Giant magnetoimpedance in crystalline Mumetal

We studied giant magnetoimpedance (GMI) effect in commercial crystalline Mumetal, with the emphasis to sample thickness dependence and annealing effects. By using appropriate heat treatment one can achieve GMI ratios as high as 310%, and field sensitivity of about 20%/Oe, which is comparable to the best GMI characteristics obtained for amorphous and nanocrystalline soft magnetic materials.Comment: 8 pages, 3 figure

Comprehensive Evaluation of Endophytic Fungi and Rhizosphere Soil Fungi on the Growth of \u3cem\u3eAchnatherum inebrians\u3c/em\u3e

This study was conducted to clarify the effect of endophytic fungi and rhizosphere soil fungi on the growth of Achnatherum inebrians. In this study, the seeds of A. inebrians with endophyte-infected (EI) and endophyte-free (EF) were used as materials. Eight fungi isolated from rhizosphere soil were inoculated through germination and greenhouse pot experiment. The results showed that the endophytes, rhizosphere soil fungi and their combined effect all had significant effect on the seed germination and plant growth of A. inebrians, and the affected factors varied with the tested materials and strains. Through comprehensive evaluation of principal component analysis and subordinate function, it was found that the overall growth performance of EI was better than that of EF plants, and the strains that inhibited the growth of A. inebrians were Cladosporium. sp2 and Fusarium sp1

Unsupervised Feature Selection with Adaptive Structure Learning

The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data. However, the estimated intrinsic structures are unreliable/inaccurate when the redundant and noisy features are not removed. Therefore, we face a dilemma here: one need the true structures of data to identify the informative features, and one need the informative features to accurately estimate the true structures of data. To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously. The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data. By leveraging the interactions between these two essential tasks, we are able to capture accurate structures and select more informative features. Experimental results on many benchmark data sets demonstrate that the proposed method outperforms many state of the art unsupervised feature selection methods

Strawberry Verticillium Wilt Detection Network Based on Multi-Task Learning and Attention

© 2013 IEEE. Plant disease detection has an inestimable effect on plant cultivation. Accurate detection of plant disease can control the spread of disease early and prevent unnecessary loss. Strawberry verticillium wilt is a soil-borne, multi-symptomatic disease. To detect strawberry verticillium wilt accurately, we first propose a disease detection network based on Faster R-CNN and multi-task learning to detect strawberry verticillium wilt. Then, the strawberry verticillium wilt detection network (SVWDN), which uses attention mechanisms in the feature extraction of the disease detection network, is proposed. SVWDN detects verticillium wilt according to the symptoms of detected plant components (i.e.,young leaves and petioles). Compared with other existing methods for detecting disease from the whole plant appearance, the SVWDN automatically classifies the petioles and young leaves while determining whether the strawberry has verticillium wilt. To provide a dataset for evaluating and testing our method, we construct a large dataset that contains 3, 531 images with 4 categories (Healthy-leaf, Healthy-petiole, Verticillium-leaf and Verticillium-petiole). Each image also has a label to indicate whether the strawberry is suffering from verticillium wilt. With the proposed strawberry verticillium wilt detection network, we achieved a mAP of 77.54% on object detection of 4 categories and 99.95% accuracy for strawberry verticillium wilt detection

Biot-Rayleigh theory of wave propagation in double-porosity media

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Optical spectroscopy study of Nd(O,F)BiS2 single crystals

We present an optical spectroscopy study on F-substituted NdOBiS$_2$ superconducting single crystals grown using KCl/LiCl flux method. The measurement reveals a simple metallic response with a relatively low screened plasma edge near 5000 \cm. The plasma frequency is estimated to be 2.1 eV, which is much smaller than the value expected from the first-principles calculations for an electron doping level of x=0.5, but very close to the value based on a doping level of 7$\%$ of itinerant electrons per Bi site as determined by ARPES experiment. The energy scales of the interband transitions are also well reproduced by the first-principles calculations. The results suggest an absence of correlation effect in the compound, which essentially rules out the exotic pairing mechanism for superconductivity or scenario based on the strong electronic correlation effect. The study also reveals that the system is far from a CDW instability as being widely discussed for a doping level of x=0.5.Comment: 5 pages, 5 figure

Air/water interfacial waves with a droplet at the tip of their crest

In nature, it is common to observe water wave crests with a droplet at their tip. This fascinating configuration remains unexplained from the physical point of view. The present study explores such a unique local configuration numerically. Solitary waves that propagate at the interface between two layers of irrotational fluids are considered. Extending the work of Guan et al. [“A local model for the limiting configuration of interfacial solitary waves,” J. Fluid Mech. 921, A9 (2021)], the density ratio has been decreased to a very small value equal to 0.001, which is close to the air/water density ratio at sea level (0.0013). A highly accurate solution for the limiting configuration of solitary waves is obtained by solving the irrotational Euler equations using the boundary integral method and Newton's iterations. It is confirmed that the limiting configuration consisting of a droplet sitting on a crest with a [Formula: see text] angle exists for very small density ratios. This limiting configuration obviously does not exist for surface waves with a void on the top, thus stressing the crucial role played by the air. The droplet is stationary in a frame of reference moving with the wave and experiences intense shear at its tip. From the energy point of view, the formation of a crest with a droplet is accompanied by a remarkable drop of kinetic and potential energies of water in the vicinity of the crest. Furthermore, we present a simple set of scaling relations for the fall of the droplet

Molecular Characterization of the Ghrelin and Ghrelin Receptor Genes and Effects on Fat Deposition in Chicken and Duck

Ghrelin (GHRL) and its receptor (GHSR) are involved in various bioactivities. In this study, the complete cDNA and 5′ flanking region of the duck GHRL (dGHRL) gene and a 3717 bp fragment of the duck GHSR (dGHSR) gene were obtained. A total of 19, 8, 43, and 48 SNPs identified in 2751, 1358, 3671, and 3567 bp of the chicken GHRL (cGHRL), chicken GHSR (cGHSR), dGHRL, and dGHSR genes, respectively. Both cGHRL and dGHRL were expressed predominantly in the proventriculus, whereas the highest mRNA levels of cGHSR and dGHSR were detected in the breast muscle and pituitary. Association analysis showed that C-2047G, A-2355C, and A-2220C of the cGHRL gene were significantly associated with abdominal fat weight (AFW; P = .01), crude protein content of leg muscle (CPCLM; P = .02), and CPCLM (P = .0009), respectively. C-1459T of the cGHSR gene was also significantly associated with CPCLM (P = .0004). C-729T of dGHRL and A3427T of dGHSR were both significantly associated with subcutaneous fat thickness (SFT; P = .04). It was indicated by this study that the GHRL and GHSR genes were related to fat deposition in both chicken and duck