In this work, we adopt the diquark model, construct the diquark−diquark−antiquark type currents with the light quarks uus in two octets, and study the uuscc¯ pentaquark states in the framework of the QCD sum rules systematically. We obtain the mass spectrum of the hidden-charm-singly-strange pentaquark states with the quantum numbers IJP=112−, 132− and 152−. And we can search for those Pcs states in the processes Σb+→Pcs+ϕ→J/ψΣ+ϕ and Ξb0→Pcs+K−→J/ψΣ+K−.
Transforming existing grid-following (GFL) doubly-fed induction generator (DFIG) wind farms into grid-forming (GFM) DFIG ones is essential for enhancing system frequency support. Studying frequency response and parameters tuning in hybrid wind farms with both unit types establishes a foundation for frequency control. Frequency response modeling for GFL units is well established, small-signal models of GFM DFIG units fail to capture frequency-active power dynamics due to multi-variable coupling, hindering unified hybrid wind farm modeling. Furthermore, frequency control parameters tuning relies on inefficient trial-and-error methods. These defects limit the application of hybrid wind farms. This paper proposes a comprehensive frequency response model for hybrid wind farms and an efficient parameter tuning method. By analyzing the frequency support process in GFM DFIG turbines, a frequency-active power response model is derived. Considering system inertia sources, wind farm outputs are transformed and aggregated to build a system frequency response model that includes hybrid DFIG wind farms. A multi-objective function based on this model integrates the maximum frequency variation rate, deviation, and steady-state deviation. The improved genetic particle swarm optimization (GPSO) algorithm is applied for parameters tuning under generator output and stability constraints. Simulation results demonstrate that the proposed model accurately captures frequency and turbine output dynamics, and the tuning method improves system frequency response and stability.
Retrieval-Augmented Generation enhances large language models by incorporating external knowledge, but deploying it in sensitive scenarios risks privacy leakage via malicious prompts. To address this, we propose a multi-agent framework that sanitizes retrieved content through semantic rewriting. By employing three specialized agents for privacy extraction, semantic analysis, and reconstruction, our approach collaboratively removes sensitive identifiers while preserving the semantic core. We evaluate the framework on the ChatDoctor and Wiki-PII datasets across six large language models. Experimental results demonstrate a significant reduction in privacy leakage under targeted attacks. For instance, we reduced targeted information exposure in LLaMA-3-8B from 144 instances in the baseline to just 1. Furthermore, we maintain strong contextual fidelity with a BLEU-1 score of 0.122, outperforming the existing SAGE method's 0.117. Finally, the framework operates as an asynchronous preprocessing module, introducing no additional latency to online inference, as all rewriting is executed as a one-time offline preprocessing step. To promote reproducibility, the source code of this work is publicly available at https://github.com/foursoils/Privacy-Preserving-RAG.
The integration of high-penetration distributed photovoltaics intensifies the spatiotemporal imbalance between sources and loads in distribution networks, making it difficult to achieve safe and economic operation under radial topology constraints. Traditional optimization methods also suffer from limited solution efficiency and scheduling performance under uncertain source-load conditions. To address these issues, this paper proposes a deep reinforcement learning (DRL)-based spatiotemporal cooperative optimal scheduling method for an energy router (ER). First, a spatiotemporal cooperative scheduling framework for the ER is established, in which spatial power mutual support is realized through multi-port power transfer, while temporal energy shifting is achieved by coordinating an internal energy storage system (ESS). Second, an optimization model is formulated with the objectives of minimizing voltage violation deviation and network losses, and the scheduling process of the ER and its internal ESS is modeled as a Markov decision process. Furthermore, a soft actor-critic algorithm based on feature grouping and self-attention, namely FGA-SAC, is proposed to enhance the policy network’s ability to identify key state features. Finally, the proposed method is validated on the IEEE 33-bus system, IEEE 69-bus system, and an extreme load-drop scenario. The results show that the proposed method can effectively ensure voltage security and reduce network losses. Compared with SAC, TD3, DDPG, and traditional optimization methods, it also demonstrates better stability and online decision-making efficiency.
In this work, the microstructural evolution and hardening behaviors of Mo-3Nb and Mo-6Nb alloys induced by Fe-ion irradiation at room temperature were investigated. The 3 MeV Fe ions were implanted into Mo-Nb alloys at doses of 0.5, 1.0, and 10 dpa at room temperature. The results show that vacancy-type defects and dislocation loops are the dominant irradiation-induced defects in Mo-Nb alloys. In both alloys, the concentration of positron-detectable vacancy-type open-volume defects and the size and density of dislocation loops increase with increasing dose. Compared to Mo-3Nb, the Mo-6Nb alloy exhibits a higher density but smaller dislocation loops at 0.5 dpa, whereas it exhibits both higher density and larger loops at 1.0 and 10 dpa, indicating that higher Nb content benefits both loop nucleation and growth. Furthermore, pronounced irradiation-induced hardening is observed in both alloys, with the Mo-6Nb alloy displaying a greater hardening increment relative to the Mo-3Nb alloy under identical irradiation doses.
