Specific features of thermal decomposition of ammonium perchlorate subjected to γ radiation

Results of studying thermal decomposition of ammonium perchlorate (AP) samples in the original form and after irradiation by γ-quanta of ⁶⁰Co by methods of differential scanning calorimetry and dynamic thermogravimetry with heating rates b = 0.1–0.3 K/sec are described. Irradiation is performed in air at a temperature of 298 ± 2 K and a dose rate of ≈0.2 Gy/sec in the range of absorbed doses D = 0–150 kGy. Preliminary irradiation is demonstrated to lead to substantial transformations of the pattern of thermal decomposition of ammonium perchlorate in the dynamic regime of heating: the single-stage process of decomposition of non-irradiated samples proceeding at b = 0.107 K/sec in the temperature range of 625 to 743 K is replaced by a multistage process. At D = 150 kGy, exothermal transformations accompanied by noticeable losses of sample mass are observed starting from 473 K. Within experimental errors, the total thermal effect of AP decomposition is found to be independent of the absorbed dose and amounts to −1150 kJ/kg on the average. __________ Translated from Fizika Goreniya i Vzryva, Vol. 43, No. 6, pp. 69–74, November–December, 2007.     

基于多分辨率奇异值分解的图像融合

提出一种新的基于多分辨率奇异值分解（ MSVD）图像融合算法。算法对源图像进行MSVD处理,使其分解为互不相关的平滑和细节分量,并对平滑分量进行多层次的分解与处理。类似于小波变换,多分辨率奇异值分解的基本思想是在平滑分量的每一层上用奇异值分解（ SVD）来取代滤波,最终利用融合规则对图像进行MSVD融合。利用5种评价算子来评价算法,得到的融合效果很好。与基于小波分解的算法相比,算法计算简单、实时性突出,对复杂、高像素图像处理更简单方便。     

Adaptive variational models for image decomposition

Image decomposition refers to the splitting of an image into two or more components.In this paper,a clean image is separated into two parts:one is the cartoon component,consisting only of geometric structure,and the other is the oscillatory component,consisting of texture.Three parts for noisy image are considered:cartoon,texture,and noise.To better decompose an image,we propose two new variational models.In our models,two adaptive regularization terms are introduced.The two regularization terms are determined by an adaptive function which can discriminate the cartoon and texture of an image automatically.Experimental results illustrate the efectiveness of the proposed models for image decomposition.     

珍珠粉、珍珠层粉、珍珠母粉的热重及热重动力学

采用热重及热重动力学方法 ,分别研究珍珠粉、珍珠层粉和珍珠母粉的热分解过程及其反应机理。结果表明 ,珍珠粉和珍珠层粉的热分解可分为二个阶段 :第一阶段为 2 50～ 370℃之间 ,失重率约 3% ,反应级数为 1 .6,活化能为 1 0 5～ 1 1 0 k J/ mol属于有机质的热分解 ;第二阶段为 60 0～ 770℃ ,失重率约为 40 % ,反应级数为 1 .3,活化能为 2 80～ 2 90 k J/ mol,属于碳酸钙的分解。珍珠母粉在 2 50～ 370℃之间不存在明显的失重。根据这一现象 ,可以通过热重分析来鉴别珍珠及其制品。     

Measurement of intrinsic rates for homogeneous gas‐phase reactions at high temperatures

A coiled quartz tubular reactor has been designed to measure the intrinsic reaction kinetics for homogeneous reactions at high temperatures up to 1100°C. Actual gas residence times were less than 100 ms. A simple and well‐studied test reaction (i.e., the decomposition of nitrous oxide, N₂O), with published intrinsic kinetics, was used to verify the operation of the experimental reactor. For this system, Peclet numbers (Pe = uL/DL) computed from experimental conversion data were greater than 1000, indicating that the plug flow assumption could be used with this reactor system to determine intrinsic rate expressions with errors of less than 5% for the conditions studied.     

