基于无人机多光谱图像的土壤水分检测方法研究

以表层土壤为对象,探究土壤的多光谱反射率与土壤水分含量相关性,进行基于无人机多光谱图像的土壤水分含量预测模型方法的探究。选取中国农业大学通州实验站为研究区域,实地采集试验田的土壤样本100组,按照一定梯度配制土壤含水量,配成的土壤含水率为10%~50%之间,土壤含量的真实值采用土壤烘干法进行测定。多光谱相机灵巧便捷,可搭载在无人机上对土壤进行监测。将RedEdged-M型多光谱相机搭载在Phantom 3型无人机上,选择阳光充足的采集环境,实时采集土壤样本的多光谱图像,建立土壤多光谱信息与水分含量之间的模型。利用处理光谱数据的ENVI5.3软件提取土壤样本多光谱信息,以多光谱相机自带的标准白板反射率为100%,计算出土壤样本在蓝、绿、红、红边、近红外五个波段的光谱反射率。采用BP神经网络算法、支持向量机算法、偏最小二乘算法分别建立基于无人机多光谱图像的土壤水分含量的预测模型。以80组土壤样本数据作为训练集,建立基于多光谱图像的土壤水分含量预测模型。采用莱文贝格-马夸特算法对BPNN进行改进,提高了其训练速度,当网络结构为5-10-1时,训练效果最好,本文选择该网络结构;SVM采取高斯核函数,当参数为0.56时,模型效果最好。本研究采用归一化均方根误差(NRMSE)和决策系数(R 2)对三种土壤水分含量的预测模型进行定量对比。以20组土壤样本数据作为测试集,结果可知,基于BP神经网络土壤水分含量预测模型的NRMSE为0.268,R 2为0.872;基于支持向量机的土壤水分含量预测模型的NRMSE为0.298,R 2为0.821;基于偏最小二乘土壤水分含量预测模型的NRMSE为0.316,R 2为0.789。对三种模型分析可知,基于BPNN的土壤水分含量预测模型效果均较好。结果可知,土壤的光谱反射率与含水率间存在较密切的相关性,将多光谱相机搭载在无人机上可以对土壤水分含量进行有效的实时监测,对监测土壤墒情提供技术支持和理论支撑。     

Experimental Confirmation of a Predicted Porous Hydrogen-Bonded Organic Framework

Hydrogen-bonded organic frameworks (HOFs) with low densities and high porosities are rare and challenging to design because most molecules have a strong energetic preference for close packing. Crystal structure prediction (CSP) can rank the crystal packings available to an organic molecule based on their relative lattice energies. This has become a powerful tool for the a priori design of porous molecular crystals. Previously, we combined CSP with structure-property predictions to generate energy-structure-function (ESF) maps for a series of triptycene-based molecules with quinoxaline groups. From these ESF maps, triptycene trisquinoxalinedione (TH5) was predicted to form a previously unknown low-energy HOF (TH5-A) with a remarkably low density of 0.374 g cm⁻³ and three-dimensional (3D) pores. Here, we demonstrate the reliability of those ESF maps by discovering this TH5-A polymorph experimentally. This material has a high accessible surface area of 3,284 m² g⁻¹, as measured by nitrogen adsorption, making it one of the most porous HOFs reported to date.     

Network-based ranking methods for prediction of novel disease associated microRNAs

BackgroundMany studies have shown roles of microRNAs on human disease and a number of computational methods have been proposed to predict such associations by ranking candidate microRNAs according to their relevance to a disease. Among them, machine learning-based methods usually have a limitation in specifying non-disease microRNAs as negative training samples. Meanwhile, network-based methods are becoming dominant since they well exploit a “disease module” principle in microRNA functional similarity networks. Of which, random walk with restart (RWR) algorithm-based method is currently state-of-the-art. The use of this algorithm was inspired from its success in predicting disease gene because the “disease module” principle also exists in protein interaction networks. Besides, many algorithms designed for webpage ranking have been successfully applied in ranking disease candidate genes because web networks share topological properties with protein interaction networks. However, these algorithms have not yet been utilized for disease microRNA prediction.MethodsWe constructed microRNA functional similarity networks based on shared targets of microRNAs, and then we integrated them with a microRNA functional synergistic network, which was recently identified. After analyzing topological properties of these networks, in addition to RWR, we assessed the performance of (i) PRINCE (PRIoritizatioN and Complex Elucidation), which was proposed for disease gene prediction; (ii) PageRank with Priors (PRP) and K-Step Markov (KSM), which were used for studying web networks; and (iii) a neighborhood-based algorithm.ResultsAnalyses on topological properties showed that all microRNA functional similarity networks are small-worldness and scale-free. The performance of each algorithm was assessed based on average AUC values on 35 disease phenotypes and average rankings of newly discovered disease microRNAs. As a result, the performance on the integrated network was better than that on individual ones. In addition, the performance of PRINCE, PRP and KSM was comparable with that of RWR, whereas it was worst for the neighborhood-based algorithm. Moreover, all the algorithms were stable with the change of parameters. Final, using the integrated network, we predicted six novel miRNAs (i.e., hsa-miR-101, hsa-miR-181d, hsa-miR-192, hsa-miR-423-3p, hsa-miR-484 and hsa-miR-98) associated with breast cancer.ConclusionsNetwork-based ranking algorithms, which were successfully applied for either disease gene prediction or for studying social/web networks, can be also used effectively for disease microRNA prediction.     

