Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

This article presents the theoretical foundations of an artificial neural network from both mathematical and algorithmic perspectives, grounded in a comprehensive review of the available literature and informed by the author extensive experience in teaching related courses. In this context, the content was directed toward single-layer, shallow, and multilayer neural networks with supervised training, complemented by examples and practical applications in classification and prediction.. For this purpose, the MATLAB Deep Learning Toolbox was employed in conjunction with publicly accessible datasets, facilitating the implementation of examples that range from simple to complex. This progression was achieved by varying parameters such as the number of neurons, input features, network layers, training iterations, and the selection of diverse activation functions. Thus, to demonstrate the applicability of the theoretical concepts, six practical examples and four real-world applications were developed.

This paper presents the Blackman's Theorem, a simple and general tool for calculating unambiguously equivalent impedances seen from any pair of terminals in electrical and analog circuits. Some application examples of this Theorem will be analyzed, demonstrating its usefulness even in complicated circuits.

In natural languages, spelling errors and ambiguities occur that can be resolved by humans; this is not the case in formal languages. Natural language processing aims to enable humans to communicate with machines in natural language; however, spelling errors can be more difficult for machines to identify. These errors can be of various types, including lexicographic errors. This study aims to demonstrate how lexical-morphological analysis can improve accuracy and efficiency in natural language processing tasks by combining advanced tokenization techniques and deep morphological analysis for applications in specialized domains.

In a world convulsed by war, climate change, and other serious crises that threaten the existence and well-being of humanity, engineering is evolving at an unprecedented pace and is positioning itself as the cornerstone for addressing the most pressing challenges facing our society. The convergence of the disciplines of civil engineering, electronics, computer science, and mechatronics fosters a transdisciplinary space that allows us to prepare to face the challenge of complex phenomena and the quantum revolution, as well as to promote more favorable conditions for life in all its manifestations and forms.

This paper explores the implementation and assessment of didactic interventions aimed at fostering metacognitive skills in primary school children, particularly in science classes. The study was conducted in rural riverside schools in the northeastern region of Pará, Brazil, focusing on fourth-grade students. The research aimed to investigate the educational benefits of using metacognitive strategies to enhance students' ability to 'learn how to learn' and improve their scientific literacy.   The theoretical framework emphasizes the importance of metacognition, defined as "thinking about one's own thinking," in educational settings. Metacognitive strategies can help students become more aware of their learning processes, thereby improving their ability to monitor, control, and adjust their thinking during tasks. The study builds on previous research suggesting that metacognitive skills are not solely dependent on age but are significantly influenced by education and experience.   The methodology involved the design and implementation of three sets of metacognitive activities: “Reflection Apples”, “Timeline”, and “Learning Thermometer”. These activities were tailored to the regional context and the limited resources available in the rural schools. The activities were conducted over three sessions, each lasting approximately four hours, and involved students in tasks that required them to reflect on their learning, organize information, and evaluate their understanding. Data were collected through ethnographic techniques, including video recordings, audio recordings, and written notes, to capture students' interactions and behaviors during the activities.   The results indicated that the activities successfully engaged students and encouraged them to participate actively in the learning process. The “Reflection Apples” activity, which involved students reflecting on environmental issues, showed that students were able to activate prior knowledge and engage in discussions with peers and teachers. The “Timeline” activity, which required students to organize historical events related to the history of chocolate, demonstrated that students could effectively sequence information and collaborate with their peers. The “Learning Thermometer” activity, which focused on understanding microorganisms, revealed that students were able to self-assess their understanding and seek clarification when needed.   The analysis of the data revealed that students exhibited various metacognitive behaviors, such as monitoring their learning, asking for clarification, and revising their work. However, the study also found that students had limited opportunities to practice higher-order metacognitive skills, such as planning and evaluating their learning strategies. The authors suggest that the lack of these skills may be due to the limited exposure to such practices in traditional classroom settings. In conclusion, the study highlights the potential of metacognitive activities to create a favorable learning environment, enhance student motivation, and promote the acquisition of metacognitive skills. The authors recommend further research to explore the long-term effects of these activities and to develop more comprehensive strategies for integrating metacognitive practices into primary education. They also emphasize the need for teacher training programs to incorporate metacognitive strategies to improve both student learning outcomes and teaching practices. Overall, the study contributes to the growing body of research on metacognition in education and provides practical insights into how metacognitive strategies can be effectively implemented in primary school science classes, particularly in resource-limited settings.

