An Advanced eLearning Environment Developed for Engineering Learners

Monitoring and evaluating engineering learners through computer-based laboratory exercises is a difficult task, especially under classroom conditions. A complete diagnosis requires the capability to assess both the competence of the learner to use the scientific software and the understanding of the theoretical principles. This monitoring and evaluation needs to be continuous, unobtrusive and personalized in order to be effective. This study presents the results of the pilot application of an eLearning environment developed specifically with engineering learners in mind. As its name suggests, the Learner Diagnosis, Assistance, and Evaluation System based on Artificial Intelligence (StuDiAsE) is an Open Learning Environment that can perform unattended diagnostic, evaluation and feedback tasks based on both quantitative and qualitative parameters. The base architecture of the system, the user interface and its effect on the performance of postgraduate engineering learners are being presented

Use of a probabilistic model to design energy transmission and distribution networks for low enthalpy geothermal multiple use schemes

A probabilistic model is suggested for the design of transmission and distribution network of geothermal energy to potential consumption sites, in cases where the development of various competitive or complementary non-electrical uses is probable, within the broader area of a field. The model can be used to find out (a) the optimum network that may offer the best economic results to the agent who will undertake the development of the field, and (b) the corresponding selling price at which the thermal fluids will be supplied to the end users who are assumed to be other than the above agent. Model input data can be collected in the frame of an appropriate market study, which is roughly specified in this work and commented according to relevant experience from a Greek geothermal field. Finally, applicability of the model is demonstrated through an indicative example.Geothermal energy Multiple use Probabilistic model Probability Location-allocation