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Sample records for Artificial Neural Network, Prediction, Supercritical extraction, Efficiency, Oregano bract essential oil.

  1. Predicting the supercritical carbon dioxide extraction of oregano bract essential oil

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    Abdolreza Moghadassi

    2011-10-01

    Full Text Available The extraction of essential oils using compressed carbon dioxide is a modern technique offering significant advantagesover more conventional methods, especially in particular applications. The prediction of extraction efficiency is a powerful toolfor designing and optimizing the process. The current work proposed a new method based on the artificial neural network(ANN for the estimation of the extraction efficiency of the essential oil oregano bract. In addition, the work used the backpropagationlearning algorithm, incorporating different training methods. The required data were collected; pre-treating wasused for ANN training. The accuracy and trend stability of the trained networks were verified according to their ability to predictunseen data. The Levenberg-Marquardt algorithm has been found to be the most suitable algorithm, with the appropriatenumber of neurons (i.e., ten neurons in the hidden layer and a minimum average absolute relative error (i.e., 0.019164. Inaddition, some excellent predictions with maximum error of 0.039313 were observed. The results demonstrated the ANN’scapability to predict the measured data. The ANN model performance was also compared to a suitable mathematical model,thereby confirming the superiority of the ANN model.

  2. Artificial Neural Network Approach to Predict Biodiesel Production in Supercritical tert-Butyl Methyl Ether

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    Obie Farobie

    2016-05-01

    Full Text Available In this study, for the first time artificial neural network was used to predict biodiesel yield in supercritical tert-butyl methyl ether (MTBE. The experimental data of biodiesel yield conducted by varying four input factors (i.e. temperature, pressure, oil-to-MTBE molar ratio, and reaction time were used to elucidate artificial neural network model in order to predict biodiesel yield. The main goal of this study was to assess how accurately this artificial neural network model to predict biodiesel yield conducted under supercritical MTBE condition. The result shows that artificial neural network is a powerful tool for modeling and predicting biodiesel yield conducted under supercritical MTBE condition that was proven by a high value of coefficient of determination (R of 0.9969, 0.9899, and 0.9658 for training, validation, and testing, respectively. Using this approach, the highest biodiesel yield was determined of 0.93 mol/mol (corresponding to the actual biodiesel yield of 0.94 mol/mol that was achieved at 400 °C, under the reactor pressure of 10 MPa, oil-to-MTBE molar ratio of 1:40 within 15 min of reaction time.

  3. Chemical composition and bioactivity of different oregano (Origanum vulgare) extracts and essential oil.

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    Teixeira, Bárbara; Marques, António; Ramos, Cristina; Serrano, Carmo; Matos, Olívia; Neng, Nuno R; Nogueira, José M F; Saraiva, Jorge Alexandre; Nunes, Maria Leonor

    2013-08-30

    There is a growing interest in industry to replace synthetic chemicals by natural products with bioactive properties. Aromatic plants are excellent sources of bioactive compounds that can be extracted using several processes. As far as oregano is concerned, studies are lacking addressing the effect of extraction processes in bioactivity of extracts. This study aimed to characterise the in vitro antioxidant and antibacterial properties of oregano (Origanum vulgare) essential oil and extracts (in hot and cold water, and ethanol), and the chemical composition of its essential oil. The major components of oregano essential oil were carvacrol, β-fenchyl alcohol, thymol, and γ-terpinene. Hot water extract had the strongest antioxidant properties and the highest phenolic content. All extracts were ineffective in inhibiting the growth of the seven tested bacteria. In contrast, the essential oil inhibited the growth of all bacteria, causing greater reductions on both Listeria strains (L. monocytogenes and L. innocua). O. vulgare extracts and essential oil from Portuguese origin are strong candidates to replace synthetic chemicals used by the industry. © 2013 Society of Chemical Industry.

  4. Supercritical fluid extraction of oregano (Origanum vulgare) essentials oils: anti-inflammatory properties based on cytokine response on THP-1 macrophages.

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    Ocaña-Fuentes, A; Arranz-Gutiérrez, E; Señorans, F J; Reglero, G

    2010-06-01

    Two fractions (S1 and S2) of an oregano (Origanum vulgare) extract obtained by supercritical fluid extraction have been used to test anti-inflammatory effects on activated human THP-1 cells. The main compounds present in the supercritical extract fractions of oregano were trans-sabinene hydrate, thymol and carvacrol. Fractions toxicity was assessed using the mitochondrial-respiration-dependent 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium (MTT) reduction method for several concentrations during 24 and 48 h of incubation. Concentrations higher than 30 microg/mL of both supercritical S1 and S2 oregano fractions caused a reduction in cell viability in a dose-dependent manner. Oxidized-LDLs (oxLDLs) activated THP-1 macrophages were used as cellular model of atherogenesis and the release/secretion of cytokines (TNT-alpha, IL-1beta, IL-6 and IL-10) and their respective mRNA expressions were quantified both in presence or absence of supercritical oregano extracts. The results showed a decrease in pro-inflammatory TNF-alpha, IL-1beta and IL-6 cytokines synthesis, as well as an increase in the production of anti-inflammatory cytokine IL-10. These results may suggest an anti-inflammatory effect of oregano extracts and their compounds in a cellular model of atherosclerosis. Copyright 2010 Elsevier Ltd. All rights reserved.

  5. Phase equilibrium of binary system carbon dioxide - methanol at high pressure using artificial neural network

    International Nuclear Information System (INIS)

    Nasri, F.; Hatami, T.

    2012-01-01

    Interest in supercritical fluids extraction (SFE ) is increasing throughout many scientific and industrial fields. The common solvent for use in SFE is carbon dioxide. However, pure carbon dioxide frequently fails to efficiently extract the essential oil from a sample matrix, and modifier fluids such as methanol should be used to enhance extraction yield. A more efficient use of SFE requires quantitative prediction of phase equilibrium of this binary system, carbon dioxide - methanol. The purpose of the current research is modeling carbon dioxide - methanol system using artificial neural network (ANN). Results of ANN modeling has been compared with experimental data as well as thermodynamic equations of state. The comparison shows that the ANN modeling has a higher accuracy than thermodynamic models. (author)

  6. ANTIMICROBIAL POTENTIAL OF GARLIC AND OREGANO EXTRACTS AND ESSENTIAL OILS AGAINST DIFFERENT BACTERIAL STRAINS

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    Ionica Deliu

    2017-12-01

    Full Text Available The modern world is often concerned about the bacterial diseases and the diversity of treatment possibilities. The herbal medicines overreach the medical world because the less number of side effects than synthetic drugs and their low costs. In addition to conventional drugs, the natural remedies can solve exceptional health problems. In this study the antibacterial actions of ethanolic, methanolic and aqueous plant extracts (Allium sativum L. and Origanum vulgare L. were tested. Also, we tested the antimicrobial effects of garlic and oregano essential oils against three bacterial strains. The extracts were tested by diffusion method and certain variants were used. The antibacterial effects were read after 24h of incubation at 37°C. The most obvious effect was observed for oregano essential oil and the smallest growth inhibition was registered for aqueous extracts. The alcoholic extracts were more efficient after concentration by evaporation. The most sensitive bacterial strain was Staphylococcus aureus strain. However the Citrobacter freundii clinical strain had not so high sensitivity at plant extracts, we shall consider the plant extracts as a good alternative to synthetic drugs.

  7. Artificial neural network for modeling the extraction of aromatic hydrocarbons from lube oil cuts

    Energy Technology Data Exchange (ETDEWEB)

    Mehrkesh, A.H.; Hajimirzaee, S. [Islamic Azad University, Majlesi Branch, Isfahan (Iran, Islamic Republic of); Hatamipour, M.S.; Tavakoli, T. [Department of Chemical Engineering, University of Isfahan, Isfahan (Iran, Islamic Republic of)

    2011-03-15

    An artificial neural network (ANN) approach was used to obtain a simulation model to predict the rotating disc contactor (RDC) performance during the extraction of aromatic hydrocarbons from lube oil cuts, to produce a lubricating base oil using furfural as solvent. The field data used for training the ANN model was obtained from a lubricating oil production company. The input parameters of the ANN model were the volumetric flow rates of feed and solvent, the temperatures of feed and solvent, and the disc rotation rate. The output parameters were the volumetric flow rate of the raffinate phase and the extraction yield. In this study, a feed-forward multi-layer perceptron neural network was successfully used to demonstrate the complex relationship between the mentioned input and output parameters. (Copyright copyright 2011 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  8. Parametric optimization design for supercritical CO2 power cycle using genetic algorithm and artificial neural network

    International Nuclear Information System (INIS)

    Wang Jiangfeng; Sun Zhixin; Dai Yiping; Ma Shaolin

    2010-01-01

    Supercritical CO 2 power cycle shows a high potential to recover low-grade waste heat due to its better temperature glide matching between heat source and working fluid in the heat recovery vapor generator (HRVG). Parametric analysis and exergy analysis are conducted to examine the effects of thermodynamic parameters on the cycle performance and exergy destruction in each component. The thermodynamic parameters of the supercritical CO 2 power cycle is optimized with exergy efficiency as an objective function by means of genetic algorithm (GA) under the given waste heat condition. An artificial neural network (ANN) with the multi-layer feed-forward network type and back-propagation training is used to achieve parametric optimization design rapidly. It is shown that the key thermodynamic parameters, such as turbine inlet pressure, turbine inlet temperature and environment temperature have significant effects on the performance of the supercritical CO 2 power cycle and exergy destruction in each component. It is also shown that the optimum thermodynamic parameters of supercritical CO 2 power cycle can be predicted with good accuracy using artificial neural network under variable waste heat conditions.

  9. Whey protein-based films incorporated with oregano essential oil

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    Sandra Prestes Lessa Fernandes Oliveira

    Full Text Available Abstract This study aimed to prepare whey protein-based films incorporated with oregano essential oil at different concentrations, and evaluate their properties and antimicrobial activity. Films were more flexible with increasing the concentration of oregano oil and water vapor permeability was higher in the films with oregano oil. Increasing the concentration of essential oil decreased the water solubility. The solubility of control film and film with 1.5% oregano oil was 20.2 and 14.0%, respectively. The addition of 1% of oregano oil improved the resistance of the films. The tensile strength for the control film was 66.0 MPa, while for the film with 1% of oregano oil was 108.7 MPa. Films containing 1.5% oregano oil showed higher antimicrobial activity. The zone of inhibition ranged from 0 to 1.7 cm. The results showed that the whey protein-based films incorporated with oregano essential oil has potential application as active packaging.

  10. Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils.

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    Daynac, Mathieu; Cortes-Cabrera, Alvaro; Prieto, Jose M

    2015-01-01

    Essential oils (EOs) are vastly used as natural antibiotics in Complementary and Alternative Medicine (CAM). Their intrinsic chemical variability and synergisms/antagonisms between its components make difficult to ensure consistent effects through different batches. Our aim is to evaluate the use of artificial neural networks (ANNs) for the prediction of their antimicrobial activity. Methods. The chemical composition and antimicrobial activity of 49 EOs, extracts, and/or fractions was extracted from NCCLS compliant works. The fast artificial neural networks (FANN) software was used and the output data reflected the antimicrobial activity of these EOs against four common pathogens: Staphylococcus aureus, Escherichia coli, Candida albicans, and Clostridium perfringens as measured by standardised disk diffusion assays. Results. ANNs were able to predict >70% of the antimicrobial activities within a 10 mm maximum error range. Similarly, ANNs were able to predict 2 or 3 different bioactivities at the same time. The accuracy of the prediction was only limited by the inherent errors of the popular antimicrobial disk susceptibility test and the nature of the pathogens. Conclusions. ANNs can be reliable, fast, and cheap tools for the prediction of the antimicrobial activity of EOs thus improving their use in CAM.

  11. Modeling of mass transfer of Phospholipids in separation process with supercritical CO2 fluid by RBF artificial neural networks

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    An artificial Radial Basis Function (RBF) neural network model was developed for the prediction of mass transfer of the phospholipids from canola meal in supercritical CO2 fluid. The RBF kind of artificial neural networks (ANN) with orthogonal least squares (OLS) learning algorithm were used for mod...

  12. Supercritical Extraction Process of Allspice Essential Oil

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    Yasvet Y. Andrade-Avila

    2017-01-01

    Full Text Available Allspice essential oil was extracted with supercritical carbon dioxide (SC-CO2 in a static process at three different temperatures (308.15, 313.15, and 318.15 K and four levels of pressure (100, 200, 300, and 360 bar. The amount of oil extracted was measured at intervals of 1, 2, 3, 4, 5, and 6 h; the most extraction yield reached was of 68.47% at 318.15 K, 360 bar, and 6 h of contact time. In this supercritical extraction process, the distribution coefficient (KD, the mean effective diffusion coefficient (Def, the energy of activation (Ea, the thermodynamic properties (ΔG0, ΔH0, and ΔS0, and the apparent solubility (S expressed as mass fraction (w/w were evaluated for the first time. At the equilibrium the experimental apparent solubility data were successfully correlated with the modified Chrastil equation.

  13. Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols.

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    Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong

    2013-11-01

    In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

  14. Supercritical extraction of carqueja essential oil: experiments and modeling

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    R. M. F. Vargas

    2006-09-01

    Full Text Available Baccharis trimera is a native Brazilian plant which has medicinal properties. In this work a method of supercritical extraction was studied to obtain the popularly essential oil from Baccharis trimera, known as carqueja. The aim was to obtain experimental data and to compare two mathematical models used in the simulation of carqueja (Baccharis trimera oil extraction by supercritical CO2. The two mathematical models are based on mass transfer. One of the models, proposed by Reverchon, is solved numerically and requires two adjustable parameters from the experimental data. The other model chosen is the one proposed by Sovová. This model is solved analytically and requires four adjustable parameters. Numerical results are presented and discussed for the adjusted parameters. The experimental results are obtained in a temperature range of 313.15 K to 343.15 K at 90 bar. The extraction yield of carqueja essential oil using supercritical carbon dioxide ranged between 1.72 % (w/w at 323.15 K and 2.34 % (w/w at 343.15 K, 90 bar with a CO2 flow rate of 3.34.10-8 m³/s for a 0.0015 kg sample of Baccharis trimera.

  15. Possibility for use essential oils in veterinary medicine and animal husbandry with special emphasis on oregano oil

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    Vučinić Marijana

    2012-01-01

    Full Text Available The paper reviews the latest studies on possible applications of oregano essential oil in veterinary medicine and animal livestock production. The first part of the paper deals with the definition of essential oils, possibilities for their extraction from plants, possibilities for their application in human and veterinary medicine, the interest of a science in essential oils, and, essential oils classification based on their use in human and veterinary medicine. The second part of the review deals with the properties of oregano essential oil, its main active principles, carvacrol and thymol and its application in veterinary medicine and animal livestock production. Oregano essential oil may be applied in animal feed, in the treatment of coccidiosis of domestic animals and candidiasis. It can be applied as a larvicide, repellent, insecticide and acaricide. It is used in aquaculture to treat fish diseases caused by bacteria and parasites or in the hatchery industry as a disinfectant for eggs or for disinfection of manure. The greatest potential of oregano essential oil is the possibility of its application in organic agriculture and organic animal husbandry. [Projekat Ministarstva nauke Republike Srbije, br. TR 31087

  16. Comparative analysis of essential oil composition of Iranian and Indian Nigella sativa L. extracted using supercritical fluid extraction and solvent extraction

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    Ghahramanloo KH

    2017-07-01

    Full Text Available Kourosh Hasanzadeh Ghahramanloo,1 Behnam Kamalidehghan,2 Hamid Akbari Javar,3 Riyanto Teguh Widodo,1 Keivan Majidzadeh,4 Mohamed Ibrahim Noordin1 1Department of Pharmacy, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; 2Medical Genetics Department, National Institute of Genetic Engineering and Biotechnology (NIGEB, 3Department of Pharmaceutics, Faculty of Pharmacy, Tehran University of Medical Sciences (TUMS, 4Breast Cancer Research Center (BCRC Academic Center for Education, Culture and Research, Tehran, Iran Abstract: The objective of this study was to compare the oil extraction yield and essential oil composition of Indian and Iranian Nigella sativa L. extracted by using Supercritical Fluid Extraction (SFE and solvent extraction methods. In this study, a gas chromatography equipped with a mass spectrophotometer detector was employed for qualitative analysis of the essential oil composition of Indian and Iranian N. sativa L. The results indicated that the main fatty acid composition identified in the essential oils extracted by using SFE and solvent extraction were linoleic acid (22.4%–61.85% and oleic acid (1.64%–18.97%. Thymoquinone (0.72%–21.03% was found to be the major volatile compound in the extracted N. sativa oil. It was observed that the oil extraction efficiency obtained from SFE was significantly (P<0.05 higher than that achieved by the solvent extraction technique. The present study showed that SFE can be used as a more efficient technique for extraction of N. Sativa L. essential oil, which is composed of higher linoleic acid and thymoquinone contents compared to the essential oil obtained by the solvent extraction technique. Keywords: Nigella sativa L., essential oil extraction, supercritical fluid extraction, solvent extraction, fatty acid composition, thymoquinone, linoleic acid

  17. Extraction of citronella (Cymbopogon nardus essential oil using supercritical co2: experimental data and mathematical modeling

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    C. F. Silva

    2011-06-01

    Full Text Available Citronella essential oil has more than eighty components, of which the most important ones are citronellal, geranial and limonene. They are present at high concentrations in the oil and are responsible for the repellent properties of the oil. The oil was extracted using supercritical carbon dioxide due to the high selectivity of the solvent. The operational conditions studied varied from 313.15 to 353.15 K for the temperature and the applied pressures were 6.2, 10.0, 15.0 and 180.0 MPa. Better values of efficiency of the extracted oil were obtained at higher pressure conditions. At constant temperature, the amount of extracted oil increased when the pressure increased, but the opposite occurred when the temperature increased at constant pressure. The composition of the essential oil was complex, although there were several main components in the oil and some waxes were presented in the extracted oils above 10.0 MPa. The results were modeled using a mathematical model in a predictive way, reproducing the extraction curves over the maximum time of the process.

  18. Comparative analysis of essential oil composition of Iranian and Indian Nigella sativa L. extracted using supercritical fluid extraction and solvent extraction.

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    Ghahramanloo, Kourosh Hasanzadeh; Kamalidehghan, Behnam; Akbari Javar, Hamid; Teguh Widodo, Riyanto; Majidzadeh, Keivan; Noordin, Mohamed Ibrahim

    2017-01-01

    The objective of this study was to compare the oil extraction yield and essential oil composition of Indian and Iranian Nigella sativa L. extracted by using Supercritical Fluid Extraction (SFE) and solvent extraction methods. In this study, a gas chromatography equipped with a mass spectrophotometer detector was employed for qualitative analysis of the essential oil composition of Indian and Iranian N. sativa L. The results indicated that the main fatty acid composition identified in the essential oils extracted by using SFE and solvent extraction were linoleic acid (22.4%-61.85%) and oleic acid (1.64%-18.97%). Thymoquinone (0.72%-21.03%) was found to be the major volatile compound in the extracted N. sativa oil. It was observed that the oil extraction efficiency obtained from SFE was significantly ( P essential oil, which is composed of higher linoleic acid and thymoquinone contents compared to the essential oil obtained by the solvent extraction technique.

  19. Development of artificial neural network models for supercritical fluid solvency in presence of co-solvents

    Energy Technology Data Exchange (ETDEWEB)

    Shokir, Eissa Mohamed El-Moghawry; El-Midany, Ayman Abdel-Hamid [Cairo University, Giza (Egypt); Al-Homadhi, Emad Souliman; Al-Mahdy, Osama [King Saud University, Riyadh (Saudi Arabia)

    2014-08-15

    This paper presents the application of artificial neural networks (ANN) to develop new models of liquid solvent dissolution of supercritical fluids with solutes in the presence of cosolvents. The neural network model of the liquid solvent dissolution of CO{sub 2} was built as a function of pressure, temperature, and concentrations of the solutes and cosolvents. Different experimental measurements of liquid solvent dissolution of supercritical fluids (CO{sub 2}) with solutes in the presence of cosolvents were collected. The collected data are divided into two parts. The first part was used in building the models, and the second part was used to test and validate the developed models against the Peng- Robinson equation of state. The developed ANN models showed high accuracy, within the studied variables range, in predicting the solubility of the 2-naphthol, anthracene, and aspirin in the supercritical fluid in the presence and absence of co-solvents compared to (EoS). Therefore, the developed ANN models could be considered as a good tool in predicting the solubility of tested solutes in supercritical fluid.

  20. Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions

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    Ganapathy Thirunavukkarasu

    2009-01-01

    Full Text Available This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively.

  1. Antioxidant effect of poleo and oregano essential oil on roasted sunflower seeds.

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    Quiroga, Patricia R; Grosso, Nelson R; Nepote, Valeria

    2013-12-01

    The objective was to evaluate the stability of sensory and chemical parameters in roasted sunflower seeds supplemented with oregano and poleo essential oils; and the consumer acceptability of this product. Four samples were prepared: plain roasted sunflower seeds (Control = RS-C), and sunflower seeds added with oregano (RS-O) or poleo (RS-P) essential oils or BHT (RS-BHT). Consumer acceptance was determined on fresh samples. The overall acceptance averages were 6.13 for RS-C, 5.62 for RS-P, and 5.50 for RS-O (9-point hedonic scale). The addition of BHT showed greater protection against the oxidation process in the roasted sunflower seeds. Oregano essential oil exhibited a greater antioxidant effect during storage than poleo essential oil. Both essential oils (oregano and poleo) provided protection to the product, inhibiting the formation of undesirable flavors (oxidized and cardboard). The antioxidant activity that presents essential oils of oregano and poleo could be used to preserve roasted sunflower seeds. © 2013 Institute of Food Technologists®

  2. Sensory attribute preservation in extra virgin olive oil with addition of oregano essential oil as natural antioxidant.

    Science.gov (United States)

    Asensio, Claudia M; Nepote, Valeria; Grosso, Nelson R

    2012-09-01

    Four commercial varieties of oregano are farmed in Argentina: "Compacto,"Cordobes,"Criollo," y "Mendocino." Oregano essential oil is known for antioxidant properties. The objective of this study was to evaluate changes in the intensities of positive and negative attributes in extra virgin olive oil with addition of essential oil obtained from the 4 Argentinean oregano types. Oregano essential oil was added into olive oil at 0.05% w/w. The samples were stored in darkness and light exposure during 126 d at room temperature. The intensity ratings of fruity, pungency, bitterness, oregano flavor, and rancid flavor were evaluated every 21 d by a trained sensory panel. In general, samples with addition of oregano essential oil in olive oil exhibited higher and lower intensity ratings of positive and negative attributes, respectively, during storage compared with the control samples. The first 2 principal components explained 72.3% of the variability in the olive oil samples. In general, positive attributes of olive oil were highly associated with the addition of oregano essential oil in darkness, whereas rancid flavor was negatively associated with them. Olive oil with oregano "Cordobes" essential oil was oppositely associated with light exposure treatments and negative attribute (rancid flavor) suggesting better performance as natural antioxidant of this essential oil in olive oil. The result of this study showed that the presence of oregano essential oil, specially "Cordobes" type, preserve sensory quality of extra virgin olive oil prolonging the shelf life of this product. Extra virgin olive oil is highly appreciated for its health benefits, taste, and aroma. These properties are an important aspect in this product quality and need to be preserved. The addition of natural additives instead of synthetic ones covers the present trend in food technology. This research showed that the addition of oregano essential oil preserved the intensity ratings of positive attributes

  3. Oregano powder substitution and shelf life in pork chorizo using Mexican oregano essential oil

    Science.gov (United States)

    The aim of this study was to evaluate the effect of oregano essential oil (OEO) from Mexican oregano, Lippia berlandieri Schauer, as a substitute for oregano powder on pork chorizo physicochemical characteristics, texture, antioxidant capacity, aerobic bacteria colony counts, and sensory evaluation ...

  4. Effect of matrix pretreatment on the supercritical CO2 extraction of Satureja montana essential oil

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    Damjanović-Vratnica Biljana

    2016-01-01

    Full Text Available The effect of different matrix pretreatment of winter savory(Satureja montana L. on the supercritical CO2(SC-CO2 extraction - yield, composition and antimicrobial activity of extracts and essential oil (EO was investigated. Herb matrix was submitted to conventional mechanical grinding, physical disruption by fast decompression of supercritical and subcritical CO2 and physical disruption by mechanical compression. The analyses of the essential oil obtained by SC-CO2 extraction and hydrodistillation were done by GC/FID method. Major compounds in winter savory EO obtained by SC-CO2 extraction and hydrodistillation were: thymol (30.4-35.4% and 35.3%, carvacrol (11.5-14.1% and 14.1%, γ-terpinene (10.2-11.4% and 9.1% and p-cymene (8.3-10.1% and 8.6%, respectively. The gained results revealed that physical disruption of essential oils glands by fast CO2 decompression in supercritical region (FDS achieved the highest essential oil yield as well as highest content of thymol, carvacrol and thymoquinone. Antimicrobial activity of obtained winter savory SC-CO2 extracts was the same (FDS or weaker compared to essential oil obtained by hydrodistillation.

  5. Dietary oregano essential oil alleviates experimentally induced coccidiosis in broilers.

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    Mohiti-Asli, M; Ghanaatparast-Rashti, M

    2015-06-15

    An experiment was conducted to determine the effects of oregano essential oil on growth performance and coccidiosis prevention in mild challenged broilers. A total of 250 1-d-old chicks were used in a completely randomized design with 5 treatments and 5 replicates with 10 birds in each replication. Experimental treatments included: (1) negative control (NC; unchallenged), (2) positive control (PC; challenged with sporulated oocysts of Eimeria), (3) PC fed 200 ppm Diclazuril in diet, (4) PC fed 300 ppm oregano oil in diet, and (5) PC fed 500 ppm oregano oil in diet. At 22 d of age, all the experimental groups except for NC were challenged with 50-fold dose of Livacox T as a trivalent live attenuated coccidiosis vaccine. On d 28, two birds were slaughtered and intestinal coccidiosis lesions were scored 0-4. Moreover, dropping was scored in the scale of 0-3, and oocysts per gram feces (OPG) were measured. Oregano oil at either supplementation rate increased body weight gain (P=0.039) and improved feed conversion ratio (P=0.010) from d 22 to 28, when compared with PC group. Using 500 ppm oregano oil in challenged broilers diet increased European efficiency factor than PC group (P=0.020). Moreover, challenged broilers fed 500 ppm oregano oil or Diclazuril in diets displayed lower coccidiosis lesions scores in upper (P=0.003) and middle (P=0.018) regions of intestine than PC group, with the effect being similar to unchallenged birds. In general, challenged birds fed 500 ppm oregano oil or Diclazuril in diets had lower OPG (P=0.001), dropping scores (P=0.001), litter scores (P=0.001), and pH of litter (P=0.001) than PC group. It could be concluded that supplementation of oregano oil at the dose of 500 ppm in diet may have beneficial effect on prevention of coccidiosis in broilers. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. Biomolecular characterization of wild sicilian oregano: phytochemical screening of essential oils and extracts, and evaluation of their antioxidant activities.

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    Tuttolomondo, Teresa; La Bella, Salvatore; Licata, Mario; Virga, Giuseppe; Leto, Claudio; Saija, Antonella; Trombetta, Domenico; Tomaino, Antonio; Speciale, Antonio; Napoli, Edoardo M; Siracusa, Laura; Pasquale, Andrea; Curcuruto, Giusy; Ruberto, Giuseppe

    2013-03-01

    An extensive survey of wild Sicilian oregano was made. A total of 57 samples were collected from various sites, followed by taxonomic characterization from an agronomic perspective. Based on morphological and production characteristics obtained from the 57 samples, cluster analysis was used to divide the samples into homogeneous groups, to identify the best biotypes. All samples were analyzed for their phytochemical content, applying a cascade-extraction protocol and hydrodistillation, to obtain the non volatile components and the essential oils, respectively. The extracts contained thirteen polyphenol derivatives, i.e., four flavanones, seven flavones, and two organic acids. Their qualitative and quantitative characterization was carried out by LC/MS analyses. The essential oils were characterized using a combination of GC-FID and GC/MS analyses; a total of 81 components were identified. The major components of the oils were thymol, p-cymene, and γ-terpinene. Cluster analysis was carried out on both phytochemical profiles and resulted in the division of the oregano samples into different chemical groups. The antioxidant activity of the essential oils and extracts was investigated by the Folin-Ciocalteau (FC) colorimetric assay, by UV radiation-induced peroxidation in liposomal membranes (UV-IP test), and by determining the O(2)(∙-)-scavenging activity. Copyright © 2013 Verlag Helvetica Chimica Acta AG, Zürich.

  7. Antimicrobial effect of dietary oregano essential oil against Vibrio bacteria in shrimps

    Directory of Open Access Journals (Sweden)

    Gracia-Valenzuela M.H.

    2014-01-01

    Full Text Available The effect of dietary oregano essential oils on the growth of Vibrio bacteria in shrimps was evaluated. Shrimps were fed: (i food with oregano oil with a high level of thymol; (ii food with oregano oil with a high level of carvacrol, and (iii food without oregano oil (the control. The animals were infected by three species of Vibrio (vulnificus, parahaemolyticus and cholerae. The microbial counts of Vibrio species were significantly lower (p <0.05 in tissues from animals whose food was supplemented with oregano oil. We concluded that dietary supplementation of shrimps with oregano oil provides antimicrobial activity into the body of the penaeids.

  8. Separation prediction in two dimensional boundary layer flows using artificial neural networks

    International Nuclear Information System (INIS)

    Sabetghadam, F.; Ghomi, H.A.

    2003-01-01

    In this article, the ability of artificial neural networks in prediction of separation in steady two dimensional boundary layer flows is studied. Data for network training is extracted from numerical solution of an ODE obtained from Von Karman integral equation with approximate one parameter Pohlhousen velocity profile. As an appropriate neural network, a two layer radial basis generalized regression artificial neural network is used. The results shows good agreements between the overall behavior of the flow fields predicted by the artificial neural network and the actual flow fields for some cases. The method easily can be extended to unsteady separation and turbulent as well as compressible boundary layer flows. (author)

  9. Supercritical CO2 Extraction of Essential Oil from Yarrow.

    Czech Academy of Sciences Publication Activity Database

    Bocevska, M.; Sovová, Helena

    2007-01-01

    Roč. 40, 3 (2007) , s. 360-367 ISSN 0896-8446 R&D Projects: GA AV ČR(CZ) KSK4040110 Grant - others:BEMUSAC(XE) G1MA/CT/2002/04019 Institutional research plan: CEZ:AV0Z40720504 Keywords : supercritical CO2 * essential oil * extraction curves Subject RIV: CI - Industrial Chemistry, Chemical Engineering Impact factor: 2.189, year: 2007

  10. Chemical composition and antimicrobial activity of oregano (Lippia palmeri S. Wats) essential oil

    OpenAIRE

    Ortega-Nieblas, Ma. Magdalena; Robles-Burgueño, Ma. Refugio; Acedo-Félix, Evelia; González-León, Alberto; Morales-Trejo, Adriana; Vázquez-Moreno, Luz

    2011-01-01

    The chemical composition and antimicrobial activity of Lippia palmeri S. Wats essential oil extracted from plants collected of two localities (Álamos and Puerto del Orégano) in the State of Sonora, México, was examined. Essential oils (EO) were obtained from oregano leaves by steam distillation, analyzed by gas chromatography coupled with a mass spectrometer, and their antimicrobial activity against human pathogens investigated by disc diffusion. Álamos and Puerto del Orégano essential oils (...

  11. Differentiating Agar wood Oil Quality Using Artificial Neural Network

    International Nuclear Information System (INIS)

    Nurlaila Ismail; Nor Azah Mohd Ali; Mailina Jamil; Saiful Nizam Tajuddin; Mohd Nasir Taib

    2013-01-01

    Agar wood oil is well known as expensive oil extracted from the resinous of fragrant heartwood. The oil is getting high demand in the market especially from the Middle East countries, China and Japan because of its unique odor. As part of an on-going research in grading the agar wood oil quality, the application of Artificial Neural Network (ANN) is proposed in this study to analyze agar wood oil quality using its chemical profiles. The work involves of selected agar wood oil from low and high quality, the extraction of chemical compounds using GC-MS and Z-score to identify of the significant compounds as input to the network. The ANN programming algorithm was developed and computed automatically via Matlab software version R2010a. Back-propagation training algorithm and sigmoid transfer function were used to optimize the parameters in the training network. The result obtained showed the capability of ANN in analyzing the agar wood oil quality hence beneficial for the further application such as grading and classification for agar wood oil. (author)

  12. Modeling the Supercritical Fluid Extraction of Essential Oils from Plant Materials

    Czech Academy of Sciences Publication Activity Database

    Sovová, Helena

    2012-01-01

    Roč. 1250, SI (2012), s. 27-33 ISSN 0021-9673 R&D Projects: GA TA ČR TA01010578 Institutional support: RVO:67985858 Keywords : supercritical fluid extraction * essential oils * model for kinetics Subject RIV: CI - Industrial Chemistry, Chemical Engineering Impact factor: 4.612, year: 2012

  13. Supercritical carbon dioxide (SC-CO2) extraction of essential oil from Swietenia mahagoni seeds

    Science.gov (United States)

    Norodin, N. S. M.; Salleh, L. M.; Hartati; Mustafa, N. M.