The article analyzes the impact of Artificial Intelligence (AI) in the banking and financial sector, specifically in the detection of fraud in financial transactions. It highlights the evolution of AI and its transformative role in the banking and financial context. The central focus is on the complexity of financial frauds, addressing various types and their economic impact. Emblematic cases such as the Jerome Kerviel's fraud at Société Générale (2008) and Nick Leeson's Fraud at Barings Banks (1995) are mentioned, underscoring the need for effective methods in preventing and identifying these incidents. The article outlines modern techniques and AI employed in detecting banking frauds, emphasizing the use of advanced algorithms such as machine learning and neural networks. The proposal is to use these approaches to identify suspicious patterns in financial transactions, emphasizing not only their effectiveness but also their potential in mitigating risks and protecting financial institutions against fraudulent activities. The study provides a detailed overview from older to more current AI methods, highlighting the use of advanced algorithms to achieve more precise and efficient detection. The results underscore the capability of these methods in identifying and preventing incidents similar to the cases presented. It is expected that advanced algorithms will provide an effective response in identifying suspicious transactions. The study concludes by emphasizing not only the effectiveness of modern AI approaches in detecting banking frauds but also the potential of these techniques in protecting financial institutions against fraudulent activities. It highlights the ongoing importance of developing and implementing advanced AI methods to strengthen security in the banking sector, mitigating risks, and safeguarding clients and institutions against financial threats.       

As data generation grows exponentially, companies face increasing challenges in storage, processing, and efficient analysis of this information. The adoption of Lambda functions has proven to be a viable and cost-effective solution for handling large volumes of data. In this context, comparing the performance between the Go and Java programming languages is of interest to developers and companies seeking improved performance of their Lambda functions. This study aims to analyze and compare the performance of these functions in both languages and provide relevant information for selecting the most suitable language based on performance needs and goals. The methodology employed involves implementing different approaches for the Fibonacci series in both languages, with measurement of execution time in nanoseconds. The results indicate better performance of the Go language compared to Java, offering insights for developers in choosing the most suitable language. Furthermore, given the growing demand for cloud applications, it is crucial to deepen knowledge about cloud computing and the use of related technologies to efficiently handle large volumes of data.

Low-code structures, consisting of platforms that enable the rapid and efficient development of web applications, emerge as a potential solution to the challenges of digital transformation in Brazilian and Latin American companies. These platforms provide web environments that simplify the creation of business processes with steps, conditions, and automation, requiring little technical programming knowledge. The text reveals that the digital transformation process in these regions is in its early stages, emphasizing the need for advancements in automation and digitization. Low-code platforms, such as Asana, Ploomes, Kissflow, Zeev, and Pipefy, emerge as possible solutions to these challenges. Furthermore, the article emphasizes that large companies and organizations have already adopted these platforms to structure complex and automated business processes. It is worth noting that the figures in this article are accompanied by image descriptions, promoting accessibility for visually impaired individuals according to the 'Guidelines for Describing Images in the Generation of Accessible Digital Material – Mecdaisy.

Technological advancements have catalyzed discoveries across various fields of knowledge. As a result, innovation assumes a strategic role in organizations, as new technologies provide competitive advantages, intensifying competition in all markets. Thus, innovation is a relevant topic in organizations, as innovative ideas represent solutions to complex challenges, driving both entrepreneurship and intrapreneurship. However, many institutions still do not utilize methodologies and tools that encourage the suggestion and selection of ideas presented by employees. Moreover, they fail to manage the history of ideas, neglecting collaboration and hindering the protection of intellectual property. In light of this, this paper introduces the Égide platform, a tool designed to support innovation with a focus on entrepreneurship and intrapreneurship, integrating social network technologies and blockchain. The social network approach was employed to optimize certain principles of Design Thinking, leveraging collective and collaborative intelligence. On Égide, entrepreneurs and intrapreneurs contribute ideas to solve problems. On the other hand, blockchain technology was used to ensure the intellectual property of employees' ideas. In this way, Égide encourages active participation and values employees' ideas, recognizing their role in organizational success. The platform acts as a strategic ally in the efficient management of these ideas and the promotion of collaboration, enabling organizations to explore new markets, enhance the quality of products and services, anticipate emerging trends, and increase their competitiveness in the global landscape.

The Igreja Evangelizadora Deus é Contigo (IEDC), founded in 2023, faced several challenges in its process of establishment, consolidation, and growth. To overcome these difficulties, the IEDC applied the SECI model by Nonaka and Takeuchi (1995), which proposes the conversion between tacit and explicit knowledge, allowing for the creation, dissemination, and formalization of theological and organizational knowledge within the church. This study explores how this model, combined with Maxwell’s (2016) leadership concepts and Ross’s (2000) molecular leadership approach, helped the IEDC strengthen its spiritual leadership and develop an adaptable and collaborative ecclesiastical structure. Through the processes of socialization, externalization, combination, and internalization of knowledge, the IEDC created a continuous learning environment that facilitated the identification of problems, the implementation of practical solutions, and the improvement of administrative processes, such as donation management and community event organization. The externalization of knowledge resulted in the formalization of practices, such as the creation of procedure manuals and church bylaws, while the combination of theological and administrative knowledge generated new evangelization strategies and community growth. Furthermore, internalization fostered the training of new pastors and spiritual leaders, turning the IEDC into a center for leadership development. This article highlights the importance of knowledge management and collaborative leadership in the sustainable development of religious organizations and how the IEDC managed to grow its faith community and enable various initiatives. The study concludes that the combination of knowledge management and the formation of spiritual leadership is a decisive factor for the ongoing success and growth of churches.