Integrated nutrient management is important for sustainable agricultural production and protecting environment quality and has been widely investigated around the world. In this article the spatial variability of soil nutrients was investigated and a regionalized nutrient management system was developed using geostatistics and geographic information system technologies. A total of 511 GPS-referenced soil samples were taken in Yongji County, Shanxi province, China, and analysed for major soil nutrients: soil total nitrogen (TN), Olsen extractable phosphorus (OLSENP) and extractable potassium (EXTK). Low concentrations of nitrogen (N) and phosphorus (P) were found and they are likely to be the main limiting nutrients for crop growth in this county. Within the county moderate spatial dependence was found for all three soil variables, but at different spatial scales. The spatial distributions of TN, OLSENP and EXTK were estimated by using kriging interpolation. The cropped areas of the county were divided into fertilizer management categories consisting of four classes of TN, three classes of OLSENP and two classes of EXTK. For the targeted crop yields, regionalized fertilization maps of N, P and K in the county were produced using geographic information system. In 3-year field verification trials in two villages the crop yields of the wheat–maize rotation system increased by 10–20%, and farmers' cash income increased by 1550–2610 RMB ha−1 year−1 where regional fertilization recommendations were implemented, in comparison with traditional farmers' practices. The regionalized maps are a practical alternative to site-specific soil nutrient management approaches in areas where it is not practical, because of small farm size or other constraints, to use intensive soil sampling and chemical analyses.
Extracting river channels from remote sensing images is crucial for locating river water bodies and efficiently managing water resources, especially in cold and arid regions. The dynamic nature of river channels in these regions during the flood season necessitates a method that can finely delineate the edges of perennially changing river channels and accurately capture information about variable fine river branches. To address this need, we propose a river channel extraction method designed specifically for detecting fine river branches in remote sensing images within cold and arid regions. The method introduces a novel river attention U-shaped network structure (RAU-Net++), leveraging the rich convolutional features of VGG16 for effective feature extraction. For optimal feature extraction along channel edges and fine river branches, we incorporate a CBAM attention module into the upper sampling area at the end of the encoder. Additionally, a residual attention feature fusion module (RAFF) is embedded at each short jump connection in the dense jump connection. Dense skip connections play a crucial role in extracting detailed texture features from river channel features with varying receptive fields obtained during the downsampling process. The integration of the RAFF module mitigates the loss of river information, optimizing the extraction of lost river detail feature information in the original dense jump connection. This tightens the combination between the detailed texture features of the river and the high-level semantic features. To enhance network performance and reduce pixel-level segmentation errors in medium-resolution remote sensing imagery, we employ a weighted loss function comprising cross-entropy (CE) loss, dice loss, focal loss, and Jaccard loss. The RAU-Net++ demonstrates impressive performance metrics, with precision, IOU, recall, and F1 scores reaching 99.78%, 99.39%, 99.71%, and 99.75%, respectively. Meanwhile, both ED and ED′ of the RAU-Net++ are optimal, with values of 1.411 and 0.003, respectively. Moreover, its effectiveness has been validated on NWPU-RESISC45 datasets. Experimental results conclusively demonstrate the superiority of the proposed network over existing mainstream methods.
Amongst the rare-earth perovskite nickelates, LaNiO$_3$ (LNO) is an exception. While the former have insulating and antiferromagnetic ground states, LNO remains metallic and non-magnetic down to the lowest temperatures. It is believed that LNO is a strange metal, on the verge of an antiferromagnetic instability. Our work suggests that LNO is a quantum critical metal, close to an antiferromagnetic quantum critical point (QCP). The QCP behavior in LNO is manifested in epitaxial thin films with unprecedented high purities. We find that the temperature and magnetic field dependences of the resistivity of LNO at low temperatures are consistent with scatterings of charge carriers from weak disorder and quantum fluctuations of an antiferromagnetic nature. Furthermore, we find that the introduction of a small concentration of magnetic impurities qualitatively changes the magnetotransport properties of LNO, resembling that found in some heavy-fermion Kondo lattice systems in the vicinity of an antiferromagnetic QCP.
The diagnosis of bearing faults is a crucial aspect of ensuring the optimal functioning of mechanical equipment. However, in practice, the use of small samples and variable operating conditions may result in suboptimal generalization performance, reduced accuracy, and overfitting for these methods. To address this challenge, this study proposes a bearing fault diagnosis method based on a symmetric two-stream convolutional neural network (CNN). The method employs hybrid signal processing techniques to address the issue of limited data. The method employs a symmetric parallel convolutional neural network (CNN) for the analysis of bearing data. Initially, the data are transformed into time–frequency maps through the utilization of the short-time Fourier transform (STFT) and the simultaneous compressed wavelet transform (SCWT). Subsequently, two sets of one-dimensional vectors are generated by reconstructing the high-resolution features of the faulty samples using a symmetric parallel convolutional neural network (CNN). Feature splicing and fusion are then performed to generate bearing fault diagnosis information and assist fault classification. The experimental results demonstrate that the proposed mixed-signal processing method is effective on small-sample datasets, and verify the feasibility and generality of the symmetric parallel CNN-support vector machine (SVM) model for bearing fault diagnosis under small-sample conditions.
Abstract The performance of superconducting qubits is degraded by a poorly characterized set of energy sources breaking the Cooper pairs responsible for superconductivity, creating a condition often called “quasiparticle poisoning”. Both superconducting qubits and low threshold dark matter calorimeters have observed excess bursts of quasiparticles or phonons that decrease in rate with time. Here, we show that a silicon crystal glued to its holder exhibits a rate of low-energy phonon events that is more than two orders of magnitude larger than in a functionally identical crystal suspended from its holder in a low-stress state. The excess phonon event rate in the glued crystal decreases with time since cooldown, consistent with a source of phonon bursts which contributes to quasiparticle poisoning in quantum circuits and the low-energy events observed in cryogenic calorimeters. We argue that relaxation of thermally induced stress between the glue and crystal is the source of these events.
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