Transient two-dimensional model of heat and mass transfer in a PEM fuel cell membrane

In the present work, a numerical study of heat and mass transfer within the membrane of a proton exchange membrane fuel cell is presented. The electrolyte membrane is considered an isotropic porous medium and ideal insulator for electrons and reactants. The adopted model in this study is based on the assumption of single-phase and multi-spices flow, supposed two-dimensional and unsteady. For the water transport, the major considered forces are; the convective force, resulting from the pressure gradient, the osmotic force, due to the concentration gradient and the electric force caused by the proton migration from the anode to the cathode. Based on a one-dimensional model, found in the literature, a transient two-dimensional one was proposed. The set of governing equations, written in velocity–pressure formulation, is solved by the implicit finite difference method. An alternating Direct Implicit scheme was used for the calculation. The numerical resolution gives the time- and space-dependent temperature and water concentration. The main focus lies on the influence of different cases of boundary conditions on water concentration and heat transfer variation with the intention of testing the reliability of the proposed computational fluid dynamic (CFD) code.     

Synthesis and characterization of maleic acid polymer for use as scale deposits inhibitors

The problem of scale formation in oil field production facilities is encountered as a result of mixing of injection water (Lias) rich in sulfate with formation water (Ordovicien) rich in barium. 1 A large number of methods have been developed for the prevention of these deposits. In this work, poly(maleic acid) was studied to ascertain its suitability as an inhibitor of barium sulfate scale deposits. It was synthesized by a free radical solution polymerization. Two different initiators were used namely a potassium persulfate and a redox system composed of potassium persulfate/sodium‐hypophosphite monohydrate. Their concentrations were varied to obtain low‐molecular weight polymers. Physical and chemical properties of the polymers obtained were measured by infrared and UV/VIS spectrometry, solubility test, viscosimetry, refractometry, and liquid chromatography. The effectiveness of poly (maleic acid) formulations as scale inhibitor was studied by turbidimetric tests. The most effective inhibitor formulation was found to be that based on poly(maleic acid) prepared via a redox initiating system. This polymer, used at a concentration of 3 ppm, presented the highest effectiveness namely 96% and 88% at room and oil well temperature, respectively, when compared with two commercial inhibitors. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010     

A method to improve and automate flat defect detection during ultrasonic inspection

In the nondestructive testing of materials, ultrasonic imagery can detect and characterize defects that are present in a structure. Data are displayed in the form of images, and processing algorithms can be applied for automatic detection and characterization. However, when using diffracted waves, the amplitude is often too low, and the signals are difficult to distinguish from the noise. Other times, the volume of data requires significant computation time. In this paper, we propose a method that can avoid image formation by replacing it with a sparse matrix and significantly reducing the amount of data to process; this allows for the enhancement and the automation of the detection of thin and flat defects such as cracks. The elements of the sparse matrix form a curve, which is sufficient to characterize defects in many cases. These elements are selected from diffracted signals using the split‐spectrum processing method. In this way, the signal‐to‐noise ratio is improved, and the position of the echo signal is accurately determined. When a crack is present in a material, the points of the sparse matrix form a parabola and classical tools of pattern recognition such as the Hough transform can detect it, thus providing significant help in decision‐making processes. Copyright © 2011 John Wiley & Sons, Ltd.     

Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

Digital signal processing of electroencephalography (EEG) data is now widely utilized in various applications, including motor imagery classification, seizure detection and prediction, emotion classification, mental task classification, drug impact identification and sleep state classification. With the increasing number of recorded EEG channels, it has become clear that effective channel selection algorithms are required for various applications. Guided Whale Optimization Method (Guided WOA), a suggested feature selection algorithm based on Stochastic Fractal Search (SFS) technique, evaluates the chosen subset of channels. This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces (BCIs), the method for identifying essential and irrelevant characteristics in a dataset, and the complexity to be eliminated. This enables (SFS-Guided WOA) algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset. The (SFS-Guided WOA) algorithm is superior in performance metrics, and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.     

