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Proton exchange membrane fuel cell (PEMFC) is a competitive energy conversion device with the zero emission of greenhouse gas and high efficiency. However, cell inconsistency in stack and non-uniformity in single cell affect the performance and accelerate localized degradation. A 7-channel serpentine flow field PEMFC is modeled based on the practical one. In this paper, numerical simulation method based on computational fluid dynamics is applied to investigate the distribution of physical field in membrane. The accuracy of simulation results is verified by experimental polarization curve. The current density, temperature and water content distribution under two working states (0.7665 V and 0.5311 V) are selected for analysis, and the distribution non-uniformity is quantitatively described and compared. The results demonstrate that numerical simulation is a convenient and inexpensive method assisting to PEMFC analysis in engineering application.

Proton exchange membrane fuel cell (PEMFC) has advantages of high energy conversion efficiency and pollution free, and is one of the ideal energy devices. As a key component, flow field design influences the useful life, mass transfer and overall output performance of PEMFC, and has always been a research hotspot. In this paper, three-dimensional numerical models of parallel flow field have been established to study the influence of vertical inlet direction and horizontal inlet direction on the PEMFC’s performance. The results show that parallel flow field with horizonal inlet has higher net power output and also has significant advantages over the uniform distribution of pressure, oxygen mass fraction and temperature.

Researches have found that tumor immunotherapy can only work for some patients, and the intestinal microbiota is one of the important factors affecting the responses of patients with cancer to immune checkpoint blockade therapy. It is highly desirable to develop computational methods that can predict whether a patient with cancer will have positive effects on cancer immunotherapy by analyzing intestinal microorganisms of the patient. In this study, a multi-task model is introduced to predict the efficacy of cancer immunotherapy on a patient who suffered from non-small cell lung cancer or renal cell carcinoma. The results demonstrate the multi-task model outperforms several single-task methods. Therefore, we believe the multi-task idea can be used to predict the efficacy of cancer immunotherapy based on the gut microbe, which would be important to cancer patients.

Traditional methods of analyzing genes mainly focus on a single gene. However, they are time consuming, costly, and neglect the interaction between genes. To overcome the shortcomings of traditional methods, many computational techniques have been proposed for gene analysis. Computational methods posit that the interaction between genes forms a network. By analyzing the topological characteristics of the network, a type of node, “hubs,”is identified. Only a small fraction of nodes is called “hubs,”but they interact with many partners in the network. Several methods are used to find “hubs”in a network. However, they are not comprehensive. Nevertheless, searching for more information about genes in a network remains challenging. In this paper, we integrate HumanNet and a framework to find a structural hole (SH) spanner. We classify genes into three classes (SH, normal, and non-SH). We also classify genes into three classes according to degree and betweenness. Enrichment analysis reveals that the SH genes are enriched for an essential one. Furthermore, enrichment analyses of disease mutations, viruses, and drugs consistently show that the SH genes are preferred targets and are important in cellular state transition. Functional enrichment shows that the biological function that the SH genes over represent is related to cell function. We demonstrate that by finding the structural hole spanner in HumanNet, we can find the SH genes that are essential and indispensable in cellular life. In addition, we identify the SH genes that are key players in cellular state transition.

With the widespread use of antibiotics, many bacteria have developed resistance. Antimicrobial peptides have broad applications in medicine because of their high antibacterial activity. In this paper, a neural network model is introduced to recognize and detect antimicrobial peptides. Our model consists of an embedded, convolutional, bidirectional LSTM, and full connection layers. The embedded layer is used to code different amino acid residues into different vectors. The convolutional layer and bidirectional LSTM extract peptide amino acid residue sequence information. The full connection layer maps the sequence information linearly to the interval from 0 to 1, as the peptide for the probability of antimicrobial peptides. Training and testing on several different datasets reveal that our model performs better than other proposed models.

