Internet of Things (IoT) is a technology that consists of interconnected entities (intelligent physical objects, services and software systems) via the Internet, which serve automatically and in a coordinated way in data management to achieve greater efficiency and productivity in industrial processes and contribute to improve the quality of people life. As the use of devices grows, so do the challenges for creating secure, operable, scalable and cost-effective IoT systems. The specialized literature shows interest in the application of best practices for the construction of IoT systems and so improving the compatibility of hardware and software systems, communication problems, security and privacy. This paper searchs in indexed journals in order to identify best practices and their current components to create IoT systems. The study applies a systematic literature review method implemented in six phases (planning, primary search, preliminary selection, selection, data extraction and analysis) that allows information to be extracted consistently. We obtain 97 papers applying the method to carry out the analysis. The results show there are different proposals that use some mechanism of knowledge representation for the creation or improvement IoT systems. The paper is a theoretical basis for creating better IoT systems in organizations and reducing costs.

The document presents a low-cost, open-source device designed to facilitate the learning of technologies like artificial intelligence in embedded systems through vibration analysis. It also aims to enhance students’ skills by introducing industrial challenges into the classroom via a scaled-down prototype. This study analyzes the vibrations generated by bearings to classify, using Artificial Intelligence (AI), whether they are defective. The device integrates electronic, mechanical, and software components, leveraging online technologies and platforms like Arduino to support hands-on learning. The document provides detailed instructions on the components used, circuit connections, step-by-step construction, and implementation, allowing replication of the prototype. This device fosters the development of STEM skills, promotes the application of AI and TinyML in real-world contexts, and enriches educational programs by encouraging interdisciplinary learning.

Gate-Based Rules for Extracting Attribute Values

Introduction: Suicide is one of the leading causes of death among young people, to the extent that in many countries it is considered a public health issue. It is important to attempt to reduce the growth of this trend, especially among susceptible individuals, considering that it increased because of the COVID-19 pandemic. Natural language processing (NLP) provides various tools that allow for the analysis of texts to predict the presence of suicidal ideation. This work aims to conduct a systematic literature review to extract the computational techniques for identifying suicidal ideation in texts written in natural language. Methods: The PRISMA 2020 method was used, which was divided into nine phases, and three inclusion criteria and two exclusion criteria were established for the selection of studies. The searches were conducted through high-level academic databases such as Scopus, IEEE Xplore, ACM Digital Library, Springer, and Web of Science. The risk of bias was assessed using AMSTAR 2. Potential biases identified include a lack of linguistic and cultural diversity and the predominance of data from social networks. A narrative synthesis was used to analyze and compare the findings qualitatively. Results: In the end, 25 studies related to computational methods for detecting suicidal ideation in texts written in natural language were identified. The techniques mainly focus on transformer-based models such as BERT and hybrid methods, which combine this architecture with neural networks such as CNN and LSTM. There are also approaches with hierarchical attention mechanisms. Some studies employed additional techniques such as feature extraction with TF-IDF and pre-trained embeddings to improve model performance. Discussion: Limitations in the evidence include the lack of linguistic and cultural diversity and the predominance of data from social networks. These results indicate that computational techniques have high potential to support early prevention strategies for suicidal ideation. However, expanding the diversity of linguistic contexts and improving understanding of the models among non-experts, such as physicians and other interested individuals, is necessary.

A trend study consists to review and analyze a great amount of information that refers to a discipline, it is based on many sources of information. This paper shows the main trend topics in computer science around the world, this base on the advances in industry 4.0. The background shows a great interest in the practical application of theoretical topics studied in the academy inside the productive and governmental sectors. Then, it is relevant to present the current trend issues in many sectors and contexts. In this work, a study is presented from the perspective of governments, business, and academia, which evidences the relevant topics for computer sciences. This article pretends to contribute to the articulation between the academic, governmental, and productive sectors as a contribution to the development of the countries.

Complaint Process Management in an Electric Power Company