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The regeneration of EDTA-FeII is a key step in electrobiofilm reduction-integrated systems for NOx removal from industrial boiler flue gas. The current and carbon sources are proposed to be the two crucial electron donors for EDTA-FeII regeneration. These parameters strongly influence the reactivity of EDTA-FeII-generated products in the system. Therefore, their effects on EDTA-FeII-NO and EDTA-FeIII reduction and the EDTA-FeII generation mechanism were studied. The results showed that the electrobiofilm method has obvious advantages over biological or electrochemical methods used alone for EDTA-FeII regeneration. Under the optimal conditions at a current of 22.9A m-3 net cathode chamber, the rate of EDTA-FeII regeneration reached 98.35%. The glucose concentration is the primary factor influencing the reduction of both EDTA-FeII-NO and EDTA-FeIII, while the current significantly promotes both processes. Comparison of the Km values of the two substrates indicated that microbial activity was crucial to the reduction of EDTA-FeII-NO, but the biological reduction of EDTA-FeIII had a competitive influence on EDTA-FeII-NO reduction, which limited the abundance and effectiveness of the bacteria responsible for EDTA-FeII-NO reduction in the electrobiofilm system.

In extreme learning machines (ELM), the hidden node parameters are randomly generated and the output weights can be analytically computed. To overcome the bad feature extraction ability of the shallow architecture of ELM, the hierarchical ELM has been extensively studied as a deep architecture with multilayer neural network. However, the commonly used mean square error (MSE) criterion is very sensitive to outliers and impulsive noises, generally existing in real world data. In this paper, we investigate the correntropy to improve the robustness of the multilayer ELM and provide sparser representation. The correntropy, as a nonlinear measure of similarity, is robust to outliers and can approximate different norms (from l0 to l2). A new full correntropy based multilayer extreme learning machine (FC-MELM) algorithm is proposed to handle the classification of datasets which are corrupted by impulsive noises or outliers. The contributions of this paper are three-folds: (1) The MSE based reconstruction loss is replaced by the correntropy based loss function; In this way, the robustness of the ELM based multilayer algorithms is enhanced. (2) The traditional l1-based sparsity penalty term is also replaced by a correntropy-based sparsity penalty term, which can further improve the performance of the proposed algorithm with a sparser representation of the data. The combination of (1) and (2) provides the correntropy-based ELM autoencoder. (3) The FC-MELM is proposed by using the correntropy-based ELM autoencoder as a building block. It is notable that the FC-MELM is trained in a forward manner, which means fine-tuning procedure is not required. Thus, the FC-MELM has great advantage in learning efficiently when compared with traditional deep learning algorithms. The good property of the proposed algorithm is confirmed by the experiments on well-known benchmark datasets, including the MNIST datasets, the NYU Object Recognition Benchmark dataset, and the Moore network traffic dataset. Finally, the proposed FC-MELM algorithm is applied to address Computer Aided Cancer Diagnosis. Experiments conducted on the well-known Wisconsin Breast Cancer Data (Diagnostic) dataset are presented and show that the proposed FC-MELM outperforms state-of-the-art methods in solving computer aided cancer diagnosis problems.

The biological reduction of ferrous ethylenediaminetetraacetic acid (EDTA-FeII-NO and EDTA-FeIII) is an important process in integrated electrobiofilm reduction method, and this method has been regarded as a promising alternative for removing NOx from industrial boiler flue gas. EDTA-FeII-NO and EDTA-FeIII are crucial substrates that should be biologically reduced at a high rate. However, they would inhibit one another’s reduction processes when they are present together, which might limited the further promotion of this integrated method. In this study, an integrated electrobiofilm reduction system with high reduction rate of EDTA-FeII- NO and EDTA-FeIII was established. The microbial communities in electrobiofilms were mainly studied to analyze their changes during the reduction of these two substrates under different conditions. It presents a better performance of substrates- resistance shock loading and high microbial diversities compared with the conventional chemical absorption-biological reduction system. High-throughput sequencing analysis showed that the changes in concentrations of EDTA-FeII-NO and EDTA-FeIII significantly impacted the genera of the microbial community. Alicycliphilus, Enterobacteriaceae and Raoultella were found to be the dominant genera (>25%, respectively) involved in EDTA-FeII-NO reduction. As an EDTA-FeIII reducing bacteria, Chryseobacterium can endure shock loading of substrates. Ochrobactrum can reduce nitrate using electrons and exhibited better stability under shock loading. Furthermore, higher microbial diversity and stable reactor operation could be achieved when the concentrations of EDTA-FeII-NO and EDTA-FeIII approached the same value.

