当归、甘草、芦荟美白护肤霜的研制

以常见的中草药当归、甘草、芦荟为初始原料,从中提取出有效成分作为美白护肤霜的天然添加剂,研制成水包油型的膏霜类高级化妆品,探讨了配制工艺对美白护肤霜效果的影响,并对美白护肤霜的性能进行了评价,证明产品具有良好的美白功效,质量符合要求。     

高职化工仿真实训教学实践与探索

文章从常规的化工仿真实训系统教学方式入手,分析了化工仿真实训教学过程中存在的不足和问题。提出了将理论知识、单元操作实训、下厂实习与化工仿真实训有机结合,完善教学手段,拓宽仿真教学内容,建立科学、合理的评价体系等改进措施,以提高化工仿真教学效果和质量。     

纳米材料及其三维结构修饰电极检测多巴胺的研究进展

纳米材料构建的化学修饰电极用于多巴胺电化学检测已经被广泛研究。当前电极研究策略主要为采用复合材料并构建三维结构以增大吸附表面积。本综述总结了目前常用的纳米碳修饰材料与纳米金属修饰材料,比较分析了其表面结构形貌对多巴胺吸附的影响及吸附机制,总结了目前常用的构建三维修饰电极的方法。分析结果表明,相对于一维形貌,三维形貌结构的修饰电极往往具有更好的检测能力,三维结构不仅能显著增大表面积,提供更多的吸附位点,还可能产生“薄膜效应”,该效应在一定程度上能提高响应灵敏度,但超过一定限度会减缓响应时间,第一性原理分析表明,多巴胺在纳米修饰电极材料表面的吸附机制是化学吸附。最后指出了在改进修饰电极结构设计合理性、增强抗干扰性和提高选择性,以及新的检测控制机制分析方面需要进一步研究。     

A parallel and robust object tracking approach synthesizing adaptive Bayesian learning and improved incremental subspace learning

This paper presents a novel tracking algorithm which integrates two complementary trackers. Firstly, an improved Bayesian tracker(B-tracker) with adaptive learning rate is presented. The classification score of B-tracker reflects tracking reliability, and a low score usually results from large appearance change. Therefore, if the score is low, we decrease the learning rate to update the classifier fast so that B-tracker can adapt to the variation and vice versa. In this way, B-tracker is more suitable than its traditional version to solve appearance change problem. Secondly, we present an improved incremental subspace learning method tracker(Stracker). We propose to calculate projected coordinates using maximum posterior probability, which results in a more accurate reconstruction error than traditional subspace learning tracker. Instead of updating at every time, we present a stopstrategy to deal with occlusion problem. Finally, we present an integrated framework(BAST), in which the pair of trackers run in parallel and return two candidate target states separately. For each candidate state, we define a tracking reliability metrics to measure whether the candidate state is reliable or not, and the reliable candidate state will be chosen as the target state at the end of each frame. Experimental results on challenging sequences show that the proposed approach is very robust and effective in comparison to the state-of-the-art trackers.     

A non-invasive learning branch to capture leaf-image attention for tree species classification

Multimedia Tools and Applications - Tree species classification is a necessary and challenging task for both multimedia technique and forestry engineering. Continuous developments in multimedia and...     

A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation

Image segmentation plays an important role in the computer vision . However, it is extremely challenging due to low resolution, high noise and blurry boundaries. Recently, region-based models have been widely used to segment such images. The existing models often utilized Gaussian filtering to filter images, which caused the loss of edge gradient information. Accordingly, in this paper, a novel local region model based on adaptive bilateral filter is presented for segmenting noisy images. Specifically, we firstly construct a range-based adaptive bilateral filter, in which an image can well be preserved edge structures as well as resisted noise. Secondly, we present a data-driven energy model, which utilizes local information of regions centered at each pixel of image to approximate intensities inside and outside of the circular contour. The estimation approach has improved the accuracy of noisy image segmentation. Thirdly, under the premise of keeping the image original shape, a regularization function is used to accelerate the convergence speed and smoothen the segmentation contour. Experimental results of both synthetic and real images demonstrate that the proposed model is more efficient and robust to noise than the state-of-art region-based models.

