A benchmark data set for two-phase Coriolis metering

For more than a decade there has been growing interest in the use of Coriolis mass flow metering applied to two-phase (gas/liquid) and multiphase (oil/water/gas) conditions. It is well-established that the mass flow and density measurements generated from multiphase flows are subject to large errors, and a variety of physical models and correction techniques have been proposed to explain and/or to compensate for these errors. One difficulty is the absence of a common basis for comparing correction techniques, because different flowtube designs and configurations, as well as liquid and gas properties, may result in quite different error curves. Furthermore, some researchers with interests in the modelling aspects of the field may not have suitable multiphase laboratory facilities to generate their own data sets. This paper offers a small data set that may be used by researchers as a benchmark i.e. a common data set for comparing correction techniques. The data set was collected at the UK National Flow Laboratory TUV-NEL, using air and a viscous oil, and provides experimental points over a wide flow range (8:1 turndown) and with Gas Volume Fraction (GVF) values up to 60%. As a first investigation using the benchmark data set, we consider how data sparsity (i.e. the flow rate and GVF spacing in the experimental grid) affects the accuracy of a correction model. A range of neural network models are evaluated, based on different subsets of the benchmark data set. The data set and some exemplary code are provided with the paper. Additional data sets are available on a web site created to support this initiative.     

Can lanthanum doping enhance catalytic performance of silver in direct propylene epoxidation over NaMoAg/SiO_2?

In the attempt to reduce surface free energy of silica to improve interaction of silica with silver, silica was doped by different amounts of low surface energy lanthanum, La_2O_3, through impregnation. The doped and undoped silica were used as supports for preparation of Na/Ag/Mo/La_2O_3-SiO_2 catalysts. Catalytic performances of the catalysts were evaluated in direct epoxidation of propylene(DPO) using molecular oxygen under atmospheric pressure and without adding hydrogen. Adding 5 wt%La to the Na/Ag/Mo/SiO_2 catalyst improves both the catalysts electivity in DPO and its stability over 20h of time-on-stream.The characterization results show that La_2O_3 species interact strongly with silver particles on the silica surface which result in significant improvement in the dispersion profile of silver and marked decrease in the size of silver nanoparticles(AgNPs). The estimated mean diameter of AgNPs is ca. 4.0 nm in Na/Ag/Mo/5 wt%La_2O_3-SiO_2, which is smaller than that(53.9 nm) found in Na/Ag/SiO_2. The presence of subnanometer AgNPs on Ag/La_2O_3-SiO_2 prior addition of MoO_3 and NaCl to the sample can enhance the mutual electronic synergism between Ag, MoO_3 and Na for selective production of propylene oxide.     

基于模糊谱聚类的不确定蛋白质相互作用网络功能模块挖掘

针对谱聚类融合模糊C-means（FCM）聚类的蛋白质相互作用（PPI）网络功能模块挖掘方法准确率不高、执行效率较低和易受假阳性影响的问题，提出一种基于模糊谱聚类的不确定PPI网络功能模块挖掘（FSC-FM）方法。首先，构建一个不确定PPI网络模型，使用边聚集系数给每一条蛋白质交互作用赋予一个存在概率测度，克服假阳性对实验结果的影响；第二，利用基于边聚集系数流行距离（FEC）策略改进谱聚类中的相似度计算，解决谱聚类算法对尺度参数敏感的问题，进而利用谱聚类算法对不确定PPI网络数据进行预处理，降低数据的维数，提高聚类的准确率；第三，设计基于密度的概率中心选取策略（DPCS）解决模糊C-means算法对初始聚类中心和聚类数目敏感的问题，并对预处理后的PPI数据进行FCM聚类，提高聚类的执行效率以及灵敏度；最后，采用改进的边期望稠密度（EED）对挖掘出的蛋白质功能模块进行过滤。在酵母菌DIP数据集上运行各个算法可知，FSC-FM与基于不确定图模型的检测蛋白质复合物（DCU）算法相比，F-measure值提高了27.92%，执行效率提高了27.92%；与在动态蛋白质相互作用网络中识别复合物的方法（CDUN）、演化算法（EA）、医学基因或蛋白质预测算法（MGPPA）相比也有更高的F-measure值和执行效率。实验结果表明，在不确定PPI网络中，FSC-FM适合用于功能模块的挖掘。     

