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Artificial Intelligence (AI) is changing the way people live in society. New technologies powered by AI have been applied in different sectors of the economy and the educational context is no different. AI has been considered a key to the development of learning strategies, especially in distance education. In this sense, this research aimed to identify the current state of Brazilian literature on AI applied to distance education. The Higher Education market in Brazil, which is the biggest in Latin America regarding the number of individuals able to enroll in a program, is still developing and distance education has grown rapidly. To reach the purpose of this paper, it was performed a Systematic Literature Review (SLR) to find the research conducted in graduate programs that investigate the subject of AI applied to distance education. The final analysis used a total of 63 studies -- 26 master's theses and 37 doctoral dissertations. The main results show that most of the research on AI in distance education in Brazil was conducted in Computer Science (56%) and Engineering (27%). Only 6% of the studies reviewed are from masters' or doctoral programs in Education. The result also shows that limited attention is paid to critical topics related to the growing introduction of AI in distance education, as such teachers' employability and technological training or the ethical implications of using AI for the educational process. As a result of this SLR, it was possible to suggest research opportunities considering the international agenda on AI.

Purpose: This paper aims to assist higher education institutions (HEIs) in their decision-making process to define initiatives and foster research projects contributing to sustainable development (SD) and minimizing the deficits found in the municipalities. Design/methodology/approach: A documental analysis was performed to select HEIs and Brazilian regional development indicators. Then, the assessment of the sustainable and institutional performance of Brazilian municipalities that have HEIs consisted of three parts: clustering with an unsupervised machine learning model, ranking with a hybrid multi-criteria decision making method and visualization of sustainability performance with the dashboard. Findings: The critical analysis of institutional and sustainability indicators contributes to a more active role of HEIs in matters of social responsibility, with a more holistic view of the performance and quality of municipal education. Furthermore, this critical analysis creates a scenario where HEIs can develop public policy proposals in partnership with the government to mitigate the main issues identified. Social implications: With this study, HEIs will be able to direct their actions to minimize the deficits found in the municipalities, consolidating their social responsibility. Originality/value: This study proposes a new decision-support tool with a dashboard of indicators so that HEIs can foster research projects with a focus on regional SD.

Copyright of Revista Científica Arbitrada Multidisciplinaria PENTACIENCIAS is the property of Revista Científica Arbitrada Multidisciplinaria PENTACIENCIAS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

While genetic variation in any species is potentially shaped by a range of processes, phylogeography and landscape genetics are largely concerned with inferring how environmental conditions and landscape features impact neutral intraspecific diversity. However, even as both disciplines have come to utilize SNP data over the last decades, analytical approaches have remained for the most part focused on either broad-scale inferences of historical processes (phylogeography) or on more localized inferences about environmental and/or landscape features (landscape genetics). Here we demonstrate that an artificial intelligence model-based analytical framework can consider both deeper historical factors and landscape-level processes in an integrated analysis. We implement this framework using data collected from two Brazilian anurans, the Brazilian sibilator frog (Leptodactylus troglodytes) and granular toad (Rhinella granulosa). Our results indicate that historical demographic processes shape most the genetic variation in the sibulator frog, while landscape processes primarily influence variation in the granular toad. The machine learning framework used here allows both historical and landscape processes to be considered equally, rather than requiring researchers to make an a priori decision about which factors are important., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The transition to a low-carbon economy is one of the main challenges of our time. In this context, solar energy, along with many other technologies, has been developed to optimize performance. For example, solar trackers follow the sun's path to increase the generation capacity of photovoltaic plants. However, several factors need consideration to further optimize this process. Important variables include the distance between panels, surface reflectivity, bifacial panels, and climate variations throughout the day. Thus, this paper proposes an artificial intelligence-based algorithm for solar trackers that takes all these factors into account—mainly weather variations and the distance between solar panels. The methodology can be replicated anywhere in the world, and its effectiveness has been validated in a real solar plant with bifacial panels located in northeastern Brazil. The algorithm achieved gains of up to 7.83% on a cloudy day and obtained an average energy gain of approximately 1.2% when compared to a commercial solar tracker algorithm. [ABSTRACT FROM AUTHOR]

