Search

The deformation of open-pit mine slopes is influenced by various factors such as geological structure, hydrogeological conditions, mining activities, etc., making the prediction model complex. It is difficult to accurately capture all influencing factors. At present, a large number of monitoring devices are deployed around the slope of open-pit mines to record real-time displacement data of open-pit mine slopes. The data has the features of high-dimensional, temporal correlation, and nonlinear. Traditional slope stability analysis methods cannot effectively predict slope deformation without knowing other conditions and only data, it is necessary to use a data-based model to predict the displacement data of open-pit mine slopes in advance for slope stability analysis. In order to solve the above problems, a deformation prediction model for open-pit mine slopes based on the golden jackal optimized multilayer perception machine (GJO-MLP) is proposed. Each agent in GJO is independent of each other and can accelerate the training process of optimizing MLP through parallel computing. GJO can combine the nonlinear modeling and feature extraction capabilities of MLP, making the optimized MLP more advantageous in dealing with complex problems. To test the feasibility and effectiveness of GJO-MLP, GJO-MLP is compared and analyzed with ant colony algorithm optimization based MLP (ACO-MLP), gravity search algorithm optimization based MLP (GSA-MLP), and differential evolution algorithm optimization based MLP (DE-MLP). The simulation results on six datasets show that under the same experimental conditions, GJO-MLP shows better optimization performance compared to the other three algorithms. The slope deformation prediction model based on GJO-MLP is applied to the slope deformation prediction of Baorixile open-pit mine and Huapingzi slope deformation prediction. The results show that under the same conditions, compared to the other three algorithms, the slope deformation prediction model based on GJO-MLP not only show better predictive performance in predicting slope deformation data, but also has better feasibility and robustness.

Peanut web blotch is an important leaf disease caused by Phoma arachidicola, which seriously affects the quality and yield of peanuts. However, the molecular mechanisms of peanut resistance to peanut web blotch are not well understood. In this study, a transcriptome analysis of the interaction between peanut (Arachis hypogaea) and P. arachidicola revealed that total 2989 (779 up- and 2210 down-regulated) genes were all differentially expressed in peanut leaves infected by P. arachidicola at 7, 14, 21 days post inoculation. The pathways that were strongly differentially expressed were the flavone or isoflavone biosynthesis pathways. In addition, two 2-hydroxy isoflavanone synthase genes, IFS1 and IFS2, were strongly induced by P. arachidicola infection. Overexpression of the two genes enhanced resistance to Phytophthora parasitica in Nicotiana benthamiana. Knockout of AhIFS genes in peanut reduced disease resistance to P. arachidicola. These findings demonstrated that AhIFS genes play key roles in peanut resistance to P. arachidicola infection. Promoter analysis of the two AhIFS genes showed several defense-related cis-elements distributed in the promoter region. This study improves our understanding of the molecular mechanisms behind resistance of peanut infection by P. arachidicola, and provides important information that could be used to undertake greater detailed characterization of web blotch resistance genes in peanut. [ABSTRACT FROM AUTHOR]

In order to solve the problem of unmanned truck transportation scheduling in open-pit mines, the minimum sum of fuel cost, fixed start-up cost, breakdown maintenance cost, and network base station construction and maintenance cost are taken as the objective functions. The mining amount of mining station, crushing amount of crushing station, truck number and truck transportation workload are taken as the constraint conditions. The optimization model of unmanned truck transportation scheduling in open-pit mines is established. To solve the problem of imbalance between global exploration and local mining ability in the tunicate swarm algorithm, an improved tunicate swarm algorithm (ITSA) based on Singer mapping and adaptive updating mechanism of parameter position is proposed. And it is applied to solve the optimization model of unmanned truck transportation scheduling in open-pit mines. Singer mapping is introduced to enhance the distribution of the initial tunicate swarm in the solution space and accelerate the compression of the solution space, thus improving the convergence speed of the algorithm. Through the adaptive updating mechanism of parameter position, the positions of the tunicate and the optimal tunicate are adjusted to increase the search range of the solution space. Therefore, the algorithm jumps out of the local optimization. The simulation results show that ITSA has better convergence precision, convergence speed and stability compared with the four population intelligent optimization algorithms of grey wolf optimization algorithm (GWO), whale optimization algorithm (WOA), atom search optimization algorithm (ASO) and tunicate swarm algorithm (TSA). In the unimodal benchmark function, the evaluation indexes of ITSA are far better than those of the other four algorithms, which shows that ITSA has better local mining capacity. In the multi-peak benchmark function, the evaluation indexes of ITSA show better optimization performance, which indicates that ITSA has better global exploration performance. The practical application scenario verification shows that ITSA has faster convergence speed and higher convergence precision when used for solving the unmanned truck transportation scheduling optimization model. And ITSA reduces the truck transportation cost and transportation distance.

