关键词： Moringa oleifera   Ziziphus jujuba   Antioxidant activity   Enzyme inhibitory activities   Phenolic composition  

摘要： Medicinal plants constitute valuable sources of chemical substances with potential therapeutic effects. In this study, phenolic pools have been extracted using MeOH from leaves of jujube (Ziziphus jujuba Mill.) and moringa (Moringa oleifera Lam.) and twelve phenolic compounds have been identified. Invitro antioxidant activities (DPPH, NO center dot, ORAC, metal chelating, FRAP, and CUPRAC) and enzyme (alpha-amylase and alpha-glucosidase) inhibitory effects of total phenolics have been evaluated. The amount of total phenolics was significantly higher in jujube (18.12 +/- 1.92 mg/g DW) than in moringa (3.09 +/- 0.25 mg/g DW).Separated compounds were also more abundant in jujube excepted for chlorogenic acid that was present at higher level (0.41 +/- 0.09 mg/g DW) in moringa than in jujube (0.03 +/- 0.00 mg/g DW). Among the major compounds, rutin was the most represented one in both species with 65% (11.80 +/- 1.43 mg/g DW) and 50% (1.56 +/- 0.11 mg/g DW) respectively in jujube and moringa. Concomitantly, methanolic extracts from jujube leaves displayed greater potential biological capacities against free radicals, chelating/reducing of iron or cupric forms and inhibitory enzymes as rescued by the lowest IC50 and EC50 values. The recorded data treated by PCA and Bi-plot analysis highlighted a positive correlation between phenolic pools and antioxidant activities particularly in jujube specie. Besides, statistic clustering of scored results on the inhibiton of alpha-amylase and alpha-glucosidase enzymes, showed a strong contribution of phenolic and flavonoid pools in anti-diabetic activity. All these data suggest the importance of Z jujuba specie as a source of bioactive compounds, especially against diabetic diseases. (C) 2021 SAAB. Published by Elsevier B.V. All rights reserved.

关键词： 电力市场   数据处理   英文  

ISBN: (纸本)9787030715166

摘要： 本书以电力市场领域近年来的研究工作成果为基础，力图系统性地介绍电力市场中的数据价值挖掘方法以支撑市场组织者和市场参与者的决策问题。本书围绕电力市场中的公开数据和机器学习方法理论与应用展开，结合电力市场规则和物理特征，期望解决市场规则解析和数据结构化两大核心难点，并从负荷与电价预测、报价行为解析、金融衍生品投机等方面，构建了电力市场数据分析理论和技术方法体系。全书共13章，第1章介绍了世界各地的电力市场数据概况。除第1章外，剩余内容分为三部分。第一部分为负荷建模与预测，包括了基于智能电表数据的负荷预测方法等。第二部分为电价建模与预测，包括了节点电价数据的子空间特性建模等。第三部分为市场投标行为分析，包括了机组投标行为的特征提取方法等。

关键词： stockpile   volume estimation   LiDAR   terrestrial laser scanner (TLS)   segmentation   registration   rotation estimation  

摘要： Stockpile quantity monitoring is vital for agencies and businesses to maintain inventory of bulk material such as salt, sand, aggregate, lime, and many other materials commonly used in agriculture, highways, and industrial applications. Traditional approaches for volumetric assessment of bulk material stockpiles, e.g., truckload counting, are inaccurate and prone to cumulative errors over long time. Modern aerial and terrestrial remote sensing platforms equipped with camera and/or light detection and ranging (LiDAR) units have been increasingly popular for conducting high-fidelity geometric measurements. Current use of these sensing technologies for stockpile volume estimation is impacted by environmental conditions such as lack of global navigation satellite system (GNSS) signals, poor lighting, and/or featureless surfaces. This study addresses these limitations through a new mapping platform denoted as Stockpile Monitoring and Reporting Technology (SMART), which is designed and integrated as a time-efficient, cost-effective stockpile monitoring solution. The novel mapping framework is realized through camera and LiDAR data-fusion that facilitates stockpile volume estimation in challenging environmental conditions. LiDAR point clouds are derived through a sequence of data collections from different scans. In order to handle the sparse nature of the collected data at a given scan, an automated image-aided LiDAR coarse registration technique is developed followed by a new segmentation approach to derive features, which are used for fine registration. The resulting 3D point cloud is subsequently used for accurate volume estimation. Field surveys were conducted on stockpiles of varying size and shape complexity. Independent assessment of stockpile volume using terrestrial laser scanners (TLS) shows that the developed framework had close to 1% relative error.

ISBN: (纸本)9787547854730

关键词： Y-box-binding protein 1 (YB-1)   poly(ADP-ribosyl)ation (PARylation)   poly(ADP-ribose) (PAR)   poly(ADP-ribose) polymerase 1 (PARP1)   RNA-binding protein (RBP)  

摘要： Poly(ADP-ribosyl)ation is a post-translational modification of proteins that performs an essential regulatory function in the cellular response to DNA damage. The key enzyme synthesizing poly(ADP-ribose) (PAR) in the cells is poly(ADP-ribose) polymerase 1 (PARP1). Understanding the mechanisms of the PARP1 activity regulation within the cells is necessary for development of the PARP1-targeted antitumor therapy. This review is devoted to the studies of the role of the RNA-binding protein YB-1 in the PARP1-catalyzed PARylation. The mechanisms of PARP1 activity stimulation by YB-1 protein can possibly be extended to other RNA-binding proteins involved in the maintenance of the genome stability.

