Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance

Engineering with Computers - Prediction of tunnel boring machine (TBM) performance parameters can be caused to reduce the risks associated with tunneling projects. This study is aimed to introduce...     

A probabilistic model for assessing the reliability of wind farms in a power system

Modeling the generation of a wind farm and its effect on power system reliability is a challenging task, largely due to the random behavior of the output power. In this paper, we propose a new probabilistic model for assessing the reliability of wind farms in a power system at hierarchical level II (HLII), using a Monte Carlo simulation. The proposed model shows the effect of correlation between wind and load on reliability calculation. It can also be used for identifying the priority of various points of the network for installing new wind farms, to promote the reliability of the whole system. A simple grid at hierarchical level I (HLI) and a network in the north-eastern region of Iran are studied. Simulation results showed that the correlation between wind and load significantly affects the reliability.     

A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network

One of the undesirable phenomena in the surface mines, which results in various hazards for human and facilities, is flyrock. It seems that the careful study of the subject and its effects on the environment can affect the control of flyrock hazards in the studied area. Therefore, the use of intelligent models and methods which are capable of predicting and simulating the risk of flyrock can be considered as an appropriate solution in this regard. The current research was conducted using nonlinear models and Monte Carlo (MC) simulation. The data used in this study consist of 260 samples of rock thrown from a mine in Malaysia. The parameters used in these models include hole’s diameter (D), hole’s depth (HD), burden to spacing (BS), stemming (ST), maximum charge per delay (MC), and powder factor (PF). At first, multiple regression analysis (MRA) and artificial neural network (ANN) models were used in order to develop a non-linear relationship between dependent and independent parameters. The ANN model was an appropriate predictor of flyrock in the mine. Then using the best implemented model of ANN, the flyrock environmental phenomenon was simulated using MC technique. MC simulation showed a proper level of accuracy of flyrock ranges in the mine. Using this simulation, it can be concluded with 90% accuracy that the Flyrock phenomenon does not exceed 331 m. Under these conditions, this simulation can be used for various areas requiring risk assessment. Finally, a sensitive analysis was carried out on data. This analysis showed MC has the greatest effect on flyrock.     

Pattern synthesize of a cylindrical conformal array antenna by PSO algorithm

Pattern synthesize of conformal array antennas is often a challenging problem. Various optimization algorithms such as genetic, particle swarm optimization (PSO), and invasive weed optimization have already been used for pattern synthesizing of conformal arrays. In this paper, a focused beam is synthesized for a quarter cylindrical conformal array antenna using the PSO algorithm with small computations. The desired pattern is a focused beam at θ = 90° and ? = 45° with 10° beamwidth in elevation and 15° beamwidth in azimuth with ?20 dB side‐lobe level. This method can be used in general for synthesizing arbitrary desired patterns and array geometries.     

Model tree approach for predicting uniaxial compressive strength and Young’s modulus of carbonate rocks

Bulletin of Engineering Geology and the Environment - The uniaxial compressive strength (UCS) and Young’s modulus (E) of rock are important parameters for evaluating the strength,...     

Synthesis,characterization, thermal and optical properties of soluble polyimides derived from an unsymmetrical diamine

A new unsymmetrical diamine monomer, 2,4‐diaminophenyl [4′‐(2′′,6′′‐diphenyl‐4′′‐pyridyl)phenyl]ether, was successfully synthesized by nucleophilic substitution of 1‐chloro‐2,4‐dinitrobenzene with 4‐(2′,6′‐diphenyl‐4′‐pyridyl) phenol. The diamine monomer was characterized by FTIR, ¹H and ¹³C NMR, and elemental analysis techniques and used for the preparation of novel polyimides (PIs) by reaction with commercially available tetracarboxylic dianhydrides such as pyromellitic dianhydride, 3,3′,4,4′‐benzophenone tetracarboxylic dianhydride, and bicyclo[2.2.2]‐oct‐7‐ene‐2,3,5,6‐tetracarboxylic dianhydride. These PIs with inherent viscosities ranged from 0.43 to 0.48 dL/g were readily soluble in many organic solvents and afforded tough and flexible films by solution casting. These polymers exhibited T_gs between 237 and 294°C, and 10% weight loss temperatures in excess of 500°C with up to 56% char yield at 600°C in air. Their maximum fluorescence emission in dilute (0.2 g/dL) NMP solution appeared at 450 nm. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010     

The impact of alphabet size on pattern complexity of maxmin-$$omega$$ω cellular automata

Natural Computing - We present an analysis of an additive cellular automaton (CA) under asynchronous dynamics. The asynchronous scheme is maxmin-$$omega$$, a deterministic system, introduced in...     

