丙烯酸酯接枝NR乳液的稳定性研究

以天然胶乳和丙烯酸酯为主单体进行了种子乳液聚合,设计正交实验,详细考查了天然胶乳/丙烯酸酯比例、丙烯酸酯软/硬单体比例、丙烯酸酯功能单体用量、引发剂用量、乳化剂用量、反应温度及反应时间对丙烯酸酯单体转化率和接枝率的影响,得到了稳定的丙烯酸酯接枝NR乳液的优化工艺和配比。     

Nurr1 Is Not an Essential Regulator of BDNF in Mouse Cortical Neurons

Nurr1 and brain-derived neurotrophic factor (BDNF) play major roles in cognition. Nurr1 regulates BDNF in midbrain dopaminergic neurons and cerebellar granule cells. Nurr1 and BDNF are also highly expressed in the cerebral cortex, a brain area important in cognition. Due to Nurr1 and BDNF tissue specificity, the regulatory effect of Nurr1 on BDNF in different brain areas cannot be generalized. The relationship between Nurr1 and BDNF in the cortex has not been investigated previously. Therefore, we examined Nurr1-mediated BDNF regulation in cortical neurons in activity-dependent and activity-independent states. Mouse primary cortical neurons were treated with the Nurr1 agonist, amodiaquine (AQ). Membrane depolarization was induced by KCl or veratridine and reversed by nimodipine. AQ and membrane depolarization significantly increased Nurr1 (p < 0.001) and BDNF (p_AQ < 0.001, p_KCl < 0.01) as assessed by real-time qRT-PCR. However, Nurr1 knockdown did not affect BDNF gene expression in resting or depolarized neurons. Accordingly, the positive correlation between Nurr1 and BDNF expression in AQ and membrane depolarization experiments does not imply co-regulation because Nurr1 knockdown did not affect BDNF gene expression in resting or depolarized cortical neurons. Therefore, in contrast to midbrain dopaminergic neurons and cerebellar granule cells, Nurr1 does not regulate BDNF in cortical neurons.     

UASB+SBR工艺处理糖果废水

针对糖果废水的特点,采用UASB SBR工艺处理箭牌糖果(中国)有限公司番禺工厂的糖果废水,通过介绍处理工艺流程和工艺调试运行过程,提出了影响工艺调试的因素及相应的控制措施。工程实践证明,UASB SBR工艺是糖果废水有效的处理工艺,出水水质达到《水污染物排放限值》(DB44/26-2001)中第二时段的三级标准。     

UNITANK系统不同运行方式下污泥膨胀的特点与控制

由于连续进水、连续出水的UNITANK系统在流态上接近完全混合,基质浓度梯度大大降低,这对于控制污泥膨胀是较为不利的。试验表明,UNITANK系统的单级好氧处理系统在较低或较高的有机负荷下易发生污泥膨胀,在中等负荷下若使得进水池溶解氧充足则可控制污泥膨胀的发生;脱氮除磷处理系统由于进行缺氧搅拌的边池可起到缺氧选择器的作用,其不发生污泥膨胀的有机负荷限值范围较单级好氧处理系统大;与SBR系统相比,UNITANK系统控制污泥膨胀的能力相对较弱。     

纳米氧化锌对NR/BR胎面胶性能的影响

试验研究纳米氧化锌对不同并用比NR/BR并用胶性能的影响及其减量替代间接法氧化锌在载重轮胎胎面胶中的应用。结果表明,以纳米氧化锌等量替代间接法氧化锌用于不同并用比NR/BR并用胶中,胶料的t10和t90延长,且随着BR用量的增大,延迟时间进一步延长;硫化胶的300%定伸应力、拉断强度和回弹值增大,二者300%定伸应力和拉断强度的差值随着NR/BR并用比的变化而无明显变化,回弹值的差值随着BR用量的增大而增大;抗撕裂性能和耐疲劳性能下降;以纳米氧化锌减量25%替代间接法氧化锌用于NR/BR(并用比60/40)胎面胶中,可改善胶料的加工安全性,降低生热,提高老化后性能,成品轮胎的耐久性能有所提高。     

Evaluation of thermal and mechanical properties of rubber compositions based on SBR extended with safe oils

The extender oil usually employed in compositions of rubbers based on styrene and butadiene (SBR) 1712 is the distilled aromatic extract (DAE). In this work, this oil was substituted by oils with low levels of polycyclic aromatic hydrocarbons (PAHs): treated residual aromatic extract (TRAE) and two naphthenic oils from different suppliers (HN1 and HN2). This substitution was performed in response to REACH Regulation (EC No. 1907/2006 European Parliament and the Council of December 18, 2006, Annex XVII), which state that the sum of individual PAHs should be below 10 mg/kg and the levels of benzo (a) pyrene (BaP) should not exceed 1 mg/kg. Infrared spectroscopy was employed to characterize the structure of crude oils and respective SBR compositions. Thermal properties of the materials were evaluated by thermogravimetry and differential scanning calorimetry. Mechanical properties as tension tests, hardness, abrasion resistance, and resilience were also determined. The final results showed that it is possible to replace the extender oil DAE for any of the oils tested, specially the naphthenic HN1, without any loss of the properties evaluated in this study for the SBR compositions. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Blind image quality assessment with improved natural scene statistics model