Traffic Sign Recognition (TSR) is one of the many utilities made possible by embedded systems with internet connections. Through the usage of vehicular cameras, it's possible to capture and classify traffic signs in real time with Artificial Intelligence (AI), more specifically, Convolutional Neural Networks (CNNs) based techniques. This article discusses the implementation of such TSR systems, and the building process of datasets for AI training. Such datasets include a brand new class to be used in TSR, vegetation occlusion. The results show that this approach is useful in making traffic sign maintenance faster since this application turns vehicles into moving sensors in that context. Leaning on the proposed technique, identified irregularities in traffic signs can be reported to a responsible body so they will eventually be fixed, contributing to a safer traffic environment. This paper also discusses the usage and performance of different YOLO models according to our case studies.

Objetivo: To describe the initial baseline results of a population-based study, as well as a protocol in order to evaluate the performance of different machine learning algorithms with the objective of predicting the demand for urgent and emergency services in a representative sample of adults from the urban area of Pelotas, Southern Brazil., Methods: The study is entitled "Emergency department use and Artificial Intelligence in PELOTAS (RS) (EAI PELOTAS)" (https://wp.ufpel.edu.br/eaipelotas/). Between September and December 2021, a baseline was carried out with participants. A follow-up was planned to be conducted after 12 months in order to assess the use of urgent and emergency services in the last year. Afterwards, machine learning algorithms will be tested to predict the use of urgent and emergency services over one year., Results: In total, 5,722 participants answered the survey, mostly females (66.8%), with an average age of 50.3 years. The mean number of household people was 2.6. Most of the sample has white skin color and incomplete elementary school or less. Around 30% of the sample has obesity, 14% diabetes, and 39% hypertension., Conclusion: The present paper presented a protocol describing the steps that were and will be taken to produce a model capable of predicting the demand for urgent and emergency services in one year among residents of Pelotas, in Rio Grande do Sul state.

User eXperience (UX) has been used to achieve improvements in digital information systems based on how people perceive them. In particular, this paper establishes a framework that employs methods for eye and mouse tracking, keyboard input, self-assessment questionnaire and artificial intelligence algorithms to evaluate user experience and categorize users in terms of performance profiles. The results obtained with this framework are artifacts that can be used to support customizations of the User Interface (UI) on the websites. Moreover, the established framework is generic and flexible and can be applied to any information system, such as the case study shown in the website of the Federal Revenue of Brazil (RFB). The main objectives of this paper are as follows: (i) to set out a powerful UX framework based on three tracking techniques – the AIT2-UX; (ii) to provide the T2-UXT to collect, collate, process and visualize data obtained from users' interactions (iii) to use and compare machine learning algorithms with the classification of user performance profiles; (iv) to use the artifacts generated by the framework to manually customize the UI with the website. [ABSTRACT FROM AUTHOR]

Through the transformation that the electrical sector has been passing by, improvements in asset management and the guarantee of sustainable and quality services have become essential aspects for power companies. Thus, the digitalization of energy utilities presents itself as an important and crucial process. A concept that involves a variety of innovative trends is the digital twin. It consists of a 3D virtual replica of existing physical objects and real-time monitoring of certain measures. By developing a digital twin in the electrical power grid, a virtual replica of the network is obtained providing network virtual maps, 3D asset models, dynamic and real-time data of grid assets, and IoT sensing. All these data can feed a platform where AI-based models and advanced field operation technologies and solutions will be applied. With a Network Digital Twin©development, applications involving on-field activities can be improved through augmented reality (AR) and virtual reality (VR) to enhance workforce operations. This paper discusses the best practices for the development of a digital twin for the electrical power sector. These practices were found during the development of a project carried out by Enel Distribuição São Paulo, applying a living lab concept in the densest region of Brazil. The results of this paper present 3D images captured with specialized tools, and how they influence the workforce activities of human interface operation. Furthermore, financial and operational returns are presented through a cost–benefit analysis for each relevant aspect. [ABSTRACT FROM AUTHOR]

