The proliferation of cyber-physical systems is driving the growing need to establish links between these systems and virtual environments. Particularly in the smart grid, the high costs of some devices and the imminent need to prevent manipulation of these devices in production environments make mechanisms that allow the manipulation of these physical objects in virtual environments necessary; this has been called a digital twin (DT). Conversley, cyberattacks are growing in all cyber-physical systems and cybersecurity is essential to the critical infrastructure of the smart grid. This work shows a small implementation of a DT system for smart metering systems in a smart home environment for testing cybersecurity issues. The results show that the use of DTs is feasible in various contexts of the smart grid, particularly in cybersecurity testing.

The smart grid revolution has only been possible, thanks to the development and proliferation of smart meters. The increasingly growing computing capabilities for Internet of Things devices have made it possible for data to be processed directly from the devices where it is produced; this has been called edge computing. Edge computing is allowing the smart grid to become increasingly intelligent to solve problems that make electricity consumption more efficient and environmentally friendly. This work presents the implementation of a smart metering system that allows data analytics using a multiprocessing architecture directly on the smart meter. The results show that the development of smart meters with data analytics capabilities at the edge is a reality today, and the use of multiprocessing permits the improvement of data processing.

The physicochemical characterization of pharmaceutical materials is essential for drug discovery, development and evaluation, and for understanding and predicting their interaction with physiological systems. Amongst many measurement techniques for spectroscopic characterization of pharmaceutical materials, Electrical Impedance Spectroscopy (EIS) is powerful as it can be used to model the electrical properties of pure substances and compounds in correlation with specific chemical composition. In particular, the accurate measurement of specific properties of drugs is important for evaluating physiological interaction. The electrochemical modelling of compounds is usually carried out using spectral impedance data over a wide frequency range, to fit a predetermined model of an equivalent electrochemical cell. This paper presents experimental results by EIS analysis of four drug formulations (trimethoprim/sulfamethoxazole C₁₄H₁₈N₄O₃-C₁₀H₁₁N₃O₃, ambroxol C₁₃H₁₈Br₂N₂O.HCl, metamizole sodium C₁₃H₁₆N₃NaO₄S, and ranitidine C₁₃H₂₂N₄O₃S.HCl). A wide frequency range from 20 Hz to 30 MHz is used to evaluate system identification techniques using EIS data and to obtain process models. The results suggest that arrays of linear R-C models derived using system identification techniques in the frequency domain can be used to identify different compounds.