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A fuel cell is an electrochemical device that converts the chemical energy of a fuel and oxidant into electricity. Cation-exchange and anion-exchange membranes play an important role in hydrogen fed proton-exchange membrane (PEM) and anion-exchange membrane (AEM) fuel cells, respectively. Over the past 10 years, there has been growing interest in using nanofiber electrospinning to fabricate fuel cell PEMs and AEMs with improved properties, e.g., a high ion conductivity with low in-plane water swelling and good mechanical strength under wet and dry conditions. Electrospinning is used to create either reinforcing scaffolds that can be pore-filled with an ionomer or precursor mats of interwoven ionomer and reinforcing polymers, which after suitable processing (densification) form a functional membrane. In this review paper, methods of nanofiber composite PEMs and AEMs fabrication are reviewed and the properties of these membranes are discussed and contrasted with the properties of fuel cell membranes prepared using conventional methods. The information and discussions contained herein are intended to provide inspiration for the design of high-performance next-generation fuel cell ion-exchange membranes.

Accurate wind speed forecasting is important in power grid security, power system management, operation and market economics. However, most research has focused only on improving either accuracy or stability, with few studies addressing the two issues, simultaneously. Therefore, we proposed a novel combined model based on multi-objective particle swarm optimization, which is applied to optimize the key parameters of the echo state network. Most combined wind speed forecasting methods just use the combination theory to combine individual methods, this paper uses echo state network to combine the intermediate wind speed forecasting results of three artificial neural networks. Moreover, a new dataset division mechanism based on the moving window is applied in this paper. Firstly, the length of the input data is changed from 5 to 15 for 1-step, 2-step and 3-step wind speed forecasting, after that, the optimal length of the input vector can be got. And then we apply this optimal length of the input vector to another dataset for further verifying the proposed method. In order to verify the forecasting effectiveness of the proposed forecasting model, the 80/min wind speed data of M2 tower of the National Wind Power Technology Center of the United States were taken as an example. The experimental results indicate that the proposed algorithm is superior to the other ten comparative models in prediction accuracy and stability, and it also performs better than the combined model that we have proposed before.

The atomic-scale surface fractal, which can notably affect gas adsorption, is a prominent form of geometric heterogeneity at the surfaces of the pore structure of shale. Systematically evaluating t...

Accurate wind speed forecasting plays a crucial role in wind power plants and wind systems. Numerous studies that dedicated to enhance wind speed forecasting accuracy have been proposed. Nevertheless, some classical models are not good at dealing with nonlinear time sequence, and artificial intelligence are easy fall into local optimum. Based on this, this paper proposed a novel combined model for wind speed forecasting, which combined hybrid models based on decomposition method and optimization algorithm. In this approach, four distinct hybrid models are proposed and to additionally enhance the forecasting performance, ESN (Echo state network) is initially applied to integrate all the results obtained by each hybrid model and achieve the ultimate forecasting results. Besides, a distinct data set division mechanism is adopted in the multi-step wind speed forecasting. To verify the effectiveness and versatility of the proposed combined model, the wind speed data of 20 m, 50 m, 80 m per minute of the M2 tower from the National Wind Power Technology Center, United States, are utilized as case studies. The experimental results indicate that the proposed combined model performs better than other conventional methods.

Mobile phones and tablets are becoming the primary platform of choice. However, these systems still suffer from limited battery and computation resources. A popular technique in mobile edge systems is computing outsourcing that augments the capabilities of mobile systems by migrating heavy workloads to resourceful clouds located at the edges of cellular networks. In the multi-site scenario, it is possible for mobile devices to save more time and energy by offloading to several cloud service providers. One of the most important challenges is how to choose servers to offload the jobs. In this paper, we consider a multi-site decision problem. We present a scheme to determine the proper assignment probabilities in a two-site mobile-edge computing system. We propose an open queueing network model for an offloading system with two servers and put forward performance metrics used for evaluating the system. Then in the specific scenario of a mobile chess game, where the data transmission is small but the computation jobs are relatively heavy, we conduct offloading experiments to obtain the model parameters. Given the parameters as arrival rates and service rates, we calculate the optimal probability to assign jobs to offload or locally execute and the optimal probabilities to choose different cloud servers. The analysis results confirm that our multi-site offloading scheme is beneficial in terms of response time and energy usage. In addition, sensitivity analysis has been conducted with respect to the system arrival rate to investigate wider implications of the change of parameter values.

