Mohammad Amin Rezaei

Lithium-ion batteries are widely used in electric vehicles, portable electronics, and grid energy systems. The constant current–constant voltage (CC–CV) method shows clear limits at high state of charge when polarization increases, temperature rises, and charging time is extended. These effects become stronger in the high SOC region, where the charging rate decreases. Consequently, to address this challenge, in this study we presents a hybrid charging method that combines physics-informed deep learning with an auxiliary hardware circuit. The circuit is placed between the charger and the battery without changes to the charger structure. It enables real-time control of pulse frequency and duty cycle. A one-dimensional convolutional neural network is applied during short relaxation periods for SOC estimation. The proposed method reduces polarization and thermal stress and improves charging efficiency at high SOC. It also improves open-circuit voltage estimation accuracy. Experimental tests are conducted on lithium-ion cells under controlled temperature conditions. The results confirm improved performance in fast charging applications.

Biomedical AI is increasingly shaped by policy-bound, multi-step clinical workflows and non-stationary, multimodal data and tools. In this setting, the field is moving beyond static predictors toward agentic systems, enabled by foundation models that maintain task-relevant state and operate through a closed perceive$\rightarrow$plan$\rightarrow$act$\rightarrow$observe loop under explicit oversight. However, the field lacks a coherent account that defines biomedical agency, relates foundational model capabilities to agent behaviors, and traces the pathway from pretraining to domain-adapted, deployable systems. This survey offers such an account by synthesizing operational boundaries of agency and framing six core components (memory, planning, reflection, tool use, dialogue, and collaboration) as foundational agent-enabling capabilities that drive the transition from isolated pipelines to fully realized agents. This survey situates these perspectives along the model-building pathway, from pretraining through post-training adaptation to the orchestration mechanisms that operationalize agents. We highlight safety and governance considerations for high-stakes settings, emphasizing the fidelity of process and reasoning, uncertainty and abstention, privacy and provenance, and human oversight. Taken together, this survey provides a structured synthesis of how recent work connects foundation models to governable biomedical agentic systems and distills the recurring challenges and directions identified in the literature for reliable, accountable deployment.

Biomedical AI is increasingly shaped by policybound, multi-step clinical workflows and non-stationary, multimodal data and tools. In this setting, the field is moving beyond static predictors toward agentic systems, enabled by foundation models that can perceive, plan, and act under explicit oversight-a shift that motivates this survey. However, the field lacks a coherent account that defines biomedical agency, relates foundational model capabilities to agent behaviors, and traces the pathway from pre-training to domain-adapted, deployable systems. This survey offers such an account by synthesizing operational notions of agency and reviewing capability-centered perspectives that organize memory, planning, tool use, reflection, and dialogue around a perceive-plan-act loop. This survey situates these perspectives along the model-building pathway, from pretraining through post-training adaptation to the orchestration mechanisms that operationalize agents. We highlight safety and governance considerations for high-stakes settings, emphasizing the fidelity of process and reasoning, uncertainty and abstention, privacy and provenance, and human oversight. Taken together, this survey provides a structured synthesis of how recent work connects foundation models to governable biomedical agentic systems and distills the recurring challenges and directions identified in the literature for reliable, accountable deployment.