Preparation,characterization and salt-resistance of a coal based super absorbent composite

Potassium humate was extracted from brown coal.A novel super absorbent composite,poly (acrylic acid-co-acrylamide)/potassium humate (PAA-AM/KHA),was prepared by graft polymerization of acrylic acid,acrylamide and coal based potassium humate using N,N'-methylenebisacrylamide as a crosslinker and potassium peroxydisulfate as an initiator.The effects of reaction temperature,degree of neutralization of the poly (acrylic acid) and the amounts of crosslinker,initiator and potassium humate were investigated.Salt resistance tests were also carried out.The composite prepared under optimal conditions had a potassium humate content of 10% and exhibited a water absorption of 770 g/g in distilled water,and 349,286 and 41 g/g in 0.5 mol/L KC1,MgCl2 and AlCl3 solutions respectively.The results indicate that the salt resistance of PAA-AM/KHA was superior to that of poly (acrylic acid-co-acrylamide) because of the collaborative effect of functional groups of the coal based potassium humate.The PAA-AM/KHA micro powder was characterized by IR spectroscopy and the micrographic surface was characterized by scanning electron microscopy.Introduction of potassium humate into the poly (acrylic acid-co-acrylamide) structure creates a composite more suitable for use as a water-managing material in the renewal of arid and desert environments.The salt resisting property of the composite is improved,production costs are reduced and the growth stimulant effect is still present.     

Formation and thermal stability of amorphous Ni-Mo-P alloys

The experimental researches on the chemical deposition of Ni-Mo-P amorphous alloys were carried out by adding Na2 MoO4 into acidic solutions. The optimum technology conditions were obtained by orthogonal design experiments. The structures and the relationship between compositions and their thermal stability were studied by energy spectrum (EC), scanning electron micrograph and X-ray diffraction spectrum. Compared with Ni-P amorphous alloys, the Ni-Mo-P amorphous alloys have high crystallization temperature and thermal stability, and the hardness reaches its peak when the annealing temperature is 500 ℃. With the increase of the heat treatment temperature, the surface morphology of the alloys changes.     

Video summarization via minimum sparse reconstruction

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. In this paper, we formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem. That is, the original video sequence can be best reconstructed with as few selected keyframes as possible. Different from the recently proposed convex relaxation based sparse dictionary selection method, our proposed method utilizes the true sparse constraint L₀ norm, instead of the relaxed constraint _L2,1$L_{2,1}$ norm, such that keyframes are directly selected as a sparse dictionary that can well reconstruct all the video frames. An on-line version is further developed owing to the real-time efficiency of the proposed MSR principle. In addition, a percentage of reconstruction (POR) criterion is proposed to intuitively guide users in obtaining a summary with an appropriate length. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed methods outperform the state of the art.     

Accelerating a Recurrent Neural Network to Finite-Time Convergence for Solving Time-Varying Sylvester Equation by Using a Sign-Bi-power Activation Function

Bartels–Stewart algorithm is an effective and widely used method with an O(n ³) time complexity for solving a static Sylvester equation. When applied to time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. Gradient-based recurrent neural network are able to solve the time-varying Sylvester equation in real time but there always exists an estimation error. In contrast, the recently proposed Zhang neural network has been proven to converge to the solution of the Sylvester equation ideally when time goes to infinity. However, this neural network with the suggested activation functions never converges to the desired value in finite time, which may limit its applications in realtime processing. To tackle this problem, a sign-bi-power activation function is proposed in this paper to accelerate Zhang neural network to finite-time convergence. The global convergence and finite-time convergence property are proven in theory. The upper bound of the convergence time is derived analytically. Simulations are performed to evaluate the performance of the neural network with the proposed activation function. In addition, the proposed strategy is applied to online calculating the pseudo-inverse of a matrix and nonlinear control of an inverted pendulum system. Both theoretical analysis and numerical simulations validate the effectiveness of proposed activation function.     

遗传算法与文本特征选择方法相结合的研究

对文本特征选择的传统方法进行了深入分析并对各自的优缺点进行了详细的对比;其次,通过利用遗传算法自身的寻优特点,把它与传统特征选择方法相结合,并得出了算法。最后,在对各自算法进行了分析,表明了在性能上有了明显的提高。     

Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network

Computational Economics - The study aims to analyze and forecast Internet financial risks based on the model based on deep learning and the Back Propagation Neural Network (BPNN). First, the theory...     

Unique role of non-mercapto groups in thiol-pinning-mediated Ag growth on Au nanoparticles

Owing to the strong affinity of thiols to Au and Ag, they are often employed to modify the surfaces of nanoparticles (NPs). Recently, these strong ligand-interface interactions have been employed to control NP growth, and this technique has emerged as a unique modulation strategy for creating unconventional plasmonic hybrid nanostructures. In these systems, the roles of the non-mercapto components of the thiol molecules and their structures are still unknown. Therefore, we herein present our investigation into this phenomenon. Primary amino (–NH₂) groups in thiols are found to play a key role in regulating growth kinetics, i.e., in accelerating Ag deposition on Au NPs. The–NH₂ groups are thought to bring Ag ions to the particle surface by coordinating to them, and thereby assist their reduction. The effect of molecular structure is non-trivial and thus provides the possibility of selective thiol detection. Based on the dependence of kinetic modulation on the non-mercapto components and molecular structures of molecules, we demonstrate the highly sensitive and specific detection of cysteine (limit of detection: 6 nM) in a mixture of 19 natural amino acids based on Ag growth on Au nanospheres. In addition, based on this modulation effect, we reveal the entrapping of chiral thiols within the growth layer through their plasmonic circular dichroism (PCD) responses. We believe that thiol-based growth regulation has great potential for creating plasmonic nanostructures with novel functionalities.

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基于专家系统的回转窑窑头工况识别

针对新型干法水泥生产线窑头环节非线性、多变量、强耦合、大时滞等特性导致的工况复杂,控制难度大的问题,在深入分析窑头工况特点的基础上,提出了一种基于专家系统的工况智能识别方法.该方法首先对从集散控制系统中采集的现场参数实时值进行数据预处理,运用ART-2神经网络算法对关键参数的变化趋势进行在线辨识,并输出趋势类别,同时对主要参数的实时值进行模糊化处理,输出模糊档位.基于专家规则的工况识别系统根据参数的辨识结果,通过精确推理输出当前工况.工况识别软件采用c++编写,现场实际运行表明了该方案的正确性和实用性.     

跨网段多层交换网络下的数据群发研究

本文介绍了一种基于TCP协议的数据群发方案,该方案适用跨网段多层交换网络中的数据传输,需接收数据的机器只要都通过交换机连接,能用IP地址唯一标识即可.实验证明,该方案保证了群发数据所需时间随着数据的增大只是线性增加,在网速一定时,基本与需要接收数据机器的数量成对数级的关系,网络资源也得到了充分的利用.