黄芩的生物活性及其在化妆品中的应用

论述了黄芩的资源、主要化学成份、提取方法、生物活性及其在化妆品中的应用等。黄芩中的主要活性成份黄芩苷和黄芩素等具有广泛的生物活性，如抗菌、抗真菌、抗炎、抗过敏、抗氧化、清除自由基、抗紫外线、抗辐射和抑制酪氨酸酶等，可制备防治痤疮、防晒、祛斑关白、抗皮肤老化和防皱等功效性化妆品，防治牙周炎等口腔用品以及外用洗剂等。黄芩素和黄芩苷等黄芩中主要活性成份是集祛斑关白、防晒、抗敏、消炎和抗衰老多效合一的天然高效多功能化妆品添加剂。     

芳香疗法对动脉压过高的治疗作用——前瞻性临床研究

使用依兰依兰油、甘牛至油和香紫苏油对动脉压过高的病人进行芳香疗法按摩治疗 ,通过问卷调查表统计结果 ,考察其临床降压疗效。结果证明 ,芳香疗法按摩具有可使过高的血压逐渐降低的作用。     

Multi-label dimensionality reduction based on semi-supervised discriminant analysis

Multi-label data with high dimensionality often occurs, which will produce large time and energy overheads when directly used in classification tasks. To solve this problem, a novel algorithm called multi-label dimensionality reduction via semi-supervised discriminant analysis (MSDA) was proposed. It was expected to derive an objective discriminant function as smooth as possible on the data manifold by multi-label learning and semi-supervised learning. By virtue of the latent imformation, which was provided by the graph weighted matrix of sample attributes and the similarity correlation matrix of partial sample labels, MSDA readily made the separability between different classes achieve maximization and estimated the intrinsic geometric structure in the lower manifold space by employing unlabeled data. Extensive experimental results on several real multi-label datasets show that after dimensionality reduction using MSDA, the average classification accuracy is about 9.71% higher than that of other algorithms, and several evaluation metrices like Hamming-loss are also superior to those of other dimensionality reduction methods.     

Two-way Markov random walk transductive learning algorithm简

Researchers face many class prediction challenges stemming from a small size of training data vis-a-vis a large number of unlabeled samples to be predicted. Transductive learning is proposed to utilize information about unlabeled data to estimate labels of the unlabeled data for this condition. This work presents a new transductive learning method called two-way Markov random walk （TMRW） algorithm. The algorithm uses information about labeled and unlabeled data to predict the labels of the unlabeled data by taking random walks between the labeled and unlabeled data where data points are viewed as nodes of a graph. The labeled points correlate to unlabeled points and vice versa according to a transition probability matrix. We can get the predicted labels of unlabeled samples by combining the results of the two-way walks. Finally, ensemble learning is combined with transductive learning, and Adboost.MH is taken as the study framework to improve the performance of TMRW, which is the basic learner. Experiments show that this algorithm can predict labels of unlabeled data well.     

食品检测实验室认可关键点的控制

随着我国对食品安全问题的重视,对食品检测工作和食品检测实验室的要求也随之不断提高。据不完全统计,我国有6000多家食品检测实验室,其中通过实验室认可的涉及食品检测的实验室约为1200多家,占实验室总数的近1／3,因此,规范食品检测实验室认可工作和提高认可质量不仅是提高实验室认可水平、有效规避和降低认可风险的手段,     

IF钢连铸板坯表面夹渣缺陷的研究

连铸过程结晶器内的保护渣卷入造成的非金属夹杂物尺寸大、靠近铸坯表面，成为轿车面板等高品质冷轧薄板的主要表面缺陷。本文对鞍钢三炼钢厂生产的IF钢连铸板坯表面夹渣缺陷的分布、形态及成分进行研究，观察到表面夹渣存在的两种形式。探讨了贯通型表面夹渣的形成机理，并提出减少表面夹渣的措施。     

一种简易心肌细胞活跃度检测单元设计

基于压电薄膜传感器,提出并设计一套心肌细胞活跃度检测单元,该系统包含心肌细胞搏动信号前端采集单元,并加入合理的数字滤波算法对采集到的信号进行滤波。经过可靠的CAN总线传输,将处理后的数据送到后端触摸屏显示;后端控制单元可接受触摸屏输入信号,通过CAN总线将控制信号送到前端,从而实现培养液供给的智能控制。通过系统整体方案设计,前后端单元的硬件电路设计以及相应控制算法的软件开发,经初步试验,实现心肌细胞搏动参数实时检测和显示,为心肌细胞搏动参数实时观察以及无菌化、无污染的智能化小规模培养提供一种有效途径。     

一种适合超再生解调电路的无线数据传输协议

本文介绍了一种无线数据传输协议。该协议在曼彻斯特编码的基础上加入了同步数据头和引导序列,使发送的数据能用超再生解调电路接收并正确解码。本文同时给出了用单片机、OOK调制电路和超再生解调电路组成的无线收发电路结构,最后在系统上对协议进行了测试,给出了测试结果。     

一种MIMO-OFDM系统的信道估计算法

导频辅助的最小平方（LS）算法是MIMO—OFDM系统中常用的信道估计方法,特点是易于实现,但精度不高。基于最小均方误差（MMSE）的信道估计算法性能较好,但复杂度高,难以实现。提出了一种基于离散傅里叶变换（DFT）的信道估计算法,首先将LS信道估计循环前缀长度以外的时域响应值置零,然后选择重要路径,再通过计算参考导频的相位偏移量来进行补偿,最终实现信道的估计。算法能够满足一定的估计精度,且便于实现。仿真数据证明了算法的有效性。     

基于消息队列和XML的数据同步技术研究

数据同步技术一直是影响分布式计算机系统应用的一个决定性因素,在简单介绍了现有的数据同步技术的基础上,提出了采用Windows的消息队列和二进制XML编码技术相结合的数据库同步方案,给出了具有较高数据压缩比率的二进制XML编码算法,实现了在窄带网络环境下的数据同步,并对数据同步的导入导出策略进行了设计,仿真测试的结果表明该同步算法相较于传统的数据同步算法具有很好的网络传输特性及数据同步的可靠性,这些对于数据同步技术的研究,在理论与实践上都有一定的借鉴意义。