Abstract
Li-ion batteries are widely used in electric vehicles, grid storage, and portable electronics. Battery Management Systems play a crucial role in ensuring the safety, efficiency, and longevity of Li-ion batteries. Accurate battery modelling is essential for effective battery management functionality, enabling precise state of charge/ state of health estimation, as well as protection against hazardous conditions such as overcharging or overheating. This article explores system identification techniques for battery modelling using a piecewise LTI approach where separate LTI models are identified for different state of charge intervals. A modified Thevenin circuit is employed, where the open-circuit voltage is represented by a capacitor that models the bulk charge storage. The capacitance of this element is dependent on the state of charge, reflecting the nonlinear nature of the battery's charge storage mechanism. Additionally, parallel resistor-capacitor networks capture transient voltage recovery dynamics. The identification process estimates the battery parameters from experimental data, and the resulting piecewise models are interpolated using cubic splines to construct a linear parameter-varying (LPV) representation of the system. The proposed methodology was validated through experimental results, demonstrating its effectiveness in enhancing battery management performance. Namely, (i) the model accurately captures the battery's voltage response with minimal error. (ii) the LPV model obtained by fitting splines to the estimated parameters demonstrates a level of accuracy comparable to that of the piecewise LTI model. (iii) the model robustness was validated through a continuous discharge test, showing strong agreement with experimental data and, therefore, demonstrating its reliability in real-world operating conditions. These results highlight the potential of the proposed methodology in improving battery management systems. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
The step response of first-order systems is vital in control systems and electronics. Understanding this behaviour is key but often challenging. This article uses Arduino with PWM to teach the step response in RC circuits, since Arduino enables real-time data acquisition and visualisation, connecting theory to practice. The research seeks to illustrate the step response of an RC circuit using Arduino, deepen knowledge of first-order systems, and offer a technique for collecting experimental data. All of this, since combining practical experiments with theoretical concepts boosts student involvement and understanding of dynamic systems. The work includes theoretical foundations, experimental procedures, and a brief discussion on the educational value of these activities.

Abstract
The resting brain has been extensively investigated for low frequency synchrony between brain regions, namely Functional Connectivity. However the other main stream of the brain connectivity analysis that seeks causal interactions between brain regions, Effective Connectivity, has been still little explored. Inherent complexity of brain activities in resting-state, as observed in Blood Oxygenation-Level Dependant fluctuations, calls for exploratory methods for characterizing these causal networks [1]. To determine the structure of the network that causes this dynamics, it is developed a method of identification based on least squares, which assumes knowledge of the signals of brain activity in different regions. As there is no access to functional Magnetic Resonance Imaging, data it is developed a model to obtain the Blood Oxygenation Level Dependent signals and it is implemented a reverse hemo-dynamic function. To assess the performance of the created model Monte Carlo simulations have been used.

Abstract
This article addresses the problem of wheelchair path planning. In particular, to minimize the length of the trajectory within an environment containing a variable number of obstacles. The positions and quantities of these obstacles are pre-determined. To tackle this challenge, we present a methodology that integrates optimisation techniques and heuristic algorithms to find trajectories both optimal and collision-free. The effectiveness of this methodology is illustrated through a practical example, demonstrating how it successfully generates a collision-free trajectory, even when a large number of obstacles is present in the workspace. In the future, we intend to continue investigating the same problem, taking into account energy consumption as well as time minimisation.

Abstract
This work focuses on the application of image processing techniques to segment and analyze images of brain sections with the aim of facilitating early diagnosis of brain tumors. The aim is to delineate specific regions of the brain, such as the cranial, intracranial, and encephalic regions, for subsequent geometric analysis. The process involves image pre-processing, conversion to polar coordinates, determination of contour points, Fourier Series approximation, and the use of the Least Square Method to obtain accurate representations of the regions. The proposed approach was tested on Magnetic Resonance Images of three different brains, showing its capability to accurately delineating the targeted regions. The results highlight the potential of signal processing techniques for analyzing brain images and provide insights for further research in this area.