Abstract
The increase in digital teaching and learning methodologies creates the opportunity for new educational approaches, both in terms of pedagogical practice and in the availability of new technological tools. The flipped classroom as an active teaching methodology is one example of blended learning (b-learning), which aims to harmonize and enhance the fusion of face-to-face teaching with online teaching, allowing students to get better use of both face-to-face contact with classmates and professors and digital teaching resources. However, active teaching methodologies allow us to merge educational techniques from different methodological approaches, for example, gamification and team-based learning (TBL), among others. This study aims to demonstrate how to implement a flipped classroom with the possibility of integrating gamification and TBL, indicating possibilities and challenges to overcome, through the comparative study and research carried out with students in higher education. The study was conducted with a group of 88 students from the engineering and technology fields, which showed that students have a very positive perception of active teaching methodologies and their teaching and learning techniques, especially those involving digital. Data collection was performed by a survey submitted to quantitative analysis using the Software SPSS version 28. © 2024 the author(s)

Abstract
This paper is concerned with the semantic segmentation within domain-specific contexts, such as those pertaining to biology, physics, or material science. Under these circumstances, the objects of interest are often irregular and have fine structure, i.e., detail at arbitrarily small scales. Empirically, they are often understood as self-similar processes, a concept grounded in Multifractal Analysis. We find that this multifractal behaviour is carried out through a convolutional neural network (CNN), if we view its channel-wise responses as self-similar measures. A function of the local singularities of each measure we call Singularity Stregth Recalibration (SSR) is set forth to modulate the response at each layer of the CNN. SSR is a lightweight, plug-in module for CNNs. We observe that it improves a baseline U-Net in two biomedical tasks: skin lesion and colonic polyp segmentation, by an average of 1.36% and 1.12% Dice score, respectively. To the best of our knowledge, this is the first time multifractal-analysis is conducted end-to-end for semantic segmentation.

Abstract
This research study presents an optimized approach for charging electric vehicles (EVs) in existing residential multi dwelling buildings. The proposed solution tackles the problem in two distinct, but complementary ways. First it takes advantage, in a novel way, of the existing electrical infrastructure by taping directly into the main feeder of the building, second it distributes the power in real time by leveraging in an optimized methodology. The aim of this methodology is to minimize the discrepancy between the desired and final state of charge (SOC) of EVs by the end of each charging session. To achieve this, the method leverages on commuting and charging preferences of EV owners, as well as the electrical infrastructure of residential buildings. To dynamically adjust the charging power for each EV in real-time, an optimized charging management system is employed. This system solves a non-linear minimization optimization problem that considers various parameters, including the initial SOC of each EV, the desired final SOC, the available charging time, and the available charging power. To assess the effectiveness of the proposed methodology, comparative analysis was conducted against a baseline methodology commonly used in practice. The results show that the optimized approach significantly outperforms the non-optimized methods, particularly in high demand scenarios. In these scenarios, the optimized methodology allows for a 200% increase in the supplied energy to the buildings' EV fleet, as well as more than doubling the range made available to users when compared to traditional approaches. In conclusion, this research work offers a robust and effective solution for charging EVs in residential buildings.

Abstract
The expansion of ethanol production in Brazil sparks several sustainability concerns, including debates on “food versus fuel”, the environmental impacts of monocultures, and indirect land-use change. Since livestock farming occupies a significantly greater area than sugarcane for ethanol production in Brazil and has a large yield gap, sugarcane-livestock integration can be a promising alternative. This integrated system considers crop production systems, biorefinery processing and meat production in both intensive and extensive livestock farming. Optimizing this system for both economic and environmental aspects can be challenging to implement and computationally expensive as this system's complexity arises from nonlinear subsystems and their intertwining input-output flows. For these reasons, this paper develops metamodels from detailed models to: (i) Optimize the extensive livestock farming, (ii) Optimize the confined animal feeding, and (iii) Optimize the integrated system. The main objective is to maximize the Net Present Value relative to investment. This study contributes to the literature by developing innovative models for ethanol-beef integrated production systems and methods for optimizing such systems to avoid negative externalities on food security and environmental impacts. © 2024 Elsevier B.V.

Abstract
This systematic literature review investigates the factors influencing cloud computing adoption within both educational and organizational settings. By synthesizing a comprehensive body of research, this study finds and analyzes the determinants that shape the decision-making process about cloud technology adoption. Security, cost-effectiveness, scalability, interoperability, and regulatory compliance are examined across educational institutions and organizational contexts. Additionally, socio-economic, political, and technological factors specific to each context are explored to provide a nuanced understanding of the challenges and opportunities associated with cloud computing adoption. The review reveals commonalities and differences in adoption drivers and barriers between education and organizational environments, offering insights into tailored strategies for effective implementation. This research contributes to the existing literature by shedding light on the multifaceted nature of cloud adoption and offering valuable guidance for educators, organizational leaders, policymakers, and technology providers looking to use cloud computing to enhance operations and services.

Abstract
Renewable energy generation technologies, heat pumps or electric vehicle (EV) charging stations use active power electronics such as IGBT or MOSFET for AC to DC conversion with the consequence of emissions in the higher frequency range above 2 kHz (non-intentional supraharmonic emissions) and with an impact to the higher frequency grid impedance. In this study the impact of active power electronics on the higher frequency grid impedance in the range up to 150 kHz is analyzed. As existing grid modelling solutions do not consider these technologies sufficiently, this study analyzes the impact of a vehicle to grid (V2G) chargers to a representative distribution grid considering different grid topologies and different types of V2G chargers. The study shows that the additional capacitance and inductance (LCL filter, DC link capacitor) introduced in the electrical grid causes parallel and series resonances in a wide frequency range starting from 500 Hz up to 50 kHz. The grid topology and the number of V2G chargers connected determines the frequency range and characteristics of resonances. Finally, the major contribution of this study is outlining the importance of considering the higher frequency grid impedance for characterization of supraharmonic emissions (primary vs. secondary emissions) and their propagation.