气液螺旋环状流压降特性研究

考虑旋流衰减的影响，对气液螺旋环状流的压降特性进行研究并推导出了螺旋环状流压降预测模型。定义压降旋-直比系数为气液两相螺旋环状流和气液两相直流的压降之比，以此来表征旋流衰减对压降的影响。基于量纲分析的方法对压降旋-直比系数进行分析，推导出其表达式，压降旋-直比系数依赖于Lockhart-Martinelli 参数和气相Froude数变化。最终，得出了气液两相螺旋环状流的压降预测模型。在50 mm内径的水平管内对螺旋环状流的压降特性进行了实验研究，其中气相表观流速变化范围为10~16 m/s，体积含液率（LVF）变化范围为0.6%~4.8%。通过与实验数据进行对比，压降预测模型的相对误差在±15%以内，为工程应用提供了参考。     

SBBR反应器中耐冷微生物的驯化与识别

以模拟生活污水为处理对象，三种不同填料为生物膜载体，运行 A、B、C三个SBBR，通过逐步降温（25、20、15、10、6和5℃）培养驯化处理低温污水的生物膜。运行251周期，三个反应器COD出水浓度均低于50 mg/L；B反应器对氨氮去除效果优于A和C。特别在低温5℃时，A、B和C三个反应器氨氮平均出水浓度分别为14.1、3.79和14.1 mg/L。高通量测序结果显示，5℃时，三个反应器中均以降解有机物的微生物为主；只有B反应器中驯化富集出了嗜冷硝化菌（Candidatus Nitrotoga）；而其他有硝化作用的菌属在B反应器中的丰度也高于A和C反应器；不利于脱氮的类固氮菌（Elstera）在A和C反应器中丰度较高。此结果可从微生物角度解释三个反应器处理效果的差异性，表明B反应器处理低温污水有显著优势，其所用填料可结合低温菌进一步开发利用。     

Improvement of dielectric breakdown strength and energy storage performance in Er2O3–modified 0.95Sr0.7Ba0.3Nb2O6-0.05CaTiO3 lead-free ceramics

In this study, 0.95?Sr_0.7Ba_0.3Nb₂O₆-0.05CaTiO₃-x wt% Er₂O₃ ceramics (SBNCTEx; x?=?0–5) were synthesized using traditional solid-state method, and we investigated the microstructure, energy storage properties as well as the relationship between dielectric breakdown strength and interfacial polarization. As compared with pure 0.95?Sr_0.7Ba_0.3Nb₂O₆-0.05CaTiO₃ ceramics, the Er₂O₃ dopants suppressed the grain growth of SBNCTEx, and the doped ones showed the dense microstructure. The secondary phase was found for x?≥?1 according to the EDS results, and the influence of the secondary phase on relative dielectric breakdown strength has also been studied. The dielectric breakdown strength increased from 18.1?kV/mm to 34.4?kV/mm, which is good for energy storage. The energy storage density of 0.28?J/cm³ and the energy storage efficiency of 91.4% were obtained in the SBNCTE5 ceramics. The results indicate that SBNCTE ceramics can be used as energy storage capacitors.     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

Enhanced photo-electrochemical activity of ZnO/Cu2S nanotube arrays photocathodes

ZnO/Cu₂S nanotube arrays are fabricated firstly by a facile and capping-agent-free method, and the photo-electrochemical performance has been studied systematically. The results show that ZnO/Cu₂S nanotube arrays achieve enhanced photo-electrochemical water splitting performance and the photocurrent densities of ZnO/Cu₂S are 7.9 times than that of ZnO at 0 V versus Ag/AgCl. The performance of the ZnO/Cu₂S nanotube arrays can be adjusted by changing the amount of Cu₂S microcrystals. The results confirm that the enhanced photo-electrochemical performance of ZnO/Cu₂S is due to the significantly improved visible light absorption, effective separation of photo-induced carriers due to the well band energy match and the formed p-n junction between ZnO and Cu₂S.     

Use of heat-moisture treated maize starch to modify the properties of wheat flour and the quality of noodles

Waxy, normal and high-amylose maize starches were subjected to heat-moisture treatment (HMT) and then added to wheat flour (WF) in different ratios (1%, 5% and 10%). The properties of blends and their cooked noodles were studied to investigate the effects of HMT starches. The incorporation of HMT starch in WF led to an increase in swelling power, peak viscosity and breakdown and to a decrease in setback, thus inhibiting retrogradation, hence enhancing resultant noodle softness. Compared to the same addition ratio of native starch to WF, HMT starch led to higher tensile strength and extensibility in resultant noodles. WF with added HMT starch had higher resistant starch than with native starch. This study showed that addition of HMT maize starch has potential to bring nutritional benefits. However, it is necessary to select the proper blending ratio and amylose content of starch to add, in consideration of its effect on noodle quality.     

Specific emitter identification for satellite communication using probabilistic neural networks

Electromagnetic signal emitted by satellite communication (satcom) transmitters are used to identify specific individual uplink satcom terminals sharing the common transponder in real environment, which is known as specific emitter identification (SEI) that allows for early indications and warning (I&W) of the targets carrying satcom furnishment and furthermore the real time electromagnetic situation awareness in military operations. In this paper, the authors are the first to propose the identification of specific transmitters of satcom by using probabilistic neural networks (PNN) to reach the goal of target recognition. We have been devoted to the examination by exploring the feasibility of utilizing the Hilbert transform to signal preprocessing, applying the discrete wavelet transform to feature extraction, and employing the PNN to perform the classification of stationary signals. There are a total of 1000 sampling time series with binary phase shift keying (BPSK) modulation originated by five types of satcom transmitters in the test. The established PNNs classifier implements the data testing and finally yields satisfactory accuracy at 8 dB(±1 dB) carrier to noise ratio, which indicates the feasibility of our method, and even the keen insight of its application in military.     

基于分段加权的反向稀疏跟踪算法研究

为提高稀疏表示跟踪模型性能，提出一种分段加权的反向稀疏跟踪算法，将跟踪问题转化为在贝叶斯框架下寻找概率最高的候选对象问题，构造不同的分段权重函数来分别度量候选目标与正负模板的判别特征系数。通过池化来降低跟踪结果的不确定性干扰，选择正负模板加权系数差值最大的候选表示作为跟踪结果。实验表明，在光照变化、遮挡、快速运动、运动模糊情况下，所提出的算法可以确保跟踪结果的准确性和鲁棒性。