Quantification of the layer dispersion degree in polymer layered silicate nanocomposites by transmission electron microscopy

As the performance of polymer layered silicate nanocomposites strongly depends on their interior layer dispersion, quantification of the layer dispersion degree is needed. In this work, a new methodology was developed to determine the dispersion parameter D_0.1, based on the measurement of the free-path spacing distance between the single clay sheets from the transmission electron microscopy (TEM) images. Several examples of exfoliated, intercalated, and immiscible composites were studied. It was found that the exfoliated composites had D_0.1 over 8%, while that of intercalated composites were between 4 and 8%. In the case of intercalation, a high frequency peak appeared at a short spacing distance in the histogram, which was a characteristic of the intercalation, distinct from the exfoliation. The main utility of this TEM methodology is for the quantification of exfoliated or intercalated samples with small number of layers with stacks. The dispersion parameter D_0.1 below 4% was suggested to classify as immiscible. A unique advantage of the TEM measurement is that the dispersion degree of different fillers can be counted individually.     

Effect of processing on sphingolipid content in soybean products

Soybeans are believed to be a rich source of sphingolipids, a class of polar lipids that has received attention for their possible cancer-inhibiting activities. The effect of processing on the sphingolipid content of various soybean products has not been determined. Glucosylceramide (GlcCer), the major sphingolipid type in soybeans, was measured in several processed soybean products to illustrate which product(s) GlcCer is partitioned into during processing and where it is lost. Whole soybeans were processed into full-fat flakes, from which crude oil was extracted. Crude oil was refined by conventional methods, and defatted soy flakes were further processed into alcohol-washed and acid-washed soy protein concentrates (SPC) and soy protein isolates (SPI) by laboratory-scale methods that simulated industrial practices. GlcCer was isolated from the samples by solvent extraction, solvent partition, and TLC and was quantified by HPLC. GlcCer remained mostly within the defatted soy flakes (91%) rather than in the oil (9%) after oil extraction. Only 52, 42, and 26% of GlcCer from defatted soy flakes was recovered in the acid-washed SPC, alcohol-washed SPC, and SPI products, respectively. All protein products had a similar GlcCer concentration of about 281 nmol/g (dry wt basis). The minor quantity of GlcCer in the crude oil was almost completely removed by water degumming.     

二醇柱高效液相色谱法测定人参提取液中的人参皂苷

采用二醇柱,在等度洗脱条件下,建立了高效液相色谱(HPLC)定量分析人参提取液中人参皂苷的检测方法.优化的色谱条件:LiChrospher100 Diol柱(250 mm x2 mm i.d.,5μm),流动相为乙腈-水(体积比为82∶18),流速为1.0 L.min-1,检测波长为200 nm,柱温为15℃.研究结果表明:在较低温度下,采用二醇柱分析7种主要人参皂苷,具有分析速度快,不需要梯度洗脱程序,方法简单等优点.     

Pavement crack detection using hessian structure propagation

Pavement images are widely used in transportation agencies to detect cracks accurately so that the best proper plans of maintenance and rehabilitation could be made. Although crack in a pavement image is perceived because the intensity of crack pixels contrasts with that of the pavement background, there are still challenges in distinguishing cracks from complex textures, heavy noise, and interference. Unlike the intensity or the first-order edge feature of crack, this paper proposes the second-order directional derivative to characterize the directional valley-like structure of crack. The multi-scale Hessian structure is first proposed to analytically adapt to the direction and valley of cracking in the Gaussian scale space. The crack structure field is then proposed to mimic the curvilinear propagation of crack in the local area, which is iteratively applied at every point of the crack curve to infer the crack structure at the gaps and intersections. Finally, the most salient centerline of the crack within its curvilinear buffer is exactly located with non-maximum suppression along the perpendicular direction of crack. The experiments on large numbers of images of various crack types and with diverse conditions of noise, illumination and interference demonstrate the proposed method can detect pavement cracks well with an average Precision, Recall and F-measure of 92.4%, 88.4%, and 90.4% respectively. Also, the proposed method achieves the best performance of crack detection on the benchmark datasets among methods that also require no training and publicly offer the detection results for every image.     

