Simulation Games - A Creative Tool for Capacity Building: Experience from the Learning for Sustainability Approach

Games that simulate complex realities to be dealt with in teams are an effective tool for fostering interactive learning processes. they link different levels of decision-making in the household, community and societal contexts. Negotiation and harmonisation of different perceptions and interests, be it within or between different households, form the basis of a common strategy for sustainable development.

Facial Nerve Image Enhancement from CBCT using Supervised Learning Technique

Facial nerve segmentation plays an important role in surgical planning of cochlear implantation. Clinically available CBCT images are used for surgical planning. However, its relatively low resolution renders the identification of the facial nerve difficult. In this work, we present a supervised learning approach to enhance facial nerve image information from CBCT. A supervised learning approach based on multi-output random forest was employed to learn the mapping between CBCT and micro-CT images. Evaluation was performed qualitatively and quantitatively by using the predicted image as input for a previously published dedicated facial nerve segmentation, and cochlear implantation surgical planning software, OtoPlan. Results show the potential of the proposed approach to improve facial nerve image quality as imaged by CBCT and to leverage its segmentation using OtoPlan.

Granular flow in polymineralic rocks bearing sheet silicates: New evidence from natural examples

Natural deformation in carbonate mylonites bearing sheet silicates occurs via a complex interaction of granular flow and solution transfer processes and involves continuous cycles of dissolution, grain boundary diffusion, nucleation and growth. In this way, new sheet silicates (a) nucleate within voids formed by grain boundary sliding of calcite grains. (b) grow, and (c) rotate towards the shear plane. As a consequence, small mica grains show a wide range of orientations with respect to the shear plane, but moderate to large grains are subparallel both to each other and to the shear plane. Increases of average grain sizes with increasing temperature of sheet silicates in mica-rich layers is more pronounced than in mica-poor layers. In the calcitic matrix however, sheet silicates can only grow via solution-precipitation and mass transfer processes. Therefore, the observed grain size variability indicates drastic differences in mass transfer behavior between the individual layers, which might be related to differences in the fluid flux. Based on these observations, a conceptual model for the microfabric evolution in sheet silicate bearing mylonites is presented. © 2001 Elsevier Science B.V. All rights reserved.

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.

Context Aware e-Learning Objects and their Types from the Perspective of the Object-Oriented Paradigm

The contribution of this article demonstrates how to identify context-aware types of e-Learning objects (eLOs) derived from the subject domains. This perspective is taken from an engineering point of view and is applied during requirements elicitation and analysis relating to present work in constructing an object-oriented (OO), dynamic, and adaptive model to build and deliver packaged e-Learning courses. Consequently, three preliminary subject domains are presented and, as a result, three primitive types of eLOs are posited. These types educed from the subject domains are of structural, conceptual, and granular nature. Structural objects are responsible for the course itself, conceptual objects incorporate adaptive and logical interoperability, while granular objects congregate granular assets. Their differences, interrelationships, and responsibilities are discussed. A major design challenge relates to adaptive behaviour. Future research addresses refinement on the subject domains and adaptive hypermedia systems.