Angular momentum transport in convectively unstable shear flows

Angular momentum transport owing to hydrodynamic turbulent convection is studied using local three dimensional numerical simulations employing the shearing box approximation. We determine the turbulent viscosity from non-rotating runs over a range of values of the shear parameter and use a simple analytical model in order to extract the non-diffusive contribution (Lambda-effect) to the stress in runs where rotation is included. Our results suggest that the turbulent viscosity is of the order of the mixing length estimate and weakly affected by rotation. The Lambda-effect is non-zero and a factor of 2-4 smaller than the turbulent viscosity in the slow rotation regime. We demonstrate that for Keplerian shear, the angular momentum transport can change sign and be outward when the rotation period is greater than the turnover time, i.e. when the Coriolis number is below unity. This result seems to be relatively independent of the value of the Rayleigh number.Comment: 10 pages, 12 figures, published version. Version with higher resolution figures can be found at http://www.helsinki.fi/~kapyla/publ.htm

Chlorophylls and Bacteriochlorophylls: Biochemistry, Biophysics, Functions and Applications

Supplementary material to this book contains the following Adobe-Writer (.pdf) files: an overview of the material, the color coding for the map on the title page), and supporting information for chapter 1, 14, 20, 22 and 30. Some of the files contain further links to materials on this server, in particular is there a collection of structural formulas accessible from the material to chapter 1. Please, click on the respective files for downloading. Any reference should cite the full title of the book

Lambda-effect from forced turbulence simulations

Aims: We determine the components of the $\Lambda$-effect tensor that quantifies the contributions to the turbulent momentum transport even for uniform rotation. Methods: Three-dimensional numerical simulations are used to study turbulent transport in triply periodic cubes under the influence of rotation and anisotropic forcing. Comparison is made with analytical results obtained via the so-called minimal tau-approximation. Results: In the case where the turbulence intensity in the vertical direction dominates, the vertical stress is always negative. This situation is expected to occur in stellar convection zones. The horizontal component of the stress is weaker and exhibits a maximum at latitude 30 degrees - regardless of how rapid the rotation is. The minimal tau-approximation captures many of the qualitative features of the numerical results, provided the relaxation time tau is close to the turnover time, i.e. the Strouhal number is of order unity.Comment: 20 pages, 14 figures, accepted for publication in Astronomy & Astrophysic

Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201