Fuzzy Superpixels based Semi-supervised Similarity-constrained CNN for PolSAR Image Classification

Recently, deep learning has been highly successful in image classification. Labeling the PolSAR data, however, is time-consuming and laborious and in response semi-supervised deep learning has been increasingly investigated in PolSAR image classification. Semi-supervised deep learning methods for PolSAR image classification can be broadly divided into two categories, namely pixels-based methods and superpixels-based methods. Pixels-based semi-supervised methods are liable to be affected by speckle noises and have a relatively high computational complexity. Superpixels-based methods focus on the superpixels and ignore tiny detail-preserving represented by pixels. In this paper, a Fuzzy superpixels based Semi-supervised Similarity-constrained CNN (FS-SCNN) is proposed. To reduce the effect of speckle noises and preserve the details, FS-SCNN uses a fuzzy superpixels algorithm to segment an image into two parts, superpixels and undetermined pixels. Moreover, the fuzzy superpixels algorithm can also reduce the number of mixed superpixels and improve classification performance. To exploit unlabeled data effectively, we also propose a Similarity-constrained Convolutional Neural Network (SCNN) model to assign pseudo labels to unlabeled data. The final training set consists of the initial labeled data and these pseudo labeled data. Three PolSAR images are used to demonstrate the excellent classification performance of the FS-SCNN method with data of limited labels

Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification

The increasing applications of polarimetric synthetic aperture radar (PolSAR) image classification demand for effective superpixels’ algorithms. Fuzzy superpixels’ algorithms reduce the misclassification rate by dividing pixels into superpixels, which are groups of pixels of homogenous appearance and undetermined pixels. However, two key issues remain to be addressed in designing a fuzzy superpixel algorithm for PolSAR image classification. First, the polarimetric scattering information, which is unique in PolSAR images, is not effectively used. Such information can be utilized to generate superpixels more suitable for PolSAR images. Second, the ratio of undetermined pixels is fixed for each image in the existing techniques, ignoring the fact that the difficulty of classifying different objects varies in an image. To address these two issues, we propose a polarimetric scattering information-based adaptive fuzzy superpixel (AFS) algorithm for PolSAR images classification. In AFS, the correlation between pixels’ polarimetric scattering information, for the first time, is considered through fuzzy rough set theory to generate superpixels. This correlation is further used to dynamically and adaptively update the ratio of undetermined pixels. AFS is evaluated extensively against different evaluation metrics and compared with the state-of-the-art superpixels’ algorithms on three PolSAR images. The experimental results demonstrate the superiority of AFS on PolSAR image classification problems

Mutant p53 drives clonal hematopoiesis through modulating epigenetic pathway

Clonal hematopoiesis of indeterminate potential (CHIP) increases with age and is associated with increased risks of hematological malignancies. While TP53 mutations have been identified in CHIP, the molecular mechanisms by which mutant p53 promotes hematopoietic stem and progenitor cell (HSPC) expansion are largely unknown. Here we discover that mutant p53 confers a competitive advantage to HSPCs following transplantation and promotes HSPC expansion after radiation-induced stress. Mechanistically, mutant p53 interacts with EZH2 and enhances its association with the chromatin, thereby increasing the levels of H3K27me3 in genes regulating HSPC self-renewal and differentiation. Furthermore, genetic and pharmacological inhibition of EZH2 decreases the repopulating potential of p53 mutant HSPCs. Thus, we uncover an epigenetic mechanism by which mutant p53 drives clonal hematopoiesis. Our work will likely establish epigenetic regulator EZH2 as a novel therapeutic target for preventing CHIP progression and treating hematological malignancies with TP53 mutations

Author Correction: Mutant p53 drives clonal hematopoiesis through modulating epigenetic pathway

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Overview of Research Status of DC Bias and Its Suppression in Power Transformers

In this article, the sources of DC bias and its effects on power transformers are first summarized. Secondly, the article classifies and summarizes the current DC bias calculation problems of power transformers, and puts forward some interesting viewpoints on the research logic of related calculations. The current processing methods of DC bias effect are classified and discussed, their advantages and disadvantages are compared, and the logic flow of DC bias effect processing is proposed. Finally, the current research on DC bias voltage of power transformers is summarized, and the progress and deficiencies of current research are pointed out, which has certain reference value for future research on DC bias voltage and its suppression

Multi-Conditional Optimization of a High-Specific-Speed Axial Flow Pump Impeller Based on Machine Learning