     

A correlative denoising autoencoder to model social influence for top-N recommender system

In recent years,there are numerous works been proposed to leverage the techniques of deep learning to improve social-aware recommendation performance.In most cases,it requires a larger number of data to train a robust deep learning model,which contains a lot of parameters to fit training data.However,both data of user ratings and social networks are facing critical sparse problem,which makes it not easy to train a robust deep neural network model.Towards this problem,we propose a novel correlative denoising autoencoder(CoDAE)method by taking correlations between users with multiple roles into account to learn robust representations from sparse inputs of ratings and social networks for recommendation.We develop the CoDAE model by utilizing three separated autoencoders to learn user features with roles of rater,truster and trustee,respectively.Especially,on account of that each input unit of user vectors with roles of truster and trustee is corresponding to a particular user,we propose to utilize shared parameters to learn common information of the units that corresponding to same users.Moreover,we propose a related regularization term to learn correlations between user features that learnt by the three subnetworks of CoDAE model.We further conduct a series of experiments to evaluate the proposed method on two public datasets for Top-N recommendation task.The experimental results demonstrate that the proposed model outperforms state-of-the-art algorithms on rank-sensitive metrics of MAP and NDCG.     

基于离散傅里叶变换的风电场储能容量的优化研究

文中分析了在风电场中配置储能系统的3种应用。基于风电并网功率波动率,采用离散傅里叶分析法对风电功率进行频谱分析,以获得满足风电并网技术要求的风电并网功率,并通过优化得出储能及系统备用容量。以某实际风电场的历史出力数据为例,对储能容量进行优化,优化结果表明,该方法在配置较小储能容量的情况下能够达到平抑风电功率波动的目的,大幅减小系统的备用容量需求,缓解了备用机组的负担,提高了系统对风电的接纳能力。     

1.2 m厚大空间预应力空心楼板混凝土的浇筑施工

1．2m库大空间预应力空心楼板混凝土的施工质量关键是密实和裂缝控制。通过材料优选、混凝土配比试验，1：1结构模拟试验，并采取控制混凝土和易性，确定合理的浇筑步骤，及时抹压拉毛、保湿保温养护等措施，取得了浇筑质量的理想效果。     

发酵小麦制酒精残渣中寡肽的抗氧化及免疫活性

研究从发酵小麦制酒精残渣中提取分离出的寡肽抗氧化和免疫活性。将112 只4 周龄雌性健康昆明小鼠随机分成14 组,基础对照组和实验1～6组饲喂正常基础料的同时分别灌胃生理盐水、低、中、高3 个剂量的寡肽（五肽和七肽）,高脂对照组和实验7～12组饲喂高脂料的同时分别灌胃生理盐水、低、中、高3 个剂量的寡肽（五肽和七肽）,连续灌胃28 d后,测定基础对照组和实验1～6组血清中丙二醛（malondialdehyde,MDA）含量,肝匀浆中MDA含量、超氧化物歧化酶（superoxide dismutase,SOD）、过氧化氢酶（catalase,CAT）和谷胱甘肽过氧化物酶（glutathione peroxidase,GSH-Px）活性,脾脏中淋巴T细胞CD3＋、CD4＋和CD8＋数量,测定基础对照组、高脂对照组和实验7～12组十二指肠、空肠和回肠中MDA含量、SOD、CAT、GSH-Px活性。结果显示,与高脂对照组相比,灌胃寡肽后,血清、肝脏和肠道中MDA含量显著下降（P＜0.05）,SOD、CAT、GSH-Px活性显著升高（P＜0.05）,CD4＋/CD8＋显著升高（P＜0.05）。结果表明寡肽均具有体内抗氧化能力和免疫活性,而且能够清除高脂饮食带来的氧化损伤。