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It started with the contribution of software engineering in the construction and inclusion of digital educational games for teaching and learning about probability. The evaluation of the digital game “Probability in Action” performed by 29 students (10 and 11 years old) from two classes at a public school in the city of Barueri, São Paulo, Brazil, is presented, according to the quality model of educational games, considering three subcomponents (motivation, user experience and learning) and 11 dimensions (attention; relevance; trust; satisfaction; immersion; social interaction; challenge; fun; competence; short- and long-term learning). The results obtained through the analysis of the students' responses are characterized as positive, in addition to being configured as an option to support the teaching and learning process of probability for Elementary School.

The objective of this work was to systematically analyze the literature in Brazil that used or created paradidactic books for the teaching of probability aimed at the final years of Elementary School. Twelve studies were considered as results, from 2014 to 2021, and the IRaMuTeQ software was used for the multidimensional analysis from the organization in texts (corpus). Each text was prepared considering what was emphasized in the research, the methodology or methodological approach used, the context in which it was developed and the types of study and areas involved and the main results and conclusions. We emphasize that research on the use or creation of paradidactic books in the teaching of probability for Elementary School is still incipient, with the concern with the student's profile and its relations with experiential knowledge and everyday life being evidenced.

Traditional Non-Destructive Testing (NDT) methods for heat exchangers inspections struggle with both efficiency and accuracy. These methods are time-consuming, prone to errors in data interpretation, and heavily reliant on skilled technicians, who are becoming a scarce resource. Artificial intelligence (AI) tools offer a promising alternative, by providing a faster, more automated solution. This study assesses an AI system's performance using Probability of Detection (POD), a key NDT metric. Due to the cost of experimental POD, we leverage numerical simulation with CIVA software to generate large datasets. POD curves are created to evaluate the AI's capabilities across various heat exchangers conditions. Simulations demonstrate the AI's ability to detect indications even with significant noise affecting the signal. The findings are corroborated by real-world testing, which achieved a 97.7% POD for a selected set of indications deemed critical. This study highlights the potential of AI and simulation to improve NDT for critical infrastructure.

ABSTRACT Sugarcane irrigation is one of the main technologies to increase productivity and profitability in the sugar-energy sector. However, to improve the management of water resources in Brazil, growers need to obtain a water rights permitfrom regulatory agencies. The calculation of water rights is determined from data based on estimates of rainfall and reference evapotranspiration (ETo) provided by the Agência Nacional de Águas e Saneamento Básico (ANA). The primary hypothesis of this study was that the method proposed by ANA to calculate water rights did not provide operational security since it lacked an adequate probabilistic character. As a corollary, we included a secondary hypothesis stating that determining flows through the simplified FAO method proposed in the CROPWAT 8.0 manual displayed vulnerability due to the criteria to choose the representative years. This research aimed to compare such methodologies with a more detailed case study (standard method), taking into account the temporal variability of these estimates, the irrigation criteria, and the probability of occurrence of ETo and rainfall to determine water rights for sugarcane irrigation. During the period between Apr and Sept (dry period), the calculation method proposed by ANA met the needs of the project. However, when rainfall was concentrated (Oct to Mar), the calculation method tended to underestimate the monthly values of available flows. The simplified method proposed by FAO and the alternative method proposed here approached ideal conditions.

The aim of the study is to estimate a nonlinear regression logit model to calculate the probability of a road accident occurring in Algeria. Referring to the literature, this probability depends on several variables related to the driver, the vehicle and the road environment. Thus, the objective is to show which explanatory variables favour the occurrence of a traffic accident and to what extent. In addition, we seek to estimate the probability that a driver with given characteristics, driving a vehicle with given characteristics, and having a driving licence for a given length of time may have an accident. The structure of the estimation is based on disaggregated data collected following the analysis of files proposed by an Algerian insurance company. The results obtained make it possible to determine the category of variables that has a significance and an important role in explaining the probability of accidents occurring.