    2016-11-01

    Swietenia mahagoni (Mahogany) is a traditional plant that is rich with bioactive compounds. In this study, process parameters such as particle size, extraction time, solvent flowrate, temperature and pressure were studied on the extraction of essential oil from Swietenia mahagoni seeds by using supercritical carbon dioxide (SC-CO2) extraction. Swietenia mahagoni seeds was extracted at a pressure of 20-30 MPa and a temperature of 40-60°C. The effect of particle size on overall extraction of essential oil was done at 30 MPa and 50°C while the extraction time of essential oil at various temperatures and at a constant pressure of 30 MPa was studied. Meanwhile, the effect of flowrate CO2 was determined at the flowrate of 2, 3 and 4 ml/min. From the experimental data, the extraction time of 120 minutes, particle size of 0.5 mm, the flowrate of CO2 of 4 ml/min, at a pressure of 30 MPa and the temperature of 60°C were the best conditions to obtain the highest yield of essential oil.

  14. THE INFLUENCE OF OREGANO ESSENTIAL OIL AND BEE PRODUCTS ON QUALITATIVE PARAMETERS AND MICROBIOLOGICAL INDICATORS OF TABLE EGGS CONTENT

    Directory of Open Access Journals (Sweden)

    Henrieta Arpášová

    2013-02-01

    Full Text Available Phytobiotics are a new group of natural products. They are defined as products derived from plants, which may have a beneficial effect on the gastrointestinal microflora of animals, performance and quality of animal products. In this experiment the effects of supplementation of the diet for laying hens with oregano essential oil, propolis and pollen extract addition on physical and microbiological egg parameters were studied. Hens of laying hybrid Hy-Line Brown (n=40 were randomly divided into 4 groups (n=10 and fed for 23 weeks with diets with oregano essential oiland propolis or pollen supplemented. In the control group hens received feed mixture with no additions. The diets in the first experimental groups was supplemented with 0.5 g/kg oregano essential oil. The feed for second and third experimental groups of birds consisted of basal diet supplemented with propolis extract and pollen extract of the same dose at 0.5 g/kg. The results suggest that the most of qualitative parameters of egg internal content were not significantly influenced with oregano oil or bee products addition (P>0.05. A statistically significant differencein favor of the experimental groups compared with the control group was observed in two indicators of albumen quality. In the index of albumen and in the Haugh Units was significantly higher difference in favor of the experimental group with addition of oregano essential oilat a dose of0.5 g/kg and in the group with pollen supplement (P<0.05. The highest total number of bacteria and count of coliforms bacteria was found in the control group. The number of lactobacilli was zero in all groups.

  15. Comparison study of moisture content, colour properties and essential oil compounds extracted by hydrodistillation and supercritical fluid extraction between stem and leaves of lemongrass (Cymbopogun citratus)

    Science.gov (United States)

    Kamaruddin, Shazlin; Mustapha, Wan Aida Wan; Haiyee, Zaibunnisa Abdul

    2018-04-01

    The objectives of this study were to compare the properties of moisture content, colour and essential oil compounds between stem and leaves of lemongrass (Cymbopogun citratus). The essential oil was extracted using two different methods which are hydrodistillation and supercritical fluid extraction (SFE). There was no significant difference of moisture content between stem and leaves of lemongrass. The lightness (L) and yellowness (+b) values of the stems were significantly higher (pleaves. The highest yield of essential oil was obtained by extraction using supercritical fluid extraction (SFE) in leaves (˜ 0.7%) by treatment at 1700psi and 50°C. The main compound of extracted essential oil was citral (geranial and neral).

  16. Network traffic anomaly prediction using Artificial Neural Network

    Science.gov (United States)

    Ciptaningtyas, Hening Titi; Fatichah, Chastine; Sabila, Altea

    2017-03-01

    As the excessive increase of internet usage, the malicious software (malware) has also increase significantly. Malware is software developed by hacker for illegal purpose(s), such as stealing data and identity, causing computer damage, or denying service to other user[1]. Malware which attack computer or server often triggers network traffic anomaly phenomena. Based on Sophos's report[2], Indonesia is the riskiest country of malware attack and it also has high network traffic anomaly. This research uses Artificial Neural Network (ANN) to predict network traffic anomaly based on malware attack in Indonesia which is recorded by Id-SIRTII/CC (Indonesia Security Incident Response Team on Internet Infrastructure/Coordination Center). The case study is the highest malware attack (SQL injection) which has happened in three consecutive years: 2012, 2013, and 2014[4]. The data series is preprocessed first, then the network traffic anomaly is predicted using Artificial Neural Network and using two weight update algorithms: Gradient Descent and Momentum. Error of prediction is calculated using Mean Squared Error (MSE) [7]. The experimental result shows that MSE for SQL Injection is 0.03856. So, this approach can be used to predict network traffic anomaly.

  17. Critical review of supercritical carbon dioxide extraction of selected oil seeds

    Directory of Open Access Journals (Sweden)

    Sovilj Milan N.

    2010-01-01

    Full Text Available Supercritical carbon dioxide extraction, as a relatively new separation technique, can be used as a very efficient process in the production of essential oils and oleoresins from many of plant materials. The extracts from these materials are a good basis for the new pharmaceutical products and ingredients in the functional foods. This paper deals with supercritical carbon dioxide extraction of selected oil seeds which are of little interest in classical extraction in the food industry. In this article the process parameters in the supercritical carbon dioxide extraction, such as pressure, temperature, solvent flow rate, diameter of gound materials, and moisture of oil seed were presented for the following seeds: almond fruits, borage seed, corn germ, grape seed, evening primrose, hazelnut, linseed, pumpkin seed, walnut, and wheat germ. The values of investigated parameters in supercritical extraction were: pressure from 100 to 600 bar, temperature from 10 to 70oC, diameter of grinding material from 0.16 to 2.0 mm, solvent flow used from 0.06 to 30.0 kg/h, amount of oil in the feed from 10.0 to 74.0%, and moisture of oil seed from 1.1 to 7.5%. The yield and quality of the extracts of all the oil seeds as well as the possibility of their application in the pharmaceutical and food, industries were analyzed.

  18. Basil (Ocimum basilicum L. essential oil and extracts obtained by supercritical fluid extraction

    Directory of Open Access Journals (Sweden)

    Zeković Zoran P.

    2015-01-01

    Full Text Available The extracts obtained from sweet basil (Ocimum basilicum L. by hydrodistillation and supercritical fluid extraction (SFE were qualitative and quantitative analyzed by GC-MS and GC-FID. Essential oil (EO content of basil sample, determined by an official method, was 0.565% (V/w. The yields of basil obtained by SFE were from 0.719 to 1.483% (w/w, depending on the supercritical fluid (carbon dioxide density (from 0.378 to 0.929 g mL-1. The dominant compounds detected in all investigated samples (EO obtained by hydrodistillation and different SFE extracts were: linalool, as the major compound of basil EO (content from 10.14 to 49.79%, w/w, eugenol (from 3.74 to 9.78% and ä-cardinene (from 3.94 to 8.07%. The quantitative results of GC-MS from peak areas and by GC-FID using external standard method involving main standards, were compared and discussed. [Projekat Ministarstva nauke Republike Srbije, br. TR 31013

  19. Effect of dietary oregano ( Origanum vulgare L.) essential oil on ...

    African Journals Online (AJOL)

    Effect of dietary oregano ( Origanum vulgare L.) essential oil on growth performance, cecal microflora and serum antioxidant activity of broiler chickens. ... promoting effects and also displayed potent antibacterial effects against cecal E. coli.

  20. Artificial neural network intelligent method for prediction

    Science.gov (United States)

    Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

    2017-09-01

    Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

  1. Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers.

    Science.gov (United States)

    Taheri, Mahboobeh; Mohebbi, Ali

    2008-08-30

    In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm (GA), has been used to predict collection efficiency in venturi scrubbers. The experimental input data, including particle diameter, throat gas velocity, liquid to gas flow rate ratio, throat hydraulic diameter, pressure drop across the venturi scrubber and collection efficiency as an output, have been used to create a GA-artificial neural network (ANN) model. The testing results from the model are in good agreement with the experimental data. Comparison of the results of the GA optimized ANN model with the results from the trial-and-error calibrated ANN model indicates that the GA-ANN model is more efficient. Finally, the effects of operating parameters such as liquid to gas flow rate ratio, throat gas velocity, and particle diameter on collection efficiency were determined.

  2. Optimization of extraction of linarin from Flos chrysanthemi indici by response surface methodology and artificial neural network.

    Science.gov (United States)

    Pan, Hongye; Zhang, Qing; Cui, Keke; Chen, Guoquan; Liu, Xuesong; Wang, Longhu

    2017-05-01

    The extraction of linarin from Flos chrysanthemi indici by ethanol was investigated. Two modeling techniques, response surface methodology and artificial neural network, were adopted to optimize the process parameters, such as, ethanol concentration, extraction period, extraction frequency, and solvent to material ratio. We showed that both methods provided good predictions, but artificial neural network provided a better and more accurate result. The optimum process parameters include, ethanol concentration of 74%, extraction period of 2 h, extraction three times, solvent to material ratio of 12 mL/g. The experiment yield of linarin was 90.5% that deviated less than 1.6% from that obtained by predicted result. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Evaluation of the essential oil of Foeniculum vulgare Mill (fennel) fruits extracted by three different extraction methods by GC/MS.

    Science.gov (United States)

    Hammouda, Faiza M; Saleh, Mahmoud A; Abdel-Azim, Nahla S; Shams, Khaled A; Ismail, Shams I; Shahat, Abdelaaty A; Saleh, Ibrahim A

    2014-01-01

    Hydrodistillation (HD) and steam-distillation, or solvent extraction methods of essential oils have some disadvantages like thermal decomposition of extracts, its contamination with solvent or solvent residues and the pollution of residual vegetal material with solvent which can be also an environmental problem. Thus, new green techniques, such as supercritical fluid extraction and microwave assisted techniques, are potential solutions to overcome these disadvantages. The aim of this study was to evaluate the essential oil of Foeniculum vulgare subsp. Piperitum fruits extracted by three different extraction methods viz. Supercritical fluid extraction (SFE) using CO2, microwave-assisted extraction (MAE) and hydro-distillation (HD) using gas chromatography-mass spectrometry (GC/MS). The results revealed that both MAE and SFE enhanced the extraction efficiency of the interested components. MAE gave the highest yield of oil as well as higher percentage of Fenchone (28%), whereas SFE gave the highest percentage of anethol (72%). Microwave-assisted extraction (MAE) and supercritical fluid extraction (SFE) not only enhanced the essential oil extraction but also saved time, reduced the solvents use and produced, ecologically, green technologies.

  4. Disinfection of vegetable seed by treatment with essential oils, organic acids and plant extract

    NARCIS (Netherlands)

    Wolf, van der J.M.; Birnbaum, Y.E.; Zouwen, van der P.S.; Groot, S.P.C.

    2008-01-01

    Various essential oils, organic acids, Biosept, (grapefruit extract), Tillecur and extracts of stinging nettle and golden rod were tested for their antimicrobial properties in order to disinfect vegetable seed. In in vitro assays, thyme oil, oregano oil, cinnamon oil, clove oil and Biosept had the

  5. Performance Comparison Between Support Vector Regression and Artificial Neural Network for Prediction of Oil Palm Production

    Directory of Open Access Journals (Sweden)

    Mustakim Mustakim

    2016-02-01

    Full Text Available The largest region that produces oil palm in Indonesia has an important role in improving the welfare of society and economy. Oil palm has increased significantly in Riau Province in every period, to determine the production development for the next few years with the functions and benefits of oil palm carried prediction production results that were seen from time series data last 8 years (2005-2013. In its prediction implementation, it was done by comparing the performance of Support Vector Regression (SVR method and Artificial Neural Network (ANN. From the experiment, SVR produced the best model compared with ANN. It is indicated by the correlation coefficient of 95% and 6% for MSE in the kernel Radial Basis Function (RBF, whereas ANN produced only 74% for R2 and 9% for MSE on the 8th experiment with hiden neuron 20 and learning rate 0,1. SVR model generates predictions for next 3 years which increased between 3% - 6% from actual data and RBF model predictions.

  6. Prediction of littoral drift with artificial neural networks

    Digital Repository Service at National Institute of Oceanography (India)

    Singh, A.K.; Deo, M.C.; SanilKumar, V.

    of the rate of sand drift has still remained as a problem. The current study addresses this issue through the use of artificial neural networks (ANN). Feed forward networks were developed to predict the sand drift from a variety of causative variables...

  7. Validation of artificial neural network models for predicting biochemical markers associated with male infertility.

    Science.gov (United States)

    Vickram, A S; Kamini, A Rao; Das, Raja; Pathy, M Ramesh; Parameswari, R; Archana, K; Sridharan, T B

    2016-08-01

    Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous

  8. Effect of Oregano Essential Oil (Origanum vulgare subsp. hirtum) on the Storage Stability and Quality Parameters of Ground Chicken Breast Meat

    Science.gov (United States)

    Al-Hijazeen, Marwan; Lee, Eun Joo; Mendonca, Aubrey; Ahn, Dong Uk

    2016-01-01

    A study was conducted to investigate the effect of oregano essential oil on the oxidative stability and color of raw and cooked chicken breast meats. Five treatments, including (1) control (none added); (2) 100 ppm oregano essential oil; (3) 300 ppm oregano essential oil; (4) 400 ppm oregano essential oil; and (5) 5 ppm butylated hydroxyanisole (BHA), were prepared with ground boneless, skinless chicken breast meat and used for both raw and cooked meat studies. For raw meat study, samples were individually packaged in oxygen-permeable bags and stored in a cold room (4 °C) for 7 days. For cooked meat study, the raw meat samples were vacuum-packaged in oxygen-impermeable vacuum bags and then cooked in-bag to an internal temperature of 75 °C. After cooling to room temperature, the cooked meats were repackaged in new oxygen-permeable bags and then stored at 4 °C for 7 days. Both raw and cooked meats were analyzed for lipid and protein oxidation, volatiles, and color at 0, 3, and 7 days of storage. Oregano essential oil significantly reduced (p oil at 400 ppm showed the strongest effect for all these parameters. Hexanal was the major aldehyde, which was decreased significantly (p oil treatment, in cooked meat. Overall, oregano essential oil at 100–400 ppm levels could be a good preservative that can replace the synthetic antioxidant in chicken meat. PMID:27338486

  9. Effect of Oregano Essential Oil (Origanum vulgare subsp. hirtum) on the Storage Stability and Quality Parameters of Ground Chicken Breast Meat.

    Science.gov (United States)

    Al-Hijazeen, Marwan; Lee, Eun Joo; Mendonca, Aubrey; Ahn, Dong Uk

    2016-06-07

    A study was conducted to investigate the effect of oregano essential oil on the oxidative stability and color of raw and cooked chicken breast meats. Five treatments, including (1) control (none added); (2) 100 ppm oregano essential oil; (3) 300 ppm oregano essential oil; (4) 400 ppm oregano essential oil; and (5) 5 ppm butylated hydroxyanisole (BHA), were prepared with ground boneless, skinless chicken breast meat and used for both raw and cooked meat studies. For raw meat study, samples were individually packaged in oxygen-permeable bags and stored in a cold room (4 °C) for 7 days. For cooked meat study, the raw meat samples were vacuum-packaged in oxygen-impermeable vacuum bags and then cooked in-bag to an internal temperature of 75 °C. After cooling to room temperature, the cooked meats were repackaged in new oxygen-permeable bags and then stored at 4 °C for 7 days. Both raw and cooked meats were analyzed for lipid and protein oxidation, volatiles, and color at 0, 3, and 7 days of storage. Oregano essential oil significantly reduced (p oil at 400 ppm showed the strongest effect for all these parameters. Hexanal was the major aldehyde, which was decreased significantly (p oil treatment, in cooked meat. Overall, oregano essential oil at 100-400 ppm levels could be a good preservative that can replace the synthetic antioxidant in chicken meat.

  10. Use of reverse osmosis membranes for the separation of lemongrass essential oil and supercritical CO2

    Directory of Open Access Journals (Sweden)

    L.A.V. Sarmento

    2004-06-01

    Full Text Available Although it is still used very little by industry, the process of essential oil extraction from vegetable matrices with supercritical CO2 is regarded as a potentially viable technique. The operation of separating the extract from the solvent is carried out by reducing the pressure in the system. Separation by membranes is an alternative that offers lower energy consumption and easier operation than traditional methods of separation. Combining the processes essential oil extraction with supercritical CO2 and separation by membranes permits the separation of solvent and oil without the need for large variations in extraction conditions. This results in a large energy savings in the case of solvent repressurisation and reuse. In this study, the effectiveness of reverse osmosis membranes in separating lemongrass essential oil from mixtures with supercritical CO2 was tested. The effects of feed oil concentration and transmembrane pressure on CO2 permeate flux and oil retention were studied for three membrane models.

  11. Selection and production of oregano rich in essential oil and carvacrol

    NARCIS (Netherlands)

    Mheen, van der H.J.C.J.

    2005-01-01

    There is an increasing interest in oregano essential oil and its component carvacrol for the use as a feed additive with antimicrobial properties, enhancing the health of poultry and pigs. This chapter describes the initial agronomic attempts (in the years 2001-2004) to acquire and develop Origanum

  12. Fluvial facies reservoir productivity prediction method based on principal component analysis and artificial neural network

    Directory of Open Access Journals (Sweden)

    Pengyu Gao

    2016-03-01

    Full Text Available It is difficult to forecast the well productivity because of the complexity of vertical and horizontal developments in fluvial facies reservoir. This paper proposes a method based on Principal Component Analysis and Artificial Neural Network to predict well productivity of fluvial facies reservoir. The method summarizes the statistical reservoir factors and engineering factors that affect the well productivity, extracts information by applying the principal component analysis method and approximates arbitrary functions of the neural network to realize an accurate and efficient prediction on the fluvial facies reservoir well productivity. This method provides an effective way for forecasting the productivity of fluvial facies reservoir which is affected by multi-factors and complex mechanism. The study result shows that this method is a practical, effective, accurate and indirect productivity forecast method and is suitable for field application.

  13. Comparison of composition and antifungal activity of Artemisia argyi Lévl. et Vant inflorescence essential oil extracted by hydrodistillation and supercritical carbon dioxide.

    Science.gov (United States)

    Wenqiang, Guan; Shufen, Li; Ruixiang, Yan; Yanfeng, Huang

    2006-09-01

    Essential oil of Artemisia argyi Lévl. et Vant inflorescence was obtained by supercritical CO(2) extraction and hydrodistillation. The oil was analyzed by gas chromatography/mass spectrometry to characterize its components and was also tested for antifungal activity. A total of 61 compounds were identified in the hydrodistilled oil. The major components were 1,8-cineole (4.46%), borneol (3.58%), terpinol (10.18%), spathulenol (10.03%), caryophyllene oxide (6.51%), juniper camphor (8.74%), Camazulene (2.05%), and camphor (3.49%). By using supercritical CO(2) at 50 degrees C and 10 MPa, the concentrations of previous main components were lower than oil obtained by hydrodistillation, while miscellaneous compounds were higher. The essential oil extracted by these two methods exhibited antifungal activity against Botrytis cinerea and Alternaria alternate, two common storage pathogens of fruits and vegetables. The inhibition of B. cinerea and A. alternate were 93.3 and 84.7% for oil extracted by hydrodistillation when exposed to a concentration of 1,000 mg L(-1), while values of 70.8 and 60.5% were observed from oil extracted by supercritical CO(2).

  14. Development of Artificial Neural Network Model of Crude Oil Distillation Column

    Directory of Open Access Journals (Sweden)

    Ali Hussein Khalaf

    2016-02-01

    Full Text Available Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. Thirteen inputs, six outputs and over 1487 data set are used to model the actual unit. Nonlinear autoregressive network with exogenous inputs (NARXand back propagation algorithm are used for training. Seventy percent of data are used for training the network while the remaining  thirty percent are used for testing  and validating the network to determine its prediction accuracy. One hidden layer and 34 hidden neurons are used for the proposed network with MSE of 0.25 is obtained. The number of neuron are selected based on less MSE for the network. The model founded to predict the optimal operating conditions for different objective functions within the training limit since ANN models are poor extrapolators. They are usually only reliable within the range of data that they had been trained for.

  15. Development of Artificial Neural Network Model of Crude Oil Distillation Column

    Directory of Open Access Journals (Sweden)

    Duraid F. Ahmed

    2016-02-01

    Full Text Available Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. Thirteen inputs, six outputs and over 1487 data set are used to model the actual unit. Nonlinear autoregressive network with exogenous inputs (NARX and back propagation algorithm are used for training. Seventy percent of data are used for training the network while the remaining thirty percent are used for testing and validating the network to determine its prediction accuracy. One hidden layer and 34 hidden neurons are used for the proposed network with MSE of 0.25 is obtained. The number of neuron are selected based on less MSE for the network. The model founded to predict the optimal operating conditions for different objective functions within the training limit since ANN models are poor extrapolators. They are usually only reliable within the range of data that they had been trained for.

  16. The Use of Artificial Neural Networks in Prediction of Congenital CMV Outcome from Sequence Data

    Directory of Open Access Journals (Sweden)

    Ravit Arav-Boger

    2008-01-01

    Full Text Available A large number of CMV strains has been reported to circulate in the human population, and the biological significance of these strains is currently an active area of research. The analysis of complex genetic information may be limited using conventional phylogenetic techniques. We constructed artificial neural networks to determine their feasibility in predicting the outcome of congenital CMV disease (defined as presence of CMV symptoms at birth based on two data sets: 54 sequences of CMV gene UL144 obtained from 54 amniotic fluids of women who contracted acute CMV infection during their pregnancy, and 80 sequences of 4 genes (US28, UL144, UL146 and UL147 obtained from urine, saliva or blood of 20 congenitally infected infants that displayed different outcomes at birth. When data from all four genes was used in the 20-infants’ set, the artificial neural network model accurately identified outcome in 90% of cases. While US28 and UL147 had low yield in predicting outcome, UL144 and UL146 predicted outcome in 80% and 85% respectively when used separately. The model identified specific nucleotide positions that were highly relevant to prediction of outcome. The artificial neural network classified genotypes in agreement with classic phylogenetic analysis. We suggest that artificial neural networks can accurately and efficiently analyze sequences obtained from larger cohorts to determine specific outcomes.The ANN training and analysis code is commercially available from Optimal Neural Informatics (Pikesville, MD.

  17. Salinity independent volume fraction prediction in water-gas-oil multiphase flows using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Salgado, C.M.; Pereira, Claudio M.N.A.; Brandao, Luis E.B., E-mail: otero@ien.gov.b, E-mail: cmnap@ien.gov.b, E-mail: brandao@ien.gov.b [Instituto de Engenharia Nuclear (DIRA/IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil). Div. de Radiofarmacos

    2011-07-01

    This work investigates the response of a volume fraction prediction system for water-gas-oil multiphase flows considering variations on water salinity. The approach is based on gamma-ray pulse height distributions pattern recognition by means the artificial neural networks (ANNs). The detection system uses appropriate fan beam geometry, comprised of a dual-energy gamma-ray source and two NaI(Tl) detectors adequately positioned outside the pipe in order measure transmitted and scattered beams. An ideal and static theoretical model for annular flow regime have been developed using MCNP-X code, which was used to provide training, test and validation data for the ANN. More than 500 simulations have been done, in which water salinity have been ranged from 0 to 16% in order to cover a most practical situations. Validation tests have included values of volume fractions and water salinity different from those used in ANN training phase. The results presented here show that the proposed approach may be successfully applied to material volume fraction prediction on watergas- oil multiphase flows considering practical (real) levels of variations in water salinity. (author)

  18. Salinity independent volume fraction prediction in water-gas-oil multiphase flows using artificial neural networks

    International Nuclear Information System (INIS)

    Salgado, C.M.; Pereira, Claudio M.N.A.; Brandao, Luis E.B.

    2011-01-01

    This work investigates the response of a volume fraction prediction system for water-gas-oil multiphase flows considering variations on water salinity. The approach is based on gamma-ray pulse height distributions pattern recognition by means the artificial neural networks (ANNs). The detection system uses appropriate fan beam geometry, comprised of a dual-energy gamma-ray source and two NaI(Tl) detectors adequately positioned outside the pipe in order measure transmitted and scattered beams. An ideal and static theoretical model for annular flow regime have been developed using MCNP-X code, which was used to provide training, test and validation data for the ANN. More than 500 simulations have been done, in which water salinity have been ranged from 0 to 16% in order to cover a most practical situations. Validation tests have included values of volume fractions and water salinity different from those used in ANN training phase. The results presented here show that the proposed approach may be successfully applied to material volume fraction prediction on watergas- oil multiphase flows considering practical (real) levels of variations in water salinity. (author)

  19. What are artificial neural networks?

    DEFF Research Database (Denmark)

    Krogh, Anders

    2008-01-01

    Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb......Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb...

  20. Single and combined effects of vitamin C and oregano essential oil in diet, on growth performance, and blood parameters of broiler chicks reared under heat stress condition

    Science.gov (United States)

    Ghazi, Shahab; Amjadian, Tahere; Norouzi, Shokufeh

    2015-08-01

    This study was conducted to evaluate the effects of adding vitamin C (VC), oregano essential oil (OR), or their combination in diet, on growth performance, and blood parameters of broiler chicks reared under heat stress (HS) condition (38 °C). One-day-old 240 male broilers were randomly assigned to four treatment groups, six replicates of ten birds each. The birds were fed with either a basal diet or a basal diet supplemented with either 200 mg L-ascorbic acid/kg of diet, 250 mg of oregano essential oil/kg of diet, or 200 mg L-ascorbic acid plus 250 mg of oregano essential oil/kg of diet. Average daily feed intake (ADFI), average daily gain (ADG), and feed conversion ratio (FCR) were obtained for 42 days of age and at the end of the experiment (day 42); birds were bled to determine some blood parameters and weighted for final body weight (BW). Feeding birds with diets supplemented with oregano essential oil and vitamin C in a single or combined form increased ADG ( P > 0.05). Also BW increased and feed efficiency decreased ( P vitamin C ( P > 0.05). Supplemental oregano essential oil and vitamin C in a combined form decreased the serum concentration of corticosterone, triglycerides, glucose, and MDA ( P vitamin C were seen in broiler chicks supplemented with vitamin C. From the results of the present experiment, it can be concluded that diet supplementation by combined oregano essential oil and vitamin C could have beneficial effects on some blood parameters of broiler chicks reared under heat stress condition.

  1. Effect of essential oils of thyme, oregano and their combination on quality of quail meat in comparison with virginiamycin

    Directory of Open Access Journals (Sweden)

    Sh Hajipour dehbalaei

    2016-01-01

    Full Text Available Due to its high concentration of polyunsaturated fatty acids, poultry meat is prone to oxidative deterioration. The aim of this study was to investigate the effect of essential oils of thyme, oregano and their combination in comparison with virginiamycin on quality of quail’s meat. The dietary treatments consisted of the basal control (without any added compounds or with 100 mg/kg of virginiamycin, 100 and 200 mg/kg of thyme and oregano essential oils, as well as an equal mixture of thyme and oregano essential oil (levels of 50 and 100 mg /kg.  At the end of 35 days of the experiment, two birds from each group were slaughtered for testing the meat quality (including malondialdehyde, pH, water holding capacity, dripping loss and cooking loss. Results showed that essential oils of thyme, oregano and their mixture reduced the thiobarbituric acid value, dripping loss and cooking loss; on the other hand pH value and water holding capacity was increased. Oregano and thyme essential oils contain compounds with high antioxidant properties. Therefore, the presence of these compounds in the bloodstream and their accumulation in the muscle tissue could results in an increase in the antioxidant capacity and consequently enhance the keeping quality of meat. It seems that the application of natural antioxidants such as thymol or carvacrol could be helpful to improve the quality of poultry meat.

  2. Chemical composition and antioxidant activities of essential oils from different parts of the oregano.

    Science.gov (United States)

    Han, Fei; Ma, Guang-Qiang; Yang, Ming; Yan, Li; Xiong, Wei; Shu, Ji-Cheng; Zhao, Zhi-Dong; Xu, Han-Lin

    This research was undertaken in order to characterize the chemical compositions and evaluate the antioxidant activities of essential oils obtained from different parts of the Origanum vulgare L. It is a medicinal plant used in traditional Chinese medicine for the treatment of heat stroke, fever, vomiting, acute gastroenteritis, and respiratory disorders. The chemical compositions of the three essential oils from different parts of the oregano (leaves-flowers, stems, and roots) were identified by gas chromatography-mass spectrometry (GC-MS). The antioxidant activity of each essential oil was assessed using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging assay and reducing the power test. Among the essential oils from different parts of the oregano, the leaf-flower oils have the best antioxidant activities, whereas the stem oils are the worst. The results of the DPPH free radical scavenging assay showed that the half maximal inhibitory concentration (IC 50 ) values of the essential oils were (0.332±0.040) mg/ml (leaves-flowers), (0.357±0.031) mg/ml (roots), and (0.501±0.029) mg/ml (stems), respectively. Interestingly, the results of reducing the power test also revealed that when the concentration exceeded 1.25 mg/ml, the leaf-flower oils had the highest reducing power; however, the stem oils were the lowest.

  3. The Influence of Oregano Essential Oil and Pollen on Egg Albumen Qualitative Parameters and Microbiological Indicators of Table Eggs Content

    Directory of Open Access Journals (Sweden)

    Henrieta Arpášová

    2013-10-01

    Full Text Available Essential oils are intensive fragrant, oily liquid substances contained in different parts of the plant. Besides antibacterial properties, essential oils or their components have been shown to exhibit antiviral, antimycotic, antitoxigenic, antiparasitic, and insecticidal properties. In this experiment the effects of supplementation of the diet for laying hens with oregano essential oils or pollen on egg albumen physical parameters and microbiological egg parameters were studied. Hens of laying hybrid Hy-Line Brown (n=30 were randomly divided into 3 groups (n=10 and fed for 23 weeks on diets with oregano essential oil and pollen extract supplemented. In the first experimental group the feed mixture was supplemented with oregano essential oil addition in a dose 0.25 g/kg, the feed for second experimental groups of birds consisted of basal diet supplemented with pollen extract of the dose at 0.4 g/kg. The results suggest that a statistically significant difference in favor of the experimental groups compared with the control group was observed in two indicators of albumen quality. In the index of albumen and in the Haugh Units was significantly higher difference in favor of the experimental group with addition of pollen supplement (P<0.05. The highest total number of bacteria and count of coliforms bacteria was found in the control group. The number of lactobacilli was zero in all groups.The paper abstract will be written with Times New Roman 10 pt., justify. It will contain maximum 200 words. A concise and factual abstract is required. The abstract should state briefly the purpose of the research, the principal results and major conclusions. An abstract is often presented separately from the article, so it must be able to stand alone. For this reason, references should be avoided, but if essential, then cite the author(s and year(s. Also, non-standard or uncommon abbreviations should be avoided, but if essential they must be defined at their first

  4. Artificial Neural Network for the Prediction of Chromosomal Abnormalities in Azoospermic Males.

    Science.gov (United States)

    Akinsal, Emre Can; Haznedar, Bulent; Baydilli, Numan; Kalinli, Adem; Ozturk, Ahmet; Ekmekçioğlu, Oğuz

    2018-02-04

    To evaluate whether an artifical neural network helps to diagnose any chromosomal abnormalities in azoospermic males. The data of azoospermic males attending to a tertiary academic referral center were evaluated retrospectively. Height, total testicular volume, follicle stimulating hormone, luteinising hormone, total testosterone and ejaculate volume of the patients were used for the analyses. In artificial neural network, the data of 310 azoospermics were used as the education and 115 as the test set. Logistic regression analyses and discriminant analyses were performed for statistical analyses. The tests were re-analysed with a neural network. Both logistic regression analyses and artificial neural network predicted the presence or absence of chromosomal abnormalities with more than 95% accuracy. The use of artificial neural network model has yielded satisfactory results in terms of distinguishing patients whether they have any chromosomal abnormality or not.

