Trace Elements Distribution in Red Soils under Semiarid Mediterranean Environment

This study states the potential trace elements (TE’s) content of red soils located at the centre region of Spain, characterized by low rainfall and slight acidity over prolonged weathering periods. For this purpose, three soil profiles from a catena were described, sampled and analyzed. The most notable characteristics are the low organic matter content and the predominantly acidic pH. Illite and kaolinite are the predominant clay minerals. The fertility of the soils is sufficient to provide most of the nutrients required, with very suitable potassium levels. The geochemical characters of this soil are: only few elements remain almost invariable across the profiles and over time, however the majority of them were directly linked with the clay content. These soils are characterized by relatively low levels of some trace elements such as Sr (64.35 mg?kg–1), Ba (303.67 mg?kg–1) and Sc (13.14 mg?kg–1); high levels of other trace elements such as V (103.92 mg?kg–1), Cr (79.9 mg?kg–1), Cu (15.18 mg?kg–1), Hf (10.26 mg?kg–1), Ni (38 mg?kg–1) and Zr (337 mg?kg–1); while the levels for rare earth elements (REE’s) such as La (48.36 mg?kg–1), Ce (95.07 mg?kg–1), Th (13.33 mg?kg–1) and Nd (42.65 mg?kg–1) are significantly high. The distribution of mayor and trace elements was directly re- lated to weathering processes, parent material and anthropogenic activities.

Knowledgeable Feedback via a Cast of Virtual Characters with Different Competences

DynaLearn (http://www.DynaLearn.eu) develops a cognitive artefact that engages learners in an active learning by modelling process to develop conceptual system knowledge. Learners create external representations using diagrams. The diagrams capture conceptual knowledge using the Garp3 Qualitative Reasoning (QR) formalism [2]. The expressions can be simulated, confronting learners with the logical consequences thereof. To further aid learners, DynaLearn employs a sequence of knowledge representations (Learning Spaces, LS), with increasing complexity in terms of the modelling ingredients a learner can use [1]. An online repository contains QR models created by experts/teachers and learners. The server runs semantic services [4] to generate feedback at the request of learners via the workbench. The feedback is communicated to the learner via a set of virtual characters, each having its own competence [3]. A specific feedback thus incorporates three aspects: content, character appearance, and a didactic setting (e.g. Quiz mode). In the interactive event we will demonstrate the latest achievements of the DynaLearn project. First, the 6 learning spaces for learners to work with. Second, the generation of feedback relevant to the individual needs of a learner using Semantic Web technology. Third, the verbalization of the feedback via different animated virtual characters, notably: Basic help, Critic, Recommender, Quizmaster & Teachable agen