Research Progress on Synergistic Effect between Insulation Gas Mixtures

Synergetic effect is a special gas discharge phenomenon among insulating gas mixtures, which has important reference value for gas selection of future gas-insulated power equipment. The research progress and investigation methods of synergistic effect and insulation characteristics of different gas mixtures at home and abroad are reviewed in this chapter. The synergistic effect between different kinds of gas mixtures including SF6 gas mixtures and some new insulation gases such as c-C4F8, CF3I, and C4F7N is presented. Combined with the results of multiple studies, it can be seen that the synergistic effect of the gas mixture has a certain relationship with the electronic transport parameters and discharge patterns. Besides, the synergistic effect of the same gas mixture may change with the change of external conditions such as gas pressure, voltage type, and electrode distance

Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks

Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics. As a result, these models suffer from accuracy decay over a long time and thus require frequent calibration. In this work, we address this issue by formulating BP estimation as a sequence prediction problem in which both the input and target are temporal sequences. We propose a novel deep recurrent neural network (RNN) consisting of multilayered Long Short-Term Memory (LSTM) networks, which are incorporated with (1) a bidirectional structure to access larger-scale context information of input sequence, and (2) residual connections to allow gradients in deep RNN to propagate more effectively. The proposed deep RNN model was tested on a static BP dataset, and it achieved root mean square error (RMSE) of 3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction respectively, surpassing the accuracy of traditional BP prediction models. On a multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81 mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction, respectively, which outperforms all previous models with notable improvement. The experimental results suggest that modeling the temporal dependencies in BP dynamics significantly improves the long-term BP prediction accuracy.Comment: To appear in IEEE BHI 201

Non-traditional CD4+CD25−CD69+ regulatory T cells are correlated to leukemia relapse after allogeneic hematopoietic stem cell transplantation

Background: Non-traditional CD4+CD25-CD69+ T cells were found to be involved in disease progression in tumor-bearing mouse models and cancer patients recently. We attempted to define whether this subset of T cells were related to leukemia relapse after allogeneic hematopoietic cell transplantation (allo-HSCT). Methods: The frequency of CD4+CD25-CD69+ T cells among the CD4+ T cell population from the bone marrow of relapsed patients, patients with positive minimal residual disease (MRD+) and healthy donors was examined by flow cytometry. The CD4+CD25-CD69+ T cells were also stained with the intracellular markers to determine the cytokine (TGF-beta, IL-2 and IL-10) secretion. Results: The results showed that the frequency of CD4+CD25-CD69 + T cells was markedly increased in patients in the relapsed group and the MRD + group compared to the healthy donor group. The percentage of this subset of T cells was significantly decreased after effective intervention treatment. We also analyzed the reconstitution of CD4+CD25-CD69+ T cells at various time points after allo-HSCT, and the results showed that this subset of T cells reconstituted rapidly and reached a relatively higher level at +60 d in patients compared to controls. The incidence of either MRD+ or relapse in patients with a high frequency of CD4+CD25-CD69+ T cells (>7%) was significantly higher than that of patients with a low frequency of CD4+CD25-CD69+ T cells at +60 d, +90 d and +270 d after transplant. However, our preliminary data indicated that CD4+CD25-CD69+ T cells may not exert immunoregulatory function via cytokine secretion. Conclusions: This study provides the first clinical evidence of a correlation between non-traditional CD4+CD25-CD69+ Tregs and leukemia relapse after allo-HSCT and suggests that exploration of new methods of adoptive immunotherapy may be beneficial. Further research related to regulatory mechanism behind this phenomenon would be necessary.Medicine, Research & ExperimentalSCI(E)[email protected]

BET: Explaining Deep Reinforcement Learning through The Error-Prone Decisions

Despite the impressive capabilities of Deep Reinforcement Learning (DRL) agents in many challenging scenarios, their black-box decision-making process significantly limits their deployment in safety-sensitive domains. Several previous self-interpretable works focus on revealing the critical states of the agent's decision. However, they cannot pinpoint the error-prone states. To address this issue, we propose a novel self-interpretable structure, named Backbone Extract Tree (BET), to better explain the agent's behavior by identify the error-prone states. At a high level, BET hypothesizes that states in which the agent consistently executes uniform decisions exhibit a reduced propensity for errors. To effectively model this phenomenon, BET expresses these states within neighborhoods, each defined by a curated set of representative states. Therefore, states positioned at a greater distance from these representative benchmarks are more prone to error. We evaluate BET in various popular RL environments and show its superiority over existing self-interpretable models in terms of explanation fidelity. Furthermore, we demonstrate a use case for providing explanations for the agents in StarCraft II, a sophisticated multi-agent cooperative game. To the best of our knowledge, we are the first to explain such a complex scenarios using a fully transparent structure.Comment: This is an early version of a paper that submitted to IJCAI 2024 8 pages, 4 figures and 1 tabl

Impact of a nearby subhalo on the constraint of dark matter annihilation from cosmic ray antiprotons

Numerous simulations indicate that a large number of subhalos should be hosted by the Milky Way. The potential existence of a nearby subhalo could have important implications for our understanding of dark matter (DM) annihilation. In this study, we investigate the hypothetical presence of a nearby subhalo and set the upper limits on the DM annihilation cross section by analyzing the cosmic-ray antiproton spectrum. By presenting the ratios of annihilation cross section limits for scenarios with and without a nearby subhalo, we can quantitatively evaluate the potential impact of the nearby subhalo on the limits of the DM annihilation cross section. The impacts of the concentration model and the subhalo probability distribution have been considered. We explore the antiproton contribution of the potential nearby DM subhalo accounting for the DAMPE $e^\pm$ spectrum at $\sim 1.4$ TeV and find that the current AMS-02 antiproton results do not place the constraint on this contribution.Comment: 8 pages, 5 figure