Multi-Scale Expressions of One Optimal State Regulated by Dopamine in the Prefrontal Cortex

The prefrontal cortex (PFC), which plays key roles in many higher cognitive processes, is a hierarchical system consisting of multi-scale organizations. Optimizing the working state at each scale is essential for PFC's information processing. Typical optimal working states at different scales have been separately reported, including the dopamine-mediated inverted-U profile of the working memory (WM) at the system level, critical dynamics at the network level, and detailed balance of excitatory and inhibitory currents (E/I balance) at the cellular level. However, it remains unclear whether these states are scale-specific expressions of the same optimal state and, if so, what is the underlying mechanism for its regulation traversing across scales. Here, by studying a neural network model, we show that the optimal performance of WM co-occurs with the critical dynamics at the network level and the E/I balance at the level of individual neurons, suggesting the existence of a unified, multi-scale optimal state for the PFC. Importantly, such a state could be modulated by dopamine at the synaptic level through a series of U or inverted-U profiles. These results suggest that seemingly different optimal states for specific scales are multi-scale expressions of one condition regulated by dopamine. Our work suggests a cross-scale perspective to understand the PFC function and its modulation

Seismic Vulnerability Evaluation of a Three-Span Continuous Beam Railway Bridge

In order to evaluate the seismic vulnerability of a railway bridge, a nonlinear finite element model of typical three-span continuous beam bridge on the Sichuan-Tibet railway in China was built. It further aimed at performing a probabilistic seismic demand analysis based on the seismic performance of the above-mentioned bridge. Firstly, the uncertainties of bridge parameters were analyzed while a set of finite element model samples were formulated with Latin hypercube sampling method. Secondly, under Wenchuan earthquake ground motions, an incremental dynamic method (IDA) analysis was performed, and the seismic peak responses of bridge components were recorded. Thirdly, the probabilistic seismic demand model for the bridge principal components under the prerequisite of two different kinds of bearing, with and without seismic isolation, was generated. Finally, comparison was drawn to further ascertain the effect of two different kinds of bearings on the fragility components. Based on the reliability theory, results were presented concerning the seismic fragility curves

Comparative study of thermally integrated pumped thermal energy storage based on the organic rankine cycle with different working fluid pairs

Thermal integrated pumped thermal energy storage (TIPTES) systems with the features of high efficiency, flexibility, and reliability, have attracted increasing attention since they can integrate low-grade heat sources to further improve the utilization and economic viability of renewable energy. In this study, a typical TIPTES system driven by waste flue gas is established, and the heat pump and organic Rankine cycle (ORC) are chosen as the charging and discharging cycle, respectively. Four organic fluids, including R600, R245fa, R601a, and R1336mzz(Z), are selected to compose sixteen different working fluid pairs for thermodynamic analysis. The effects of key parameters, like heat pump system evaporation temperature and hot storage tank temperature, on system performance were analyzed, and the single-objective optimization was conducted. A comparative study was carried out to identify the best working fluid pair according to the optimization results. Results show that the system’s power-to-power efficiency goes up as the evaporation temperature increases while an increase in the heat storage temperature decreases the exergy efficiency of the TIPTES system. Optimization results show that the R245fa + R245fa is the best working fluid pair, and in this system, the ORC evaporator has the largest exergy destruction at about 260.84 kW, which is 20.2% of the total. On the other hand, the ORC pump has the smallest exergy destruction only about 0.5%. This study also finds that the system’s power-to-power efficiency of using different working fluids in either heat pump cycles or ORC cycles is lower than that of using the same working fluid throughout the entire system

Rethinking GNN-based Entity Alignment on Heterogeneous Knowledge Graphs: New Datasets and A New Method

The development of knowledge graph (KG) applications has led to a rising need for entity alignment (EA) between heterogeneous KGs that are extracted from various sources. Recently, graph neural networks (GNNs) have been widely adopted in EA tasks due to GNNs' impressive ability to capture structure information. However, we have observed that the oversimplified settings of the existing common EA datasets are distant from real-world scenarios, which obstructs a full understanding of the advancements achieved by recent methods. This phenomenon makes us ponder: Do existing GNN-based EA methods really make great progress? In this paper, to study the performance of EA methods in realistic settings, we focus on the alignment of highly heterogeneous KGs (HHKGs) (e.g., event KGs and general KGs) which are different with regard to the scale and structure, and share fewer overlapping entities. First, we sweep the unreasonable settings, and propose two new HHKG datasets that closely mimic real-world EA scenarios. Then, based on the proposed datasets, we conduct extensive experiments to evaluate previous representative EA methods, and reveal interesting findings about the progress of GNN-based EA methods. We find that the structural information becomes difficult to exploit but still valuable in aligning HHKGs. This phenomenon leads to inferior performance of existing EA methods, especially GNN-based methods. Our findings shed light on the potential problems resulting from an impulsive application of GNN-based methods as a panacea for all EA datasets. Finally, we introduce a simple but effective method: Simple-HHEA, which comprehensively utilizes entity name, structure, and temporal information. Experiment results show Simple-HHEA outperforms previous models on HHKG datasets.Comment: 11 pages, 6 figure