SAFT-VNDN: A Socially-Aware Forwarding Technique in Vehicular Named Data Networking

Vehicular Social Networks (VSNs) is the bridge of social networks and Vehicular Ad-Hoc Networks (VANETs). VSNs are promising as they allow the exchange of various types of contents in large-scale through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication protocols. Vehicular Named Data Networking (VNDN) is an auspicious communication paradigm for the challenging VSN environment since it can optimize content dissemination by decoupling contents from their physical locations. However, content dissemination and caching represent crucial challenges in VSNs due to short link lifetime and intermittent connectivity caused by vehicles’ high mobility. Our aim with this paper is to improve content delivery and cache hit ratio, as well as decrease the transmission delay between end-users. In this regard, we propose a novel hybrid VNDN-VSN forwarding technique based on social communities, which allows requester vehicles to easily find the most suitable forwarder or producer among the community members in their neighborhood area. Furthermore, we introduce an effective caching mechanism by dividing the content store into two parts, one for community private contents and the second one for public contents. Simulation results show that our proposed forwarding technique can achieve a favorable performance compared with traditional VNDN, in terms of data delivery ratio, average data delivery delay, and cache hit ratio.     

A Novel Hybrid Machine Learning Approach for Classification of Brain Tumor Images

Abnormal growth of brain tissues is the real cause of brain tumor. Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient. The manual segmentation of brain tumor magnetic resonance images (MRIs) takes time and results vary significantly in low-level features. To address this issue, we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network (CNN) for reliable images segmentation by considering the low-level features of MRI. In this model, we have extracted features through ResNet-50 architecture and fed these feature maps to multi-level CNN model. To handle the classification process, we have collected a total number of 2043 MRI patients of normal, benign, and malignant tumor. Three model CNN, multi-level CNN, and ResNet-50 based multi-level CNN have been used for detection and classification of brain tumors. All the model results are calculated in terms of various numerical values identified as precision (P), recall (R), accuracy (Acc) and f1-score (F1-S). The obtained average results are much better as compared to already existing methods. This modified transfer learning architecture might help the radiologists and doctors as a better significant system for tumor diagnosis.     

Construction of flexible blending parametric surfaces via curves

The main purpose of this paper is to provide a method that allows to solve the blending problem of two parametric surfaces. The blending surface is constructed with a collection of space curves defined by point pairs on the blending boundaries of given primary surfaces. Bézier and C-cubic curves are used to interpolate the blending boundaries. The blending surface is Gⁿ continuously connected to the primary surfaces.     

An artificial immune system heuristic for two-stage multi-machine assembly scheduling problem to minimize total completion time

We address the two-stage multi-machine assembly scheduling problem. The first stage consists of m independently working machines where each machine produces its own component. The second stage consists of two independent and identical assembly machines. The objective is to come up with a schedule that minimizes total or mean completion time for all jobs. The problem has been addressed in the scheduling literature and several heuristics have been proposed. In this paper, we propose a new heuristic called artificial immune system (AIS). We conduct experimental analysis for comparing the newly proposed heuristic AIS with the best known heuristic in the literature. Experimental results show that our proposed heuristic AIS performs better than the best known existing heuristic. More specifically, our new heuristic AIS reduces the error of the best known heuristic by 60% while the computational times of both AIS and the best known heuristic are almost the same.     

Deadline-based connection setup in wavelength-routed WDM networks

This article addresses the ubiquitous topic of quality of service (QoS) aware connection provisioning in wavelength-routed WDM optical networks. The impact of the connection setup time of an optical connection has not been adequately addressed in the open literature. As such, this paper presents a novel approach that uses the optical connection setup time as a service differentiator during connection provisioning. The proposed approach utilizes the Earliest Deadline First (EDF) queueing algorithm to achieve deadline-based connection setup management with the deadline being the setup time requirement of an optical connection. The proposed EDF-based approach would allow the network operator to improve the QoS perceived by the end clients. Performance of this novel scheme is analyzed by accurately calculating various parameters, such as the fraction of connections provisioned on-time (i.e. prior to deadline expiration) and the average time it takes to successfully setup a connection. In addition, the presented approach is validated by a simulation that analyzes the performance of the proposed connection setup scheme in the specific context of the National Science Foundation Network (NSFNET). The obtained results show that a deadline-based setup strategy can minimize blocking probability while achieving QoS differentiation.     

Data Structures in Multi-Objective Evolutionary Algorithms

Data structures used for an algorithm can have a great impact on its performance,particularly for the solution of large and complex problems,such as multi-objective optimization problems(MOPs).Multi-objective evolutionary algorithms(MOEAs) are considered an attractive approach for solving MOPs,since they are able to explore several parts of the Pareto front simultaneously.The data structures for storing and updating populations and non-dominated solutions(archives) may affect the efficiency of the search process.This article describes data structures used in MOEAs for realizing populations and archives in a comparative way,emphasizing their computational requirements and general applicability reported in the original work.