在硫酸化氧化物催化剂上二氯甲烷的低温催化降解

采用色谱微反联用技术研究了在硫酸化氧化物催化剂上二氯甲烷的低温催化降解。实验发现，硫酸化氧化物催化剂如ＴｉＯ２－（ＳＯ４）在较低反应温度（如２７５℃）时表现出较高的反应活性，转化率１００％，并且其催化活性次序为：ＴｉＯ２－（ＳＯ４）＞ＺｒＯ２－（ＳＯ４）＞ＣｅＯ２－（ＳＯ４）。实验结果还表明：催化剂的酸性关联降解二氯甲烷的催化活性。同时，水汽的存在明显降低了其催化活性。     

小波包变换在火箭发动机脉动压力分析中的应用

在火箭发动机试验中,燃烧室脉动压力数据是考察推进剂燃烧状态的重要依据.通过对脉动压力数据特征分析,利用小波包变换多分辨分析的特点,以信噪比作为衡量标准,采用小波包自选阈值法对脉动压力数据进行去噪.通过与稳态参数数据的对比验证了去噪结果的准确性.采用Wicherhauser树状分解和Shannon信息熵基波选择标准等方法对脉动压力数据进行分频段分析.根据结果中各频段内的系数特征能够直观分析脉动压力在各频带中的能量分布.通过对脉动压力数据和相应振动测点数据的频谱分析,验证了分析结果的有效性.该方法在其他型号发动机和组合件试验脉动压力数据分析中有重要的应用和推广价值.     

Compositional metric learning for multi-label classification

Multi-label classification aims to assign a set of proper labels for each instance, where distance metric learning can help improve the generalization ability of instance-based multi-label classification models. Existing multi-label metric learning techniques work by utilizing pairwise constraints to enforce that examples with similar label assignments should have close distance in the embedded feature space. In this paper, a novel distance metric learning approach for multi-label classification is proposed by modeling structural interactions between instance space and label space. On one hand, compositional distance metric is employed which adopts the representation of a weighted sum of rank-1 PSD matrices based on component bases. On the other hand, compositional weights are optimized by exploiting triplet similarity constraints derived from both instance and label spaces. Due to the compositional nature of employed distance metric, the resulting problem admits quadratic programming formulation with linear optimization complexity w.r.t. the number of training examples.We also derive the generalization bound for the proposed approach based on algorithmic robustness analysis of the compositional metric. Extensive experiments on sixteen benchmark data sets clearly validate the usefulness of compositional metric in yielding effective distance metric for multi-label classification.     

Radio frequency interference suppression algorithm in the spatial domian for high-frequency radars

Radio frequency interference suppression is a very important issue in applications of high-frequency surface wave radar. This paper proposes a new interference suppression algorithm which makes use of RF interference azimuth information on the basis of learning common RF interference suppression algorithms and spatial spectrum estimation. First, we apply the MUSIC algorithm to estimate the interference azimuth and obtains the interference's signal space after the first Fourier transform; next, we use space decomposition to decompose the radar echo and obtain interference components; last, we subtract radio frequency interference components from the original signals. This algorithm can do great amount of RF interference suppression in actual data and get good results.     

苯并吡喃酮类化合物与Skp2蛋白的分子识别及其抑制机理

S期激酶相关蛋白2(S-phase kinase-associated protein 2,Skp2)与Skp1形成的蛋白质聚合物在调控癌细胞生长周期中发挥着重要作用,而苯并吡喃酮类抑制剂(简称BPC)可有效抑制Skp1-Skp2的形成,但其分子识别机制尚不明确.通过生物信息学统计分析已报道的Skp1-Skp2晶体结构,确定模拟体系后,首先用同源模建对其模拟体系缺失的结构进行补全;然后用分子对接方法获得Skp1-Skp2-BPC复合物模型并用于后续分子动力学模拟.计算结果表明:疏水相互作用是促使BPC特异性结合在由Skp2 W109、D110、L117、I120、R138和W139所构成口袋中的主要驱动力,自由能计算值与实验数据吻合较好.Skp2结合BPC后,结合口袋周围的氢键网络有所加强,口袋附近的溶剂化水分子数量明显减少,导致Skp1-Skp2的体系稳定性下降.体系构象成簇与运动性分析显示,Skp1-Skp2在结合BPC抑制剂后,Skp1的运动更加剧烈,这可能是BPC主要的抑制机理.