The saturated hydraulic conductivity, k sat , is a crucial variable to describe the hydromechanical behavior of soils. The value of k sat of lateritic soils that are typically found in tropical regions is highly affected by the soil's structure, void ratio, and fine particle aggregation. As a result, the determination of k sat in the field or in the laboratory is complex and involves greater variability, depending on the type of test and on the spatial location of sampling. This paper presents a study of k sat values of lateritic soils, analyzing them using Statistic, Multilayer Perceptron Artificial Neural Networks (ANN) and Decision Trees (CHAID). This study aims to support decision-making regarding the type of test and depth chosen for sampling in laterite soils and understanding the factors influencing the permeability of such soils. An extensive literature review on the k sat values of lateritic soils was performed, providing data for the establishment of a database comprise of 722 registries. According to agronomic and geotechnical soil classifications, the Brazilian lateritic soils presents a "moderate" hydraulic conductivity. A significant variation of permeability values along the depth was identified, particularly for depths between 0.1 and 0.2 m. Regarding the importance of testing variables, the ANN indicated a high dependency on the type of test. The decision tree divided field test and laboratory test automatically, inferring the relevance of the type of test to the determination of k sat ., (© 2022. The Author(s).)

[Display omitted] • Assessment of the status and trends of PV DG in Brazil based on big data processing; • Application of machine learning algorithms to estimate the installed power of individual systems; • Application of forecasting models to predict the installed power of PV DG over time in Brazil and its regions; • Application of the DEA method to rank Brazilian states in terms of efficiency in PV DG deployment; • The paper is expected to be valuable for regulatory agencies, researchers, and PV aggregator companies. The deployment of photovoltaic (PV) distributed generation (DG) has been increasing substantially in Brazil. In this context, the present paper exploits a robust dataset provided by the Brazilian Electricity Regulatory Agency (ANEEL) to evaluate the status and trends concerning PV DG. As of Nov/2022, such a dataset consists of more than 1,4 million lines (one for each system) and thirty columns (various information concerning the systems). For an in-depth assessment, three items are addressed: (i) the application of machine learning algorithms to estimate the installed power of individual systems based on other information available in the dataset, (ii) the application of forecasting models to predict the installed power of PV DG over time in Brazil and its regions, and (iii) the application of the data envelopment analysis (DEA) method to rank Brazilian states in terms of efficiency in PV DG deployment. While items (i)-(iii) present distinct specific goals, they use the same dataset and provide essential insights concerning PV DG in Brazil. In item (i), elastic net (EN), decision tree (DT), random forest (RF), extra tree (ET), AdaBoost (AB), and gradient boosting (GB) are applied to select the most accurate algorithm. In item (ii), autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), Holt-Winters exponential smoothing (HWES), Bass diffusion model (BDM), and multilayer perceptron artificial neural network (MLP-ANN) are applied. In item (iii), output-oriented DEA CCR and BCC are applied. Concerning item (i), results demonstrate that estimating the installed power of individual systems is not very simple. Nonetheless, the machine learning algorithms imply a significant accuracy increase compared to taking the dataset's average as the estimation, especially the DT and RF algorithms with a coefficient of determination of 29%. Regarding item (ii), results demonstrate that the installed capacity in Brazil and its regions is expected to increase approximately linearly in the horizon of months but with weekly seasonal characteristics. Moreover, the most accurate model depends on the region, i.e., the ANN is superior for the Northeast (MAPE of 3.25%) and South (MAPE of 1.28%) regions, the HWES method is superior for Brazil (MAPE of 0.51%) and the Southeast region (MAPE of 2.26%), and the BDM is superior for the Mid-west (MAPE of 1.95%) and North (MAPE of 3.37%) regions. Concerning item (iii), results demonstrate that DEA CCR is more representative of the Brazilian reality since DEA BCC assigns several states as efficient, even if they showcase a very low installed capacity, particularly small states. DEA CCR indicates that Minas Gerais, Rio Grande do Sul, and Mato Grosso are the most efficient states in PV DG deployment relative to their inputs, closely followed by São Paulo with an efficiency of 95%. Furthermore, states located in the North and Northeast region require more attention from ANEEL since their efficiencies are generally low. A literature review demonstrated that this paper is the first of its kind in exploiting so thoroughly and robustly the dataset provided by ANEEL. Therefore, the paper is expected to assist ANEEL in the task of properly regulating the sector and also assist researchers and PV aggregator companies through in-depth insights. Moreover, the conducted procedures can also be applied to other countries with available datasets, thus ensuring high applicability for the research. [ABSTRACT FROM AUTHOR]

