The continuous evolution of digital technologies enriches the panorama of possible opportunities that digital transformation offers to the advantage of the educational system in reference to new ecological systems and sustainable teaching methodologies for the benefit of increasingly interactive, personalized and effective learning.

Among the different types of interactive learning environments, there is a growing expanse of literature on virtual worlds as risk-free educational contexts capable of enhancing today's critical life skills such as critical thinking, creativity, communication and collaboration, of increasing new digital skills and of supporting students in their learning process.

At the same time, we are witnessing an increase in the use of Artificial Intelligence in education and, in particular, among the Adaptive Learning Systems, Intelligent Tutoring Systems are configured as valid solutions to support students in an increasingly personalized and student-centered learning process.

Based on recent studies in the literature on the use of virtual worlds in the processes that accompany students in effective learning and that offer collaborative learning spaces in which to co-construct knowledge, this paper aims to point out the characteristics of virtual worlds, through the analysis of recent case studies, and those of Intelligent Tutoring Systems in order to outline similarities between the two learning systems, defining the possible future challenges that affect the combined use of the Metaverse and Artificial Intelligence in education.

Arboviruses are diseases caused by viruses transmitted by mosquitoes, with dengue, chikungunya, and Zika being the most common in urban environments, transmitted by Aedes aegypti. In Brazil, dengue poses a constant threat, with over 3 million cases reported in 2024. Arboviruses share similar characteristics, complicating accurate diagnosis and treatment, and under-reporting remains a significant challenge. To improve diagnosis and treatment, a system is proposed that integrates and standardizes data collected at health posts. This system utilizes artificial intelligence algorithms to diagnose and suggest treatments.

Challenges include collecting relevant patient data, such as temperature, symptoms, and travel history, through electronic medical records (EMRs), which avoid paper waste and ensure information security and organization. Additionally, ensuring the accuracy and completeness of data collection is crucial for the effectiveness of the proposed system. After collection, the data must be stored in appropriate database systems, depending on whether they are structured or unstructured.

Subsequently, the ETL (extraction, transformation, and loading) process is necessary to move the data to suitable repositories, enabling the use of machine learning algorithms. These measures aim to improve diagnostic accuracy and treatment effectiveness for arboviruses, contributing to saving lives.

Furthermore, continuous monitoring and updating of the system are required to adapt to new strains of arboviruses and emerging health threats. Public health education and awareness campaigns are also essential to encourage the public to participate in prevention measures, such as eliminating mosquito breeding sites. By addressing these challenges, the proposed system can significantly enhance public health responses to arboviruses outbreaks, ultimately reducing the burden of these diseases and saving lives.

In the didactic approach, Lev Bygotsky developed what he would classify as ZDP: Zone of Proximal Development, whose main objective wold be to measure student learning. This demonstrate how data engineering can facilitate the classification of ZDP for use in brazilian public schools.
Therefore, the use of mining tools, like Scrapy or Beautiful Soup, was essential for collecting students' pedagogical data from the platforms of the Alagoas state government. With the data in hand, it was up to the teacher to define the student's learning zones, based on teaching goals under the subject syllabus: assessments, activities and playful moments for the student's understanding of the subject studied.
Thus, with the influence of the teacher and the information collected from the student, it was possible to create a machine learning model, specifically a supervised classification model, which evaluates the student's performance and returns the current learning level, taking into account their pedagogical needs to be delivered to the teacher.
With the application, public school teachers were able to diagnose students according to their pedagogical needs, directly influencing student performance when these needs were met. This data architecture was able to directly meet a need that is still evident in public schools in the state of Alagoas in Brazil.

Public transportation plays a vital role in the development of mid-sized cities, significantly impacting urban mobility, economic growth, and overall quality of life. This article explores the potential benefits of implementing a free public transport system on designated market days, focusing on its economic, social, and environmental advantages. The proposed initiative aims to improve access to markets, encourage the use of public transportation, and reduce reliance on private vehicles, contributing to the reduction of traffic congestion and CO2 emissions. The study incorporates data science and machine learning methodologies, with an emphasis on time series analysis, to forecast key metrics such as passenger demand, vehicle circulation, and pollutant emissions. These predictive models also evaluate the financial implications of the initiative, accounting for operational expenses including depreciation, staffing, fuel consumption, and maintenance costs. By assessing both the short-term and long-term impacts, we demonstrate that free transport policies can optimize the efficiency of the transportation system, lower operational costs over time, and contribute to improved air quality. In addition to the environmental and economic benefits, this initiative has the potential to provide significant social value by increasing accessibility to commercial areas, services, and public spaces. It can stimulate local economies by enhancing consumer access to markets and promoting local business activities.

This research focuses on the classification of pediatric pneumonia diagnosis through X-ray images. The database utilized in this study consists of anteroposterior chest X-ray images obtained from retrospective cohorts of pediatric patients aged one to five years at the Guangzhou Women and Children's Medical Center. These images were selected based on their relevance to the study of pneumonia, specifically concerning the identification of bacterial infections.

