一种植物油中苯并（a）芘含量测定的固相萃取前处理方法

为了建立一种准确性高、成本低的植物油中苯并(a)芘测定的样品前处理方法，基于GB 5009.7—2016以自制氧化铝柱为固相萃取柱，采用单因素试验对称样量、洗脱剂量、洗脱流速、吸附剂存放时间、吸附剂量这5个影响植物油中苯并（a）芘洗脱的因素进行研究，优化前处理条件，并对测定方法进行考察。结果表明：优化的前处理条件为称样量0.100 0 g、洗脱剂量120 mL、吸附剂（氧化铝）量22 g、洗脱流速1滴/2 s、吸附剂存放时间少于12周；方法检出限为0.2 μg/L，样品加标回收率为94.23%～100.00%，RSD为1.20%～7.36%；同一样品测定结果与SGS测定值接近，相对平均偏差为2.36%～3.50%。说明本试验方法测定结果准确，可应用于油脂企业植物油中痕量苯并（a）芘的测定。     

Prediction of Flooding in Packed Liquid-Liquid and High-Pressure Extraction Columns Using a Gaussian Process

Reliable prediction of flooding conditions is needed for sizing and operating packed extraction columns. Due to the complex interplay of physicochemical properties, operational parameters and the packing-specific properties, it is challenging to develop accurate semi-empirical or rigorous models with a high validity range. State of the art models may therefore fail to predict flooding accurately. To overcome this problem, a data-driven model based on Gaussian processes is developed to predict flooding for packed liquid-liquid and high-pressure extraction columns. The optimized Gaussian process for the liquid-liquid extraction column results in an average absolute relative error (AARE) of 15.23 %, whereas the algorithm for the high-pressure extraction column results in an AARE of 13.68 %. Both algorithms can predict flooding curves for different packing geometries and chemical systems precisely.     

基于面元素重排的三维网格模型数据隐藏算法

目的 为了解决目前三维数据隐藏算法不能兼顾无失真和盲提取的问题，提出一种新的完全无失真的三维网格模型数据隐藏盲算法。方法 首先使用混沌逻辑映射选择嵌入与提取模式，保证数据的安全性。然后利用面元素重排，完全不会造成三维模型失真的性质，通过不同嵌入模式规则对三角面元素进行重排，以嵌入秘密数据。接收端则可根据相应的提取模式规则提取秘密数据。结果 仿真结果与分析表明，该算法不会对三维模型造成任何失真，嵌入容量为每顶点2比特，且能抵抗仿射变换攻击、噪声攻击和平滑攻击等。结论 这种三维数据隐藏盲算法无失真，容量大、安全性高、鲁棒性强，适用于三维载体不容修改的情形，如军事、医学、秘密通信和版权保护等。     

Simultaneous fractionation of multiple classes of polyphenols from honeybush tea using solid-phase extraction

The structural diversity of polyphenols and the inherent limitations of current extraction techniques pose a challenge to extract polyphenols using a simple and green method. Hence, in this study, a method was developed to simultaneously fractionate multiple classes of polyphenols by only varying ethanol-water solutions. Honeybush tea, which is rich in polyphenols, was selected as a model for this study. Solvent extraction followed by solid-phase extraction (SPE) was developed to obtain a polyphenol-rich fraction from six honeybush samples. Based on a gradient elution programme (10%, 30%, 50%, 70% and 90% (v/v) ethanol-water solution) of SPE, the Strata X cartridge showed a better recovery of most targeted polyphenols under 0.9 mL of the drying volume and 1 mL min⁻¹ of the dispensing speed. The elution programme for fractionating most polyphenols was as follows: single elution with 50% ethanol, followed by twice elution with 70% ethanol. The antioxidant capacity was used to analyse the differences among the polyphenol-rich fractions from six honeybush samples. Principal component analysis (PCA) revealed that unfermented C. genistoides (GG) has the greatest antioxidant capacity among the honeybush species studied. Additionally, mangiferin, isomangiferin and vicenin-2 were the main contributors to the antioxidant capacity in six honeybush fractions according to the correlation study.     