Copyright of Revista Pauta Geral is the property of Revista Pauta Geral and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This paper aims to present a systematic review of the development process of Industry 4.0 in the textile and apparel sector, as well as to show some concepts, examples found in the literature on the application of the principles and technologies involved like the internet of things (IoT), cloud computing, Big Data, autonomous robots, three-dimensional (3D) printing, augmented reality, virtual prototyping, horizontal and vertical system integration and cybersecurity. Design/methodology/approach: The methodology adopted in this study was a systematic literature review aided by the use of SciMAT, a scientific mapping software. Documents were collected from the Web of Science and Scopus database from 2011 to 2020 using the words "Textile" and "Industry 4.0" that result in 865 documents and 115 were analyzed. Findings: The literature review showed that the textile industry in the international context is at an incipient stage of the implementation of Industry 4.0. The main aspects of Industry 4.0 that were identified in the textile industry initially focus on the implementation of technologies aimed at computerization and automation of processes, whose main focuses are increasing productivity and reducing costs. Projects for the implementation of augmented reality and 3D printing and simulation technologies in the textile industry, clothing and apparel area are still embryonic, normally implemented through tools and software oriented toward the creation and development of new models of processes, products and commerce. Research limitations/implications: The search in the databases was carried out on October 17, 2020. Therefore, for future study, other combinations of search terms and time update are suggested, in addition to including more databases besides Scopus and Web of Science. Practical implications: This literature review served as the basis for the development of a questionnaire that was applied to 72 people in an industry in the clothing sector, located in the state of Santa Catarina, southern Brazil. Social implications: The benefits of industry 4.0 are perceived in people with its implementation, such as a reduction in energy consumption of around 15%, an increase of up to 25% in work efficiency, in addition to more assertive decision-making, improvement of processes and balance between life and work. Originality/value: Machine learning, artificial intelligence, smart fabrics, IoT, supply chain management, environmental protection, Big Data, autonomation and cyber physics were the strongest terms found, consolidating as a prominent field for current and future studies. From emerging and/or still unexplored areas of Industry 4.0 in the textile sector, there is real-time communication, computer applications, carbon, fibers, health care and sustainable development. Some strategic actions that are taking place in some countries are summarized and in Brazil the adoption rate is 29% for this sector, revealing itself as a needy area and suitable for the development of studies that address the subject. [ABSTRACT FROM AUTHOR]

A documentary review was carried out on the production and publication of research papers related to the study of the variables INTELLIGENT SYSTEMS, ARTIFICIAL INTELLIGENCE and AUTOMATIC LEARNING. The purpose of the bibliometric analysis proposed in this document was to know the main characteristics of the volume of publications registered in the Scopus database during the period 2017-2022 by Latin American institutions, achieving the identification of 196 publications. The information provided by this platform was organized through graphs and figures categorizing the information by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics have been described, the position of different authors towards the proposed theme is referenced through a qualitative analysis. Among the main findings made through this research, it is found that Brazil with 104 publications was the Latin American country with the highest scientific production registered in the name of authors affiliated with institutions of that nation. The Area of Knowledge that made the greatest contribution to the construction of bibliographic material referring to the study of Intelligent Systems, Artificial Intelligence and Machine Learning was Computer Science with 151 published documents, and the Type of Publication most used during the period indicated above were Conference Articles with 55% of the total scientific production. [ABSTRACT FROM AUTHOR]

