Effect of nitriding atmosphere on the morphology of AlN nanofibers from solution blow spinning

High purity AlN fiber is a promising thermal conductive material. In this work, AlN fibers were prepared using solution blow spinning followed by nitridation under N₂ or NH₃ atmosphere. Soluble polymer, such as polyaluminoxane, and allyl-functional novolac resin were adopted as raw materials to form homogeneous distribution of Al₂O₃ and C nanoparticles within the fibers, which could inhibit the growth of alumina crystal and promote their nitridation process. The effect of nitriding atmosphere on the fiber morphology was investigated. XRD results showed that complete nitridation was achieved at 1300 °C in the NH₃ or at 1500 °C in the N₂ atmosphere. Hollowed fiber structure was observed when fiber was nitrided in N₂ at high temperature, which was caused by gaseous Al gas diffusion, and this phenomenon was eliminated in NH₃ atmosphere. The nitridation mechanisms in different atmosphere were analyzed in detail. It was demonstrated that the nitridation of Al₂O₃ fibers in the NH₃ atmosphere offered the favored AlN morphology and chemical quality. Flexible AlN fiber with O content of 0.7 wt% was achieved after nitriding in NH₃ at 1400 °C. The high quality AlN can be used in thermal conductive composite materials.     

Learning shape and texture progression for young child face aging

Face aging (FA) for young faces refers to rendering the aging faces at target age for an individual, generally under 20s, which is an important topic of facial age analysis. Unlike traditional FA for adults, it is challenging to age children with one deep learning-based FA network, since there are deformations of facial shapes and variations of textural details. To alleviate the deficiency, a unified FA framework for young faces is proposed, which consists of two decoupled networks to apply aging image translation. It explicitly models transformations of geometry and appearance using two components: GD-GAN, which simulates the Geometric Deformation using Generative Adversarial Network; TV-GAN, which simulates the Textural Variations guided by the age-related saliency map. Extensive experiments demonstrate that our method has advantages over the state-of-the-art methods in terms of synthesizing visually plausible images for young faces, as well as preserving the personalized features.     

Micro-expression recognition from local facial regions

MiE is a facial involuntary reaction that reflects the real emotion and thoughts of a human being. It is very difficult for a normal human to detect a Micro-Expression (MiE), since it is a very fast and local face reaction with low intensity. As a consequence, it is a challenging task for researchers to build an automatic system for MiE recognition. Previous works for MiE recognition have attempted to use the whole face, yet a facial MiE appears in a small region of the face, which makes the extraction of relevant features a hard task. In this paper, we propose a novel deep learning approach that leverages the locality aspect of MiEs by learning spatio-temporal features from local facial regions using a composite architecture of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The proposed solution succeeds to extract relevant local features for MiEs recognition. Experimental results on benchmark datasets demonstrate the highest recognition accuracy of our solution with respect to state-of-the-art methods.     

控制爆破法处理卡钻事故

介绍了在包头市某工程实施管道穿越黄河施工中,采用爆破法处理卡钻的经验。针对深水环境条件及钻杆内径小不宜采用集团装药的条件,确定采用"小直径爆破筒,钻杆内部装药"的爆破方案,阐述了爆破设计及施工注意事项。可供类似工程参考。     

Dielectric and ferroelectric properties of multilayer BaTiO3/NiFe2O4 thin films prepared by solution deposition technique

In this work different lead-free multilayered structures, composed of perovskite BaTiO₃ and spinel NiFe₂O₄ thin layers, were obtained by solution deposition method. Structural characterization of the sintered thin films confirmed the well-defined layered structure with overall thickness from 160 to 600 nm, crystalline nature of perovskite BaTiO₃ and spinel NiFe₂O₄ phases without secondary phases (after sintering below 900 °C) and grains on nanometer scale. Dielectric properties of the multiferroic multilayer BaTiO₃/NiFe₂O₄ thin films were analyzed in temperature and frequency range from 30 °C to 200 °C and 100 Hz to 1 MHz, respectively. In comparison to the pure BaTiO₃ films, the introduction of ferrite layer reduces dielectric response and increases low frequency permittivity dispersion of the multilayer thin films. The multilayer samples have shown relatively low dielectric loss with stronger contribution of conductivity at higher temperatures, and characteristic broad peak representing “relaxation” of the interface charge accumulation.     