  5. Probabilistic Wind Power Forecasting with Hybrid Artificial Neural Networks

    DEFF Research Database (Denmark)

    Wan, Can; Song, Yonghua; Xu, Zhao

    2016-01-01

    probabilities of prediction errors provide an alternative yet effective solution. This article proposes a hybrid artificial neural network approach to generate prediction intervals of wind power. An extreme learning machine is applied to conduct point prediction of wind power and estimate model uncertainties...... via a bootstrap technique. Subsequently, the maximum likelihood estimation method is employed to construct a distinct neural network to estimate the noise variance of forecasting results. The proposed approach has been tested on multi-step forecasting of high-resolution (10-min) wind power using...... actual wind power data from Denmark. The numerical results demonstrate that the proposed hybrid artificial neural network approach is effective and efficient for probabilistic forecasting of wind power and has high potential in practical applications....

  6. [The antibacterial activity of oregano essential oil (Origanum heracleoticum L.) against clinical strains of Escherichia coli and Pseudomonas aeruginosa].

    Science.gov (United States)

    Sienkiewicz, Monika; Wasiela, Małgorzata; Głowacka, Anna

    2012-01-01

    The aim of this study was to investigate the antibacterial properties of oregano (Origanum heracleoticum L.) essential oil against clinical strains of Escherichia coli and Pseudomonas aeruginosa. The antibacterial activity of oregano essential oil was investigate against 2 tested and 20 clinical bacterial strains of Escherichia coli and 20 clinical strains o Pseudomonas aeruginosa come from patients with different clinical conditions. The agar dilution method was used for microbial growth inhibition at various concentrations ofoil. Susceptibility testing to antibiotics was carried out using disc-diffusion method. The results of experiments showed that the tested oil was active against all of the clinical strains from both genus of bacteria, but strains of Escherichia coli were more sensitive to tested oil. Essential oil from Origanum heracleoticum L. inhibited the growth of Escherichia coli and Pseudomonas aeruginosa clinical strains with different patters of resistance. The obtained outcomes will enable further investigations using oregano essential oil obtained from Origanum heracleoticum L. as alternative antibacterial remedies enhancing healing process in bacterial infections and as an effective means for the prevention of antibiotic-resistant strain development.

  7. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  8. Application of response surface methodology for the optimization of supercritical fluid extraction of essential oil from pomegranate (Punica granatum L.) peel.

    Science.gov (United States)

    Ara, Katayoun Mahdavi; Raofie, Farhad

    2016-07-01

    Essential oils and volatile components of pomegranate ( Punica granatum L.) peel of the Malas variety from Meybod, Iran, were extracted using supercritical fluid extraction (SFE) and hydro-distillation methods. The experimental parameters of SFE that is pressure, temperature, extraction time, and modifier (methanol) volume were optimized using a central composite design after a (2 4-1 ) fractional factorial design. Detailed chemical composition of the essential oils and volatile components obtained by hydro-distillation and optimum condition of the supercritical CO 2 extraction were analyzed by GC-MS, and seventy-three and forty-six compounds were identified according to their retention indices and mass spectra, respectively. The optimum SFE conditions were 350 atm pressure, 55 °C temperature, 30 min extraction time, and 150 µL methanol. Results showed that oleic acid, palmitic acid and (-)-Borneol were major compounds in both extracts. The optimum extraction yield was 1.18 % (w/w) for SFE and 0.21 % (v/w) for hydro-distillation.

  9. Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks

    International Nuclear Information System (INIS)

    Kavaklioglu, Kadir; Ozturk, Harun Kemal; Canyurt, Olcay Ersel; Ceylan, Halim

    2009-01-01

    Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption. (author)

  10. Artificial neural network modelling of pharmaceutical residue retention times in wastewater extracts using gradient liquid chromatography-high resolution mass spectrometry data.

    Science.gov (United States)

    Munro, Kelly; Miller, Thomas H; Martins, Claudia P B; Edge, Anthony M; Cowan, David A; Barron, Leon P

    2015-05-29

    The modelling and prediction of reversed-phase chromatographic retention time (tR) under gradient elution conditions for 166 pharmaceuticals in wastewater extracts is presented using artificial neural networks for the first time. Radial basis function, multilayer perceptron and generalised regression neural networks were investigated and a comparison of their predictive ability for model solutions discussed. For real world application, the effect of matrix complexity on tR measurements is presented. Measured tR for some compounds in influent wastewater varied by >1min in comparison to tR in model solutions. Similarly, matrix impact on artificial neural network predictive ability was addressed towards developing a more robust approach for routine screening applications. Overall, the best neural network had a predictive accuracy of <1.3min at the 75th percentile of all measured tR data in wastewater samples (<10% of the total runtime). Coefficients of determination for 30 blind test compounds in wastewater matrices lay at or above R(2)=0.92. Finally, the model was evaluated for application to the semi-targeted identification of pharmaceutical residues during a weeklong wastewater sampling campaign. The model successfully identified native compounds at a rate of 83±4% and 73±5% in influent and effluent extracts, respectively. The use of an HRMS database and the optimised ANN model was also applied to shortlisting of 37 additional compounds in wastewater. Ultimately, this research will potentially enable faster identification of emerging contaminants in the environment through more efficient post-acquisition data mining. Copyright © 2015 Elsevier B.V. All rights reserved.

  11. Predicting Developmental Disorder in Infants Using an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Farin Soleimani

    2013-06-01

    Full Text Available Early recognition of developmental disorders is an important goal, and equally important is avoiding misdiagnosing a disorder in a healthy child without pathology. The aim of the present study was to develop an artificial neural network using perinatal information to predict developmental disorder at infancy. A total of 1,232 mother–child dyads were recruited from 6,150 in the original data of Karaj, Alborz Province, Iran. Thousands of variables are examined in this data including basic characteristics, medical history, and variables related to infants. The validated Infant Neurological International Battery test was employed to assess the infant’s development. The concordance indexes showed that true prediction of developmental disorder in the artificial neural network model, compared to the logistic regression model, was 83.1% vs. 79.5% and the area under ROC curves, calculated from testing data, were 0.79 and 0.68, respectively. In addition, specificity and sensitivity of the ANN model vs. LR model was calculated 93.2% vs. 92.7% and 39.1% vs. 21.7%. An artificial neural network performed significantly better than a logistic regression model.

  12. Predicting CYP2C19 Catalytic Parameters for Enantioselective Oxidations Using Artificial Neural Networks and a Chirality Code

    Science.gov (United States)

    Hartman, Jessica H.; Cothren, Steven D.; Park, Sun-Ha; Yun, Chul-Ho; Darsey, Jerry A.; Miller, Grover P.

    2013-01-01

    Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (kcat, Km, and kcat/Km), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (kcat and Km) were more consistent with experimental values than those for catalytic efficiency (kcat/Km). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds. PMID:23673224

  13. Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data

    Directory of Open Access Journals (Sweden)

    Yasir Hassan Ali

    2015-01-01

    Full Text Available The thickness of an oil film lubricant can contribute to less gear tooth wear and surface failure. The purpose of this research is to use artificial neural network (ANN computational modelling to correlate spur gear data from acoustic emissions, lubricant temperature, and specific film thickness (λ. The approach is using an algorithm to monitor the oil film thickness and to detect which lubrication regime the gearbox is running either hydrodynamic, elastohydrodynamic, or boundary. This monitoring can aid identification of fault development. Feed-forward and recurrent Elman neural network algorithms were used to develop ANN models, which are subjected to training, testing, and validation process. The Levenberg-Marquardt back-propagation algorithm was applied to reduce errors. Log-sigmoid and Purelin were identified as suitable transfer functions for hidden and output nodes. The methods used in this paper shows accurate predictions from ANN and the feed-forward network performance is superior to the Elman neural network.

  14. Measurement and correlation study of silymarin solubility in supercritical carbon dioxide with and without a cosolvent using semi-empirical models and back-propagation artificial neural networks

    Directory of Open Access Journals (Sweden)

    Gang Yang

    2017-09-01

    Full Text Available The solubility data of compounds in supercritical fluids and the correlation between the experimental solubility data and predicted solubility data are crucial to the development of supercritical technologies. In the present work, the solubility data of silymarin (SM in both pure supercritical carbon dioxide (SCCO2 and SCCO2 with added cosolvent was measured at temperatures ranging from 308 to 338 K and pressures from 8 to 22 MPa. The experimental data were fit with three semi-empirical density-based models (Chrastil, Bartle and Mendez-Santiago and Teja models and a back-propagation artificial neural networks (BPANN model. Interaction parameters for the models were obtained and the percentage of average absolute relative deviation (AARD% in each calculation was determined. The correlation results were in good agreement with the experimental data. A comparison among the four models revealed that the experimental solubility data were more fit with the BPANN model with AARDs ranging from 1.14% to 2.15% for silymarin in pure SCCO2 and with added cosolvent. The results provide fundamental data for designing the extraction of SM or the preparation of its particle using SCCO2 techniques.

  15. Effect of Oregano Essential Oil and Aqueous Oregano Infusion Application on Microbiological Properties of Samarella (Tsamarella), a Traditional Meat Product of Cyprus

    OpenAIRE

    Beyza Ulusoy; Canan Hecer; Doruk Kaynarca; Şifa Berkan

    2018-01-01

    Different types of dried meat products manufactured by different drying and curing methods are very common and well-known with a long history all over the world. Samarella (tsamarella) is one of these products and is famous among traditionally produced meat products in Cypriot gastronomy. The aim of this study was to investigate the effect of oregano essential oil (OEO) and aqueous oregano infusion (AOI) applications on the microbiological properties of samarella. In order to carry out this s...

  16. PREDICTION OF DEMAND FOR PRIMARY BOND OFFERINGS USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Michal Tkac

    2014-12-01

    Full Text Available Purpose: Primary bond markets represent an interesting investment opportunity not only for banks, insurance companies, and other institutional investors, but also for individuals looking for capital gains. Since offered securities vary in terms of their rating, industrial classification, coupon, or maturity, demand of buyers for particular offerings often overcomes issued volume and price of given bond on secondary market consequently rises. Investors might be regarded as consumers purchasing required service according to their specific preferences at desired price. This paper aims at analysis of demand for bonds on primary market using artificial neural networks.Design/methodology: We design a multilayered feedforward neural network trained by Levenberg-Marquardt algorithm in order to estimate demand for individual bonds based on parameters of particular offerings. Outcomes obtained by artificial neural network are compared with conventional econometric methods.Findings: Our results indicate that artificial neural network significantly outperformed standard econometric techniques and on examined sample of primary bond offerings achieved considerably better performance in terms of prediction accuracy and mean squared error.Originality: We show that proposed neural network is able to successfully predict demand for primary obligation offerings based on their specifications. Moreover, we identify relevant parameters of issues which are able to considerably affect total demand for given security.  Our findings might not only help investors to detect marketable securities, but also enable issuing entities to increase demand for their bonds in order to decrease their offering price. 

  17. Supercritical solvent extraction of oil sand bitumen

    Science.gov (United States)

    Imanbayev, Ye. I.; Ongarbayev, Ye. K.; Tileuberdi, Ye.; Mansurov, Z. A.; Golovko, A. K.; Rudyk, S.

    2017-08-01

    The supercritical solvent extraction of bitumen from oil sand studied with organic solvents. The experiments were performed in autoclave reactor at temperature above 255 °C and pressure 29 atm with stirring for 6 h. The reaction resulted in the formation of coke products with mineral part of oil sands. The remaining products separated into SARA fractions. The properties of the obtained products were studied. The supercritical solvent extraction significantly upgraded extracted natural bitumen.

  18. Prediction of tides using back-propagation neural networks

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.

    Prediction of tides is very much essential for human activities and to reduce the construction cost in marine environment. This paper presents an application of the artificial neural network with back-propagation procedures for accurate prediction...

  19. Artificial neural networks for prediction of percentage of water ...

    Indian Academy of Sciences (India)

    have high compressive strengths in comparison with con- crete specimens ... presenting suitable model based on artificial neural networks. (ANNs) to ... by experimental ones to evaluate the software power for pre- dicting the ..... Figure 7. Correlation of measured and predicted percentage of water absorption values of.

  20. Wavelet neural network modeling in QSPR for prediction of solubility of 25 anthraquinone dyes at different temperatures and pressures in supercritical carbon dioxide.

    Science.gov (United States)

    Tabaraki, R; Khayamian, T; Ensafi, A A

    2006-09-01

    A wavelet neural network (WNN) model in quantitative structure property relationship (QSPR) was developed for predicting solubility of 25 anthraquinone dyes in supercritical carbon dioxide over a wide range of pressures (70-770 bar) and temperatures (291-423 K). A large number of descriptors were calculated with Dragon software and a subset of calculated descriptors was selected from 18 classes of Dragon descriptors with a stepwise multiple linear regression (MLR) as a feature selection technique. Six calculated and two experimental descriptors, pressure and temperature, were selected as the most feasible descriptors. The selected descriptors were used as input nodes in a wavelet neural network (WNN) model. The wavelet neural network architecture and its parameters were optimized simultaneously. The data was randomly divided to the training, prediction and validation sets. The predictive ability of the model was evaluated using validation data set. The root mean squares error (RMSE) and mean absolute errors were 0.339 and 0.221, respectively, for the validation data set. The performance of the WNN model was also compared with artificial neural network (ANN) model and the results showed the superiority of the WNN over ANN model.

  1. Damage Level Prediction of Reinforced Concrete Building Based on Earthquake Time History Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Suryanita Reni

    2017-01-01

    Full Text Available The strong motion earthquake could cause the building damage in case of the building not considered in the earthquake design of the building. The study aims to predict the damage-level of building due to earthquake using Artificial Neural Networks method. The building model is a reinforced concrete building with ten floors and height between floors is 3.6 m. The model building received a load of the earthquake based on nine earthquake time history records. Each time history scaled to 0,5g, 0,75g, and 1,0g. The Artificial Neural Networks are designed in 4 architectural models using the MATLAB program. Model 1 used the displacement, velocity, and acceleration as input and Model 2 used the displacement only as the input. Model 3 used the velocity as input, and Model 4 used the acceleration just as input. The output of the Neural Networks is the damage level of the building with the category of Safe (1, Immediate Occupancy (2, Life Safety (3 or in a condition of Collapse Prevention (4. According to the results, Neural Network models have the prediction rate of the damage level between 85%-95%. Therefore, one of the solutions for analyzing the structural responses and the damage level promptly and efficiently when the earthquake occurred is by using Artificial Neural Network

  2. Classification and prediction of the critical heat flux using fuzzy theory and artificial neural networks

    International Nuclear Information System (INIS)

    Moon, Sang Ki; Chang, Soon Heung

    1994-01-01

    A new method to predict the critical heat flux (CHF) is proposed, based on the fuzzy clustering and artificial neural network. The fuzzy clustering classifies the experimental CHF data into a few data clusters (data groups) according to the data characteristics. After classification of the experimental data, the characteristics of the resulting clusters are discussed with emphasis on the distribution of the experimental conditions and physical mechanism. The CHF data in each group are trained in an artificial neural network to predict the CHF. The artificial neural network adjusts the weight so as to minimize the prediction error within the corresponding cluster. Application of the proposed method to the KAIST CHF data bank shows good prediction capability of the CHF, better than other existing methods. ((orig.))

  3. Modeling the Effect of Crude Oil Impacted Sand on the Properties of Concrete Using Artificial Neural Networks

    OpenAIRE

    W. O. Ajagbe; A. A. Ganiyu; M. O. Owoyele; J. O. Labiran

    2013-01-01

    A network of the feedforward-type artificial neural networks (ANNs) was used to predict the compressive strength of concrete made from crude oil contaminated soil samples at 3, 7, 14, 28, 56, 84, and 168 days at different degrees of contamination of 2.5%, 5%, 10%, 15%, 20% and 25%. A total of 49 samples were used in the training, testing, and prediction phase of the modeling in the ratio 32 : 11 : 7. The TANH activation function was used and the maximum number of iterations was limited to 20,...

  4. Artificial neural networks application for solid fuel slagging intensity predictions

    Directory of Open Access Journals (Sweden)

    Kakietek Sławomir

    2017-01-01

    Full Text Available Slagging issues present in pulverized steam boilers very often lead to heat transfer problems, corrosion and not planned outages of boilers which increase the cost of energy production and decrease the efficiency of energy production. Slagging especially occurs in regions with reductive atmospheres which nowadays are very common due to very strict limitations in NOx emissions. Moreover alternative fuels like biomass which are also used in combustion systems from two decades in order to decrease CO2 emissions also usually increase the risk of slagging. Thus the prediction of slagging properties of fuels is not the minor issue which can be neglected before purchasing or mixing of fuels. This however is rather difficult to estimate and even commonly known standard laboratory methods like fusion temperature determination or special indexers calculated on the basis of proximate and ultimate analyses, very often have no reasonable correlation to real boiler fuel behaviour. In this paper the method of determination of slagging properties of solid fuels based on laboratory investigation and artificial neural networks were presented. A fuel data base with over 40 fuels was created. Neural networks simulations were carried out in order to predict the beginning temperature and intensity of slagging. Reasonable results were obtained for some of tested neural networks, especially for hybrid feedforward networks with PCA technique. Consequently neural network model will be used in Common Intelligent Boiler Operation Platform (CIBOP being elaborated within CERUBIS research project for two BP-1150 and BB-1150 steam boilers. The model among others enables proper fuel selection in order to minimize slagging risk.

  5. Incidents Prediction in Road Junctions Using Artificial Neural Networks

    Science.gov (United States)

    Hajji, Tarik; Alami Hassani, Aicha; Ouazzani Jamil, Mohammed

    2018-05-01

    The implementation of an incident detection system (IDS) is an indispensable operation in the analysis of the road traffics. However the IDS may, in no case, represent an alternative to the classical monitoring system controlled by the human eye. The aim of this work is to increase detection and prediction probability of incidents in camera-monitored areas. Knowing that, these areas are monitored by multiple cameras and few supervisors. Our solution is to use Artificial Neural Networks (ANN) to analyze moving objects trajectories on captured images. We first propose a modelling of the trajectories and their characteristics, after we develop a learning database for valid and invalid trajectories, and then we carry out a comparative study to find the artificial neural network architecture that maximizes the rate of valid and invalid trajectories recognition.

  6. Artificial neural networks for prediction of percentage of water

    Indian Academy of Sciences (India)

    ... Lecture Workshops · Refresher Courses · Symposia · Live Streaming. Home; Journals; Bulletin of Materials Science; Volume 35; Issue 6. Artificial neural networks for prediction of percentage of water absorption of geopolymers produced by waste ashes. Ali Nazari. Volume 35 Issue 6 November 2012 pp 1019-1029 ...

  7. Fungal inactivation by Mexican oregano (Lippia berlandieri Schauer) essential oil added to amaranth, chitosan, or starch edible films.

    Science.gov (United States)

    Avila-Sosa, Raúl; Hernández-Zamoran, Erika; López-Mendoza, Ingrid; Palou, Enrique; Jiménez Munguía, María Teresa; Nevárez-Moorillón, Guadalupe Virginia; López-Malo, Aurelio

    2010-04-01

    Edible films can incorporate antimicrobial agents to provide microbiological stability, since they can be used as carriers of a wide number of additives that can extend product shelf life and reduce the risk of pathogenic bacteria growth on food surfaces. Addition of antimicrobial agents to edible films offers advantages such as the use of low antimicrobial concentrations and low diffusion rates. The aim of this study was to evaluate inhibition of Aspergillus niger and Penicillium spp. by selected concentrations of Mexican oregano (Lippia berlandieri Schauer) essential oil added to amaranth, chitosan, or starch edible films. Oregano essential oil was characterized by gas chromatography-mass spectrometry (GC/MS) analysis. Amaranth, chitosan, and starch edible films were formulated with essential oil concentrations of 0%, 0.25%, 0.50%, 0.75%, 1%, 2%, and 4%. Mold radial growth was evaluated inoculating spores in 2 ways: edible films were placed over inoculated agar, Film/Inoculum mode (F/I), or the edible films were first placed in the agar and then films were inoculated, Inoculum/Film mode (I/F). The modified Gompertz model adequately described growth curves. There was no significant difference (P > 0.05) in growth parameters between the 2 modes of inoculation. Antifungal effectiveness of edible films was starch > chitosan > amaranth. In starch edible films, both studied molds were inhibited with 0.50% of essential oil. Edible films added with Mexican oregano essential oil could improve the quality of foods by controlling surface growth of molds.

  8. Prediction of U-Mo dispersion nuclear fuels with Al-Si alloy using artificial neural network

    International Nuclear Information System (INIS)

    Susmikanti, Mike; Sulistyo, Jos

    2014-01-01

    Dispersion nuclear fuels, consisting of U-Mo particles dispersed in an Al-Si matrix, are being developed as fuel for research reactors. The equilibrium relationship for a mixture component can be expressed in the phase diagram. It is important to analyze whether a mixture component is in equilibrium phase or another phase. The purpose of this research it is needed to built the model of the phase diagram, so the mixture component is in the stable or melting condition. Artificial neural network (ANN) is a modeling tool for processes involving multivariable non-linear relationships. The objective of the present work is to develop code based on artificial neural network models of system equilibrium relationship of U-Mo in Al-Si matrix. This model can be used for prediction of type of resulting mixture, and whether the point is on the equilibrium phase or in another phase region. The equilibrium model data for prediction and modeling generated from experimentally data. The artificial neural network with resilient backpropagation method was chosen to predict the dispersion of nuclear fuels U-Mo in Al-Si matrix. This developed code was built with some function in MATLAB. For simulations using ANN, the Levenberg-Marquardt method was also used for optimization. The artificial neural network is able to predict the equilibrium phase or in the phase region. The develop code based on artificial neural network models was built, for analyze equilibrium relationship of U-Mo in Al-Si matrix

  9. Application of Artificial Neural Network to Predict the use of Runway at Juanda International Airport

    Science.gov (United States)

    Putra, J. C. P.; Safrilah

    2017-06-01

    Artificial neural network approaches are useful to solve many complicated problems. It solves a number of problems in various areas such as engineering, medicine, business, manufacturing, etc. This paper presents an application of artificial neural network to predict a runway capacity at Juanda International Airport. An artificial neural network model of backpropagation and multi-layer perceptron is adopted to this research to learning process of runway capacity at Juanda International Airport. The results indicate that the training data is successfully recognizing the certain pattern of runway use at Juanda International Airport. Whereas, testing data indicate vice versa. Finally, it can be concluded that the approach of uniformity data and network architecture is the critical part to determine the accuracy of prediction results.

  10. Artificial neural network application for predicting soil distribution coefficient of nickel

    International Nuclear Information System (INIS)

    Falamaki, Amin

    2013-01-01

    The distribution (or partition) coefficient (K d ) is an applicable parameter for modeling contaminant and radionuclide transport as well as risk analysis. Selection of this parameter may cause significant error in predicting the impacts of contaminant migration or site-remediation options. In this regards, various models were presented to predict K d values for different contaminants specially heavy metals and radionuclides. In this study, artificial neural network (ANN) is used to present simplified model for predicting K d of nickel. The main objective is to develop a more accurate model with a minimal number of parameters, which can be determined experimentally or select by review of different studies. In addition, the effects of training as well as the type of the network are considered. The K d values of Ni is strongly dependent on pH of the soil and mathematical relationships were presented between pH and K d of nickel recently. In this study, the same database of these presented models was used to verify that neural network may be more useful tools for predicting of K d . Two different types of ANN, multilayer perceptron and redial basis function, were used to investigate the effect of the network geometry on the results. In addition, each network was trained by 80 and 90% of the data and tested for 20 and 10% of the rest data. Then the results of the networks compared with the results of the mathematical models. Although the networks trained by 80 and 90% of the data the results show that all the networks predict with higher accuracy relative to mathematical models which were derived by 100% of data. More training of a network increases the accuracy of the network. Multilayer perceptron network used in this study predicts better than redial basis function network. - Highlights: ► Simplified models for predicting K d of nickel presented using artificial neural networks. ► Multilayer perceptron and redial basis function used to predict K d of nickel in

  11. Applications of supercritical fluid extraction (SFE) of palm oil and oil from natural sources.

    Science.gov (United States)

    Akanda, Mohammed Jahurul Haque; Sarker, Mohammed Zaidul Islam; Ferdosh, Sahena; Manap, Mohd Yazid Abdul; Ab Rahman, Nik Norulaini Nik; Ab Kadir, Mohd Omar

    2012-02-10

    Supercritical fluid extraction (SFE), which has received much interest in its use and further development for industrial applications, is a method that offers some advantages over conventional methods, especially for the palm oil industry. SC-CO₂ refers to supercritical fluid extraction (SFE) that uses carbon dioxide (CO₂) as a solvent which is a nontoxic, inexpensive, nonflammable, and nonpolluting supercritical fluid solvent for the extraction of natural products. Almost 100% oil can be extracted and it is regarded as safe, with organic solvent-free extracts having superior organoleptic profiles. The palm oil industry is one of the major industries in Malaysia that provides a major contribution to the national income. Malaysia is the second largest palm oil and palm kernel oil producer in the World. This paper reviews advances in applications of supercritical carbon dioxide (SC-CO₂) extraction of oils from natural sources, in particular palm oil, minor constituents in palm oil, producing fractionated, refined, bleached, and deodorized palm oil, palm kernel oil and purified fatty acid fractions commendable for downstream uses as in toiletries and confectionaries.

  12. Applications of Supercritical Fluid Extraction (SFE of Palm Oil and Oil from Natural Sources

    Directory of Open Access Journals (Sweden)

    Mohd Omar Ab Kadir

    2012-02-01

    Full Text Available Supercritical fluid extraction (SFE, which has received much interest in its use and further development for industrial applications, is a method that offers some advantages over conventional methods, especially for the palm oil industry. SC-CO2 refers to supercritical fluid extraction (SFE that uses carbon dioxide (CO2 as a solvent which is a nontoxic, inexpensive, nonflammable, and nonpolluting supercritical fluid solvent for the extraction of natural products. Almost 100% oil can be extracted and it is regarded as safe, with organic solvent-free extracts having superior organoleptic profiles. The palm oil industry is one of the major industries in Malaysia that provides a major contribution to the national income. Malaysia is the second largest palm oil and palm kernel oil producer in the World. This paper reviews advances in applications of supercritical carbon dioxide (SC-CO2 extraction of oils from natural sources, in particular palm oil, minor constituents in palm oil, producing fractionated, refined, bleached, and deodorized palm oil, palm kernel oil and purified fatty acid fractions commendable for downstream uses as in toiletries and confectionaries.

  13. Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey.

    Science.gov (United States)

    Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

    2012-01-01

    The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.

  14. Optimisation of supercritical carbon dioxide extraction of essential oil of flowers of tea (Camellia sinensis L.) plants and its antioxidative activity.

    Science.gov (United States)

    Chen, Zhenchun; Mei, Xin; Jin, Yuxia; Kim, Eun-Hye; Yang, Ziyin; Tu, Youying

    2014-01-30

    To extract natural volatile compounds from tea (Camellia sinensis) flowers without thermal degradation and residue of organic solvents, supercritical fluid extraction (SFE) using carbon dioxide was employed to prepare essential oil of tea flowers in the present study. Four important parameters--pressure, temperature, static extraction time, and dynamic extraction time--were selected as independent variables in the SFE. The optimum extraction conditions were the pressure of 30 MPa, temperature of 50°C, static time of 10 min, and dynamic time of 90 min. Based on gas chromatography-mass spectrometry analysis, 59 compounds, including alkanes (45.4%), esters (10.5%), ketones (7.1%), aldehydes (3.7%), terpenes (3.7%), acids (2.1%), alcohols (1.6%), ethers (1.3%) and others (10.3%) were identified in the essential oil of tea flowers. Moreover, the essential oil of tea flowers showed relatively stronger DPPH radical scavenging activity than essential oils of geranium and peppermint, although its antioxidative activity was weaker than those of essential oil of clove, ascorbic acid, tert-butylhydroquinone, and butylated hydroxyanisole. Essential oil of tea flowers using SFE contained many types of volatile compounds and showed considerable DPPH scavenging activity. The information will contribute to the future application of tea flowers as raw materials in health-care food and food flavour industries. © 2013 Society of Chemical Industry.

  15. Efficient computation in adaptive artificial spiking neural networks

    NARCIS (Netherlands)

    D. Zambrano (Davide); R.B.P. Nusselder (Roeland); H.S. Scholte; S.M. Bohte (Sander)

    2017-01-01

    textabstractArtificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs exchange analog values in a frame-based manner, a computationally and energetically inefficient form of

  16. Artificial neural networks for prediction of quality in resistance spot welding

    International Nuclear Information System (INIS)

    Martin, O.; Lopez, M.; Martin, F.

    2006-01-01

    An artificial neural network is proposed as a tool for predicting from three parameters (weld time, current intensity and electrode sort) if the quality of a resistance spot weld reaches a certain level or not. The quality id determined by cross tension testing. The fact of reaching this quality level or not is the desired output that goes with each input of the artificial neural network during its supervised learning. The available data set is made up of input/desired output pairs and is split randomly into a training subset (to update synaptic weight values) and a validation subset (to avoid overfitting phenomenon by means of cross validation). (Author) 44 refs

  17. Prediction of two-phase mixture density using artificial neural networks

    International Nuclear Information System (INIS)

    Lombardi, C.; Mazzola, A.

    1997-01-01

    In nuclear power plants, the density of boiling mixtures has a significant relevance due to its influence on the neutronic balance, the power distribution and the reactor dynamics. Since the determination of the two-phase mixture density on a purely analytical basis is in fact impractical in many situations of interest, heuristic relationships have been developed based on the parameters describing the two-phase system. However, the best or even a good structure for the correlation cannot be determined in advance, also considering that it is usually desired to represent the experimental data with the most compact equation. A possible alternative to empirical correlations is the use of artificial neural networks, which allow one to model complex systems without requiring the explicit formulation of the relationships existing among the variables. In this work, the neural network methodology was applied to predict the density data of two-phase mixtures up-flowing in adiabatic channels under different experimental conditions. The trained network predicts the density data with a root-mean-square error of 5.33%, being ∼ 93% of the data points predicted within 10%. When compared with those of two conventional well-proven correlations, i.e. the Zuber-Findlay and the CISE correlations, the neural network performances are significantly better. In spite of the good accuracy of the neural network predictions, the 'black-box' characteristic of the neural model does not allow an easy physical interpretation of the knowledge integrated in the network weights. Therefore, the neural network methodology has the advantage of not requiring a formal correlation structure and of giving very accurate results, but at the expense of a loss of model transparency. (author)

  18. Artificial neural network optimisation for monthly average daily global solar radiation prediction

    International Nuclear Information System (INIS)

    Alsina, Emanuel Federico; Bortolini, Marco; Gamberi, Mauro; Regattieri, Alberto

    2016-01-01

    Highlights: • Prediction of the monthly average daily global solar radiation over Italy. • Multi-location Artificial Neural Network (ANN) model: 45 locations considered. • Optimal ANN configuration with 7 input climatologic/geographical parameters. • Statistical indicators: MAPE, NRMSE, MPBE. - Abstract: The availability of reliable climatologic data is essential for multiple purposes in a wide set of anthropic activities and operative sectors. Frequently direct measures present spatial and temporal lacks so that predictive approaches become of interest. This paper focuses on the prediction of the Monthly Average Daily Global Solar Radiation (MADGSR) over Italy using Artificial Neural Networks (ANNs). Data from 45 locations compose the multi-location ANN training and testing sets. For each location, 13 input parameters are considered, including the geographical coordinates and the monthly values for the most frequently adopted climatologic parameters. A subset of 17 locations is used for ANN training, while the testing step is against data from the remaining 28 locations. Furthermore, the Automatic Relevance Determination method (ARD) is used to point out the most relevant input for the accurate MADGSR prediction. The ANN best configuration includes 7 parameters, only, i.e. Top of Atmosphere (TOA) radiation, day length, number of rainy days and average rainfall, latitude and altitude. The correlation performances, expressed through statistical indicators as the Mean Absolute Percentage Error (MAPE), range between 1.67% and 4.25%, depending on the number and type of the chosen input, representing a good solution compared to the current standards.

