This study presents a multi-user virtual reality (VR) tool designed to enhance hands-on learning in engineering education through real-time sensorized digital twins. The motivation stems from the limitations of traditional laboratory settings, such as time constraints and restricted access to physical equipment, which can hinder practical learning. The developed environment allows multiple students, wearing VR headsets, to interact simultaneously with a real-time synchronized virtual model of an engine, replicating its physical counterpart at the Mechanical Engineering Laboratory of the Pontificia Universidad Católica de Valparaíso, Chile. This novel integration of VR and digital twin technology offers students a unique opportunity to observe engine behavior in operation within a safe, controlled virtual space. By bridging theoretical knowledge with practical experience, this approach deepens understanding of complex mechanical concepts while fostering the development of key technical skills. Additionally, the use of real-time data visualization and digital twins provides a safer, more interactive, and efficient alternative to traditional laboratory practices, overcoming constraints like time limitations and equipment availability. This innovative method introduces students to Industry 4.0 principles, encouraging data-driven analysis and informed decision making.

A characterization of composite materials of a polyester matrix reinforced with natural fibers of Cuban henequen and organophilized Cuban bentonite was carried out, the experimental results of which are used to formulate, implement, and validate a scalar damage model. The fibers were added on a weight basis of 15, 20, and 25% as a reinforcing material, and organophilized Cuban bentonite, in amounts of 3, 5, and 7% by weight, was added. Samples containing only polymer and fibers showed the best mechanical performance for 25% of fiber, with increases of 51% in the tensile strength and 169% on the impact resistance. Samples containing polymer and clay showed the best results for 5% of bentonite, with increases of 89% in the tensile strength and 83% on the impact resistance, and samples containing 25% fiber and 5% clay were also prepared and achieved a 98% increase in the tensile strength and 219% in the impact strength. The scalar damage model deals with an internal variable, which corresponds to the damage variable, together with a failure surface that allows identifying the mechanical state of the material dependent on the mechanical stress, obtaining an adequate correlation with the results of the experimental tests.

The influence of the addition of bentonite nanoparticles on the tensile and flexural strength of a thermosetting polymer matrix composite material reinforced with hemp fibers was de-terminated. All composites were manufactured with 5% of bentonite in the polymer mass–weight ratios and 10 to 45 wt% of fibers with a step of 5%. For mechanical characterization, tensile and flexural tests were performed: scanning electron microscopy and energy-dispersive X-ray spectroscopy analyses were carried out. The tensile strength of the samples containing bentonite compared to the polymer samples with the fiber addition was affected for all fiber addition percentages, except for 35% while the flexural resistance improved with the addition of bentonite in the percentages of 20, 30, 35, and 45% of fiber addition. With the addition of bentonite, the maximum values of tensile and flexural strength were both obtained for the 35% addition of fibers, with values of 34.28 MPa and 98.04 MPa, respectively. The presence of bentonite favored the rigidity of the material to traction and bending, which was reflected through an increase in the elastic modulus compared to the composite that only had fiber. The maximum values obtained were 9065 MPa in tension and 8453 MPa in flexion for the 40% and 35% of addition of fiber, respectively. Microscopy showed a good distribution of fibers in the matrix, the absence of internal porosities, and a good interaction between matrix and reinforcement.

Composites with natural lignocellulosic fillers are being cited as a viable and sustainable alternative to conventional materials, as they combine lower costs with lower weight. In many tropical countries, such as Brazil, there is a considerable amount of lignocellulosic waste that is improperly discarded, which causes pollution of the environment. The Amazon region has huge deposits of clay silicate materials in the Negro River basin, such as kaolin, which can be used as fillers in polymeric composite materials. This work investigates a new composite material (ETK) made of epoxy resin (ER), powdered tucumã endocarp (PTE), and kaolin (K), without coupling agents, with the aim of producing a composite with lower environmental impact. The ETK samples, totaling 25 different compositions, were prepared by cold molding. Characterizations of the samples were performed using a scanning electron microscope (SEM) and a Fourier-transform infrared spectrometer (FTIR). In addition, the mechanical properties were determined via tensile, compressive, three-point flexural and impact tests. The FTIR and SEM results showed an interaction between ER, PTE, and K, and the incorporation of PTE and K reduced the mechanical properties of the ETK samples. Nonetheless, these composites can be considered potential materials to be used for sustainable engineering applications in which high mechanical strength is not a main requirement of the material.

Background/Objectives: Extended reality (XR) technologies have been increasingly applied in inclusive education settings to assist neurodivergent children. However, the existing evidence remains fragmented across diverse contexts and disciplines. This systematic review synthesizes current research to identify the educational purposes, implementation characteristics, and reported outcomes associated with the use of XR in inclusive educational environments. Methods: A comprehensive literature search was conducted in major academic databases using predefined keyword combinations related to XR, inclusive education, and neurodivergence. Peer-reviewed articles that applied XR tools in educational settings for neurodivergent children were screened against predefined inclusion and exclusion criteria. Data were extracted regarding study design, participant characteristics, XR modality, educational objectives, and outcome indicators. Results: The reviewed studies report heterogeneous applications of XR technologies, including virtual and augmented reality, to support cognitive, social, and behavioral skill development in neurodivergent learners. Most studies employed small sample sizes and quasi-experimental or exploratory designs. Although several studies reported improvements in engagement, communication skills, and task performance, outcome measures varied substantially and methodological rigor was limited in many cases. Conclusions: Current evidence suggests that XR technologies hold potential as complementary tools in inclusive education for neurodivergent children. Nonetheless, the heterogeneity of study designs and the lack of standardized assessment metrics limit the generalizability of the results. More robust empirical investigations are required to establish evidence-based guidelines for the implementation of XR in inclusive educational contexts.

This paper presents an integrated methodology for remote monitoring, technical training and early warning systems in virtual reality environments oriented towards Industry 4.0. The proposal incorporates an engine modeled as a digital twin in Unity 3D, connected to physical sensors, and transmitted through an IoT platform. This architecture allows continuous monitoring and immersive visualization, in addition to generating alerts when operating conditions exceed critical limits, allowing users to simulate fault conditions in real time and perform interactive training without putting equipment or operators at risk. This work proposes a low-cost simulation platform, based on virtual reality and real-time digital twins, designed to support training and capacity building in industrial environments. This methodology seeks to propose general guidelines that allow the integration of these technologies in a general way to enrich both preventive and predictive maintenance mechanisms, as well as training processes.