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Physical activity (PA) enhances proximal femur bone mass, as assessed using projectional imaging techniques. However, these techniques average data over large volumes, obscuring spatially heterogeneous adaptations. The current study used quantitative computed tomography, statistical parameter mapping, and subject-specific finite element (FE) modeling to explore spatial adaptation of the proximal femur to PA. In particular, we were interested in adaptation occurring at the superior femoral neck and improving strength under loading from a fall onto the greater trochanter. High/long jump athletes (n = 16) and baseball pitchers (n = 16) were utilized as within-subject controlled models as they preferentially load their take-off leg and leg contralateral to their throwing arm, respectively. Controls (n = 15) were included but did not show any dominant-to-nondominant (D-to-ND) leg differences. Jumping athletes showed some D-to-ND leg differences but less than pitchers. Pitchers had 5.8% (95% confidence interval [CI] 3.9%-7.6%) D-to-ND leg differences in total hip volumetric bone mineral density (vBMD), with increased vBMD in the cortical compartment of the femoral neck and trochanteric cortical and trabecular compartments. Voxel-based morphometry analyses and cortical bone mapping showed pitchers had D-to-ND leg differences within the regions of the primary compressive trabeculae, inferior femoral neck, and greater trochanter but not the superior femoral neck. FE modeling revealed pitchers had 4.1% (95% CI 1.4%-6.7%) D-to-ND leg differences in ultimate strength under single-leg stance loading but no differences in ultimate strength to a fall onto the greater trochanter. These data indicate the asymmetrical loading associated with baseball pitching induces proximal femur adaptation in regions associated with weight bearing and muscle contractile forces and increases strength under single-leg stance loading. However, there were no benefits evident at the superior femoral neck and no measurable improvement in ultimate strength to common injurious loading during aging (ie, fall onto the greater trochanter), raising questions as to how to better target these variables with PA. © 2019 American Society for Bone and Mineral Research.

ObjectiveTo address the problems of low efficiency and high cost of traditional manual identification of pests and diseases, improve the automatic recognition of pests and diseases by introducing advanced technical means, and provide feasible technical solutions for agricultural pest and disease monitoring and prevention and control, a rice image recognition model GE-DenseNet (G-ECA DenseNet) based on improved ECA (Efficient Channel Attention) mechanism with DenseNet201 was proposed.MethodsThe leaf images of three pests and diseases, namely, brownspot, hispa, leafblast and healthy rice were selected as experimental materials. The images were captured at the Zhuanghe Rice Professional Cooperative in Yizheng, Jiangsu Province, and the camera was used to manually take pictures from multiple angles such as the top and side of rice every 2 h, thus acquiring 1250 images of rice leaves under different lighting conditions, different perspectives, and different shading environments. In addition, samples about pests and diseases were collected in the Kaggle database. There were 1488 healthy leaves, 523 images of brownspot, 565 images of hispa, and 779 images of leafblast in the dataset. Since the original features of the pest and disease data were relatively close, firstly, the dataset was divided into a training set and a test set according to the ratio of 9:1, and then data enhancement was performed on the training set. A region of interest (ROI) was randomly selected to achieve a local scale of 1.1 to 1.25 for the sample images of the dataset, thus simulating the situation that only part of the leaves were captured in the actual shooting process due to the different distance of the plants from the camera. In addition, a random rotation of a certain angle was used to crop the image to simulate the different angles of the leaves. Finally, the experimental training set contains 18,018 images and the test set contains 352 images. The GE-DenseNet model firstly introduces the idea of Ghost module on the ECA attention mechanism to constitute the G-ECA Layer structure, which replaces the convolution operation with linear transformation to perform efficient fusion of channel features while avoiding dimensionality reduction when learning channel attention information and effectively enhancing its ability to extract features. Secondly, since the original Dense Block only considered the correlation between different layers and ignores the extraction of important channel information in the image recognition process, introducing G-ECA Layer before the original Dense Block of DenseNet201 gives the model a better channel feature extraction capability and thus improved the recognition accuracy. Due to the small dataset used in the experiment, the weight parameters of DenseNet201 pre-trained on the ImageNet dataset were migrated to GE-DenseNet. During the training process, the BatchSize size was set to 32, the number of iterations (Epoch) was set to 50, and the Focal Loss function was used to solve the problem of unbalanced samples for each classification. Meanwhile, the adaptive moment estimation (Adam) optimizer was used to avoid the problem of drastic gradient changes in back propagation due to random initialization of some weights at the early stage of model training, which weakened the uncertainty of network training to a certain extent.Results and DiscussionsExperimental tests were conducted on a homemade dataset of rice pests and diseases, and the recognition accuracy reached 83.52%. Comparing the accuracy change graphs and loss rate change graphs of GE-DenseNet and DenseNet201, it could be found that the proposed method in this study was effective in training stability, which could accelerate the speed of model convergence and improve the stability of the model, making the network training process more stable. And observing the visualization results of GE-DenseNet and DenseNet201 corresponding feature layers, it could be found that the features were more densely reflected around the pests and diseases after adding the G-ECA Layer structure. From the ablation comparison experiments of the GE-DenseNet model, it could be obtained that the model accuracy increased by 2.27% after the introduction of the Focal Loss function with the G-ECA Layer layer. Comparing the proposed model with the classical NasNet (4@1056), VGG-16 and ResNet50 models, the classification accuracy increased by 6.53%, 4.83% and 3.69%, respectively. Compared with the original DenseNet201, the recognition accuracy of hispa improved 20.32%.ConclusionsThe experimental results showed that the addition of G-ECA Layer structure enables the model to more accurately capture feature information suitable for rice pest recognition, thus enabling the GE-DenseNet model to achieve more accurate recognition of different rice pest images. This provides reliable technical support for timely pest and disease control, reducing crop yield loss and pesticide use. Future research can lighten the model and reduce its size without significantly reducing the recognition accuracy, so that it can be deployed in UAVs, tractors and various distributed image detection edge devices to facilitate farmers to conduct real-time inspection of farmland and further enhance the intelligence of agricultural production.

