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Pb-contaminated soil poses inherent risks for the spread of antibiotic resistance genes (ARGs). However, few reports have investigated the distribution of lead resistance genes (LRGs), particularly their role in ARGs dynamics during Pb-contaminated soil remediation. This study explored the mechanisms driving ARGs variation under different remediation strategies. The results indicated that an increase in the total abundance of ARGs after applying montmorillonite (Imvite), the composite remediation agents of MgO and Ca(H2PO4)2 (MgO-Ca(H2PO4)2), and the composite remediation agents of montmorillonite, MgO, and Ca(H2PO4)2 (Imvite-MgO-Ca(H2PO4)2). Bioelectrochemical systems (BES) effectively reduced ARGs abundance, and when combined with Imvite-MgO-Ca(H2PO4)2, lowered the risk of ARGs proliferation linked to antibiotic deactivation. Changes in Pb concentration and pH reshaped microbial communities, impacting both LRGs and ARGs. To reduce the risk of ARGs proliferation and transmission during, various control strategies, such as modifying Pb stress, adjusting pH, and manipulating microbial community structure, have been proposed. The study provided theoretical foundation and practical strategies for controlling ARGs dissemination during the remediation of heavy metal-contaminated soil. [ABSTRACT FROM AUTHOR]

Accurate extraction of farmland boundaries is crucial for improving the efficiency of farmland surveys, achieving precise agricultural management, enhancing farmers’ production conditions, protecting the ecological environment, and promoting local economic development. Remote sensing and deep learning are feasible methods for creating large-scale farmland boundary maps. However, existing neural network models have limitations that restrict the accuracy and reliability of agricultural parcel extraction using remote sensing technology. In this study, we used high-resolution satellite images (2 m, 1 m, and 0.8 m) and the U2-Net++ model based on the RSU module, deep separable convolution, and the channel-spatial attention mechanism module to extract different types of fields. Our model exhibited significant improvements in farmland parcel extraction compared with the other models. It achieved an F1-score of 97.13%, which is a 7.36% to 17.63% improvement over older models such as U-Net and FCN and a more than 2% improvement over advanced models such as DeepLabv3+ and U2-Net. These results indicate that U2-Net++ holds the potential for widespread application in the production of large-scale farmland boundary maps.

This study proposes a method for recognizing the drill depth in low-light underground environments, with the aim of addressing the issues of low efficiency and susceptibility to manual changes in the current methods. The method is based on an improved You Only Look Once X model. Initially, image data undergo enhancement and annotation. Secondly, it incorporates an attention mechanism to improve the feature extraction capability. The feature pyramid is utilized to minimize feature loss and facilitate better multi-scale feature fusion. Additionally, the loss function is optimized to enhance the localization ability of the prediction box. The enhanced model achieves an accuracy of 91.3 |$\%$|⁠ , representing a 4.4 |$\%$| increase compared to the pre-improvement performance, and demonstrates improved positioning accuracy. Successful drilling depth measurements were carried out with the acquired positioning information. [ABSTRACT FROM AUTHOR]