Different from traditional single-element and phased-array antennas, frequency diverse array (FDA) uses a slight frequency offset across the elements to produce angle-, range- and even time-dependent transmit beampattern, which provides promising potentials to develop new jamming techniques on synthetic aperture radar (SAR) imaging. In this paper, we propose a novel deception jamming method against SAR imaging, which uses FDA to quickly generate false targets. Moreover, the position of the false target is controlled by the jammer, and the number of false targets is determined by the number of FDA elements. The proposed methods are validated by extensive simulation results with range-Doppler imaging algorithm.

The weighted structure tensor (ST) can be usually used on edge detection of multi-channel images, including polarimetric synthetic aperture radar (polSAR) images. The traditional weighting method is average weighting, each channel considered to provide the same edge information. But in fact different channels tend to contain different amount of edge information, so the traditional weighting method cannot be adopted to extract full edge information of the multi-channel images. This letter proposes a novel weighting method for ST, in which the weight of each channel is obtained by the eigenvalue-measured way. Experimental results show that the proposed method outperforms the traditional method on edge detection.

This paper proposes a simple two-dimensional imaging via stationary nonuniform frequency diverse array (FDA) transmitter by exploiting FDA both range and angle dependent transmit beampattern. The nonuniform FDA transmitter only generates range-dependent beampattern, so that two-dimensional (range-angle dimension) imaging of targets can be achieved without the employment of moving radar platform, by jointly utilizing the only range-dependent transmitter beampattern and general single-antenna or phased-array antenna. The effectiveness of the proposed method is verified by simulation results.

Intelligent event recognition along the fiber is still a challenging problem in long distance pipeline monitoring with distributed optical fiber sensors (DOFS), because the complicated burying environments are changing from time to time, and the interference sources are unpredictable at different fiber locations. The fixed hand crafted feature extraction is always time-consuming and laborious, and the update of algorithm always lags behind the environmental change, which restricts its practical scale applications. Thus in this paper, we propose a real-time distributed deep learning model by using the efficient 1-D convolutional neural network (1-D CNN), to learn the distinguishable features of different disturbances and identify them automatically by training the raw event data (signal), which also can be updated easily. The experimental results from the real field data for the safety monitoring of oil pipelines demonstrate the effectiveness of the proposed method, which performs better than the 2-D CNN in terms of both recognition metrics and speed.

Now many researches have been studying the optimization between throughput/energy consuming and delay with a couple of independent input flows in network coding (NC). However, the implementation of network coding (NC) requires a thorough investigation of its impact on relevant queueing systems which little work focuses on. In this paper, the scenario with three independent Poisson input flows based on our previous work about the pdf with two independent Poisson input flows., and one output flow is considered. The asynchronous NC-based strategy is that a new arrival evicts a head packet holding in its queue when waiting for another packet from the other flow to encode. The three flows are divided into two parts for getting the probability density function (pdf) of the output flow. The pdf for the output flow which contains both coded and uncoded packets is derived. Besides, the statistic characteristics of this strategy are analyzed. These results are verified by numerical simulations.

We tackle the problem of mobile visual search. Moving pictures experts group (MPEG) has completed a standard named compact descriptor for visual search (CDVS) to provide a standardized syntax in the context of image retrieval application. CDVS applies principal components analysis to reduce the dimension of local feature descriptor as the input of global descriptor pipeline, and utilizes traditional fisher vector as the local feature descriptor aggregation algorithm. However, the descriptor components of SIFT and Fisher Vector (FV) have highly non-Gaussian statistics, and applying a single PCA transform can in-fact hurt compression performance at high rates. We develop a net-based architecture combining neural networks with FV layer to obtain fisher vector. There are two advantages in our architecture comparing with CDVS global descriptor pipeline. One is that we employ “autoencoder” networks to reduce the dimensionality of data, the other is that we exploit a trainable system to learn parameters after the FV codebook obtained. The experiments demonstrate an obvious advantage of our proposed architecture in terms of CDVS retrieval task.

