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This essay establishes three factors, i.e. organic fertilizer, chemical fertilizer and organic fertilizer with cow carbide. The purpose of the present study was to elucidate influence of organic fertilizer and chemical fertilizer on yield and inner quality in spinach with fifteen treatments at five levels. The results showed that the contents of reducing sugar and nitrate nitrogen of spinach in organic fertilizer handling area was less than those in chemical fertilizer. No significant difference was found among the fresh weight, total sugar content and vitamin C content by utilization of organic fertilizer or chemical fertilizer.

Multi-label classification has been extensively researched and utilized for several decades. However, the performance of these methods is highly susceptible to the presence of noisy data samples, resulting in a significant decrease in accuracy when noise levels are high. To address this issue, we propose a robust ranking support vector machine (Rank-SVM) method that incorporates manifold regularized matrix factorization. Unlike traditional Rank-SVM methods, our approach integrates feature selection and multi-label learning into a unified framework. Within this framework, we employ matrix factorization to learn a low-rank robust subspace within the input space, thereby enhancing the robustness of data representation in high-noise conditions. Additionally, we incorporate manifold structure regularization into the framework to preserve manifold relationships among low-rank samples, which further improves the robustness of the low-rank representation. Leveraging on this robust low-rank representation, we extract a resilient low-rank features and employ them to construct a more effective classifier. Finally, the proposed framework is extended to derive a kernelized ranking approach, for the creation of nonlinear multi-label classifiers. To effectively solve this non-convex kernelized method, we employ the augmented Lagrangian multiplier (ALM) and alternating direction method of multipliers (ADMM) techniques to obtain the optimal solution. Experimental evaluations conducted on various datasets demonstrate that our framework achieves superior classification results and significantly enhances performance in high-noise scenarios. [ABSTRACT FROM AUTHOR]

In order to solve the problems of difficult decomposition of returning corn straw and long decomposition period in Xiliaohe Plain of Inner Mongolia, the composting experiments of field straw were carried out in 2020. Four treatments were set including straw decomposition agent+straw (SRT), fresh cow dung+straw (SRN), straw decomposition agent+fresh cow dung+straw (SRW) and single straw (CK). The temperature, water content and the activities of cellulase, urease and lignin catalase in different treatments during corn straw composting were determined. The results showed that adding straw decomposition agent and fresh cow dung could improve the decomposition rate of straw and shorten the decomposition time. Compared with single straw composting, the addition of fresh cow dung and straw decomposition agent could significantly improve the temperature and water content of straw composting. Compared with CK, the cellulase activities of SRW, SRT and SRN treatments increased by 15.64%, 4.87% and 7.96%, respectively; the urease activities increased by 10.29%, 3.43% and 6.86%,respectively; and the lignin catalase activities increased by 71.43%, 48.05% and 53.25%, respectively. Therefore, the addition of straw decomposition agent and fresh cow dung could improve the hydrothermal conditions of straw composting and the activity of straw hydrolase, and accelerate the process of straw decomposition. So the coupling effect of straw decomposition agent and fresh cow dung was better, which could be applied as an efficient straw composting method in the Xiliaohe Plain of Inner Mongolia. [ABSTRACT FROM AUTHOR]

Quantum dot (QD) co-catalysts can efficiently facilitate photocatalytic processes due to their extremely small size and unique optical properties. However, metal quantum dots have high surface energy and tend to agglomerate to form larger particles during the synthesis process and lose their advantages. Herein, a simple and novel functional group anchoring strategy was designed for the in situ synthesis of small-sized and uniformly dispersed Cu2O QDs on commercial P25 surfaces. The average diameter of the obtained Cu2O QDs is only 1.8 nm. The hydrogen production rate of the photocatalysts deposited with Cu2O QDs is increased by 72 times compared with that of pristine commercial P25. The experimental results demonstrate that the OH-group on the surface of commercial P25 is able to act as an anchor point to induce the in situ generation of Cu2O QDs. After deposition of Cu2O QDs, the light absorption range of commercial P25 is extended to the visible region. In addition, the photogenerated carrier separation efficiency and photoreduction capacity are significantly enhanced after deposition of Cu2O QDs on the surface of commercial P25. This work provides a new strategy for the synthesis of small-sized and uniformly dispersed nanomaterials. [ABSTRACT FROM AUTHOR]