Noise Reduction Method of Nanopore Based on Wavelet and Kalman Filter

Nanopore detection technology has now developed into an indispensable tool for single molecule experiments, in which useful information on molecular properties can be obtained from the ion current flow induced by individual molecules, but the relatively high background noise affects the data analysis. Therefore, a nanopore signal noise reduction method based on wavelet transform and Kalman filter is proposed, which can achieve multi-scale decomposition and optimal estimation of the nanopore signal. The real measurement shows that the Kalman filter based on the wavelet mode maxima method reduces the root mean square (RMS) of the background noise by 17.8%, and the noise reduction effect is better than the traditional Kalman filter method

Noise Reduction Method of Nanopore Based on Wavelet and Kalman Filter

Nanopore detection technology has now developed into an indispensable tool for single molecule experiments, in which useful information on molecular properties can be obtained from the ion current flow induced by individual molecules, but the relatively high background noise affects the data analysis. Therefore, a nanopore signal noise reduction method based on wavelet transform and Kalman filter is proposed, which can achieve multi-scale decomposition and optimal estimation of the nanopore signal. The real measurement shows that the Kalman filter based on the wavelet mode maxima method reduces the root mean square (RMS) of the background noise by 17.8%, and the noise reduction effect is better than the traditional Kalman filter method

A study of the parsing strategy and the generative algorithm for dependency relation network of Chinese sentences(一种汉语语句依存关系网分析策略与生成算法研究)

本文以依存语法作为语言模型的基础，首先提出了一种全新的句子分析策略：分析在两级上进行。一级是基于中心动词同其他成分间的约束关系，通过寻找汉语语义类之间可能存在的句法关系，实现句子成分过滤，完成句子主干提取。另一级是上下文级，将语法、语义和语境信息一体化，结合依存语法确定汉语句子中各成分间的依存关系。之后给出了一种快速有效的用于完成句子分析的松弛匹配迭代算法。通过实验表明了该分析策略和算法的可行性

A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry

The use of phase-sensitive optical time-domain reflectometry (Φ-OTDR)-distributed fiber vibration sensors to detect and identify damaged bags in bag dust collectors has the potential to overcome the inadequacy of traditional damaged bag detection methods. In our previous study, we verified the feasibility of applying this technique in the field of damaged bag detection in bag filters. However, many problems still occur in engineering applications when using this technology to detect and identify damaged filter bags in pulse-jet dust-cleaning bag dust collectors. Further studies are needed to characterize the fiber vibration signals inside different types of rectangular damaged filter bags. A filter bag damage identification and detection method based on empirical mode decomposition (EMD) and a backpropagation (BP) neural network is proposed. The signal feature differences between intact filter bags and damaged filter bags with different rectangular hole sizes and positions are comparatively analyzed, and optimal feature difference parameters are proposed. Support vector machine (SVM) and a BP neural network are used to recognize different types of filter bag signals, and the comparison results show that the BP neural network algorithm is better at recognizing different types of filter bags, obtaining the highest recognition rate of 97.3%

Efficient vegetation restoration in Mu Us desert reduces microbial diversity due to the transformation of nutrient requirements

Various nutrient requirements of soil microorganisms often occur in restoration ecosystems, but the responses of microbial communities in different vegetation types remain unclear. In this study, we selected four types of vegetation (grassland desert (GD), desert steppe (DS), typical steppe (TS), and artificial forest (AF)) on the Mu Us Desert, and examined the soil physicochemical properties, extracellular enzyme activities (carbon (C)-, nitrogen (N)-, and phosphorus (P)-acquisition enzymes), and community characteristics. Our results revealed that the N-requirement of microorganisms in the area was higher than that of other elements, particularly when organic C was scarce, whereas severe P requirement was detected in the presence of abundant organic matter. Compared with TS, we detected a higher diversity of microorganisms in GD, DS, and AF, and the microbial communities were dominated by a few taxa with loose internal connections and higher C and N requirements. Stronger N and P requirements reduced the diversity of microorganisms and the relative abundance of dominant taxa in TS, but increased the stability of bacterial communities. Our results further indicate that bacteria play a more active role in coping with the transformation of nutrient requirements. When C- and N-requirements of microorganisms were transformed to N and P, the abundance of dominant and sub-dominant taxa decreased and increased, respectively. Collectively, the results of this study indicate that efficient vegetation restoration in desert areas may lead to stronger nutrient requirements for microorganisms, thus reducing the diversity of microorganisms and causing unpredictable consequences for ecological sustainable development