Multiple controllers are connected to each other to construct a scalable SDN (Software Defined Network) control plane, and every switch is connected to a dedicated controller in data plane. The load on controllers changes dynamically in accordance to the services varying in switches; and switches need to be migrated from their master controllers to slave controllers to make the load balanced for SDN controllers. A dynamic switch migration algorithm with Q-learning towards scalable SDN control plane is proposed in this paper, which models switch migration problem, redefines parameters of Q-learning based on the switch migration model, and gets decisions of switch migration dynamically with the changing load on controllers using the proposed algorithm in SDN.

Device-to-Device (D2D) communication has been proposed as a promising implementation of green communication to benefit the existed cellular network. In order to limit cross-tier interference while explore the gain of short-range communication, we devise a series of distributed power control (DPC) schemes for energy conservation (EC) and enhancement of radio resource utilization in the hybrid system. Firstly, a constrained opportunistic power control model is built up to take advantage of the interference avoidance methodology in the presence of service requirement and power constraint. Then, biasing scheme and admission control are added to evade ineffective power consumption and maintain the feasibility of the system. Upon feasibility, a non-cooperative game is further formulated to exploit the proft in EC with minor influence on spectral efficiency (SE). The convergence of the DPC schemes is validated and their performance is confrmed via simulation results.

Traffic classification has been widely applied for networking. Previous works paid much attention to static network traffic. In this paper, we propose a new strategy for the semi-supervised clustering algorithm to deal the concept drift in a dynamic network, as well as updating the model incrementally. Moreover, our algorithm can find new clusters and reduce the impact of noises. The results of simulation demonstrate the effectiveness of semi-supervised clustering algorithm.

Femtocell is a promising technology for improving indoor coverage and offloading the macrocell. Femtocells tend to be densely deployed in populated areas such as the dormitories. However, the inter-tier interference seriously exists in the co-channel Densely Deployed Femtocell Network (DDFN). Since the Femtocell Access Points (FAPs) are randomly deployed by their customers, the interference cannot be predicted in advance. Meanwhile, new characteristics such as the short radius of femtocell and the small number of users lead to the inefficiency of the traditional frequency reuse algorithms such as Fractional Frequency Reuse (FFR). Aiming for the downlink interference coordination in the DDFN, in this paper, we propose a User-oriented Graph based Frequency Allocation (UGFA) algorithm. Firstly, we construct the interference graph for users in the network. Secondly, we study the conventional graph based resources allocation algorithm. Then an improved two steps graph based frequency allocation mechanism is proposed. Simulation results show that UGFA has a high frequency reuse ratio mean while guarantees a better throughput.

Overlay networks have emerged as a useful approach to providing a general framework for new applications and services that are to be implemented without significantly changing the IP-layer network infrastructure. Overlay routing has been used as an alternative to the default best effort Internet routing for the absence of end-to-end Quality of Service (QoS). While the former has recently been investigated, the conflict of QoS restraints and resource optimization remains unsolved. Recent studies have shown that overlay paths can give better latency, loss rate and TCP throughput. In this paper, a multi-dimensional QoS objective model based on the analysis of multiple QoS constraints has been presented, and a routing algorithm to optimise the overlay resource of its nodes and links is then proposed. In fact, the algorithm obtained multiple QoS values using probability theory to achieve the routing according to the multi-dimensional QoS objective vector of the QoS objective model. Simulation results reveals that the algorithm works better than other existing algorithms in balancing the network resources, and applications with stringent QoS requirements could be run.

This paper proposes a method for improving the precision of Network Traffic Prediction based on the Maximum Correntropy Criterion (NTPMCC), where the nonlinear characteristics of network traffic are considered. This method utilizes the MCC as a new error evaluation criterion or named the cost function (CF) to train neural networks (NN). MCC is based on a new similarity function (Generalized correlation entropy function, Correntropy), which has as its foundation the Parzen window evaluation and Renyi entropy of error probability density function. At the same time, by combining the MCC with the Mean Square Error (MSE), a mixed evaluation criterion with MCC and MSE is proposed as a cost function of NN training. According to the traffic network characteristics including the nonlinear, non-Gaussian, and mutation, the Elman neural network is trained by MCC and MCC-MSE, and then the trained neural network is used as the model for predicting network traffic. The simulation results based on the evaluation by Mean Absolute Error (MAE), MSE, and Sum Squared Error (SSE) show that the accuracy of the prediction based on MCC is superior to the results of the Elman neural network with MSE. The overall performance is improved by about 0.0131.