  19. Extraction/fractionation and deacidification of wheat germ oil using supercritical carbon dioxide

    Directory of Open Access Journals (Sweden)

    P. Zacchi

    2006-03-01

    Full Text Available Wheat germ oil was obtained by mechanical pressing using a small-scale screw press and by supercritical extraction in a pilot plant. With this last method, different pressures and temperatures were tested and the tocopherol concentration in the extract was monitored during extraction. Then supercritical extracted oil as well as commercial pressed oil were deacidified in a countercurrent column using supercritical carbon dioxide as solvent under different operating conditions. Samples of extract, refined oil and feed oil were analyzed for free fatty acids (FFA and tocopherol contents. The results show that oil with a higher tocopherol content can be obtained by supercritical extraction-fractionation and that FFA can be effectively removed by countercurrent rectification while the tocopherol content is only slightly reduced.

  20. Prediction of Full-Scale Propulsion Power using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Pedersen, Benjamin Pjedsted; Larsen, Jan

    2009-01-01

    Full scale measurements of the propulsion power, ship speed, wind speed and direction, sea and air temperature from four different loading conditions, together with hind cast data of wind and sea properties; and noon report data has been used to train an Artificial Neural Network for prediction...

  1. Consumer profile and acceptability of cooked beef steaks with edible and active coating containing oregano and rosemary essential oils.

    Science.gov (United States)

    Vital, Ana Carolina Pelaes; Guerrero, Ana; Kempinski, Emilia Maria Barbosa Carvalho; Monteschio, Jessica de Oliveira; Sary, Cesar; Ramos, Tatiane Rogelio; Campo, María Del Mar; Prado, Ivanor Nunes do

    2018-09-01

    Fresh animal products are highly perishable and characterized by a short shelf-life. Edible coatings with natural antioxidants (essential oils: EOs) could improve stability, ensure quality, and increase the shelf-life of fresh products. Due to the strong flavor of EOs, their use should consider consumer preferences and sensory acceptability. This study evaluated the effects of edible coating (with oregano and rosemary essential oil) on beef in relation to consumer preferences, besides the determination of habits of consumption and buying intentions of consumers. Acceptability scores from three clusters of consumers was described. Coating with oregano was the preferred. The higher consumer acceptance and willingness to buy this product indicate a great potential and possibility of using coatings with essential oils in fresh animal products. Copyright © 2018. Published by Elsevier Ltd.

  2. Transport energy demand modeling of South Korea using artificial neural network

    International Nuclear Information System (INIS)

    Geem, Zong Woo

    2011-01-01

    Artificial neural network models were developed to forecast South Korea's transport energy demand. Various independent variables, such as GDP, population, oil price, number of vehicle registrations, and passenger transport amount, were considered and several good models (Model 1 with GDP, population, and passenger transport amount; Model 2 with GDP, number of vehicle registrations, and passenger transport amount; and Model 3 with oil price, number of vehicle registrations, and passenger transport amount) were selected by comparing with multiple linear regression models. Although certain regression models obtained better R-squared values than neural network models, this does not guarantee the fact that the former is better than the latter because root mean squared errors of the former were much inferior to those of the latter. Also, certain regression model had structural weakness based on P-value. Instead, neural network models produced more robust results. Forecasted results using the neural network models show that South Korea will consume around 37 MTOE of transport energy in 2025. - Highlights: → Transport energy demand of South Korea was forecasted using artificial neural network. → Various variables (GDP, population, oil price, number of registrations, etc.) were considered. → Results of artificial neural network were compared with those of multiple linear regression.

  3. Artificial Neural Networks in the prediction of insolvency. A paradigm shift to traditional business practices recipes

    Directory of Open Access Journals (Sweden)

    Marcia M. Lastre Valdes

    2014-06-01

    Full Text Available In this paper a review and analysis of the major theories and models that address the prediction of corporate bankruptcy and insolvency is made. Neural networks are a tool of most recent appearance, although in recent years have received considerable attention from the academic and professional world, and have started to be implemented in different models testing organizations insolvency based on neural computation. The purpose of this paper is to yield evidence of the usefulness of Artificial Neural Networks in the problem of bankruptcy prediction insolence or so compare its predictive ability with the methods commonly used in that context. The findings suggest that high predictive capabilities can be achieved  using artificial neural networks, with qualitative and quantitative variables.

  4. Prediction of thermal hydraulic parameters in the loss of coolant accident by using artificial neural networks

    International Nuclear Information System (INIS)

    Vaziri, N.; Erfani, A.; Monsefi, M.; Hajabri, A.

    2008-01-01

    In a reactor accident like loss of coolant accident , one or more signals may not be monitored by control panel for some reasons such as interruptions and so on. Therefore a fast alternative method could guarantee the safe and reliable exploration of nuclear power planets. In this study, we used artificial neural network with Elman recurrent structure to predict six thermal hydraulic signals in a loss of coolant accident after upper plenum break. In the prediction procedure, a few previous samples are fed to the artificial neural network and the output value or next time step is estimated by the network output. The Elman recurrent network is trained with the data obtained from the benchmark simulation of loss of coolant accident in VVER. The results reveal that the predicted values follow the real trends well and artificial neural network can be used as a fast alternative prediction tool in loss of coolant accident

  5. INTEGRATING ARTIFICIAL NEURAL NETWORKS FOR DEVELOPING TELEMEDICINE SOLUTION

    Directory of Open Access Journals (Sweden)

    Mihaela GHEORGHE

    2015-06-01

    Full Text Available Artificial intelligence is assuming an increasing important role in the telemedicine field, especially neural networks with their ability to achieve meaning from large sets of data characterized by lacking exactness and accuracy. These can be used for assisting physicians or other clinical staff in the process of taking decisions under uncertainty. Thus, machine learning methods which are specific to this technology are offering an approach for prediction based on pattern classification. This paper aims to present the importance of neural networks in detecting trends and extracting patterns which can be used within telemedicine domains, particularly for taking medical diagnosis decisions.

  6. An Artificial Neural Network Based Short-term Dynamic Prediction of Algae Bloom

    Directory of Open Access Journals (Sweden)

    Yao Junyang

    2014-06-01

    Full Text Available This paper proposes a method of short-term prediction of algae bloom based on artificial neural network. Firstly, principal component analysis is applied to water environmental factors in algae bloom raceway ponds to get main factors that influence the formation of algae blooms. Then, a model of short-term dynamic prediction based on neural network is built with the current chlorophyll_a values as input and the chlorophyll_a values in the next moment as output to realize short-term dynamic prediction of algae bloom. Simulation results show that the model can realize short-term prediction of algae bloom effectively.

  7. Effect of Edible and Active Coating (with Rosemary and Oregano Essential Oils) on Beef Characteristics and Consumer Acceptability

    Science.gov (United States)

    Vital, Ana Carolina Pelaes; Guerrero, Ana; Monteschio, Jessica de Oliveira; Valero, Maribel Velandia; Carvalho, Camila Barbosa; de Abreu Filho, Benício Alves; Madrona, Grasiele Scaramal; do Prado, Ivanor Nunes

    2016-01-01

    The effects of an alginate-based edible coating containing natural antioxidants (rosemary and oregano essential oils) on lipid oxidation, color preservation, water losses, texture and pH of beef steaks during 14 days of display were studied. The essential oil, edible coating and beef antioxidant activities, and beef consumer acceptability were also investigated. The edible coatings decreased lipid oxidation of the meat compared to the control. The coating with oregano was most effective (46.81% decrease in lipid oxidation) and also showed the highest antioxidant activity. The coatings significantly decreased color losses, water losses and shear force compared to the control. The coatings had a significant effect on consumer perception of odor, flavor and overall acceptance of the beef. In particular, the oregano coating showed significantly high values (approximately 7 in a 9-point scale). Active edible coatings containing natural antioxidants could improve meat product stability and therefore have potential use in the food industry. PMID:27504957

  8. Effect of Edible and Active Coating (with Rosemary and Oregano Essential Oils on Beef Characteristics and Consumer Acceptability.

    Directory of Open Access Journals (Sweden)

    Ana Carolina Pelaes Vital

    Full Text Available The effects of an alginate-based edible coating containing natural antioxidants (rosemary and oregano essential oils on lipid oxidation, color preservation, water losses, texture and pH of beef steaks during 14 days of display were studied. The essential oil, edible coating and beef antioxidant activities, and beef consumer acceptability were also investigated. The edible coatings decreased lipid oxidation of the meat compared to the control. The coating with oregano was most effective (46.81% decrease in lipid oxidation and also showed the highest antioxidant activity. The coatings significantly decreased color losses, water losses and shear force compared to the control. The coatings had a significant effect on consumer perception of odor, flavor and overall acceptance of the beef. In particular, the oregano coating showed significantly high values (approximately 7 in a 9-point scale. Active edible coatings containing natural antioxidants could improve meat product stability and therefore have potential use in the food industry.

  9. Effect of Edible and Active Coating (with Rosemary and Oregano Essential Oils) on Beef Characteristics and Consumer Acceptability.

    Science.gov (United States)

    Vital, Ana Carolina Pelaes; Guerrero, Ana; Monteschio, Jessica de Oliveira; Valero, Maribel Velandia; Carvalho, Camila Barbosa; de Abreu Filho, Benício Alves; Madrona, Grasiele Scaramal; do Prado, Ivanor Nunes

    2016-01-01

    The effects of an alginate-based edible coating containing natural antioxidants (rosemary and oregano essential oils) on lipid oxidation, color preservation, water losses, texture and pH of beef steaks during 14 days of display were studied. The essential oil, edible coating and beef antioxidant activities, and beef consumer acceptability were also investigated. The edible coatings decreased lipid oxidation of the meat compared to the control. The coating with oregano was most effective (46.81% decrease in lipid oxidation) and also showed the highest antioxidant activity. The coatings significantly decreased color losses, water losses and shear force compared to the control. The coatings had a significant effect on consumer perception of odor, flavor and overall acceptance of the beef. In particular, the oregano coating showed significantly high values (approximately 7 in a 9-point scale). Active edible coatings containing natural antioxidants could improve meat product stability and therefore have potential use in the food industry.

  10. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

    International Nuclear Information System (INIS)

    Yu, Lean; Wang, Shouyang; Lai, Kin Keung

    2008-01-01

    In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). Then a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally, the prediction results of all IMFs are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price series. For verification and testing, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price, are used to test the effectiveness of the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm. (author)

  11. Artificial Neural Networks For Hadron Hadron Cross-sections

    International Nuclear Information System (INIS)

    ELMashad, M.; ELBakry, M.Y.; Tantawy, M.; Habashy, D.M.

    2011-01-01

    In recent years artificial neural networks (ANN ) have emerged as a mature and viable framework with many applications in various areas. Artificial neural networks theory is sometimes used to refer to a branch of computational science that uses neural networks as models to either simulate or analyze complex phenomena and/or study the principles of operation of neural networks analytically. In this work a model of hadron- hadron collision using the ANN technique is present, the hadron- hadron based ANN model calculates the cross sections of hadron- hadron collision. The results amply demonstrate the feasibility of such new technique in extracting the collision features and prove its effectiveness

  12. Chemical composition and antifungal activity of essential oils and supercritical CO2 extracts of Apium nodiflorum (L.) Lag.

    Science.gov (United States)

    Maxia, Andrea; Falconieri, Danilo; Piras, Alessandra; Porcedda, Silvia; Marongiu, Bruno; Frau, Maria Assunta; Gonçalves, Maria J; Cabral, Célia; Cavaleiro, Carlos; Salgueiro, Lígia

    2012-07-01

    Aerial parts of Apium nodiflorum collected in Portugal and Italy were submitted to hydrodistillation; also a supercritical fluid extract was obtained from Italian plants. The extracts were analyzed by GC and GC/MS. Both essential oils, obtained from Portuguese and Italian plants, posses high content of phenylpropanoids (51.6 vs. 70.8%); in the former, the percentage split in myristicin (29.1%) and dillapiol (22.5%), whereas in the latter, the total percentage is only of dillapiol (70.8%). The co-occurrence of myristicin and dillapiol is frequent because dillapiol results from enzymatic methoxylation of myristicin. Antimicrobial activity of phenylpropanoids has been patented, what suggest the potential of plants with high amounts of these compounds. Minimal inhibitory concentration (MIC) and minimal lethal concentration, determined according to NCCLS, were used to evaluate the antifungal activity of the essential oils against yeasts, Aspergillus species and dermatophytes. Essential oils exhibited higher antifungal activity than other Apiaceae against dermatophytes, with MIC ranging from 0.04 to 0.32 μl/ml. These results support the potential of A. nodiflorum oil in the treatment of dermatophytosis and candidosis.

  13. Neural networks to predict exosphere temperature corrections

    Science.gov (United States)

    Choury, Anna; Bruinsma, Sean; Schaeffer, Philippe

    2013-10-01

    Precise orbit prediction requires a forecast of the atmospheric drag force with a high degree of accuracy. Artificial neural networks are universal approximators derived from artificial intelligence and are widely used for prediction. This paper presents a method of artificial neural networking for prediction of the thermosphere density by forecasting exospheric temperature, which will be used by the semiempirical thermosphere Drag Temperature Model (DTM) currently developed. Artificial neural network has shown to be an effective and robust forecasting model for temperature prediction. The proposed model can be used for any mission from which temperature can be deduced accurately, i.e., it does not require specific training. Although the primary goal of the study was to create a model for 1 day ahead forecast, the proposed architecture has been generalized to 2 and 3 days prediction as well. The impact of artificial neural network predictions has been quantified for the low-orbiting satellite Gravity Field and Steady-State Ocean Circulation Explorer in 2011, and an order of magnitude smaller orbit errors were found when compared with orbits propagated using the thermosphere model DTM2009.

  14. COMBINING PCA ANALYSIS AND ARTIFICIAL NEURAL NETWORKS IN MODELLING ENTREPRENEURIAL INTENTIONS OF STUDENTS

    Directory of Open Access Journals (Sweden)

    Marijana Zekić-Sušac

    2013-02-01

    Full Text Available Despite increased interest in the entrepreneurial intentions and career choices of young adults, reliable prediction models are yet to be developed. Two nonparametric methods were used in this paper to model entrepreneurial intentions: principal component analysis (PCA and artificial neural networks (ANNs. PCA was used to perform feature extraction in the first stage of modelling, while artificial neural networks were used to classify students according to their entrepreneurial intentions in the second stage. Four modelling strategies were tested in order to find the most efficient model. Dataset was collected in an international survey on entrepreneurship self-efficacy and identity. Variables describe students’ demographics, education, attitudes, social and cultural norms, self-efficacy and other characteristics. The research reveals benefits from the combination of the PCA and ANNs in modeling entrepreneurial intentions, and provides some ideas for further research.

  15. Artificial neural network based particle size prediction of polymeric nanoparticles.

    Science.gov (United States)

    Youshia, John; Ali, Mohamed Ehab; Lamprecht, Alf

    2017-10-01

    Particle size of nanoparticles and the respective polydispersity are key factors influencing their biopharmaceutical behavior in a large variety of therapeutic applications. Predicting these attributes would skip many preliminary studies usually required to optimize formulations. The aim was to build a mathematical model capable of predicting the particle size of polymeric nanoparticles produced by a pharmaceutical polymer of choice. Polymer properties controlling the particle size were identified as molecular weight, hydrophobicity and surface activity, and were quantified by measuring polymer viscosity, contact angle and interfacial tension, respectively. A model was built using artificial neural network including these properties as input with particle size and polydispersity index as output. The established model successfully predicted particle size of nanoparticles covering a range of 70-400nm prepared from other polymers. The percentage bias for particle prediction was 2%, 4% and 6%, for the training, validation and testing data, respectively. Polymer surface activity was found to have the highest impact on the particle size followed by viscosity and finally hydrophobicity. Results of this study successfully highlighted polymer properties affecting particle size and confirmed the usefulness of artificial neural networks in predicting the particle size and polydispersity of polymeric nanoparticles. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Prediction of Austenite Formation Temperatures Using Artificial Neural Networks

    International Nuclear Information System (INIS)

    Schulze, P; Schmidl, E; Grund, T; Lampke, T

    2016-01-01

    For the modeling and design of heat treatments, in consideration of the development/ transformation of the microstructure, different material data depending on the chemical composition, the respective microstructure/phases and the temperature are necessary. Material data are, e.g. the thermal conductivity, heat capacity, thermal expansion and transformation data etc. The quality of thermal simulations strongly depends on the accuracy of the material data. For many materials, the required data - in particular for different microstructures and temperatures - are rare in the literature. In addition, a different chemical composition within the permitted limits of the considered steel alloy cannot be predicted. A solution for this problem is provided by the calculation of material data using Artificial Neural Networks (ANN). In the present study, the start and finish temperatures of the transformation from the bcc lattice to the fcc lattice structure of hypoeutectoid steels are calculated using an Artificial Neural Network. An appropriate database containing different transformation temperatures (austenite formation temperatures) to train the ANN is selected from the literature. In order to find a suitable feedforward network, the network topologies as well as the activation functions of the hidden layers are varied and subsequently evaluated in terms of the prediction accuracy. The transformation temperatures calculated by the ANN exhibit a very good compliance compared to the experimental data. The results show that the prediction performance is even higher compared to classical empirical equations such as Andrews or Brandis. Therefore, it can be assumed that the presented ANN is a convenient tool to distinguish between bcc and fcc phases in hypoeutectoid steels. (paper)

  17. Prediction of Austenite Formation Temperatures Using Artificial Neural Networks

    Science.gov (United States)

    Schulze, P.; Schmidl, E.; Grund, T.; Lampke, T.

    2016-03-01

    For the modeling and design of heat treatments, in consideration of the development/ transformation of the microstructure, different material data depending on the chemical composition, the respective microstructure/phases and the temperature are necessary. Material data are, e.g. the thermal conductivity, heat capacity, thermal expansion and transformation data etc. The quality of thermal simulations strongly depends on the accuracy of the material data. For many materials, the required data - in particular for different microstructures and temperatures - are rare in the literature. In addition, a different chemical composition within the permitted limits of the considered steel alloy cannot be predicted. A solution for this problem is provided by the calculation of material data using Artificial Neural Networks (ANN). In the present study, the start and finish temperatures of the transformation from the bcc lattice to the fcc lattice structure of hypoeutectoid steels are calculated using an Artificial Neural Network. An appropriate database containing different transformation temperatures (austenite formation temperatures) to train the ANN is selected from the literature. In order to find a suitable feedforward network, the network topologies as well as the activation functions of the hidden layers are varied and subsequently evaluated in terms of the prediction accuracy. The transformation temperatures calculated by the ANN exhibit a very good compliance compared to the experimental data. The results show that the prediction performance is even higher compared to classical empirical equations such as Andrews or Brandis. Therefore, it can be assumed that the presented ANN is a convenient tool to distinguish between bcc and fcc phases in hypoeutectoid steels.

  18. Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

    Science.gov (United States)

    Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari

    2018-01-01

    Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg

  19. Training algorithms evaluation for artificial neural network to temporal prediction of photovoltaic generation

    International Nuclear Information System (INIS)

    Arantes Monteiro, Raul Vitor; Caixeta Guimarães, Geraldo; Rocio Castillo, Madeleine; Matheus Moura, Fabrício Augusto; Tamashiro, Márcio Augusto

    2016-01-01

    Current energy policies are encouraging the connection of power generation based on low-polluting technologies, mainly those using renewable sources, to distribution networks. Hence, it becomes increasingly important to understand technical challenges, facing high penetration of PV systems at the grid, especially considering the effects of intermittence of this source on the power quality, reliability and stability of the electric distribution system. This fact can affect the distribution networks on which they are attached causing overvoltage, undervoltage and frequency oscillations. In order to predict these disturbs, artificial neural networks are used. This article aims to analyze 3 training algorithms used in artificial neural networks for temporal prediction of the generated active power thru photovoltaic panels. As a result it was concluded that the algorithm with the best performance among the 3 analyzed was the Levenberg-Marquadrt.

  20. Supercritical fluid extraction of silicone oil from uranate microspheres prepared by sol-gel process

    International Nuclear Information System (INIS)

    Kumar, R.; Venkatakrishnan, R.; Sivaraman, N.; Srinivasan, T.G.; Vasudeva Rao, P.R.

    2005-01-01

    Supercritical fluid extraction of silicone oil from urania microspheres prepared through sol-gel route was investigated. The influence of pressure, temperature, and flow rate on the extraction efficiency was studied. Experimental conditions were optimised for the complete removal of silicone oil from urania microspheres. (author)

  1. Energy efficiency optimisation for distillation column using artificial neural network models

    International Nuclear Information System (INIS)

    Osuolale, Funmilayo N.; Zhang, Jie

    2016-01-01

    This paper presents a neural network based strategy for the modelling and optimisation of energy efficiency in distillation columns incorporating the second law of thermodynamics. Real-time optimisation of distillation columns based on mechanistic models is often infeasible due to the effort in model development and the large computation effort associated with mechanistic model computation. This issue can be addressed by using neural network models which can be quickly developed from process operation data. The computation time in neural network model evaluation is very short making them ideal for real-time optimisation. Bootstrap aggregated neural networks are used in this study for enhanced model accuracy and reliability. Aspen HYSYS is used for the simulation of the distillation systems. Neural network models for exergy efficiency and product compositions are developed from simulated process operation data and are used to maximise exergy efficiency while satisfying products qualities constraints. Applications to binary systems of methanol-water and benzene-toluene separations culminate in a reduction of utility consumption of 8.2% and 28.2% respectively. Application to multi-component separation columns also demonstrate the effectiveness of the proposed method with a 32.4% improvement in the exergy efficiency. - Highlights: • Neural networks can accurately model exergy efficiency in distillation columns. • Bootstrap aggregated neural network offers improved model prediction accuracy. • Improved exergy efficiency is obtained through model based optimisation. • Reductions of utility consumption by 8.2% and 28.2% were achieved for binary systems. • The exergy efficiency for multi-component distillation is increased by 32.4%.

  2. Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

    Science.gov (United States)

    Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

    2015-01-01

    Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

  3. Antioxidant activity of oregano, parsley, and olive mill wastewaters in bulk oils and oil-in-water emulsions enriched in fish oil.

    Science.gov (United States)

    Jimenez-Alvarez, D; Giuffrida, F; Golay, P A; Cotting, C; Lardeau, A; Keely, Brendan J

    2008-08-27

    The antioxidant activity of oregano, parsley, olive mill wastewaters (OMWW), Trolox, and ethylenediaminetetraacetic acid (EDTA) was evaluated in bulk oils and oil-in-water (o/w) emulsions enriched with 5% tuna oil by monitoring the formation of hydroperoxides, hexanal, and t-t-2,4-heptadienal in samples stored at 37 degrees C for 14 days. In bulk oil, the order of antioxidant activity was, in decreasing order (p oregano > parsley > EDTA > Trolox. The antioxidant activity in o/w emulsion followed the same order except that EDTA was as efficient an antioxidant as OMWW. In addition, the total phenolic content, the radical scavenging properties, the reducing capacity, and the iron chelating activity of OMWW, parsley, and oregano extracts were determined by the Folin-Ciocalteau, oxygen radical absorbance capacity, ferric reducing antioxidant power, and iron(II) chelating activity assays, respectively. The antioxidant activity of OMWW, parsley, and oregano in food systems was related to their total phenolic content and radical scavenging capacity but not to their ability to chelate iron in vitro. OMWW was identified as a promising source of antioxidants to retard lipid oxidation in fish oil-enriched food products.

  4. Reconstruction of magnetic configurations in W7-X using artificial neural networks

    Science.gov (United States)

    Böckenhoff, Daniel; Blatzheim, Marko; Hölbe, Hauke; Niemann, Holger; Pisano, Fabio; Labahn, Roger; Pedersen, Thomas Sunn; The W7-X Team

    2018-05-01

    It is demonstrated that artificial neural networks can be used to accurately and efficiently predict details of the magnetic topology at the plasma edge of the Wendelstein 7-X stellarator, based on simulated as well as measured heat load patterns onto plasma-facing components observed with infrared cameras. The connection between heat load patterns and the magnetic topology is a challenging regression problem, but one that suits artificial neural networks well. The use of a neural network makes it feasible to analyze and control the plasma exhaust in real-time, an important goal for Wendelstein 7-X, and for magnetic confinement fusion research in general.

  5. Artificial neural network models for prediction of intestinal permeability of oligopeptides

    Directory of Open Access Journals (Sweden)

    Kim Min-Kook

    2007-07-01

    Full Text Available Abstract Background Oral delivery is a highly desirable property for candidate drugs under development. Computational modeling could provide a quick and inexpensive way to assess the intestinal permeability of a molecule. Although there have been several studies aimed at predicting the intestinal absorption of chemical compounds, there have been no attempts to predict intestinal permeability on the basis of peptide sequence information. To develop models for predicting the intestinal permeability of peptides, we adopted an artificial neural network as a machine-learning algorithm. The positive control data consisted of intestinal barrier-permeable peptides obtained by the peroral phage display technique, and the negative control data were prepared from random sequences. Results The capacity of our models to make appropriate predictions was validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC curve (the ROC score. The training and test set statistics indicated that our models were of strikingly good quality and could discriminate between permeable and random sequences with a high level of confidence. Conclusion We developed artificial neural network models to predict the intestinal permeabilities of oligopeptides on the basis of peptide sequence information. Both binary and VHSE (principal components score Vectors of Hydrophobic, Steric and Electronic properties descriptors produced statistically significant training models; the models with simple neural network architectures showed slightly greater predictive power than those with complex ones. We anticipate that our models will be applicable to the selection of intestinal barrier-permeable peptides for generating peptide drugs or peptidomimetics.

  6. Compositional and functional difference in cumin (Cuminum cyminum essential oil extracted by hydrodistillation and SCFE

    Directory of Open Access Journals (Sweden)

    Supradip Saha

    2016-12-01

    Full Text Available Essential oils were obtained from same raw material of cumin seed by extraction with hydrodistillation and super critical fluid extraction (SCFE. For SCFE, supercritical carbon dioxide at 45°C and 100 bar was used as variable for the extraction. The composition of the extracts was determined by gas chromatography-mass spectrometry. Yield of essential oil was more in the SCFE method. Extract obtained by supercritical fluid extraction technique using CO2 was heavier than the hydrodistilled volatile oil. Cumin oil obtained by hydrodistillation contained higher percentage of cuminaldehyde (52.6%, then did oil obtained by SCFE (37.3%, whereas cumin oil obtained by hydrodistillation had the lower percentage of cuminic alcohol (13.3% as compared to 19.3% in SCFE method. However, cuminal (2-caren-10-al content was almost similar in cumin oil obtained by the SCFE and hydrodistillation method (24.5–25.8%. Hydrodistilled volatile oil showed better antioxidant activity measured by DPPH and FRAP assay and more total phenol content. The results indicated that though essential oil yield was more in the SCFE method, antioxidant property was more in conventional hydrodistillation method. SCFE extracted non polar (wax materials compounds along with volatile oil and it was recorded that enhanced aroma of signature compounds of cumin.

  7. Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

    Science.gov (United States)

    Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

    2012-06-01

    This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement.

  8. A microsensor array for quantification of lubricant contaminants using a back propagation artificial neural network

    International Nuclear Information System (INIS)

    Zhu, Xiaoliang; Du, Li; Zhe, Jiang; Liu, Bendong

    2016-01-01

    We present a method based on an electrochemical sensor array and a back propagation artificial neural network for detection and quantification of four properties of lubrication oil, namely water (0, 500 ppm, 1000 ppm), total acid number (TAN) (13.1, 13.7, 14.4, 15.6 mg KOH g −1 ), soot (0, 1%, 2%, 3%) and sulfur content (1.3%, 1.37%, 1.44%, 1.51%). The sensor array, consisting of four micromachined electrochemical sensors, detects the four properties with overlapping sensitivities. A total set of 36 oil samples containing mixtures of water, soot, and sulfuric acid with different concentrations were prepared for testing. The sensor array’s responses were then divided to three sets: training sets (80% data), validation sets (10%) and testing sets (10%). Several back propagation artificial neural network architectures were trained with the training and validation sets; one architecture with four input neurons, 50 and 5 neurons in the first and second hidden layer, and four neurons in the output layer was selected. The selected neural network was then tested using the four sets of testing data (10%). Test results demonstrated that the developed artificial neural network is able to quantitatively determine the four lubrication properties (water, TAN, soot, and sulfur content) with a maximum prediction error of 18.8%, 6.0%, 6.7%, and 5.4%, respectively, indicting a good match between the target and predicted values. With the developed network, the sensor array could be potentially used for online lubricant oil condition monitoring. (paper)

  9. Automated system for load flow prediction in power substations using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Arlys Michel Lastre Aleaga

    2015-09-01

    Full Text Available The load flow is of great importance in assisting the process of decision making and planning of generation, distribution and transmission of electricity. Ignorance of the values in this indicator, as well as their inappropriate prediction, difficult decision making and efficiency of the electricity service, and can cause undesirable situations such as; the on demand, overheating of the components that make up a substation, and incorrect planning processes electricity generation and distribution. Given the need for prediction of flow of electric charge of the substations in Ecuador this research proposes the concept for the development of an automated prediction system employing the use of Artificial Neural Networks.

  10. Prediction of dissolved oxygen in the Mediterranean Sea along Gaza, Palestine - an artificial neural network approach.

    Science.gov (United States)

    Zaqoot, Hossam Adel; Ansari, Abdul Khalique; Unar, Mukhtiar Ali; Khan, Shaukat Hyat

    2009-01-01

    Artificial Neural Networks (ANNs) are flexible tools which are being used increasingly to predict and forecast water resources variables. The human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. The presence of dissolved oxygen is essential for the survival of most organisms in the water bodies. This paper is concerned with the use of ANNs - Multilayer Perceptron (MLP) and Radial Basis Function neural networks for predicting the next fortnight's dissolved oxygen concentrations in the Mediterranean Sea water along Gaza. MLP and Radial Basis Function (RBF) neural networks are trained and developed with reference to five important oceanographic variables including water temperature, wind velocity, turbidity, pH and conductivity. These variables are considered as inputs of the network. The data sets used in this study consist of four years and collected from nine locations along Gaza coast. The network performance has been tested with different data sets and the results show satisfactory performance. Prediction results prove that neural network approach has good adaptability and extensive applicability for modelling the dissolved oxygen in the Mediterranean Sea along Gaza. We hope that the established model will help in assisting the local authorities in developing plans and policies to reduce the pollution along Gaza coastal waters to acceptable levels.

  11. Liquefaction Microzonation of Babol City Using Artificial Neural Network

    DEFF Research Database (Denmark)

    Farrokhzad, F.; Choobbasti, A.J.; Barari, Amin

    2012-01-01

    that will be less susceptible to damage during earthquakes. The scope of present study is to prepare the liquefaction microzonation map for the Babol city based on Seed and Idriss (1983) method using artificial neural network. Artificial neural network (ANN) is one of the artificial intelligence (AI) approaches...... microzonation map is produced for research area. Based on the obtained results, it can be stated that the trained neural network is capable in prediction of liquefaction potential with an acceptable level of confidence. At the end, zoning of the city is carried out based on the prediction of liquefaction...... that can be classified as machine learning. Simplified methods have been practiced by researchers to assess nonlinear liquefaction potential of soil. In order to address the collective knowledge built-up in conventional liquefaction engineering, an alternative general regression neural network model...

  12. Application of Functional Link Artificial Neural Network for Prediction of Machinery Noise in Opencast Mines

    Directory of Open Access Journals (Sweden)

    Santosh Kumar Nanda

    2011-01-01

    Full Text Available Functional link-based neural network models were applied to predict opencast mining machineries noise. The paper analyzes the prediction capabilities of functional link neural network based noise prediction models vis-à-vis existing statistical models. In order to find the actual noise status in opencast mines, some of the popular noise prediction models, for example, ISO-9613-2, CONCAWE, VDI, and ENM, have been applied in mining and allied industries to predict the machineries noise by considering various attenuation factors. Functional link artificial neural network (FLANN, polynomial perceptron network (PPN, and Legendre neural network (LeNN were used to predict the machinery noise in opencast mines. The case study is based on data collected from an opencast coal mine of Orissa, India. From the present investigations, it could be concluded that the FLANN model give better noise prediction than the PPN and LeNN model.

  13. Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks.

    Science.gov (United States)

    Jin, Junghwan; Kim, Jinsoo

    2015-01-01

    Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency.

  14. Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks.

    Directory of Open Access Journals (Sweden)

    Junghwan Jin

    Full Text Available Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency.

  15. Combined effect of vacuum-packaging and oregano essential oil on the shelf-life of Mediterranean octopus (Octopus vulgaris) from the Aegean Sea stored at 4 degrees C.

