The global health implications of fine particulate matter (PM2.5) underscore the imperative need for research into its toxicity and chemical composition. In this study, zebrafish embryos exposed to the water-soluble components of PM2.5 from two cities (Harbin and Hangzhou) with differences in air quality, underwent microscopic examination to identify primary target organs. The Harbin PM2.5 induced dose-dependent organ malformation in zebrafish, indicating a higher level of toxicity than that of the Hangzhou sample. Harbin PM2.5 led to severe deformities such as pericardial edema and a high mortality rate, while the Hangzhou sample exhibited hepatotoxicity, causing delayed yolk sac absorption. The experimental determination of PM2.5 constituents was followed by the application of four algorithms for predictive toxicological assessment. The random forest algorithm correctly predicted each of the effect classes and showed the best performance, suggesting that zebrafish malformation rates were strongly correlated with water-soluble components of PM2.5. Feature selection identified the water-soluble ions F- and Cl- and metallic elements Al, K, Mn, and Be as potential key components affecting zebrafish development. This study provides new insights into the developmental toxicity of PM2.5 and offers a new approach for predicting and exploring the health effects of PM2.5.

本文采用化学气相沉积(CVD)方法,利用平板式外延炉,在5英寸<111>晶向,2×10-3Ω·cm重掺As衬底上,生长N/N+型硅外延片;试验中采用电容-电压方法,利用汞探针CV测试仪,通过金属汞与硅外延片表面接触形成肖特基势垒,测试势垒电容并转换为外延层载流子浓度和电阻率,实现对外延层电阻率的测量.在此过程中,我们采用H2O2水浴处理硅外延片表面,形成10~15?的氧化层,并对比分析了氧化温度、氧化反应时间、H2O2浓度对电阻率测试结果的影响.

Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users’ buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized. Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then, two factor-user matrices can be used to construct a so-called ‘preference dictionary’ that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.

In this paper, we propose a novel recommender algorithm for the data competition launched by the Alibaba Group based on the intuition that a user's buying behaviors will be influenced by the user's browsing behaviors on the web, which means that the latent preferences that lie behind these two behaviors are consistent. We present a matrix factorization framework that fuses a user-item buying matrix with a user-item browsing matrix using joint nonnegative matrix factorization. This approach assumes that the two factorized coefficient matrices obtained from the browsing matrix and the buying matrix should be regularized toward a common consensus. The experimental results show that our algorithm outperforms other algorithms based only on a single matrix factorization.

[Purpose] Smartphones video cameras can be used to detect the photoplethysmograph (PPG) signal.The pulse wave signal detected by smartphone always mixed mass noise because of finger moving, unevenness of pressure and outer light interference. Previous studies limit to the filtering algorithm that denoising signals, without considering characteristics information of pulse wave itself. [Method] In this paper, we propose an algorithm based on wavelet to detect qualified PPG, which captures three critical characteristic quantities through wavelet high frequency coefficient. [Results] Experiment illustrates that the detected PPG signal contain dicrotic wave, and whats more, further experiment on artery elasticity indexes indicates good robust of the algorithm. [Conclusions] Wavelet Based Measurement on Photoplethysmography by Smartphone Imaging can be used for the calculation of cardiovascular parameter such as angiosclerosis, arrhythmia, and vascular resistance.

提出通过图表标题信息来检测在线生物文献中核磁共振图像的新方法.学术文献中每张图表都有对应的图表标题,而图表一般由多个嵌图组成,图表标题中不同文本是对不同嵌图的文字解释.将图表标题分割成与嵌图匹配的嵌图标注,利用嵌图标注来完成核磁共振图像的检测.依托正则语言理论,寻找图表标题中指向嵌图的图像指针,图像指针将图表标题分割成嵌图标注并与对应嵌图进行匹配.在分析嵌图标注的基础上,提出嵌图混合标注方法,根据图表仅包含同类型嵌图和包含不同类型嵌图2种情况,分别采用嵌图标注或者整个未分割标题作为图像识别的文本特征.实验结果表明,该方法可以很好地识别在线生物文献中的核磁共振图像.

The actual and theoretical power consumption of ESP was calculated and analyzed.The result shows that the operational power consumption of ESP can be classified into effective power consumption,non-effective power consumption and adversely effective power consumption.The pulsed energy-saving and power supply technology was developed to reduce non-effective and adversely effective power consumption greatly,improve power utilization efficiency and realize the energysaving operation of ESP.This technology was widely verified in many industrial applications.