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Item type:Publication, The Impact of Artificial Intelligence on Second Language Acquisition Experiences(2026)Zambrano JhonArtificial intelligenceArtificial intelligencein educationArtificial intelligence in education has transformed foreign language learning, but also raises pedagogical, ethical, and technical challenges, including data privacy, algorithmic transparency, equitable access, and shifting teacher roles. This study examines how students perceive the use of adaptive personalizationAdaptive personalization tools supported by intelligent technologies in second languageSecond language learning. It focuses on student experiences with digital platforms that automatically adjust content difficulty, learning pace, and feedback according to individual progress and performance. The research seeks to provide empirical evidence on both the educational potential and current limitations of such systems. A mixed-method approach was applied. Quantitative data were collected through structured questionnaires measuring user satisfactionUser satisfaction, perceived effectivenessEffectiveness of learning, motivation, and self-regulation. Qualitative insights were gathered through open-ended responses, allowing participants to share detailed perspectives on their interaction with these adaptive systems. The study involved university students participating in technology-assisted language learning programs, offering a representative context for analyzing emerging practices in higher educationHigher education. The findings indicate that students generally view these adaptive systems as effective in fostering personalized learningPersonalized learning pathways, enhancing motivation, and enabling timely feedback. Many participants emphasized the advantage of progressing at their own pace while receiving immediate support when facing difficulties. Overall, results show a positive impact on learner engagement and autonomy, although limitations persist regarding the handling of complex language skills and the reduction of human interaction. Nevertheless, some concerns emerged regarding the limited flexibility of these systems in addressing complex language aspects, occasional reduction of human interaction, and potential dependency on automated guidance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Research Trends in Mixed Reality for Cognitive Aging: A Bibliometric Perspective(2026)In recent years, research on mixed realityMixed reality technologies encompassing virtual reality and augmented reality has gained momentum in the context of aging and cognitiveCognitive aging health. This bibliometric analysisBibliometric analysis explores global trends in scholarly output related to applications for cognitive rehabilitation and support in older adultOlder adults populations. The study analyzed 75 documents indexed in Web of Science from 2009 to 2025, using VOSviewer for visualization and co-occurrence mapping. The temporal distribution of publications indicates a sustained rise from 2018 onward, with 2024 being the most prolific year, suggesting an academic consolidation of mixed reality as a valid approach to non-pharmacological intervention for aging-related decline. The co-occurrence analysis reveals three interrelated thematic clusters: (1) technological innovation, centered around keywords like virtual reality, augmented reality, and design, reflecting the evolution of immersive interfaces adapted to cognitive and physical limitations; (2) clinical and cognitive application, including rehabilitation, impairment, dementia, and alzheimer’s disease, highlighting the alignment of mixed realityMixed reality with neurodegenerative conditions; and (3) behavioral and lifestyle engagement, with terms such as physical activity, exercise, adherence, and quality of life, indicating a growing research trend that integrates embodied interaction with well-being and behavioral change. Notably, older adultsOlder adults emerge as the most connected term, acting as a central bridge between technology and health-related objectives. This convergence suggests a shift from purely exploratory work to user-centered, context-aware applications aimed at improving autonomy and engagement in aging populations. Future research should focus on comparative regional implementations, long-term effectivenessEffectiveness, and inclusive design to ensure equitable access and sustainable deployment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Smart Material Phase Classification Using Machine Learning on Ising Lattice SimulationsUnderstanding and classifying phase transitionsPhase transitions in lattice-based systems remain central challenges in statistical physics and material science, particularly when conventional order parameters become ambiguous. Recent advances in machine learningMachine learning have shown promise in automating phase detection, yet many approaches either focus narrowly on identifying the critical temperature or rely on raw spin configurations, which can hinder interpretability and robustness. In this work, we develop a hybrid computational framework that integrates Monte Carlo simulations of the two-dimensional Ising modelIsing model with unsupervised machine learningMachine learning techniques to address these challenges. By extracting high-level thermodynamic observables—magnetization, energy, specific heat, and susceptibility—we construct a feature-rich dataset that captures both average behavior and fluctuation-driven response. Dimensionality reduction via Principal Component Analysis (PCA), followed by KMeans clustering, enables data-driven classification of ferromagnetic, paramagnetic, and antiferromagnetic regimes. The framework successfully identifies first-order transitions through hysteresis loops, resolves second-order critical phenomena near Tc ≈ 2.269, and detects the Néel transition in antiferromagnetic systems where traditional magnetization fails. These findings highlight the novelty of using thermodynamic observables for interpretable ML-based phase classification and demonstrate the potential of this approach for analyzing smart materialsSmart materials and adaptive technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.23 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, VPD Monitoring with ESP32 and Flask API for Early Detection of Powdery Mildew in Rose Greenhouses(2026)Herrera, Vicente-D.In this study, a vapor pressure deficit (VPD) monitoring system was developed using an ESP32 and a Flask-based API for the early detection of powdery mildew in rose greenhouses. The research demonstrated that the integration of intelligent systems and real-time analysis of environmental conditions allows for the rapid identification of factors that favor the development of powdery mildew. The SVM model employed achieved high accuracy, with a classification accuracy rate of 96% in identifying conditions conducive to this disease, which is crucial for reducing unnecessary interventions and optimizing resource management in greenhouses. Additionally, the use of an API facilitates the integration of the system with other management platforms, enhancing data accessibility and decision-making. This approach promotes more responsible agricultural practices aligned with environmental sustainability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Integration of Simulation in Strategic Decision-Making in Dairy Industry(2026)Herrera, Vicente-D.In the context of Industry 4.0, the challenge of improving process efficiency and decision-making in the dairy industry remains critical. This study addresses this problem by evaluating the application of Flexsim simulation software to optimize a dairy plant’s production processes. The main objective of the work is to demonstrate how simulation can enhance production efficiency and reduce material blockages. The methodology involved developing a detailed simulation model of the production line using Flexsim, where multiple scenarios were analyzed to identify bottlenecks and optimize resource allocation. Key results showed an 11.11% improvement in production efficiency and a reduction in blocked material from 36.09 to 10.48%. These findings highlight the ability of Flexsim to support tactical decision-making based on precise and real data. Despite some limitations, such as the complexity of model development, the study concludes that simulation is an essential tool for continuous improvement and maintaining competitiveness in the dairy industry, with potential applications in various industrial sectors. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Risks and Ethical Challenges of Emotional Intelligence in Conversational AgentsThe development of emotionally intelligent conversational agents has attracted growing interest due to their potential to enhance human–machine interaction. These systems aim to simulate empathy by recognizing and responding to human emotions, enabling more fluid, personalized, and engaging communication. Yet, this simulated empathy raises significant technical, ethical, and social concerns, particularly in domains such as healthcare, education, and commerce, where emotional influence can shape decision-making and user well-being. This article presents a narrative review that critically examines the integration of emotional intelligenceEmotional intelligence into conversational AI. It draws on interdisciplinary literature in artificial intelligenceArtificial intelligence, affective computing, and ethicsEthics, reviewing peer-reviewed sources published between 2015 and 2024. The analysis applied a thematic approach to identify recurrent patterns, conceptual tensions, and sector-specific risks. Findings show that while advances in voice analysis, natural language processing, and deep learning have improved emotion detection, important limitations persist in multicultural and linguistically diverse contexts. These gaps risk misinterpretations, inappropriate responses, or discriminatory interactions. Moreover, users may mistakenly interpret emotionally tailored responses as genuine empathy, fostering emotional confusion or dependence. The potential for manipulative or persuasive uses further complicates their ethical deployment. This study provides a novel contribution by explicitly linking simulated empathy with risks of anthropomorphization, autonomy loss, and regulatory gaps, thereby bridging technical advances with their socio-ethical implications. It highlights practical challenges for applied contexts such as clinical support, education, and digital services, offering insights for both researchers and practitioners. Ethical principles, inclusive design, and regulatory frameworks are needed to ensure that emotionally intelligent AIArtificial intelligence supports rather than exploits human emotional experience. