![]() ![]() We seek Original Research, Review, Mini-Review, Hypothesis and Theory, Perspective, Clinical Trial, Case Report, and Opinion that cover, but are not limited to, the following topics: 1. Revealing the mechanisms of physiological oscillations is also important for the direction of this topic. Large-scale recording and reliable decoding technologies are crucial for discovering the correlations between pathological oscillations and some symptoms, while time- and space-targeted intervention technologies are necessary for studying their causal relationships, Such data will eventually allow the development of neuroprosthesis devices for pathological oscillations. Good animal models that accurately reflect neurological and psychiatric symptoms of patients are necessary for providing the proof-of-concept toward future translational research. This Research Topic will also examine correlations or causal relationships between pathological oscillations and the symptoms of disorders with a view to the possible use of oscillations as biomarkers or therapeutic targets. This Research Topic focused on understanding and controlling pathological oscillations in the brain will provide a comprehensive overview of pathological oscillations in neurological and psychiatric disorders. Furthermore, novel brain stimulation technologies will enable time- and space-targeted interventions of the pathological oscillations of both neurological and psychiatric disorders as possible therapeutic targets for regulating their symptoms. Recent advances in brain activity measurements and analyses have allowed us to study the pathological oscillations of each disorder as a possible biomarker of symptoms. Therefore, these disorders can be considered as common oscillation defects “oscillopathies” despite having distinct behavioral manifestations. Abnormal oscillatory brain activities are commonly observed in neurological and psychiatric disorders including epilepsy, Parkinson’s disease, Alzheimer’s disease, schizophrenia, anxiety/trauma-related disorders, major depressive disorders, addiction, etc. They support many physiological functions from motor control to cognition and emotion. ![]() ![]() According to the results, (a) different RLs show promise in CSO and flooding mitigation (b) Voting selects a relatively reliable and optimal control trajectory compared with any single RL agents (c) the performances of RL agents have certain robustness when facing different rainfall events and imperfect input.Oscillatory brain activities reflect and affect network activities in the brain. All the RLs were evaluated through simulation based on a Storm Water Management Model of a UDS located in Eastern China. ![]() Finally, the robustness of the RL agents was validated using uncertainty analysis. After that, an innovative hybrid RTC system, called Voting, was developed by coupling multiple RLs and the independent security system through a model predictive control framework to avoid the fluctuations of RLs' output. Then, an independent security system based on SWMM was provided to forecast and evaluate the consequence of RL control strategy. First, five RTC systems based on five individual RLs were designed to distinguish different RLs' performance in the context of UDS. This study conducted four tasks to address these problems. Meanwhile, the risk of handing over the control process to a RL agent is still unavoidable because of the fluctuations of RLs' output and the unknown consequences of implementing RLs control strategy. While these studies have made contributions to enable an improved urban water management, they are insufficient to allow for deeply understanding of the effectiveness of different RLs in UDS. Recently, new RTC approaches based on reinforcement learning (RL) were developed for flooding mitigation in stormwater systems. Real-time control (RTC) of urban drainage systems (UDS) has been proved an efficient tool in combined sewer overflow (CSO) and flooding mitigation. ![]()
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