19672 research outputs found
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Green transmission for C-RAN based on SWIPT in 5G: a review
No full textC-RAN is a promising new design for the next generation, an important aspect of it in the energy efficiency consideration. Hence, it is considering an innovative candidate to use it as an alternative cellular network instead of the traditional. Investigation green transmission of mobile cloud radio access networks based on SWIPT for 5G cellular networks. Especially, with considering SWIPT as a future solution for increasing the lifetime of end-user battery’s, that’s mean this technique will improving energy efficiency (EE). Addressing SWIPT into C-RAN is a challenging and it is needed to developing a new algorithm to use it on the cellular network with many trying to ensure the success of the system performance. C-RAN as a network and SWIPT as a promising technique with the suggesting green wireless network are discussed besides the importance of energy efficiency for the next generation. Furthermore, there was a study on fifth enabling technologies that can be used for 5G with emphasis on two of them (C-RAN and energy efficiency). Lastly, research challenges and future direction that require substantial research efforts are summarized. © 2018, Springer Science+Business Media, LLC, part of Springer Nature
Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting
No full textThe neural network is a technique to reduce cost and time that can be a good alternative to practical testing. This technique, which has become more important with the advancement of computer science, can also be used to predict the properties of nanofluids. To prove this claim, in this research, an optimal artificial neural network (ANN) was designed to evaluation the thermal conductivity enhancement of the SWCNTs/EG-water nanofluid using experimental data. For this goal, reported experimental enhancement for various concentrations and temperatures were employed. 35 measured data obtained from experiments have been applied to utilize ANN modeling. 80% data were chosen for network training and the remaining data were adopted for network testing. Based on the minimum mean square error (MSE), ANN model with two hidden layers and 4 neurons in each layer was selected. In addition, a new correlation was presented for predicting the thermal conductivity enhancement. Comparative results showed ANN model can forecast the thermal conductivity enhancement of nanofluids appropriately. © 2019 Elsevier B.V
An Outlier Detection Method for Circular Data Using Covratio Statistics
No full textThe existence of outlier may affect data aberrantly. However, outlier detection problem has been frequently discussed for linear data but limited on circular data. Thus, this paper discusses an outlier detection method on circular data. We focus on circular data with equal error concentration parameters where the data is studied using linear functional relationship model. In this paper, the data and the error terms are distributed with von Mises distribution. We modify the covratio statistics in which the correction factor is applied to the estimation of concentration parameter. We develop the cut-off equation based on the 5% upper percentile of the covratio statistics and the power of performance of outlier detection is examined by a Monte Carlo simulation study. The simulation result shows that the power of performance increases when the concentration and the level of contamination increase. The applicability of the proposed method is illustrated by using the wind direction data collected from the Holderness Coastline at the Humberside Coast in North Sea, United Kingdom. © 2019 Malaysian Abstracting and Indexing System. All rights reserved
Standardized bioassays: An improved method for studying Fusarium oxysporum f. sp. cubense race 4 (FocR4) pathogen stress response in musa acuminata cv. ‘Berangan’
No full textTo date, there is no standardized Fusarium bioassay protocol established owing partly to the wide variety of Fusarium oxysporum f. sp. cubense (Foc) isolates and banana cultivars present. Thus, validation of the infection parameters is deemed essential prior to each bioassay experiment. In the current study, a simple standardized workflow was developed based on available assays for testing Fusarium wilt disease response in Musa acuminata using M. acuminata cv. ‘Berangan’ of tissue-culture origin as a model. The phenotypic assays were able to detect external disease symptoms less than one week post-inoculation, while the molecular approach using RT-qPCR identified differential expression of catalase (CAT), pathogenesis-related 10 (PR10), phenylalanine ammonia-lyase (PAL) and xylanase (XYL) genes as early as day 0. The transcript levels of PR10 and XYL fluctuated over 4 days of Foc Race 4 (FocR4 C1 HIR isolate) infection while the expression of CAT steadily increased over time. In contrast, PAL was highly upregulated at 2 days post-inoculation. These signature changes suggest that all genes tested might be involved in the early defense response of ‘Berangan’ plants against FocR4 infection. ‘Berangan’ cultivar was found to be highly susceptible to Foc Race 4 (C1 HIR isolate) with leaf symptoms index (LSI) and rhizome discoloration index (RDI) scores of 4.257 and 5.971, respectively. The procedure elaborated in this study can be used as a reference Foc bioassay for reproducible and comparable results possibly across cultivars and test isolates due to its simple steps aided by integration of phenotypic and molecular approach. © 2019, University of Malaya. All rights reserved
