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The electricity-gas interconnected system includes the electricity and gas subsystems. It has a variety of energy coupling and storage devices with obvious differences in operation. And with the access of new energy power generation， the flexible and coordinated operation of the system is challenged. In this regard， an optimal scheduling method for integrated electricity-gas energy system using multiple time scales to call multiple types of resources is proposed. Firstly， a quasi-steady-state output model of energy equipment in the system is established. The proposed model takes the optimal daily operation cost of the system as the optimization goal， considers the different operation characteristics of energy sources and equipment， uses different time scales and step sizes to optimize the operation output of the power storage and other equipment， and dispatches different resources in the system in an orderly manner to fast track the fluctuations of the load and the new energy. Secondly， the random chance constraints are introduced to describe the remaining flexible resource supply capacity to improve the flexibility and robustness of the system. Finally， the distribution network and gas coupling system are used to conduct a case study to verify the effectiveness of the proposed method.

The power system will gradually step into the era of high percentage of renewable energy， and the shared energy storage can improve the power system resilience and thus effectively consume renewable energy. Since self-built energy storage of users may result in unnecessary sunk costs due to excess capacity and the business model of centralized investment in energy storage user sharing by operators has problems such as high investment costs， centralized operation and non-transparency， a distributed energy storage peer-to-peer transaction model based on blockchain smart contracts is proposed. On the basis of energy storage sharing， a load optimization model of users under the demand response mechanism is established. Finally， a typical daily electric load in spring， summer， autumn and winter in a region of the United States is simulated as an example. The results show that the charging and discharging decision model based on the rational use of shared energy storage can help users to smooth the load curve and realize “low storage and high discharge”， and the shared energy storage model based on blockchain architecture can bring economic benefits to users.

ObjectivesTo explore the suicide risk-specific disruption of cortical hierarchy in major depressive disorder (MDD) patients with diverse suicide risks.MethodsNinety-two MDD patients with diverse suicide risks and 38 matched controls underwent resting-state functional MRI. Connectome gradient analysis and stepwise functional connectivity (SFC) analysis were used to characterize the suicide risk-specific alterations of cortical hierarchy in MDD patients.ResultsRelative to controls, patients with suicide attempts (SA) had a prominent compression from the sensorimotor system; patients with suicide ideations (SI) had a prominent compression from the higher-level systems; non-suicide patients had a compression from both the sensorimotor system and higher-level systems, although it was less prominent relative to SA and SI patients. SFC analysis further validated this depolarization phenomenon.ConclusionThis study revealed MDD patients had suicide risk-specific disruptions of cortical hierarchy, which advance our understanding of the neuromechanisms of suicidality in MDD patients.

Reinforcement learning (RL) for dynamic asset allocation is an emerging field of study. Total return, the common performance metric, is useful for comparing algorithms but does not help us determine how close an RL algorithm is to an optimal solution. In real-world financial applications, a bad decision could prove to be fatal. One of the key ideas of our work is to combine the two paradigms of the mean-variance optimization approach (Markowitz criteria) and the optimal capital growth approach (Kelly criteria) via the actor-critic approach. By using an actor-critic approach, we can balance optimization of risk and growth by configuring the actor to optimize the mean-variance while the critic is configured to maximize growth. We propose a Geometric Policy Score used by the critic to assess the quality of the actions taken by the actor. This could allow portfolio manager practitioners to better understand the investment RL policy. We present an extensive and in-depth study of RL algorithms for use in portfolio management (PM). We studied eight published policy-based RL algorithms which are preferred over value-based RL because they are better suited for continuous action spaces and are considered to be state of the art, Deterministic Policy Gradient (DPG), Stochastic Policy Gradients (SPG), Deep Deterministic Policy Gradient (DDPG), Trust Region Policy Optimization (TRPO), Proximal Policy Optimization (PPO), Twin Delayed Deep Deterministic Policy Gradient (TD3), Soft Actor Critic (SAC), and Evolution Strategies (ES) for Policy Optimization. We implemented all eight and we were able to modify all of them for PM but our initial testing determined that their performance was not satisfactory. Most algorithms showed difficulty converging during the training process due to the non-stationary and noisy nature of financial environments, along with other challenges. We selected the four most promising algorithms DPG, SPG, DDPG, PPO for further improvements. The modification of RL algorithms to finance required unconventional changes. We have developed a novel approach for encoding multi-type financial data in a way that is compatible with RL. We use a multi-channel convolutional neural network (CNN-RL) framework, where each channel corresponds to a specific type of data such as high-low-open-close prices and volumes. We also designed a reward function based on concepts such as alpha, beta, and diversification that are financially meaningful while still being learnable by RL. In addition, portfolio managers will typically use a blend of time series analysis and cross-sectional analysis before making a decision. We extend our approach to incorporate, for the first time, cross-sectional deep RL in addition to time series RL. Finally, we demonstrate the performance of the RL agents and benchmark them against commonly used passive and active trading strategies, such as the uniform buy-and-hold (UBAH) index and the dynamical multi-period Mean-Variance-Optimization (MVO) model. [ABSTRACT FROM AUTHOR]

As one of the key regions in the "social brain" network, the middle temporal gyrus (MTG) has been widely reported to be associated with autism spectrum disorder (ASD), but there have been contradictory results in terms of whether it shows hyperconnectivity or hypoconnectivity. Delineating roles of MTG at the subregional level may eliminate the observed inconsistencies and provide a new avenue to reveal the neurophysiologic mechanism of ASD. Thus, we first performed connectivity‐based parcellation using the BrainMap database to identify fine‐grained functional topography of the MTG. Then, the MTG subregions were used to investigate differences in the functional connectivity in children and adults with ASD using two data sets from Autism Brain Imaging Data Exchange database. Four distinct subregions in the human left and right MTG were identified, including the anterior MTG (aMTG), middle‐anterior MTG (maMTG), middle‐posterior MTG, and posterior MTG (pMTG). The bilateral pMTG was more vulnerable in both children and adults with ASD than in the typically developing (TD) group, mainly showing hypoconnectivity with different brain regions. In addition, the bilateral aMTG and right maMTG also showed altered functional connectivity in adults with ASD compared to the TD group. Moreover, all these altered MTG subregions were mainly associated with social cognition and language, as revealed by functional characterization. Further correlation analyses also showed trends of association between altered connectivity of the left aMTG and the Autism Diagnostic Observation Schedule scores in adults with ASD. Together, these results suggest a potential objective way to explore sub‐regional differences associated with such disorders. Autism Res 2020, 13: 410–422. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary: Four distinct subregions in the human left and right middle temporal gyrus (MTG) were identified, including the anterior MTG (aMTG), middle‐anterior MTG (maMTG), middle‐posterior MTG, and posterior MTG (pMTG). The bilateral pMTG was more vulnerable in both children and adults with autism spectrum disorder (ASD) than in the typically developing (TD) group, mainly showing hypoconnectivity with different brain regions. In addition, the bilateral aMTG and right maMTG also showed altered functional connectivity in adults with ASD compared to the TD group. Moreover, all these altered MTG subregions were mainly associated with social cognition and language, as revealed by functional characterization. Further correlation analyses also showed trends of association between altered connectivity of the left aMTG and the Autism Diagnostic Observation Schedule scores in adults with ASD. [ABSTRACT FROM AUTHOR]