Vertical One-dimensional (1-D) Simulations of Horizontal Velocity Profiles

Details of a vertical 1-D hydrodynamic model to simulate the horizontal velocity profiles for tidal estuarial flows with possible stratifications caused by salinity or Suspended Sediment Concentration (SSC) are presented. The standard 2nd order k-ε model was implemented to address the turbulent flow with possible stratification effects. Simulation results are verified with two field measurements for steady nonstratified flows and a field measurement for tidal estuary non-stratified flow. The stratification effect of salinity and suspended sediment concentration are also checked with the following descriptions: “Salinity stratification will change the typical logarithmic velocity profile to a linear profile for most of the water column. It appears that the possible high gradient of near-bottom (less than 0.5 m) SSC when the nearbed SSC is high only significantly alter the velocity profile when the turbulence is weak. The source codes, in FORTRAN 90, samples of the ASCII input data files, and a post process codes for plotting results using Matlab are attached for future uses

Study on Optimized Elman Neural Network Classification Algorithm Based on PLS and CA

High-dimensional large sample data sets, between feature variables and between samples, may cause some correlative or repetitive factors, occupy lots of storage space, and consume much computing time. Using the Elman neural network to deal with them, too many inputs will influence the operating efficiency and recognition accuracy; too many simultaneous training samples, as well as being not able to get precise neural network model, also restrict the recognition accuracy. Aiming at these series of problems, we introduce the partial least squares (PLS) and cluster analysis (CA) into Elman neural network algorithm, by the PLS for dimension reduction which can eliminate the correlative and repetitive factors of the features. Using CA eliminates the correlative and repetitive factors of the sample. If some subclass becomes small sample, with high-dimensional feature and fewer numbers, PLS shows a unique advantage. Each subclass is regarded as one training sample to train the different precise neural network models. Then simulation samples are discriminated and classified into different subclasses, using the corresponding neural network to recognize it. An optimized Elman neural network classification algorithm based on PLS and CA (PLS-CA-Elman algorithm) is established. The new algorithm aims at improving the operating efficiency and recognition accuracy. By the case analysis, the new algorithm has unique superiority, worthy of further promotion

Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors

The prevalence of short video platforms has spawned a lot of fake news videos, which have stronger propagation ability than textual fake news. Thus, automatically detecting fake news videos has been an important countermeasure in practice. Previous works commonly verify each news video individually with multimodal information. Nevertheless, news videos from different perspectives regarding the same event are commonly posted together, which contain complementary or contradictory information and thus can be used to evaluate each other mutually. To this end, we introduce a new and practical paradigm, i.e., cross-sample fake news video detection, and propose a novel framework, Neighbor-Enhanced fakE news video Detection (NEED), which integrates the neighborhood relationship of new videos belonging to the same event. NEED can be readily combined with existing single-sample detectors and further enhance their performances with the proposed graph aggregation (GA) and debunking rectification (DR) modules. Specifically, given the feature representations obtained from single-sample detectors, GA aggregates the neighborhood information with the dynamic graph to enrich the features of independent samples. After that, DR explicitly leverages the relationship between debunking videos and fake news videos to refute the candidate videos via textual and visual consistency. Extensive experiments on the public benchmark demonstrate that NEED greatly improves the performance of both single-modal (up to 8.34% in accuracy) and multimodal (up to 4.97% in accuracy) base detectors. Codes are available in https://github.com/ICTMCG/NEED.Comment: To appear in ACL 2023 Finding

Effect of C/N on Water State during Composting of Kitchen Waste and Vegetable Waste Mixture

The objective of this study was to evaluate the effects of the C/N ratio on the water state changes during the composting of kitchen waste (KW) and vegetable waste (VW) mixtures. The C/N ratios in KW and VW were 50.70 and 27.07, respectively, and the VW was added to the KW to amend the C/N ratio. Five composting treatments were used, R1 with 0% KW, R2 with 25% KW, R3 with 50% KW, R4 with 75% KW, and R5 with 100% KW, and the initial C/N ratios increased in the order R1 < R2 < R3 < R4 < R5. As the composting process progressed, the capillary water (CW) and multi-molecular-layer water (MMLW) were changed into entrapped water (EW), and a high C/N ratio could delay the changes in the water state. The percentage of EW and CW significantly positively correlated with the C/N ratio during the composting of KW. The composting process performed better in treatments R2 and R3 than in the other treatments, and it was found that treatments R2 and R3 each had a lag phase of around 4 d until the water states started to change

Identifying veraison process of colored wine grapes in field conditions combining deep learning and image analysis