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Digital Twins in Higher Education: Applications, Challenges, and Future Perspectives(2026)The Fourth Industrial Revolution has introduced emerging technologies that are reshaping educational models, among them digital twins, originally developed in the aerospace industry as virtual representations of real systems. Their integration into higher education now enables the simulation of complex learning environments, monitoring of student performance, and personalization of learning processes. However, their adoption within university settings remains incipient, fragmented, and lacking in standardization, which limits their transformative potential. This situation underscores the need to systematize dispersed knowledge on their educational use. This study presents a review with a descriptive and exploratory approach, based on peer-reviewed scientific articles selected from databases such as Scopus, IEEE Xplore, ScienceDirect, SpringerLink, and Web of Science, published between 2018 and 2024. The findings indicate that digital twins are primarily applied for simulations (68%), learning monitoring (52%), and educational personalization (44%). Reported outcomes highlight improvements not only in learner autonomy, motivation, and acquisition of practical skills, but also in conceptual understanding, problem-solving abilities, and collaboration among students in project-based environments. At the institutional level, benefits include greater efficiency in resource management, reduction of physical laboratory costs, and continuity of teaching in hybrid or remote learning contexts. Nevertheless, challenges remain, including limited connectivity, high implementation costs, insufficient teacher training, and ethical risks. Digital twins represent a strategic technology for transforming higher education into more flexible, personalized, and sustainable models, provided they are supported by inclusive policies and adequate investment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Dual Regimes of Nonlinear Energy Dynamics in FPUT Lattices: From Recurrence to Chaos in Communication-Oriented SystemsNonlinear oscillator networks such as the Fermi–Pasta–Ulam–Tsingou (FPUT) chain exhibit rich dynamical behavior that bridges integrable motion and chaotic energy diffusion. In this work, a comprehensive numerical and theoretical framework is developed to analyze the transition from coherent modal excitation to ergodicity, with a particular focus on its implications for signal propagation and wave-based computation. A dual decay model is introduced to characterize the critical energy threshold required for ergodic behavior, revealing distinct exponential and power-law scaling regimes as functions of the nonlinear coupling parameter. Numerical simulations further demonstrate that this threshold scales inversely with system size, highlighting the role of mode density in enabling energy delocalization. Spectral analyses reveal both reversible modal recurrences and irreversible energy cascades, while phase-space diagnostics via Poincaré sections uncover the progressive breakdown of invariant structures and the emergence of global chaos. The results provide a predictive framework for tuning energy and nonlinearity to preserve coherence or induce controlled randomness in oscillator-based communication and computing systems. The integration of dynamical systems theory with applied nonlinear modeling offers new tools for the design of robust, tunable, and scalable information technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Intelligent Automation of Quantum Transport Simulation: AI-Driven Modeling of Tunneling and Resonance in One-Dimensional PotentialsAccurate simulation and analysis of quantum transport phenomena are essential for designing next-generation nanoscale devices in communication and computing. A hybrid computational framework is developed to simulate Gaussian wave packet dynamics using the Crank–Nicolson method, integrated with automationAutomation pipelines and AI-based modeling. Quantum interactions with single barriers, double barriers, and potential wellsPotential wells are explored in detail, revealing transmission behaviors consistent with analytical predictions. Resonance conditions are identified through automated signal processing, and surrogate models trained via machine learningMachine learning enable fast, accurate prediction of transmission coefficients across varying geometries and energies. Numerical results demonstrate excellent agreement with theoretical models, with deviations below 3%, validating the approach. The intelligent system supports inverse design and classification of quantum behaviors, offering practical tools for engineering resonant tunneling diodes, quantum logic filters, and photonic structures. By combining applied numerical physics with AI and automationAutomation, this work exemplifies intelligent solutions for quantum system design © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhancing Simulation Accuracy in Radioactive Decay Models: Comparative Analysis of Euler and RK2 Methods for Interactive SystemsThe modeling of radioactive decay processes has long been essential in fields such as nuclear physics, embedded systems, and educational simulations. Traditionally, the Euler method has been used to numerically solve the differential equations governing these phenomena. However, Euler’s method exhibits significant limitations in terms of accuracyAccuracy and stability, particularly in coupled systems or stiff mathematical configurations. To address this issue, this study presents a detailed comparison between the Euler method and the second-order Runge-Kutta method (RK2), aiming to evaluate their performance in terms of error accumulation, temporal fidelity, and the ability to preserve system dynamics. The goal is to determine which method is more suitable for simulations that demand high precision and computational efficiency. Numerical simulations were conducted for dual decay, cyclic decay, and thermistor cooling models using normalized time steps (Δt = 0.01 and 0.05). The results were compared against analytical solutions and analyzed through logarithmic error plots. RK2 consistently outperformed the Euler method, reducing absolute error by up to 80%. For instance, in the dual decay model, Euler’s error reached 3.41 units, while RK2 kept it below 0.62. Moreover, RK2 preserved the oscillatory dynamics in cyclic models and accurately predicted thermal thresholds in thermistor simulations. RK2 proves to be significantly more suitable for dynamic simulations where numerical precision and stability are critical. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.15