Abstract
The current trend in energy sustainability and the energy growing demand have given emergence to distributed hybrid energy systems based on renewable energy sources. This study proposes a strategy for the optimal sizing of an autonomous hybrid energy system integrating a photovoltaic park, a wind energy conversion, a diesel group, and a storage system. The problem is formulated as a uni-objective function subjected to economical and technical constraints, combined with evolutionary approaches mainly particle swarm optimization algorithm and genetic algorithm to determine the number of installation elements for a reduced system cost. The computational results have revealed an optimal configuration for the hybrid energy system.

Abstract
The significant growth of interconnected Internet-of-Things (IoT) devices, the use of social networks, along with the evolution of technology in different domains, lead to a rise in the volume of data generated continuously from multiple systems. Valuable information can be derived from these evolving data streams by applying machine learning. In practice, several critical issues emerge when extracting useful knowledge from these potentially infinite data, mainly because of their evolving nature and high arrival rate which implies an inability to store them entirely. In this work, we provide a comprehensive survey that discusses the research constraints and the current state-of-the-art in this vibrant framework. Moreover, we present an updated overview of the latest contributions proposed in different stream mining tasks, particularly classification, regression, clustering, and frequent patterns. This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

Abstract
Self-assessment is one of the strategies used in active teaching to engage students in the entire learning process, in the form of self-regulated academic learning. This study aims to assess the possibility of including self-evaluation in the student's final grade, not just as a self-assessment that allows students to predict the grade obtained but also as something to weigh on the final grade. Two different curricular units are used, both from the first year of graduation, one from the international relations course (N = 29) and the other from the computer science and computer engineering courses (N = 50). Students were asked to self-assess at each of the two evaluation moments of each unit, after submitting their work/test and after knowing the correct answers. This study uses statistical analysis as well as a clustering algorithm (K-means) on the data to try to gain deeper knowledge and visual insights into the data and the patterns among them. It was verified that there are no differences between the obtained grade and the thought grade by gender and age variables, but a direct correlation was found between the thought grade averages and the grade level. The difference is less accentuated at the second moment of evaluation-which suggests that an improvement in the self-assessment skill occurs from the first to the second evaluation moment.

Abstract
Optical waveguides were fabricated at the surface of Eagle2000 glass substrates, using femtosecond laser direct writing and wet etching, and their potential as intensity-modulated refractometers was assessed. Through the analysis of their broadband spectral response to different refractive index oils, we observed that mode mismatch is present when the guided mode reaches the surface of the substrate and interacts with the external medium, thus enabling the use of such optical waveguides in refractive index sensing. Refractive indices equal to or greater than that of the substrate also induced a coupling mechanism that was shown not to be suitable in these devices. The device's wavelength of operation was found to be tunable by controlling the distance between the surface and the center of the optical waveguide. However, the sensitivity was seen to diminish by increasing the latter, being nonexistent for distances greater than 5.5 mu m. In this study, the maximum sensitivity values were found for a surface to core center distance between 1 and 2 mu m, in the biological range, and 2.5 to 3 mu m, for a refractive index nearing that of the substrate. Accordingly, maximum sensitivities of approximate to 25 dB/RIU and approximate to 1200 dB/RIU were found between 1.300 < n(D)(25)degrees(C) < 1.400 and 1.490 < n(D)(25)degrees(C) < 1.500, respectively.

Abstract
The objective of this study is to present a new coupling method in order to measure the electrical properties of titanium alloy pedicle screws used in spinal surgery and to compare it with other common methods of measurement. An experimental setup was devised to test the electrical resistance of two specimens of pedicle screws using four methods for coupling the sensing leads, including the use of multimeter probes, alligator clips, wrapped wires and encapsulation with thermo-retractable sleeves. The electrical resistance of the pedicle screw under testing was measured at a current of 10 mA for each coupling method, and the results compared. Our findings show that although widely used in electrical analysis, the alligator clips do not perform as well as the other methods, such as simple wrapping of wires around the screw or the direct application of multimeter probes. The use of thermo-retractable sleeves provides the lowest resistance and inter-quartile range and is closer to the tabled values for the screw's titanium alloy. Additionally, only this method allows the measurement of identical resistivity values between different screw models manufactured with the same titanium alloy. We then concluded that the use of wrapped wires encapsulated with thermo-retractable sleeves allow more accurate measurements of the pedicle screw's electrical properties.

Abstract
Rescuing survivors in unknown environment can be extreme difficulty. The use of UAVs to map the environment and also to obtain remote information can benefit the rescue tasks. This paper proposes an organizational system for multi-UAVs to map indoor environments that have been affected by a natural disaster. The robot’s organization is focused on avoiding possible collisions between swarm’s members, and also to prevent searching in locations that have already discovered. This organizational approach is inspired by bees behavior. Thus, the multi- UAVs must search, in a collaborative way, in order to map the scenario in the shortest possible time and, consequently, to travel the shortest reasonable distance. Therefore, three strategies were evaluated in a simulation scenario created in the V-REP software. The results indicate the feasibility of the proposed approach and compare the three plans based on the number of locations discovered and the path taken by each UAV. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.