Acknowledgments This work was supported by the National Key R&D Program Project of China (Grant No. 2019YFD1002500) and Guangxi Key R&D Program Project (Grant No. Gui Ke AB21076001) The authors would like to thank the anonymous reviewers for their helpful comments and suggestions.Peer reviewedPostprin

Development of a mobile application for identification of grapevine (Vitis vinifera L.) cultivars via deep learning

Acknowledgements: The authors would like to express their gratitude to the Teaching Experiment Farm of Ningxia University, for their kind help. This study was supported by the Key R & D projects of Ningxia Hui Autonomous Region (Grant No. 2019BBF02013)Peer reviewedPublisher PD

Segmentation of field grape bunches via an improved pyramid scene parsing network

With the continuous expansion of wine grape planting areas, the mechanization and intelligence of grape harvesting have gradually become the future development trend. In order to guide the picking robot to pick grapes more efficiently in the vineyard, this study proposed a grape bunches segmentation method based on Pyramid Scene Parsing Network (PSPNet) deep semantic segmentation network for different varieties of grapes in the natural field environments. To this end, the Convolutional Block Attention Module (CBAM) attention mechanism and the atrous convolution were first embedded in the backbone feature extraction network of the PSPNet model to improve the feature extraction capability. Meanwhile, the proposed model also improved the PSPNet semantic segmentation model by fusing multiple feature layers (with more contextual information) extracted by the backbone network. The improved PSPNet was compared against the original PSPNet on a newly collected grape image dataset, and it was shown that the improved PSPNet model had an Intersection-over-Union (IoU) and Pixel Accuracy (PA) of 87.42% and 95.73%, respectively, implying an improvement of 4.36% and 9.95% over the original PSPNet model. The improved PSPNet was also compared against the state-of-the-art DeepLab-V3+ and U-Net in terms of IoU, PA, computation efficiency and robustness, and showed promising performance. It is concluded that the improved PSPNet can quickly and accurately segment grape bunches of different varieties in the natural field environments, which provides a certain technical basis for intelligent harvesting by grape picking robots

MicroRNA-483 amelioration of experimental pulmonary hypertension.

Endothelial dysfunction is critically involved in the pathogenesis of pulmonary arterial hypertension (PAH) and that exogenously administered microRNA may be of therapeutic benefit. Lower levels of miR-483 were found in serum from patients with idiopathic pulmonary arterial hypertension (IPAH), particularly those with more severe disease. RNA-seq and bioinformatics analyses showed that miR-483 targets several PAH-related genes, including transforming growth factor-β (TGF-β), TGF-β receptor 2 (TGFBR2), β-catenin, connective tissue growth factor (CTGF), interleukin-1β (IL-1β), and endothelin-1 (ET-1). Overexpression of miR-483 in ECs inhibited inflammatory and fibrogenic responses, revealed by the decreased expression of TGF-β, TGFBR2, β-catenin, CTGF, IL-1β, and ET-1. In contrast, inhibition of miR-483 increased these genes in ECs. Rats with EC-specific miR-483 overexpression exhibited ameliorated pulmonary hypertension (PH) and reduced right ventricular hypertrophy on challenge with monocrotaline (MCT) or Sugen + hypoxia. A reversal effect was observed in rats that received MCT with inhaled lentivirus overexpressing miR-483. These results indicate that PAH is associated with a reduced level of miR-483 and that miR-483 might reduce experimental PH by inhibition of multiple adverse responses

Identification of Puccinia striiformis races from the spring wheat crop in Xinjiang, China

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a foliar disease that affects both winter and spring wheat crops in Xinjiang, China, which is linked to Central Asia. Race identification of Pst from spring wheat in Xinjiang was not done before. In this study, a total of 216 isolates were recovered from stripe rust samples of spring wheat in the region in 2021 and multiplied using the susceptible cultivar Mingxian 169. These isolates were tested on the Chinese set of 19 wheat differential lines for identifying Pst races. A total of 46 races were identified. Races Suwon-11-1, Suwon11-12, and CYR32 had high frequencies in the spring wheat region. The frequencies of virulence factors on differentials “Fulhard” and “Early Premium” were high (&gt;95%), whereas the virulence factor to differential “Triticum spelta var. Album” (Yr5) was not detected, while virulence to other differentials showed variable frequency within different counties. The predominant races in winter wheat in the same season were also detected from spring wheat cultivars, indicating Pst spreading from winter wheat to spring wheat crops. Deploying resistance genes in spring and winter wheat cultivars is critical for control stripe rust