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Combined with the monitoring data of the new distribution network Feeder Terminal Unit (FTU), an innovative three-phase real-time line loss calculation method based on the feeder segment rather than the entire feeder as the basic calculation unit is proposed. This method uses the variable structure dissipative network theory, divides the feeder into feeder segments, and conducts mathematical modeling for each equipment in the segment based on the feeder segment. The detailed line loss of each equipment makes the line loss distribution in the system clear at a glance, which is convenient for operation and maintenance personnel to scientifically manage and carry out distribution network line loss analysis and calculation, and provides a favorable basis for further optimization of the distribution network system.

Seed storage underpins global agriculture and the seed trade and revealing the mechanisms of seed aging is essential for enhancing seed longevity management. Safflower is a multipurpose oil crop, rich in unsaturated fatty acids that are at high risk of peroxidation as a contributory factor to seed aging. However, the molecular mechanisms responsible for safflower seed viability loss are not yet elucidated. We used controlled deterioration (CDT) conditions of 60% relative humidity and 50 °C to reduce germination in freshly harvested safflower seeds and analyzed aged seeds using biochemical and molecular techniques. While seed malondialdehyde (MDA) and fatty acid content increased significantly during CDT, catalase activity and soluble sugar content decreased. KEGG analysis of gene function and qPCR validation indicated that aging severely impaired several key functional and biosynthetic pathways including glycolysis, fatty acid metabolism, antioxidant activity, and DNA replication and repair. Furthermore, exogenous sucrose and diethyl aminoethyl hexanoate (DA-6) treatment partially promoted germination in aged seeds, further demonstrating the vital role of impaired sugar and fatty acid metabolism during the aging and recovery processes. We concluded that energy metabolism and genetic integrity are impaired during aging, which contributes to the loss of seed vigor. Such energy metabolic pathways as glycolysis, fatty acid degradation, and the tricarboxylic acid cycle (TCA) are impaired, especially fatty acids produced by the hydrolysis of triacylglycerols during aging, as they are not efficiently converted to sucrose via the glyoxylate cycle to provide energy supply for safflower seed germination and seedling growth. At the same time, the reduced capacity for nucleotide synthesis capacity and the deterioration of DNA repair ability further aggravate the damage to DNA, reducing seed vitality.

Image compression aims to minimize the amount of data in image representation while maintaining a certain visual quality for humans, which is an essential technique for storage and transmission. Recently, along with the development of computer vision, machines have become another primary receiver for images and require compressed images at a certain quality level, which may be different from that of human vision. In many scenarios, compressed images should serve both human and machine vision tasks, but few compression methods are designed for both goals simultaneously. In this article, we propose a unified and scalable deep image compression (USDIC) framework that jointly optimizes the image quality according to human and machine vision in an end-to-end style. For the encoder, we propose an information splitting mechanism (ISM) to separate images into semantic and visual features, which mainly aims at machine analysis and human viewing tasks. For the decoder, we design a scalable decoding architecture. The encoded semantic feature is first decoded for machine analysis tasks, and the image is decoded and reconstructed further by leveraging the decoded semantic features. Herein, to further remove the redundancy between the semantic and visual features of images, we propose a scalable entropy model (SEM) with a joint optimization strategy to reconstruct the image using the two kinds of decoded features. Extensive experimental results show that the proposed USDIC achieves much better performance on the image analysis task while maintaining competitive performance on the traditional image reconstruction task compared with popular image compression methods. [ABSTRACT FROM AUTHOR]

Although deep learning technique has achieved significant improvement on image compression, but its advantages are not fully explored in video compression, which leads to the performance of deep-learning-based video compression (DLVC) is obviously inferior to that of hybrid video coding framework. In this article, we proposed a novel network to improve the performance of DLVC from its most important modules, including Motion Process (MP), Residual Compression (RC), and Frame Reconstruction (FR). In MP, we design a split second-order attention and multi-scale feature extraction module to fully remove the warping artifacts from multi-scale feature space and pixel space, which can help reduce the distortion in the following process. In RC, we propose a channel selection mechanism to gradually drop redundant information while preserving informative channels for a better rate-distortion performance. Finally, in FR, we introduce a residual multi-scale recurrent network to improve the quality of the current reconstructed frame by progressively exploiting temporal context information between it and its several previous reconstructed frames. Extensive experiments are conducted on the three widely used video compression datasets (HEVC, UVG, and MCL-JVC), and the performance demonstrates the superiority of our proposed approach over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Demand response adjusts demand through market signals such as price to promote grid reliability. By changing the demand for electricity, the demand response can realize the friendly interaction of sourcenetwork-load, promote the absorption of new energy, and thus applying to the rapid growth of the scale of new energy installation. Considering the characteristics of provincial power grid, this paper studies the power demand response trading organization and technology realization from the point of view of promoting clean energy consumption, and realizes it systematically. This method has universal application value for the same type of provincial power grid.