This paper has researched on the leading nodes fluctuation of the CPI (the consumer price Index) based on Complex Network. The price fluctuations of consumer often affect each other. However, in the fluctuation of a certain amount of commodity prices, several products play an important role in effecting the rest of commodity price changes. The paper has Selected representative commodities including Guangzhou CPI as nodes, on the basis of time Series of above-mentioned commodities prices indexes from 2001 to 2009, researched on the correlation coefficients (named as P) of time series between any two, when P≥0.3, the two nodes are connected. The authors have established price fluctuation weighted complex network to study the complex relationship of commodity price fluctuation. By searching for the leading commodity goods (i.e. leading node) from a number of goods, the authors has proposed that great importance should be attached to monitor these leading commodities, which will play a vital role in controlling the whole consumer price changes and establishing a price mechanism on emergency management.

In the ground-based FSR, the strong ground reflected signal seriously cancel the useful signal, as a result the estimation methods of the target images in current SISAR imaging technology are no longer applicable. The height difference of the target can't be estimated from the amplitude of complex profile function (CPF). In this paper, firstly, the multipath interference on the forward scatter (FS) signal model and imaging results of the moving target are analyzed by the two ray path model. Then, the estimation methods of the target profile image under multipath interference condition are presented. As a result, the accurate target images that cannot be achieved by using the current imaging algorithm can be extracted using the modified imaging algorithm. Finally, the effectiveness of the estimation methods is validated by the simulations.

the main advantage of biomolecular computing devices in vivo is their ability to interact directly with biological systems. All components of the molecular computers, including the input module, computation module and output module are molecules that interact in solution along a cascade of programmable chemical events. Here, a biomolecular computing model in vivo for SAT problem is presented. A synthetic gene network is constructed by RNAi and lactose operon in living cell. This model explores further the ability to solve hard problems based on organism processing signal.

Quantitative modeling and simulation based on Anterior Chamber OCT images plays a very important role in acute angle-closure glaucoma (AACG) pathology, clinic and treatment. In order to attain quantitative analysis of the results, a series quantitative modeling methods and simulation analysis of the pathogenesis of AACG were proposed in this paper. The displacement and stress of anterior segment were obtained as the simulation results by using the Finite Element Method (FEM). The results proved the feasibility of the quantitative modeling and simulation of anterior chamber based on Anterior Chamber OCT images.

Laser has been used clinically as a therapeutic tool for at least twenty years and has achieved good clinical results. An understanding of optical properties of tissue is important for these applications. However, applying the rigorous electromagnetic theory to analyze light propagation in tissue is almost impossible. Therefore, several approximate analysis approaches to model the light propagation in tissue were introduced. In this study we tried to measure the power of diffuse light on body surface non-invasively and brought the data into comparison. Five positions which the superficial tissues are different were selected and Automation Control Technology was used to improve measurement efficiency and accuracy. Our study suggests that the distributions of diffuse light along different paths on the surface of skin are conformed to exponential attenuation law but the attenuation coefficients are different. Otherwise, the exponential attenuation coefficient of the same position is also influenced by the gender of subject.

DNA computing is a bio-computing paradigm based on the specific hybridization of DNA strands. Most of DNA computing models and experiments were held in vitro. In this paper, a DNA computing paradigm in vivo is presented. A synthetic gene network is constructed by RNAi and lactose operon in living cell for solving minimum dominating set problem. This model explores further the ability to solve hard problems based on organism processing signal.

This paper builds a new collaborative contract that stresses on long-term relationship between a single supplier and a single retailer in a supply chain. This relationship is based on commitment and trust within the supply chain. Both participants of the supply chain take effort to improve quality. The objective function of the proposed model is to maximize the total revenue of the supplier as well as that of the buyer. The new contract takes the quality issue in addition to the unobservable commitment issue into consideration.