Single-layered Ni/Ni3N, inter-layered Ni/Ni3N/Ni, and double-layered Ni/Ni3N composite films were produced on an AZ31 magnesium alloy substrate through a magnetron sputtering technique, utilizing precise control over the N2 flow's modulation. An investigation was conducted to examine the phase composition, structural characteristics, tribological behavior, and corrosion resistance of the developed composite films. The experimental findings reveal that the composite film exhibits a stratified structure, wherein layers of Ni and Ni3N are superimposed with distinct interlaminar boundaries, and the layers have a tight connection between them. Relative to the AZ31 magnesium alloy, the wear volume for the single-layered Ni/Ni3N, inter-layered Ni/Ni3N/Ni, and double-layered Ni/Ni3N composite films was significantly reduced, exhibiting reductions of 68.1%, 80.4%, and 90.1%, respectively. The electrochemical corrosion current density was substantially reduced for the composite film deposition, with respective decrements of 89.5%, 91.4%, and 88.2%. The influence of Ni and Ni3N on the wear and corrosion resistance of composite films varies with different laminated configurations, resulting in distinct levels of wear and corrosion resistance among the various films. [ABSTRACT FROM AUTHOR]

The oil pump equipped with E-CVT has the function of providing cooling lubrication flow and brake flow.In order to study the use characteristics of cycloidal pump as the power source of E-CVT hydraulic system，the mathematical model is established based on theoretical analysis，and the multi-domain simulation model including signal domain，hydraulic domain and mechanical domain is built by AMESim platform.The model is verified by experimental data.Based on the simulation model，the effects of oil temperature，rotation speed and load pressure on volumetric efficiency and flow characteristics are dynamically studied.At the same time，the low oil temperature start failure of the oil pump in the E-CVT is predicted by the model connection filter，oil sump and pipeline，which provides a reference for the subsequent fuel pump motor related strategy design.

Accurate and efficient 3D medical image segmentation models are critical for clinical applications. However, many models which performed well in recent years have consumed significant computational cost, especially those based on self‐attention. For more efficient and accurate segmentation of medical 3D images, we develop an efficient multiscale spatial attention model for 3D abdominal multiorgan segmentation. We use 1D and 2D kernels in the encoder and decoder modules to simulate 3D kernels to reduce the number of parameters and computational cost. Furthermore, we use the lightweight spatial attention and mixed pooling module to capture cross‐dimensional information, multiscale features, and long‐range contextual information. We achieved a Dice Similarity Coefficient (DSC) of 89.86 and a Normalized Surface Dice (NSD) of 78.2 on the FLARE dataset with only 1183 M GPU memory usage, 9 M parameters, and 193G floating point operations(FLOPs). [ABSTRACT FROM AUTHOR]

Introduction The cornea is primarily innervated by small-diameter C-fiber sensory nerves and is the most densely innervated mammalian tissue (1). Axons from the trigeminal ganglion terminate in delicate endings among [...], Diabetic peripheral neuropathy (DPN) often leads to neurotrophic ulcerations in the cornea and skin; however, the underlying cellular mechanisms of this complication are poorly understood. Here, we used post-wound corneal sensory degeneration and regeneration as a model and tested the hypothesis that diabetes adversely affects DC populations and infiltration, resulting in disrupted DC-nerve communication and DPN. In streptozotocin-induced type 1 diabetic mice, there was a substantial reduction in sensory nerve density and the number of intraepithelial DCs in unwounded (UW) corneas. In wounded corneas, diabetes markedly delayed sensory nerve regeneration and reduced the number of infiltrating DCs, which were a major source of ciliary neurotrophic factor (CNTF) in the cornea. While CNTF neutralization retarded reinnervation in normal corneas, exogenous CNTF accelerated nerve regeneration in the wounded corneas of diabetic mice and healthy animals, in which DCs had been locally depleted. Moreover, blockade of the CNTF-specific receptor CNTFRα induced sensory nerve degeneration and retarded regeneration in normal corneas. Soluble CNTFRα also partially restored the branching of diabetes-suppressed sensory nerve endings and regeneration in the diabetic corneas. Collectively, our data show that DCs mediate sensory nerve innervation and regeneration through CNTF and that diabetes reduces DC populations in UW and wounded corneas, resulting in decreased CNTF and impaired sensory nerve innervation and regeneration.

The reason for the formation of pinhole defects on electrophoretic coating of HC180BD+ZF plate from an original equipment manufacturer (OEM) was analyzed. It was found that the higher iron content in coating on the surface of hot-dip zinc alloy coated steel sheet was, the more δ phases it had, resulting in the formation of thinner phosphating film for its lower activity in phosphating solution. Spark discharge phenomenon occurred more easily on the thin phosphating film with low surface resistance during cathodic electrophoretic coating, leading to the formation of pinhole defects. [ABSTRACT FROM AUTHOR]