Frequency regulation strategy of direct drive permanent magnet synchronous wind power generation system based on RPC principle

The continuous integration of renewable energy into the grid has reduced its inertia and damping levels. When disturbances occur, the grid is prone to frequency excursion issues, which restrict the further utilization of renewable energy. Consequently, an increasing number of grid codes require active participation of renewable energy sources in the system’s frequency regulation (FR). Direct-drive permanent magnet synchronous wind power systems, characterized by their simple structure and high reliability, have gradually become the mainstream in wind power systems. By controlling the pitch angle to reserve surplus power, the wind turbines can actively engage in frequency regulation during disturbances. However, due to limited power reserve capacity, traditional FR methods struggle with parameters tuning, thus failing to achieve the desired effect. To this end, this paper proposes an FR strategy for direct-drive permanent magnet synchronous wind power systems based on the principle of rapid power compensation (RPC). It circumvents the challenges associated with parameter tuning, and achieves optimal FR performance for wind turbine inverter under power-limited conditions. Firstly, it is demonstrated that the proposed RPC control, when making full use of power reserves, can achieve FR effects equivalent to optimal PD control through rigorous mathematical analysis. Subsequently, the RPC control is divided into four operating modes to address FR requirements under different conditions. The transitions between these modes are explained, and the detailed implementation of the RPC control is provided. Finally, the effectiveness and superiority of the proposed control strategy are validated through simulation based on Matlab/Simulink

miR-22 has a potent anti-tumour role with therapeutic potential in acute myeloid leukaemia

MicroRNAs are subject to precise regulation and have key roles in tumorigenesis. In contrast to the oncogenic role of miR-22 reported in myelodysplastic syndrome (MDS) and breast cancer, here we show that miR-22 is an essential anti-tumour gatekeeper in de novo acute myeloid leukaemia (AML) where it is significantly downregulated. Forced expression of miR-22 significantly suppresses leukaemic cell viability and growth in vitro, and substantially inhibits leukaemia development and maintenance in vivo. Mechanistically, miR-22 targets multiple oncogenes, including CRTC1, FLT3 and MYCBP, and thus represses the CREB and MYC pathways. The downregulation of miR-22 in AML is caused by TET1/GFI1/EZH2/SIN3A-mediated epigenetic repression and/or DNA copy-number loss. Furthermore, nanoparticles carrying miR-22 oligos significantly inhibit leukaemia progression in vivo. Together, our study uncovers a TET1/GFI1/EZH2/SIN3A/miR-22/CREB-MYC signalling circuit and thereby provides insights into epigenetic/genetic mechanisms underlying the pathogenesis of AML, and also highlights the clinical potential of miR-22-based AML therapy

Transcriptome Dataset of Soybean (Glycine max) Grown under Phosphorus-Deficient and -Sufficient Conditions

This data descriptor introduces the dataset of the transcriptome of low-phosphorus tolerant soybean (Glycine max) variety NN94-156 under phosphorus-deficient and -sufficient conditions. This data is comprised of the transcriptome datasets (four libraries) acquired from roots and leaves of the soybean plants challenged with low-phosphorus, which allows further analysis whether systemic tolerance response to low phosphorus stress occurred. We describe the detailed procedure of how plants were prepared and treated and how the data were generated and pre-processed. Further analyses of this data would be helpful to improve our understanding of molecular mechanisms of low-phosphorus stress in soybean

RNA-seq data comparisons of wild soybean genotypes in response to soybean cyst nematode (Heterodera glycines)

Soybean [Glycine max (L.) Merr.] is an important crop rich in vegetable protein and oil, and is a staple food for human and animals worldwide. However, soybean plants have been challenged by soybean cyst nematode (SCN, Heterodera glycines), one of the most damaging pests found in soybean fields. Applying SCN-resistant cultivars is the most efficient and environmentally friendly strategy to manage SCN. Currently, soybean breeding and further improvement in soybean agriculture are hindered by severely limited genetic diversity in cultivated soybeans. G. soja is a soybean wild progenitor with much higher levels of genetic diversity compared to cultivated soybeans. In this study, transcriptomes of the resistant and susceptible genotypes of the wild soybean, Glycine soja Sieb & Zucc, were sequenced to examine the genetic basis of SCN resistance. Seedling roots were treated with infective second-stage juveniles (J2s) of the soybean cyst nematode (HG type 2.5.7) for 3, 5, 8 days and pooled for library construction and RNA sequencing. The transcriptome sequencing generated approximately 245 million (M) high quality (Q > 30) raw sequence reads (125 bp in length) for twelve libraries. The raw sequence reads were deposited in NCBI sequence read archive (SRA) database, with the accession numbers SRR5227314-25. Further analysis of this data would be helpful to improve our understanding of the molecular mechanisms of soybean-SCN interaction and facilitate the development of diverse SCN resistance cultivars

Local Similarity-Based Fuzzy Multiple Kernel One-Class Support Vector Machine

One-class support vector machine (OCSVM) is one of the most popular algorithms in the one-class classification problem, but it has one obvious disadvantage: it is sensitive to noise. In order to solve this problem, the fuzzy membership degree is introduced into OCSVM, which makes the samples with different importance have different influences on the determination of classification hyperplane and enhances the robustness. In this paper, a new calculation method of membership degree is proposed and introduced into the fuzzy multiple kernel OCSVM (FMKOCSVM). The combined kernel is used to measure the local similarity between samples, and then, the importance of samples is determined based on the local similarity between training samples, so as to determine the membership degree and reduce the impact of noise. The proposed membership requires only positive data in the calculation process, which is consistent with the training set of OCSVM. In this method, the noise has a smaller membership value, which can reduce the negative impact of noise on the classification boundary. Simultaneously, this method of calculating membership has a higher efficiency. The experimental results show that FMKOCSVM based on proposed local similarity membership is efficient and more robust to outliers than the ordinary multiple kernel OCSVMs

SV-FPN: Small Object Feature Enhancement and Variance-Guided RoI Fusion for Feature Pyramid Networks

Small object detection is one of the research difficulties in object detection, and Feature Pyramid Networks (FPN) is a common feature extractor in deep learning; thus, improving the results of small object detection based on FPN is of great significance in this field. In this paper, SV-FPN is proposed for a small object detection task, which consists of Small Object Feature Enhancement (SOFE) and Variance-guided Region of Interest Fusion (VRoIF). When using FPN as a feature extractor, an SOFE module is designed to enhance the finer-resolution level feature maps from which the small object features are extracted. VRoIF takes the variance of RoI features as the data driver to learn the completeness of several RoI features from different feature layers, which avoids wasting information and introducing noise. Ablation experiments on three public datasets (KITTI, PASCAL VOC 07+12 and MS COCO 2017) demonstrate the effectiveness of SV-FPN, and the mean Average Precision (mAP) of SV-FPN in the three datasets achieves 41.5%, 53.9% and 38.3%, respectively