In order to widen the range of high-efficiency area of a high-specific-speed axial flow pump and to improve the operating efficiency under non-design conditions, the parameters of the axial flow pump blades were optimized. An optimization system based on computational fluid dynamics (CFD), optimized Latin hypercube sampling (OLHS), machine learning (ML), and multi-island genetic algorithm (MIGA) was established. The prediction effects of three machine learning models based on Bayesian optimization, support vector machine regression (SVR), Gaussian process regression (GPR), and fully connected neural network (FNN) on the performance of the axial flow pump were compared. The results show that the GPR model has the highest prediction accuracy for the impeller head and weighted efficiency. Compared to the original impeller, the optimized impeller is forward skewed and backward swept, and the weighted efficiency of the impeller increases by 1.31 percentage points. The efficiency of the pump section at 0.8Qd, 1.0Qd, and 1.2Qd increases by about 1.1, 1.4, and 1.6 percentage points, respectively, which meets the optimization requirements. After optimization, the internal flow field of the impeller is more stable; the entropy production in the impeller reduces; the spanwise distribution of the total pressure coefficient and the axial velocity coefficient at the impeller outlet are more uniform; and the flow separation near the hub at the blade trailing edge is restrained. This research can provide a reference for the efficient operation of pumping stations and the optimal design of axial flow pumps under multiple working conditions

Multi-Conditional Optimization of a High-Specific-Speed Axial Flow Pump Impeller Based on Machine Learning

In order to widen the range of high-efficiency area of a high-specific-speed axial flow pump and to improve the operating efficiency under non-design conditions, the parameters of the axial flow pump blades were optimized. An optimization system based on computational fluid dynamics (CFD), optimized Latin hypercube sampling (OLHS), machine learning (ML), and multi-island genetic algorithm (MIGA) was established. The prediction effects of three machine learning models based on Bayesian optimization, support vector machine regression (SVR), Gaussian process regression (GPR), and fully connected neural network (FNN) on the performance of the axial flow pump were compared. The results show that the GPR model has the highest prediction accuracy for the impeller head and weighted efficiency. Compared to the original impeller, the optimized impeller is forward skewed and backward swept, and the weighted efficiency of the impeller increases by 1.31 percentage points. The efficiency of the pump section at 0.8Qd, 1.0Qd, and 1.2Qd increases by about 1.1, 1.4, and 1.6 percentage points, respectively, which meets the optimization requirements. After optimization, the internal flow field of the impeller is more stable; the entropy production in the impeller reduces; the spanwise distribution of the total pressure coefficient and the axial velocity coefficient at the impeller outlet are more uniform; and the flow separation near the hub at the blade trailing edge is restrained. This research can provide a reference for the efficient operation of pumping stations and the optimal design of axial flow pumps under multiple working conditions

Effects of Bamboo (Phyllostachys praecox) Cultivation on Soil Nitrogen Fractions and Mineralization

The mineralization of soil organic nitrogen (N) is the key process in the cycling of N in terrestrial ecosystems. Land-use change to bamboo (Phyllostachys praecox) cultivation that later entails organic material mulching combined with chemical fertilizer application will inevitably influence soil N mineralization (Nmin) and availability dynamics. However, the soil Nmin rates associated with various N fractions of P. praecox in response to land-use change and mulching are not well understood. The present study aimed to understand the effects of land-use change to P. praecox bamboo cultivation and organic material mulching on soil Nmin and availability. Soil properties and organic N fractions were measured in a P. praecox field planted on former paddy fields, a mulched P. praecox field, and a rice (Oryza sativa L.) field. Soil Nmin was determined using a batch incubation method, with mathematical models used to predict soil Nmin kinetics and potential. The conversion from a paddy field to P. praecox plantation decreased the soil pH, soil total N, and soil organic matter (SOM) content significantly (p < 0.05); the mulching method induced further soil acidification. The mulching treatment significantly augmented the SOM content by 7.08% compared with the no-mulching treatment (p < 0.05), but it decreased soil hydrolyzable N and increased the nonhydrolyzable N (NHN) content. Both the Nmin rate and cumulative mineralized N were lowest in the mulched bamboo field. The kinetics of Nmin was best described by the ‘two-pool model’ and ‘special model’. The Pearson’s correlation analysis and the Mantel test suggested soil pH was the dominant factor controlling the soil cumulative mineralized N and mineralization potential in the bamboo fields. These findings could help us better understand the N cycling and N availability under mulching conditions for shifts in land use, and provide a scientific basis for the sustainable management of bamboo plantations