A Feature Extraction Method for scRNA-seq Processing and Its Application on COVID-19 Data Analysis

Single-cell RNA-sequencing (scRNA-seq) is a rapidly increasing research area in biomedical signal processing. However, the high complexity of single-cell data makes efficient and accurate analysis difficult. To improve the performance of single-cell RNA data processing, two single-cell features calculation method and corresponding dual-input neural network structures are proposed. In this feature extraction and fusion scheme, the features at the cluster level are extracted by hierarchical clustering and differential gene analysis, and the features at the cell level are extracted by the calculation of gene frequency and cross cell frequency. Our experiments on COVID-19 data demonstrate that the combined use of these two feature achieves great results and high robustness for classification tasks.     

基于SVD和SSA-VMD降噪的轴承故障特征提取

针对强噪声背景下轴承故障特征提取困难的问题，提出一种基于奇异值分解和参数优化变分模态分解联合降噪的轴承故障特征提取方法(SSVMD)：首先，对原始信号进行奇异值分解(Singular Value Decomposition，SVD)处理，运用奇异值差分谱法选取有效奇异值并将原始信号重构得到初步降噪信号；其次，为防止故障信息丢失，将残余信号进行麻雀算法(Sparrow Search Algorithm，SSA)优化的变分模态分解(Variational Mode Decomposition，VMD）算法处理，得到最佳的模态个数K和惩罚参数α，选取峭度值最大、包络熵最小的IMF分量与初步降噪信号叠加得到最终降噪信号，并对信号进行包络分析；最后，通过仿真和试验数据分析得出，该方法能在信噪比很低的情况下降低噪声含量并提取轴承故障特征，为设备的状态监测和故障诊断提供理论依据。     

基于人脸卡口数据的行人共现关系图谱构建

随着人脸识别技术的不断进步以及人脸卡口的大范围且密集的部署, 本文针对团伙犯罪案件侦察这一应用场景, 对人脸卡口数据进行深入挖掘, 探究其中行人间的共现关系, 获取所关注的嫌疑人的现实社交网络, 锁定团伙其余人员. 经过实验比对和论证, 本文使用Chinese Whispers聚类算法对行人节点进行识别, 通过Faiss加速邻接边的构建, 加速图的初始化步骤, 解决其聚类效率低下的问题. 在此基础上, 使用共现频次和Apriori算法中的置信度挖掘行人间的共现关系, 构建行人共现关系图谱.     

南美白对虾肝胰腺蛋白提取方法优化及无水保活环境胁迫对细胞凋亡蛋白的影响

为提高Western Blotting结果的可靠性，以蛋白溶出率和凝胶电泳蛋白条带完整性为指标，比较提取液种类、研磨方式、酶抑制剂种类及其体积分数和提取时间对南美白对虾肝胰腺蛋白提取效果的影响。采用优化后的提取方法获得高质量蛋白样品，并采用Western Blotting法分析无水环境胁迫后南美白对虾肝胰腺组织中细胞凋亡信号通路相关蛋白的表达水平。结果表明：RIPA裂解液作为提取溶剂所得的蛋白溶出率高于水提和磷酸盐缓冲液，电动匀浆和液氮研磨所得蛋白条带更完整，4%蛋白酶磷酸酶混合抑制剂能有效抑制肝胰腺内源酶引起的蛋白降解；采用Western Blotting法分析无水保活期间南美白对虾肝胰腺蛋白，发现低温诱导休眠的同时会引起细胞轻微凋亡，且凋亡水平呈应激时间依赖性增加，环境胁迫解除后有所回调。     

高面板坝全寿命周期变形控制方法及应用

高面板坝的变形对面板的安全运行有着特别重要的影响，国内外已建的高面板坝工程中，因坝体变形大导致防渗面板挤压破损，坝体渗漏量大的实例较多，不得不降低水库水位进行修复处理，造成较大的经济损失乃至给大坝的长期运行留下安全隐患。通过发生挤压破损的实例分析，发现变形控制缺乏系统性是发生面板挤压破损的主要因素，为预防面板破损，系统提出了“控制坝体总变形，转化有害变形，适应纵向变形”的坝体变形控制方法，并在使用软硬岩混合料筑坝的董箐面板堆石坝中得到的应用，取得了良好效果，该工程运行至今达十余年，未见面板有挤压破损迹象，该方法对建设200 m以上乃至300 m级超高面板坝具有重要借鉴意义。