The rise of streaming data processing is driven by mass deployment of sensors, the increasing popularity of mobile devices, and the rapid growth of online financial trading. Apache Kafka is often used as a real-time messaging system for many stream processors. However, efficiently running Kafka as a reliable data source is challenging, especially in the case of real-time processing with unstable network connection. We find that changing configuration parameters can significantly impact the guarantee of message delivery in Kafka. Therefore the key to solving the above problem is to predict the reliability of Kafka given various configurations and network conditions. We define two reliability metrics to be predicted, the probability of message loss and the probability of message duplication. Artificial neural networks (ANN) are applied in our prediction model and we select some key parameters, as well as network metrics as the features. To collect sufficient training data for our model we build a Kafka testbed based on Docker containers. With the neural network model we can predict Kafka's reliability for different application scenarios given various network environments. Combining with other metrics that a streaming application user may care for, a weighted key performance indicator (KPI) of Kafka is proposed for selecting proper configuration parameters. In the experiments we propose a rough dynamic configuration scheme, which significantly improves the reliability while guaranteeing message timeliness.

Wind energy has attracted much attention because it is sustainable and renewable energy with a much smaller impact on the environment than fossil fuels. Accurate wind speed forecasting can pave the way to the goal of sustainable development. However, many papers only use wind speed series but ignore weather factors, which lead to poor forecasting results. In this paper, we propose an ensemble system that considers historical wind speed series and other weather factors to make wind speed forecasting. The proposed system integrates noise reduction, clustering, and multi-objective optimization. Firstly, the historical wind speed series are decomposed by complementary ensemble empirical mode decomposition (CEEMD). Secondly, the samples are clustered by self-organizing map (SOM) according to the weather factors. Finally, a regularized extreme learning machine (RELM) model is trained to forecast the wind speed for each cluster, and multi-objective grey wolf optimizer (MOGWO) is employed to optimize the parameters in the model. An empirical study using three datasets from the M2 tower of the national wind power technology center (NWTC) of the national renewable energy laboratory (NREL) illustrates that the proposed system performs better than other comparative models in terms of four performance indicators.

Particulate Matters such as PM10, PM2.5 may contain heavy metal oxides and harmful substances that threaten human health and environmental quality. In this paper, we propose a new combined neural n...

Irinotecan (Ir) is a potent antitumor chemotherapeutics in clinic and used for the treatment of a various cancers, but the degree of its application is critically limited by toxic side-effects and marked heterogeneities. Nano-formulation of prodrugs, based on “all-in-one” carrier-free self-assemblies offers an effective approach to alter pharmacokinetics and safety profiles of cytotoxic agents. In this study, a novel vitamin E succinate-based formulation of Ir (VES-Ir) combined with nanoscaled characteristics and synergistic combination was constructed through esterification. The conjugation makes amphiphilic VES-Ir prodrug self-assemble into nanoparticles with a fine diameter (VES-Ir NPs, 75.4 nm) of spherical morphology. Furthermore, VES-Ir NPs with a 1:1 drug-to-drug ratio was demonstrated to possess respectable physiological stability within 72 h test, while can react to pH/esterase-sensitive drug release in lysosomes internalized into tumor cells, potentially highlighting their alleviating side effects. Compared with single and mixture drugs administration, the nanoformulated VES-Ir NPs codelivered both VES and Ir with different anticancer mechanisms to induce the highest suppress proliferation of MCF-7 (IC50 0.18 μM) and A549 (IC50 0.29 μM) cells in a synergistic way (CI

This electronic Because of the limited capacity of a single controller, multiple controllers need to be deployed in a large software defined-network. The assignment of controllers and switches is a significant problem that may affect the flow setup time. Since the network traffic is dynamic, static controller assignment may lead to high flow setup time. To reduce the flow setup time and improve the performance of networks, we focus on the dynamic controller assignment problem in this paper. We propose a controller assignment scheme for multiple controllers. It adjusts the assignment among controllers and switches dynamically based on the load of switches and the capacity of controllers. We model each controller as an M/PH/1 queue and use a heuristic called LANS to optimize the controller assignment based on the model. We compare our method with DCP-SA in two cases. The results show that LANS can balance the load of controllers better than DCP-SA. The flow setup time under LANS is 8% and 17% lower than DCP-SA and LANS makes 0.8 and 1 fewer migrations on average than DCP-SA in the two cases.

