Understanding the mechanical behavior of cells is a complex challenge at the crossroads of physics, biology, and engineering.The cytoskeleton which is a dynamic network of filaments which helps cells maintain shape, move, and respond to their environment.Tensegrity structures, made of interconnected tensile and compressive elements, offer a compelling way to model these internal forces.In this work, we use Reinforcement Learning (RL) to simulate and optimize cellular mechanics.We propose an RL framework where an agent learns to minimize the total mechanical energy of tensegrity-based cell models by adjusting node positions.We consider diverse shapes from simple shapes like lines and triangles, to more complex shapes like cell-like geometries.Our approach shows that RL can effectively model mechanical adaptations in cells and opens the door to intelligent, bio-inspired simulations.This work bridges biophysics, AI, and structural mechanics, offering new ways to predict and understand how cells respond to mechanical stress.

Every year about 30 million people travel by ship worldwide often in extreme weather conditions and polluted environments and many other factors that impact the health of passengers and crew staff.Such issues require medical staff for passenger health care.We introduce a model based on Reinforcement learning(RL) which is used in the dialogue system.We incorporate the Hierarchical reinforcement learning (HRL) model with the layers of Deep Q-Network for dialogue oriented diagnosis system.Policy learning is integrated as policy gradients are already defined.We created a two-stage hierarchical strategy.We used the hierarchical structure with double-layer policies for automatic disease diagnosis.A double layer means it splits the task into sub-tasks named high-state strategy and low-level strategy.It has a user simulator component that communicates with the patient for symptom collection low-level agents inquire about symptoms.Once it's done collecting it sends results to the high-level agent which activates the D-classifier for the last diagnosis.When it's done its sent back by the user simulator to patients to verify the diagnosis made.Every single diagnosis made has its reward that trains the system

The allocation of healthcare resources on ships is crucial for safety and well-being due to limited access to external aid.Proficient medical staff on board provide a mobile healthcare facility, offering a range of services from first aid to complex procedures.This paper presents a system model utilizing Reinforcement Learning (RL) to optimize doctor-patient assignments and resource allocation in maritime settings.The RL approach focuses on dynamic, sequential decision-making, employing Q-learning to adapt to changing conditions and maximize cumulative rewards.Our experimental setup involves a simulated healthcare environment with variable patient conditions and doctor availability, operating within a 24-hour cycle.The Qlearning algorithm iteratively learns optimal strategies to enhance resource utilization and patient outcomes, prioritizing emergency cases while balancing the availability of medical staff.The results highlight the potential of RL in improving healthcare delivery on ships, demonstrating the system's effectiveness in dynamic, time-constrained scenarios and contributing to overall maritime safety and operational resilience.