Super-Resolution Reconstruction of Images Based on Microarray Camera

In the field of images and imaging, super-resolution (SR) reconstruction of images is a technique that converts one or more low-resolution (LR) images into a highresolution (HR) image. The classical two types of SR methods are mainly based on applying a single image or multiple images captured by a single camera. Microarray camera has the characteristics of small size, multi views, and the possibility of applying to portable devices. It has become a research hotspot in image processing. In this paper, we propose a SR reconstruction of images based on a microarray camera for sharpening and registration processing of array images. The array images are interpolated to obtain a HR image initially followed by a convolution neural network (CNN) procedure for enhancement. The convolution layers of our convolution neural network are 3×3 or 1×1 layers, of which the 1×1 layers are used to improve the network performance particularly. A bottleneck structure is applied to reduce the parameter numbers of the nonlinear mapping and to improve the nonlinear capability of the whole network. Finally, we use a 3×3 deconvolution layer to significantly reduce the number of parameters compared to the deconvolution layer of FSRCNN-s. The experiments show that the proposed method can not only ameliorate effectively the texture quality of the target image based on the array images information, but also further enhance the quality of the initial high resolution image by the improved CNN.     

Design of Feedback Shift Register of Against Power Analysis Attack

Stream ciphers based on linear feedback shift register (LFSR) are suitable for constrained environments, such as satellite communications, radio frequency identification devices tag, sensor networks and Internet of Things, due to its simple hardware structures, high speed encryption and lower power consumption. LFSR, as a cryptographic primitive, has been used to generate a maximum period sequence. Because the switching of the status bits is regular, the power consumption of the LFSR is correlated in a linear way. As a result, the power consumption characteristics of stream cipher based on LFSR are vulnerable to leaking initialization vectors under the power attacks. In this paper, a new design of LFSR against power attacks is proposed. The power consumption characteristics of LFSR can be masked by using an additional LFSR and confused by adding a new filter Boolean function and a flip-flop. The design method has been implemented easily by circuits in this new design in comparison with the others.     

面向数据流结构的指令映射优化方法

在高性能计算领域,数据流是一类重要的计算结构,也在很多实际场景表现出很好的性能和适用性。在数据流计算模式中,程序是以数据流图来表示的,数据流计算中一个关键的问题是如何将数据流图映射到多个执行单元上。通过分析现有数据流结构的指令映射方法及其不足,提出了基于数据流结构的新型指令映射优化方法。主要是根据多地址共享数据包的特性对指令映射方法进行优化,延迟多地址共享数据路由包的拆分,减少网络拥堵。     

Microwave assisted synthesis and antimicrobial activity of Fe3O4-doped ZrO2 nanoparticles

Composites play important role in dental filling by controlling shrinkage along with correction in teeth's shape and position. Rehabilitation of severely worn dentition can be achieved using mechanically strong composites. This study aims to synthesize zirconia-based composites to be used as dental fillers. Effect of microwave powers (100–900?W) along with Fe₃O₄ doping are studied on the structural, mechanical and magnetic properties of stabilized zirconia. SEM and TEM reveal formation of spherical nanoparticles with diameter of ～30?nm. XRD results shows phase pure tetragonal zirconia (t-ZrO₂) at microwave power of 500?W without any post heat treatment. Crystallite size calculated from XRD data (～23?nm) matches well with the previously reported value for stabilization of t-ZrO₂. Microwave energy dissipation results in stresses causing volume shrinkage leading to monoclinic to tetragonal phase transformation with higher X-ray density and hardness of ～1347HV. VSM results show ferromagnetic response with low coercivity (600Oe) value and saturation magnetization (～2emu/g). It is worth mentioning here that this is one of its kind study reporting synthesis of room temperature stabilized Fe₃O₄ doped zirconia composites at microwave power of 500?W. Antibacterial studies reveal inhibition zone of ～32?mm against bacillus bacteria suggesting their potential use as dental filler.     

Common due date assignment scheduling for a no-wait flowshop with convex resource allocation and learning effect

This article addresses the no-wait flowshop scheduling problem with simultaneous consideration of common due date assignment, convex resource allocation and learning effect in a two machine setting. The processing time of each job can be controlled by its position in a sequence and also by allocating extra resource, which is a convex function of the amount of a common continuously divisible resource allocated to the job. The objective is to determine the optimal common due date, the resource allocation and the schedule of jobs such that the total earliness, tardiness and common due date cost (the total resource consumption cost) are minimized under the constraint condition that the total resource consumption cost (the total earliness, tardiness and common due date cost) is limited. Polynomial time algorithms are developed for two versions of the problem.