Image analysis for interfacial area and cocontinuity detection in polymer blends

A novel image processing method was developed to extract interfacial area concentration measurements from 2D micrographs of immiscible polymer blends. Although this method can be used for analyzing different types of 2D micrographs such as optical or transmission electron microscopy images, it was designed for analyzing scanning electron microscopy (SEM) images. The method operates by detecting edges within the images and using standard image processing operations to selectively eliminate false edges. SEM images of polyethylene oxide/polystyrene (PEO/PS) blends were analyzed using this image processing method to measure the amount of interfacial area in the samples. Interfacial area per unit volume exhibits maxima for blend compositions at the boundary between droplet and cocontinuous morphologies. In addition to the detection of cocontinuity, the interfacial area measurements facilitated by this method may be used in future investigations of blend dynamics, including coalescence, drop deformation, and blend rheology studies. These measurements may also be used to quantify the effects of compatibilizers on blend morphology.     

RT-PCR法检测庚型肝炎病毒C(GBV-C)RNA

应用RT－PCR法在非甲～非戊型肝炎患者血清及单采浆站供血员血浆中检测到GBV－CRNA，扩增产物经序列测定进一步确证，在30例非甲～非戊型肝炎患者中阳性率为6．6％；在600例单采浆供血者血浆中阳性率为0．33％。提示GBV－C可能不是非甲～非戊型肝炎的主要致病因子，可能存在GBV－C健康携带者。     

催化剂烧炭再生模拟和分析

催化剂烧炭再生是非定态过程。本文建立了绝热反应器内催化剂烧炭模型,利用移动边界正交配置法求解。以结炭的BO2催化剂再生为工作反应,模拟预测与实验值吻合良好。模拟计算表明,用递升进口温度序列烧炭,使再生时间明显缩短。     

基于引力搜索算法的分数阶变异时序回归GSA-TSGM(1,1)模型

为了利用分数阶累加算子在灰色短期预测中的高效性能,首次将分数阶累加算子引入变异时序回归模型以期取得更高的预测精度。主要方法如下：首先取湖北省链子崖某监测点1978—1987年的十年数据作为训练集并使用引力搜索算法确定最佳分数阶累加阶数,而1988—1993年的六年数据作为验证集验证提出的模型;其次对比了经典灰色模型GM(1,1)、分数阶累加灰色模型、变异时序回归模型TSGM(1,1)三种灰色模型。结果如下：首先修正了陈西江等人变异时序回归模型仿真时出现的错误,其次表明了相比于其他的模型,基于引力搜索算法的分数阶累加时序回归模型在进行灰色长期预测中具有较高的预测精度。因此,通过分数阶累加算子提高了灰色理论中长期预测模型的精度,为灰色长期预测提供了指导。     

A vibrational study of the activation sequence of C–H and C–C bonds of isobutene and 1-butene on Mo(110) and (4 × 4)-C/Mo(110) surfaces

The thermal decomposition pathways of isobutene and 1-butene on both Mo(110) and 4 × 4-C/Mo(110) surfaces have been studied using high-resolution electron energy loss spectroscopy (HREELS) in order to highlight the substantially different activities of these two surfaces towards the cleavage of C–H and C–C bonds. On clean Mo(110), the CH₂ group of isobutene decomposes upon heating to 150 K, producing either a /-bonded isobutenylidene [(CH₃)₂CCH] species or a 1,1-di-/-bonded isobutenyl [(CH₃)₂CC] species. Upon further heating, extensive C–H bond scission occurs to form hydrocarbon fragments which do not contain CH₃ or CH₂ groups, but appear to have largely intact carbon skeletons. By contrast, isobutene is molecularly adsorbed on the carbide-modified surface at 150 K. Further heating produces isobutylidyne [(CH₃)₂HCC] by 300 K, which subsequently decomposes via C–C bond scission to generate surface methyl groups. The different activation sequence of the C–H and C–C bonds of isobutene on clean and carbide-modified Mo(110) surfaces is also qualitatively confirmed by comparative studies of 1-butene on the two surfaces.     

Enumerating maximal bicliques in bipartite graphs with favorable degree sequences

We propose an output-sensitive algorithm for the enumeration of all maximal bicliques in a bipartite graph, tailored to the case when the degree distribution in one partite set is very skewed. We accomplish a worst-case bound better than previously known general bounds if, e.g., the degree sequence follows a power law.     

A general non-local denoising model using multi-kernel-induced measures

Noises are inevitably introduced in digital image acquisition processes, and thus image denoising is still a hot research problem. Different from local methods operating on local regions of images, the non-local methods utilize non-local information (even the whole image) to accomplish image denoising. Due to their superior performance, the non-local methods have recently drawn more and more attention in the image denoising community. However, these methods generally do not work well in handling complicated noises with different levels and types. Inspired by the fact in machine learning field that multi-kernel methods are more robust and effective in tackling complex problems than single-kernel ones, we establish a general non-local denoising model based on multi-kernel-induced measures (GNLMKIM for short), which provides us a platform to analyze some existing and design new filters. With the help of GNLMKIM, we reinterpret two well-known non-local filters in the united view and extend them to their novel multi-kernel counterparts. The comprehensive experiments indicate that these novel filters achieve encouraging denoising results in both visual effect and PSNR index.     

Efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization

The fuzzy c-partition entropy has been widely adopted as a global optimization technique for finding the optimized thresholds for multilevel image segmentation. However, it involves expensive computation as the number of thresholds increases and often yields noisy segmentation results since spatial coherence is not enforced. In this paper, an iterative calculation scheme is presented for reducing redundant computations in entropy evaluation. The efficiency of threshold selection is further improved through utilizing the artificial bee colony algorithm as the optimization technique. Finally, instead of performing thresholding for each pixel independently, the presented algorithm oversegments the input image into small regions and uses the probabilities of fuzzy events to define the costs of different label assignments for each region. The final segmentation results is computed using graph cut, which produces smooth segmentation results. The experimental results demonstrate the presented iterative calculation scheme can greatly reduce the running time and keep it stable as the number of required thresholds increases. Quantitative evaluations over 20 classic images also show that the presented algorithm outperforms existing multilevel segmentation approaches.     

Enhanced user performance in an image gallery application with a mobile autostereoscopic touch display

In this study, we explored how stereoscopic depth affects performance and user experience in a mobile device with an autostereoscopic touch display. Participants conducted a visual search task with an image gallery application on three layouts with different depth ranges. The task completion times were recorded, and the participants were asked to rate their experiences. The results revealed that the image search times were facilitated by a mild depth effect and that too great a depth slowed search times and decreased user-experience ratings.