To fly or not to fly? An empirical study of trust, post-recovery satisfaction and loyalty of Malaysia Airlines passengers
No full textPurpose: The purpose of this paper is to investigate the importance of successful service recovery in the airline sector by examining the interrelationship between perceived justice, recovery satisfaction and overall satisfaction, customer trust and customer loyalty. Furthermore, the research assesses the mediating effect of overall satisfaction and customer trust on customer loyalty. Design/methodology/approach: Data were collected via an airport intercept survey of Malaysia Airlines passengers who had experienced service failure. In total, 380 responses were used for the final analysis. The study uses partial least squares structural equation modelling technique with SmartPLS 3.0, in order to test and validate the research model and hypotheses posited. Findings: The results reveal that: recovery satisfaction is significantly affected by procedural and interactional justice; distributive and procedural justice, as well as recovery satisfaction influenced overall satisfaction; customer trust is most influenced by interactional justice, distributive justice and recovery satisfaction; customer loyalty is positively affected by customer trust, overall satisfaction and recovery satisfaction; and the influence amongst recovery satisfaction and customer loyalty is partially mediated by customer trust and overall satisfaction. Originality/value: The study contributes to a whole conceptual comprehension of the essential determinants of customer loyalty from the combined perspectives of three theories, namely, justice theory, expectancy disconfirmation theory and commitment-trust theory. This study successfully differentiates the three dimensions of perceived justice and assesses them individually to discern and compare their influence on overall satisfaction, recovery satisfaction and trust. In addition, the study finds that the influence of recovery satisfaction on loyalty is partially and sequentially mediated by trust and overall satisfaction. © 2019, Emerald Publishing Limited
Mean Monte Carlo Finite Difference Method for Random Sampling of a Nonlinear Epidemic System
No full textIn this article, a numerical method integrated with statistical data simulation technique is introduced to solve a nonlinear system of ordinary differential equations with multiple random variable coefficients. The utilization of Monte Carlo simulation with central divided difference formula of finite difference (FD) method is repeated n times to simulate values of the variable coefficients as random sampling instead being limited as real values with respect to time. The mean of the n final solutions via this integrated technique, named in short as mean Monte Carlo finite difference (MMCFD) method, represents the final solution of the system. This method is proposed for the first time to calculate the numerical solution obtained for each subpopulation as a vector distribution. The numerical outputs are tabulated, graphed, and compared with previous statistical estimations for 2013, 2015, and 2030, respectively. The solutions of FD and MMCFD are found to be in good agreement with small standard deviation of the means, and small measure of difference. The new MMCFD method is useful to predict intervals of random distributions for the numerical solutions of this epidemiology model with better approximation and agreement between existing statistical estimations and FD numerical solutions. © The Author(s) 2016
Micropropagation and Cellular Behaviour Changes During in vitro Flowering of Impatiens balsamina
No full textIn this study, the micropropagation of Impatiens balsamina was established from stem and shoot explants. The effects of GA3 and glutathione on the morphogenesis of this species were also investigated, in order to induce in vitro flowering. It was found that the optimum in vitro plant regeneration was achieved on MS medium supplemented with 1.0 mg L-1 GA3 and in vitro flowering was also obtained from the same medium after 4 weeks of culture. To understand cellular behavior during in vitro flowering, Mitotic Index (MI), chromosome counts, measurement of mean cell and nuclear areas, DNA measurements and ploidy levels were analyzed from in vivo plants, in vitro grown plants and plantlets that flowered in vitro. The chromosome count was the same for all, 2x=2n=14 or n=7. However, it was observed that in vitro flowering plants of Impatiens balsamina had the highest percentage of polyploid cells (30.7%), based on a histogram plotted by the AxioVision 4.7 software. It was found that plant growth regulators, especially GA3, increased the polyploidy level of the meristematic root cells. © 2019, Sociedade Brasileira da Ciencia das Plantas Daninha. All rights reserved
