Sustainable Pasture Management : LforS learning and simulation game

Three teams consisting of 2 to 5 persons each play the game. Each team represents a farm. Each team decides jointly on its strategy. In annual meetings in winter, the farm teams jointly discuss, evaluate and decide on how to proceed and actions to be taken. The farms make use of three different pasture areas (village pasture, intensive pasture and summer pasture) for grazing their livestock. The carrying capacity of each pasture area is different and varies according to the season. In each season, the farms have to decide on how many livestock units to graze on which pasture. Overgrazing and pasture degradation occur if the total number of livestock units exceeds the carrying capacity of a specific pasture area. Overgrazing results in a reduction of pasture productivity. To diversify and improve their livelihood strategy farms can make individual investments to increase productivity at the farm level, eg. in fodder production or in income generating activities. At the community level, collective investments can be made which may influence livestock and household economy, e.g. rehabilitate and improve pasture productivity, improve living conditions on remote pastures etc. Events occurring in the course of the game represent different types of (risk) factors such as meteorology, market, politics etc. that may positively or negatively influence livestock production and household economy. A sustainable management of pastures requires that farms actively regulate the development of their herds, that they take measures to prevent pasture degradation and to improve pasture productivity, and that they find a balance between livestock economy and other productive activities. The game has a double aim: a) each farm aims at its economic success and prosperity, and b) the three farm teams jointly have to find and implement strategies for a sustainable use of pasture areas.

Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography using Supervised Learning and a Novel Morphometric Feature Descriptor

Patient-specific biomechanical models including local bone mineral density and anisotropy have gained importance for assessing musculoskeletal disorders. However the trabecular bone anisotropy captured by high-resolution imaging is only available at the peripheral skeleton in clinical practice. In this work, we propose a supervised learning approach to predict trabecular bone anisotropy that builds on a novel set of pose invariant feature descriptors. The statistical relationship between trabecular bone anisotropy and feature descriptors were learned from a database of pairs of high resolution QCT and clinical QCT reconstructions. On a set of leave-one-out experiments, we compared the accuracy of the proposed approach to previous ones, and report a mean prediction error of 6% for the tensor norm, 6% for the degree of anisotropy and 19◦ for the principal tensor direction. These findings show the potential of the proposed approach to predict trabecular bone anisotropy from clinically available QCT images.

Synesthetic experiences enhance unconscious learning

Synesthesia is characterized by consistent extra perceptual experiences in response to normal sensory input. Recent studies provide evidence for a specific profile of enhanced memory performance in synesthesia, but focus exclusively on explicit memory paradigms for which the learned content is consciously accessible. In this study, for the first time, we demonstrate with an implicit memory paradigm that synesthetic experiences also enhance memory performance relating to unconscious knowledge.