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Recent technological breakthroughs have allowed unmanned aerial vehicles (UAVs) to be utilized in a broad range of new operations. Among these various applications, herein, we focus on the use of UAVs for search and rescue missions in emergency and postdisaster scenarios. In this context, self-charging technologies for drones create new challenges in the routing of UAVs with charging stations. We present a variant of the vehicle routing problem (VRP) to address the integrated use of UAVs and mobile charging stations and define the VRP with synchronized networks (VRPSN), a new class of VRPs involving the routing of UAVs whose recharge platforms can travel to different locations during an operation. This leads to two networks within the VRP that must be integrated and synchronized. This research develops a mixed-integer linear program model for the VRPSN that considers the use of UAVs and mobile charging stations in a synchronized manner. To overcome the computational limits of the MILP model, this research presents a construct-and-adjust heuristic method integrated with a genetic algorithm. As a numerical example, we test the proposed model on the Corrego do Feijao Mine located in Minas Gerais, Brazil, where a dam recently collapsed, killing many workers. Numerical tests show that the new methodology is an attractive planning method for providing efficient and rapid responses in search and rescue missions.

A atual conjuntura global aponta para um futuro no qual Drones serão essenciais como resposta a desastres. Neste âmbito, um dos desafios tecnológicos é permitir o uso integrado de Drones com plataformas de recarga móveis, aumentando, assim, o tempo de voo em missões de busca e resgate. Trata-se de um problema de otimização de difícil solução por resolvedores convencionais. Por essa razão, desenvolvemos uma abordagem heurística para encontrar boas respostas. Simulamos a metodologia proposta na Mina Córrego do Feijão, em Brumadinho, Minas Gerais, onde houve o rompimento de uma barragem de rejeitos de minério. Os resultados obtidos apontam que a metodologia proposta pode auxiliar operações de busca e resgate, fornecendo uma resposta rápida e, portanto, aumentando as chances de encontrar vítimas.

Forecasting in Renewable Energy Systems is a challenging problem since their inputs present some uncertainties in the data distribution. On the other hand, there is an increasing volume of information recorded by such systems that can be explored by a forecasting model with the expectation of improved performance. This work introduces e-MVFTS (evolving Multivariate Fuzzy Time Series), an evolving forecasting model based on Fuzzy Time Series, and an evolving clustering method based on TEDA (Typicality and Eccentricity Data Analytics) Framework, which uses multivariate time series in a spatio-temporal context. The model has an adaptation mechanism to deal with changes in the data distribution or concept drifts in data streams. The evolving clustering method is adjusted as the data points arrive and are processed, in an online manner. Its performance is evaluated in the application to problems of solar and wind energy forecasting as well as concept drift events. The model was developed in Python programming language using pyFTS library. To contribute to the replication of all the results, we provide all source codes in a public repository. The good results in the different experiments enable the e-MVFTS model to be used in forecasting problems with streaming data in renewable energy systems.

The Covid-19 pandemic affected Brazil with severity. Brazil is a large country characterized by significant socioeconomic inequalities among its regions. This study aimed to check the sociodemographic factors associated with the Covid-19 mortality rate in Brazilian municipalities. This paper is an ecological study that analyzed data of the first three death waves, with Brazilian municipalities as a unit of analysis. Municipalities were clusterized based on their vulnerabilities in the face of Covid-19. The results show that, from the second death wave in the country, the municipalities with lower social vulnerability were more likely to have a higher mortality level than the other municipalities. The results also show that, among the attributes analyzed, the urban population percentage was the one that most strongly contributed to a municipality being more likely to have a higher level of mortality. Finally, this work demonstrated that using municipality clustering contributes to a more thoughtful and accurate analysis of correlations in ecological studies.

Sign language recognition is considered the most important and challenging application in gesture recognition, involving the fields of pattern recognition, machine learning and computer vision. This is mainly due to the complex visual–gestural nature of sign languages and the availability of few databases and studies related to automatic recognition. This work presents the development and validation of a Brazilian sign language (Libras) public database. The recording protocol describes (1) the chosen signs, (2) the signaller characteristics, (3) the sensors and software used for video acquisition, (4) the recording scenario and (5) the data structure. Provided that these steps are well defined, a database with more than 1000 videos of 20 Libras signs recorded by twelve different people is created using an RGB-D sensor and an RGB camera. Each sign was recorded five times by each signaller. This corresponds to a database with 1200 samples of the following data: (1) RGB video frames, (2) depth, (3) body points and (4) face information. Some approaches using deep learning-based models were applied to classify these signs based on 3D and 2D convolutional neural networks. The best result shows an average accuracy of 93.3%. This paper presents an important contribution for the research community by providing a publicly available sign language dataset and baseline results for comparison.