Automatic hierarchy classification in venation networks using directional morphological filtering for hierarchical structure traits extraction

The extraction of vein traits from venation networks is of great significance to the development of a variety of research fields, such as evolutionary biology. However, traditional studies normally target to the extraction of reticulate structure traits (ReSTs), which is not sufficient enough to distinguish the difference between vein orders. For hierarchical structure traits (HiSTs), only a few tools have made attempts with human assistance, and obviously are not practical for large-scale traits extraction. Thus, there is a necessity to develop the method of automated vein hierarchy classification, raising a new challenge yet to be addressed. We propose a novel vein hierarchy classification method based on directional morphological filtering to automatically classify vein orders. Different from traditional methods, our method classify vein orders from highly dense venation networks for the extraction of traits with ecological significance. To the best of our knowledge, this is the first attempt to automatically classify vein hierarchy. To evaluate the performance of our method, we prepare a soybean transmission image dataset (STID) composed of 1200 soybean leaf images and the vein orders of these leaves are manually coarsely annotated by experts as ground truth. We apply our method to classify vein orders of each leaf in the dataset. Compared with ground truth, the proposed method achieves great performance, while the average deviation on major vein is less than 5 pixels and the average completeness on second-order veins reaches 54.28%.     

Diffusion-direction-dependent imaging: a novel MRI approach for peripheral nerve imaging

A novel magnetic resonance imaging approach, called diffusion-direction-dependent imaging (DDI), is introduced. Due to inherent anisotropic diffusion properties, peripheral nerves can be visualized on diffusion tensor imaging (DTI). The largest signal attenuation on DTI correlates with the direction of a nerve fiber, and the least signal attenuation correlates with the direction perpendicular to the nerve fiber. Since low signal-to-noise ratio is a concern in peripheral nerve DTI, we explored a new approach focusing on the perpendicular diffusion direction. A 36-gradient diffusion direction scheme was used. A mean expected curve specific for peripheral nerves was calculated based on the sciatic nerve and its division into the common peroneal nerve and the tibial nerve in three healthy volunteers. By a simple postprocessing method, a comparison of the mean expected curve and the measured curve was made voxel by voxel, and the sciatic nerve and its division were reconstructed, excluding other tissues. More studies are needed to investigate whether other postprocessing methods or other diffusion direction schemes are more suited for peripheral nerve imaging with DDI. Further studies may also be of interest to investigate whether DDI can be a complementary method to conventional T(1)-weighted and T(2)-weighted sequences in the imaging of peripheral nerve pathology or even in the visualization of other tissues, possibly with different diffusion direction schemes.     

Characteristic X-ray detector: In-situ imaging of radioactive contaminant distributions

The characteristic X-ray detector (CXRD), a CsI(Tl) scintillator with a 50-mm diameter, is a directional X-ray sensor that measures characteristic X-rays from radioactive material, such as ¹³⁷Cs, and identifies the direction of radioactive contamination. We evaluated a CXRD and visualized the distribution of radioactivity in the contaminated area near the Fukushima Dai-ichi nuclear power station, where the ambient dose equivalent rate was 2.1 μSv/h at 1 m above ground level. We found a good correlation between the characteristic X-ray fluxes and the distribution of radioactive contaminants with a 0.823 Pearson product–moment correlation coefficient.     

Analytical and empirical studies on the characteristic resistances of no-insulation GdBCO racetrack pancake coil under various operating currents

This paper presents the effects of the Lorentz force on the electrical behaviors of the no-insulation (NI) GdBCO racetrack pancake (RP) coil without turn-to-turn insulation by performing charging/discharging and sudden-discharging tests. The simulation results of the 2-dimensional finite element method showed that the Lorentz forces were generated towards the center of the winding pack consisting of 60-turn GdBCO CCs, which could enhanced the turn-to-turn contact within the RP coil. The charging/discharging test results indicated that the characteristic resistance (R_c) of the NI RP coil decreased with increasing operating current, which was caused by an increase of the Lorentz force. Further, the results of the sudden-discharging tests exhibited that the Lorentz force was dissipated instantly during the sudden-discharge of the coil, since the operating current immediately decreased to zero. Overall, this study clearly demonstrated that the Lorentz force induced by the operating current exerts the electrical behaviors of an NI RP coil.     

环形光纤声发射传感器的相位调制特性研究

提出了一种基于光纤Sagnac干涉仪的环形传感器,用于固体表面传播的超声波的检测.这种传感器的特点是能够精确地检测由固体表面传播的超声波产生的微弱振动.当超声波信号通过光纤传感器的两个臂到达探测器时,干涉仪的输出光强度受到了超声信号的调制.通过检测干涉仪的输出光强度并利用Fourier变换,测得了超声信号的振幅和频率.而且对传感系统的位相调制特性进行了仿真,并对实验结果进行了分析,结果表明该系统可用于固体表面传播的超声波频率特征的识别.     

石墨炉原子吸收光谱法绝对分析

石墨炉原子吸收光谱法(GFAAS)绝对分析近年来得到了较显著的发展,其主要原因是由于GFAAS中基体干扰问题获得了解决;用以描述GFAAS性能的特征质量MOE值相对稳定;以及用L’vov提出的计算MOT值的式子算出的32个元素的MOT值已比较接近其实验MOE值。大量数据表明完全有可能实现GFAAS绝对分析。     

Comment on “Kirchhoff index in line,subdivision and total graphs of a regular graph”

Let G$G$ be a connected regular graph. Denoted by t(G)$t\left(G\right)$ and Kf(G)$Kf\left(G\right)$ the total graph and Kirchhoff index of G$G$, respectively. This paper is to point out that Theorem 3.7 and Corollary 3.8 from “Kirchhoff index in line, subdivision and total graphs of a regular graph” [X. Gao, Y.F. Luo, W.W. Liu, Kirchhoff index in line, subdivision and total graphs of a regular graph, Discrete Appl. Math. 160(2012) 560–565] are incorrect, since the conclusion of a lemma is essentially wrong. Moreover, we first show the Laplacian characteristic polynomial of t(G)$t\left(G\right)$, where G$G$ is a regular graph. Consequently, by using Kf(G)$Kf\left(G\right)$, we give an expression on Kf(t(G))$Kf\left(t\left(G\right)\right)$ and a lower bound on Kf(t(G))$Kf\left(t\left(G\right)\right)$ of a regular graph G$G$, which correct Theorem 3.7 and Corollary 3.8 in Gao et al. (2012)  [2].