This paper deals with the master-slave synchronization scheme for partially known nonlinear chaotic systems, where the unknown dynamics is considered as the master system and we propose the slave system structure which estimates the unknown states. It introduced a new reduced order observer, using the concept of Algebraic Observability; we applied the results to a Sundarapandian chaotic system, and by means of some numerical simulations we show the effectiveness of the suggested approach. Finally, the proposed observer is utilized for encryption, where encryption key is the master system and decryption key is the slave system.

Hexavalent chromium [Cr(VI)] is a high-priority environmental pollutant because of its toxicity and potential to contaminate water sources. Biosorption, using low-cost biomaterials, is an emerging technology for removing pollutants from water. In this study, Long Short-Term Memory (LSTM) and bidirectional LSTM (Bi-LSTM) neural networks were used to model and predict the kinetics of the removal capacity of Cr(VI) and total chromium [Cr(T)] using Cupressus lusitanica bark (CLB) particles. The models were developed using 34 experimental kinetics datasets under various temperature, pH, particle size, and initial Cr(VI) concentration conditions. Data preprocessing via interpolation was implemented to augment the sparse time-series data. Early stopping regularization prevented overfitting, and dropout techniques enhanced model robustness. The Bi-LSTM models demonstrated a superior performance compared to the LSTM models. The inherent complexities of the process and data limitations resulted in a heavy-tailed and left-skewed residual distribution, indicating occasional deviations in the predictions of capacities obtained under extreme conditions. K-fold cross-validation demonstrated the stability of Bi-LSTM models 38 and 43, while response surfaces and validation with unseen datasets assessed their predictive accuracy and generalization capabilities. Shapley additive explanations analysis (SHAP) identified the initial Cr(VI) concentration and time as the most influential input features for the models. This study highlights the capabilities of deep recurrent neural networks in comprehending and predicting complex pollutant removal kinetic phenomena for environmental applications.

Este artículo analiza la problemática de la integridad académica y las nuevas formas de deshonestidad derivadas del uso de inteligencia artificial generativa (IAg) en contextos educativos, explorando cómo las herramientas de generación de texto —como chatbots avanzados— transforman las nociones tradicionales de autoría, originalidad y plagio académico. El objetivo es comprender de qué manera la IA generativa desafía las concepciones tradicionales de integridad académica y proponer lineamientos éticos e institucionales para su uso responsable. Metodológicamente, se realiza una revisión bibliográfica cualitativa de investigaciones recientes (2019–2024) sobre ética, autoría, plagio y equidad tecnológica en la educación superior. Los resultados muestran que la IA introduce dilemas éticos inéditos: redefine la autoría humana, dificulta la detección del plagio automatizado y puede ampliar la brecha digital entre instituciones con distinto acceso tecnológico; además, se identifican enfoques emergentes para la detección de contenido generado por IA y la promoción de prácticas de transparencia en la colaboración humano-IA. Las conclusiones subrayan la necesidad de fortalecer la educación ética digital, desarrollar políticas institucionales que regulen el uso académico de la IA y garantizar un aprovechamiento equitativo de sus beneficios mediante herramientas de código abierto, promoviendo así una reflexión crítica sobre la autoría y la responsabilidad intelectual en la era de la inteligencia artificial generativa.

Breakthrough-curve prediction is central to fixed-bed biosorption column design, but data-driven modeling remains challenging with small, unevenly distributed datasets. This study compares a feedforward multilayer perceptron (MLP) with recurrent long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) networks for simultaneous prediction of hexavalent chromium [Cr(VI)] and total chromium breakthrough curves in columns packed with Quercus crassipes acorn shell. A dataset of 14 experimental curves was used, covering variations in pH, influent concentration, flow rate, bed height, and biosorbent mass. Model performance was assessed using Mahalanobis distance (D_M)-based holdout selection, curve-shape evaluation, inter-model coefficient of variation (CoV), and SHAP attribution. All architectures achieved high internal cross-validation accuracy (R² = 0.9879–0.9979; RMSE = 0.0149–0.0309). However, complete-curve holdout performance depended on the excluded condition and chromium response. Under the central holdout condition (D_M = 0.317), R² remained high for both outputs (0.954–0.990). In non-central holdouts, errors also affected curve shape, including breakthrough timing and the approach to saturation. The largest deterioration occurred for Cr(VI) under the peripheral low-flow condition, where the MLP decreased to R² = 0.325, while LSTM and Bi-LSTM retained higher agreement. SHAP attribution confirmed process-consistent effects, with influent concentration and flow rate as dominant predictors and pH contributing more strongly to Cr(VI). These findings show that recurrent architectures can provide more robust representations of complex breakthrough profiles, while CoV–D_M analysis helps identify regions where additional experiments are needed.

RESUMEN Este estudio analiza aplicaciones, beneficios y limitaciones de la Realidad Extendida (XR) en la educación básica hasta la superior. El objetivo es analizar su impacto en enseñanza y aprendizaje: comprensión conceptual, motivación y competencias prácticas. Se realizó una revisión de literatura (2015–2025) en bases indexadas, seleccionando estudios sobre implementación de XR y sus efectos. Los hallazgos muestran que la XR favorece el aprendizaje experiencial y la simulación de escenarios de riesgo; facilita la visualización de contenidos abstractos y el análisis de información compleja, y mejora la capacitación en entornos virtuales. Persisten barreras: costos, formación docente y accesibilidad; su integración exige diseño pedagógico y estrategias sostenibles a gran escala.

Heavy metal pollution from anthropogenic sources poses significant risks to ecosystems and human health. Biosorption offers a sustainable removal method, but kinetics are poorly captured by traditional neural networks. This study introduces optimized Bidirectional Long Short-Term Memory (Bi-LSTM) networks for multivariate modeling of Ni(II) biosorption on Quercus crassipes acorn shells, trained using experimental (EKD), synthetic (SKD), and combined (CKD) datasets. A two-stage hyperparameter optimization with Optuna yielded models with R² above 0.995 and low RMSE in 5-fold cross-validation. Second-stage models showed high stability, with coefficient of variation (CoV) values below 10% for RMSE. Based on unseen kinetics, production models showed slightly lower performance (R² = 0.89–0.996): EKD1, EKD2, and CKD1 showed the most consistent performance across challenging conditions with R² values of 0.9617, 0.9769, and 0.9415, respectively; SKD models achieved strong results under standard conditions (kinetic 1, SKD1 R² = 0.9963). SHapley Additive exPlanations (SHAP) analysis identified contact time and initial Ni(II) concentration as key variables, with temperature, cation charge, and a salt interference code also contributing to model interpretability. These findings demonstrate that optimized Bi-LSTM networks offer a robust and interpretable data-driven solution for modeling Ni(II) removal under multivariate conditions, including the presence of salts.