Student stress in higher education remains a pervasive problem, yet many institutions lack affordable, scalable, and interpretable tools for its detection and management. Existing methods frequently depend on costly physiological sensors and opaque machine learning models, limiting their applicability in resource-constrained settings. The objective of this research is to develop a cost-effective, survey-based stress classification model using multiple machine learning algorithms and eXplainable Artificial Intelligence (XAI) to support transparent and actionable decision-making in educational environments. Drawing on a dataset of university students, the research applies a supervised machine learning pipeline to classify stress levels and identify key contributing variables. Six classification algorithms—Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting, and XGBoost—were employed and optimized using grid search and cross-validation for hyperparameter tuning. Evaluation metrics included precision, recall, F1-score, and overall accuracy. The Random Forest model achieved the highest classification accuracy of 0.89, followed by XGBoost at 0.87, Gradient Boosting at 0.85, Decision Tree at 0.83, SVM at 0.82, and Logistic Regression at 0.81. SHAP (SHapley Additive exPlanations) analysis was conducted to interpret model predictions and rank feature importance. The analysis revealed five principal predictors: blood pressure, perceived safety, sleep quality, teacher-student relationship, and participation in extracurricular activities. Results demonstrate that both physiological indicators and psychosocial conditions contribute meaningfully to stress prediction. The study concludes that institutional interventions targeting health monitoring, campus safety, behavioral support, relational pedagogy, and extracurricular engagement can effectively mitigate student stress. These findings provide an empirical foundation for the development of integrated policies in higher education aimed at promoting student well-being.

The analytic solution of the gravimetric tensor components, making use of the gravitational potential equation for a three-dimensional volumetric assembly composed of unit prisms of constant density, demands a high computational cost. This is due to the gravitational potential of each one of these prisms must be calculated for all of the points of a previously defined observation grid, which turns out in a large scale computational cost. In this work we introduce a hybrid design and its parallel implementation, based on OpenMP and MPI, for the calculation of the vectorial components of the gravimetric field and the components of the gravimetric tensor. Since the computing time is drastically reduced, the obtained performance leads close to optimal speed-up ratios. The applied parallelization technique consists of decomposing the problem into groups of prisms and using different memory allocations per processing core to avoid bottleneck issues when accessing the main memory in one cluster node, which are generally produced when using too many execution threads over the same region in OpenMP. Due OpenMP can be only used on shared memory systems is necessary to use MPI for the calculation distribution among cluster nodes, giving as a result a hybrid code (OpenMP+MPI) highly efficient and with a nearly perfect speed-up. Additionally the numerical results were validated with respect to its sequential counterpart

Water body classification is a topic of great interest, especially for the effective management of floods. Synthetic aperture radar (SAR) imaging has demonstrated a great potential for water monitoring, given its capacity to register images independent of weather conditions. Several algorithms for water detection using SAR images are based on optimal thresholding techniques. However, these simple methodologies produce false classification results when small water bodies embedded in mountain ranges are presented in the image. We present an unsupervised and easy-to-implement methodology, based on local Moran index of spatial association in combination with morphological closing operations, for inland water body extraction. According to several experiments, we demonstrate that our method is capable of effectively extracting lakes and rivers located at different land surface reliefs without the requirement of a training step. In addition, comparisons with the state-of-the-art techniques demonstrate the effectiveness of our procedure, performing an overall accuracy of 96.37% and Kappa = 0.927.