The interaction between the control plane and data plane can lead to an additional performance penalty in OpenFlow networks. The performance penalty is very severe in large networks and packet bursts make the penalty worse. Upon arrival of a burst, the controller receives many flow installation requests in a very short time. In such cases, the controller may become the bottleneck of the OpenFlow network. Therefore, it is very important to avoid overload of the controller. In this paper, we focus on bursts of packet-in messages in OpenFlow networks. We build a queueing model for bursts of packet-in messages and present a method that decreases the number of packet-in messages using buffers in OpenFlow switches. The mismatched packets are stored in the buffers of the switches and are sent to the controller at a lower rate. We evaluate our method by queueing analysis and simulation. The results show that the bursts of packet-in messages decrease the performance of OpenFlow networks and the proposed method can reduce this effect.

Network traffic prediction performs a main function in characterizing network community performance. An approach which could appropriately seize the salient characteristics of the network visitors could be very useful for network analysis and simulation. Network traffic prediction methods could be divided into two classes: one is the single models and the opposite is the hybrid fashions. The hybrid models integrate the merits of several single models and consequently can enhance the network traffic prediction accuracy. In this paper, a new hybrid network traffic prediction method (EPSVM) primarily based on Empirical Mode Decomposition (EMD), Particle Swarm Optimization (PSO), and Support Vector Machines (SVM) is presented. The EPSVM first utilizes EMD to eliminate the impact of noise signals. Then, SVM is applied to model training and fitting, and the parameters of SVM are optimized by PSO. The effectiveness of the presented method is examined by evaluating it with different methods, including basic SVM (BSVM), Empirical Mode Decomposition processed by SVM (ESVM), and SVM optimized by Particle Swarm Optimization (PSVM). Case studies have demonstrated that EPSVM performed better than the other three network traffic prediction models.

The Monte Carlo adaptation rule has been proposed to design asymmetric neural network. By adjusting the degree of the symmetry of the networks designed by this rule, the spurious memories or unwanted attractors of the networks can be suppressed completely. We have extended this rule to design full-connected neural networks and diluted neural networks. Comparing the dynamics of these two neural networks, the simulation results indicated that the performance of diluted neural network was poorer than the performance of full-connected neural network. As to this point, further research is needed. In this paper, we use the annealed dilution method to design a diluted neural network with fixed degree of dilution. By analyzing the dynamics of the diluted neural network, it is verified that asymmetric full-connected neural network do have significant advantages over the asymmetric diluted neural network.

Particulate Matters such as PM

SDN (Software Defined Networking) provides a way to flexible networks and makes the management easy. This is achieved by the programmable controllers. OpenFlow is a popular SDN protocol. In an OpenFlow network, the controller is the only part implemented logically, all the switches can only execute the instructions from the controller. Therefore, it is important to understand how a controller impacts an OpenFlow network for researchers and network managers. In this paper, we present a user-friendly OpenFlow controller performance evaluation tool that aims to help network researchers building performance models of OpenFlow controllers and network manager to understand the behavior of OpenFlow controllers. The tool uses a virtual OpenFlow switch sending OpenFlow messages to a controller and measures the response time. It fits the response time to a hyper-Erlang distribution. Through the fitted distribution, The tool can offer more clearly performance characteristic than the existing tools. The tool can export its result into JMT, it helps users to build and evaluate their performance models.

Classic fluorescent dyes, such as coumarin, naphthalimide, fluorescein, BODIPY, rhodamine, and cyanines, are cornerstones of various spectroscopic and microscopic methods, which hold a prominent position in biological studies. We recently found that 9-amino-benzo[c]cinnoliniums make up a novel group of fluorophores that can be used in biological studies. They are featured with a succinct conjugative push-pull backbone, a broad absorption band, and a large Stokes shift. They are potentially useful as a small-molecule alternative to R-phycoerythrin to pair with fluorescein in multiplexing applications.