浅析如何在溶剂萃取机理研究中正确使用斜率法

斜率法是萃取机理研究最为常用的方法之一。尽管其理论基础和使用方法非常简单,但该方法却常常被误用,导致其非但不能有效的确定萃取机理,反而产生了误导性结论。本文以t-BAMBP萃取碱金属离子(K~+、Rb~+、Cs~+)体系为例讨论了常见的错误用法以及造成这类情况的根源问题,之后对其他萃取体系中类似错误用法进行了简要评述,表明斜率法的错误使用主要源于对萃取反应方程式的错误假定及对工作方程推导过程中引入的近似处理理解不当。最后总结了在萃取机理研究中正确使用斜率法的几点注意事项。     

番红砗磲外套膜色素提取条件优化及成分分析

以番红砗磲(Tridacna crocea)为实验对象,通过单因素对比实验对其外套膜色素的提取条件进行了优化,并利用薄层层析色谱法对色素组成进行了初步分析。结果表明:丙酮和乙醇为适宜的番红砗磲外套膜色素提取剂;使用丙酮为提取剂时,浸提时间为4 h,提取温度为25℃,料液比为1︰40。使用乙醇为提取剂时,浸提时间为4 h,提取温度为30℃,料液比为1︰200;利用薄层色谱法分离外套膜颜色为蓝色和红棕色的番红砗磲外套膜色素后发现,蓝色番红砗磲外套膜色素由9种色素组分构成,红棕色个体番红砗磲外套膜色素由10种色素组分构成。     

GPS/PWV时间序列特征提取方法的研究

针对GAMIT/GLOBK软件解算得到的4 a GPS/PWV时间序列的特征提取问题,提出基于滤波辅助的部分集成经验模态分解（PEEMD）与Hilbert谱分析相结合的特征提取方法。首先,在PEEMD方法的基础上,结合滤波辅助的PEEMD方法与Hilbert谱分析,建立GPS/PWV时间序列特征提取模型;然后,将所提出的方法应用于TNML测站4 a的GPS/PWV长时间序列和7 d的GPS/PWV短时间序列分析中,并将滤波辅助的PEEMD结果与传统的小波分解结果进行对比。结果表明,该特征提取方法能准确有效地提取出 GPS/PWV时间序列中的周年周期和日周期特征分量,滤波辅助的PEEMD分解结果与小波分解结果一致,且提取的特征分量与原始信号更加吻合。     

A literature synthesis of LiDAR applications in transportation: feature extraction and geometric assessments of highways

Interest in using Light Detection and Ranging (LiDAR) technology in Transportation Engineering has grown over the past decade. The high accuracy of LiDAR datasets and the efficiency by which they can be collected has led many transportation agencies to consider mobile LiDAR as an alternative to conventional tools when surveying roadway infrastructure. Nonetheless, extracting semantic information from LiDAR datasets can be extremely challenging. Although extracting roadway features from LiDAR has been considered in previous research, the extraction of some features has received more attention than others. In fact, for some roadway design elements, attempts to extract those elements from LiDAR have been extremely scarce. To document the research that has been done in this area, this paper conducts a thorough review of existing studies while also highlighting areas where more research is required. Unlike previous research, this paper includes a thorough review of the previous attempts at data extraction from LiDAR while summarizing the detailed steps of the extraction procedure proposed in each study. Moreover, the paper also identifies common tools and techniques used to extract information from LiDAR for transportation applications. The paper also highlights common limitations in existing algorithms that could be improved in future research. This paper represents a valuable resource for researchers and practitioners interested in knowing the current state of research on the applications of LiDAR in the field of Transportation Engineering while also understanding the opportunities and challenges that lie ahead.     

