Nowadays, Blockchain has become one of the most widely used tools in recent years due to its efficiency for various uses, one of them being smart contracts. The main objective is to identify the state of the art of research on Blockchain in Smart Contracts over the last 7 years. A systematic review of the literature was conducted, defining bibliographic sources such as Scopus, Web of Science, Science Direct, IEEE Xplore, EBSCOhost, and ARDI to carry out search strategies, resulting in 13,476 articles. Exclusion filters were applied using the PRISMA Flow Diagram to finally obtain 76 quality articles for review. The results of the SLR indicate that China is one of the countries that contributes the most research on this topic. Additionally, Solidity and Vyper are the most widely used programming languages for developing Blockchain. Interesting findings have been provided that give context to the research conducted on this topic.
BackgroundThe rapid expansion of artificial intelligence tools in higher education demands understanding whether students' moral development is associated with more responsible AI use. Moral development, conceptualized through Kohlberg's stage theory, provides a theoretically grounded framework for examining this relationship, yet few empirical studies have tested it in a Latin American university context.MethodsA quantitative, cross-sectional, correlational design was used with a quota sample of 5,487 Peruvian university students recruited through institutional channels. Moral development was assessed using the Defining Issues Test-2 (DIT-2) and responsible AI use was measured with the EURIA-ES, a 24-item self-report scale with evidence of reliability and factorial structure comprising four dimensions: transparency, academic honesty, critical algorithmic awareness, and responsibility in content dissemination.ResultsMost participants fell in the middle descriptive N2 band (61.4%; N2 mean = 32.14), using descriptive cut points rather than validated Kohlbergian stage diagnoses. EURIA-ES scores were moderate (M = 72.34 out of 120), with academic honesty scoring highest and critical algorithmic awareness lowest. The N2 index showed an important standardized association with EURIA-ES scores (beta = .31). The final model accounted for 23.5% of the variance (R2 = .235), and entering N2 increased explained variance by 9.2 percentage points beyond the sociodemographic block (ΔR2 = .092). Interpretation emphasizes effect magnitude and 95% confidence intervals rather than statistical significance alone. Year of study and field of knowledge showed adjusted associations, whereas regional differences were small in magnitude.Conclusionswithin a cross-sectional design, higher DIT-2 N2 scores were associated with higher self-reported responsible academic use of generative AI. N2 should be interpreted as an important correlate rather than a primary determinant of responsible AI use. These findings pertain specifically to academic uses of generative AI and should not be generalized to all artificial intelligence systems or to objectively observed student behavior.
Higher education is undergoing a transition in which static multimodal resources are giving way to immersive learning environments powered by generative artificial intelligence (GenAI). This PRISMA 2020-compliant systematic review, prospectively registered in INPLASY (202610066), synthesizes evidence on immersive GenAI-based strategies in higher education, examining their reported contributions to sustainability, inclusion, and learning outcomes. Searches across Scopus, ScienceDirect, and ERIC (2022–2026) identified 1364 records; after quality appraisal using an adapted CASP instrument, 25 studies were included in a narrative and descriptive synthesis. Five strategy types emerged—VR-based simulations, virtual patient platforms, adaptive LLM tutoring systems, mixed/augmented reality environments, and 3D/metaverse configurations—with GPT-family models predominating (56%). The central finding is a structural reporting asymmetry: learning outcomes were explicitly documented in 23 studies (92%), whereas sustainability and inclusion were explicitly reported as outcome domains in only one study each (4%). Health sciences (36%) and educational technology (28%) dominated the evidence base, while Latin American, African, and most STEM contexts remained underrepresented. Immersive GenAI strategies are being evaluated for short-term instructional value, while their contribution to sustainable higher education remains underexamined. Advancing SDG 4 requires longitudinal designs, equity-oriented frameworks, and indicators capable of evaluating inclusion and durable learning gains across institutional contexts.
This article presents the design of a graphene-based microstrip patch antenna, operating frequency range: (1-5) THz for terahertz (THz) applications. This paper presents simulations and a machine learning (ML) approach to characterize the performance characteristics, such as S11, Voltage Standing Wave Ratio (VSWR), gain, radiation, and total efficiencies, as well as the radiation pattern in horizontal (H, XZ) and vertical (V, XY) planes. The Computer Simulation Tool (CST) full microwave studio is used to model the antenna with dimensions of Length (L) and × Width (W): 93 μm × 113 μm, a return loss around - 40 dB, with 11 multi-band frequencies, achieving a maximum gain of 7.5 dBi at 3.2 THz. To analyze the effect of geometric parameters like length ([Formula: see text]) and width ([Formula: see text]) of the patch and graphene properties such as chemical potential ([Formula: see text]) and relaxation time (τ) on the performance characteristics of the patch antenna, three ML models are developed, such as Artificial Neural Networks (ANN), Random Forest ([Formula: see text]), and Support Vector Machine (SVM). The training data is collected for 784 simulations. The ANN architecture is built with four features in the input layer and one output layer to predict performance characteristics. The performance of the developed models is evaluated using metrics such as Mean Squared Error (MSE) and R-Squared (R2). Out of three developed models, ANN predicts the performance within 0.7 milliseconds (ms) with high accuracy, achieving an R2 of 0.99 for all performance characteristics. The predicted results discuss that the regression-based predictive models can capture the nonlinear relationship between antenna geometry and electromagnetic (EM) responses. These advantages, such as faster predictions and higher prediction accuracy, make these models, especially the ANN model, a replacement for traditional EM simulations by reducing computation time. Such qualities made the proposed ML models a powerful alternative to traditional simulation tools, making these antennas useful for next-generation wireless communication systems in the THz frequency range and beyond 6G.
Electronic systems are increasingly used to support pediatric gait assessment by enabling objective measurement of biomechanical parameters beyond traditional laboratory settings. However, although technological development has expanded in adult populations, the extent to which embedded technologies and human-machine interaction (HMI) modalities have been integrated into pediatric monitoring systems remains unclear. This systematic review synthesizes evidence published between 2015 and 2025 on electronic systems applied to pediatric gait biomechanics. The review followed PRISMA guidelines, was registered in PROSPERO (CRD420251230372), and adopted a descriptive synthesis approach. A total of 2619 records were identified, and after eligibility assessment and methodological quality appraisal using CASP, 34 studies were included in the final synthesis. The studies were examined according to system type, interaction characteristics, and biomechanical outcomes. The findings indicate a predominance of wearable architectures and inertial sensing technologies in the literature on electronic systems for pediatric gait monitoring. However, HMI modalities were rarely described, and most systems functioned primarily as passive data acquisition tools. Biomechanical outcomes focused mainly on motion-derived parameters, whereas region-specific plantar-load distribution was infrequently assessed, and no studies reported the use of force-sensitive resistors for zonal pressure monitoring. These findings suggest that future advances may depend on integrative approaches that combine multimodal sensing, interaction mechanisms, and functional load characterization.
