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Organic fertilizers produced from agricultural waste materials using earthworms have many advantages. The impact of vermicompost bio-organic fertilizer (VcF) prepared from waste mushroom bran on the quality of Chinese herbal medicine and soil health deserves attention. We conducted a field experiment to explore the quality of Rhizoma atractylodis and soil, using chemical fertilizer and VcF at dosages of 30 t·ha−1 and 40 t·ha−1. The results demonstrated that VcF significantly increased the contents of atractylenolide II, β-eudesmol, atractylenol, and atractylenolone by 34%, 175%, 72%, and 24%, respectively, along with a 70% increase in yield compared to the control. VcF significantly increased the available phosphorus content and the activity of the soil enzymes (α-Galactosidase, β-Galactosidase, and peroxidase), while reducing the nitrate nitrogen content. The addition of vermicompost did not change the soil bacterial diversity, while it significantly increased the soil fungal diversity. VcF improved the soil fungal diversity and significantly enhanced the relative abundance of the bacterial phylum Actinobacteriota and WPS-2, as well as the fungal phylum Ascomycota. Vermicompost significantly increased the relative abundance of bacteria phylum Actinobacteriota and WPS-2, as well as the fungi phylum Ascomycota. Actinobacteria mainly participate in the decomposition of detritus and the heterotrophic nutrient cycle. A Principal Coordinates Analysis of the soil bacterial and fungi communities suggested a significant impact of vermicompost application on the community structure of the soil bacteria. Conversely, no significant variance was detected in the bacterial community composition across the two levels of vermicompost. This study contributes to the enhancement of fertilization strategies for Atractylodes macrocephala, broadens the application scope of vermicompost, and advances the growth of organic agriculture. [ABSTRACT FROM AUTHOR]

Geosynchronous spaceborne–airborne (GEO-SA) ultra-high-frequency ultra-wideband bistatic synthetic aperture radar (UHF UWB BiSAR) provides high-precision images for marine and polar environments, which are pivotal in glacier monitoring and sea ice thickness measurement for polar ocean mapping and navigation. Contrasting with traditional high-frequency BiSAR, it faces unique challenges, such as the considerable spatial variability, significant range–azimuth coupling, and vast volumes of echo data, which impede high-resolution image reconstruction. This paper presents an improved bistatic nonlinear chirp scaling (NLCS) algorithm for imaging oceanic scenes with GEO-SA UHF UWB BiSAR. This methodology extends the two-dimensional (2-D) spectrum up to the sixth order via the method of series reversion (MSR) to meet accuracy demands and then employs an elliptical model to elucidate the alterations in the azimuth frequency modulation (FM) rate mismatch. Initially, the imaging geometry and signal model are introduced, and then a separation of bistatic slant ranges based on the configuration is proposed. In addition, during range processing, after eliminating linear range cell migration (RCM), the derivation process for the sixth-order 2-D spectrum is detailed and an improved filter is applied to correct the high-order RCM. Finally, during azimuth processing, the causes of the FM rate mismatch are analyzed, a cubic perturbation function derived from the elliptical model is used for FM rate equalization, and a unified sixth-order filter is applied to complete the azimuth compression. Experimental results with point targets and natural oceanic scenes validate the outstanding efficacy of the proposed NLCS algorithm, particularly in imaging quality enhancements for GEO-SA UHF UWB BiSAR. [ABSTRACT FROM AUTHOR]

The breeding of laying hens and broilers in China has increased tremendously. Wet organic fertilizer prepared from hen manure using high-temperature container fermentation preserves high levels of nutrients and a diverse microbial community. We applied low doses of organic fertilizer to peanuts in the black soil area of China's northeastern region. Based on the calculation of nitrogen content, treatments were set as follows: chemical fertilizer (PCF), organic fertilizer (POF, 4500 kg·ha−1), and replacement of 50% chemical fertilizer with organic fertilizer (PR, 2250 kg·ha−1). Compared to the plots with chemical fertilizers, the use of organic fertilizer and replacing 50% of the chemical fertilizers with organic fertilizer significantly increased peanut yields. Both the organic fertilizer and replacing 50% of the chemical fertilizers with organic fertilizer did not significantly affect the activities of the most tested soil enzymes related to carbon transformation and the absolute abundance of microorganisms. However, they did significantly enhance soil dehydrogenase and α-glucosidase. The community abundance ratio of fungi/bacteria trended downward, leading to soil with a high-fertility bacterial composition. The replacement of 50% chemical fertilizer with organic fertilizer significantly enhanced the species richness and diversity of the bacterial and fungal communities. Organic fertilizer treatment significantly increased the relative abundance of Gemmatimonas and Sphingomonas. The relative abundance of Mycobacterium in the treatment where 50% of the chemical fertilizers were replaced with organic fertilizer was significantly lower than that in the organic fertilizer treatment. PCoA results showed that the low-dose organic fertilizer treatment, replacing 50% of the chemical fertilizers with organic fertilizer, had a significant impact on the composition of soil bacterial communities. [ABSTRACT FROM AUTHOR]

Ship classification using the synthetic aperture radar (SAR) images has a significant role in remote sensing applications. Aiming at the problems of excessive model parameters numbers and high energy consumption in the traditional deep learning methods for the SAR ship classification, this paper provides an energy-efficient SAR ship classification paradigm that combines spiking neural networks (SNNs) with Siamese network architecture, for the first time in the field of SAR ship classification, which is called the Siam-SpikingShipCLSNet. It combines the advantage of SNNs in energy consumption and the advantage of the idea in performances that use the Siamese neuron network to fuse the features from dual-polarized SAR images. Additionally, we migrated the feature fusion strategy from CNN-based Siamese neural networks to the SNN domain and analyzed the effects of various spiking feature fusion methods on the Siamese SNN. Finally, an end-to-end error backpropagation optimization method based on the surrogate gradient has been adopted to train this model. Experimental results tested on the OpenSARShip2.0 dataset have demonstrated the correctness and effectiveness of the proposed SAR ship classification strategy, which has the advantages of the higher accuracy, fewer parameters and lower energy consumption compared with the mainstream deep learning method of the SAR ship classification. [ABSTRACT FROM AUTHOR]

Large nitrogen (N) losses during fertilization in agricultural production may result in energy wastage, soil and water contamination, and potentially influence crop development. Thus, with the help of a 15N-labeled tracer, we carried out a field monitoring analysis of NH3 emissions in a long-term (9-year) conservation tillage agroecosystem of Mollisols in northeast China, in order to determine whether a no-tillage regime and four levels of stover mulching (0%, 33%, 67%, and 100%), combined with urease and nitrification inhibitors, could improve fertilizer utilization efficiency in agricultural systems by reducing ammonia volatilization. Our results showed that in comparison with ridge tillage, no-tillage with stover mulching levels of 33%, 67%, and 100% significantly reduced NH3 emission rates and cumulative volatilization from 159.67 to 130.42 g N ha−1 and 15N-NH3 cumulative volatilization emission by 26% (on average). Furthermore, the application of urease and nitrification inhibitors significantly reduced 15N-NH3 volatilization levels from 1.19 to 0.98 g N ha−1. Our research results demonstrate that a long-term no-tillage regime and straw mulching can significantly reduce NH3 volatilization in fertilizers. Furthermore, when combined with the use of urease and nitrification inhibitors, these practices further enhance the reduction in NH3 volatilization. Although the volatilization of 15N-NH3 is minimally studied in Mollisols, these findings provide a solid foundation for improving fertilizer utilization efficiency, reducing crop production costs and mitigating subsequent environmental pollution. [ABSTRACT FROM AUTHOR]