Understanding the transcriptional regulatory characteristics throughout the embryogenesis of plant-parasitic nematodes is crucial for elucidating their developmental processes' uniqueness. However, a challenge arises due to the lack of suitable technical methods for synchronizing the age of plant-parasitic nematodes embryo, it is difficult to collect detailed transcriptome data at each stage of embryonic development. Here, we recorded the 11 embryonic developmental time-points of endophytic nematode Meloidogyne incognita (isolated from Wuhan, China), Heterodera glycines (isolated from Wuhan, China), and Ditylenchus destructor (isolated from Jinan, China) species, and constructed transcriptome datasets of single embryos of these three species utilizing low-input smart-seq2 technology. The datasets encompassed 11 complete embryonic development stages, including Zygote, 2-cell, 4-cell, 8-cell, 24–44 cell, 64–78 cell, Comma, 1.5-fold, 2-fold, Moving, and L1, each stage generated four to five replicates, resulting in a total of 162 high-resolution transcriptome libraries. This high-resolution cross-species dataset serves as a crucial resource for comprehending the embryonic developmental properties of plant-parasitic nematodes and for identifying functional regulatory genes during embryogenesis. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a serious ocular complication that can pose a serious risk to a patient's vision and overall health. Currently, the automatic grading of DR is mainly using deep learning techniques. However, the lesion information in DR images is complex, variable in shape and size, and randomly distributed in the images, which leads to some shortcomings of the current research methods, i.e., it is difficult to effectively extract the information of these various features, and it is difficult to establish the connection between the lesion information in different regions. To address these shortcomings, we design a multi-scale dynamic fusion (MSDF) module and combine it with graph convolution operations to propose a multi-scale dynamic graph convolutional network (MDGNet) in this paper. MDGNet firstly uses convolution kernels with different sizes to extract features with different shapes and sizes in the lesion regions, and then automatically learns the corresponding weights for feature fusion according to the contribution of different features to model grading. Finally, the graph convolution operation is used to link the lesion features in different regions. As a result, our proposed method can effectively combine local and global features, which is beneficial for the correct DR grading. We evaluate the effectiveness of method on two publicly available datasets, namely APTOS and DDR. Extensive experiments demonstrate that our proposed MDGNet achieves the best grading results on APTOS and DDR, and is more accurate and diverse for the extraction of lesion information. [ABSTRACT FROM AUTHOR]

Peanut leaf spot is a worldwide disease whose prevalence poses a major threat to peanut yield and quality, and accurate prediction models are urgently needed for timely disease management. In this study, we proposed a novel peanut leaf spot prediction method based on an improved long short-term memory (LSTM) model and multi-year meteorological data combined with disease survey records. Our method employed a combination of convolutional neural networks (CNNs) and LSTMs to capture spatial–temporal patterns from the data and improve the model's ability to recognize dynamic features of the disease. In addition, we introduced a Squeeze-and-Excitation (SE) Network attention mechanism module to enhance model performance by focusing on key features. Through several hyper-parameter optimization adjustments, we identified a peanut leaf spot disease condition index prediction model with a learning rate of 0.001, a number of cycles (Epoch) of 800, and an optimizer of Adma. The results showed that the integrated model demonstrated excellent prediction ability, obtaining an RMSE of 0.063 and an R 2 of 0.951, which reduced the RMSE by 0.253 and 0.204, and raised the R 2 by 0.155 and 0.122, respectively, compared to the single CNN and LSTM. Predicting the occurrence and severity of peanut leaf spot disease based on the meteorological conditions and neural networks is feasible and valuable to help growers make accurate management decisions and reduce disease impacts through optimal fungicide application timing. [ABSTRACT FROM AUTHOR]

There are environmental problems such as pollutant emission in the construction and service of road asphalt pavement. In order to explore the hazards of road asphalt to the environment, the types of pollutants from the construction and use of road asphalt pavement are analyzed. The various harmful components of pavement asphalt materials and their influencing factors are discussed. The corresponding pollution inhibition measures are introduced. The existing research work of asphalt pavement pollution is analyzed and suggestions for future research are proposed.