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With the rapid development of artificial intelligence technology, DRL has shown great potential in solving complex real-time optimal power flow problems of modern power systems. Nevertheless, traditional DRL methodologies confront dual bottlenecks: (a) suboptimal coordination between exploratory behavior policies and experience-based data exploitation in practical applications, compounded by (b) users’ distrust from the opacity of model decision mechanics. To address these, a model–data hybrid-driven physics-informed reinforcement learning (PIRL) algorithm is proposed in this paper. Specifically, the proposed methodology uses the proximal policy optimization (PPO) algorithm as the agent’s foundational framework and constructs a PI-actor network embedded with prior model knowledge derived from power flow sensitivity into the agent’s actor network via the PINN method, which achieves dual optimization objectives: (a) enhanced environmental perceptibility to improve experience utilization efficiency via gradient-awareness from model knowledge during actor network updates, and (b) improved user trustworthiness through mathematically constrained action gradient information derived from explicit model knowledge, ensuring actor updates adhere to safety boundaries. The simulation and validation results show that the PIRL algorithm outperforms the baseline PPO algorithm in terms of training stability, exploration efficiency, economy, and security.

Monkeypox virus (MPXV) has re-emerged globally since May 2022, posing a significant public health threat. To address this, we develop two multivalent mRNA vaccine candidates—AAL, encoding three MPXV antigens, and AALI, which combines AAL with an immune-enhancing IFN-α protein. Both vaccines are delivered via mannose-modified lipid nanoparticles to target dendritic cells. Here we show that these vaccines elicit strong antibody responses against vaccinia virus and multiple MPXV clades, induce robust memory B-cell and T-cell responses, and promote dendritic cell maturation. In mouse challenge models, both vaccines provide protection against clade IIb MPXV and vaccinia virus, significantly reducing viral loads and preventing lung damage. Immune profiling reveals enhanced B- and T-cell receptor diversity and distinct CDR3 motifs post-vaccination. These findings demonstrate the potential of using mRNA-based multivalent vaccines as an effective strategy for preventing mpox and related Orthopoxvirus infections. Increased incidence of Monkey pox infection in humans has raised the need for efficacious interventional approaches. Here the authors administer a multivalent mRNA vaccine for monkey pox virus in a mouse model, showing protection and characterising the induced immune responses.

We have previously established a clade IIb mpox virus (MPXV) pathogenic BALB/c mouse model for developing mpox countermeasures. Here, we comprehensively investigated the susceptibility of BALB/c and C57BL/6 mice to MPXV and found that Clade IIb MPXV was capable of rapid replication in the lungs of both mouse strains, thus triggering similar dynamic pathological changes and antibody responses. However, C57BL/6 mice, compared to BALB/c mice, seem less susceptible to MPXV, evidenced by no significant weight loss, lower viral load, faster viral clearance, and earlier pathological improvement, as well as weaker antibody response. Interestingly, C57BL/6 mice intranasally infected with MPXV displayed a significant body weight loss, indicating the crucial role of innate immunity in the susceptibility to MPXV. The C57BL/6 model mimics clinical characteristics of asymptomatic or mildly symptomatic patients with mild mpox, which will be beneficial for exploring MPXV infection, transmission, pathogenesis, and immune responses.

Accurately determining load model parameters is of the utmost importance for conducting power system simulation analysis and designing effective control strategies. Measurement-based approaches are commonly employed to identify load model parameters that closely reflect the actual operating conditions. However, these methods typically rely on iterative parameter search processes, which can be time-consuming, particularly when dealing with complex models. To address this challenge, this paper introduces a parameter identification method for the generalized synthetic load model (SLM) using the Extreme Learning Machine (ELM) technique, with the aim of enhancing computational efficiency. Furthermore, to achieve better alignment with load response curves, a Quantum Particle Swarm Optimization (QPSO) algorithm is adopted to train the ELM model. The proposed QPSO-ELM-based SLM parameter identification method is subsequently evaluated using a standard test system. To assess its effectiveness, parameter sensitivity analysis is performed, and simulation results are analyzed. The findings demonstrate that the proposed method yields favorable outcomes, offering improved computation efficiency in load model parameter identification tasks.

The interconnection of power and natural gas systems can improve the flexibility of system operation and the capacity of renewable energy consumption. It is necessary to consider the interaction between both, and carry out collaborative optimization of energy flow. For space-time related line packs, this paper studies the optimal multi-energy flow (OMEF) model of an integrated electricity-gas system, taking into account the dynamic characteristics of a natural gas system. Besides, in order to avoid the problem of large data collection in centralized algorithms and consider the characteristics of decentralized autonomous decision-making for each subsystem, this paper proposes a decentralized algorithm for the OMEF problem. This algorithm transforms the original non-convex OMEF problem into an iterative convex programming problem through penalty convex-concave procedure (PCCP), and then, uses the alternating direction method of multipliers (ADMM) algorithm at each iteration of PCCP to develop a decentralized collaborative optimization of power flow and natural gas flow. Finally, numerical simulations verify the effectiveness and accuracy of the algorithm proposed in this paper, and analyze the effects of dynamic characteristics of networks on system operation.

With the joint optimization of the electricity–gas–heating system (EGHS) attracting more and more attention, a distributed optimized scheduling framework for EGHS based on an improved alternating direction method of multipliers (ADMM) algorithm is put forward in this paper. The framework of the proposed algorithm is a co-ordinated convex distribution framework with inner and outer layers. The outer layer is a penalty convex–concave procedure (PCCP), the inner layer is an ADMM-FE (forced equality) procedure. In this framework, the outer layer optimization uses the convex and concave procedure to turn the non-convex airflow equation into a second-order cone constraint with successive iterations, and the inner layer ADMM-FE algorithm solves the convex model to obtain a convergent solution. In the end, we compare the established algorithm with the traditional ADMM algorithm and the centralized optimization algorithm through example simulation analysis, and the results verify the effectiveness of the proposed model and optimization algorithm framework.

This paper proposes a novel multi-searcher optimization (MSO) algorithm for the optimal energy dispatch (OED) of combined heat and power-thermal-wind-photovoltaic systems. The available power of wind turbine (WT) units and photovoltaic (PV) units is approximated with the probability density functions of wind speed and solar irradiance, respectively. The chaos theory is used to implement a wide global search, which can effectively avoid a low-quality local optimum for OED. Besides, a double-layer searcher is designed to guarantee fast convergence to a high-quality optimal solution. Finally, three benchmark functions and an energy system with 27 units are used for testing the performance of the MSO compared with nine other frequently used heuristic algorithms. The simulation results demonstrate that the proposed technique not only can solve the highly nonlinear, non-smooth, and non-convex OED problem of an energy system, but can also achieve a superior performance for the convergence speed and the optimum quality.

The stability problem of power grids has become increasingly serious in recent years as the size of novel power systems increases. In order to improve and ensure the stable operation of the novel power system, this study proposes an artificial emotional lazy Q-learning method, which combines artificial emotion, lazy learning, and reinforcement learning for static security and stability analysis of power systems. Moreover, this study compares the analysis results of the proposed method with those of the small disturbance method for a stand-alone power system and verifies that the proposed lazy Q-learning method is able to effectively screen useful data for learning, and improve the static security stability of the new type of power system more effectively than the traditional proportional-integral-differential control and Q-learning methods.