into both the nature of individual objects and the whole asteroid population. With a larger statistical sample we can study the physical properties of asteroid populations, such as main-belt asteroids or individual asteroid families, in more detail. Shape models can also be used in combination with other types of observational data (IR, adaptive optics images, stellar occultations), e.g., to determine sizes and thermal properties. We use all available photometric data of asteroids to derive their physical models by the lightcurve inversion method and compare the observed pole latitude distributions of all asteroids with known convex shape models with the simulated pole latitude distributions. We used classical dense photometric lightcurves from several sources and sparse-in-time photometry from the U.S. Naval Observatory in Flagstaff, Catalina Sky Survey, and La Palma surveys (IAU codes 689, 703, 950) in the lightcurve inversion method to determine asteroid convex models and their rotational states. We also extended a simple dynamical model for the spin evolution of asteroids used in our previous paper. We present 119 new asteroid models derived from combined dense and sparse-in-time photometry. We discuss the reliability of asteroid shape models derived only from Catalina Sky Survey data (IAU code 703) and present 20 such models. By using different values for a scaling parameter cYORP (corresponds to the magnitude of the YORP momentum) in the dynamical model for the spin evolution and by comparing synthetics and observed pole-latitude distributions, we were able to constrain the typical values of the cYORP parameter as between 0.05 and 0.6.Comment: Accepted for publication in A&A, January 15, 201

Immigration and the Diffusion of Technology: The Huguenot Diaspora in Prussia

This paper analyzes the long-term effect of technological diffusion on productivity caused by immigration of skilled workers. In 1685 religious persecution drove highly skilled Huguenots into the backward Brandenburg-Prussia where they established themselves and transferred technological knowledge to natives. We find that textile manufactories installed in towns hosting the Huguenots achieved higher productivity than others due to diffusion, even 100 years after immigration. Identification is based on an instrumental variable approach exploiting variation in the settlement of Huguenots which results from population losses due to plagues during the Thirty Years' War, effectively eliminating worries of selectivity in the settlement pattern

Recurring opinions or productive improvements—what agile teams actually discuss in retrospectives

Team-level retrospectives are widely used in agile and lean software development, yet little is known about what is actually discussed during retrospectives or their outcomes. In this paper, we synthesise the outcomes of sprint retrospectives in a large, distributed, agile software development organisation. This longitudinal case study analyses data from 37 team-level retrospectives for almost 3 years. We report the outcomes of the retrospectives, their perceived importance for process improvement and relatVed action proposals. Most discussions were related to topics close to and controllable by the team. However, the discussions might suffer from participant bias, and in cases where they are not supported by hard evidence, they might not reflect reality, but rather the sometimes strong opinions of the participants. Some discussions were related to topics that could not be resolved at the team level due to their complexity. Certain topics recurred over a long period of time, either reflecting issues that can and have been solved previously, but that recur naturally as development proceeds, or reflecting waste since they cannot be resolved or improved on by the team due to a lack of controllability or their complexity. For example, the discussion on estimation accuracy did not reflect the true situation and improving the estimates was complicated. On the other hand, discussions on the high number of known bugs recurred despite effective improvements as development proceeded.Peer reviewe