Modeling and Optimization of SE and SI of Copper Flotation via Hybrid GA–ANN

In the present paper, a new attitude has been proposed for optimization of the separation efficiency (SE) and the Gaudin’s selectivity index (SI) in a flotation process by Hybrid artificial neural network (ANN) and genetic algorithm (GA). The chemical reagent’s dosage (collector, frother and fuel oil), pH, solid percentage, feed rate, Cu, Mo, and Fe grades in the flotation feed were selected as input variables and the SE-Cu and SI-Mo and SI-Fe were selected as output ones. Multilayer NN with back propagation (BP) algorithm was trained by the standard Bayesian regulation algorithm in which the validation data set did not required to be apart from its training. This algorithm with four-layer was used to relate output and input variables. Employment of Hybrid GA–ANN method resulted in significant improvement on GA fitnesses, as SE-Cu = 88, SI-Mo = 4.47 and SI of Fe = 12.85 were achieved. The input parameters corresponding to the fitnesses were as follows: pH = 12.25; the grade of Cu = 0.55%, Mo = 0.04% and Fe = 5.53%; the collector, frother and fuel–oil concentrations being 16.55, 15.54 and 2.71 (g/ton), respectively; the solid percentage was 25.84% and feed rate was 38,380 ton/day. The best fitness of GA was obtained after 10 generations by MSE value of 2.23.     

Controlled Growth of Conductive AlN Thin Films by Plasma-Assisted Reactive Evaporation

Metallurgical and Materials Transactions A - In this work, the growth of conductive AlN thin films by plasma-assisted reactive evaporation at different filament-to-substrate distances was presented...     

Inhibitory effect of magnetic iron‐oxide nanoparticles on the pattern of expression of lanosterol 14α ‐demethylase (ERG11) in fluconazole‐resistant colonising isolate of Candida albicans

Fluconazole‐resistant Candida albicans is a big scary reality. The authors assessed the antifungal effects of magnetic iron‐oxide nanoparticles on fluconazole‐resistant colonising isolate of C. albicans and determined the expression of ERG11 gene, protein sequence similarity and ergosterol content. C. albicans isolates were characterised and fluconazole resistance is recognised using World Health Organization''s WHONET software. Susceptibility testing of magnetic iron‐oxide nanoparticles against fluconazole‐resistant colonising isolate of C. albicans was performed according to Clinical and Laboratory Standards Institute guidelines. The expression patterns of ERG11 and protein sequence similarity were investigated. Ergosterol quantification has been used to gauge the antifungal activity of magnetic iron‐oxide nanoparticles. The findings indicated that 93% of C. albicans isolates were resistant to fluconazole. Magnetic iron‐oxide nanoparticles were presented activity against fluconazole‐resistant colonising isolate of C. albicans with minimum inhibitory concentration at 250–500 µg/ml. The expression level of ERG11 gene was downregulated in fluconazole‐resistant colonising isolate of C. albicans. The results revealed no differences in fluconazole‐resistant colonising isolate of C. albicans by comparison with ERG11 reference sequences. Moreover, significant reduction was noted in ergosterol content. The findings shed a novel light on the application of magnetic iron‐oxide nanoparticles in fighting against resistant C. albicans.Inspec keywords: microorganisms, biochemistry, molecular biophysics, antibacterial activity, iron compounds, proteins, cellular biophysics, nanoparticles, drugs, geneticsOther keywords: albicans isolates, magnetic iron‐oxide nanoparticles, fluconazole‐resistant colonising isolate, fluconazole resistance, ERG11, candida albicans, protein sequence similarity, ergosterol content, WHONET, ergosterol quantification, susceptibility testing, antifungal activity, gene expression