Numerical investigation of flow field configuration and contact resistance for PEM fuel cell performance

A steady-state three-dimensional non-isothermal computational fluid dynamics (CFD) model of a proton exchange membrane fuel cell is presented. Conservation of mass, momentum, species, energy, and charge, as well as electrochemical kinetics are considered. In this model, the effect of interfacial contact resistance is also included. The numerical solution is based on a finite-volume method. In this study the effects of flow channel dimensions on the cell performance are investigated. Simulation results indicate that increasing the channel width will improve the limiting current density. However, it is observed that an optimum shoulder size of the flow channels exists for which the cell performance is the highest. Polarization curves are obtained for different operating conditions which, in general, compare favorably with the corresponding experimental data. Such a CFD model can be used as a tool in the development and optimization of PEM fuel cells.     

A linear theory for bending stress-strain analysis of a beam with an edge crack

In this paper, a new linear theory for bending stress-strain analysis of a cracked beam has been developed. A displacement field has been suggested for the beam strain and stress calculations. The bending differential equation for the beam has been written using equilibrium equations. The required constant for this model is also obtained from fracture mechanics. The bending equation has been solved for a simply supported beam with rectangular cross-section and the results are compared with finite element and empirical results. There is an excellent agreement between theoretical results and those obtained by numerical and empirical methods. The model developed in this research is a simple and precise approximation of the behavior of the cracked beams in bending.     

Many-Objective Virtual Machine Placement

The process of selecting which virtual machines (VMs) should be executed at each physical machine (PM) of a virtualized infrastructure is commonly known as Virtual Machine Placement (VMP). This work presents a general many-objective optimization framework that is able to consider as many objective functions as needed when solving a VMP problem in a pure multi-objective context. As an example of utilization of the proposed framework, a formulation of a many-objective VMP problem (MaVMP) is proposed, considering the simultaneous optimization of the following five objective functions: (1) power consumption, (2) network traffic, (3) economical revenue, (4) quality of service and (5) network load balancing. To solve the formulated MaVMP problem, an interactive memetic algorithm is proposed. Experimental results prove the correctness of the proposed algorithm, its effectiveness converging to a manageable number of solutions and its capabilities to solve problem instances with large numbers of PMs and VMs.     

Effect of Messaging Model on the Reliable Data Transfer Latency in a Fog System

Journal of Network and Systems Management - IoT applications have become a pillar for enhancing the quality of life. However, the increasing amount of data generated by IoT devices places pressure...     

Speaker independent feature selection for speech emotion recognition: A multi-task approach

Nowadays, automatic speech emotion recognition has numerous applications. One of the important steps of these systems is the feature selection step. Because it is not known which acoustic features of person’s speech are related to speech emotion, much effort has been made to introduce several acoustic features. However, since employing all of these features will lower the learning efficiency of classifiers, it is necessary to select some features. Moreover, when there are several speakers, choosing speaker-independent features is required. For this reason, the present paper attempts to select features which are not only related to the emotion of speech, but are also speaker-independent. For this purpose, the current study proposes a multi-task approach which selects the proper speaker-independent features for each pair of classes. The selected features are then given to the classifier. Finally, the outputs of the classifiers are appropriately combined to achieve an output of a multi-class problem. Simulation results reveal that the proposed approach outperforms other methods and offers higher efficiency in terms of detection accuracy and runtime.

     

Adaptive content-and-deadline aware chunk scheduling in mesh-based P2P video streaming

In mesh-based Peer-to-Peer (P2P) live video streaming systems packet scheduling is an important factor in overall video playback quality. In mesh based P2P video streaming systems, each video sequence is divided into chunks, which are then distributed by multiple suppliers to the receivers. The suppliers need to be coordinated by the receiver through specifying a transmission schedule for each of them. Many previous studies on scheduling of P2P streaming tend to mainly focus on networking issues which strongly depend on a particular P2P architecture such as tree or mesh. These algorithms suffer from some design issues: 1) they are too complex to deploy, 2) they do not take video characteristics into account and 3) they do not have sender-side transmission policy. To address all three of these problems, we propose a new chunk scheduling scheme which consists of two parts: i) receiver-side scheduler and ii) sender-side transmission order scheme. The proposed receiver-side scheduler considers the contribution level of each video frame as well as the frame’s urgency in order to define a priority for each video frame. It attempts to request frames with highest priority from peers which can deliver them in a shorter time. We also design a new chunk transmission order scheme that decides which requested chunk will be sent out first based on its importance to the requesting neighbor. Our simulation results show that the proposed scheduling scheme improves the overall quality of the perceived video in mesh-based P2P video streaming architectures substantially.     

