This study examined whether luteolin may exert an anti-inflammatory effect in microglia and may be neuroprotective by regulating microglia activation. We treated BV2 microglia with 1.0 μg/ml lipopolysaccharide (LPS) after incubation with luteolin for 1 hour, the nitric oxide (NO) levels were determined by a Griess reaction, the inducible NO synthase (iNOS), cyclooxygenase-2 (COX-2), tumor necrosis factor-alpha (TNF-alpha), and interleukin 1beta (IL-1beta) mRNA expression were determined by real-time PCR analysis, the iNOS and COX-2 protein induction were determined by Western blot analysis, and the levels of prostaglandin E(2) (PGE(2)), TNF-alpha, and IL-1beta were determined by enzyme-linked immunosorbent assay (ELISA) kits. Rat primary hippocampal neurons were co-cultured with LPS-activated BV2 microglia with 20 μM luteolin for 24 hours, the hippocampal neurons viability was determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, and the number of apoptotic hippocampal neurons was determined by immunofluorescence detection. Luteolin significantly inhibited the expression of iNOS and COX-2 in LPS-induced BV2 microglia. Moreover, the compound down-regulated the proinflammatory cytokines (TNF-alpha and IL-1beta) as well as the production of NO and PGE(2) in these cells. When hippocampal neurons were co-cultured with LPS-stimulated BV2 microglia, the administration of 20 μM luteolin increased the neurons viability and reduced the number of apoptotic neurons. These data demonstrate that anti-inflammatory activity of luteolin in microglia contributes to its neuroprotective effect and suggest that it may have a potential therapeutic application in the treatment of neurodegenerative diseases.

In modern power systems, the high penetration of source-load equipment with uncertain characteristics poses significant challenges to the stable operation of the grid. Particularly in scenarios where large scale electric vehicles (EVs) are connected to the distribution network, whose disordered charging behavior can easily lead to voltage violations and threaten system operational security. To address this issue, this paper first models the state of charge (SOC) of electric vehicles as a Markov chain and then employs the Monte Carlo simulation method to fit the model parameters. In a modified IEEE 33-node distribution network system, the proposed method is compared and analyzed against traditional fitting methods such as support vector machines and regression forests. The results demonstrate that the proposed method exhibits superior fitting performance, providing reliable prediction support considering the uncertainty of EV charging.

Using electric vehicles (EVs) for grid support requires reliable flexibility estimates and effective voltage control from limited online information. We propose a behavior-informed interacting multiple-model estimator (BIMM) and an availability-aware voltage-control strategy. The estimator combines predicted behavior-mode probabilities with station-level measurements to estimate aggregate upward and downward energy flexibility and its uncertainty. The controller combines behavior-derived availability priors with online measurements to update station-level reactive-power limits and allocate voltage support. In paired simulations, explicit behavior-prior pooling reduces the normalized continuous ranked probability score (nCRPS) for energy by 54.0% and 61.8% in IID and OOD, respectively, relative to the same-bank no-pooling reference. Relative to the standard interacting multiple-model estimator (Standard-IMM), the normalized root-mean-square error (NRMSE) for energy decreases by 23.36% and 39.48% in the batch-charging scenarios S1 and S2, respectively; energy losses in ordinary IID/OOD operation are higher than those of Standard-IMM. In ordinary control scenarios, mean action-time request-exceedance rates are lower than for fast-measurement-only voltage control (FastOnly). These results demonstrate the use of aggregate behavior information for probabilistic flexibility estimation and availability-aware reactive-power dispatch.

The findings of this study underscore the efficacy of integrating the time modelling capability of LSTM with the robustness of integrated methods to enhance the stability and operational reliability of the power grid. This study has contributed to the development of elastic DSE technology that can adapt to the complexity challenges of modern power grids.