Socio-economic constructs and urban topology are crucial drivers of human mobility patterns. During the coronavirus disease 2019 pandemic, these patterns were reshaped in their components: the spatial dimension represented by the daily travelled distance, and the temporal dimension expressed as the synchronization time of commuting routines. Here, leveraging location-based data from de-identified mobile phone users, we observed that, during lockdowns restrictions, the decrease of spatial mobility is interwoven with the emergence of asynchronous mobility dynamics. The lifting of restriction in urban mobility allowed a faster recovery of the spatial dimension compared with the temporal one. Moreover, the recovery in mobility was different depending on urbanization levels and economic stratification. In rural and low-income areas, the spatial mobility dimension suffered a more considerable disruption when compared with urbanized and high-income areas. In contrast, the temporal dimension was more affected in urbanized and high-income areas than in rural and low-income areas.

This paper performs an investigation on the application of well-known Particle Swarm Optimization based algorithms for clustering tasks, namely, PSOClustering, Hybrid PSOClustering with K-means and Particle Swarm Clustering. The case study is to tackle a significant problem related to grouping students of an on-line educational database, aiming to increase their learning process through the recommendation of specific grammar lessons. The clustering process is performed based on the type, and the number of errors done by the students. The results show that the PSOClustering algorithm can achieve the best performance when compared to the other PSO-based algorithms and the standard K-means algorithm.

The behavior of domestic cats inspired the Cat Swarm Optimization (CSO) algorithm. The algorithm presents two modes of operation: seeking mode, representing the resting state, and tracing mode, in which the cats are hunting preys. In this paper, we introduce a boolean based operator, resulting in the Boolean Binary Cat Swarm Optimization algorithm (BBCSO). In our proposal, we change the selection of the agents to different modes and present a new way to determine the position and the velocity of the agents, using Boolean functions. The computational results demonstrate the viability of the proposal since it overcomes the original versions of the binary genetic algorithm and the binary particle swarm optimization in the 0/1 knapsack problem, especially in high dimensional instances.

Up to 50% of the students drop out of school in Brazilian universities. Because of the heterogeneity of individuals, it is difficult to determine which are the main causes of this high percentage of students not finishing their degree. In this paper, we employed the Fuzzy C-Means algorithm on a dataset composed of real-world registers of the Biology Undergraduate course from Brazilian universities. We applied the transactional distance theory to select the set of variables which were utilized in the clustering process. The results indicate that the data is better divided into five groups. We observed that the Fuzzy C-Means generated groups based on how engaged the students are, and, in each group, there are two subgroups: students that drop out and do not drop out the course. The type of analysis presented in this work can generate inputs for the institutions to establish new policies to reduce the dropout rate.