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In light of the growing urgency surrounding energy and environmental concerns, this paper presents a two-layer iterative energy dispatch strategy tailored for a multi-energy-flow virtual power plant (VPP) operating within the distribution power grid. The proposed strategy unfolds in two key phases. First, it establishes an energy dispatch framework designed specifically for the multi-energy-flow VPP within the distribution power grid. Subsequently, it introduces an improved ant colony algorithm aimed at optimizing the output power of each VPP. In addition, the paper presents an optimization method for substation energy dispatch. This method uses a delay-aware consensus algorithm with the substation dispatch cost increment rate as the consensus variable, taking into account the communication delay between VPPs. Integrating a proportional-derivative (PD) control mechanism enhances the convergence speed of the delay-aware consensus algorithm and enables real-time energy dispatch of the multi-energy-flow VPP. The paper presents its conclusions by validating the efficacy of the proposed approach through simulation, thereby addressing the challenges and adapting to the shifting energy and environmental landscape. [ABSTRACT FROM AUTHOR]

The existing remote sensing (RS) image dehazing methods based on deep learning have sought help from the convolutional frameworks. Nevertheless, the inherent limitations of convolution, i.e., local receptive fields and independent input elements, curtail the network from learning the long-range dependencies and nonuniform distributions. To this end, we design an effective RS image dehazing Transformer architecture, denoted as RSDformer. First, given the irregular shapes and nonuniform distributions of haze in RS images, capturing both local and non-local features is crucial for RS image dehazing models. Hence, we propose a detail-compensated transposed attention (DCTA) to extract the global and local dependencies (LDs) across channels. Second, to enhance the ability to learn degraded features and better guide the restoration process, we develop a dual-frequency adaptive block (DFAB) with dynamic filters. Finally, a dynamic gated fusion block (DGFB) is designed to achieve fuse and exchange features across different scales effectively. In this way, the model exhibits robust capabilities to capture dependencies from both global and local areas, resulting in improving image content recovery. Extensive experiments prove that the proposed method obtains more appealing performances against other competitive methods. [ABSTRACT FROM AUTHOR]

Computer vision technology is increasingly being used in areas such as intelligent security and autonomous driving. Users need accurate and reliable visual information, but the images obtained under severe weather conditions are often disturbed by rainy weather, causing image scenes to look blurry. Many current single image deraining algorithms achieve good performance but have limitations in retaining detailed image information. In this paper, we design a Scale-space Feature Recalibration Network (SFR-Net) for single image deraining. The proposed network improves the image feature extraction and characterization capability of a Multi-scale Extraction Recalibration Block (MERB) using dilated convolution with different convolution kernel sizes, which results in rich multi-scale rain streaks features. In addition, we develop a Subspace Coordinated Attention Mechanism (SCAM) and embed it into MERB, which combines coordinated attention recalibration and a subspace attention mechanism to recalibrate the rain streaks feature information learned from the feature extraction phase and eliminate redundant feature information to enhance the transfer of important feature information. Meanwhile, the overall SFR-Net structure uses dense connection and cross-layer feature fusion to repeatedly utilize the feature maps, thus enhancing the understanding of the network and avoiding gradient disappearance. Through extensive experiments on synthetic and real datasets, the proposed method outperforms the recent state-of-the-art deraining algorithms in terms of both the rain removal effect and the preservation of image detail information. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to investigate differences in baseline and treatment characteristics, and in‐hospital mortality according to the aetiologies of cardiogenic shock in patients undergoing veno‐arterial‐extracorporeal membrane oxygenation (VA‐ECMO). Methods and results: The RESCUE registry is a multicentre, observational cohort that includes 1247 patients with cardiogenic shock from 12 centres. A total of 496 patients requiring VA‐ECMO were finally selected, and the study population was stratified by cardiogenic shock aetiology [ischaemic cardiomyopathy (ICM, n = 342) and non‐ICM (NICM, n = 154)]. The primary outcome of interest was in‐hospital mortality. Sensitivity analyses including propensity‐score matching adjustments were performed. Mean age of the entire population was 61.8 ± 14.2, and 30.8% were women. There were significant differences in baseline characteristics; notable differences included the older age of patients with ICM (65.1 ± 13.7 vs. 58.2 ± 13.8, P < 0.001), preponderance of males [258 (75.4%) vs. 85 (55.2%), P < 0.001], and higher prevalence of diabetes mellitus [140 (40.9%) vs. 39 (25.3%), P = 0.001] compared with patients in the NICM aetiology group. Patients with ischaemic cardiogenic shock were more likely to have longer shock duration before VA‐ECMO implantation (518.7 ± 941.4 min vs. 292.4 ± 707.8 min, P = 0.003) and were less likely to undergo distal limb perfusion than those with NICM [108 (31.6%) vs. 79 (51.3%), P < 0.001]. In‐hospital mortality in the overall cohort was 52.2%; patients with ICM had a higher unadjusted risk of in‐hospital mortality [203 (59.4%) vs. 56 (36.4%); unadjusted hazard ratio, 2.295; 95% confidence interval, 1.698–3.100; P < 0.001]. There were no significant differences in the primary outcome between the two aetiologies following propensity‐score matching multiple adjustments (adjusted hazard ratio, 1.265; 95% confidence interval, 0.840–1.906; P = 0.260). Conclusions: Results of the current study indicated among patients with cardiogenic shock undergoing VA‐ECMO, ischaemic aetiology does not seem to impact in‐hospital mortality. These findings underline that early initiation and appropriate treatment strategies of VA‐ECMO for patients with ICM shock are required. [ABSTRACT FROM AUTHOR]