    Science.gov (United States)

    Atrea, I; Papavergou, A; Amvrosiadis, I; Savvaidis, I N

    2009-04-01

    The present study evaluated the use of vacuum packaging (alone) or with addition of oregano essential oil (EO), as an antimicrobial treatment for shelf-life extension of fresh Mediterranean octopus stored under refrigeration for a period of 23 days. Four different treatments were tested: A, control sample; under aerobic storage in the absence of oregano essential oil; VP, under vacuum packaging in the absence of oregano essential oil; and VO1, VO2, treated samples with oregano essential oil 0.2 and 0.4% (v/w), respectively, under VP. Of all the microorganisms enumerated, Pseudomonas spp., H2S-producing bacteria and lactic acid bacteria (LAB) were the groups that prevailed in octopus samples, irrespective of antimicrobial treatment. With regard to the chemical freshness indices determined, thiobarbituric acid (TBA) values were low in all octopus samples, as could have been expected from the low fat content of the product. Both trimethylamine nitrogen (TMA-N) and total volatile basic nitrogen (TVB-N) values of oregano treated under VP octopus samples were significantly lower compared to control samples during the entire refrigerated storage period. Based primarily on sensory evaluation (odor), the use of VP, VO1 and VO2 extended the shelf-life of fresh Mediterranean octopus by ca. 3, 11 and 20 days, respectively.

  16. Trimaran Resistance Artificial Neural Network

    Science.gov (United States)

    2011-01-01

    11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to

  17. Multiple simultaneous fault diagnosis via hierarchical and single artificial neural networks

    International Nuclear Information System (INIS)

    Eslamloueyan, R.; Shahrokhi, M.; Bozorgmehri, R.

    2003-01-01

    Process fault diagnosis involves interpreting the current status of the plant given sensor reading and process knowledge. There has been considerable work done in this area with a variety of approaches being proposed for process fault diagnosis. Neural networks have been used to solve process fault diagnosis problems in chemical process, as they are well suited for recognizing multi-dimensional nonlinear patterns. In this work, the use of Hierarchical Artificial Neural Networks in diagnosing the multi-faults of a chemical process are discussed and compared with that of Single Artificial Neural Networks. The lower efficiency of Hierarchical Artificial Neural Networks , in comparison to Single Artificial Neural Networks, in process fault diagnosis is elaborated and analyzed. Also, the concept of a multi-level selection switch is presented and developed to improve the performance of hierarchical artificial neural networks. Simulation results indicate that application of multi-level selection switch increase the performance of the hierarchical artificial neural networks considerably

  18. Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network.

    Science.gov (United States)

    Roffman, David; Hart, Gregory; Girardi, Michael; Ko, Christine J; Deng, Jun

    2018-01-26

    Ultraviolet radiation (UVR) exposure and family history are major associated risk factors for the development of non-melanoma skin cancer (NMSC). The objective of this study was to develop and validate a multi-parameterized artificial neural network based on available personal health information for early detection of NMSC with high sensitivity and specificity, even in the absence of known UVR exposure and family history. The 1997-2015 NHIS adult survey data used to train and validate our neural network (NN) comprised of 2,056 NMSC and 460,574 non-cancer cases. We extracted 13 parameters for our NN: gender, age, BMI, diabetic status, smoking status, emphysema, asthma, race, Hispanic ethnicity, hypertension, heart diseases, vigorous exercise habits, and history of stroke. This study yielded an area under the ROC curve of 0.81 and 0.81 for training and validation, respectively. Our results (training sensitivity 88.5% and specificity 62.2%, validation sensitivity 86.2% and specificity 62.7%) were comparable to a previous study of basal and squamous cell carcinoma prediction that also included UVR exposure and family history information. These results indicate that our NN is robust enough to make predictions, suggesting that we have identified novel associations and potential predictive parameters of NMSC.

  19. LEARNING ALGORITHM EFFECT ON MULTILAYER FEED FORWARD ARTIFICIAL NEURAL NETWORK PERFORMANCE IN IMAGE CODING

    Directory of Open Access Journals (Sweden)

    OMER MAHMOUD

    2007-08-01

    Full Text Available One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. The performance of Multilayer Feed Forward Artificial Neural Network performance in image compression using different learning algorithms is examined in this paper. Based on Gradient Descent, Conjugate Gradient, Quasi-Newton techniques three different error back propagation algorithms have been developed for use in training two types of neural networks, a single hidden layer network and three hidden layers network. The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms.

  20. An artificial neural network approach and sensitivity analysis in predicting skeletal muscle forces.

    Science.gov (United States)

    Vilimek, Miloslav

    2014-01-01

    This paper presents the use of an artificial neural network (NN) approach for predicting the muscle forces around the elbow joint. The main goal was to create an artificial NN which could predict the musculotendon forces for any general muscle without significant errors. The input parameters for the network were morphological and anatomical musculotendon parameters, plus an activation level experimentally measured during a flexion/extension movement in the elbow. The muscle forces calculated by the 'Virtual Muscle System' provide the output. The cross-correlation coefficient expressing the ability of an artificial NN to predict the "true" force was in the range 0.97-0.98. A sensitivity analysis was used to eliminate the less sensitive inputs, and the final number of inputs for a sufficient prediction was nine. A variant of an artificial NN for a single specific muscle was also studied. The artificial NN for one specific muscle gives better results than a network for general muscles. This method is a good alternative to other approaches to calculation of muscle force.

  1. Oregano Essential Oil as an Antimicrobial and Antioxidant Additive in Food Products.

    Science.gov (United States)

    Rodriguez-Garcia, I; Silva-Espinoza, B A; Ortega-Ramirez, L A; Leyva, J M; Siddiqui, M W; Cruz-Valenzuela, M R; Gonzalez-Aguilar, G A; Ayala-Zavala, J F

    2016-07-26

    Food consumers and industries urged the need of natural alternatives to assure food safety and quality. As a response, the use of natural compounds from herbs and spices is an alternative to synthetic additives associated with toxic problems. This review discusses the antimicrobial and antioxidant activity of oregano essential oil (OEO) and its potential as a food additive. Oregano is a plant that has been used as a food seasoning since ancient times. The common name of oregano is given to several species: Origanum (family: Lamiaceae) and Lippia (family: Verbenaceae), amongst others. The main compounds identified in the different OEOs are carvacrol and thymol, which are responsible for the characteristic odor, antimicrobial, and antioxidant activity; however, their content may vary according to the species, harvesting season, and geographical sources. These substances as antibacterial agents make the cell membrane permeable due to its impregnation in the hydrophobic domains, this effect is higher against gram positive bacteria. In addition, the OEO has antioxidant properties effective in retarding the process of lipid peroxidation in fatty foods, and scavenging free radicals. In this perspective, the present review analyzes and discusses the state of the art about the actual and potential uses of OEO as an antimicrobial and antioxidant food additives.

  2. Prediction of incrustation thickness in pipes used in transport of petroleum using gamma radiation and artificial neural network

    International Nuclear Information System (INIS)

    Teixeira, Tâmara Porfíro

    2018-01-01

    This work presents a methodology for predicting concentric and eccentric scales in pipelines used in the offshore oil industry. The approximation is based on the principles of gamma densitometry and artificial neural networks. A preliminary study model was developed to define the compositions of the duct and scale. In order to do so, the influence of pipeline transmission with four different types of steel used in oil platforms was evaluated, as well as the influence of the main inorganic deposit formations. The divergence of the radioactive source was also considered in this evaluation, with collimation openings of 2 mm to 7 mm, with steps of 2.5 mm. After defining the composition of the duct and scale, a measurement geometry was defined by means of the MCNP-X code to calculate the scale thickness by means of analytical equations, independent of the fluids present in the duct (salt water, gas and oil). The representative geometry uses a duct composed of iron, with inorganic scale formed by barium sulfate (BaSO 4 ). Concentric scale models were simulated and the data obtained were used for training and validation of an artificial neural network, as well as eccentric scale models. The simulated detection system consisted of a narrow-beam geometry with a 2 mm collimation aperture, comprising a gamma ray source ( 137 Cs) and 2 x 2 “NaI (Tl) sensors suitably positioned around the duct-scale-fluid system for calculation of the scale thickness considering the transmitted beam and the scattered beam. Compton scattering was considered in cases of eccentric scale to aid in the determination and location of maximum scale thicknesses. The theoretical models were developed using the mathematical code MCNP-X and used for training, testing and validation of artificial neural networks. The proposed methodology was able to predict the concentric and eccentric scale thicknesses with satisfactory results for these two types of inorganic formations. (author)

  3. Analysis Resilient Algorithm on Artificial Neural Network Backpropagation

    Science.gov (United States)

    Saputra, Widodo; Tulus; Zarlis, Muhammad; Widia Sembiring, Rahmat; Hartama, Dedy

    2017-12-01

    Prediction required by decision makers to anticipate future planning. Artificial Neural Network (ANN) Backpropagation is one of method. This method however still has weakness, for long training time. This is a reason to improve a method to accelerate the training. One of Artificial Neural Network (ANN) Backpropagation method is a resilient method. Resilient method of changing weights and bias network with direct adaptation process of weighting based on local gradient information from every learning iteration. Predicting data result of Istanbul Stock Exchange training getting better. Mean Square Error (MSE) value is getting smaller and increasing accuracy.

  4. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  5. Artificial Neural Network Analysis System

    Science.gov (United States)

    2001-02-27

    Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

  6. Artificial neural network applications in ionospheric studies

    Directory of Open Access Journals (Sweden)

    L. R. Cander

    1998-06-01

    Full Text Available The ionosphere of Earth exhibits considerable spatial changes and has large temporal variability of various timescales related to the mechanisms of creation, decay and transport of space ionospheric plasma. Many techniques for modelling electron density profiles through entire ionosphere have been developed in order to solve the "age-old problem" of ionospheric physics which has not yet been fully solved. A new way to address this problem is by applying artificial intelligence methodologies to current large amounts of solar-terrestrial and ionospheric data. It is the aim of this paper to show by the most recent examples that modern development of numerical models for ionospheric monthly median long-term prediction and daily hourly short-term forecasting may proceed successfully applying the artificial neural networks. The performance of these techniques is illustrated with different artificial neural networks developed to model and predict the temporal and spatial variations of ionospheric critical frequency, f0F2 and Total Electron Content (TEC. Comparisons between results obtained by the proposed approaches and measured f0F2 and TEC data provide prospects for future applications of the artificial neural networks in ionospheric studies.

  7. Comparison of classical statistical methods and artificial neural network in traffic noise prediction

    International Nuclear Information System (INIS)

    Nedic, Vladimir; Despotovic, Danijela; Cvetanovic, Slobodan; Despotovic, Milan; Babic, Sasa

    2014-01-01

    Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application of artificial neural networks (ANNs) for the prediction of traffic noise is presented. As input variables of the neural network, the proposed structure of the traffic flow and the average speed of the traffic flow are chosen. The output variable of the network is the equivalent noise level in the given time period L eq . Based on these parameters, the network is modeled, trained and tested through a comparative analysis of the calculated values and measured levels of traffic noise using the originally developed user friendly software package. It is shown that the artificial neural networks can be a useful tool for the prediction of noise with sufficient accuracy. In addition, the measured values were also used to calculate equivalent noise level by means of classical methods, and comparative analysis is given. The results clearly show that ANN approach is superior in traffic noise level prediction to any other statistical method. - Highlights: • We proposed an ANN model for prediction of traffic noise. • We developed originally designed user friendly software package. • The results are compared with classical statistical methods. • The results are much better predictive capabilities of ANN model

  8. Comparison of classical statistical methods and artificial neural network in traffic noise prediction

    Energy Technology Data Exchange (ETDEWEB)

    Nedic, Vladimir, E-mail: vnedic@kg.ac.rs [Faculty of Philology and Arts, University of Kragujevac, Jovana Cvijića bb, 34000 Kragujevac (Serbia); Despotovic, Danijela, E-mail: ddespotovic@kg.ac.rs [Faculty of Economics, University of Kragujevac, Djure Pucara Starog 3, 34000 Kragujevac (Serbia); Cvetanovic, Slobodan, E-mail: slobodan.cvetanovic@eknfak.ni.ac.rs [Faculty of Economics, University of Niš, Trg kralja Aleksandra Ujedinitelja, 18000 Niš (Serbia); Despotovic, Milan, E-mail: mdespotovic@kg.ac.rs [Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac (Serbia); Babic, Sasa, E-mail: babicsf@yahoo.com [College of Applied Mechanical Engineering, Trstenik (Serbia)

    2014-11-15

    Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application of artificial neural networks (ANNs) for the prediction of traffic noise is presented. As input variables of the neural network, the proposed structure of the traffic flow and the average speed of the traffic flow are chosen. The output variable of the network is the equivalent noise level in the given time period L{sub eq}. Based on these parameters, the network is modeled, trained and tested through a comparative analysis of the calculated values and measured levels of traffic noise using the originally developed user friendly software package. It is shown that the artificial neural networks can be a useful tool for the prediction of noise with sufficient accuracy. In addition, the measured values were also used to calculate equivalent noise level by means of classical methods, and comparative analysis is given. The results clearly show that ANN approach is superior in traffic noise level prediction to any other statistical method. - Highlights: • We proposed an ANN model for prediction of traffic noise. • We developed originally designed user friendly software package. • The results are compared with classical statistical methods. • The results are much better predictive capabilities of ANN model.

  9. Efficient and Invariant Convolutional Neural Networks for Dense Prediction

    OpenAIRE

    Gao, Hongyang; Ji, Shuiwang

    2017-01-01

    Convolutional neural networks have shown great success on feature extraction from raw input data such as images. Although convolutional neural networks are invariant to translations on the inputs, they are not invariant to other transformations, including rotation and flip. Recent attempts have been made to incorporate more invariance in image recognition applications, but they are not applicable to dense prediction tasks, such as image segmentation. In this paper, we propose a set of methods...

  10. APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF AIR POLLUTION LEVELS IN ENVIRONMENTAL MONITORING

    Directory of Open Access Journals (Sweden)

    Małgorzata Pawul

    2016-09-01

    Full Text Available Recently, a lot of attention was paid to the improvement of methods which are used to air quality forecasting. Artificial neural networks can be applied to model these problems. Their advantage is that they can solve the problem in the conditions of incomplete information, without the knowledge of the analytical relationship between the input and output data. In this paper we applied artificial neural networks to predict the PM 10 concentrations as factors determining the occurrence of smog phenomena. To create these networks we used meteorological data and concentrations of PM 10. The data were recorded in 2014 and 2015 at three measuring stations operating in Krakow under the State Environmental Monitoring. The best results were obtained by three-layer perceptron with back-propagation algorithm. The neural networks received a good fit in all cases.

  11. Construction of a predictive model for concentration of nickel and vanadium in vacuum residues of crude oils using artificial neural networks and LIBS.

    Science.gov (United States)

    Tarazona, José L; Guerrero, Jáder; Cabanzo, Rafael; Mejía-Ospino, E

    2012-03-01

    A predictive model to determine the concentration of nickel and vanadium in vacuum residues of Colombian crude oils using laser-induced breakdown spectroscopy (LIBS) and artificial neural networks (ANNs) with nodes distributed in multiple layers (multilayer perceptron) is presented. ANN inputs are intensity values in the vicinity of the emission lines 300.248, 301.200 and 305.081 nm of the Ni(I), and 309.310, 310.229, and 311.070 nm of the V(II). The effects of varying number of nodes and the initial weights and biases in the ANNs were systematically explored. Average relative error of calibration/prediction (REC/REP) and average relative standard deviation (RSD) metrics were used to evaluate the performance of the ANN in the prediction of concentrations of two elements studied here. © 2012 Optical Society of America

  12. Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model

    Directory of Open Access Journals (Sweden)

    Ani Shabri

    2014-01-01

    Full Text Available A new method based on integrating discrete wavelet transform and artificial neural networks (WANN model for daily crude oil price forecasting is proposed. The discrete Mallat wavelet transform is used to decompose the crude price series into one approximation series and some details series (DS. The new series obtained by adding the effective one approximation series and DS component is then used as input into the ANN model to forecast crude oil price. The relative performance of WANN model was compared to regular ANN model for crude oil forecasting at lead times of 1 day for two main crude oil price series, West Texas Intermediate (WTI and Brent crude oil spot prices. In both cases, WANN model was found to provide more accurate crude oil prices forecasts than individual ANN model.

  13. The Study on the Composition of Yongdamsagan-tang(龍膽瀉肝湯’s Essential Oil Obtained by Supercritical Carbon Dioxide Extraction

    Directory of Open Access Journals (Sweden)

    Shin Min-Seop

    2008-03-01

    Full Text Available Objectives : This study was performed to analyze the effective components of essential oil obtained from Yongdamsagan-tang, which has been efficacious against leukorrhea in gynecologic diseases. Methods : I obtained the essential oils of Yongdamsagan-tang by hydrodistillation extraction method and supercritical fluid extraction(SFE method, and then I analyzed those by GC/MS(Gas Chromatography/Mass Spectrum. Results : 1. The optimum SFE(Supercritical Fluid Extraction condition was obtained in the following experiment conditions: pressure 200atm, temperature 45℃, duration of extraction 25minutes. 2. With GC(Gas Chromatography and GC/MS(Gas Chromato- graphy/Mass Spectrum analysis, I identified 37 compounds in the Yongdamsagan-tang's essential oil obtained through the SFE method. The main compounds were as follows : 3-Methyl-but-2-enoic acid,2,2-dimethyl-8-oxo-3,4-dihydro-2H,8H -pyrano[3,2-g]chromen-3-yl ester(49.81 %, (Z-6-Pentadecen-1 -ol(3.19%, (--Spathulenol(2.40%. 3. I identified 4 compounds in the Yongdamsagan-tang's essential oil obtained through the hydrodistillation method. The main compounds were as follows : 3-Methyl-but-2-enoic acid, 2,2-dimethyl-8-oxo-3,4-dihydro-2H,8H-pyrano[3,2-g]chromen-3-yl ester(2.61%. 4. 3 - Methy l - but - 2 - enoic acid, 2, 2 - dimethyl - 8 - oxo - 3, 4 - dihydro - 2H, 8H - pyrano[3, 2 - g] chromen - 3 - yl ester, all were identified in both the SFE method and the hydrodistillation method, but the others were not identified in common. 5. I also conducted an additional test in order to examine the essential oil's antimicrobial action against bacteria. Both MIC(Minimum Inhibitory Concentrations and MBC(Minimum Bactericidal Concentrations were 0.125㎎/㎖ against N. meningitidis, however MIC and MBC were 1.0㎎/㎖ in antimicrobial action against 12 different genera of bacteria.

  14. Supercritical CO2 Extraction of Salvia officinalis L

    Czech Academy of Sciences Publication Activity Database

    Aleksovski, S.A.; Sovová, Helena

    2007-01-01

    Roč. 40, č. 2 (2007), s. 239-245 ISSN 0896-8446 R&D Projects: GA AV ČR(CZ) IAA4072102 Grant - others:GA_(MK) 40108601/0 Institutional research plan: CEZ:AV0Z40720504 Keywords : supercritical fluid extraction * essential oil * collection efficiency Subject RIV: CI - Industrial Chemistry, Chemical Engineering Impact factor: 2.189, year: 2007

  15. Daily Nigerian peak load forecasting using artificial neural network ...

    African Journals Online (AJOL)

    A daily peak load forecasting technique that uses artificial neural network with seasonal indices is presented in this paper. A neural network of relatively smaller size than the main prediction network is used to predict the daily peak load for a period of one year over which the actual daily load data are available using one ...

  16. Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

    Science.gov (United States)

    Yao, Weigang; Liou, Meng-Sing

    2012-01-01

    The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

  17. A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

    Science.gov (United States)

    Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

    2017-12-19

    Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

  18. Aspects of artificial neural networks - with applications in high energy physics

    International Nuclear Information System (INIS)

    Roegnvaldsson, T.S.

    1994-02-01

    Different aspects of artificial neural networks are studied and discussed. They are demonstrated to be powerful general purpose algorithms, applicable to many different problem areas like pattern recognition, function fitting and prediction. Multi-layer perceptron (MPL) models are shown to out perform previous standard approaches on both off-line and on-line analysis tasks in high energy physics, like quark flavour tagging and mass reconstruction, as well as being powerful tools for prediction tasks. It is also demonstrated how a self-organizing network can be employed to extract information from data, for instance to track down origins of unexpected model discrepancies. Furthermore, it is proved that the MPL is more efficient than the learning vector quantization technique on classification problems, by producing smoother discrimination surfaces, and that an MPL network should be trained with a noisy updating schedule if the Hessian is ill-conditioned - A result that is especially important for MPL network with more than just one hidden layer. 81 refs, 6 figs

  19. Distribution network fault section identification and fault location using artificial neural network

    DEFF Research Database (Denmark)

    Dashtdar, Masoud; Dashti, Rahman; Shaker, Hamid Reza

    2018-01-01

    In this paper, a method for fault location in power distribution network is presented. The proposed method uses artificial neural network. In order to train the neural network, a series of specific characteristic are extracted from the recorded fault signals in relay. These characteristics...... components of the sequences as well as three-phase signals could be obtained using statistics to extract the hidden features inside them and present them separately to train the neural network. Also, since the obtained inputs for the training of the neural network strongly depend on the fault angle, fault...... resistance, and fault location, the training data should be selected such that these differences are properly presented so that the neural network does not face any issues for identification. Therefore, selecting the signal processing function, data spectrum and subsequently, statistical parameters...

  20. Phase equilibrium data for systems composed of oregano essential oil compounds and hydroalcoholic solvents at T = 298.2 K

    International Nuclear Information System (INIS)

    Capellini, Maria C.; Carvalho, Fernanda H.; Koshima, Cristina C.; Aracava, Keila K.; Gonçalves, Cintia B.; Rodrigues, Christianne E.C.

    2015-01-01

    Highlights: • (Liquid + liquid) equilibrium data for p-cymene, thymol, terpinen-4-ol, α-terpineol, ethanol and water were determined. • Complete second order models were fitted to the experimental data. • Distribution coefficients of thymol, terpinen-4-ol and α-terpineol in pure and mixed solute were evaluated. • Mass fractions of oxygenated compounds and water influenced the distribution coefficients of the essential oil components. • NRTL and UNIQUAC thermodynamic models satisfactorily describe the partition of components and solvent selectivity. - Abstract: The deterpenation process of essential oils consists of terpene removal and a consequent concentration of oxygenated compounds, which increases the sensorial quality, the aromatic potential and the oxidative stability of the oil. Deterpenation of oregano (Origanum vulgare L., Lamiaceae) essential oil, which has been used extensively as a popular medication and as an antimicrobial, antifungal, antimutagenic and a powerful antioxidant agent, can be performed by (liquid + liquid) extraction using hydroalcoholic solvents. This research presents (liquid + liquid) equilibrium data for model systems composed of p-cymene, thymol, terpinen-4-ol and α-terpineol, some of the main components of oregano essential oil, using hydrous ethanol as the solvent with the water mass fraction ranging from 0.28 to 0.41 at T = (298.2 ± 0.1) K. The results show that an increase in the hydration of the alcoholic solvent causes a negative influence on the values of the distribution coefficient for the three oxygenated compounds (thymol, terpinen-4-ol and α-terpineol), with an increase in solvent selectivity. An increase in the content of oxygenated compounds in the terpene-rich phase reduces their distribution coefficients and the selectivity values. In addition, binary interaction parameters were estimated correlating the experimental data using the NRTL and UNIQUAC thermodynamic models, and the global deviations were

  1. Detecting and Predicting Muscle Fatigue during Typing By SEMG Signal Processing and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Elham Ghoochani

    2011-03-01

    Full Text Available Introduction: Repetitive strain injuries are one of the most prevalent problems in occupational diseases. Repetition, vibration and bad postures of the extremities are physical risk factors related to work that can cause chronic musculoskeletal disorders. Repetitive work on a computer with low level contraction requires the posture to be maintained for a long time, which can cause muscle fatigue. Muscle fatigue in shoulders and neck is one of the most prevalent problems reported with computer users especially during typing. Surface electromyography (SEMG signals are used for detecting muscle fatigue as a non-invasive method. Material and Methods: Nine healthy females volunteered for signal recoding during typing. EMG signals were recorded from the trapezius muscle, which is subjected to muscle fatigue during typing.  After signal analysis and feature extraction, detecting and predicting muscle fatigue was performed by using the MLP artificial neural network. Results: Recorded signals were analyzed in time and frequency domains for feature extraction. Results of classification showed that the MLP neural network can detect and predict muscle fatigue during typing with 80.79 % ± 1.04% accuracy. Conclusion: Intelligent classification and prediction of muscle fatigue can have many applications in human factors engineering (ergonomics, rehabilitation engineering and biofeedback equipment for mitigating the injuries of repetitive works.

  2. Extraction of Fuzzy Logic Rules from Data by Means of Artificial Neural Networks

    Czech Academy of Sciences Publication Activity Database

    Holeňa, Martin

    2005-01-01

    Roč. 41, č. 3 (2005), s. 297-314 ISSN 0023-5954 R&D Projects: GA AV ČR IAA1030004 Institutional research plan: CEZ:AV0Z10300504 Keywords : knowledge extraction from data * artificial neural networks * fuzzy logic * Lukasiewicz logic * disjunctive normal form Subject RIV: BA - General Mathematics Impact factor: 0.343, year: 2005 http://dml.cz/handle/10338.dmlcz/135657

  3. Prediction of ferric iron precipitation in bioleaching process using partial least squares and artificial neural network

    Directory of Open Access Journals (Sweden)

    Golmohammadi Hassan

    2013-01-01

    Full Text Available A quantitative structure-property relationship (QSPR study based on partial least squares (PLS and artificial neural network (ANN was developed for the prediction of ferric iron precipitation in bioleaching process. The leaching temperature, initial pH, oxidation/reduction potential (ORP, ferrous concentration and particle size of ore were used as inputs to the network. The output of the model was ferric iron precipitation. The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. After optimization and training of the network according to back-propagation algorithm, a 5-5-1 neural network was generated for prediction of ferric iron precipitation. The root mean square error for the neural network calculated ferric iron precipitation for training, prediction and validation set are 32.860, 40.739 and 35.890, respectively, which are smaller than those obtained by PLS model (180.972, 165.047 and 149.950, respectively. Results obtained reveal the reliability and good predictivity of neural network model for the prediction of ferric iron precipitation in bioleaching process.

  4. Prediction of ozone tropospheric degradation rate constant of organic compounds by using artificial neural networks

    International Nuclear Information System (INIS)

    Fatemi, M.H.

    2006-01-01

    Ozone tropospheric degradation of organic compound is very important in environmental chemistry. The lifetime of organic chemicals in the atmosphere can be calculated from the knowledge of the rate constant of their reaction with free radicals such as OH and NO 3 or O 3 . In the present work, the rate constant for the tropospheric degradation of 137 organic compounds by reaction with ozone, the least widely and successfully modeled degradation process, are predicted by quantitative structure activity relationships modeling based on a variety of theoretical descriptors, which screened and selected by genetic algorithm variable subset selection procedure. These descriptors which can be used as inputs for generated artificial neural networks are; HOMO-LUMO gap, number of double bonds, number of single bonds, maximum net charge on C atom, minimum (>0.1) bond order of C atom and Minimum e-e repulsion of H atom. After generation, optimization and training of artificial neural network, network was used for the prediction of log KO 3 for the validation set. The root mean square error for the neural network calculated log KO 3 for training, prediction and validation set are 0.357, 0.460 and 0.481, respectively, which are smaller than those obtained by multiple linear regressions model (1.217, 0.870 and 0.968, respectively). Results obtained reveal the reliability and good predictivity of neural network model for the prediction of ozone tropospheric degradations rate constant of organic compounds

  5. Effect of direct adding oregano essential oil (Origanum syriacum L.) on quality and stability of chicken meat patties

    OpenAIRE

    AL-HIJAZEEN, Marwan

    2017-01-01

    Abstract Evaluate of Origanum syriacum L. essential oil grown in Jordan, and other comparable antioxidant on TBARS, total carbonyl, color values, and sensory attributes of raw chicken meat was investigated. Six treatments were prepared: (1) control (no additive); (2) 100 ppm oregano essential oil (OE); (3) 150 ppm OE; (4) 300 ppm L-ascorbic acid (E-300); (5) 5 and 14 ppm butylatedhydroxyanisole (BHA/E-320) for both breast and thigh meat respectively, and 6) 150 ppm Sodium nitrite (E-250), wer...

  6. Application of Artificial Neural Networks in Canola Crop Yield Prediction

    Directory of Open Access Journals (Sweden)

    S. J. Sajadi

    2014-02-01

    Full Text Available Crop yield prediction has an important role in agricultural policies such as specification of the crop price. Crop yield prediction researches have been based on regression analysis. In this research canola yield was predicted using Artificial Neural Networks (ANN using 11 crop year climate data (1998-2009 in Gonbad-e-Kavoos region of Golestan province. ANN inputs were mean weekly rainfall, mean weekly temperature, mean weekly relative humidity and mean weekly sun shine hours and ANN output was canola yield (kg/ha. Multi-Layer Perceptron networks (MLP with Levenberg-Marquardt backpropagation learning algorithm was used for crop yield prediction and Root Mean Square Error (RMSE and square of the Correlation Coefficient (R2 criterions were used to evaluate the performance of the ANN. The obtained results show that the 13-20-1 network has the lowest RMSE equal to 101.235 and maximum value of R2 equal to 0.997 and is suitable for predicting canola yield with climate factors.

  7. Prediction of biodiesel ignition delay in a diesel engine using artificial neural networks

    International Nuclear Information System (INIS)

    Piloto-Rodríguez, Ramón; Sánchez-Borroto, Yisel

    2017-01-01

    Ignition delay is one of the most important parameters of the combustion process and have a strong influence in exhaust emissions and engines performance. In the present work, the results of the mathematical modeling of ignition delay through artificial neural networks are shown. The modeling starts from input values that cover thermodynamic variables, engines parameters and biodiesel properties. The model obtained is only useful for biodiesel samples and several neural network algorithms were applied in order to predict the ignition delay. From its correlation coefficient, prediction capability and lowest absolute error, the best model was selected. Among other network’s input parameters, the cetane number was taken into account, also previously predicted by the use of ANN. (author)

  8. Empirical modeling of a dewaxing system of lubricant oil using Artificial Neural Network (ANN); Modelagem empirica de um sistema de desparafinacao de oleo lubrificante usando redes neurais artificiais

    Energy Technology Data Exchange (ETDEWEB)

    Fontes, Cristiano Hora de Oliveira; Medeiros, Ana Claudia Gondim de; Silva, Marcone Lopes; Neves, Sergio Bello; Carvalho, Luciene Santos de; Guimaraes, Paulo Roberto Britto; Pereira, Magnus; Vianna, Regina Ferreira [Universidade Salvador (UNIFACS), Salvador, BA (Brazil). Dept. de Engenharia e Arquitetura]. E-mail: paulorbg@unifacs.br; Santos, Nilza Maria Querino dos [PETROBRAS S.A., Rio de Janeiro, RJ (Brazil)]. E-mail: nilzaq@petrobras.com.br

    2003-07-01

    The MIBK (m-i-b-ketone) dewaxing unit, located at the Landulpho Alves refinery, allows two different operating modes: dewaxing ND oil removal. The former is comprised of an oil-wax separation process, which generates a wax stream with 2 - 5% oil. The latter involves the reprocessing of the wax stream to reduce its oil content. Both involve a two-stage filtration process (primary and secondary) with rotative filters. The general aim of this research is to develop empirical models to predict variables, for both unit-operating modes, to be used in control algorithms, since many data are not available during normal plant operation and therefore need to be estimated. Studies have suggested that the oil content is an essential variable to develop reliable empirical models and this work is concerned with the development of an empirical model for the prediction of the oil content in the wax stream leaving the primary filters. The model is based on a feed forward Artificial Neural Network (ANN) and tests with one and two hidden layers indicate very good agreement between experimental and predicted values. (author)

  9. Optimizing oil and xanthorrhizol extraction from Curcuma xanthorrhiza Roxb. rhizome by supercritical carbon dioxide.

    Science.gov (United States)

    Salea, Rinaldi; Widjojokusumo, Edward; Veriansyah, Bambang; Tjandrawinata, Raymond R

    2014-09-01

    Oil and xanthorrhizol extraction from Curcuma xanthorrhiza Roxb. rhizome by supercritical carbon dioxide was optimized using Taguchi method. The factors considered were pressure, temperature, carbon dioxide flowrate and time at levels ranging between 10-25 MPa, 35-60 °C, 10-25 g/min and 60-240 min respectively. The highest oil yield (8.0 %) was achieved at factor combination of 15 MPa, 50 °C, 20 g/min and 180 min whereas the highest xanthorrhizol content (128.3 mg/g oil) in Curcuma xanthorrhiza oil was achieved at a factor combination of 25 MPa, 50 °C, 15 g/min and 60 min. Soxhlet extraction with n-hexane and percolation with ethanol gave oil yield of 5.88 %, 11.73 % and xanthorrhizol content of 42.6 mg/g oil, 75.5 mg/g oil, respectively. The experimental oil yield and xanthorrhizol content at optimum conditions agreed favourably with values predicted by computational process. The xanthorrizol content extracted using supercritical carbon dioxide was higher than extracted using Soxhlet extraction and percolation process.

