The restrictions associated with the current SARS-Cov-2 pandemic are having a major impact on nearly all human activities and interactions; among these, the didactic and education field certainly has a prominent place. In the case of either complete lockdown or reduced accessibility to facilities and classrooms, the participation of students was severely limited for safety reasons. In particular, laboratory activities, already compromised by a large number of students of some classes, suffered from the restrictions, thus making it difficult to guarantee the possibility of live operating on instruments for the execution of measurements of different quantities of interest. To overcome the considered problem, this article proposes the use of enabling technologies of the Internet of Things and the fourth industrial revolution to allow students to achieve correct and complete training in laboratory activities. To this aim, remotely controlled instruments are used and displayed in augmented reality on consumer devices, such as mobile phones, tablets, or personal computers. The continuous availability of access to the instruments along with the realism in their representation and use ensures a positive and effective involvement of the students.

Thanks to their peculiar features, organic transistors are proving to be a valuable alternative to traditional semiconducting devices in several application fields; however, before releasing their exploitation, simulating their behaviour through adequate circuital models could be advisable during the design stage of electronic circuits and/or boards. Consequently, accurately extracting the parameter value of those models is fundamental to developing useful libraries for hardware design environments. To face the considered problem, the authors present a method based on successive application of Single- and Multi-Objective Evolutionary Algorithm for the optimal tuning of model parameters of organic transistors on thin film (OTFT). In particular, parameters are first roughly estimated to assure the best fit with the experimental transfer characteristics; the estimates are successively refined through the multi-objective strategy by also matching the values of the experimental mobility. The performance of the method has been assessed by estimating the parameters value of both P-type and N-type OTFTs characterized by different values of channel lengths; the obtained results evidence that the proposed method can obtain suitable parameters values for all the considered channel lengths.