Copyright of Paradigma is the property of Universidad Pedagogica Experimental Libertador and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The increasing dispute between the United States of America and China finds a main area of competition around 5G, which will enable a substantial improvement in the capacity for vehicle auto-guidance, remote operations, robotics, artificial intelligence and other dual-use applications. While 5G is still emerging, the global shaping of the network infrastructure, regulations, patents and standards involves the Global South. It is an eminently global dispute for markets, but also for national security, which has gained a central place on the foreign policy agenda of most of the countries around the globe. In this context, the objective of this text is to analyze the emergent conditions of 5G in two of the main countries of the Global South, India and Brazil, within the dispute between the US and China. Through the contextualization of this dispute and its implications in India and Brazil, we seek to extract a series of preliminary reflections for other countries of the Global South. Thus, this paper contributes to a key area understanding the emergent conditions of a new phase of the mobile telecommunications paradigm developing two main cases still unexplored systematically by the literature in Spanish. [ABSTRACT FROM AUTHOR]

Seeking asylum presents itself as a surviving strategy to a part of the population in the 21st century globalization period due to political, social and economic crisis. In this regard, this paper aims to analyze if the Brazilian economic crisis discourse stimulates xenophobia towards refugees sheltered in Brazil. Therefore, this article is divided into two stages. Initially, the migratory context in the 21st century is exposed, followed by and explanation of the international and national legal support regarding the refugee issue. Then, the possibility of the economic discourse as a xenophobia stimulator was pointed out. The research is based on a bibliographic methodology, pure, qualitative, descriptive and exploratory, using a legislative and data analysis of the National Refugee Committee. Thus, it is concluded that the economic crisis discourse does promote xenophobic practices, and those behaviors must be efficiently combated to there by guarantee human rights to refugees in Brazil. [ABSTRACT FROM AUTHOR]

This Article investigates the feasibility and consequences of granting legal personality to Artificial Intelligences (AIs) in the context of Brazilian law, with a special focus on copyright law. It conducts a thorough analysis of how such a grant can enhance legal security and encourage innovation in AI technologies. Through an integrative review of the literature and a comparative analysis of national and international legislation and jurisprudence, the study explores the implications of this legislative innovation. This Article highlights the importance of legal clarity for companies and investors in the AI sector, emphasizing that granting legal personality to AIs can simplify the identification of the copyright holder and protect investments. However, this Article also recognizes challenges, such as the complexity of assigning authorship and evaluating the originality of works created by AIs. A careful debate is proposed on criteria for determining which AIs should be considered legal persons and how to balance the rights and duties of AIs and their creators. This Article suggests adapting the legal structure of the limited liability company (LTDA) to incorporate AIs as operational entities, aiming for an effective legal framework for managing risks associated with AI. This Article concludes that granting legal personality to AIs in Brazil is a promising strategy, requiring careful consideration and forward-looking vision, emphasizing the need for Brazilian law to prepare for the opportunities and challenges of the AI era. [ABSTRACT FROM AUTHOR]

This article investigates the historical uses and types of artificial intelligence (AI) systems and resources in Brazilian journalistic products. It is a work anchored in critically analyzing the literature on the subject, mapping and observing cases, seeking to identify uses and innovative processes, and analyzing AI projects for journalism. A search was carried out in web repositories, specifically Google, Google Scholar, and Scopus, using the terms: "inteligência artificial" + "jornalismo", "bot + jornalismo", "Geração de linguagem natural [NLG] + jornalismo", "aprendizado de máquina [machine learning] + jornalismo", and "algoritmos + jornalismo". The corpus analysis (N = 45) includes the evaluation of the impacts of AI on the production and distribution of news in the context of Brazilian digital journalism. We try to answer questions about the uses of databases, approximation with platforms, uses of shared codes, connections with other Ais, and sources of funding, and whether they are backend or frontend initiatives. In a parallel investigation, we try to identify if Brazilian newsrooms are officially using ChatGPT, a generative AI. The findings point to advances in using low-cost and low-impact AI, with the predominance of bots. The great availability of this kind of AI in web repositories is believed to facilitate native digital media to incorporate innovative processes in using these technologies. [ABSTRACT FROM AUTHOR]

