Abstract
Organizations assume a key role in the goal of achieving sustainable development and are influential elements on the path to sustainability. Allied with competitiveness, today, there is also a strategy based on sustainability, anchored in the concept of responsibility, minimizing the potential negative effects of our actions through innovative products, services, processes, and models. Measuring and monitoring these efforts is currently a challenge for organizations. This study adopts a mixed methods approach to address this challenge and identifies 13 tools and 16 dimensions that are central elements in the process of measuring and monitoring sustainable innovation. The findings indicate that the dimensions related to social and governance components are the most relevant in sustainable innovation, while inclusion and entrepreneurship are dimensions that are not highly valued by these tools. © 2024 The Authors

Abstract
Inspecting and maintaining power lines is essential for ensuring the safety, reliability, and efficiency of electrical infrastructure. This process involves regular assessment to identify hazards such as damaged wires, corrosion, or vegetation encroachment, followed by timely maintenance to prevent accidents and power outages. By conducting routine inspections and maintenance, utilities can comply with regulations, enhance operational efficiency, and extend the lifespan of power lines and equipment. Unmanned Aerial Vehicles (UAVs) can play a relevant role in this process by increasing efficiency through rapid coverage of large areas and access to difficult-to-reach locations, enhanced safety by minimizing risks to personnel in hazardous environments, and cost-effectiveness compared to traditional methods. UAVs equipped with sensors such as visual and thermographic cameras enable the accurate collection of high-resolution data, facilitating early detection of defects and other potential issues. To ensure the safety of the autonomous inspection process, UAVs must be capable of performing onboard processing, particularly for detection of power lines and obstacles. In this paper, we address the development of a deep learning approach with YOLOv8 for power line detection based on visual and thermographic images. The developed solution was validated with a UAV during a power line inspection mission, obtaining mAP@0.5 results of over 90.5% on visible images and over 96.9% on thermographic images.

Abstract
The correction of artifacts in Magnetic Resonance Imaging (MRI) is increasingly relevant as voluntary and involuntary artifacts can hinder data acquisition. Reverting from corrupted to artifact-free images is a complex task. Deep Learning (DL) models have been employed to preserve data characteristics and to identify and correct those artifacts. We propose MOANA, a novel DL-based solution to correct artifacts in multi-contrast brain MRI scans. MOANA offers two models: the simulation and the correction models. The simulation model introduces perturbations similar to those occurring in an exam while preserving the original image as ground truth; this is required as publicly available datasets rarely have motion-corrupted images. It allows the addition of three types of artifacts with different degrees of severity. The DL-based correction model adds a fourth contrast to state-of-the-art solutions while improving the overall performance of the models. MOANA achieved the highest results in the FLAIR contrast, with a Structural Similarity Index Measure (SSIM) of 0.9803 and a Normalized Mutual Information (NMI) of 0.8030. With this, the MOANA model can correct large volumes of images in less time and adapt to different levels of artifact severity, allowing for better diagnosis.

Abstract
In the current context of smart grids, microgrids have proven to be an effective solution to meet the energy needs of neighborhoods and collective buildings. This study investigates the voltage behavior and other critical parameters within a direct current (DC) microgrid to enhance system efficiency, stability, and reliability. The dynamic performance of a DC microgrid is analyzed under varying load and generation conditions, with particular emphasis on the voltage response and load-sharing mechanisms required to ensure stable operation. The findings indicate that specific control strategies, particularly droop methods, are effective in mitigating voltage fluctuations, enhancing power quality, and ensuring proper load distribution across multiple sources. This study also addresses significant challenges, including voltage regulation and fault resilience, to provide guidelines for designing robust and efficient DC microgrids. These insights are essential to inspire further advancements in control strategies and facilitate the practical deployment of DC microgrids as a sustainable solution for distributed energy systems, especially in scenarios prioritizing high DC load penetration and renewable energy integration.

Abstract
Increasing the number of Electric Vehicles (EVs) imposes several challenges in power distribution networks. Developed Electric Vehicle Supply Equipment (EVSE) provides fast and efficient charging of EVs at the Public Charging Stations (PCS). These chargers benefit from balanced three-phase chargers with considerable power consumption. Hence, the optimal management and task scheduling for EVSE should be arranged in such a way as to avoid overloading network infrastructure or imposing new peaks on the distribution networks. On the other hand, energy management in the presence of high renewable energy penetration due to installed Photovoltaic (PV) panels at the low-voltage (LV) distribution network should be elaborated to minimize the renewable power curtailment. Hence, this paper presents a novel model to address the optimal scheduling of charging stations availability and unlocking the Demand Response (DR) potentials at the distribution networks with highly penetrated PV panels. The energy management model is represented as a standard Mixed-Integer Linear Programming (MILP) problem which can be solved by open-source solvers. The proposed model is tested for a real case study in Portugal to demonstrate the functionality of the developed tool. © 2024 IEEE.

Abstract
Introduction/Objective: the objective of this article is to analyze the current academic literature on smart cities in Brazil with evidence of the application of Digital Twin or Digital Shadow technology. Method: Integrative Literature Review was used as the research instrument, analyzing in the articles: a) objective; b) research method; c) study subject (location); d) application of Digital Twin or Digital Shadow; e) Results and conclusions. Results: portfolio with 25 articles on the topic and qualitative analysis regarding objective, method, study location, Digital Twin technology, Digital Shadow, and results. Studies with elements of Digital Shadow are perceived timidly in two cases of smart cities in Brazil. Conclusions: smart city technologies should be centered on the interests of users to not lose their humanity. It is worth adding that people’s needs change and, therefore, smart technologies should have a forward-looking vision to anticipate the needs of future generations. Digital Twin technology is a model that can contribute in this sense, monitoring and providing readings of future scenarios for smart cities. © 2024, Programa de Pos-Graduacao em Gestao da Informacao, Universidade Federal do Parana. All rights reserved.