Superconductivity in nearly single-phase Bi-Pb-Sr-Ca-Cu-O samples with different nominal compositions

The preparation conditions, phase composition, and superconducting properties of Bi-Pb-Sr-Ca-Cu oxide materials from different nominal compositions have been investigated. Nearly single-phase samples from Bi₂Pb_0.4Sr₂Ca₃Cu₄O _y , as well as from the proposed compositions Bi_1.8Pb_0.4Sr₂Ca₃Cu₄O _y and Bi_1.8Pb_0.4Sr₂Ca_2.5Cu_3.5O _y were obtained by a solid-state reaction in air. Samples with the third nominal composition showed the best superconducting properties (T _on=111 K and zero resistance atT ₀=103 K). A possible mechanism for the 2223 phase formation in the three investigated compositions has been discussed.     

Al-Cu-Fe QUASICRYSTALLINE PHASE FORMATION BY MECHANICAL ALLOYING

Al-Cu-Fe alloys were prepared from elemental powders in a high-energy planetary ball mill. A sequence of solid state reactions resulting in quasicrystal (QC) phase formation takes place during heating of the as-milled powder. These reactions were studied by both differential scanning calorimetry and x-ray diffraction methods. Mechanically alloyed powders were consolidated by cold and hot pressing, as well as by explosive compaction. After annealing at sufficiently high temperatures, the consolidated samples are single-phase QC, except the ones consolidated by explosion. The high reactivity of the as-milled alloys causes the appearance of high porosity of the consolidated samples after the annealing.     

硫脲型整合树脂在分析上的应用 Ⅰ.岩样中微量银的中子活化分析

前言银和其他贵金属元素一样,在地壳中比较稀少,按其自然丰度为第67号元素,平均含量仅0.07 ppm。由于地质样品成分非常复杂,为测定其微量银,必须进行严格的化学分离。但是迄今文献中报道的许多分离分法,步骤都相当冗长,一般采用氯化银反     

Are Samples in Randomized Controlled Trials of Psychotherapy Representative of Community Outpatients? A New Methodology and Initial Findings.

To determine the extent to which published randomized controlled trials (RCTs) of psychotherapy can be generalized to a sample of community outpatients, the authors used a method of matching information obtained from outpatient charts to inclusion and exclusion criteria from published RCT studies. They found that 80% of the patients in their sample who had diagnoses represented in the RCT literature were judged eligible for at least 1 published RCT; however, 58% of the patients had primary diagnoses such as adjustment disorder or dysthymia, which were not represented in the existing psychotherapy outcome literature. The most common reasons that patients in their sample did not match with published RCTs for psychotherapy are listed, and the implications of these findings for research and practice are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

EASY2000色谱处理系统在酒样色谱分析中的运用

介绍了EASY2000色谱处理系统在酒类产品分析实际应用过程中的方法，步骤，特点及注意事项等。     

Microplastic and other anthropogenic microparticles in water and sediments of Lake Simcoe

Microplastics (plastic particles <5 mm in size) have been reported in ecosystems worldwide and have been shown to cause adverse negative impacts on organisms. This study provides the first report of microplastics and other anthropogenic microparticles in the surface waters and sediments of Lake Simcoe in Ontario, Canada, a popular recreational and fishing lake. Surface waters (low volume grabs and manta trawls) and sediments were sampled from eight sites to determine microparticle abundances and character (size, morphology, material identity). Concentrations ranged from 0 to 0.7 particles/L in surface water grab samples, 0.4–1.3 particles/m³ in manta trawl samples, and 8.3–1070 particles/kg in sediment samples. Spectroscopic analysis confirmed that 72% of particles were anthropogenic, with 64% unambiguously identified as microplastics. However, confirmed microplastic concentrations were approximately 6–7 times lower than unadjusted counts in sediments, demonstrating the importance of verifying and adjusting reported values. Fibers were only quantified and characterized in surface water grab samples and sediments, and were the most common morphology, accounting for 82% and 89% of anthropogenic particles, respectively. Fragments were most common in manta trawl samples (75%, excluding fibers) and consisted predominantly of polyethylene (41%) and polypropylene (22%). The influence of proximity to urban centers and prevailing winds on the distribution of microplastics was apparent in surface water manta trawls. Microplastics and other anthropogenic microparticles are present in Lake Simcoe but at lower abundances than at locations impacted by larger population centers in other large freshwater lakes such as Lakes Ontario and Erie of the Great Lakes.     

Speech Enhancement via Residual Dense Generative Adversarial Network

Generative adversarial networks (GANs) are paid more attention to dealing with the end-to-end speech enhancement in recent years. Various GAN-based enhancement methods are presented to improve the quality of reconstructed speech. However, the performance of these GAN-based methods is worse than those of masking-based methods. To tackle this problem, we propose speech enhancement method with a residual dense generative adversarial network (RDGAN) contributing to map the log-power spectrum (LPS) of degraded speech to the clean one. In detail, a residual dense block (RDB) architecture is designed to better estimate the LPS of clean speech, which can extract rich local features of LPS through densely connected convolution layers. Meanwhile, sequential RDB connections are incorporated on various scales of LPS. It significantly increases the feature learning flexibility and robustness in the time-frequency domain. Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments. Specifically, in the untrained acoustic test with limited priors, e.g., unmatched signal-to-noise ratio (SNR) and unmatched noise category, RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes. It indicates that our method is more generalized in untrained conditions.     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.     

基于改进自注意力机制生成对抗网络的智能电网GPS欺骗攻击防御方法

为了避免全球定位系统欺骗攻击(GSA)对相量测量装置造成的危害,提出了一种基于改进自注意力机制生成对抗网络(SAGAN)的智能电网GSA防御方法.首先,通过引入深度学习参数,构建了改进网络-物理模型,利用历史数据计算得到当前时刻的量测值.然后,在SAGAN的生成器和判别器网络中分别融入一个时间注意力模块,提出了一种用于实现网络-物理模型的改进SAGAN防御方法.通过训练改进SAGAN,得到一对判别器和生成器,利用判别器检测采集的量测值是否遭受GSA,当检测到攻击时,利用生成器生成的数据替换欺骗数据,从而实现智能电网对GSA的主动防御.最后,基于IEEE 14节点和IEEE 118节点系统进行仿真测试,结果验证了所提方法的可行性和有效性.     

基于面向分类准则的维数约简及其在人脸识别中的应用

针对高维数据导致的维数灾难问题,提出了一种基于面向分类准则的维数约简方法。所提准则使每个训练样本在特征空间中与同类样本尽可能接近,而与异类样本尽可能疏远。首先对每个训练样本定义同类样本加权平均距离和异类样本加权平均距离。然后基于上述两个概念分别定义总体同类距离和总体异类距离。以最小化总体同类距离和最大化总体异类距离为目的提出了面向分类的准则(Classification Oriented Criterion,COC)。最后,基于面向分类的准则推导出了一种新的维数约简方法。在公共人脸数据库ORL和Yale上的实验表明所提方法性能优于有代表性的维数约简方法。