The global surge in urbanization and population growth has led to a significant increase in municipal solid waste generation, posing a considerable challenge in identifying suitable landfill sites. This study proposes a novel framework that enhances landfill site monitoring and assessment by combining GIS-based hierarchical analytical processes with a fuzzy inference system (FIS). The study employs a systematic approach involving phases such as feature selection, spatial analysis, criteria weighting, FIS building, and a case study conducted in São Paulo State, Brazil. The proposed framework effectively assesses landfill suitability and offers practical recommendations for landfill management and future site selection. This framework provides actionable recommendations for landfill monitoring and assessment, supporting landfill management while minimizing environmental and social impacts. It offers a comprehensive approach to landfill assessment, enhancing the sustainability of waste management practices. Further research can improve the proposed framework by refining feature selection and incorporating real-time data for continuous monitoring. Additionally, exploring the integration of emerging technologies, such as remote sensing and artificial intelligence, can further enhance landfill site monitoring and assessment. [ABSTRACT FROM AUTHOR]

Countries have been developing and deploying anti-corruption tools based on artificial intelligence with hopes of them having positive capabilities. Yet, we still lack empirical analyses of these automated systems designed to identify and curb corruption. Hence, this article explores novel data on 31 bottom-up and top-down initiatives in Brazil, presented as a case study. Methodologically, it uses a qualitative analysis and draws on secondary data and interviews to assess the most common features, usages and constraints of these tools. Data collected are scrutinised under a new conceptual framework that considers how these tools operate, who created them for what purpose, who uses and monitors these tools, what types of corruption they are targeting, and what their tangible outcomes are. Findings suggest that in Brazil, AI-based anti-corruption technology has been tailored by tech-savvy civil servants working for law enforcement agencies and by concerned citizens with tech skills to take over the key tasks of mining and crosschecking large datasets, aiming to monitor, identify, report and predict risks and flag suspicions related to clear-cut unlawful cases. The target is corruption in key governmental functions, mainly public spending. While most of the governmental tools still lack transparency, bottom-up initiatives struggle to expand their scope due to high dependence on and limited access to open data. Because this new technology is seen as supporting human action, a low level of concern related to biased codes has been observed. [ABSTRACT FROM AUTHOR]

Despite the concern about climate change and the associated negative impacts, fossil fuels continue to prevail in the global energy consumption. This paper aimed to propose the first model that relates CO2 emissions of Sao Paulo, the main urban center emitter in Brazil, with gross national product and energy consumption. Thus, we investigated the accuracy of three different methods: multivariate linear regression, elastic-net regression, and multilayer perceptron artificial neural networks. Comparing the results, we clearly demonstrated the superiority of artificial neural networks when compared with the other models. They presented better results of mean absolute percentage error (MAPE = 0.76%) and the highest possible coefficient of determination (R2 = 1.00). This investigation provides an innovative integrated climate-economic approach for the accurate prediction of carbon emissions. Therefore, it can be considered as a potential valuable decision-support tool for policymakers to design and implement effective environmental policies. [ABSTRACT FROM AUTHOR]

Here, we develop and test an artificial intelligence (AI)‐based approach to monitor major Brazilian aquifers. The approach combines Gravity Recovery and Climate Experiment (GRACE) data and ground‐based hydrogeological measurements from Brazil's Integrated Groundwater Monitoring Network at hundreds of wells distributed in 12 aquifers across the country. We tested model ensembles based on three AI approaches: Extreme Gradient Boost, Light Gradient Boosting Model and CatBoost, followed by a Linear Regression step. The approach is further boosted with wavelet and seasonal decomposition processes applied to GRACE data. To determine the AI‐based model's sensitivity to data availability, we propose four experiments combining hydrogeological measurements from different aquifers. Groundwater storage (GWS) estimates from the Global Land Data Assimilation System (GLDAS) are used as benchmark. A sensitivity analysis shows that the LR‐based model ensemble is the best suited and to reproduce GWS change in all studied Brazilian aquifers. Results show that the proposed approach outperforms GLDAS in all experiments, with an root mean square error (RMSE) value of 2.68 cm for the experiment that covers all monitored wells in Brazil. GLDAS resulted in RMSE = 6.76 cm. Using our AI model outputs, we quantified the GWS change of two major aquifers, Urucuia and Bauru‐Caiuá, over the past two decades: −31 and −6 km3, respectively. Water loss is driven by a prolonged drought across most of the country and intensification of groundwater pumping for irrigation. This study demonstrates that combining satellite data and AI can be a cost‐effective alternative to monitor poorly equipped aquifers at the continental scale, with possible global replicability. Key Points: An Artificial Intelligence (AI)‐based model was built to monitor groundwater in Brazilian aquifers using satellite gravimetry dataAI‐based groundwater changes outperformed Global Land Data Assimilation System (GLDAS) estimates in all proposed experimentsResults show that satellite‐based AI techniques can be an effective solution for groundwater monitoring in poorly equipped regions [ABSTRACT FROM AUTHOR]