Handover is one of the most important parts in mobility management. In the LTE-Advanced system, new technologies such as relay and carrier aggregation are introduced, which put forward higher requirements and challenges to the handover mechanism. By studying the handover process of the traditional LTE-Advanced system and LTE-Advanced Relay system, this paper proposes a cooperative handover algorithm with the consideration of the auxiliary carrier in LTE-Advanced relay system. This algorithm takes the advantages of wide spreading of low-frequency carrier, rich resources of high-frequency carrier, and in conjunction with relay technology and carrier aggregation technology. The propose algorithm reduces the handover outage probability and improve the throughput of the system.

We have proposed a call admission control in LTE-Advanced relay system which is based on resource sharing. Each cell has 7 sites, including 1 eNB and 6 relay stations, if the shared resources are used up for handover users and new users in the requested site, handover users can use the reserved resources of the requested site, while new users can utilize the resources of other sites in the same cell. Simulation results show that our proposed algorithm can effectively reduce the dropping rate of handover users and blocking rate of new users when compared with the conventional ones and, in the meantime, improve the system resource utilization.

QoS routing is one of the key issues in the research of the overlay network. This paper proposes a routing algorithm with Multiple Constrained Balanced Path(MCBP) based on analyzing multiple QoS constraints. It solves the weights allocation among multiple QoS constraints by introducing the methods AHP and Parameter Normalization with including multiple QoS parameters of nodes and links considered. Meanwhile, it also balanced the QoS parameters Bandwidth and Nodes Processing Capacity according to the features of the overlay network. Simulation results show that MCBP is better than other similar algorithms in balancing the network resources with all the QoS parameters are equally considered.

In this paper, we establish an analog of the Beale-Kato-Majda type criterion for singularities of smooth solutions of a hydrodynamic system modeling vesicle and fluid interactions. The result shows that the maximum norm of the vorticity alone controls the breakdown of smooth solutions., Comment: 16 pages

In this paper we study existence of solutions for the Cauchy problem of the Debye-H��ckel system with low regularity initial data. By using the Chemin-Lerner time-space estimate for the heat equation, we prove that there exists a unique local solution if the initial data belongs to the Besov space $\dot{B}^{s}_{p,q}(\mathbb{R}^{n})$ for $-3/2, 9 pages

In order to improve survivability of Multi-Domain Optical Network (MDON) and differentiate service reliability, this paper provides a Protection mechanism with Differentiated Service Reliability in MDON based on Conditional Risk Disjunction Degree (CRDD), called PDRC. Firstly, defines CRDD, provides Intra-domain virtual links Reflection arithmetic based on CRDD considering wavelength Continuity (IRCC) to form aggregation topology, then provides Arithmetic of Computing work route and backup route with Differentiated service reliability based on CRDD (ACDC) to compute work route and backup route in aggregation topology. The results verify that the proposed protection mechanism not only differentiate service reliability, but also reduce blocking rate of service and improve usability of network.

In contemporary society, the momentum of development is largely based on the level of the scientific and technological revolution. The main contributor to this revolution forms a special group: the intellectuals. In the process of China's modernization reform, the importance of science and technology is crucial. Therefore, one of the key predictions is the incentives and originality in the contribution of China's intellectuals in this current reform. Beginning in the early 1980s, the concept of economic returns was introduced into the upcoming, more competitive society in China, and people of all social ranks became more aware of the monetary returns they received. What about the Chinese intellectuals' contribution to the national economy and their economic returns? This is the main question that this article will explore. In this regard, consider the following: 1. In the report on ten major targets that the government should achieve in the next five years, Premier Li Peng listed the following as the numb...

In this paper, we concentrate on the problem of routing and bandwidth assignment in GMPLS networks. We proposed a new restoration routing algorithm in GMPLS networks with optimized bandwidth. In this new algorithm, we consider multiple network optimizing objectives to setup a LSP with maximize profit and shortest path under the limited resources. By comparing and analyzing with the traditional algorithm, it is much better than the algorithm, which only considers one optimizing objective. In practice it is enough to find a solution, which does not use more bandwidth than the available technology allows.

Based on the kernel methods and the nonlinear feature of chaotic time series, we develop a new algorithm called kernel least mean kurtosis (KLMK) by applying the kernel trick to the least mean kurtosis (LMK) algorithm, which maps the input data to a high dimensional feature space. The KLMK algorithm can overcome the shortcomings of the original LMK for nonlinear time series prediction, and it is easy to implement a sample by sample adaptation procedure. Theoretical analysis suggests that the KLMK algorithm may converge in a mean square sense in nonlinear chaotic time series prediction under certain conditions. Simulation results show that the performance of KLMK is better than those of LMK and the kernel least mean square (KLMS) algorithm.