Nanodispersions stabilized by β-cyclodextrin nanosponges: application for simultaneous enhancement of bioactivity and stability of sage essential oil

The aim of this work was to stabilize oil-in-water nanoemulsion containing sage (salvia officinalis) essential oil, for enhancing its physicochemical stability and enlarging its industrial applications. New β-cyclodextrin nanosponges were synthesized by polycondensation using naphthalene dicarboxylic acid as cross-linking agent, the latter system was characterized by FTIR spectroscopy, SEM, BET, and powder XRD. Nanoemulsions stabilized by free β-cyclodextrin or nanosponges were prepared, their physicochemical properties were determined (particles size, zeta potential, viscosity, turbidity, and essential oil content) and their stability was studied at different storage temperatures (4?°C, 20?°C, and 40?°C) during 3?months. Pharmaceutical application of prepared nanoemulsions was investigated in vitro by dissolution test study and in vivo by their antidiabetic activity evaluation in rats. Sage essential oil nanoemulsion stabilized by β-cyclodextrin-naphthalene dicarboxylic acid nanosponges presents very high stability and promising uses in pharmaceutical industry.     

高强度大桥缆索用盘条生产现状分析

最近5年，大桥缆索用镀锌钢丝抗拉强度有了大幅提高，从1 770 MPa级、1 860 MPa级、1 960 MPa级到目前的2 000 MPa及以上级别，扭转指标有了颠覆性的改变，从之前的几转到目前的十几转、二十几转。论述了国内外钢铁企业生产大桥缆索镀锌钢丝用盘条的不同现状。从成分设计、生产工艺流程、盘条韧化处理方式和组织特点等方面进行阐述，讨论了国内外不同生产工艺特点及发展趋势，为中国大桥缆索镀锌钢丝用盘条的生产提供理论支持。     

Essential oil nanoemulsions: antibacterial activity in contaminated fruit juices

Many human acid tolerant bacterial and fungal pathogens can be transmitted through the consumption of the contaminated fruit juices. We aim to formulate essential oil nanoemulsions (basil, black seed, turmeric, clove & cinnamon), determine their ability to clear contamination by food borne bacterial pathogens from fruit juices. The antibacterial activity of the optimised formulations was tested in the fruit juices against bacterial pathogens causing gastrointestinal tract infections. The minimum bactericidal concentration (MBC) of clove emulsions ranged from 15.6 to 25 μL mL⁻¹. Cinnamon oil emulsion had an MBC ranging between 15 and 31 μL mL⁻¹. At MBC, cinnamon oil emulsions caused a 6log₁₀ decrease in viable counts by 8 h and maintained the sterility of fruit juices for 7 days at ambient temperature. Thus, clove and cinnamon microemulsions can be used as juice additives to control food borne bacterial pathogens and maintain the bacterial sterility of fruit juices.     

基于聚类的数据加权优化在犯罪预测中的应用

近年来，我国传统暴力犯罪与成年人犯罪呈下降态势，但是，犯罪案由层出不穷。为有效提升公安实践工作中犯罪预测能力，打击各类违法犯罪事件,本文针对犯罪数据，提出一种新型犯罪预测模型。利用密度聚类分析方法将犯罪数据分类，然后进行数据降维提取关键属性生成特征数据，继而对特征数据进行加权优化并采用机器学习的方式对特征数据进行学习，从而预测犯罪案由。实验结果表明，与传统方法相比，本文方法具有更好的预测效果，为公安实践工作中类似案件的侦破和预防，提供新的路径支撑。     

基于卷积神经网络优化TLD运动手势跟踪算法

针对TLD（Tracking-Learning-Detection）算法在光照变化不均、遮挡严重、跟踪目标模糊等情况下会出现跟踪失败的问题，提出一种基于卷积神经网络优化TLD运动手势跟踪算法。选取手势特征作正样本，其背景作负样本，获取手势HOG特征并投入到卷积神经网络中加以训练，得到手势检测分类器，从而确定目标手势区域，实现手势的自动识别；再利用TLD算法对手势进行跟踪与学习，对正负样本进行估计检测并实时校正，同时运用SURF特征匹配更新跟踪器。实验结果验证，该算法对比TLD经典算法跟踪精度提高了4.24%，增强了运动手势的跟踪效果，相比经典跟踪算法拥有更高鲁棒性。     

Hybrid Segmentation Scheme for Skin Features Extraction Using Dermoscopy Images

Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration. Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively, but they have unavoidable disadvantages when used to analyze skin features accurately. This study proposes a hybrid segmentation scheme consisting of Deeplab v3+ with an Inception-ResNet-v2 backbone, LightGBM, and morphological processing (MP) to overcome the shortcomings of handcraft-based approaches. First, we apply Deeplab v3+ with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells. Then, LightGBM and MP are used to enhance the pixel segmentation quality. Finally, we determine several skin features based on the results of wrinkle and cell segmentation. Our proposed segmentation scheme achieved a mean accuracy of 0.854, mean of intersection over union of 0.749, and mean boundary F1 score of 0.852, which achieved 1.1%, 6.7%, and 14.8% improvement over the panoptic-based semantic segmentation method, respectively.     

DCNN深度特征与交替方向乘子的相关滤波跟踪

针对采用相关滤波的判别式目标跟踪遇到的瓶颈问题：由于目标快速移动引起边界效应，使得相关滤波器在学习与更新过程中可能会引入错误，最终错误的累积将导致跟踪失败。在采集深度学习特征与样本相似性度量的基础上，提出一种引入交替方向乘子方法的改进相关滤波目标跟踪算法，选择DCNN深度特征有效地表征待跟踪目标的初始状态，通过在线分类过程中样本相似性比对与半监督学习，辅助解决相关滤波器在学习过程中存在的自学习问题。所提目标跟踪算法特别适合训练样本为持续获得的、同时存储空间较小的机器学习过程，提高目标在快速运动与部分遮挡等复杂情况下的跟踪成功率，针对VOT2016标准测试视频的实验表明：当目标面临快速运动时，对比CN、SAMF、STC算法，所提DA-CFT跟踪算法将跟踪成功率分别由60.4%～73.4%、67.2%～82.9%、80.9%～88.1%提升至85.6%～91.0%。