Using a MATLAB program, seven relevant characteristics were extracted from each X-ray image. These features were essential in determining whether the patient exhibited signs of a bacterial infection associated with pneumonia or if the diagnosis was normal. The classification process was carried out using two distinct methodologies: neural networks and the k-nearest neighbors (K-NN) algorithm. A comparison of these classifiers was performed to evaluate their effectiveness in diagnosing pediatric pneumonia.

The dataset included a total of 49 images diagnosed as normal and 48 images indicating the presence of the bacteria linked to pneumonia. The characteristics considered for analysis included mean, standard deviation, entropy, contrast, correlation, energy, and homogeneity, which play a critical role in image analysis. The results demonstrated an impressive efficiency of 89% for the k-nearest neighbors algorithm and over 96.9% for the neural-network-based classifier, indicating the potential for these methodologies to aid in accurate pediatric pneumonia diagnosis through X-ray imaging.

This study presents an innovative approach to processing vibration signals in bridge structures, focusing on enhancing the accuracy of dynamic response measurements and structural health assessment. The research addresses the critical challenges in signal processing, particularly the uncertainties in determining filtering parameters for isolating dynamic components from static displacements.

A novel method for adaptive filter parameter selection is proposed, taking into account the variability of resonant frequencies and the non-linearity of quasi-static displacements due to moving loads. This approach significantly reduces errors in determining forced and natural vibration parameters, leading to more accurate assessments of the bridge's mechanical characteristics.

The study introduces an optimized algorithm for processing acceleration and velocity signals, improving the resolution in identifying natural frequencies of the structures. This method combines traditional Fast Fourier Transform (FFT) techniques with an innovative approach to spectral analysis, enabling more precise identification of resonant frequencies and damping coefficients.

A comprehensive evaluation framework is developed, integrating the analysis of vibration amplitudes, frequencies, and damping ratios. This framework provides a more robust assessment of the bridge's structural health, enhancing the ability to detect and characterize potential defects or changes in load-bearing capacity.

The practical value of this research lies in its application to real-world bridge diagnostics. Guidelines for sensor selection and configuration are provided, tailored to different bridge types and sizes. The proposed methods demonstrate significant improvements in the accuracy of dynamic coefficient determination and overall structural assessment, potentially reducing maintenance costs and enhancing safety.

Modern advances in nanoscience and nanotechnology offer innovative applications in the food sector, a relatively new field compared to biological and pharmaceutical uses. Nanostructured materials, including nanosensors, packaging materials, and encapsulated components, enhance food science. Nanostructured food systems, such as polymeric nanoparticles and liposomes, improve solubility, bioavailability, and controlled release, safeguarding bioactive components. Organic molecules (proteins, lipids, and saccharides) and inorganics (metal and metal oxides, carbon-based materials, and clays) make up the building blocks of food nanostructures. Nanostructured colloids in food include fat globules in homogenised milk, casein micelles, and β-lactoglobulin fibres in milk. Synthetic nanostructures are commonly used in food to increase solubility, improve bioavailability, preserve biologically active chemicals from degradation, extend shelf life, colour, and flavour, and provide nutritional value. These materials, which comprise nanoparticles, nanocomposites, and nanoemulsions, have increased solubility, stability, and other unique properties. Nanostructured materials detect contaminants like Salmonella or E. coli in food, ensuring consumer safety. Nanostructured materials reduce the energy consumption and environmental impact of food processing. This study aims to provide insights into nanotechnology’s benefits and risks, informing the development of novel, functional food products with improved attributes and prolonged shelf life. By exploring the potential of nanostructured materials, we can enhance food safety, quality control, and consumer acceptance.

Coffee cultivation is of extreme economic importance in many regions of the world. However, diseases and pests pose serious challenges, significantly affecting productivity. To solve this problem, deep neural network techniques are emerging as promising solutions, offering precision and efficiency in identifying plant leaf pathologies under different environmental conditions. This study proposes the analysis of a two-stage methodology, detecting the diseased regions of coffee leaves and classifying the diseases into Miner, Rust, Cercospora and Phoma. The experiments were conducted using two public datasets with a total of 1747 images of Arabica coffee leaves. The complete dataset was used for the detection stage and a subset of data with 4104 cropped images of the diseased region of the leaves was generated for the classification stage. The early stopping technique was used to train the models with a patience of 20 and a total of 300 epochs. The YOLOv8 model was chosen to detect the affected regions on the leaves due to its established real-time detection capability and low computational cost. After detection, the clipped regions of interest were submitted to the InceptionResNetv2, DenseNet169 and Resnet50 models, which are state-of-the-art methodologies used for disease classification. The results show that YOLOv8 obtained an mAP of 85.1% and, for classification, the InceptionResNetv2 model obtained the highest average accuracy with 98.18%, which can be seen in the robustness of this architecture compared to the others. The use of the two-stage methodology makes it possible to optimize each stage separately, making it easier to adjust other architectures for new types of diseases or plants.