  10. An integrated artificial neural networks approach for predicting global radiation

    International Nuclear Information System (INIS)

    Azadeh, A.; Maghsoudi, A.; Sohrabkhani, S.

    2009-01-01

    This article presents an integrated artificial neural network (ANN) approach for predicting solar global radiation by climatological variables. The integrated ANN trains and tests data with multi layer perceptron (MLP) approach which has the lowest mean absolute percentage error (MAPE). The proposed approach is particularly useful for locations where no available measurement equipment. Also, it considers all related climatological and meteorological parameters as input variables. To show the applicability and superiority of the integrated ANN approach, monthly data were collected for 6 years (1995-2000) in six nominal cities in Iran. Separate model for each city is considered and the quantity of solar global radiation in each city is calculated. Furthermore an integrated ANN model has been introduced for prediction of solar global radiation. The acquired results of the integrated model have shown high accuracy of about 94%. The results of the integrated model have been compared with traditional angstrom's model to show its considerable accuracy. Therefore, the proposed approach can be used as an efficient tool for prediction of solar radiation in the remote and rural locations with no direct measurement equipment.

  11. Application of artificial neural networks in analysis of CHF experimental data in round tubes

    International Nuclear Information System (INIS)

    Huang Yanping; Chen Bingde; Lang Xuemei; Wang Xiaojun; Shan Jianqiang; Jia Dounan

    2004-01-01

    Artificial neural networks (ANNs) are applied successfully to analyze the critical heat flux (CHF) experimental data from some round tubes in this paper. A set of software adopting artificial neural network method for predicting CHF in round tube and a set of CHF database are gotten. Comparing with common CHF correlations and CHF look-up table, ANN method has stronger ability of allow-wrong and nice robustness. The CHF predicting software adopting artificial neural network technology can improve the predicting accuracy in a wider parameter range, and is easier to update and to use. The artificial neural network method used in this paper can be applied to some similar physical problems. (authors)

  12. Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Høgsberg, Jan Becker; Winther, Ole

    2011-01-01

    It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is ab...... to perform accurate response prediction much faster than the corresponding finite element model. Initial result indicate a reduction in cpu time by two orders of magnitude....

  13. The use of the oregano (Origanum vulgare L.) essential oil and hydrosol in green olive fermentation

    OpenAIRE

    M. Musa Özcan; Derya Arslan; Ali Osman Aydar

    2008-01-01

    The effect of oregano the (Origanum vulgare L.) oil at the levels of 0.05, 0.1 and 0.3% and oregano hydrosol on the physicochemical, microbiological and sensory characteristics of the green olive (Edremit variety) fermentation was investigated. The initial pH of the oregano oil added samples were higher than the other treatments, which were above 5.8. The highest final acidity was observed in the hydrosol+brine combination (0.53%). The initial LAB population on the first day of the fermentati...

  14. A GIS-based multi-criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks

    NARCIS (Netherlands)

    Sheikhian, Hossein; Delavar, Mahmoud Reza; Stein, Alfred

    2017-01-01

    This study proposes multi‐criteria group decision‐making to address seismic physical vulnerability assessment. Granular computing rule extraction is combined with a feed forward artificial neural network to form a classifier capable of training a neural network on the basis of the rules provided by

  15. Artificial neural network based approach to transmission lines protection

    International Nuclear Information System (INIS)

    Joorabian, M.

    1999-05-01

    The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection

  16. Effect on tomato plant and fruit of the application of biopolymer-oregano essential oil coatings.

    Science.gov (United States)

    Perdones, Ángela; Tur, Núria; Chiralt, Amparo; Vargas, Maria

    2016-10-01

    Oregano essential oil (EO) was incorporated into film-forming dispersions (FFDs) based on biopolymers (chitosan and/or methylcellulose) at two different concentrations. The effect of the application of the FFDs was evaluated on tomato plants (cultivar Micro-Tom) at three different stages of development, and on pre-harvest and postharvest applications on tomato fruit. The application of the FFDs at '3 Leaves' stage caused phytotoxic problems, which were lethal when the EO was applied without biopolymers. Even though plant growth and development were delayed, the total biomass and the crop yield were not affected by biopolymer-EO treatments. When the FFDs were applied in the 'Fruit' stage the pre-harvest application of FFDs had no negative effects. All FFDs containing EO significantly reduced the respiration rate of tomato fruit and diminished weight loss during storage. Moreover, biopolymer-EO FFDs led to a decrease in the fungal decay of tomato fruit inoculated with Rhizopus stolonifer spores, as compared with non-treated tomato fruit and those coated with FFDs without EO. The application of biopolymer-oregano essential oil coatings has been proven to be an effective treatment to control R. stolonifer in tomato fruit. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

  17. Prediction of Irradiation Damage by Artificial Neural Network for Austenitic Stainless Steels

    International Nuclear Information System (INIS)

    Kim, Won Sam; Kim, Dae Whan; Hwang, Seong Sik

    2007-01-01

    The internal structures of pressurized water reactors (PWR) located close to the reactor core are used to support the fuel assemblies, to maintain the alignment between assemblies and the control bars and to canalize the primary water. In general these internal structures consist of baffle plates in solution annealed (SA) 304 stainless steel and baffle bolts in cold worked (CW) 316 stainless steel. These components undergo a large neutron flux at temperatures between 280 and 380 .deg. C. Well-controlled irradiation-assisted stress corrosion cracking (IASCC) data from properly irradiated, and properly characterized, materials are sorely lacking due to the experimental difficulties and financial limitations related to working with highly activated materials. In this work, we tried to apply the artificial neural network (ANN) approach, predicted the susceptibility to an IASCC for an austenitic stainless steel SA 304 and CW 316. G.S. Was and J.-P. Massoud experimental data are used. Because there is fewer experimental data, we need to prediction for radiation damage under the internal structure of PWR. Besides, we compared experimental data with prediction data by the artificial neural network

  18. Evaluation and scoring of radiotherapy treatment plans using an artificial neural network

    International Nuclear Information System (INIS)

    Willoughby, Twyla R.; Starkschall, George; Janjan, Nora A.; Rosen, Isaac I.

    1996-01-01

    Purpose: The objective of this work was to demonstrate the feasibility of using an artificial neural network to predict the clinical evaluation of radiotherapy treatment plans. Methods and Materials: Approximately 150 treatment plans were developed for 16 patients who received external-beam radiotherapy for soft-tissue sarcomas of the lower extremity. Plans were assigned a figure of merit by a radiation oncologist using a five-point rating scale. Plan scoring was performed by a single physician to ensure consistency in rating. Dose-volume information extracted from a training set of 511 treatment plans on 14 patients was correlated to the physician-generated figure of merit using an artificial neural network. The neural network was tested with a test set of 19 treatment plans on two patients whose plans were not used in the training of the neural net. Results: Physician scoring of treatment plans was consistent to within one point on the rating scale 88% of the time. The neural net reproduced the physician scores in the training set to within one point approximately 90% of the time. It reproduced the physician scores in the test set to within one point approximately 83% of the time. Conclusions: An artificial neural network can be trained to generate a score for a treatment plan that can be correlated to a clinically-based figure of merit. The accuracy of the neural net in scoring plans compares well with the reproducibility of the clinical scoring. The system of radiotherapy treatment plan evaluation using an artificial neural network demonstrates promise as a method for generating a clinically relevant figure of merit

  19. Extraction of heavy oil by supercritical carbon dioxide

    DEFF Research Database (Denmark)

    Rudyk, Svetlana Nikolayevna; Spirov, Pavel; Søgaard, Erik Gydesen

    2010-01-01

    The present study deals with the extraction of heavy oil by supercritical carbon dioxide at the pressure values changing from 16 to 56 MPa at the fixed value of temperature: 60oC. The amount of the recovered liquid phase of oil was calculated as a percentage of the extracted amount to the initial...... 40 gm of oil. The noticeable breackover point in the graph of the oil recovery versus pressure was observed at 27 MPa, which was in concordance with the conclusions from chromatographic analysis of the extracted oil samples. But the recovery rate of 14 % at this pressure value was not high enough...

  20. Prediction of pelvic organ prolapse using an artificial neural network.

    Science.gov (United States)

    Robinson, Christopher J; Swift, Steven; Johnson, Donna D; Almeida, Jonas S

    2008-08-01

    The objective of this investigation was to test the ability of a feedforward artificial neural network (ANN) to differentiate patients who have pelvic organ prolapse (POP) from those who retain good pelvic organ support. Following institutional review board approval, patients with POP (n = 87) and controls with good pelvic organ support (n = 368) were identified from the urogynecology research database. Historical and clinical information was extracted from the database. Data analysis included the training of a feedforward ANN, variable selection, and external validation of the model with an independent data set. Twenty variables were used. The median-performing ANN model used a median of 3 (quartile 1:3 to quartile 3:5) variables and achieved an area under the receiver operator curve of 0.90 (external, independent validation set). Ninety percent sensitivity and 83% specificity were obtained in the external validation by ANN classification. Feedforward ANN modeling is applicable to the identification and prediction of POP.

  1. Artificial Astrocytes Improve Neural Network Performance

    Science.gov (United States)

    Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-01-01

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

  2. Artificial astrocytes improve neural network performance.

    Directory of Open Access Journals (Sweden)

    Ana B Porto-Pazos

    Full Text Available Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN and artificial neuron-glia networks (NGN to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  3. Artificial astrocytes improve neural network performance.

    Science.gov (United States)

    Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-04-19

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  4. A comparative study of solvent and supercritical Co2 extraction of Simarouba gluaca seed oil

    International Nuclear Information System (INIS)

    Anjaneyulu, B.; Satyannarayana, S.; Kanjilal, S.; Siddaiah, V.; Prasanna Rani, K.N.

    2017-01-01

    In the present study, the supercritical carbon dioxide (Co2) extraction of oil from Simarouba gluaca seeds was carried out at varying conditions of pressure (300–500 bar), temperature (50–70 °C) and CO2 flow rate (10–30 g·min-1). The extraction condition for maximum oil yield was obtained at 500 bar pressure, 70 °C and at 30 g·min-1 flow rate of CO2. The extracted oil was analyzed thoroughly for physico-chemical properties and compared with those of conventional solvent extracted oil. An interesting observation is a significant reduction in the phosphorus content of the oil (8.4 mg·kg-1) extracted using supercritical CO2 compared to the phosphorous content of the solvent extracted oil (97 mg·kg-1). Moreover, the content of total tocopherols in supercritically extracted oil (135.6 mg·kg-1) was found to be higher than the solvent extracted oil (111 mg·kg-1). The rest of the physico-chemical properties of the two differently extracted oils matched well with each other. The results indicated the possible benefits of supercritical CO2 extraction over solvent extraction of Simarouba gluaca seed oil. [es

  5. A comparative study of solvent and supercritical CO2 extraction of Simarouba gluaca seed oil

    Directory of Open Access Journals (Sweden)

    B. Anjaneyulu

    2017-09-01

    Full Text Available In the present study, the supercritical carbon dioxide (CO2 extraction of oil from Simarouba gluaca seeds was carried out at varying conditions of pressure (300–500 bar, temperature (50–70 °C and CO2 flow rate (10–30 g·min-1. The extraction condition for maximum oil yield was obtained at 500 bar pressure, 70 °C and at 30 g·min-1 flow rate of CO2. The extracted oil was analyzed thoroughly for physico-chemical properties and compared with those of conventional solvent extracted oil. An interesting observation is a significant reduction in the phosphorus content of the oil (8.4 mg·kg-1 extracted using supercritical CO2 compared to the phosphorous content of the solvent extracted oil (97 mg·kg-1. Moreover, the content of total tocopherols in supercritically extracted oil (135.6 mg·kg-1 was found to be higher than the solvent extracted oil (111 mg·kg-1. The rest of the physico-chemical properties of the two differently extracted oils matched well with each other. The results indicated the possible benefits of supercritical CO2 extraction over solvent extraction of Simarouba gluaca seed oil.

  6. Supercritical fluid extraction for the determination of optimum oil recovery conditions

    Energy Technology Data Exchange (ETDEWEB)

    Al-Marzouqi, Ali H.; Zekri, Abdulrazag Y.; Jobe, Baboucarr; Dowaidar, Ali [Chemical and Petroleum Engineering Department, U.A.E. University, P.O. Box: 17555, Al-Ain (United Arab Emirates)

    2007-01-15

    CO{sub 2} under supercritical (SC) conditions is a powerful solvent capable of extracting hydrocarbons from crude oil. The extraction capacity of CO{sub 2} is a function of pressure, temperature and composition of the crude oil. This paper presents the results of a laboratory study investigating the capacity of CO{sub 2} to extract hydrocarbons from an oil-saturated soil under a wide range of pressures and temperatures (80-120 bar for temperatures ranging from 40 to 60 C and 200-300 bar for temperatures varying from 100 to 140 C). The soil samples were collected from Sahel oil filed, which is near Bu Hasa oil field (Abu Dhabi, UAE) where the crude oil was obtained from. The extracted oil from the SC CO{sub 2} process and the residual oil remaining in the soil sample were analyzed by gas chromatography to shed more light on the extraction phenomenon. Extraction efficiency of CO{sub 2} increased with pressure and decreased with temperature. Moreover, the amount of extracted heavy fractions increased with pressure for all temperatures. On the other hand, the amount of extracted heavy hydrocarbons decreased with temperature for the low pressure range (80-120 bar) and remained the same for the pressure range of 250-300 bar. The maximum extraction efficiency of CO{sub 2} was 72.4%, which was obtained at the highest pressure (300 bar) and a temperature of 100 C. (author)

  7. Optimization the Initial Weights of Artificial Neural Networks via Genetic Algorithm Applied to Hip Bone Fracture Prediction

    Directory of Open Access Journals (Sweden)

    Yu-Tzu Chang

    2012-01-01

    Full Text Available This paper aims to find the optimal set of initial weights to enhance the accuracy of artificial neural networks (ANNs by using genetic algorithms (GA. The sample in this study included 228 patients with first low-trauma hip fracture and 215 patients without hip fracture, both of them were interviewed with 78 questions. We used logistic regression to select 5 important factors (i.e., bone mineral density, experience of fracture, average hand grip strength, intake of coffee, and peak expiratory flow rate for building artificial neural networks to predict the probabilities of hip fractures. Three-layer (one hidden layer ANNs models with back-propagation training algorithms were adopted. The purpose in this paper is to find the optimal initial weights of neural networks via genetic algorithm to improve the predictability. Area under the ROC curve (AUC was used to assess the performance of neural networks. The study results showed the genetic algorithm obtained an AUC of 0.858±0.00493 on modeling data and 0.802 ± 0.03318 on testing data. They were slightly better than the results of our previous study (0.868±0.00387 and 0.796±0.02559, resp.. Thus, the preliminary study for only using simple GA has been proved to be effective for improving the accuracy of artificial neural networks.

  8. Prediction of Weld Penetration in FCAW of HSLA steel using Artificial Neural Networks

    International Nuclear Information System (INIS)

    Asl, Y. Dadgar; Mostafa, N. B.; Panahizadeh, V. R.; Seyedkashi, S. M. H.

    2011-01-01

    Flux-cored arc welding (FCAW) is a semiautomatic or automatic arc welding process that requires a continuously-fed consumable tubular electrode containing a flux. The main FCAW process parameters affecting the depth of penetration are welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed. Shallow depth of penetration may contribute to failure of a welded structure since penetration determines the stress-carrying capacity of a welded joint. To avoid such occurrences; the welding process parameters influencing the weld penetration must be properly selected to obtain an acceptable weld penetration and hence a high quality joint. Artificial neural networks (ANN), also called neural networks (NN), are computational models used to express complex non-linear relationships between input and output data. In this paper, artificial neural network (ANN) method is used to predict the effects of welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed on weld penetration depth in gas shielded FCAW of a grade of high strength low alloy steel. 32 experimental runs were carried out using the bead-on-plate welding technique. Weld penetrations were measured and on the basis of these 32 sets of experimental data, a feed-forward back-propagation neural network was created. 28 sets of the experiments were used as the training data and the remaining 4 sets were used for the testing phase of the network. The ANN has one hidden layer with eight neurons and is trained after 840 iterations. The comparison between the experimental results and ANN results showed that the trained network could predict the effects of the FCAW process parameters on weld penetration adequately.

  9. A MODELLING APPROACH TO EXTRA VIRGIN OLIVE OIL EXTRACTION

    Directory of Open Access Journals (Sweden)

    Marco Daou

    2007-12-01

    Full Text Available In the present work is described a feasibility assessment for a new approach in virgin olive oil production control system. A predicting or simulating algorithm is implemented as artificial neural network based software, using literature found data concerning parameters related to olive grove, process, machine. Test and validation proved this tool is able to answer two different frequently asked questions by olive oil mill operators, using few agronomic and technological parameters with time and cost saving: – which quality level is up to oil extracted from defined olive lot following a defined process (predicting mode; – which process and machine parameters set would determine highest quality level for oil extracted from a defined olive lot (simulating mode.

  10. Application of artificial neural network for heat transfer in porous cone

    Science.gov (United States)

    Athani, Abdulgaphur; Ahamad, N. Ameer; Badruddin, Irfan Anjum

    2018-05-01

    Heat transfer in porous medium is one of the classical areas of research that has been active for many decades. The heat transfer in porous medium is generally studied by using numerical methods such as finite element method; finite difference method etc. that solves coupled partial differential equations by converting them into simpler forms. The current work utilizes an alternate method known as artificial neural network that mimics the learning characteristics of neurons. The heat transfer in porous medium fixed in a cone is predicted using backpropagation neural network. The artificial neural network is able to predict this behavior quite accurately.

  11. Artificial neural network as the tool in prediction rheological features of raw minced meat.

    Science.gov (United States)

    Balejko, Jerzy A; Nowak, Zbigniew; Balejko, Edyta

    2012-01-01

    The aim of the study was to elaborate a method of modelling and forecasting rheological features which could be applied to raw minced meat at the stage of mixture preparation with a given ingredient composition. The investigated material contained pork and beef meat, pork fat, fat substitutes, ice and curing mixture in various proportions. Seven texture parameters were measured for each sample of raw minced meat. The data obtained were processed using the artificial neural network module in Statistica 9.0 software. The model that reached the lowest training error was a multi-layer perceptron MLP with three neural layers and architecture 7:7-11-7:7. Correlation coefficients between the experimental and calculated values in training, verification and testing subsets were similar and rather high (around 0.65) which indicated good network performance. High percentage of the total variance explained in PCA analysis (73.5%) indicated that the percentage composition of raw minced meat can be successfully used in the prediction of its rheological features. Statistical analysis of the results revealed, that artificial neural network model is able to predict rheological parameters and thus a complete texture profile of raw minced meat.

  12. Prediction of compression strength of high performance concrete using artificial neural networks

    International Nuclear Information System (INIS)

    Torre, A; Moromi, I; Garcia, F; Espinoza, P; Acuña, L

    2015-01-01

    High-strength concrete is undoubtedly one of the most innovative materials in construction. Its manufacture is simple and is carried out starting from essential components (water, cement, fine and aggregates) and a number of additives. Their proportions have a high influence on the final strength of the product. This relations do not seem to follow a mathematical formula and yet their knowledge is crucial to optimize the quantities of raw materials used in the manufacture of concrete. Of all mechanical properties, concrete compressive strength at 28 days is most often used for quality control. Therefore, it would be important to have a tool to numerically model such relationships, even before processing. In this aspect, artificial neural networks have proven to be a powerful modeling tool especially when obtaining a result with higher reliability than knowledge of the relationships between the variables involved in the process. This research has designed an artificial neural network to model the compressive strength of concrete based on their manufacturing parameters, obtaining correlations of the order of 0.94

  13. Antibacterial Effects of Allspice, Garlic, and Oregano Essential Oils in Tomato Films Determined by Overlay and Vapor-Phase Methods

    Science.gov (United States)

    Physical properties as well as antimicrobial activities against Escherichia coli O157:H7, Salmonella enterica and Listeria monocytogenes of allspice, garlic and oregano essential oils (EOs) in tomato puree film forming solutions (TPFFS) formulated into edible films at 0.5-3.0% (w/w) concentrations w...

  14. Antioxidant capacity and larvicidal activity of essential oil and extracts from Lippia grandis

    Directory of Open Access Journals (Sweden)

    Evelyn Ivana T. Damasceno

    2011-02-01

    Full Text Available The leaves and thin branches of Lippia grandis Schauer, Verbenaceae, are used for flavoring of food in the Brazilian Amazon, as substitute for oregano. In this study the constituents of the essential oil were identified and the antioxidant capacity and larvicidal activity of the oil and methanol extract and its sub-fractions were evaluated. A sensory evaluation was determined in view of absence of toxicity. The oil showed a yield of 2.1% and its main constituents were thymol (45.8%, p-cymene (14.3%, γ-terpinene (10.5%, carvacrol (9.9% and thymol methyl ether (4.8%, totalizing 85%. The DPPH radical scavenging activity showed values for the EC50 between 9.0 and 130.5 µg mL-1 and the TEAC/ABTS values varied from 131.1 to 336.0 mg TE/g, indicating significant antioxidant activity for the plant. The total phenolic content ranged from 223.0 to 761.4 mg GAE/g, contributing to the antioxidant activity observed. The crude extracts inhibited the bleaching of β-carotene and the oil showed the greatest inhibition (42.5%. The oil (LgO, 7.6±2.4 µg mL-1 showed strong larvicidal activity against the brine shrimp bioassay. The sensory evaluation was highly satisfactory in comparison to oregano. The results are very promising for the use of L. grandis in seasoning and antioxidant products.

  15. The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network.

    Science.gov (United States)

    Agharezaei, Laleh; Agharezaei, Zhila; Nemati, Ali; Bahaadinbeigy, Kambiz; Keynia, Farshid; Baneshi, Mohammad Reza; Iranpour, Abedin; Agharezaei, Moslem

    2016-10-01

    Venous thromboembolism is a common cause of mortality among hospitalized patients and yet it is preventable through detecting the precipitating factors and a prompt diagnosis by specialists. The present study has been carried out in order to assist specialists in the diagnosis and prediction of the risk level of pulmonary embolism in patients, by means of artificial neural network. A number of 31 risk factors have been used in this study in order to evaluate the conditions of 294 patients hospitalized in 3 educational hospitals affiliated with Kerman University of Medical Sciences. Two types of artificial neural networks, namely Feed-Forward Back Propagation and Elman Back Propagation, were compared in this study. Through an optimized artificial neural network model, an accuracy and risk level index of 93.23 percent was achieved and, subsequently, the results have been compared with those obtained from the perfusion scan of the patients. 86.61 percent of high risk patients diagnosed through perfusion scan diagnostic method were also diagnosed correctly through the method proposed in the present study. The results of this study can be a good resource for physicians, medical assistants, and healthcare staff to diagnose high risk patients more precisely and prevent the mortalities. Additionally, expenses and other unnecessary diagnostic methods such as perfusion scans can be efficiently reduced.

  16. Effect of Oregano Essential Oil and Aqueous Oregano Infusion Application on Microbiological Properties of Samarella (Tsamarella), a Traditional Meat Product of Cyprus.

    Science.gov (United States)

    Ulusoy, Beyza; Hecer, Canan; Kaynarca, Doruk; Berkan, Şifa

    2018-03-21

    Different types of dried meat products manufactured by different drying and curing methods are very common and well-known with a long history all over the world. Samarella (tsamarella) is one of these products and is famous among traditionally produced meat products in Cypriot gastronomy. The aim of this study was to investigate the effect of oregano essential oil (OEO) and aqueous oregano infusion (AOI) applications on the microbiological properties of samarella. In order to carry out this study, traditional methods were followed for experimental production of samarella. As a result of this study, five percent OEO application was found to be more effective to reduce microbiological counts but this ratio of OEO application was not accepted by panelists. According to all microbiological results correlated with the sensorial scores, it is concluded that one percent OEO application can be used for samarella production as an alternative preservative method.

  17. Effect of Oregano Essential Oil and Aqueous Oregano Infusion Application on Microbiological Properties of Samarella (Tsamarella, a Traditional Meat Product of Cyprus

    Directory of Open Access Journals (Sweden)

    Beyza Ulusoy

    2018-03-01

    Full Text Available Different types of dried meat products manufactured by different drying and curing methods are very common and well-known with a long history all over the world. Samarella (tsamarella is one of these products and is famous among traditionally produced meat products in Cypriot gastronomy. The aim of this study was to investigate the effect of oregano essential oil (OEO and aqueous oregano infusion (AOI applications on the microbiological properties of samarella. In order to carry out this study, traditional methods were followed for experimental production of samarella. As a result of this study, five percent OEO application was found to be more effective to reduce microbiological counts but this ratio of OEO application was not accepted by panelists. According to all microbiological results correlated with the sensorial scores, it is concluded that one percent OEO application can be used for samarella production as an alternative preservative method.

  18. Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.

    Science.gov (United States)

    Ching, Travers; Zhu, Xun; Garmire, Lana X

    2018-04-01

    Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet.

  19. Supercritical CO2 extraction of candlenut oil: process optimization using Taguchi orthogonal array and physicochemical properties of the oil.

    Science.gov (United States)

    Subroto, Erna; Widjojokusumo, Edward; Veriansyah, Bambang; Tjandrawinata, Raymond R

    2017-04-01

    A series of experiments was conducted to determine optimum conditions for supercritical carbon dioxide extraction of candlenut oil. A Taguchi experimental design with L 9 orthogonal array (four factors in three levels) was employed to evaluate the effects of pressure of 25-35 MPa, temperature of 40-60 °C, CO 2 flow rate of 10-20 g/min and particle size of 0.3-0.8 mm on oil solubility. The obtained results showed that increase in particle size, pressure and temperature improved the oil solubility. The supercritical carbon dioxide extraction at optimized parameters resulted in oil yield extraction of 61.4% at solubility of 9.6 g oil/kg CO 2 . The obtained candlenut oil from supercritical carbon dioxide extraction has better oil quality than oil which was extracted by Soxhlet extraction using n-hexane. The oil contains high unsaturated oil (linoleic acid and linolenic acid), which have many beneficial effects on human health.

  20. Improved Local Weather Forecasts Using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Wollsen, Morten Gill; Jørgensen, Bo Nørregaard

    2015-01-01

    Solar irradiance and temperature forecasts are used in many different control systems. Such as intelligent climate control systems in commercial greenhouses, where the solar irradiance affects the use of supplemental lighting. This paper proposes a novel method to predict the forthcoming weather...... using an artificial neural network. The neural network used is a NARX network, which is known to model non-linear systems well. The predictions are compared to both a design reference year as well as commercial weather forecasts based upon numerical modelling. The results presented in this paper show...

  1. Wheat germ oil extracted by supercritical carbon dioxide with ethanol: Fatty acid composition

    International Nuclear Information System (INIS)

    Parczewska-Plesnar, B.; Brzozowski, R.; Gwardiak, H.; Białecka-Florjańczyk, E.; Bujnowski, Z.

    2016-01-01

    In this work, supercritical fluid extraction (SFE) using CO2 with ethanol as entrainer was performed at a temperature of 40 o C under a pressure of 21 MPa. For comparison, a similar extraction without the entrainer was carried out. The extraction yield of wheat germ using supercritical CO2 with ethanol was slightly higher (10.7 wt%) than that of extraction without the entrainer (9.9 wt%). Fractions of SFE extracts were collected separately during the experiments and the composition of fatty acids in each fraction was analyzed. The SFE extracted oils were rich (63.4-71.3%) in the most valuable polyunsaturated fatty acids (PUFA) and their content in all collected fractions was approximately constant. Similar PUFA contents were found in the reference samples of oils extracted by n-hexane (66.2-67.0%), while the commercial cold-pressed oil contained significantly less PUFA (60.2%). These results show a higher nutritional value of the oil obtained by extraction with supercritical CO2 than cold pressed oil which is generally considered to be very valuable. [es

  2. The optimization of essential oils supercritical CO2 extraction from Lavandula hybrida through static-dynamic steps procedure and semi-continuous technique using response surface method

    Science.gov (United States)

    Kamali, Hossein; Aminimoghadamfarouj, Noushin; Golmakani, Ebrahim; Nematollahi, Alireza

    2015-01-01

    Aim: The aim of this study was to examine and evaluate crucial variables in essential oils extraction process from Lavandula hybrida through static-dynamic and semi-continuous techniques using response surface method. Materials and Methods: Essential oil components were extracted from Lavandula hybrida (Lavandin) flowers using supercritical carbon dioxide via static-dynamic steps (SDS) procedure, and semi-continuous (SC) technique. Results: Using response surface method the optimum extraction yield (4.768%) was obtained via SDS at 108.7 bar, 48.5°C, 120 min (static: 8×15), 24 min (dynamic: 8×3 min) in contrast to the 4.620% extraction yield for the SC at 111.6 bar, 49.2°C, 14 min (static), 121.1 min (dynamic). Conclusion: The results indicated that a substantial reduction (81.56%) solvent usage (kg CO2/g oil) is observed in the SDS method versus the conventional SC method. PMID:25598636

  3. Applying Fuzzy Artificial Neural Network OSPF to develop Smart ...

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... Fuzzy Artificial Neural Network to create Smart Routing. Protocol Algorithm. ... manufactured mental aptitude strategy. The capacity to study .... Based Energy Efficiency in Wireless Sensor Networks: A Survey",. International ...

  4. Effects of Zataria, Mentha Pulegium, Oregano spp Essential Oil and Hydroalcholic Extract of Hypericum perforatum on Cyst of Acanthamoeba spp In Vitro

    Directory of Open Access Journals (Sweden)

    Ali Arjmand Shabestary

    2017-11-01

    Full Text Available Abstract Background: Resistance of Acanthamoeba cysts causes recurrence of the disease; so, the patient should be monitored regularly ،The aim of the study was to examine the effect of a few herbal materials on Acanthamoeba cysts in vitro. Materials and Methods: Essential oils (EOs of Zataria, Mint, and Oregano were prepared by steam distillation. The EOs and Hypericum perforatum extract were prepared in three concentrations (0.6%, 1% and 10%، Acanthamoeba cysts in various time intervals (30, 60, 120, 180 and 1440 minutes were exposed with plant extracts. Then, the viability of parasite was investigated by eosin 0.1%. Results: Comparison of the parasite mortality rate between control and case groups showed that the mortality of Acanthamoeba cysts was higher in the case groups that exposed to herbal materials. At the equal concentration (10% and time (24 h, the Zataria and mint EOs produced the highest (22% and lowest (4% mortality, respectively. The results showed the mortality rate of Acanthamoeba was time-dependent. Conclusion: Zataria showed the most fatality effect against Acanthamoeba cysts. In this respect, clinical trial studies are suggested.

  5. Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

    Science.gov (United States)

    Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

    2016-01-01

    Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

  6. Prediction of the antimicrobial activity of walnut (Juglans regia L.) kernel aqueous extracts using artificial neural network and multiple linear regression.

    Science.gov (United States)

    Kavuncuoglu, Hatice; Kavuncuoglu, Erhan; Karatas, Seyda Merve; Benli, Büsra; Sagdic, Osman; Yalcin, Hasan

    2018-04-09

    The mathematical model was established to determine the diameter of inhibition zone of the walnut extract on the twelve bacterial species. Type of extraction, concentration, and pathogens were taken as input variables. Two models were used with the aim of designing this system. One of them was developed with artificial neural networks (ANN), and the other was formed with multiple linear regression (MLR). Four common training algorithms were used. Levenberg-Marquardt (LM), Bayesian regulation (BR), scaled conjugate gradient (SCG) and resilient back propagation (RP) were investigated, and the algorithms were compared. Root mean squared error and correlation coefficient were evaluated as performance criteria. When these criteria were analyzed, ANN showed high prediction performance, while MLR showed low prediction performance. As a result, it is seen that when the different input values are provided to the system developed with ANN, the most accurate inhibition zone (IZ) estimates were obtained. The results of this study could offer new perspectives, particularly in the field of microbiology, because these could be applied to other type of extraction, concentrations, and pathogens, without resorting to experiments. Copyright © 2018 Elsevier B.V. All rights reserved.

