This work establishes the technological elements that have enabled the preservation, promotion, and dissemination of tangible and intangible cultural heritage in the period from 2018 to 2022. For this, a Systematic Literature Review (SLR) was conducted in the scientific databases Scopus, Science Direct, IEEE and Web of Science, which facilitated the identification of 146 articles related to the topic. A quantitative and qualitative analysis of the journals, authors and topics was carried out, detailing the important variables required to establish the sought-out elements; for this purpose, the following were quantified in the papers: type, topic, categorization, country, and language; in the publications, the type of heritage chosen, the place of the heritage and the type of intervention were investigated. The number of publications reporting the use of some type of technology was also identified, finding that 70% of them show a technological approach to preserve cultural heritage, while 30% refer to other types of interventions. The technologies reported to be used the most are 3D digital technologies (44% of those showing technological applications), augmented reality or virtual reality, henceforth AR/VR (15%).

The Internet of Things is a new paradigm that is revolutionizing computing. It is intended that all objects around us are connected to the network, providing “anytime, anywhere” access to information. This concept is gaining ground, thanks to advances in nanotechnology which allows the creation of devices capable of connecting to the Internet efficiently. Nowdays a large number of devices are connected to the web, ranging from mobile devices to appliances. In this paper we focus on the education field, where Internet of Things can be used to create more significant learning spaces. In this sense, we propose a system that allows students to interact with physical surrounding objects which are virtualy associated with a subject of learning. We conduct an experimental validation of our approach, yielding evidence that our model improves the student's learning outcomes

This article presents a detailed review of methodologies for estimating crop yields in the context of growing global concern for food security and agricultural sustainability, with the main objective of analyzing, synthesizing, and comparing recent studies that apply artificial intelligence to yield prediction, identifying their strengths, limitations, and emerging trends. Approaches that integrate climatic variables, soil conditions, and agricultural management practices are examined. Artificial intelligence techniques, such as machine learning and neural networks, are effective at building robust predictive models. In several reviewed studies, these methods have achieved coefficients of determination (R²) greater than 0.85 and error reductions of 15% to 30% compared to traditional statistical approaches, confirming their high predictive potential. These models consider key elements such as temperature, precipitation, soil fertility, and agronomic decisions related to planting, crop choice, and fertilizer use. The article also discusses the challenges associated with model calibration and selection, given the complexity of agricultural systems and the variability of available data. The review covers studies published between 2016 and 2024, a period in which there has been a notable advance in the application of hybrid and deep learning approaches in the agricultural field. The importance of further research into hybrid approaches that integrate various techniques to improve prediction accuracy is highlighted. Finally, the strategic role of artificial intelligence in agricultural decision-making, in promoting sustainable practices, and in strengthening global food security is underlined.

In this study, a system was developed to predict anemia using blood count data and supervised learning algorithms. Anemia, a common condition characterized by low levels of red blood cells or hemoglobin, affects oxygenation and often causes symptoms, such as fatigue and shortness of breath. The diagnosis of anemia often requires laboratory tests, which can be challenging in low-resource areas where anemia is common. We built a supervised learning approach and trained three models (Linear Discriminant Analysis, Decision Trees, and Random Forest) using an anemia dataset from a previous study by Sabatini in 2022. The Random Forest model achieved an accuracy of 99.82%, highlighting its capability to subclassify anemia types (microcytic, normocytic, and macrocytic) with high precision, which is a novel advancement compared to prior studies limited to binary classification (presence/absence of anemia) of the same dataset.

The dynamics of the world today demands a change in traditional education paradigms to enable the creation of more efficient learning environments, where students will learn more effectively and will play a more active role in their education. They should interact with the knowledge at anytime-anywhere. In order to tackle this problem we should take advantage of mobile communication devices (e.g., smartphones and tablets) which are widely used by students and which have excellent processing storage and connectivity capabilities. In this research work, a context-aware system stimulating active learning by students was developed. This system places the student in an intelligent learning environment and is capable of delivering appropriate context-related learning contents, based on location, time, date, interaction of the student, profile of the student, and so on. Within its architecture, the system includes reasoning capability that using context-based ontology is able to deliver efficient learning resources. The experimental results obtained from various learning experiences in nursery, medicine, and systems engineering support the validity of our approach.

This paper presents an in-depth look at the evolution of Vehicular Ad Hoc Networks (VANET) and their increasing importance in the development of Intelligent Transportation Systems (ITS). Emerging technologies that shape the future of VANET are discussed, focusing on artificial intelligence (AI) and software-defined networking (SDN) as key solutions to the challenges inherent in these dynamic networks. Applications of AI, including machine learning (ML), deep learning, and swarm intelligence, for improving security, traffic efficiency, and information management in VANET were examined. Routing challenges in these environments are discussed and SDN is presented as a viable solution. SDN protocols, controllers, and architectures are described, and their advantages and disadvantages in the context of VANET are analyzed. It provides a comprehensive overview of the current state of VANET, the emerging technologies driving them, and the challenges and opportunities presented by their continued development. The convergence of AI, SDN, and other key technologies is crucial for intelligent transportation, paving the way for safer, more efficient, and more connected driving.