Giuseppe Costantino Giaconia

This review analyzes the transition from silicon to wide-bandgap (WBG) and ultrawide-bandgap (UWBG) semiconductor materials for power electronics, focusing on Silicon Carbide (SiC) and Gallium Nitride (GaN) technologies. Following a PRISMA-based systematic review methodology, we analyzed more than 50 peer-reviewed publications spanning device technology, converter architectures, and system applications. We employ a bottom-up approach, progressing from fundamental material properties through device architectures and converter topologies to system-level implications. We examine how intrinsic material properties enable operation at elevated temperatures, voltages, and frequencies while minimizing losses. Through analysis of Figures of Merit and system-level Key Performance Indicators, we quantify WBG benefits across automotive, industrial, renewable energy, and consumer electronics sectors, demonstrating 3--5x power density improvements and 20--40\% cost reductions. The review presents emerging device technologies including vertical GaN for medium-voltage applications and monolithic bidirectional switches (BDS) enabling single-stage power conversion. We provide the first comprehensive topology-level comparison of emerging vertical GaN and monolithic bidirectional switches against established SiC solutions, identifying specific applications where each technology offers advantages. A comprehensive topology-by-topology comparison between SiC and GaN is provided, offering design guidelines for device selection. The review addresses practical constraints including dynamic on-resistance degradation, threshold voltage instability, and electromagnetic interference challenges for both SiC and GaN. Finally, we examine emerging UWBG materials (Beta-Ga2O3, AlN, c-BN, Diamond) and their development status, manufacturing challenges, supply chain considerations, and commercialization prospects for ultra-high-voltage applications.

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique that measures brain hemodynamic activity by detecting changes in oxyhemoglobin and deoxyhemoglobin concentrations using light in the near-infrared spectrum. This study aims to provide a comprehensive characterization of fNIRS signals acquired with a prototypal continuous-wave fNIRS device during a breath-holding task, to evaluate the impact of respiratory activity on scalp hemodynamics within the framework of Network Physiology. To this end, information-theoretic and spectral analysis methods were applied to characterize the dynamics of fNIRS signals. In the time domain, time-resolved information-theoretic measures, including entropy, conditional entropy and, information storage, were employed to assess the complexity and predictability of the fNIRS signals. These measures highlighted distinct informational dynamics across the breathing and apnea phases, with conditional entropy showing a significant modulation driven by respiratory activity. In the frequency domain, power spectral density was estimated using a parametric method, allowing the identification of distinct frequency bands related to vascular and respiratory components. The analysis revealed significant modulations in both the amplitude and frequency of oscillations during the task, particularly in the high-frequency band associated with respiratory activity. Our observations demonstrate that the proposed analysis provides novel insights into the characterization of fNIRS signals, enhancing the understanding of the impact of task-induced peripheral cardiovascular responses on NIRS hemodynamics.

Active student involvement and instruction through experience in everyday contexts are pedagogical approaches suitable to promote inquiry-based learning and improve learners’ cognitive skills. Nevertheless, many university and postgraduate courses offer lecture-based instructions of theoretical concepts to the students; little attention is still devoted to design hands-on activities, to improve practical/technical competencies and enhance students’ effective understanding of the concepts. The development of a personalized, student-centered learning environment that encourages teamwork and inquiry-based learning aligns with the contemporary push for interdisciplinary education in bioengineering fields. This is particularly relevant for fostering expertise in emerging technologies like functional Near-Infrared Spectroscopy (f-NIRS). In this framework, this paper reports a lab activity for bioelectronic engineering and/or biomedical science students focused on analyzing prefrontal cortex activation during a memory task, processing the f-NIRS signals. This pilot activity, conducted at the University of Palermo (Italy), involved Master’s and Ph.D. students working in teams to address challenges in experimental design. The study combines cutting-edge biosignal detection techniques with innovative educational strategies, offering substantial contributions to both bioengineering and educational research. The outcomes suggest that a hands-on and student-centered laboratory, experienced through a methodical sequence of self-directed learning activities, could considerably boost the student motivation to learn and the level of engagement in bioengineering and biosciences.