  7. Artificial neural network detects human uncertainty

    Science.gov (United States)

    Hramov, Alexander E.; Frolov, Nikita S.; Maksimenko, Vladimir A.; Makarov, Vladimir V.; Koronovskii, Alexey A.; Garcia-Prieto, Juan; Antón-Toro, Luis Fernando; Maestú, Fernando; Pisarchik, Alexander N.

    2018-03-01

    Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.

  8. Short communication: Effect of oregano and caraway essential oils on the production and flavor of cow milk

    DEFF Research Database (Denmark)

    Lejonklev, Johan; Kidmose, Ulla; Jensen, Sidsel

    2016-01-01

    . Essential oils from caraway (Carum carvi) seeds and oregano (Origanum vulgare) plants were included in dairy cow diets to study the effects on terpene composition and sensory properties of the produced milk, as well as feed consumption, production levels of milk, and methane emissions. Two levels...... of essential oils, 0.2 and 1.0 g of oil/kg of dry matter, were added to the feed of lactating cows for 24 d. No effects on feed consumption, milk production, and methane emissions were observed. The amount and composition of volatile terpenes were altered in the produced milk based on the terpene content......Many essential oils and their terpene constituents display antimicrobial properties, which may affect rumen metabolism and influence milk production parameters. Many of these compounds also have distinct flavors and aromas that may make their way into the milk, altering its sensory properties...

  9. Short communication: Effect of oregano and caraway essential oils on the production and flavor of cow milk

    DEFF Research Database (Denmark)

    Lejonklev, Johan; Kidmose, Ulla; Jensen, Sidsel

    2016-01-01

    Many essential oils and their terpene constituents display antimicrobial properties, which may affect rumen metabolism and influence milk production parameters. Many of these compounds also have distinct flavors and aromas that may make their way into the milk, altering its sensory properties....... Essential oils from caraway (Carum carvi) seeds and oregano (Origanum vulgare) plants were included in dairy cow diets to study the effects on terpene composition and sensory properties of the produced milk, as well as feed consumption, production levels of milk, and methane emissions. Two levels...... of essential oils, 0.2 and 1.0 g of oil/kg of dry matter, were added to the feed of lactating cows for 24 d. No effects on feed consumption, milk production, and methane emissions were observed. The amount and composition of volatile terpenes were altered in the produced milk based on the terpene content...

  10. Development of a hybrid system of artificial neural networks and ...

    African Journals Online (AJOL)

    Development of a hybrid system of artificial neural networks and artificial bee colony algorithm for prediction and modeling of customer choice in the market. ... attempted to present a new method for the modeling and prediction of customer choice in the market using the combination of artificial intelligence and data mining.

  11. Introduction to Concepts in Artificial Neural Networks

    Science.gov (United States)

    Niebur, Dagmar

    1995-01-01

    This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

  12. Use of Artificial Neural Networks for Prediction of Convective Heat Transfer in Evaporative Units

    Directory of Open Access Journals (Sweden)

    Romero-Méndez Ricardo

    2014-01-01

    Full Text Available Convective heat transfer prediction of evaporative processes is more complicated than the heat transfer prediction of single-phase convective processes. This is due to the fact that physical phenomena involved in evaporative processes are very complex and vary with the vapor quality that increases gradually as more fluid is evaporated. Power-law correlations used for prediction of evaporative convection have proved little accuracy when used in practical cases. In this investigation, neural-network-based models have been used as a tool for prediction of the thermal performance of evaporative units. For this purpose, experimental data were obtained in a facility that includes a counter-flow concentric pipes heat exchanger with R134a refrigerant flowing inside the circular section and temperature controlled warm water moving through the annular section. This work also included the construction of an inverse Rankine refrigeration cycle that was equipped with measurement devices, sensors and a data acquisition system to collect the experimental measurements under different operating conditions. Part of the data were used to train several neural-network configurations. The best neural-network model was then used for prediction purposes and the results obtained were compared with experimental data not used for training purposes. The results obtained in this investigation reveal the convenience of using artificial neural networks as accurate predictive tools for determining convective heat transfer rates of evaporative processes.

  13. The use of the oregano (Origanum vulgare L. essential oil and hydrosol in green olive fermentation

    Directory of Open Access Journals (Sweden)

    M. Musa Özcan

    2008-06-01

    Full Text Available The effect of oregano the (Origanum vulgare L. oil at the levels of 0.05, 0.1 and 0.3% and oregano hydrosol on the physicochemical, microbiological and sensory characteristics of the green olive (Edremit variety fermentation was investigated. The initial pH of the oregano oil added samples were higher than the other treatments, which were above 5.8. The highest final acidity was observed in the hydrosol+brine combination (0.53%. The initial LAB population on the first day of the fermentation was high in the diluted hydrosol (8.89 log cfu ml-1 and control (8.47 log cfu ml-1 samples. But a significant difference was not observed between the LAB counts of the treatments on the 40th day of fermentation. The control and brine+oregano hydrosol samples had the highest sensory scores.

  14. Volume fraction prediction in biphasic flow using nuclear technique and artificial neural network

    International Nuclear Information System (INIS)

    Salgado, Cesar M.; Brandao, Luis E.B.

    2015-01-01

    The volume fraction is one of the most important parameters used to characterize air-liquid two-phase flows. It is a physical value to determine other parameters, such as the phase's densities and to determine the flow rate of each phase. These parameters are important to predict the flow pattern and to determine a mathematical model for the system. To study, for example, heat transfer and pressure drop. This work presents a methodology for volume fractions prediction in water-gas stratified flow regime using the nuclear technique and artificial intelligence. The volume fractions calculate in biphasic flow systems is complex and the analysis by means of analytical equations becomes very difficult. The approach is based on gamma-ray pulse height distributions pattern recognition by means of the artificial neural network. The detection system uses appropriate broad beam geometry, comprised of a ( 137 Cs) energy gamma-ray source and a NaI(Tl) scintillation detector in order measure transmitted beam whose the counts rates are influenced by the phases composition. These distributions are directly used by the network without any parameterization of the measured signal. The ideal and static theoretical models for stratified regime have been developed using MCNP-X code, which was used to provide training, test and validation data for the network. The detector also was modeled with this code and the results were compared to experimental photopeak efficiency measurements of radiation sources. The proposed network could obtain with satisfactory prediction of the volume fraction in water-gas system, demonstrating to be a promising approach for this purpose. (author)

  15. Volume fraction prediction in biphasic flow using nuclear technique and artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Salgado, Cesar M.; Brandao, Luis E.B., E-mail: otero@ien.gov.br, E-mail: brandao@ien.gov.br [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)

    2015-07-01

    The volume fraction is one of the most important parameters used to characterize air-liquid two-phase flows. It is a physical value to determine other parameters, such as the phase's densities and to determine the flow rate of each phase. These parameters are important to predict the flow pattern and to determine a mathematical model for the system. To study, for example, heat transfer and pressure drop. This work presents a methodology for volume fractions prediction in water-gas stratified flow regime using the nuclear technique and artificial intelligence. The volume fractions calculate in biphasic flow systems is complex and the analysis by means of analytical equations becomes very difficult. The approach is based on gamma-ray pulse height distributions pattern recognition by means of the artificial neural network. The detection system uses appropriate broad beam geometry, comprised of a ({sup 137}Cs) energy gamma-ray source and a NaI(Tl) scintillation detector in order measure transmitted beam whose the counts rates are influenced by the phases composition. These distributions are directly used by the network without any parameterization of the measured signal. The ideal and static theoretical models for stratified regime have been developed using MCNP-X code, which was used to provide training, test and validation data for the network. The detector also was modeled with this code and the results were compared to experimental photopeak efficiency measurements of radiation sources. The proposed network could obtain with satisfactory prediction of the volume fraction in water-gas system, demonstrating to be a promising approach for this purpose. (author)

  16. Investment Valuation Analysis with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Hüseyin İNCE

    2017-07-01

    Full Text Available This paper shows that discounted cash flow and net present value, which are traditional investment valuation models, can be combined with artificial neural network model forecasting. The main inputs for the valuation models, such as revenue, costs, capital expenditure, and their growth rates, are heavily related to sector dynamics and macroeconomics. The growth rates of those inputs are related to inflation and exchange rates. Therefore, predicting inflation and exchange rates is a critical issue for the valuation output. In this paper, the Turkish economy’s inflation rate and the exchange rate of USD/TRY are forecast by artificial neural networks and implemented to the discounted cash flow model. Finally, the results are benchmarked with conventional practices.

  17. Lifetime prediction for organic coating under alternating hydrostatic pressure by artificial neural network

    Science.gov (United States)

    Tian, Wenliang; Meng, Fandi; Liu, Li; Li, Ying; Wang, Fuhui

    2017-01-01

    A concept for prediction of organic coatings, based on the alternating hydrostatic pressure (AHP) accelerated tests, has been presented. An AHP accelerated test with different pressure values has been employed to evaluate coating degradation. And a back-propagation artificial neural network (BP-ANN) has been established to predict the service property and the service lifetime of coatings. The pressure value (P), immersion time (t) and service property (impedance modulus |Z|) are utilized as the parameters of the network. The average accuracies of the predicted service property and immersion time by the established network are 98.6% and 84.8%, respectively. The combination of accelerated test and prediction method by BP-ANN is promising to evaluate and predict coating property used in deep sea. PMID:28094340

  18. Prediction of moving bed biofilm reactor (MBBR) performance for the treatment of aniline using artificial neural networks (ANN)

    Energy Technology Data Exchange (ETDEWEB)

    Delnavaz, M. [Tarbiat Modares University, Civil Engineering Department, Environmental Engineering Division, Tehran (Iran, Islamic Republic of); Ayati, B., E-mail: ayati_bi@modares.ac.ir [Tarbiat Modares University, Civil Engineering Department, Environmental Engineering Division, Tehran (Iran, Islamic Republic of); Ganjidoust, H. [Tarbiat Modares University, Civil Engineering Department, Environmental Engineering Division, Tehran (Iran, Islamic Republic of)

    2010-07-15

    In this study, the results of 1-year efficiency forecasting using artificial neural networks (ANN) models of a moving bed biofilm reactor (MBBR) for a toxic and hard biodegradable aniline removal were investigated. The reactor was operated in an aerobic batch and continuous condition with 50% by volume which was filled with light expanded clay aggregate (LECA) as carrier. Efficiency evaluation of the reactors was obtained at different retention time (RT) of 8, 24, 48 and 72 h with an influent COD from 100 to 4000 mg/L. Exploratory data analysis was used to detect relationships between the data and dependent evaluated one. The appropriate architecture of the neural network models was determined using several steps of training and testing of the models. The ANN-based models were found to provide an efficient and a robust tool in predicting MBBR performance for treating aromatic amine compounds.

  19. Neural and hybrid modeling: an alternative route to efficiently predict the behavior of biotechnological processes aimed at biofuels obtainment.

    Science.gov (United States)

    Curcio, Stefano; Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

    2014-01-01

    The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.

  20. Predicting ventriculoperitoneal shunt infection in children with hydrocephalus using artificial neural network.

    Science.gov (United States)

    Habibi, Zohreh; Ertiaei, Abolhasan; Nikdad, Mohammad Sadegh; Mirmohseni, Atefeh Sadat; Afarideh, Mohsen; Heidari, Vahid; Saberi, Hooshang; Rezaei, Abdolreza Sheikh; Nejat, Farideh

    2016-11-01

    The relationships between shunt infection and predictive factors have not been previously investigated using Artificial Neural Network (ANN) model. The aim of this study was to develop an ANN model to predict shunt infection in a group of children with shunted hydrocephalus. Among more than 800 ventriculoperitoneal shunt procedures which had been performed between April 2000 and April 2011, 68 patients with shunt infection and 80 controls that fulfilled a set of meticulous inclusion/exclusion criteria were consecutively enrolled. Univariate analysis was performed for a long list of risk factors, and those with p value < 0.2 were used to create ANN and logistic regression (LR) models. Five variables including birth weight, age at the first shunting, shunt revision, prematurity, and myelomeningocele were significantly associated with shunt infection via univariate analysis, and two other variables (intraventricular hemorrhage and coincided infections) had a p value of less than 0.2. Using these seven input variables, ANN and LR models predicted shunt infection with an accuracy of 83.1 % (AUC; 91.98 %, 95 % CI) and 55.7 % (AUC; 76.5, 95 % CI), respectively. The contribution of the factors in the predictive performance of ANN in descending order was history of shunt revision, low birth weight (under 2000 g), history of prematurity, the age at the first shunt procedure, history of intraventricular hemorrhage, history of myelomeningocele, and coinfection. The findings show that artificial neural networks can predict shunt infection with a high level of accuracy in children with shunted hydrocephalus. Also, the contribution of different risk factors in the prediction of shunt infection can be determined using the trained network.

  1. Stability prediction of berm breakwater using neural network

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Rao, S.; Manjunath, Y.R.

    In the present study, an artificial neural network method has been applied to predict the stability of berm breakwaters. Four neural network models are constructed based on the parameters which influence the stability of breakwater. Training...

  2. Prediction of Bladder Cancer Recurrences Using Artificial Neural Networks

    Science.gov (United States)

    Zulueta Guerrero, Ekaitz; Garay, Naiara Telleria; Lopez-Guede, Jose Manuel; Vilches, Borja Ayerdi; Iragorri, Eider Egilegor; Castaños, David Lecumberri; de La Hoz Rastrollo, Ana Belén; Peña, Carlos Pertusa

    Even if considerable advances have been made in the field of early diagnosis, there is no simple, cheap and non-invasive method that can be applied to the clinical monitorisation of bladder cancer patients. Moreover, bladder cancer recurrences or the reappearance of the tumour after its surgical resection cannot be predicted in the current clinical setting. In this study, Artificial Neural Networks (ANN) were used to assess how different combinations of classical clinical parameters (stage-grade and age) and two urinary markers (growth factor and pro-inflammatory mediator) could predict post surgical recurrences in bladder cancer patients. Different ANN methods, input parameter combinations and recurrence related output variables were used and the resulting positive and negative prediction rates compared. MultiLayer Perceptron (MLP) was selected as the most predictive model and urinary markers showed the highest sensitivity, predicting correctly 50% of the patients that would recur in a 2 year follow-up period.

  3. Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique

    Energy Technology Data Exchange (ETDEWEB)

    Wijayasekara, Dumidu, E-mail: wija2589@vandals.uidaho.edu [Department of Computer Science, University of Idaho, 1776 Science Center Drive, Idaho Falls, ID 83402 (United States); Manic, Milos [Department of Computer Science, University of Idaho, 1776 Science Center Drive, Idaho Falls, ID 83402 (United States); Sabharwall, Piyush [Idaho National Laboratory, Idaho Falls, ID (United States); Utgikar, Vivek [Department of Chemical Engineering, University of Idaho, Idaho Falls, ID 83402 (United States)

    2011-07-15

    Highlights: > Performance prediction of PCHE using artificial neural networks. > Evaluating artificial neural network performance for PCHE modeling. > Selection of over-training resilient artificial neural networks. > Artificial neural network architecture selection for modeling problems with small data sets. - Abstract: Artificial Neural Networks (ANN) have been used in the past to predict the performance of printed circuit heat exchangers (PCHE) with satisfactory accuracy. Typically published literature has focused on optimizing ANN using a training dataset to train the network and a testing dataset to evaluate it. Although this may produce outputs that agree with experimental results, there is a risk of over-training or over-learning the network rather than generalizing it, which should be the ultimate goal. An over-trained network is able to produce good results with the training dataset but fails when new datasets with subtle changes are introduced. In this paper we present EBaLM-OTR (error back propagation and Levenberg-Marquardt algorithms for over training resilience) technique, which is based on a previously discussed method of selecting neural network architecture that uses a separate validation set to evaluate different network architectures based on mean square error (MSE), and standard deviation of MSE. The method uses k-fold cross validation. Therefore in order to select the optimal architecture for the problem, the dataset is divided into three parts which are used to train, validate and test each network architecture. Then each architecture is evaluated according to their generalization capability and capability to conform to original data. The method proved to be a comprehensive tool in identifying the weaknesses and advantages of different network architectures. The method also highlighted the fact that the architecture with the lowest training error is not always the most generalized and therefore not the optimal. Using the method the testing

  4. Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique

    International Nuclear Information System (INIS)

    Wijayasekara, Dumidu; Manic, Milos; Sabharwall, Piyush; Utgikar, Vivek

    2011-01-01

    Highlights: → Performance prediction of PCHE using artificial neural networks. → Evaluating artificial neural network performance for PCHE modeling. → Selection of over-training resilient artificial neural networks. → Artificial neural network architecture selection for modeling problems with small data sets. - Abstract: Artificial Neural Networks (ANN) have been used in the past to predict the performance of printed circuit heat exchangers (PCHE) with satisfactory accuracy. Typically published literature has focused on optimizing ANN using a training dataset to train the network and a testing dataset to evaluate it. Although this may produce outputs that agree with experimental results, there is a risk of over-training or over-learning the network rather than generalizing it, which should be the ultimate goal. An over-trained network is able to produce good results with the training dataset but fails when new datasets with subtle changes are introduced. In this paper we present EBaLM-OTR (error back propagation and Levenberg-Marquardt algorithms for over training resilience) technique, which is based on a previously discussed method of selecting neural network architecture that uses a separate validation set to evaluate different network architectures based on mean square error (MSE), and standard deviation of MSE. The method uses k-fold cross validation. Therefore in order to select the optimal architecture for the problem, the dataset is divided into three parts which are used to train, validate and test each network architecture. Then each architecture is evaluated according to their generalization capability and capability to conform to original data. The method proved to be a comprehensive tool in identifying the weaknesses and advantages of different network architectures. The method also highlighted the fact that the architecture with the lowest training error is not always the most generalized and therefore not the optimal. Using the method the

  5. The Evolution of a Malignancy Risk Prediction Model for Thyroid Nodules Using the Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Shahram Paydar

    2016-01-01

    fine needle aspiration and surgical histopathology results. The results matched in 63.5% of subjects. On the other hand, fine needle aspiration biopsy results falsely predicted malignant thyroid nodules in 16% of cases (false-negative. In 20.5% of subjects, fine needle aspiration was falsely positive for thyroid malignancy. The Resilient back Propagation (RP training algorithm lead to acceptable accuracy in prediction for the designed artificial neural network (64.66% by the cross- validation method. Under the cross-validation method, a back propagation algorithm that used the resilient back propagation protocol - the accuracy in prediction for the trained artificial neural network was 64.66%. Conclusion: An extensive bio-statistically validated artificial neural network of certain clinical, paraclinical and individual given inputs (predictors has the capability to stratify the malignancy risk of a thyroid nodule in order to individualize patient care. This risk assessment model (tool can virtually minimize unnecessary diagnostic thyroid surgeries as well as FNA misleading.

  6. Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Narayanan Manikandan

    2016-01-01

    Full Text Available Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

  7. Analysis of Artificial Neural Network Backpropagation Using Conjugate Gradient Fletcher Reeves In The Predicting Process

    Science.gov (United States)

    Wanto, Anjar; Zarlis, Muhammad; Sawaluddin; Hartama, Dedy

    2017-12-01

    Backpropagation is a good artificial neural network algorithm used to predict, one of which is to predict the rate of Consumer Price Index (CPI) based on the foodstuff sector. While conjugate gradient fletcher reeves is a suitable optimization method when juxtaposed with backpropagation method, because this method can shorten iteration without reducing the quality of training and testing result. Consumer Price Index (CPI) data that will be predicted to come from the Central Statistics Agency (BPS) Pematangsiantar. The results of this study will be expected to contribute to the government in making policies to improve economic growth. In this study, the data obtained will be processed by conducting training and testing with artificial neural network backpropagation by using parameter learning rate 0,01 and target error minimum that is 0.001-0,09. The training network is built with binary and bipolar sigmoid activation functions. After the results with backpropagation are obtained, it will then be optimized using the conjugate gradient fletcher reeves method by conducting the same training and testing based on 5 predefined network architectures. The result, the method used can increase the speed and accuracy result.

  8. The Use of Artificial Neural Network for Prediction of Dissolution Kinetics

    Directory of Open Access Journals (Sweden)

    H. Elçiçek

    2014-01-01

    Full Text Available Colemanite is a preferred boron mineral in industry, such as boric acid production, fabrication of heat resistant glass, and cleaning agents. Dissolution of the mineral is one of the most important processes for these industries. In this study, dissolution of colemanite was examined in water saturated with carbon dioxide solutions. Also, prediction of dissolution rate was determined using artificial neural networks (ANNs which are based on the multilayered perceptron. Reaction temperature, total pressure, stirring speed, solid/liquid ratio, particle size, and reaction time were selected as input parameters to predict the dissolution rate. Experimental dataset was used to train multilayer perceptron (MLP networks to allow for prediction of dissolution kinetics. Developing ANNs has provided highly accurate predictions in comparison with an obtained mathematical model used through regression method. We conclude that ANNs may be a preferred alternative approach instead of conventional statistical methods for prediction of boron minerals.

  9. Are leaf glandular trichomes of oregano hospitable habitats for bacterial growth?

    Science.gov (United States)

    Karamanoli, K; Thalassinos, G; Karpouzas, D; Bosabalidis, A M; Vokou, D; Constantinidou, H-I

    2012-05-01

    Phyllospheric bacteria were isolated from microsites around essential-oil-containing glands of two oregano (Origanum vulgare subsp. hirtum) lines. These bacteria, 20 isolates in total, were subjected to bioassays to examine their growth potential in the presence of essential oils at different concentrations. Although there were qualitative and quantitative differences in the essential oil composition between the two oregano lines, no differences were recorded in their antibacterial activity. In disk diffusion bioassays, four of the isolated strains could grow almost unrestrained in the presence of oregano oil, another five proved very sensitive, and the remaining 11 showed intermediate sensitivity. The strain least inhibited by oregano essential oil was further identified by complete16s rRNA gene sequencing as Pseudomonas putida. It was capable of forming biofilms even in the presence of oregano oil at high concentrations. Resistance of P. putida to oregano oil was further elaborated by microwell dilution bioassays, and its topology on oregano leaves was studied by electron microscopy. When inoculated on intact oregano plants, P. putida was able not only to colonize sites adjacent to essential oil-containing glands, but even to grow intracellularly. This is the first time that such prolific bacterial growth inside the glands has been visually observed. Results of this study further revealed that several bacteria can be established on oregano leaves, suggesting that these bacteria have attributes that allow them to tolerate or benefit from oregano secondary metabolites.

  10. Evaluation of extra virgin olive oil stability by artificial neural network.

    Science.gov (United States)

    Silva, Simone Faria; Anjos, Carlos Alberto Rodrigues; Cavalcanti, Rodrigo Nunes; Celeghini, Renata Maria dos Santos

    2015-07-15

    The stability of extra virgin olive oil in polyethylene terephthalate bottles and tinplate cans stored for 6 months under dark and light conditions was evaluated. The following analyses were carried out: free fatty acids, peroxide value, specific extinction at 232 and 270 nm, chlorophyll, L(∗)C(∗)h color, total phenolic compounds, tocopherols and squalene. The physicochemical changes were evaluated by artificial neural network (ANN) modeling with respect to light exposure conditions and packaging material. The optimized ANN structure consists of 11 input neurons, 18 hidden neurons and 5 output neurons using hyperbolic tangent and softmax activation functions in hidden and output layers, respectively. The five output neurons correspond to five possible classifications according to packaging material (PET amber, PET transparent and tinplate can) and light exposure (dark and light storage). The predicted physicochemical changes agreed very well with the experimental data showing high classification accuracy for test (>90%) and training set (>85). Sensitivity analysis showed that free fatty acid content, peroxide value, L(∗)Cab(∗)hab(∗) color parameters, tocopherol and chlorophyll contents were the physicochemical attributes with the most discriminative power. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. Artificial Neural Networks: A New Approach for Predicting Application Behavior. AIR 2001 Annual Forum Paper.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    This paper examines how predictive modeling can be used to study application behavior. A relatively new technique, artificial neural networks (ANNs), was applied to help predict which students were likely to get into a large Research I university. Data were obtained from a university in Iowa. Two cohorts were used, each containing approximately…

  12. Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach

    DEFF Research Database (Denmark)

    Buus, S.; Lauemoller, S.L.; Worning, Peder

    2003-01-01

    We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict...

  13. The principles of artificial neural network information processing

    International Nuclear Information System (INIS)

    Dai, Ru-Wei

    1993-01-01

    In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as Perceptron, Back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally, the application of artificial neural network for Chinese Character Recognition is also given. (author)

  14. The principles of artificial neural network information processing

    International Nuclear Information System (INIS)

    Dai, Ru-Wei

    1993-01-01

    In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as perception, back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally the application of artificial neural network for Chinese character recognition is also given. (author)

  15. Setup of a Parameterized FE Model for the Die Roll Prediction in Fine Blanking using Artificial Neural Networks

    Science.gov (United States)

    Stanke, J.; Trauth, D.; Feuerhack, A.; Klocke, F.

    2017-09-01

    Die roll is a morphological feature of fine blanked sheared edges. The die roll reduces the functional part of the sheared edge. To compensate for the die roll thicker sheet metal strips and secondary machining must be used. However, in order to avoid this, the influence of various fine blanking process parameters on the die roll has been experimentally and numerically studied, but there is still a lack of knowledge on the effects of some factors and especially factor interactions on the die roll. Recent changes in the field of artificial intelligence motivate the hybrid use of the finite element method and artificial neural networks to account for these non-considered parameters. Therefore, a set of simulations using a validated finite element model of fine blanking is firstly used to train an artificial neural network. Then the artificial neural network is trained with thousands of experimental trials. Thus, the objective of this contribution is to develop an artificial neural network that reliably predicts the die roll. Therefore, in this contribution, the setup of a fully parameterized 2D FE model is presented that will be used for batch training of an artificial neural network. The FE model enables an automatic variation of the edge radii of blank punch and die plate, the counter and blank holder force, the sheet metal thickness and part diameter, V-ring height and position, cutting velocity as well as material parameters covered by the Hensel-Spittel model for 16MnCr5 (1.7131, AISI/SAE 5115). The FE model is validated using experimental trails. The results of this contribution is a FE model suitable to perform 9.623 simulations and to pass the simulated die roll width and height automatically to an artificial neural network.

  16. Combined Toxicity of Three Essential Oils Against Aedes aegypti (Diptera: Culicidae) Larvae.

    Science.gov (United States)

    Muturi, Ephantus J; Ramirez, Jose L; Doll, Kenneth M; Bowman, Michael J

    2017-11-07

    Essential oils are potential alternatives to synthetic insecticides because they have low mammalian toxicity, degrade rapidly in the environment, and possess complex mixtures of bioactive constituents with multi-modal activity against the target insect populations. Twenty-one essential oils were initially screened for their toxicity against Aedes aegypti (L.) larvae and three out of the seven most toxic essential oils (Manuka, oregano, and clove bud essential oils) were examined for their chemical composition and combined toxicity against Ae. aegypti larvae. Manuka essential oil interacted synergistically with oregano essential oil and antagonistically with clove bud essential oil. GC-MS analysis revealed the presence of 21 components in Manuka essential oil and three components each in oregano and clove bud essential oils. Eugenol (84.9%) and eugenol acetate (9.6%) were the principal constituents in clove bud essential oil while carvacrol (75.8%) and m-isopropyltoluene (15.5%) were the major constituents in oregano essential oil. The major constituents in Manuka essential oil were calamenene (20%) and 3-dodecyl-furandione (11.4%). Manuka essential oil interacted synergistically with eugenol acetate and antagonistically with eugenol, suggesting that eugenol was a major contributor to the antagonistic interaction between Manuka and clove bud essential oils. In addition, Manuka interacted synergistically with carvacrol suggesting its contribution to the synergistic interaction between Manuka and oregano essential oils. These findings provide novel insights that can be used to develop new and safer alternatives to synthetic insecticides. Published by Oxford University Press on behalf of Entomological Society of America 2017. This work is written by (a) US Government employee(s) and is in the public domain in the US.

  17. Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Jorjani, E.; Poorali, H.A.; Sam, A.; Chelgani, S.C.; Mesroghli, S.; Shayestehfar, M.R. [Islam Azad University, Tehran (Iran). Dept. of Mining Engineering

    2009-10-15

    In this paper, the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate were predicted by regression and artificial neural network based on proximate and group macerals analysis. The regression method shows that the relationships between (a) in (ash), volatile matter and moisture (b) in (ash), in (liptinite), fusinite and vitrinite with combustible value can achieve the correlation coefficients (R{sup 2}) of 0.8 and 0.79, respectively. In addition, the input sets of (c) ash, volatile matter and moisture (d) ash, liptinite and fusinite can predict the combustible recovery with the correlation coefficients of 0.84 and 0.63, respectively. Feed-forward artificial neural network with 6-8-12-11-2-1 arrangement for moisture, ash and volatile matter input set was capable to estimate both combustible value and combustible recovery with correlation of 0.95. It was shown that the proposed neural network model could accurately reproduce all the effects of proximate and group macerals analysis on coal flotation system.

  18. Antimicrobial effectiveness of oregano and sage essential oils incorporated into whey protein films or cellulose-based filter paper.

    Science.gov (United States)

    Royo, Maite; Fernández-Pan, Idoya; Maté, Juan I

    2010-07-01

    In this study the antimicrobial effectiveness of oregano and sage essential oils (EOs) incorporated into two different matrices, whey protein isolate (WPI) and cellulose-based filter paper, was analysed. Antimicrobial properties of WPI-based films containing oregano and sage EOs were tested against Listeria innocua, Staphylococcus aureus and Salmonella enteritidis. Oregano EO showed antimicrobial activity against all three micro-organisms. The highest inhibition zones were against L. innocua. However, sage EO did not show antimicrobial activity against any of the micro-organisms. Antimicrobial activity was confirmed for both EOs using cellulose-based filter paper as supporting matrix, although it was significantly more intense for oregano EO. Inhibition surfaces were significantly greater when compared with those of the WPI films. This finding is likely due to the higher porosity and diffusivity of the active compounds in the filter paper. The interactions between the EOs and the films have a critical effect on the diffusivity of the active compounds and therefore on the final antimicrobial activity. As a result, to obtain active edible films, it is necessary to find the equilibrium point between the nature and concentration of the active compounds in the EO and the formulation of the film.

  19. Optimization of microwave-assisted extraction of total extract, stevioside and rebaudioside-A from Stevia rebaudiana (Bertoni) leaves, using response surface methodology (RSM) and artificial neural network (ANN) modelling.

    Science.gov (United States)

    Ameer, Kashif; Bae, Seong-Woo; Jo, Yunhee; Lee, Hyun-Gyu; Ameer, Asif; Kwon, Joong-Ho

    2017-08-15

    Stevia rebaudiana (Bertoni) consists of stevioside and rebaudioside-A (Reb-A). We compared response surface methodology (RSM) and artificial neural network (ANN) modelling for their estimation and predictive capabilities in building effective models with maximum responses. A 5-level 3-factor central composite design was used to optimize microwave-assisted extraction (MAE) to obtain maximum yield of target responses as a function of extraction time (X 1 : 1-5min), ethanol concentration, (X 2 : 0-100%) and microwave power (X 3 : 40-200W). Maximum values of the three output parameters: 7.67% total extract yield, 19.58mg/g stevioside yield, and 15.3mg/g Reb-A yield, were obtained under optimum extraction conditions of 4min X 1 , 75% X 2 , and 160W X 3 . The ANN model demonstrated higher efficiency than did the RSM model. Hence, RSM can demonstrate interaction effects of inherent MAE parameters on target responses, whereas ANN can reliably model the MAE process with better predictive and estimation capabilities. Copyright © 2017. Published by Elsevier Ltd.

  20. Reliability analysis of C-130 turboprop engine components using artificial neural network

    Science.gov (United States)

    Qattan, Nizar A.

    In this study, we predict the failure rate of Lockheed C-130 Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including (feed-forward back-propagation, radial basis neural network, and multilayer perceptron neural network model); will be utilized to perform this study. For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network (ANN) model utilizing the feed-forward back-propagation algorithm as a learning rule. The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box. In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model. By the end of the study, we forecast the general failure rate of the Lockheed C-130 Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network (MLP) model on DTREG commercial software. The results also give an insight into the reliability of the engine

  1. Livermore Big Artificial Neural Network Toolkit

    Energy Technology Data Exchange (ETDEWEB)

    2016-07-01

    LBANN is a toolkit that is designed to train artificial neural networks efficiently on high performance computing architectures. It is optimized to take advantages of key High Performance Computing features to accelerate neural network training. Specifically it is optimized for low-latency, high bandwidth interconnects, node-local NVRAM, node-local GPU accelerators, and high bandwidth parallel file systems. It is built on top of the open source Elemental distributed-memory dense and spars-direct linear algebra and optimization library that is released under the BSD license. The algorithms contained within LBANN are drawn from the academic literature and implemented to work within a distributed-memory framework.

