Ehab Seif Ghith

Precise position control of magnetically actuated micro-robotic systems is challenging due to viscous damping, parameter sensitivity, and the requirement for high transient accuracy without overshoot. This study presents an optimization-based control framework for enhancing transient and steady-state performance of a magnetically actuated micro-robotic position control system. The system dynamics are modeled based on magnetic force and viscous drag interactions on paramagnetic micro-particles suspended in fluid, yielding a second-order representation suitable for controller design. A proportional–integral–derivative (PID) controller regulates particle position, with parameters optimally tuned using the starfish optimization algorithm (SFOA), a bio-inspired metaheuristic that balances global exploration and local exploitation through biologically motivated movement, preying, and regeneration mechanisms. The tuning process is guided by the integral of squared time multiplied by squared error performance index, which suppresses late-stage tracking errors and promotes smooth transient behavior. Simulation studies are conducted for a 1000 µm step reference input, with the optimization independently repeated 25 times to assess robustness and consistency. Results demonstrate strong convergence reliability, with tightly clustered objective function values and low standard deviation across runs. Time-domain analyses confirm that the SFOA-tuned PID controller achieves a smooth, monotonic, overshoot-free response with the shortest rise and settling times compared to PID controllers tuned by arithmetic optimization, jellyfish search optimization, Harris Hawks Optimization (HHO), and a hybrid arithmetic–rat swarm approach. These findings establish the proposed method as an effective and reliable solution for high-precision micro-robotic positioning where overshoot-free operation and consistent transient performance are critically important.

Arterial conduits with more than two consecutive constrictions are classified as exhibiting multiple overlapped stenoses, a pathological morphology with critical implications for vascular health. Accurate comprehension of the associated hemodynamic behavior necessitates advanced geometric modeling and mathematical analysis. In this study, blood is idealized as a non‐Newtonian Carreau fluid to effectively capture its shear‐dependent viscosity characteristics. The stenotic model comprises two overlapping constrictions, yielding three peak and two trough locations, where velocity, wall shear stress (WSS), and temperature distributions are analyzed in detail constituting a key novelty of this work. A mathematical formulation is developed and solved using the Finite Element Method (FEM) to retrieve numerical approximations of the governing equations. Results reveal a marked acceleration of blood flow within the stenotic zones and a pre‐stenotic rise in WSS. Moreover, the temporal increase in the Nusselt number indicates a concurrent enrichment in heat transmission efficiency. It is noticed that the velocity is increasing with increment in the stenosis's height which increases the shear rate. Due to increase in shear rate, WSS also enhances and damages the vessel wall. The interaction between adjacent stenotic regions enhances flow disturbances, resulting in stronger velocity gradients and possible flow separation downstream of each constriction. These findings deliver critical insight into the physiological consequences of multi‐segmented stenotic geometries, including heightened risk for ischemic conditions and thrombotic events. The developed model provides a rigorous quantitative framework for evaluating pathological blood flow dynamics and optimizing treatment planning in cardiovascular medicine.

The segmentation of knee cartilage and bone in magnetic resonance images with supervised learning methods needs manual annotations from experts, although i