Introduction: technological developments in artificial intelligence and health are necessary for Latin American health systems. Objective: to describe the trends in scientific production on artificial intelligence and health in Latin America in Scopus. Method: This is a retrospective bibliometric study of Latin American authors' scientific production on artificial intelligence and health in Scopus between 2012 and 2021. Production, visibility and impact indicators were used. VOSviewer and SciVal were used for data analysis. Results: 2871 articles were published, with a variation between 2012 and 2021 of 94.98%. 2,397 articles were original, and 2,741 were written in English. 58.3% were published in first-quartile journals, the most productive being Sensors (Ndoc=79) and Plos One (Ndoc=66). 64,128 citations were received (mean of 22.3 citations per article). Brazil was the most productive country (Ndoc=1420), and the institution was the University of São Paulo (Ndoc=288). 498 thematic groups were identified, and 1376 themes. 54% of the articles had international collaboration and 3.3% with academic-corporation collaboration. Conclusions: there is a growing scientific production on artificial intelligence and health in Latin America, written mainly in English, medical, engineering and computer science research areas, disseminated in specialized magazines in the first quartiles. Brazil and its institutions were the top producers. The main topics were predictive models and the application of artificial intelligence for classifying, diagnosing and treating diseases. [ABSTRACT FROM AUTHOR]

This study aimed to assess monthly average daily global solar irradiation ( H g m) estimates in Southeastern Brazil from empirical models and artificial intelligence (AI) techniques using extreme air temperatures (maximum—Tx and minimum—Tn) and extraterrestrial solar irradiation ( H 0 m ) and evaluate the geographic and climate factors in the performance of the models or the AI techniques. The H g m , Tx and Tn data series from 95 automatic weather stations of the Brazilian National Institute of Meteorology (INMET) were used. The Hargreaves-Samani (HS) and Bristow-Campbell (BC) models were fitted, and AI techniques (artificial neural network—ANN and support vector machines—SVM) were trained, and assessed according to local climate conditions. Three input schemes in the AI techniques were assessed: (i) H 0 m , Tx, and Tn; (ii) H 0 m and air temperature amplitude (ΔT); and (iii) H 0 m , Tx, Tn, and ΔT. The coefficients of the HS (0.134 ≤ kr ≤ 0.262) and BC (0.539 ≤ β0 ≤ 0.796; 0.004 ≤ β1 ≤ 0.843; 0.35 ≤ β2 ≤ 2.60) models and their performance were influenced by climate, continentality/maritime effects, altitude and weather systems. The performance of the HS model was inferior to that of BC model and the SVM and ANN techniques. Overall, the SVN and ANN techniques performed better than the BC method. However, BC had similar or superior precision and accuracy to SVM and ANN for some climatic conditions and combinations of input variables. The quality of the H g m series, the combination of input variables, and climate influenced the estimates of the SVM and ANN techniques. [ABSTRACT FROM AUTHOR]

Studies on safety in aviation are necessary for the development of new technologies to forecast and prevent aeronautical accidents and incidents. When predicting these occurrences, the literature frequently considers the internal characteristics of aeronautical operations, such as aircraft telemetry and flight procedures, or external characteristics, such as meteorological conditions, with only few relationships being identified between the two. In this study, data from 6,188 aeronautical occurrences involving accidents, incidents, and serious incidents, in Brazil between January 2010 and October 2021, as well as meteorological data from two automatic weather stations, totaling more than 2.8 million observations, were investigated using machine learning tools. For data analysis, decision tree, extra trees, Gaussian naive Bayes, gradient boosting, and k-nearest neighbor classifiers with a high identification accuracy of 96.20% were used. Consequently, the developed algorithm can predict occurrences as functions of operational and meteorological patterns. Variables such as maximum take-off weight, aircraft registration and model, and wind direction are among the main forecasters of aeronautical accidents or incidents. This study provides insight into the development of new technologies and measures to prevent such occurrences. [ABSTRACT FROM AUTHOR]