关键词： Doppler radar   millimetre wave radar   radar imaging   radar signal processing  

摘要： Using a microwave or millimetre wave radar system, this study aims to achieve highly accurate Doppler velocity estimation and radar imaging that would be suitable for various remote-sensing sensors, such as self-driving, surveillance, or security applications. In particular, micro-Doppler signatures are one of the most promising approaches for human recognition;however, the traditional limitations of both velocity and temporal resolution need to be addressed. The weighted kernel density (WKD) algorithm using super-resolution Doppler velocity estimation has been one solution. Using WKD, this study incorporates the range points migration (RPM) method of radar imaging to enhance accuracy in both Doppler velocity and radar imaging using an iterative data selection scheme. Furthermore, to obtain more informative Doppler-associated RPM-based images using less data by exploiting a unique feature of WKD and RPM methods, this study introduces the image integration approach along the slow-time profile. In this framework, bi-directional data processing between Doppler velocity and imaging analysis is achieved. At each pulse hit sequence, both numerical and experimental tests demonstrate that the proposed method yields a more accurate Doppler-associated radar image compared with the methods in previous studies.

关键词： 地下空间   应变   加权融合   温度效应   预测模型  

摘要： 随着我国城市化建设加快,城市交通流量不断增高,城市地下空间建设成为城市发展的重要方向。为了城市地下空间安全施工、正常运营,期间需要依据施工安全需求、客户使用需求、运营安全需求等,搭建高精度结构监测系统,采集、传输、处理、分析监测数据,提高应变检测精度,获取相关结构信息,为结构变化的监测与预测提供可靠的数据支撑。本文在参与国家重点研发计划项目“城市地下大空间施工安全可视化自动监控系统”基础上,按照“数据预处理——高精度应变算法——快速高效预测算法”的技术路线,展开城市地下空间结构高精度应变数据处理及预测算法研究。首先,对采集的原始数据进行了异常值剔除、降噪等预处理,为后续数据处理打下良好基础。然后,为进一步得到高精度应变监测结果,提出基于数据融合与小波分解的温度效应消除算法,算法分为两步,第一步通过分析不同数据融合算法的权重求解方式的优缺点,提出了长短时间分析的双权重动态加权融合算法,对多应变传感器监测的应变数据、温度数据分别进行融合,经过实验分析,长短时间分析的双权重动态加权融合算法经过权重的优化,可以有效降低融合算法误差,提高算法稳定性;第二步为消除温度效应对结构应变的影响,对融合后的应变数据、温度数据通过小波分量相关性分析、二次光滑局部线性回归算法建立温度-应变回归模型,进而消除结构应变的温度效应。最后,为了准确预测应变变化趋势,在宽度学习算法的基础上,提出SR-BLS、MG-BLS两种算法,SR-BLS相对于MG-BLS训练模型时间更少,MG-BLS相对于SR-BLS精度更高,但这两种算法精度都优于G-BLS、LSTM,根据两种算法性能构建了CEEMD-SR-MG-BLS预测模型,实现了高效、准确的结构应变预测。

关键词： 气体像素探测器   软X射线   偏振探测   机器学习   力矩分析  

摘要： X射线探测是多信使天文时代重要组成部分,本论文主要研究空间X射线偏振信息的探测,可用于研究极端重力环境和极端磁场环境下的物理规律。探测空间X射线偏振所用的仪器为气体像素探测器,通过统计待测X射线与探测器中工作气体发生光电效应产生光电子的发射方向,来反映X射线的偏振信息。目前国内外使用气体像素探测器探测X射线偏振的项目有:极光计划,成像X射线探测仪(IXPE),增强X射线时变与偏振平台(e XTP)等,均会产生大量关于气体像素探测器的科学数据。本文探究了两种由Python编写的气体像素探测器数据处理方法,分别是力矩分析法和深度学习法。第一章主要介绍了空间X射线偏振探测的发展历史和最新成果,并介绍了四个近现代使用气体像素探测器探测空间X射线偏振的科学项目,使用四个案例分析了探测天文X射线偏振的物理意义。具体介绍了气体像素探测器的构成,以及气体像素探测器探测X射线偏振的物理依据。第二章介绍了气体像素探测器的数据,从实验和仿真两个角度分别介绍了气体像素探测器科学数据的构成与数据结构。第三章介绍了如何使用力矩分析法处理气体像素探测器的数据,分别从数学原理、Python代码、方法优化、以及结果分析的角度,详细介绍了如何编写Python程序使用力矩分析法分析X射线的偏振。根据实验数据和仿真数据的不同,分别介绍了使用力矩分析法进行数据处理的具体操作,包括数据读取、数据切片、解码赋值、径迹还原、数据降噪、求解力矩、重建发射方向和绘制调制曲线等。最后通过四个具体的案例,对力矩分析法的数据处理结果进行介绍,讨论了力矩分析法的优势与不足。第四章介绍了如何使用深度学习法处理气体像素探测器的数据,分别从深度学习的发展、卷积神经网络的构成、主流的深度学习框架、搭建卷积神经网络和使用模型进行回归预测等角度详细介绍了如何使用Tensor Flow框架,搭建卷积神经网络处理气体像素探测器的数据。根据实验数据和仿真数据的不同,分别介绍了数据读入、数据赋值形成矩阵、建立卷积神经网络、进行机器学习、保存模型和使用训练好的模型进行数据处理等步骤,并对以上操作所需要的程序进行了介绍。第五章对结果进行讨论,本文使用了针对气体像素探测器数据处理的力矩分析法和深度学习法,均能根据气体像素探测器的数据分析出所测X射线的偏振情况。根据对两种数据处理方法的结果进行分析,可以发现力矩分析法具有速度快,稳定性强的优点,但有平均误差大的缺点,深度学习法具有准确度高、成长性好的优点,但有速度慢、迁移性差的缺点。