River centerline extraction using the multiple direction integration algorithm for mixed and pure water pixels

The river centerline is a basic hydrological characteristic. Most prior studies have used remote sensing data to extract the river centerline from the open water region in a pure water pixel region. Extracting this type of river is relatively easy. However, extracting the centerline of a micro-river, which is mainly composed of mixed water pixels, is challenging. This paper presents a novel method, called the Multiple Direction Integration Algorithm (MDIA), to extract the river centerline using an image-enhancing method combined with river morphology. MDIA can be applied to regions mainly composed of pure water pixels, as well as to regions consisting of mixed water pixels in the index image. The method first calculates the normalized difference vegetation index (NDVI) and enhances the river linear structure using a Hessian matrix. Second, a small window is constructed as a circular structural element. In the window region, the local threshold is automatically obtained using water-oriented clustering segmentation and prior river knowledge to judge the pixel type. After completing the river centerline extraction in the current window, the next detecting window is generated to continue judgment. The structural element automatically executes river centerline judgment until the entire river centerline is extracted. The Landsat 8 images of six regions with different geomorphologies were chosen to analyze the method’s performance. The test sites include high mountain region, low mountain region, plains region with farmland and a residential region. The experimental results show that the optimal threshold of the processing results ranged from 0.2 to 0.3. In this range, the user’s accuracy is 0.813 to 0.997, and the producer’s accuracy is 0.981 to 1. The MDIA effectively and correctly extracts the river network in mixed-pixel regions. The presented method provides an effective algorithm for river centerline extraction that can be used to expand and update river datasets and provide reliable river centerline data for relevant hydrology studies.     

基于DEM数据的月表撞击坑自动提取研究

提出一种基于地形与形态学的撞击坑自动提取算法,该算法主要利用撞击坑的形态特征进行提取,利用形态学方法提高提取结果的连续性并去除提取结果中的噪声,使提取的撞击坑边界线更为简单,定位更加准确,易于存储与计算。该算法使用月球勘测轨道器照相机(LROC)提供的100 m分辨率DEM数据进行测试,测试结果显示该算法对于形态较为完整的简单撞击坑提取效果较好,提取的边界线较为准确且能够反映撞击坑的真实形态。     

基于面片裁切技术的点云轮廓线生成算法

随着点云在各个领域的广泛应用,对点云处理相关技术的研究更为活跃。目前,研究的热点主要集中在点云去噪、配准、分割、增强、特征提取和重建等方面。其中,特征提取起着承前启后的作用,具有十分重要的研究价值。基于面片裁切技术的点云轮廓线生成算法。首先,对点云数据进行面片提取,通过面片生长、融合生成特征面片,并根据提出的面片双向索引法快速构造特征线,最终实现点云平面轮廓线的生成。实验证明,本算法可快速、准确、有效地实现点云轮廓线的生成。     

海洋微藻多糖微波提取法研究

选取自养小球藻(Chlorella autotrophica)为实验材料,采用微波法提取多糖,以温度、时间、pH以及微波功率4个因子建立正交实验,考察单因素影响情况。实验结果表明,在温度70℃、时间30min、pH为7、功率为600W条件下粗多糖的产率最高,为291.13mg/g(粗多糖/干重),远高于传统热水浸提法所得粗多糖产率(78.52mg/g,粗多糖/干重),且所得粗多糖与热水浸提法获得的粗多糖具有相似的红外光谱。说明,在一定的实验条件下,与热水浸提法相比,微波提取法不仅不会破坏多糖的结构,还有耗时短、有较高的多糖产率的优势。     

Screening of extraction methods for glycoproteins from jellyfish (Rhopilema esculentum) oral-arms by high performance liquid chromatography

In order to select an optimum extraction method for the target glycoprotein (TGP) from jellyfish (Rhopilema esculentum) oral-arms, a high performance liquid chromatography (HPLC)-assay for the determination of the TGP was developed. Purified target glycoprotein was taken as a standard glycoprotein. The results showed that the calibration curves for peak area plotted against concentration for TGP were linear (r = 0.9984, y = 4.5895x+47.601) over concentrations ranging from 50 to 400 mgL-1. The mean extraction recovery was 97.84% (CV2.60%). The fractions containing TGP were isolated from jellyfish (R. esculentum) oral-arms by four extraction methods: 1) water extraction (WE), 2) phosphate buffer solution (PBS) extraction (PE), 3) ultrasound-assisted water extraction (UA-WE), 4) ultrasound-assisted PBS extraction (UA-PE). The lyophilized extract was dissolved in Milli-Q water and analyzed directly on a short TSK-GEL G4000PWXL (7.8 mm×300 mm) column. Our results indicated that the UA-PE method was the optimum extraction method selected by HPLC.