The three-dimensional magnetic vector structure (magnetization intensity and direction) of the planet can be effectively used to analyze the characteristics of its formation and operation. However, the quick acquisition of a large region of the magnetic vector structure of the planet with bigger observation surfaces undulation is hard and indispensable. We firstly proposed a fast magnetization vector inversion method for the inversion of a magnetic anomaly with the undulating observation surfaces in the spherical coordinate system, which first transforms the data to a plane when the data are distributed on a surface. Then, it uses a block-Toeplitz-Toeplitz-block (BTTB)-FFT to achieve fast inversion with the constraint that the magnetization intensities of the grids between the transformed observation surfaces and the terrain are zero. In addition, Gramian constraint term is used to reduce the ambiguity of the magnetic vector inversion. The theoretical model tests show that the proposed method can effectively improve the computational efficiency by 23 times in the 60 × 60 × 10 grid division compared to the conventional inversion method, and the accuracy of the two computation methods is comparable. The root-mean-square error of the magnetization intensity is only 0.017, and the angle error is within 1°. The magnetization vector structure shows that the largest crater diameter does not exceed 340 km in the Mare Australe region, the amplitude of the magnetic anomaly is much higher than the current meteorite impact simulation results, and the depth of the magnetic source is less than 10 km, which cannot be explained by the impact simulation experiments. In addition, the magnetization directions of adjacent sources differ by 122° (or 238°), and the high-frequency dynamics of the Moon as well as the short-lived dynamics may be responsible for this phenomenon. The magnetization directions of the three adjacent sources in the Mare Crisium region are close to each other and differ in depth with different cooling times, making it difficult to record the transient fields produced by meteorite impacts. In addition to the above characteristics, the magnetization direction of the magnetic sources in both regions is uniformly distributed without reflecting the dispersion of the magnetization direction of the meteorite impact magnetic field. Therefore, it can be inferred that the magnetic anomalies in these two regions are related to the generator hypothesis. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) genesis is initiated by convective precursors or "seeds" and influenced by environmental conditions along the seed-to-TC trajectories. Genesis potential indices (GPIs) provide a simple way to evaluate TC genesis likelihood from environmental conditions but have two limitations that may introduce bias. First, the globally fixed GPIs fail to represent interbasin differences in the relationship between environments and genesis. Second, existing GPIs are only functions of local environmental conditions, whereas nonlocal factors may have a significant impact. We address the first limitation by constructing basin- and time-scale-specific GPIs (local-GPIs) over the eastern North Pacific (ENP) and North Atlantic (NA) using Poisson regression. A sequential feature selection (SFS) algorithm identifies vertical wind shear and a heating condition as leading factors controlling TC genesis in the ENP and the NA, respectively. However, only a slight improvement in performance is achieved, motivating us to tackle the second limitation with a novel trajectory-based GPI (traj-GPI). We merge adjacent nonlocal environments into each grid point based on observed seed trajectory densities. The seed activity, driven mainly by upward motion, and the transition to TCs, controlled primarily by vertical wind shear or heating conditions, are captured simultaneously in the traj-GPI, yielding a better performance than the original GPIs. This study illustrates the importance of seed activity in modeling TC genesis and identifies key environmental factors that influence the process of TC genesis at different stages. Significance Statement: The genesis potential index (GPI) is an effective tool for modeling the likelihood of tropical cyclone (TC) genesis for a given time and location. This study reveals that existing GPIs are primarily biased by a lack of information about nonlocal TC seed activity, since they are based only on local large-scale environmental variables. According to our study, upward motion and vertical wind shear are the most influential environmental factors in seed genesis and the transition from seed to TC, respectively. Based on the observed seed trajectories, we build trajectory-based GPIs that include the information from seed activity. Spatiotemporal performances of TC genesis are significantly improved over the original GPIs. [ABSTRACT FROM AUTHOR]

When an Autonomous Underwater Vehicle(AUV) sails near the surface of the sea,it will inevitably be subjected to wave disturbance. The heave and pitch motion caused by wave disturbance not only affects the navigation attitude of the AUV,but also leads to an increase in sailing resistance. As such, its energy consumption is increased. In this paper,the six degrees of freedom model of AUVs is established and linearized in order to achieve the weighted optimization of the sailing attitude and the resistance of the AUVs. The drag force model of the AUV is derived using the theory of potential flow. The Q matrix and R matrix are determined in the controller based on research into the drag force model. The Linear Quadratic Regulator(LQR)controller of the AUV is designed using the drag force model as the performance index. The simulation results show that after adding the LQR controller,the effects of reducing heave motion and pitch motion are 46.64% and 77.62% respectively, and the increased resistance caused by the pitch motion is reduced to 1/6 of its original value. The results show that the multiple optimum of attitude and sailing resistance is realized,the energy consumption is decreased and the endurance of the AUV is increased.