No-reference (NR)/blind image quality assessment (IQA) metrics play an important role in the area of image processing. Natural scene statistics (NSS) model assumes that natural images possess certain regular statistical properties and is widely used in NR IQA metrics. Most existing NSS-based NR algorithms are achieved by measuring the variation of image statistics, which are characterized by the fitting parameters of NSS model, across different distortions. However, distortions not only change the image statistics, but also disturb the statistical regularity held by natural images. As a result, the distribution of distorted images can not well follow the NSS model. There exists fitting error between the real distribution of the distorted image and the fitted one under certain NSS model. In this paper, the statistical distributions of the distorted images are discussed in detail. We suggest to take the fitting errors into account as well as the fitting parameters for feature extraction, and propose a novel NR IQA algorithm. Experimental results on several image databases demonstrate that the proposed metric performs highly consistent with human visual perception.     

No reference image blurriness assessment with local binary patterns

In this paper, we put forward an effective and efficient no reference image blurriness assessment metric on the basis of local binary pattern (LBP) features. In this proposal, we reveal that part of the LBP histogram bins present monotonously with the degree of blurriness. The proposed method contains the following steps. Firstly, the LBP maps of an input image are extracted with multiple radiuses. And then, the frequency of pattern histogram is analyzed before part of bins are chosen as the features. In addition, we also take the entropy of these bins as another feature. Finally, we learn the extracted features to predict the image blurriness score. Validation of the proposed method is conducted on the blurred images of LIVE-II, CSIQ, TID2008, TID2013, LIVE3D IQA Phase I and LIVE3D IQA Phase II. Experimental results demonstrate that compared with the state-of-the-art image quality assessment (IQA) methods, the proposed algorithm has notable advantage in correlation with subjective perception and computational complexity.     

Design study of an AnSBBR for hydrogen production by co-digestion of whey with glycerin: Interaction effects of organic load,cycle time and feed strategy

An anaerobic sequencing batch biofilm reactor (AnSBBR) treating a mixture of dairy industry wastewater and biodiesel production wastewater (co-digestion of whey with glycerin) was applied to hydrogen production. The influence of fed-batch and batch mode, cycle time and interactions effects between influent concentration and cycle time (2, 3 and 4 h) over the organic loading rate were assessed in order to obtain a sensitivity analysis for important operational variables to the reactor. It was possible to find an optimal cycle time of 3 h with an influent concentration of 7000 mgCOD L^?1 (molar productivity 129.0 molH₂ m^?3 d^?1 and yield 5.4 molH₂ kgCOD^?1). Reactor operation in fed-batch mode allowed higher hydrogen production rates. Increasing the influent concentration (with a constant cycle time) was better for the hydrogen production process than decreasing the cycle length (with a constant influent concentration), which means that these two parameters have different weights in the organic loading rate. The best operational conditions produce hydrogen via acetic, butyric and valeric acids similarly. The system is able to produce 1.3 kJ per gram of COD applied.     

Blind quality assessment for screen content images via convolutional neural network

With the wide propagation of cloud and mobile computing, screen content images (SCIs) have become more indispensable in our daily lives. Compared to natural scene images (NSIs), SCIs possess many particular characteristics, like mixed contents, extremely sharp edges, and text graphics. Consequently, more challenges occur in the feature extraction, which is used to reflect the distortion, during the quality assessment of SCIs. Recently, some convolutional neural network (CNN) models have been designed by automatically learning feature to evaluate the quality. In this paper, we develop a novel blind quality assessment method for SCIs via the CNN. First, compared with existing CNN-based methods, the proposed method avoids the disadvantage of training with image patches, and it is the pioneering attempt that takes the entire image as inputs. Second, instead of the image gray value, the original image is decomposed into two portions, i.e., the predicted and unpredicted portions, according to the internal generative mechanism (IGM) theory as the input of CNN. Through the CNN, all features of the image are learned automatically from beginning to end, and the network finally outputs the predicted score. Since existing SCI database is too small, to fully train the network, we collected 30000 SCIs and employed a high-accuracy full-reference quality assessment metric of SCI to compute scores as the training labels. Experimental results on SIQAD database demonstrate that the proposed method is comparable to reference-based SCI quality assessment metrics and is superior to the state-of-the-art NSI quality assessment metrics.