Automated design of a new integrated intelligent computing paradigm for constructing a constitutive model applicable to predicting rock fractures

Making a relation between strains and stresses is an important subject in the rock engineering field. Shear behaviors of rock fractures have been extensively investigated by different researchers. Literature mostly consists of constitutive models in the form of empirical functions that represent experimental data using mathematical regression techniques. As an alternative, this study aims to present a new integrated intelligent computing paradigm to form a constitutive model applicable to rock fractures. To this end, an RBFNN-GWO model is presented, which integrates the radial basis function neural network (RBFNN) with grey wolf optimization (GWO). In the proposed model, the hyperparameters and weights of RBFNN were tuned using the GWO algorithm. The efficiency of the designed RBFNN-GWO was examined comparing it with the RBFNN-GA model (a combination of RBFNN and the Genetic Algorithm). The proposed models were trained based on the results of a systematic set of 84 direct shear tests gathered from the literature. The finding of the current study demonstrated the efficiency of both the RBFNN-GA and RBFNN-GWO models in predicting the dilation angle, peak shear displacement, and stress as the rock fracture properties. Among the two models proposed in this study, the statistical results revealed the superiority of RBFNN-GWO over RBFNN-GA in terms of prediction accuracy.

     

Sustainability assessment of Iranian petrochemical companies in stock exchange: A data envelopment analysis-based approach

The assessment of efficiency is always of particular importance according to different indicators from different perspectives. There are various techniques for evaluating petrochemical companies, among which the data envelopment analysis technique is one of the best techniques that can be used to calculate the relative efficiency of a set of decision-making units with network structures. In the present paper, seven petrochemical companies listed in the Iranian stock exchange were analysed. These companies were evaluated in terms of financial performance and sustainable development, and their relative efficiency was calculated during 2015–2016. According to the obtained results, only Marun Petrochemical Co. was found to be efficient in all areas and years. The results also showed that four companies were efficient in financial terms over the period under study. In the general conclusion regarding the companies' performance, Marun was ranked first, Jam was ranked second, and Zagros was ranked third.     

Numerical evaluation and the effects of geometrical and operational parameters on thermal performance of the shell and double coil tube heat exchanger

Heat exchangers are extensively used in various industries. In this study, the impact of geometric and flow parameters on the performance of a shell and double helical coil heat exchanger is studied numerically. The investigated geometric parameters include external coil pitch, internal coil pitch, internal coil diameter, and coil diameter. The influences of considered geometrical parameters are analyzed on the output temperature of the hot and cold fluid, convective heat transfer coefficient, pressure drop, and average Nusselt number. Water is considered as working fluid in both shell and tube. As an innovation, double helical coils are used instead of one in the heat exchanger. To compare the obtained results accurately, in each section, the heat transfer area (coil outer surface) is kept constant in all models. The results show that the geometrical parameters of double helical coils significantly affect the heat transfer rate.     

Dissolution Behavior of Electrodeposited Ni–W Alloys

Nickel (Ni)–Tungsten (W) alloys were electrodeposited galvanostatically (at–10 mA cm^–2) on copper substrate with 3 different W contents under the controlled hydrodynamic conditions and then the anodic dissolution behaviors of the alloys were observed by potentiodynamic polarization and electrochemical quartz crystal microbalance (EQCM) techniques. While the structure of the electrodeposited Ni–W alloy with low W content (15.90% W) was crystalline, that of the alloy with high W content (50.80% W) was nano-crystalline according to X-ray diffraction patterns. The increase in the W content of the electrodeposited Ni–W alloy resulted decrease at pH 3 and increase at pH 7 and 12.5 in the anodic currents of the alloy. The pH dependent dissolutions caused electrodeposited alloy surface to have W—enrichment at pH 3 and Ni—enrichment at pH 7 and 12.5. These observations indicated that the selective dissolution of Ni or W was the main mechanism in the anodic dissolution of the electrodeposited Ni–W alloys. The EQCM experiments conducted at pH 7 supported the presence of the selective dissolution mechanism that the anodic dissolution potential of W was 0.42 V lower than that of Ni in the electrodeposited Ni–W alloys.     

Modeling managerial behavior in real options valuation for project-based environments

Project valuation, as a decision-making tool for initiating investments in projects, should be able to value project flexibilities and incorporate reasonable risk preferences of relevant decision makers. Real options valuation methods are the available approaches for valuing project flexibilities, whereas they have shortcomings in considering managers’ reasonable risk preferences in project decisions. Therefore, researchers have suggested approximating the perspective on risk of real options methods and practitioners in project management. This study proposes a fair real options valuation for project-based environments by a behavioral economic approach, which adopts binomial lattice method, Monte-Carlo simulation, and cumulative prospect theory. The results show that behavioral factors such as ‘risk attitude’ and ‘loss aversion’ should be accepted in project investment decisions while limited to an acceptable amount depending on the project conditions (e.g. uniqueness of decision-making scenarios). This research contributes to the project management domain by enhancing project investment decisions that include project flexibilities.     

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.     

Audio Steganalysis based on collaboration of fractal dimensions and convolutional neural networks

Multimedia Tools and Applications - Steganography is the art of concealing a message within a cover media with the least understandable changes. On the other hand, steganalysis algorithms try to...