Design of forging process variables under uncertainties

Forging is a complex nonlinear process that is vulnerable to various manufacturing anomalies, such as variations in billet geometry, billet/die temperatures, material properties, and workpiece and forging equipment positional errors. A combination of these uncertainties could induce heavy manufacturing losses through premature die failure, final part geometric distortion, and reduced productivity. Identifying, quantifying, and controlling the uncertainties will reduce variability risk in a manufacturing environment, which will minimize the overall production cost. In this article, various uncertainties that affect the forging process are identified, and their cumulative effect on the forging tool life is evaluated. Because the forging process simulation is time-consuming, a response surface model is used to reduce computation time by establishing a relationship between the process performance and the critical process variables. A robust design methodology is developed by incorporating reliability-based optimization techniques to obtain sound forging components. A case study of an automotive-component forging-process design is presented to demonstrate the applicability of the method.     

Connectionist simulation of quantification skills

The study of numerical abilities, and how they are acquired, is being used to explore the continuity between ontogenesis and environmental learning. One technique that proves useful in this exploration is the artificial simulation of numerical abilities with neural networks, using different learning paradigms to explore development. A neural network simulation of subitization, sometimes referred to as visual enumeration, and of counting, a recurrent operation, has been developed using the so-called multi-net architecture. Our numerical ability simulations use two or more neural networks combining supervised and unsupervised learning techniques to model subitization and counting. Subitization has been simulated using networks employing unsupervised self-organizing learning, the results of which agree with infant subitization experiments and are comparable with supervised neural network simulations of subitization reported in the literature. Counting has been simulated using a multi-net system of supervised static and recurrent backpropagation networks that learn their individual tasks within an unsupervised, competitive framework. The developmental profile of the counting simulation shows similarities to that of children learning to count and demonstrates how neural networks can learn how to be combined together in a process modelling development.     

Error quantification of the normalised right graph symbol for an errors-in-variables system

This paper proposes a novel method to quantify the error of a nominal normalized right graph symbol (NRGS) for an errors-in-variables (EIV) system corrupted with bounded noise. Following an identification framework for estimation of a perturbation model set, a worst-case v-gap error bound for the estimated nominal NRGS can be first determined from \textit{a priori} and \textit{a posteriori} information on the underlying EIV system. Then, an NRGS perturbation model set can be derived from a close relation between the v-gap metric of two models and ${\rm H}_\infty$-norm of their NRGSs' difference. The obtained NRGS perturbation model set paves the way for robust controller design using an ${\rm H}_\infty$ loop-shaping method because it is a standard form of the well-known NCF (normalized coprime factor) perturbation model set. Finally, a numerical simulation is used to demonstrate the effectiveness of the proposed identification method.     

Characterization of surface deformation with the Edge of LightTM technique

The aircraft lap joints are inspected with an enhanced visual inspection technique named “Edge of Light”, which is patented by the NRC Institute for Aerospace Research. This technique is applicable for rapid detection of possible hidden corrosion in lap joints. The surface deformation due to hidden corrosion can be characterized by this optical-based inspection method. In this study, a calibration procedure is developed to quantify the lap joint surface deformation. The effect of surface reflectivity is investigated with the solid film highlighting technique (SolidHi^TM), which helps achieve a uniform reflectivity during the inspection. The efficiency of the technique is demonstrated with the experimental results. This work is supported by NRC-IAR New Initiative Research Funding.     

基于国家信息安全风险评估指南的量化模型研究

以2005年颁布的国标草案《信息安全风险评估指南》为主要指导,并参考NISTSP800—30的管理控制思想、评估方法以及威胁发生可能性的计算方法,给出了一个具有可操作性的信息安全风险评估量化模型。