Solution plasma-assisted preparation of highly dispersed NiMnAl-LDO catalyst to enhance low-temperature activity of CO2 methanation

In this work, the solution plasma-assisted method was used to prepare NiMnAl-LDO (layered double oxides) catalysts with different treatment times, which were used for the CO₂ methanation reaction. Solution plasma treatment can enhance the dispersibility of the catalyst, create oxygen defects and improve the chemical adsorption capacity of the catalyst. The results show that the low-temperature activity of the catalyst has been improved after the solution plasma treatment. We demonstrate that the NiMnAl-LDO-P(20) catalyst with high dispersion has the highest catalytic activity in CO₂ methanation (81.3% CO₂ conversion and 96.7% CH₄ selectivity at 200 °C). Even though working for 70 h, the catalyst is still highly stable. This work provides a great promise for improving the low-temperature activity of Ni-based catalysts.     

Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

Solution precursor thermal spraying of gadolinium zirconate for thermal barrier coating

Gadolinium zirconate (GZ) is an attractive material for thermal barrier coatings (TBCs). However, a single layer GZ coating has poor thermal cycling life compared to Yttria Stabilized Zirconia (YSZ). In this study, Solution Precursor High Velocity Oxy-Fuel (SP-HVOF) thermal spray was used to produce a double layer GZ/YSZ TBC and compared the thermal cycling performance with the single layer YSZ TBC. The temperature behaviour of the solution precursor GZ was studied, and single splat tests were carried out to obtain an optimised spray parameter. In thermal cycling tests, the single-layer YSZ reached 20 % failure at 85 ± 5 cycles, whereas the double-layer GZ/YSZ was at 70 ± 15 cycles. The single-layer failed at the topcoat/TGO interface, whereas the double-layer failed at GZ/YSZ interface and topcoat/TGO interface. Moreover, Gd diffusion occurred near the GZ/YSZ interface, resulting in porosities in the GZ layer.     

Applications of sketches in network traffic measurement: A survey

Accurate and timely network traffic measurement is essential for network status monitoring, network fault analysis, network intrusion detection, and network security management. With the rapid development of the network, massive network traffic brings severe challenges to network traffic measurement. However, existing measurement methods suffer from many limitations for effectively recording and accurately analyzing big-volume traffic. Recently, sketches, a family of probabilistic data structures that employ hashing technology for summarizing traffic data, have been widely used to solve these problems. However, current literature still lacks a thorough review on sketch-based traffic measurement methods to offer a comprehensive insight on how to apply sketches for fulfilling various traffic measurement tasks. In this paper, we provide a detailed and comprehensive review on the applications of sketches in network traffic measurement. To this end, we classify the network traffic measurement tasks into four categories based on the target of traffic measurement, namely cardinality estimation, flow size estimation, change anomaly detection, and persistent spreader identification. First, we briefly introduce these four types of traffic measurement tasks and discuss the advantages of applying sketches. Then, we propose a series of requirements with regard to the applications of sketches in network traffic measurement. After that, we perform a fine-grained classification for each sketch-based measurement category according to the technologies applied on sketches. During the review, we evaluate the performance, advantages and disadvantages of current sketch-based traffic measurement methods based on the proposed requirements. Through the thorough review, we gain a number of valuable implications that can guide us to choose and design proper traffic measurement methods based on sketches. We also review a number of general sketches that are highly expected in modern network systems to simultaneously perform multiple traffic measurement tasks and discuss their performance based on the proposed requirements. Finally, through our serious review, we summarize a number of open issues and identify several promising research directions.     

安卓平台上的医用图像信息采集系统的开发设计

为了探讨在安卓平台上构建医用图像采集系统的开发个案,分析通过以智能手机、平板电脑为核心安卓设备通过拍照获得化验单数据后进行文本识别并提交智慧医疗系统的解决方案。本文首先通过二值化算法形成低阈值图像数据,使用卷积神经元网络算法对文本进行逐一识别,使用K-means算法对识别后的单字文本进行字段记录值的整合并形成元数据库服务于其他智慧医疗系统模块。在使用9000组数据对神经元网络进行前期训练的前提下,该系统的识别准确率达到了99.5%以上。本系统具有一定的可行性,对未来智慧医疗的系统开发有实践意义。