Efficiency evaluation of the photocatalytic degradation of zinc oxide nanoparticles immobilized on modified zeolites in the removal of styrene vapor from air

Styrene monomer is a volatile organic compound that has many applications in plastics, rubber, and paint manufacturing industries. Exposure to styrene vapor has certain effects, including suppression of the central nervous system, loss of concentration, weakness and fatigue, and nausea and there is a possibility of carcinogenesis in long-term exposure. Therefore, it is necessary to control and eliminate this vapor. The aim of this study was to investigate the performance of zinc oxide nanoparticles on modified natural zeolites in removing styrene vapor from the air. Natural zeolites of clinoptilolite were modified using hydrochloric acid and diphenyldichlorosilane. Next, zinc oxide nanoparticles with different ratios of 3, 5, and 10 wt% were stabilized on the zeolites. To determine their characteristics, samples were used from BET, SEM and XRD analyses. The input styrene concentration and the ratio of nanoparticles stabilized on zeolites were studied as effective functional parameters on the removal process. The efficiency results of natural zeolites (Ze) and modified zeolites (Mze) in styrene adsorption from the air show that the styrene breakthrough in the bed of MZe compared to that of Ze increases approximately two times. Also, the results showed that the removal by the process of UV/MZe-ZnO 3%, UV/MZe-ZnO 5%, and UV/MZe-ZnO 10%, was 36.5%, 40%, and 26%, respectively. From the results it can be concluded that MZe can increase the efficiency of photocatalytic degradation. Clinoptilolites of Iran can be used as an adsorbent to remove polluted air in industries that have low concentrations and flow rates.     

Linear nearest neighbor optimization in quantum circuits: a multiobjective perspective

Several current implementations of quantum circuits rely on the linear nearest neighbor restriction, which only allows interaction between adjacent qubits. Most methods that address the process of converting a generic circuit to an equivalent circuit which satisfies this restriction, minimize the number of additional SWAP gates required by this process. Moreover, most methods which address this problem are designed for 1D circuits. Considering the new and promising proposals for 2D quantum circuits, what we propose is a new perspective on this problem, namely that it can be seen as a multiobjective optimization problem. To test our hypothesis, we developed a multiobjective evolutionary algorithm that solves this problem by considering two objectives: minimizing the size of the 2D grid where the circuit is placed, and minimizing the number of additional SWAP gates. Of the methods designed for 2D circuits, only one considers different grid sizes which are much larger than strictly necessary. Consequently, our algorithm makes considerations which other methods do not make, since it naturally finds the grid which requires fewer SWAP gates for the circuit conversion, whether it is one-dimensional or two-dimensional. Our experimental results indicate that allowing a larger grid size results in fewer additional SWAP gates in about 73% of the tested circuits. Additionally, the average improvement we found when using larger grid sizes is about 30%, while the best improvement over using the smallest possible grid is 63.8%.     

Blood supply chain network design considering blood group compatibility under uncertainty

This paper addresses the design of a blood supply chain (SC) network considering blood group compatibility. To this aim, a bi-objective mathematical programming model is developed which minimises the total cost as well as the maximum unsatisfied demand. Due to uncertain nature of some input parameters, two novel robust possibilistic programming models are proposed based on credibility measure. The data of a real case study are then used to illustrate the applicability and performance of the proposed models as well as validating the proposed robust possibilistic programming approach. The obtained results show the superiority of the developed models and significant cost savings compared to current existed blood SC network.