Image splicing forgery detection based on low-dimensional singular value decomposition of discrete cosine transform coefficients
No full textDigital image forgery has significantly increased due to the rapid development of several tools of image manipulation. Based on the manipulation used to produce a tampered image, image forgery techniques can be characterized into three types: copy–move forgery, image splicing, and image retouching. Image splicing is achieved by adding regions from one image into another. This technique changes the content of the target image and causes variations in image features which are used to detect the forgery regions. In this study, an image splicing forgery detection method based on low-dimensional singular value decomposition of discrete cosine transform (DCT) coefficients has been presented. The suspicious input image is divided into multi-size blocks, and each block is transformed into 2D DCT. The DCT coefficients are calculated correspondingly to each block. The features from DCT are extracted using SVD algorithm. The roughness measure is calculated for the set of singular values obtained. Lastly, four types of statistical features—mean, variance, third-order moment skewness, and fourth-order moment kurtosis—are extracted from SVD features and are then arranged in a feature vector. Feature reduction has been applied by kernel principal component analysis. Finally, support vector machine is used to distinguish between the authenticated and spliced images. The proposed method was evaluated against three standard image datasets CASIA v1, DVMM v1, and DVMM v2. The proposed method shows an average detection accuracy of 97.15, 99.30, and 96.97 for DVMM v1, CASIA v1, and DVMM v2, respectively. These results outperform several current image splicing detection methods. © 2018, The Natural Computing Applications Forum
Reservoir computing based echo state networks for ventricular heart beat classification
No full textThe abnormal conduction of cardiac activity in the lower chamber of the heart (ventricular) can cause cardiac diseases and sometimes leads to sudden death. In this paper, the author proposed the Reservoir Computing (RC) based Echo State Networks (ESNs) for ventricular heartbeat classification based on a single Electrocardiogram (ECG) lead. The Association for the Advancement of Medical Instrumentation (AAMI) standards were used to preprocesses the standardized diagnostic tool (ECG signals) based on the interpatient scheme. Despite the extensive efforts and notable experiments that have been done on machine learning techniques for heartbeat classification, ESNs are yet to be considered for heartbeat classification as a is fast, scalable, and reliable approach for real-time scenarios. Our proposed method was especially designed for Medical Internet of Things (MIoT) devices, for instance wearable wireless devices for ECG monitoring or ventricular heart beat detection systems and so on. The experiments were conducted on two public datasets, namely AHA and MIT-BIH-SVDM. The performance of the proposed model was evaluated using the MIT-BIH-AR dataset and it achieved remarkable results. The positive predictive value and sensitivity are 98.98% and 98.98%, respectively for the modified lead II (MLII) and 98.96% and 97.95 for the V1 lead, respectively. However, the experimental results of the state-of-the-art approaches, namely the patient-adaptable method, improved generalization, and the multiview learning approach obtained 92.8%, 87.0%, and 98.0% positive predictive values, respectively. These obtained results of the existing studies exemplify that the performance of this method achieved higher accuracy. We believe that the improved classification accuracy opens up the possibility for implementation of this methodology in Medical Internet of Things (MIoT) devices in order to bring improvements in e-health systems. © 2019 by the authors
Tribological Properties of Steel/Steel, Steel/DLC and DLC/DLC Contacts in the Presence of Biodegradable Oil
No full textTribological properties of tetrahedral diamond-like carbon (ta-C DLC) coating were investigated to assess the effects of the counter surfaces under the boundary condition in the presence of biodegradable oil. The tribological tests were performed using steel/steel, steel/DLC and DLC/DLC contacts, and found that the coefficient of friction was 0.062, 0.059 and 0.048, and the wear loss was 5.57×10–4, 9.81×10–4 and 6.02×10–4 mm3, respectively. DLC/DLC contacts showed the lowest friction coefficient and steel/steel contacts showed the lowest wear. The high wear of steel/DLC compared to the other two contacts might be due to the high affinity between iron and carbon. The high content of oleic acid may maintain the low friction in self-mated ta-C DLC contacts and tribochemical wear was prevented by nano-scale graphitization. Therefore, material combinations are important for tribological properties and canola oil is an effective lubricant for contacts coated with ta-C DLC films. © 2019 Japan Petroleum Institute. All rights reserved