Local fracture resistance based on microstructural efficiency concept and simulation of injection molding

Local fracture resistance (FR) of short (SGF) and discontinuous long glass fibre (LGF) reinforced polypropylene (PP) was predicted using the ‘microstructural efficiency concept’ together with a simulation program for fibre orientation in injection molding. The ‘microstructural efficiency concept’ describes the relation between microstructural parameters such as the fibre content, the fibre aspect ratio and the processing (injection molding) induced layer structure taking also into account the local fibre orientation. The local fibre orientation in injection molding was predicted with the MOLDFLOW^®-software. The predicted local FR was compared with the measured one, which was determined by using compact tension samples and linear elastic fracture mechanics (LEFM). The comparison showed, that for SGF-PP good consistence between the predicted and measured FR existed, for LGF-PP the discrepancy was higher. Yet for both materials, the ‘microstructural efficiency concept’ together with the results obtained from the simulation of the fibre orientation can be used for FR prediction of an injection molded workpiece.     

Large sample prediction intervals for future geometric mean a comparative study

This paper provides simulation comparisons among the performance of 11 possible prediction intervals for the geometric.mean of a Pareto distribution with parameters (αB, ). Six different procedures were used to obtain these intervals , namely; true inter -val , pivotal interval , maximum likelihood estimation interval, centrallimit teorem interval, variance stabilizing interval and a mixture of the above intervals . Some of these intervals are valid if the observed sample size m,are large , others are valid if both, n and the future sample size m, are large. Some of these intervals require a knowledge of α or B, while others do not. The simulation validation and efficiency study shows that intervals depending on the MLE's are the best. The second best intervalsare those obtained through pivotal methods or variance stabilization transformation. The third group of intervals is that which depends on the central limit theorem when λ is known. There are two intervals which proved to be unacceptable under any criterion.     

Structure-dependent activities of Cu2O cubes in thermal decomposition of ammonium perchlorate

Catalytic activity of three kinds of Cu₂O cubes with different structures for thermal decomposition of ammonium perchlorate (AP) has been investigated in this paper. Cu₂O crystals in the form of cubic aggregate, mono-dispersed cube and {100} planes etched cube have been synthesized through a microwave-assisted solvothermal method by adjusting the composition of solvent. The decomposition of AP in the presence or absence of Cu₂O cubes has been investigated non-isothermally through thermogravimetry and differential scanning calorimetry (DSC). The data obtained from DSC have been applied for the calculation and comparison of the kinetic parameters of AP decomposition process through a model-free approach. The obtained kinetic parameters have been used to predict the reaction rate and progress of AP with Cu₂O cubes under isothermal conditions or at temperature mode corresponding to real climate changes.     

Multiple Statistical models for simulation and prediction of nonlinear processes

The paper describes a parameterisation procedure for multiple nonlinear equations with intercepts. These equations may consist of numerous members with few practical limitations on their structure. Examples are given on the application of the procedure to simulation in the areas of chemical technology and hydrology. Advantages of the proposed method are simplicity of the fitting procedure, high accuracy of simulations and more reliable simulations beyond the calibration range     

Proton Nuclear Magnetic Resonance Spectrometry-Based Metabolic Characterization of Panax Notoginseng Roots

The radix of Panax notoginseng is an important herbal resource for clinics, commercial markets, and the health industry worldwide. The quality of P. notoginseng roots is known to be associated with its cultivation age. This study used ¹H-nuclear-magnetic-resonance-spectrometry-based metabolic profiling to characterize P. notoginseng roots. Twenty metabolites—including four ginsenosides—contributed to the composition of P. notoginseng according to age. A partial least-squares regression model using a combined data set from two solvent systems was the best predictor of cultivation age. Finally, receiver-operating-characteristic analysis was used to screen potential markers of P. notoginseng root age. These results may be applied to the development of medicinal and nutraceutical products using P. notoginseng roots.     

Development of Near-Infrared Reflectance Spectroscopy Models for Quantitative Determination of Water-Soluble Carbohydrate Content in Wheat Stem and Glume

Water-soluble carbohydrate (WSC) reserved in stem is an important agronomic trait for crop improvement. The intact samples and pieces of chipped samples were employed to determine WSC content by near-infrared reflectance spectroscopy (NIRS). Three NIRS models were developed to predict WSC content in wheat stem lower internode, upmost internode, and wheat glume, respectively. Moreover, a mixed model was developed for WSC quantitative analysis in the mixed sample of the three wheat organs. Statistics analysis indicated that the four models showed a high determination coefficient (R² ≥ 0.97) and ratio of standard deviation to RMSECV (RPD ≥ 5.99). The NIRS models would allow rapid and high throughout assessments and selections of WSC contents in wheat genetics and breeding programs.