关键词： 农业环境数据   农业物联网   HDFS   Spark   聚类算法  

摘要： 随着信息技术的不断发展，现代社会的数据量已经呈现爆炸式增长，人类产生的数据量是古代人类的万倍以上，逐渐从互联网时代逐渐走向了大数据时代。在中国，农业是第一产业，随着农业机械化的不断成熟，物联网技术、互联网技术、无线传感技术的发展，我国农业逐渐迈入信息化时代和大数据时代。而如何有效挖掘这些海量农业数据，从中获得高价值信息从而更好地指导农业发展，是实现农业智能化、信息化的重要前提。我国作为一个农业大国，地域分布广泛、农业生产环境复杂，传统农业数据量大、时间跨度长、更新缓慢，而农业物联网产生新型农业数据又呈非结构化、时效性强等特点，所以这对处理农业相关数据的大数据技术提出了更高的要求。本文对农业物联网中环境数据的感知应用需求进行分析研究，设计了一个基于Spark集群的农业环境数据处理系统。该系统分为三大功能模块，实现了对农业环境数据的预处理、存储和计算分析。首先系统搭建在完全分布式的Spark集群上，通过HDFS对数据进行分布式存储:其次利用Spark MLlib的机器学习库结合聚类算法去处理农业环境数据:最后用Spark SQL实现数据查询等操作。实验选取温室西瓜生长环境数据进行聚类分析,用两种算法聚类出了西瓜最适宜的生长条件,比较了K-means和GMM算法的精确度，体现了分布式存储和并行化计算方式对聚类算法性能的有效提升，说明了聚类算法对于农业环境数据的挖掘具有重要意义。实验证明本文设计与开发的农业环境数据处理系统，在中小型温室农作物环境数据处理方面有良好的应用前景，并对农业生产与发展具有重要的指导意义。

关键词： 非煤矿山   无线传感器网络   地压监测   数据传输   数据融合  

摘要： 随着我国经济的高速发展,对非煤矿产资源的需求与日俱增。高强度的开采导致地压灾害事故频发,给我国带来巨大经济损失和造成大量人员伤亡。为确保非煤矿产资源的安全开采,亟需对非煤矿山地压灾害进行实时监测。当前,非煤矿山井下主要采取有线监测,这种方式不仅部署耗时耗力,且存在维护不便、可靠性差、鲁棒性差等问题,无线传感器网络(Wireless Sensor Network,WSN)具有自组织、体积小和低成本等优点,非常适用于非煤矿山井下环境。将WSN运用于地下非煤矿山地压灾害监测,通过理论分析和仿真模拟的方法对无线传感器网络节点定位、数据传输、数据融合三个关键技术进行了研究。(1)基于改进鸟群优化的WSN定位算法。该算法首先利用距节点最近的数个锚节点限定区域,再利用随机惯性权重对鸟群算法的觅食行为进行改进,用选择最优个体方法替代原算法中随机选取的方法对鸟群算法的警戒行为进行改进,最后运用改进的鸟群算法在限定区域内搜索求解完成节点定位。仿真结果表明,该算法有较高的定位精度和较强的抗误差性。(2)基于动态竞争半径的非均匀分簇路由传输协议。首先,利用距离和能量因素对LEACH协议的阈值公式加以改进,其次,在竞争半径的计算中引入继任能量消耗因子和前任能量消耗因子,最后,利用节点剩余能量简化下一轮簇首选举,减少网络能量开销。通过仿真分析表明,该算法网络的能耗更加均衡,网络的存活时间更长。(3)基于烟花优化BP神经网络的WSN数据融合算法。首先,在烟花算法中使用Tent混沌图改进烟花种群的初始位置分布,使初始烟花分布更加均匀,然后,利用改进的烟花算法优化BP神经网络的权重矩阵、阈值矩阵等参数进行数据融合。仿真结果表明,对比其他算法,该算法提升了数据融合的精度,降低了网络能耗,延长了网络生命周期。