Co-salient object detection (CoSOD) aims to detect common salient objects sharing the same attributes in an image group. The key issue of CoSOD is how to model the inter-saliency relations within an image group. The major limitation of previous methods is that they pre-define the group-to-one relations within an image group. In this paper, we propose a new concept of structural inter-saliency relations and solve the CoSOD with deep reinforcement learning framework. Firstly, we design a semantic relation graph (SRG) to model the structural inter-saliency relations. Then the feature selecting agent (FS-agent) aims to select the informative features, which can help the SRG effectively model structural inter-saliency relations. Finally, relation updating agent (RU-agent) progressively updates the SRG to focus on the co-salient relations like human decision-making process. Extensive experiments on co-saliency datasets show that because of well modeling inter-saliency relations in image group, our proposed method achieves superior performance compared to the state-of-the-art methods. We hope that this paper can motivate future research for visual co-analysis tasks. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel model for simultaneous stable co-saliency detection (CoSOD) and object co-segmentation (CoSEG). To detect co-saliency (segmentation) accurately, the core problem is to well model inter-image relations between an image group. Some methods design sophisticated modules, such as recurrent neural network (RNN), to address this problem. However, order-sensitive problem is the major drawback of RNN, which heavily affects the stability of proposed CoSOD (CoSEG) model. In this paper, inspired by RNN-based model, we first propose a multi-path stable recurrent unit (MSRU), containing dummy orders mechanisms (DOM) and recurrent unit (RU). Our proposed MSRU not only helps CoSOD (CoSEG) model captures robust inter-image relations, but also reduces order-sensitivity, resulting in a more stable inference and training process. Moreover, we design a cross-order contrastive loss (COCL) that can further address order-sensitive problem by pulling close the feature embedding generated from different input orders. We validate our model on five widely used CoSOD datasets (CoCA, CoSOD3k, Cosal2015, iCoseg and MSRC), and three widely used datasets (Internet, iCoseg and PASCAL-VOC) for object co-segmentation, the performance demonstrates the superiority of the proposed approach as compared to the state-of-the-art (SOTA) methods. [ABSTRACT FROM AUTHOR]

Phosphodiesterase 4 (PDE4) inhibitors with potential activities for CNS disorders provide a new therapeutic strategy for depression. To discover PDE4 inhibitors with anti‐neuroinflammation activities, reliable three‐dimensional quantitative structure‐activity relationship (3D‐QSAR) models on our previous reported catecholic PDE4 inhibitors was built with a statistically significant cross‐validated coefficient (q2), conventional coefficient (r2), and good predictive capabilities based on the molecular docking results, using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods. Based on the analysis of CoMFA and CoMSIA contour maps, a series of 2‐(3,4‐dialkoxyphenyl)‐2‐(substituted pyridazin‐3‐yl) acetonitriles 16a–i was designed and synthesized. Among these compounds, compound 16a exhibited good inhibitory activities toward PDE4B1 and PDE4D7 with mid‐nanomolar IC50 values and potential anti‐neuroinflammation activity in BV‐2 cells. Docking simulation of compound 16a in the PDE4 catalytic domain activity pocket revealed that compound 16a maybe assumed a "V‐shaped" conformation, extending the side chain to S‐pocket. In this paper, a reliable 3D‐QSAR model on catecholic PDE4 inhibitors was successfully built with a statistically significant cross‐validated coefficient (q2), conventional coefficient (r2) and good predictive capabilities, using CoMFA and CoMSIA methods. And a series of 2‐(3,4‐dialkoxyphenyl)‐2‐(substituted pyridazin‐3‐yl) acetonitriles 16a–i was designed and synthesized based on the analysis of CoMFA and CoMSIA contour maps. Among these compounds, compound 16a exhibited good inhibitory activities toward PDE4B1 and PDE4D7 with mid‐nanomole IC50 values, and potential anti‐neuroinflammation activity in BV‐2 cells. [ABSTRACT FROM AUTHOR]

This study presents a novel protection criterion of fault locating in multi‐terminal DC (MTDC) transmission systems. When a fault occurs on the DC transmission line of the MTDC system, the traditional protection cannot locate the fault because there is no smoothing reactor (equipped at the converter output) in the transmission lines. In the circumstance of AC/DC conductor contact (ADCC) fault, the varieties of the transient voltage and current components are given based on the symmetrical fault analysis. According to the Bergeron equation, the travelling wave characteristic is analysed. A new criterion of fault locating using the change rate of the current travelling wave is presented which can reduce the impacts of the related fault on the AC system. Finally, simulation results verify the effectiveness of the proposed protection criterion. [ABSTRACT FROM AUTHOR]