Financial stability in interconnected markets is increasingly challenged by nonlinear interactions that amplify local disturbances into systemic crises. This study models a financial system as a complex network of coupled chaotic nodes, where each node represents a nonlinear macroeconomic subsystem governed by endogenous feedback dynamics. In contrast to traditional centralized interventions, a pinning control strategy is proposed to stabilize a network through selective control of a small subset of influential nodes. Numerical simulations show how local crises propagate through coupling links, generating systemic instability, and how the proposed impulsive control scheme effectively suppresses chaos and restores synchronization across an entire network. Results highlight the efficiency of localized interventions for achieving global stability, offering new theoretical insights into mechanisms of financial correlation and design of control-based resilience strategies for complex economic systems.

This project presents the development of VigilEye, a prototype system for continuous monitoring of alertness in drivers on long journeys. This monitoring is performed by means of a camera through the analysis of eye movement patterns, with purpose of preventing automobile accidents. Implementation includes a gradual response protocol that includes sound alerts, visual signals and, in critical cases, controlled intervention of vehicle systems. VigilEye proposes a prototype of a two-stage system to respond to detection of decreased alertness in long-distance drivers. This system uses a detailed analysis of eye movements to detect signs of fatigue. In first stage, upon identifying that driver is showing signs of decreased alertness, system emits an acoustic signal to alert driver and stimulate an immediate reaction. If driver does not respond to this warning, system activates second stage of response in which vehicle turns on flashing lights to warn other drivers and simultaneously reduces speed progressively, in order to minimize risk of accidents and ensure safety on the road.

Hypertension, a significant cardiovascular disease, necessitates continuous monitoring and precise predictive models to optimize treatment strategies. This research proposes a Digital Twin for Hypertension, integrating a mathematical model based on Olufsen’s differential equations with artificial neural networks to dynamically adjust vascular resistance, arterial compliance, and blood flow inertia. This model is implemented in MATLAB®, utilizing the Runge-Kutta method to solve 11 differential equations.In contrast with conventional blood pressure monitoring techniques, which rely on discrete measurements, this approach provides real-time estimation of blood pressure fluctuations, allowing for improved risk stratification and personalized treatment recommendations. Expected results suggest that the use of the digital twin can enhance hypertension management by adapting to individual physiological conditions and predicting pressure variations more accurately than conventional methods.

Continuous arterial blood pressure (ABP) prediction is relevant to cardiovascular monitoring and development of adaptive patient-specific digital twins. This study presents an adaptive data-driven ABP prediction framework inspired by cardiovascular digital-twin principles for causal one-step-ahead prediction using a single physiological signal modality. The proposed methodology combines a seven-sample causal ABP representation, a compact 7–5–1 multilayer perceptron, recursive Extended Kalman Filter (EKF) adaptation of all neural-network weights and biases, and offline evolutionary optimization of EKF covariance hyperparameters. Model development and evaluation used invasive ABP recordings from five CHARIS subjects available in PhysioNet, with five non-overlapping 100 s windows per subject. A rotating 3/1/1 subject-wise protocol assigned three subjects to supervised MLP training, one independent subject to covariance-hyperparameter selection, and one completely unseen subject to final testing in each fold. A fixed MLP achieved a mean test RMSE of 3.4123 mmHg. Recursive EKF adaptation using identical manually selected covariance hyperparameters across folds reduced mean RMSE to 3.0033 mmHg, corresponding to an average improvement of 11.98%. Offline optimization of $P_0$, $Q_0$, and $R_0$ further reduced mean RMSE to 2.1546 mmHg with Differential Evolution, 2.1548 mmHg with Particle Swarm Optimization, and 2.1546 mmHg with Genetic Algorithm, representing an overall improvement of approximately 35.9% relative to the fixed-MLP baseline. Despite differences in selected covariance values, all three evolutionary methods produced nearly identical final predictive performance. These results show that recursive neural-parameter adaptation and systematic covariance tuning provide complementary improvements in causal ABP prediction while preserving strict subject-level separation among training, hyperparameter selection, and final evaluation.