融合几何光学的蒙日-安培方程方法和物理光学的迭代角谱算法，提出了一种复合型相位恢复方法。针对迭代角谱算法高度依赖初始值的问题，将蒙日-安培方程的解作为迭代初值，该初值通常比光强传输方程的解更准确。采用传统迭代角谱算法与混合输入输出算法的交替迭代策略，以避免算法过早陷入局部最优和迭代停滞。通过数值计算与仿真验证了所提复合型算法的优越性。

Because the breast cancer is an important factor that threatens women’s lives and health, early diagnosis is helpful for disease screening and a good prognosis. Exosomes are nanovesicles, secreted from cells and other body fluids, which can reflect the genetic and phenotypic status of parental cells. Compared with other methods for early diagnosis of cancer (such as circulating tumor cells (CTCs) and circulating tumor DNA), exosomes have a richer number and stronger biological stability, and have great potential in early diagnosis. Thus, it has been proposed as promising biomarkers for diagnosis of early-stage cancer. However, distinguishing different exosomes remain is a major biomedical challenge. In this paper, we used predictive Convolutional Neural model to detect and analyze exosomes of normal and cancer cells with surface-enhanced Raman scattering (SERS). As a result, it can be seen from the SERS spectra that the exosomes of MCF-7, MDA-MB-231 and MCF-10A cells have similar peaks (939, 1145 and 1380 cm−1). Based on this dataset, the predictive model can achieve 95% accuracy. Compared with principal component analysis (PCA), the trained CNN can classify exosomes from different breast cancer cells with a superior performance. The results indicate that using the sensitivity of Raman detection and exosomes stable presence in the incubation period of cancer cells, SERS detection combined with CNN screening may be used for the early diagnosis of breast cancer in the future.Because the breast cancer is an important factor that threatens women’s lives and health, early diagnosis is helpful for disease screening and a good prognosis. Exosomes are nanovesicles, secreted from cells and other body fluids, which can reflect the genetic and phenotypic status of parental cells. Compared with other methods for early diagnosis of cancer (such as circulating tumor cells (CTCs) and circulating tumor DNA), exosomes have a richer number and stronger biological stability, and have great potential in early diagnosis. Thus, it has been proposed as promising biomarkers for diagnosis of early-stage cancer. However, distinguishing different exosomes remain is a major biomedical challenge. In this paper, we used predictive Convolutional Neural model to detect and analyze exosomes of normal and cancer cells with surface-enhanced Raman scattering (SERS). As a result, it can be seen from the SERS spectra that the exosomes of MCF-7, MDA-MB-231 and MCF-10A cells have similar peaks (939, 1145 and 1380 cm−1). Based on this dataset, the predictive model can achieve 95% accuracy. Compared with principal component analysis (PCA), the trained CNN can classify exosomes from different breast cancer cells with a superior performance. The results indicate that using the sensitivity of Raman detection and exosomes stable presence in the incubation period of cancer cells, SERS detection combined with CNN screening may be used for the early diagnosis of breast cancer in the future.

低强度激光治疗(LLLT)是一种通过低强度激光照射相关皮肤、穴位等人体部位治疗心脑血管疾病、缓解疼痛、促进伤口愈合的新型物理方法。它能够刺激线粒体呼吸链的复合物Ⅳ(细胞色素c氧化酶)并增加腺苷三磷酸酯、活性氧化物、一氧化氮等物质的合成, 有助于定向调节细胞行为。高血压、高血糖和高血脂(三高)是最常见的血液疾病, 其导致的血液各参数的变化将引起其他脏器功能异常。目前, “三高”的发病群体数量日益增加, 患者偏年轻化, 因此迫切需要一种便携有效的治疗技术来应对该疾病。近年来研究发现, LLLT在血液系统疾病中有明显的作用, 能有效降低高血压。此外, LLLT还可以调节血糖, 并对因血糖过高导致的相关并发症起到一定的改善, 同时还可调节血脂的浓度, 但更多的应用侧重于前两者。这种治疗技术具有无创和便携等优势, 因此有望成为新的治疗方法。本文将对有关LLLT技术在“三高”中的应用及相关的机制进行综述。

采用激光诱导击穿光谱(LIBS)技术对复合肥样品中氮(N)、磷(P)和钾(K)等营养元素进行定量分析。实验中一共选取20个样品,由于样品量较少,为了提高预测精度,采取一种新的数据提取方式来建立训练集和预测集。利用PLS结合主成分分析(PCA)为训练集的光谱数据建立定标模型,定标过程中选取12个主成分。N、P、K三种元素定标模型的决定系数分别为0.99、0.98、0.99;20个样品中 N、P、K元素含量(质量分数,下同)预测的平均相对误差分别为2.33%、0.70%、3.00%。之后对定标模型的鲁棒性进行检验,其中N、P的平均相对误差大多维持在12%以下。采用基于统计学原理的数据抽取方式扩充样本光谱数据后,与未扩充时相比,被测元素含量的平均相对误差降幅均在10%以上。实验结果表明,当样本数量较少时,利用所提的数据提取方法结合PLS定量分析可以提高检测的准确度,实现复合肥样品中N、P、K等营养元素的快速检测。

基于激光诱导击穿光谱(LIBS)对铁矿石、锰矿石和铬矿石中的Fe元素进行定量分析。由于矿石成分复杂,采取一系列的光谱预处理来降低由激光能量波动及样品不稳定烧蚀所造成的光谱波动。本文将分类和定量分析方法结合,首先通过支持向量机对光谱进行分类以避免不同类矿石间的基体效应。然后通过相关性变量筛选偏最小二乘回归分析(R-PLS)改进算法进行分析,发现三类矿石的预测集方均根误差分别降至0.975%、0.418%、0.123%,平均相对误差分别降至1.46%、6.72%和1.09%。实验结果表明,矿石分类后再进行相关性变量筛选偏最小二乘回归分析的方法可以有效提升预测准确度,为矿石成分在线检测的应用提供了可靠依据。

中性笔油墨是司法鉴定中同一认定的重要物证。为提高油墨检验的准确性,本文利用拉曼光谱法对油墨样本进行无损检测。首先对预处理后的光谱数据进行降维处理,构建偏最小二乘判别分析模型;然后采用受试者工作特征曲线线下面积对预测效果进行验证,提取出36个变量投影重要性最高的特征变量;接着将特征变量作为数据输入到隐藏层神经元数目为13的多层感知器中,最终的训练正确率为87%且无过拟合现象。将变量投影重要性的特征提取与有监督的多层感知器训练相结合,可以有效压缩数据,缩短分析时间。感知器层间的连接权重可通过自主学习进行调节,提高了中性笔油墨分类结果的可信度与正确率。

针对传统跨域密钥协商协议安全性不足问题，提出一种新的跨域量子密钥协商协议。在无证书密钥协商体系下，采用量子密钥协商与经典密码算法结合的方案，提高了协议适应现有通信网络架构的能力。密钥协商过程使用三粒子量子隐形传态，利用量子态不可克隆定理保障协商过程中密钥的安全性。与其他方案相比，本协议具有较高的量子比特效率，并且可以抵抗中间人攻击、重放攻击等多种内部和外部攻击手段。