Health Care Resource Utilization in Patients With Atopic Dermatitis According to Treatment Response: Evidence From the BIOBADATOP Registry.

Observer-based prescribed-time optimal neural consensus control for six-rotor UAVs: A novel actor-critic reinforcement learning strategy.

Optimal control of inter-population disease spread via reaction-diffusion models.

Optimal control over split stopping times in defaultable settings and reflected BSDEs with irregular obstacles

Developing process descriptor for biological nitrogen removal in wastewater treatment.

Optimal control and techno-economic analysis of long-duration battery energy storage system applications

Forecasting and optimal control model to assess the potential role of vaccine hesitancy and supportive care in East Java measles outbreak.

AW-EL-PINNs: A multi-task learning physics-informed neural network for Euler-Lagrange systems in optimal control problems.

This study develops a reinforcement learning (RL)-based control framework with guaranteed predefined performance for nonlinear switched interconnected systems. This approach effectively addresses challenges arising from unmeasurable states and group average dwell time switching mechanisms, allowing both convergence time and accuracy to be preset via parameter configuration. First, the system equations are reconstructed to target nonlinear and interconnected terms, which are then approximated using neural networks (NNs). Additionally, an NNs-based switching state observer is designed to estimate the unmeasurable states. Second, within the backstepping synthesis framework, a distributed optimal controller is designed by integrating a performance transformation function into the cost function, with the resulting control law approximated via an identifier-actor-critic architecture. Furthermore, the group average dwell time-based stability analysis is generalized to address the optimal control challenges inherent in nonlinear switched interconnected systems. Compared with existing studies, this approach demonstrates enhanced extensibility and practicality for real-world applications. Finally, two simulation examples verify the effectiveness and superiority of the proposed method over state-of-the-art alternatives.

• Formulated a ship decarbonization design integrating PCC with the ship energy system. • Developed a hybrid model to capture the PCC dynamics under varying ship engine loads. • Designed an EMPC for energy-efficient PCC operation with high carbon capture rate. • Employed cross-entropy to efficiently solve the complex EMPC optimization problem. • Conducted extensive simulations verifying superior modeling and control performance. Implementing carbon capture technology on-board ships holds promise as a solution to facilitate the reduction of carbon intensity in international shipping, as mandated by the International Maritime Organization. In this work, we address the energy-efficient operation of shipboard carbon capture processes by proposing a hybrid modeling-based economic predictive control scheme. Specifically, we consider a comprehensive shipboard carbon capture process that encompasses the ship engine system and the shipboard post-combustion carbon capture plant. To accurately and robustly characterize the dynamic behaviors of this shipboard plant, we develop a hybrid dynamic process model that integrates available imperfect physical knowledge with neural networks trained using process operation data. An economic model predictive control approach is proposed based on the hybrid model to ensure carbon capture efficiency while minimizing energy consumption required for the carbon capture process operation. The cross-entropy method is employed to efficiently solve the complex non-convex optimization problem associated with the proposed hybrid model-based economic model predictive control method. Extensive simulations, analyses, and comparisons are conducted to verify the effectiveness and illustrate the superiority of the proposed framework. The proposed hybrid model-based economic model predictive control reduced the overall economic cost by 8.07% compared to conventional optimal set-point tracking nonlinear model predictive control and achieved a 4.20% lower economic cost with a 9.10% higher carbon capture rate than the imperfect first-principles model-based economic model predictive control.

DPP-4 inhibitors in current diabetes care: their foundational role in treating to target.

Fixed-time optimal bipartite containment fault-tolerant control for multi-agent systems under multiple faults and saturated actuation

Reinforcement learning and game-based optimal output consensus control for higher-order multi-agent systems with unknown dead-zone inputs

Artificial intelligence in revolutionizing food encapsulation: Applications ranging from discovery to deployment.

Contamination characteristics and airflow regulated transport of microbial aerosols in a hospital respiratory ward.

Promising treatment options for cerebellopontine angle meningiomas: a longitudinal retrospective cohort study.

Reinforcement learning-based optimal attitude tracking control for rigid spacecraft with external disturbances

Type 1 diabetes (T1D) is a chronic autoimmune disease that disproportionately affects children and adolescents and is associated with substantial medical and psychosocial burden. Despite advances in insulin delivery systems and continuous glucose monitoring, most patients do not achieve optimal glycemic control and remain at risk for diabetic ketoacidosis, long-term microvascular and macrovascular complications, and premature mortality. Increasing evidence demonstrates that T1D is a progressive disease characterized by years of asymptomatic autoimmunity and gradual pancreatic beta-cell loss, creating an opportunity for early disease-modifying intervention. This review summarizes the genetic and immunologic foundations of T1D, the disease classification system, and emerging strategies aimed at preserving endogenous insulin production. Particular focus is given to teplizumab (Tzield), an anti-CD3 monoclonal antibody that is the first FDA-approved therapy shown to delay progression from stage 2 to stage 3 T1D in individuals ages 8 years and older. Disease-modifying immunotherapies such as teplizumab represent a paradigm shift in T1D management, with the potential to delay disease onset and reduce long-term complications.

Online parameter identification and backpressure optimization control of a direct air-cooled system based on dynoNet

Reinforcement learning based constrained optimal control:An interpretable reward design