Super-oleophilicity Co-doping Co2+/F-/TiO2 one-dimensional nanotubes for photocatalytic denitrification of light oil

In this study, a simple hydrothermal synthesis method was adapted for the preparation of Co-doping Co²⁺/F^-/TiO₂ nanotubes photocatalyst, and the micro-nano structure of catalysts prepared by biomimetic technology which makes the catalyst have super-oleophilicity property. Co²⁺/F^-/TiO₂ revealed improved photocatalytic performance for denitrification of light oil compared to single TiO₂ photocatalysts. The enhance of photocatalytic activity can be attributed to narrowing the band gap, increasing the light response wavelength and exposing more highly active crystal surfaces due to synergistic effects of Co²⁺ and F^? in the photocatalyst.     

Stabilizing temperature-capacitance dependence of (Sr,Pb, Bi)TiO3-Bi4Ti3O12 solutions for energy storage

(1-x)Sr_0.7Pb_0.15Bi_0.1TiO₃-xBi₄Ti₃O₁₂ ((1-x)SPBT-xBIT, x = 0-0.125) bulk ceramics were developed and calcined via the solid-state method, aimed at the application of pulsed power capacitors. The phase structures, temperature stability, hysteresis loop, and discharge properties were systematically investigated. Considering both the temperature stability and dielectric properties, 0.925SPBT-0.075BIT bulk ceramics with a capacitance variation satisfying the X7R specification were developed for pulsed power capacitors. The energy storage density was 0.252 J/cm³, and the ceramics showed high temperature stability at 80 kV/cm. The discharge current waveforms of the 0.925SPBT-0.075BIT ceramics were recorded. A high discharge power density of approximately 1.01 × 10⁸ W/kg with an 8 Ω load resistor and short discharge period of 84 ns were achieved at 50 kV/cm. The good temperature stability properties and high power density show that the 0.925SPBT-0.075BIT ceramics are well suited for pulsed power capacitors with a wide temperature range.     

基于星座图对射频指纹识别方法的研究

针对星座图的射频(RF)指纹识别方案中,低信噪比环境下识别准确率低的问题,提出一种基于欧式距离与幅度距离的二维识别算法来进行RF指纹识别。该方案通过对星座图进行优化处理,可从优化后的星座图中提取识别性能更好的RF指纹,再通过二维识别算法来提高识别准确率。仿真结果表明:与仅用欧式距离作为判断依据的方法相比,所提出的二维识别算法的识别准确率最高可提升8%,在设备容量为50组的情况下识别准确率为77.8%,并且从优化后的星座图中所提取的RF指纹具有更好的唯一性和稳定性。     

调节性B细胞在自身免疫性溶血性贫血患者外周血中的表达及临床意义

目的: 探讨自身免疫性溶血性贫血（AIHA）患者外周血中调节性B细胞（Bregs）的表达及其在该病发病中的意义。方法:选择16例自身免疫性溶血性贫血患者和14例健康志愿者为研究对象，用流式细胞术分析外周血CD19+IL-10+调节性B细胞及CD19+CD24hiCD27+调节性B细胞的表达；ELISA方法检测培养上清液中IL-10的水平。结果:自身免疫性溶血性贫血患者外周血中CD19+IL-10+调节性B细胞、CD19+CD24hiCD27+调节性B细胞的表达分别为（1.27±0.39）％、（9.85±2.18）％，健康志愿者组分别为（2.92±0.71）％、（26.47±4.31）％，两组比较差异有统计学意义（P<0.05）；自身免疫性溶血性贫血患者细胞培养上清中IL-10的水平低于健康志愿者组（P<0.05）。结论:自身免疫性溶血性贫血患者外周血中CD19+IL-10+调节性B细胞及CD19+CD24hiCD27+调节性B细胞比例降低，提示调节性B细胞可能参与自身免疫性溶血性贫血的发病过程。     

Semantic word shifts in a scientific domain

Understanding semantic word shifts in scientific domains is essential for facilitating interdisciplinary communication. Using a data set of published papers in the field of information retrieval (IR), this paper studies the semantic shifts of words in IR based on mining per-word topic distribution over time. We propose that semantic word shifts not only occur over time, but also over topics. The shifts are examined from two perspectives, the topic-level and the context-level. According to the over-time word-topic distribution, stable words and unstable words are recognized. The diverging and converging trends in the unstable type reveal characteristics of the topic evolution process. The context-level shifts are further detected by similarities between word vectors. Our work associates semantic word shifts with the evolving of topics, which facilitates a better understanding of semantic word shifts from both topics and contexts.     

超高效液相色谱串联质谱法测定烟草中的噁霉灵残留

为降低烟草中噁霉灵检测中的基质干扰，提高检测灵敏度和和稳定性，通过试验建立了以QuEChERS为样品前处理方法、液相色谱串联质谱仪为检测仪器的烟草中噁霉灵残留检测方法。样品加水浸润后，经丙酮提取、盐析试剂包除水、石墨化炭黑除杂后，采用Agilent ZORBAX 300SB-C8液相色谱柱（2.1 mm×100 mm，1.8 μm）分离，甲醇-水程序洗脱，ESI雾化，串联质谱MRM检测。结果发现，该方法在0.0025~0.0500 mg/L范围内线性良好，决定系数r²=0.9996，定量检测限1.06 μg/L，回收率在90.8%~92.0%之间，相对标准偏差为5.1%。运用该方法测定烟草中的噁霉灵，操作简单，检测精度高，满足CORESTA对烟草农残检测的要求。     

Headspace fingerprinting approach to identify the major pathway influencing volatile patterns of vinasse-cured duck processed by high pressure,as well as its impact on physicochemical and sensory attributes

As a decisive attribute, flavour could be influenced by HP treatments through multiple physical and chemical pathways within the high pressure (HP)-assisted meat curing process. This investigation aimed to identify the major pathway influencing volatile flavour patterns of two representative vinasse-cured duck (VCD) products with HP treatments (150–300 MPa/15 min), including wet and dry types, by employing headspace fingerprinting as an untargeted approach. Results suggested that HP treatments greatly lowered moisture contents and increased Warner-Bratzler shear force and thiobarbituric acid reactive substances of the cured samples. According to multivariate models, the volatile flavour patterns of the HP-processed VCD could be clearly separated from the unprocessed samples, but the VCD pressurised at different intensities represented similar volatile fingerprinting, which was validated by e-nose analysis. The discriminant analysis (OPLS-DA) model outlined vinasse-derived ethanol, acetic acid, 3-methyl-1-butanol, 2-methyl-1-butanol, phenethyl alcohol and 2-methyl-3-octanone as the major discriminant aromas across the unpressurised and pressurised samples.     

Image and Feature Space Based Domain Adaptation for Vehicle Detection

The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.