Could-based vehicular networks: a taxonomy,survey, and conceptual hybrid architecture

Wireless Networks - In recent times, vehicular network research has attracted the attention of both researchers and the industry partly due to its potential applications in efficient traffic...     

Correction to: Cloud-based vehicular networks: a taxonomy,survey, and conceptual hybrid architecture

Wireless Networks - The original version of this article unfortunately contained a mistake in the title. The correct title has been published with this erratum.     

On the analysis of fracture mechanisms and mechanical behavior of AA5083-based tri-modal composites reinforced with 5 wt.%B4C and toughened by AA5083 and AA2024 coarse grain phases

In this article, the influence of AA2024 and AA5083 coarse grains on mechanical properties and failure mechanisms of AA5083-5wt. %B₄C tri-modal composite has been discussed. AA2024 and AA5083 powders (<100 µm) were added to mechanically milled AA5083-5 wt.%B₄C powders in 25 and 50 wt.% and the mixtures were consolidated using the hot press and hot extrusion techniques. Results indicated that by adding AA2024 and AA5083 powders as coarse grains, hardness and tensile strength of AA5083-5 wt.%B₄C composite decreased but ductility increased. Moreover, by adding AA2024 powders as coarse grains, fracture mode changed and cracks tended to grow through along AA2024/AA5083-5 wt.%B₄C interface rather than being arrested or deflected. It seemed that dislocation mobility and the interface between coarse grains and ultra-fine grains had the main role in determining the mechanical properties and failure mechanisms in tri-modal AA5083-B₄C composites.     

Using the red-near infrared spectral to estimate ground cover based on vegetative indices

Rapid and accurate estimation of Ground Cover (GC) at regional and global scales for agricultural management application is only possible by using remote sensing (RS). In this study, two Vegetation Indices (VIs) including the Perpendicular Vegetation Index (PVI) and Normalized Difference Vegetation Index (NDVI) were used for estimating GC. Since the parameters of the bare soil line have an important role in calculating GC based on PVI, this line was extracted based on the red-NIRmin (minimum near infrared) method with different intervals (0.0001, 0.0005, and 0.0010). In addition to traditional statistics such as Root Mean Square Error (RMSE), the sensitivity analysis (S) was also used to sharpen the accuracy of the models' estimations. The results indicated that the PVI-based method, in contrast to the NDVI-based approach, had a better performance in estimating GC of wheat. The highest correlation between the observed GC and the estimated GC based on PVI method was achieved in interval length of 0.0005 (R2 = 0.91) with RMSE equal to 8.82. This regression line (GCEST = -3.47 + 0.96 GCOBS) was not significantly different from the 1:1 line. As expected, the best estimation was achieved when the sensitivity of estimated GC based on PVI (length of the interval: 0.0005) was almost constant and low compared to the other models.     

Application of Fuzzy c-means algorithm for the estimation of Asphaltene precipitation

One of problematic topics in petroleum engineering is Asphaltene precipitation issue which causes problems such as tubing plugging and formation damage due to temperature, pressure and composition changes so the notability of this issue increases. In the present investigation a novel Fuzzy c-means (FCM) algorithm was developed to predict precipitated asphaltene as function of dilution ratio, carbon number of precipitants and temperature for solving the problem. The results showed that this novel approach has great ability to predict precipitated asphaltene in terms of aforementioned parameters. The coefficients of determination (R²) for training and testing steps are calculated as 0.9828 and 0.9387 respectively. This great degree of accuracy expresses that the predicting algorithm has potential to be utilized as software for prediction of asphaltene behavior.     

On the prediction of solubility of alkane in carbon dioxide using the LSSVM algorithm

The increasing global energy demand and declination of oil reservoir in recent years cause the researchers attention focus on the enhancement of oil recovery approaches. One of the extensive applicable methods for enhancement of oil recovery, which has great efficiency and environmental benefits, is carbon dioxide injection. The CO₂ injection has various effects on the reservoir fluid, which causes enhancement of recovery. One of these effects is extraction of lighter components of crude oil, which straightly depends on solubility of hydrocarbons in carbon dioxide. In order to better understand of this parameter, in this study, Least squares support vector machine (LSSVM) algorithm was developed as a novel predictive tool to estimate solubility of alkane in CO₂ as function of carbon number of alkane, carbon dioxide density, pressure, and temperature. The predicting model outputs were compared with the extracted experimental solubility from literature statistically and graphically. The comparison showed the great ability and high accuracy of developed model in prediction of solubility.     

Modeling and analysis of hydrogen production in steam methane reforming (SMR) process

Hydrogen is one a gas that demands continue to grow across many industries. Due to the growth for this gas the means of producing it and the ability to supply this demand is of great importance. As a result of this, steam methane reforming is a process of high significance as it is one of the most economically and popular means of producing hydrogen. The value of this process is tremendous as it is able to provide up to 48% of global demands, with this only predicted to increase. Therefore, the understanding of what occurs during this process and the steps that it experiences must be understood to ensure that an efficient system is created.

Steam methane reforming operates by converting the hydrocarbons located in methane into hydrogen and CO_x. This process will generally occur over two different stages, a reformer stage, before going into a water-gas shift reactor. After these main processes occur the product produced may undergo purification to remove any containments and ensuring that the hydrogen is at the industry standard. To help investigate this process and how various stages affect others it can be modeled through software such as Unisim which allows modifications to be made and analyzed the effect this had on the system, allowing a potential more efficient system to be designed which will help meet the growing demand.     

Scopolamine impairs human recognition memory: Data and modeling.

Eight subjects studied a set of complex visual images after administration of 0.4 mg scopolamine. Another 8 subjects performed the same task without drug administration. On a subsequent item recognition test, subjects rated, on a 5-point scale, their confidence that the studied pictures and an equal number of unstudied lures were actually presented. Results showed that scopolamine affected responses to studied items, but not unstudied lures, demonstrating an unambiguous effect of scopolamine on recognition memory. To describe the scopolamine-injected subjects' data, the authors constructed a new model of 2-process recognition that includes the A. P. Yonelinas (1994) model as a limiting case. The model analysis suggests that scopolamine affected both familiarity and recollection. In particular, scopolamine did not affect the frequency with which recollection took place, but rather, affected the amount of recollected information. (PsycINFO Database Record (c) 2010 APA, all rights reserved)