Abstract
Public lighting is crucial for maintaining the safety and well-being of communities. Current inspection methods involve examining the luminaires during the day, but this approach has drawbacks, including energy consumption, delay in detecting issues, and high costs and time investment. Utilising deep learning based automatic detection is an advanced method that can be used for identifying and locating issues in this field. This study aims to use deep learning to automatically detect burnt-out street lights, using Seville (Spain) as a case study. The study uses high-resolution night time imagery from the JL1-3B satellite to create a dataset called NLight, which is then divided into three subsets: NL1, NL2, and NT. The NL1 and NL2 datasets are used to train and evaluate YOLOv5 and YOLOv7 segmentation models for instance segmentation of streets. And then, distance outliers were detected to find the lights off. Finally, the NT dataset is used to evaluate the effectiveness of the proposed methodology. The study finds that YOLOv5 achieved a mask mAP of 57.7%, and the proposed methodology had a precision of 30.8% and a recall of 28.3%. The main goal of this work is accomplished, but there is still space for future work to improve the methodology.

Abstract
Teaching and learning are processes that must accompany the digital transition, which is one of the biggest challenges we currently face, along with the green transition. The digital transition in education is a process with several challenges that must count on the involvement and collaboration of all stakeholders, contributing to the schools of the future. For this, technology plays a decisive role, and must be integrated into classes as a relevant tool to develop and implement different types of experiments, motivating the students towards STEM areas. In this context, a project financed by IFAC made it possible to use pocket laboratories in different high schools, encouraging teachers to prepare activities supported by this equipment, stimulating students to be interested in engineering topics. This article presents the approach followed in one high school and discusses the results obtained, highlighting the usefulness and opportunity of using pocket labs, and low-cost equipment in general, in school activities, which can promote the STEM areas and, in particular, the engineering courses.

Abstract
Nowadays, Automated Guides Vehicles and Autonomous Mobile Robots are equipped with electromagnetic or optical automatic guiding devices and can navigate, interact, perform path planning and avoid obstacles. It is crucial to develop applications to support the teaching by real and/or simulated robots. In this paper the authors propose a simulation of an AGV system, that uses localization based on mounted cameras for positioning and control by a central system. Also, a real robot prototype is proposed. The mobile robot should reach the destination point precisely and kept inside the desired margins, avoiding collisions. The presented results show the developed system in operation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Reconhecendo as discussões em andamento no movimento da Economia Popular Solidáriano Brasil e tomando como base a pesquisa desenvolvida com a Rede de padariascomunitárias Fermento na Massa, este artigo conta sobre o retorno das atividades da Redepós Covid-19, a aproximação das pesquisadoras com a Rede e a troca que fizemos entreconhecimentos de design gráfico das pesquisadoras e as experiências como trabalhadorasda Economia Solidária. A partir disso buscamos discutir as relações entre a prática do designe a Economia Solidária, dialogando com os conceitos de design participativo e autogestãona prática da Economia Solidária buscamos refletir sobre as possibilidades de uma práticade um design mais solidário, co-produzido e buscando maior autonomia das trabalhadorasnos processos de comunicação e venda.

Abstract
The scoping review reported by this article aimed to analyse how security and privacy mechanism are being integrated with Fast Health Interoperability Resources (FHIR). An electronic search was conducted, and 37 studies were included in the review. The results show that 19 studies (i.e., more than half of the included studies) reported on the use of blockchain technology to (i) assure secure data sharing, (ii) provide secure Personal Health Records, (iii) support authentication and auditing mechanisms, (iv) support smart legal contracts, and (v) monitor the access to clinical data. The remainder 18 articles reported on the implementation of security and privacy mechanisms related to (i) data security at transmission, (ii) data security at storage, (iii) access control; (iv) data anonymization, and (v) management of informed consents. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Gastric cancer (GC) is still a significant public health issue, among the most common and deadly cancers globally. The identification and characterization of precancerous lesions of the stomach using endoscopy are crucial for determining the risk of cancer and guiding appropriate surveillance. In this scenario, deep learning (DL)-based computer vision methods have the potential to help us classify and identify particular patterns in endoscopic images, leading to a more accurate classification of these types of lesions. The quantity and quality of the data used heavily influence the classification performance of DL networks. However, one of the major setbacks for developing high-performance DL classification models is the typical need for more available data in the medical field. This review explores the use of Generative Adversarial Networks (GANs) and classical data augmentation techniques for improving the classification of precancerous stomach lesions. GANs are DL models that have shown promising results in generating synthetic data, which can be used to augment limited medical datasets. This review discusses recent studies that have implemented GANs and classical data augmentation methods to improve the accuracy of cancerous lesion classification. The results indicate that GANs can effectively increase the dataset's size, enhance the classification models' performance. In specific applications, such as the augmentation of endoscopic images depicting gastrointestinal polyps and Barrett's esophagus Adenocarcinoma, our review reveals instances where GANs, including models like Deep Convolutional GANs and conditional GANs, outperform classical data augmentation methods. Furthermore, this review highlights the challenges and limitations of the recent works using GANs and classical data augmentation techniques in medical imaging analysis and proposes directions for future research.