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Schizophrenia (SZ) is a complex psychiatric disorder with unclear etiology and pathological features. Neuroscientists are increasingly proposing that schizophrenia is an abnormality in the dynamic organization of brain networks. Previous studies have found that the dynamic brain networks of people with SZ are abnormal in both space and time. However, little is known about the interactions and overlaps between hubs of the brain underlying spatiotemporal dynamics. In this study, we aimed to investigate different patterns of spatial and temporal overlap of hubs between SZ patients and healthy individuals. Specifically, we obtained resting-state functional magnetic resonance imaging data from the public dataset for 43 SZ patients and 49 healthy individuals. We derived a representation of time-varying functional connectivity using the Jackknife Correlation (JC) method. We employed the Betweenness Centrality (BC) method to identify the hubs of the brain's functional connectivity network. We then applied measures of temporal overlap, spatial overlap, and hierarchical clustering to investigate differences in the organization of brain hubs between SZ patients and healthy controls. Our findings suggest significant differences between SZ patients and healthy controls at the whole-brain and subnetwork levels. Furthermore, spatial overlap and hierarchical clustering analysis showed that quasi-periodic patterns were disrupted in SZ patients. Analyses of temporal overlap revealed abnormal pairwise engagement preferences in the hubs of SZ patients. These results provide new insights into the dynamic characteristics of the network organization of the SZ brain. [ABSTRACT FROM AUTHOR]

Severe weather threatens the reliability of the power supply by damaging the network. In the case of hurricanes, tens of elements may fail, which would lead to power outages. Under such circumstances, preventive unit commitment methods can model the probabilistic failure forecasts and minimise the power outages. Preventive stochastic unit commitment is an effective method to consider failure forecasts to reduce the power outage. Although stochastic unit commitment produces high‐quality solutions, it is computationally burdensome. Thus, this paper evaluates proxy deterministic methods with lighter computational compared with stochastic unit commitment on both the solution time and quality. Adjusted spinning reserve requirements, engineering judgment‐based rules, and robust preventive operation are among the evaluated methods. Numerical results are obtained for the synthetic grid on the footprint of Texas with 2000 buses. The results suggest that while some proxy methods, such as standard spinning‐reserve and adjusted spinning‐reserve with 6% to 30% of the spinning capacity, may not be as effective as the stochastic method, others, such as robust optimisation, deliver the majority of the stochastic benefits with substantially less (85%) computational time. Monte Carlo simulations are used to evaluate the quality of solutions in reducing the expected unserved load and over‐generation. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is a complex psychiatric disorder with unclear etiology and pathological features. Neuroscientists are increasingly proposing that schizophrenia is an abnormality in the dynamic organization of brain networks. Previous studies have found that the dynamic brain networks of people with SZ are abnormal in both space and time. However, little is known about the interactions and overlaps between hubs of the brain underlying spatiotemporal dynamics. In this study, we aimed to investigate different patterns of spatial and temporal overlap of hubs between SZ patients and healthy individuals. Specifically, we obtained resting-state functional magnetic resonance imaging data from the public dataset for 43 SZ patients and 49 healthy individuals. We derived a representation of time-varying functional connectivity using the Jackknife Correlation (JC) method. We employed the Betweenness Centrality (BC) method to identify the hubs of the brain's functional connectivity network. We then applied measures of temporal overlap, spatial overlap, and hierarchical clustering to investigate differences in the organization of brain hubs between SZ patients and healthy controls. Our findings suggest significant differences between SZ patients and healthy controls at the whole-brain and subnetwork levels. Furthermore, spatial overlap and hierarchical clustering analysis showed that quasi-periodic patterns were disrupted in SZ patients. Analyses of temporal overlap revealed abnormal pairwise engagement preferences in the hubs of SZ patients. These results provide new insights into the dynamic characteristics of the network organization of the SZ brain.