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This study is a comprehensive and modern approach to predict customer churn in the example of an e-commerce retail store operating in Brazil. Our approach consists of three stages in which we combine and use three different datasets: numerical data on orders, textual after-purchase reviews and socio-geo-demographic data from the census. At the pre-processing stage, we find topics from text reviews using Latent Dirichlet Allocation, Dirichlet Multinomial Mixture and Gibbs sampling. In the spatial analysis, we apply DBSCAN to get rural/urban locations and analyse neighbourhoods of customers located with zip codes. At the modelling stage, we apply machine learning extreme gradient boosting and logistic regression. The quality of models is verified with area-under-curve and lift metrics. Explainable artificial intelligence represented with a permutation-based variable importance and a partial dependence profile help to discover the determinants of churn. We show that customers' propensity to churn depends on: (i) payment value for the first order, number of items bought and shipping cost; (ii) categories of the products bought; (iii) demographic environment of the customer; and (iv) customer location. At the same time, customers' propensity to churn is not influenced by: (i) population density in the customer's area and division into rural and urban areas; (ii) quantitative review of the first purchase; and (iii) qualitative review summarised as a topic. [ABSTRACT FROM AUTHOR]

The current scenario of a global pandemic caused by the virus SARS-CoV-2 (COVID19), highlights the importance of water studies in sewage systems. In Brazil, about 35 million Brazilians still do not have treated water and more than 100 million do not have basic sanitation. These people, already exposed to a range of diseases, are among the most vulnerable to COVID-19. According to studies, places that have poor sanitation allow the proliferation of the coronavirus, been observed a greater number of infected people being found in these regions. This social problem is strongly related to the lack of effective management of water resources, since they are the sources for the population's water supply and the recipients of effluents stemming from sanitation services (household effluents, urban drainage and solid waste). In this context, studies are needed to develop technologies and methodologies to improve the management of water resources. The application of tools such as artificial intelligence and hydrometeorological models are emerging as a promising alternative to meet the world's needs in water resources planning, assessment of environmental impacts on a region's hydrology, risk prediction and mitigation. The main model of this type, WRF-Hydro Weather Research and Forecasting Model), represents the state of the art regarding water resources, as well as being the object of study of small and medium-sized river basins that tend to have less water availability. hydrometeorological data and analysis. Thus, this article aims to analyze the feasibility of a web tool for greater software usability and computational cost use, making it possible to use the WRF-Hydro model integrated with Artificial Intelligence tools for short and medium term, optimizing the time of simulations with reduced computational cost, so that it is able to monitor and generate a predictive analysis of water bodies in the MATOPIBA region (Maranhão-Tocantins-Piauí-Bahia), constituting an instrument for water resources management. The results obtained show that the WRF-Hydro model proves to be an efficient computational tool in hydrometeorological simulation, with great potential for operational, research and technological development purposes, being considered viable to implement the web tool for analysis and management of water resources and consequently, assist in monitoring and mitigating the number of cases related to the current COVID-19 pandemic. This research are in development and represents a preliminary results with future perspectives. [ABSTRACT FROM AUTHOR]

The health sector faces a series of problems generated by patients who miss their scheduled appointments. The main challenge to this problem is to understand the patient's profile and predict potential absences. The goal of this work is to explore the main causes that contribute to a patient's no-show and develop a prediction model able to identify whether the patient will attend their scheduled appointment or not. The study was based on data from clinics that serve the Unified Health System (SUS) at the University of Vale do Itajaí in southern Brazil. The model obtained was tested on a real collected dataset with about 5000 samples. The best model result was performed by the Random Forest classifier. It had the best Recall Rate (0.91) and achieved an ROC curve rate of 0.969. This research was approved and authorized by the Ethics Committee of the University of Vale do Itajaí, under opinion 4270,234, contemplating the General Data Protection Law. [ABSTRACT FROM AUTHOR]