  2. Supercritical CO2 Extracts and Volatile Oil of Basil (Ocimum basilicum L. Comparison with Conventional Methods

    Directory of Open Access Journals (Sweden)

    José Coelho

    2018-03-01

    Full Text Available Interest in new products from aromatic plants as medical and nutritional compounds is increasing. The aim of this work was to apply different extraction methods, including the use of supercritical carbon dioxide extraction, and to test the antioxidant activity of basil (Ocimum basilicum L. extracts. In vitro efficacy assessments were performed using enzymatic assays. Essential oil obtained by hydrodistillation and volatile oil obtained from supercritical fluid extraction were analyzed by gas chromatography to quantify components. The total phenolic content in the extracts ranged from 35.5 ± 2.9 to 85.3 ± 8.6 mg of gallic acid equivalents and the total flavonoid content ranged from 35.5 ± 2.9 to 93.3 ± 3.9 micromole catechin equivalents per gram of dry weight of extract. All the extracts showed an antioxidant activity with 2,2-diphenyl-1-picrylhydrazyl (DPPH, 2,2-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid (ABTS, and the reducing power test. Extracts obtained from methanol had a higher antioxidant capacity per the DPPH test results (IC50 = 3.05 ± 0.36 mg/mL and the reducing power test assay 306.8 ± 21.8 μmol of trolox equivalents per gram of extract (TE/g compared with ethanolic or supercritical fluid extracts. However, using the ABTS assay, the extract obtained by supercritical fluid extraction had a higher antioxidant capacity with an IC50 of 1.74 ± 0.05 mg/mL. Finally, the examined extracts showed practically no acetylcholinesterase (AChE inhibitory capacity and a slight inhibitory activity against tyrosinase.

  3. Advanced Applications of Neural Networks and Artificial Intelligence: A Review

    OpenAIRE

    Koushal Kumar; Gour Sundar Mitra Thakur

    2012-01-01

    Artificial Neural Network is a branch of Artificial intelligence and has been accepted as a new computing technology in computer science fields. This paper reviews the field of Artificial intelligence and focusing on recent applications which uses Artificial Neural Networks (ANN’s) and Artificial Intelligence (AI). It also considers the integration of neural networks with other computing methods Such as fuzzy logic to enhance the interpretation ability of data. Artificial Neural Networks is c...

  4. Development of classification and prediction methods of critical heat flux using fuzzy theory and artificial neural networks

    International Nuclear Information System (INIS)

    Moon, Sang Ki

    1995-02-01

    This thesis applies new information techniques, artificial neural networks, (ANNs) and fuzzy theory, to the investigation of the critical heat flux (CHF) phenomenon for water flow in vertical round tubes. The work performed are (a) classification and prediction of CHF based on fuzzy clustering and ANN, (b) prediction and parametric trends analysis of CHF using ANN with the introduction of dimensionless parameters, and (c) detection of CHF occurrence using fuzzy rule and spatiotemporal neural network (STN). Fuzzy clustering and ANN are used for classification and prediction of the CHF using primary system parameters. The fuzzy clustering classifies the experimental CHF data into a few data clusters (data groups) according to the data characteristics. After classification of the experimental data, the characteristics of the resulted clusters are discussed with emphasis on the distribution of the experimental conditions and physical mechanisms. The CHF data in each group are trained in an artificial neural network to predict the CHF. The artificial neural network adjusts the weight so as to minimize the prediction error within the corresponding cluster. Application of the proposed method to the KAIST CHF data bank shows good prediction capability of the CHF, better than other existing methods. Parametric trends of the CHF are analyzed by applying artificial neural networks to a CHF data base for water flow in uniformly heated vertical round tubes. The analyses are performed from three viewpoints, i.e., for fixed inlet conditions, for fixed exit conditions, and based on local conditions hypothesis. In order to remove the necessity of data classification, Katto and Groeneveld et al.'s dimensionless parameters are introduced in training the ANNs with the experimental CHF data. The trained ANNs predict the CHF better than any other conventional correlations, showing RMS error of 8.9%, 13.1%, and 19.3% for fixed inlet conditions, for fixed exit conditions, and for local

  5. Predicting pressure drop in venturi scrubbers with artificial neural networks.

    Science.gov (United States)

    Nasseh, S; Mohebbi, A; Jeirani, Z; Sarrafi, A

    2007-05-08

    In this study a new approach based on artificial neural networks (ANNs) has been used to predict pressure drop in venturi scrubbers. The main parameters affecting the pressure drop are mainly the gas velocity in the throat of venturi scrubber (V(g)(th)), liquid to gas flow rate ratio (L/G), and axial distance of the venturi scrubber (z). Three sets of experimental data from five different venturi scrubbers have been applied to design three independent ANNs. Comparing the results of these ANNs and the calculated results from available models shows that the results of ANNs have a better agreement with experimental data.

  6. Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Aminmohammad Saberian

    2014-01-01

    Full Text Available This paper presents a solar power modelling method using artificial neural networks (ANNs. Two neural network structures, namely, general regression neural network (GRNN feedforward back propagation (FFBP, have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

  7. International Conference on Artificial Neural Networks (ICANN)

    CERN Document Server

    Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

    2015-01-01

    The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

  8. Influence of essential oils on the growth of aspergillus flavus

    Directory of Open Access Journals (Sweden)

    Denisa Foltinová

    2017-01-01

    Full Text Available This paper was focused on the determination of the inhibitory effect of selected essential oils on growth of ten isolates of Aspergillus flavus and their potential ability to produce mycotoxins in vitro by TLC method. The isolates were obtained from moldy bread of domestic origin. We followed the impact of five essential oils at 100% concentration - lemon, eucalyptus, oregano, sage and thyme. The effect of the essential oils we tested the gaseous diffusion method. We isolates grown on CYA (Czapek yeast extract agar, in the dark at 25 ±1 °C, 14 days. The diameter of colonies grown we continuously measured on the 3rd, 7th, 11th, and 14th day of cultivation. The results of the paper suggest that oregano and thyme essential oil had 100% inhibited the growth of all tested isolates of Aspergillus flavus. Lemon, eucalyptus and sage essential oil had not significant inhibitory effects on tested isolates Aspergillus flavus, but affected the growth of colonies throughout the cultivation. In addition to the inhibitory effect we witnessed the stimulative effect of lemon, eucalyptus and sage essential oil to some isolates. Together with the antifungal effect of essential oils, we monitored the ability of Aspergillus flavus isolates to produce mycotoxins - aflatoxin B1 (AFB1 and cyclopiazonic acid (CPA in the presence of essential oils. Production mycotoxins we have seen in the last (14th day of cultivation. Lemon and eucalyptus essential oil did not affect the production of mycotoxins. In the case of sage essential oil we were recorded cyclopiazonic acid production in three of the ten isolates from the all three repetitions, while neither isolate did not produced aflatoxin B1. The production of secondary metabolites was detected in all control samples. From the results we can say that oregano and thyme essential oil could be used as a natural preservative useful in the food industry.

  9. Artificial neural networks in neutron dosimetry

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Mercado, G.A.; Perales M, W.A.; Robles R, J.A. [Unidades Academicas de Estudios Nucleares, UAZ, A.P. 336, 98000 Zacatecas (Mexico); Gallego, E.; Lorente, A. [Depto. de Ingenieria Nuclear, Universidad Politecnica de Madrid, (Spain)

    2005-07-01

    An artificial neural network has been designed to obtain the neutron doses using only the Bonner spheres spectrometer's count rates. Ambient, personal and effective neutron doses were included. 187 neutron spectra were utilized to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in Bonner spheres spectrometer and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing was carried out in Mat lab environment. The artificial neural network performance was evaluated using the {chi}{sup 2}- test, where the original and calculated doses were compared. The use of Artificial Neural Networks in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  10. Artificial neural networks in neutron dosimetry

    International Nuclear Information System (INIS)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Mercado, G.A.; Perales M, W.A.; Robles R, J.A.; Gallego, E.; Lorente, A.

    2005-01-01

    An artificial neural network has been designed to obtain the neutron doses using only the Bonner spheres spectrometer's count rates. Ambient, personal and effective neutron doses were included. 187 neutron spectra were utilized to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in Bonner spheres spectrometer and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing was carried out in Mat lab environment. The artificial neural network performance was evaluated using the χ 2 - test, where the original and calculated doses were compared. The use of Artificial Neural Networks in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  11. Predicting the Deflections of Micromachined Electrostatic Actuators Using Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Hing Wah LEE

    2009-03-01

    Full Text Available In this study, a general purpose Artificial Neural Network (ANN model based on the feed-forward back-propagation (FFBP algorithm has been used to predict the deflections of a micromachined structures actuated electrostatically under different loadings and geometrical parameters. A limited range of simulation results obtained via CoventorWare™ numerical software will be used initially to train the neural network via back-propagation algorithm. The micromachined structures considered in the analyses are diaphragm, fixed-fixed beams and cantilevers. ANN simulation results are compared with results obtained via CoventorWare™ simulations and existing analytical work for validation purpose. The proposed ANN model accurately predicts the deflections of the micromachined structures with great reduction of simulation efforts, establishing the method superiority. This method can be extended for applications in other sensors particularly for modeling sensors applying electrostatic actuation which are difficult in nature due to the inherent non-linearity of the electro-mechanical coupling response.

  12. Predicting the wheel rolling resistance regarding important motion parameters using the artificial neural network

    Directory of Open Access Journals (Sweden)

    F Gheshlaghi

    2016-04-01

    resistance is discussed. The results indicated that in general trend of changes, the velocity is not affected by rolling resistance. Rolling resistance increases when tire pressure decreases. This is due to energy consumption for creating deflection on the body of the tire at the lower levels of tire inflation pressure. Another variable parameter is the vertical load on the wheel and its logical relation with rolling resistance using neural network. The results showed that increasing the vertical load increases the rolling resistance. Conclusions: The major purpose of this study was the feasibility of using learning algorithms for interaction between wheel and soil. The parameters of the wheel when clashes with soil are not stochastic and in spite of their complexity follow a specific model, certainly. Artificial neural network trained with a correlation coefficient of 0.92 relatively had a good performance in education, testing and validation parts. To validate the network results, the impact of some factors on the extraction process such as velocity, load and inflation pressure was simulated. The main objective of this article is comparing the network performance with basic principles and other scientific reports. In this regard, the predictions by trained neural network indicated that rolling resistance is independent of the velocity of the wheel. On the other hand, rolling resistance decreases by increasing tire inflation pressure which is a general trend similar to other studies and reports in the same mechanical condition of the soil tested. Rolling resistance changes are directly proportional to load vertical variations on the wheel in terms of quantity and quality, similar to experimental models such as Wismer and Luth.

  13. Characterization of Active Packaging Films Made from Poly(Lactic Acid)/Poly(Trimethylene Carbonate) Incorporated with Oregano Essential Oil

    OpenAIRE

    Dong Liu; Hongli Li; Lin Jiang; Yongming Chuan; Minglong Yuan; Haiyun Chen

    2016-01-01

    Antimicromial and antioxidant bioactive films based on poly(lactic acid)/poly(trimenthylene carbonate) films incorporated with different concentrations of oregano essential oil (OEO) were prepared by solvent casting. The antimicrobial, antioxidant, physical, thermal, microstructural, and mechanical properties of the resulting films were examined. Scanning electron microscopy analysis revealed that the cross-section of films became rougher when OEO was incorporated into PLA/PTMC blends. Differ...

  14. Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment

    Directory of Open Access Journals (Sweden)

    Stefano Curcio

    2014-01-01

    Full Text Available The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.

  15. Application of neural networks to signal prediction in nuclear power plant

    International Nuclear Information System (INIS)

    Wan Joo Kim; Soon Heung Chang; Byung Ho Lee

    1993-01-01

    This paper describes the feasibility study of an artificial neural network for signal prediction. The purpose of signal prediction is to estimate the value of undetected next time step signal. As the prediction method, based on the idea of auto regression, a few previous signals are inputs to the artificial neural network and the signal value of next time step is estimated with the outputs of the network. The artificial neural network can be applied to the nonlinear system and answers in short time. The training algorithm is a modified backpropagation model, which can effectively reduce the training time. The target signal of the simulation is the steam generator water level, which is one of the important parameters in nuclear power plants. The simulation result shows that the predicted value follows the real trend well

  16. Dual-modality and dual-energy gamma ray densitometry of petroleum products using an artificial neural network

    International Nuclear Information System (INIS)

    Roshani, G.H.; Feghhi, S.A.H.; Setayeshi, S.

    2015-01-01

    The prediction of volume fractions in order to measure the multiphase flow rate is a very important issue and is the key parameter of multi-phase flow meters (MPFMs). Currently, the gamma ray attenuation technique is known as one of the most precise methods for obtaining volume fractions. The gamma ray attenuation technique is based on the mass attenuation coefficient, which is sensitive to density changes; density is sensitive in turn to temperature and pressure fluctuations. Therefore, MPFM efficiency depends strongly on environmental conditions. The conventional solution to this problem is the periodical recalibration of MPFMs, which is a demanding task. In this study, a method based on dual-modality densitometry and artificial intelligence (AI) is presented, which offers the advantage of the measurement of the oil–gas–water volume fractions independent of density changes. For this purpose, several experiments were carried out and used to validate simulated dual modality densitometry results. The reference density point was established at a temperature of 20 °C and pressure of 1 bar. To cover the full range of likely density fluctuations, four additional density sets were defined (at changes of ±4% and ±8% from the reference point). An annular regime with different percentages of oil, gas and water at different densities was simulated. Four features were extracted from the transmission and scattered detectors and were applied to the artificial neural network (ANN) as inputs. The input parameters included the "2"4"1Am full energy peak, "1"3"7Cs Compton edge, "1"3"7Cs full energy peak and total scattered count, and the outputs were the oil and air percentages. A multi-layer perceptron (MLP) neural network was used to predict the volume fraction independent of the oil and water density changes. The obtained results show that the proposed ANN model achieved good agreement with the real data, with an estimated root mean square error (RMSE) of less than 3

  17. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

    2006-07-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  18. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    International Nuclear Information System (INIS)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R.

    2006-01-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  19. Cultivar classification of Apulian olive oils: Use of artificial neural networks for comparing NMR, NIR and merceological data.

    Science.gov (United States)

    Binetti, Giulio; Del Coco, Laura; Ragone, Rosa; Zelasco, Samanta; Perri, Enzo; Montemurro, Cinzia; Valentini, Raffaele; Naso, David; Fanizzi, Francesco Paolo; Schena, Francesco Paolo

    2017-03-15

    The development of an efficient and accurate method for extra-virgin olive oils cultivar and origin authentication is complicated by the broad range of variables (e.g., multiplicity of varieties, pedo-climatic aspects, production and storage conditions) influencing their properties. In this study, artificial neural networks (ANNs) were applied on several analytical datasets, namely standard merceological parameters, near-infra red data and 1 H nuclear magnetic resonance (NMR) fingerprints, obtained on mono-cultivar olive oils of four representative Apulian varieties (Coratina, Ogliarola, Cima di Mola, Peranzana). We analyzed 888 samples produced at a laboratory-scale during two crop years from 444 plants, whose variety was genetically ascertained, and on 17 industrially produced samples. ANN models based on NMR data showed the highest capability to classify cultivars (in some cases, accuracy>99%), independently on the olive oil production process and year; hence, the NMR data resulted to be the most informative variables about the cultivars. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Artificial Neural Networks and the Mass Appraisal of Real Estate

    Directory of Open Access Journals (Sweden)

    Gang Zhou

    2018-03-01

    Full Text Available With the rapid development of computer, artificial intelligence and big data technology, artificial neural networks have become one of the most powerful machine learning algorithms. In the practice, most of the applications of artificial neural networks use back propagation neural network and its variation. Besides the back propagation neural network, various neural networks have been developing in order to improve the performance of standard models. Though neural networks are well known method in the research of real estate, there is enormous space for future research in order to enhance their function. Some scholars combine genetic algorithm, geospatial information, support vector machine model, particle swarm optimization with artificial neural networks to appraise the real estate, which is helpful for the existing appraisal technology. The mass appraisal of real estate in this paper includes the real estate valuation in the transaction and the tax base valuation in the real estate holding. In this study we focus on the theoretical development of artificial neural networks and mass appraisal of real estate, artificial neural networks model evolution and algorithm improvement, artificial neural networks practice and application, and review the existing literature about artificial neural networks and mass appraisal of real estate. Finally, we provide some suggestions for the mass appraisal of China's real estate.

  1. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    Directory of Open Access Journals (Sweden)

    Hazlee Azil Illias

    Full Text Available It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN and particle swarm optimisation (PSO techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

  2. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    Science.gov (United States)

    Illias, Hazlee Azil; Chai, Xin Rui; Abu Bakar, Ab Halim; Mokhlis, Hazlie

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

  3. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

    Science.gov (United States)

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works. PMID:26103634

  4. Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods

    Directory of Open Access Journals (Sweden)

    Gregorius Satia Budhi

    2015-07-01

    Full Text Available Javanese characters are traditional characters that are used to write the Javanese language. The Javanese language is a language used by many people on the island of Java, Indonesia. The use of Javanese characters is diminishing more and more because of the difficulty of studying the Javanese characters themselves. The Javanese character set consists of basic characters, numbers, complementary characters, and so on. In this research we have developed a system to recognize Javanese characters. Input for the system is a digital image containing several handwritten Javanese characters. Preprocessing and segmentation are performed on the input image to get each character. For each character, feature extraction is done using the ICZ-ZCZ method. The output from feature extraction will become input for an artificial neural network. We used several artificial neural networks, namely a bidirectional associative memory network, a counterpropagation network, an evolutionary network, a backpropagation network, and a backpropagation network combined with chi2. From the experimental results it can be seen that the combination of chi2 and backpropagation achieved better recognition accuracy than the other methods.

  5. Face Recognition using Artificial Neural Network | Endeshaw | Zede ...

    African Journals Online (AJOL)

    Face recognition (FR) is one of the biometric methods to identify the individuals by the features of face. Two Face Recognition Systems (FRS) based on Artificial Neural Network (ANN) have been proposed in this paper based on feature extraction techniques. In the first system, Principal Component Analysis (PCA) has been ...

  6. Analysis of the essential oils of Alpiniae Officinarum Hance in different extraction methods

    Science.gov (United States)

    Yuan, Y.; Lin, L. J.; Huang, X. B.; Li, J. H.

    2017-09-01

    It was developed for the analysis of the essential oils of Alpiniae Officinarum Hance extracted by steam distillation (SD), ultrasonic assisted solvent extraction (UAE) and supercritical fluid extraction (SFE) via gas chromatography mass spectrometry (GC-MS) combined with retention index (RI) method. There were multiple volatile components of the oils extracted by the three above-mention methods respectively identified; meanwhile, each one was quantified by area normalization method. The results indicated that the content of 1,8-Cineole, the index constituent, by SD was similar as SFE, and higher than UAE. Although UAE was less time consuming and consumed less energy, the oil quality was poorer due to the use of organic solvents was hard to degrade. In addition, some constituents could be obtained by SFE but could not by SD. In conclusion, essential oil of different extraction methods from the same batch of materials had been proved broadly similarly, however, there were some differences in composition and component ratio. Therefore, development and utilization of different extraction methods must be selected according to the functional requirements of products.

  7. Artificial neural networks in NDT

    International Nuclear Information System (INIS)

    Abdul Aziz Mohamed

    2001-01-01

    Artificial neural networks, simply known as neural networks, have attracted considerable interest in recent years largely because of a growing recognition of the potential of these computational paradigms as powerful alternative models to conventional pattern recognition or function approximation techniques. The neural networks approach is having a profound effect on almost all fields, and has been utilised in fields Where experimental inter-disciplinary work is being carried out. Being a multidisciplinary subject with a broad knowledge base, Nondestructive Testing (NDT) or Nondestructive Evaluation (NDE) is no exception. This paper explains typical applications of neural networks in NDT/NDE. Three promising types of neural networks are highlighted, namely, back-propagation, binary Hopfield and Kohonen's self-organising maps. (Author)

  8. Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

    International Nuclear Information System (INIS)

    Ortiz R, J. M.; Martinez B, M. R.; Vega C, H. R.; Gallego D, E.; Lorente F, A.; Mendez V, R.; Los Arcos M, J. M.; Guerrero A, J. E.

    2011-01-01

    With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks (Ann) have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Ann still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning Ann parameters. In recent years the use of hybrid technologies, combining Ann and genetic algorithms, has been utilized to. In this work, several Ann topologies were trained and tested using Ann and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out. (Author)

  9. Bacterial radiosensitization by using radiation processing in combination with essential oil: Mechanism of action

    International Nuclear Information System (INIS)

    Lacroix, Monique; Caillet, Stephane; Shareck, Francois

    2009-01-01

    Spice extracts under the form of essential oils were tested for their efficiency to increase the relative radiosensitivity of Listeria monocytogenes and Escherichia coli O157H7 in culture media. The two pathogens were treated by gamma-irradiation alone or in combination with oregano essential oil to evaluate their mechanism of action. The membrane murein composition, and the intracellular and extracellular concentration of ATP was determined. The bacterial strains were treated with two irradiation doses: 1.2 kGy to induce cell damage and 3.5 kGy to cause cell death for L. monocytogenes. A dose of 0.4 kGy to induce cell damages, 1.1 kGy to obtain viable but nonculturable (VBNC) state and 1.3 kGy to obtain a lethal dose was also applied on E. coli O157H7. Oregano essential oil was used at 0.020% and 0.025% (w/v), which is the minimum inhibitory concentration (MIC) for L. monocytogenes. For E. coli O157H7, a concentration of 0.006% and 0.025% (w/v) which is the minimum inhibitory concentration was applied. The use of essential oils in combination with irradiation has permitted an increase of the bacterial radiosensitization by more than 3.1 times. All treatments had also a significant effect (p≤0.05) on the murein composition, although some muropeptides did not seem to be affected by the treatment. Each treatment influenced differently the relative percentage and number of muropeptides. There was a significant (p≤0.05) correlation between the reduction of intracellular ATP and increase in extracellular ATP following treatment of the cells with oregano oil. The reduction of intracellular ATP was even more important when essential oil was combined with irradiation, but irradiation of L. monocytogenes alone induced a significant decrease (p≤0.05) of the internal ATP without affecting the external ATP.

  10. Bacterial radiosensitization by using radiation processing in combination with essential oil: Mechanism of action

    Energy Technology Data Exchange (ETDEWEB)

    Lacroix, Monique [Canadian Irradiation Center, Research Laboratory in Sciences Applied to Food, INRS-Institut Armand-Frappier, 531, Boulevard des Prairies, Laval, Quebec, H7V 1B7 (Canada)], E-mail: monique.lacroix@iaf.inrs.ca; Caillet, Stephane [Canadian Irradiation Center, Research Laboratory in Sciences Applied to Food, INRS-Institut Armand-Frappier, 531, Boulevard des Prairies, Laval, Quebec, H7V 1B7 (Canada); Shareck, Francois [INRS-Institut Armand-Frappier, 531, Boulevard des Prairies, Laval, Quebec, H7V 1B7 (Canada)

    2009-07-15

    Spice extracts under the form of essential oils were tested for their efficiency to increase the relative radiosensitivity of Listeria monocytogenes and Escherichia coli O157H7 in culture media. The two pathogens were treated by gamma-irradiation alone or in combination with oregano essential oil to evaluate their mechanism of action. The membrane murein composition, and the intracellular and extracellular concentration of ATP was determined. The bacterial strains were treated with two irradiation doses: 1.2 kGy to induce cell damage and 3.5 kGy to cause cell death for L. monocytogenes. A dose of 0.4 kGy to induce cell damages, 1.1 kGy to obtain viable but nonculturable (VBNC) state and 1.3 kGy to obtain a lethal dose was also applied on E. coli O157H7. Oregano essential oil was used at 0.020% and 0.025% (w/v), which is the minimum inhibitory concentration (MIC) for L. monocytogenes. For E. coli O157H7, a concentration of 0.006% and 0.025% (w/v) which is the minimum inhibitory concentration was applied. The use of essential oils in combination with irradiation has permitted an increase of the bacterial radiosensitization by more than 3.1 times. All treatments had also a significant effect (p{<=}0.05) on the murein composition, although some muropeptides did not seem to be affected by the treatment. Each treatment influenced differently the relative percentage and number of muropeptides. There was a significant (p{<=}0.05) correlation between the reduction of intracellular ATP and increase in extracellular ATP following treatment of the cells with oregano oil. The reduction of intracellular ATP was even more important when essential oil was combined with irradiation, but irradiation of L. monocytogenes alone induced a significant decrease (p{<=}0.05) of the internal ATP without affecting the external ATP.

  11. Bacterial radiosensitization by using radiation processing in combination with essential oil: Mechanism of action

    Science.gov (United States)

    Lacroix, Monique; Caillet, Stéphane; Shareck, Francois

    2009-07-01

    Spice extracts under the form of essential oils were tested for their efficiency to increase the relative radiosensitivity of Listeria monocytogenes and Escherichia coli O157H7 in culture media. The two pathogens were treated by gamma-irradiation alone or in combination with oregano essential oil to evaluate their mechanism of action. The membrane murein composition, and the intracellular and extracellular concentration of ATP was determined. The bacterial strains were treated with two irradiation doses: 1.2 kGy to induce cell damage and 3.5 kGy to cause cell death for L. monocytogenes. A dose of 0.4 kGy to induce cell damages, 1.1 kGy to obtain viable but nonculturable (VBNC) state and 1.3 kGy to obtain a lethal dose was also applied on E. coli O157H7. Oregano essential oil was used at 0.020% and 0.025% (w/v), which is the minimum inhibitory concentration (MIC) for L. monocytogenes. For E. coli O157H7, a concentration of 0.006% and 0.025% (w/v) which is the minimum inhibitory concentration was applied. The use of essential oils in combination with irradiation has permitted an increase of the bacterial radiosensitization by more than 3.1 times. All treatments had also a significant effect ( p⩽0.05) on the murein composition, although some muropeptides did not seem to be affected by the treatment. Each treatment influenced differently the relative percentage and number of muropeptides. There was a significant ( p⩽0.05) correlation between the reduction of intracellular ATP and increase in extracellular ATP following treatment of the cells with oregano oil. The reduction of intracellular ATP was even more important when essential oil was combined with irradiation, but irradiation of L. monocytogenes alone induced a significant decrease ( p⩽0.05) of the internal ATP without affecting the external ATP.

  12. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  13. Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Kemal Fidanboylu

    2009-09-01

    Full Text Available Artificial neural network (ANN based prediction of the response of a microbend fiber optic sensor is presented. To the best of our knowledge no similar work has been previously reported in the literature. Parallel corrugated plates with three deformation cycles, 6 mm thickness of the spacer material and 16 mm mechanical periodicity between deformations were used in the microbend sensor. Multilayer Perceptron (MLP with different training algorithms, Radial Basis Function (RBF network and General Regression Neural Network (GRNN are used as ANN models in this work. All of these models can predict the sensor responses with considerable errors. RBF has the best performance with the smallest mean square error (MSE values of training and test results. Among the MLP algorithms and GRNN the Levenberg-Marquardt algorithm has good results. These models successfully predict the sensor responses, hence ANNs can be used as useful tool in the design of more robust fiber optic sensors.

  14. Prediction of compressibility parameters of the soils using artificial neural network.

    Science.gov (United States)

    Kurnaz, T Fikret; Dagdeviren, Ugur; Yildiz, Murat; Ozkan, Ozhan

    2016-01-01

    The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.

  15. Prediction of paddy drying kinetics: A comparative study between mathematical and artificial neural network modelling

    Directory of Open Access Journals (Sweden)

    Beigi Mohsen

    2017-01-01

    Full Text Available The present study aimed at investigation of deep bed drying of rough rice kernels at various thin layers at different drying air temperatures and flow rates. A comparative study was performed between mathematical thin layer models and artificial neural networks to estimate the drying curves of rough rice. The suitability of nine mathematical models in simulating the drying kinetics was examined and the Midilli model was determined as the best approach for describing drying curves. Different feed forward-back propagation artificial neural networks were examined to predict the moisture content variations of the grains. The ANN with 4-18-18-1 topology, transfer function of hyperbolic tangent sigmoid and a Levenberg-Marquardt back propagation training algorithm provided the best results with the maximum correlation coefficient and the minimum mean square error values. Furthermore, it was revealed that ANN modeling had better performance in prediction of drying curves with lower root mean square error values.

  16. Prediction of Groundwater Arsenic Contamination using Geographic Information System and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Md. Moqbul Hossain

    2013-01-01

    Full Text Available Ground water arsenic contamination is a well known health and environmental problem in Bangladesh. Sources of this heavy metal are known to be geogenic, however, the processes of its release into groundwater are poorly understood phenomena. In quest of mitigation of the problem it is necessary to predict probable contamination before it causes any damage to human health. Hence our research has been carried out to find the factor relations of arsenic contamination and develop an arsenic contamination prediction model. Researchers have generally agreed that the elevated concentration of arsenic is affected by several factors such as soil reaction (pH, organic matter content, geology, iron content, etc. However, the variability of concentration within short lateral and vertical intervals, and the inter-relationships of variables among themselves, make the statistical analyses highly non-linear and difficult to converge with a meaningful relationship. Artificial Neural Networks (ANN comes in handy for such a black box type problem. This research uses Back propagation Neural Networks (BPNN to train and validate the data derived from Geographic Information System (GIS spatial distribution grids. The neural network architecture with (6-20-1 pattern was able to predict the arsenic concentration with reasonable accuracy.

  17. Robust nonlinear autoregressive moving average model parameter estimation using stochastic recurrent artificial neural networks

    DEFF Research Database (Denmark)

    Chon, K H; Hoyer, D; Armoundas, A A

    1999-01-01

    In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...... error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate...

  18. Extraction of Thyme Oil: Comparison between Hydrodistillation and Supercritical CO2 Extraction

    Czech Academy of Sciences Publication Activity Database

    Aleksovski, S. A.; Sovová, Helena; Poposka, F. A.

    2001-01-01

    Roč. 51, č. 4 (2001), s. 305-310 ISSN 1330-0075 Institutional research plan: CEZ:AV0Z4072921 Keywords : thymus serpyllum * supercritical fluid extraction * assential oil Subject RIV: CI - Industrial Chemistry, Chemical Engineering

  19. Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger

    Science.gov (United States)

    Ghasemi, Nahid; Aghayari, Reza; Maddah, Heydar

    2018-06-01

    The present study aims at predicting and optimizing exergetic efficiency of TiO2-Al2O3/water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Function (RBF) were used to display the significant levels of the analyzed factors on the exergetic efficiency. The size of TiO2-Al2O3/water nanocomposite was 20-70 nm. The parameters of ANN model were adapted by a training algorithm of radial basis function (RBF) with a wide range of experimental data set. Total mean square error and correlation coefficient were used to evaluate the results which the best result was obtained from double layer perceptron neural network with 30 neurons in which total Mean Square Error(MSE) and correlation coefficient (R2) were equal to 0.002 and 0.999, respectively. This indicated successful prediction of the network. Moreover, the proposed equation for predicting exergetic efficiency was extremely successful. According to the optimal curves, the optimum designing parameters of double pipe heat exchanger with inner twisted tape and nanofluid under the constrains of exergetic efficiency 0.937 are found to be Reynolds number 2500, twisted ratio 2.5 and volume fraction( v/v%) 0.05.

  20. Precision Obtained Using an Artificial Neural Network for Predicting the Material Removal Rate in Ultrasonic Machining

    Directory of Open Access Journals (Sweden)

    Gaoyan Zhong

    2017-12-01

    Full Text Available The present study proposes a back propagation artificial neural network (BPANN to provide improved precision for predicting the material removal rate (MRR in ultrasonic machining. The BPANN benefits from the advantage of artificial neural networks (ANNs in dealing with complex input-output relationships without explicit mathematical functions. In our previous study, a conventional linear regression model and improved nonlinear regression model were established for modelling the MRR in ultrasonic machining to reflect the influence of machining parameters on process response. In the present work, we quantitatively compare the prediction precision obtained by the previously proposed regression models and the presently proposed BPANN model. The results of detailed analyses indicate that the BPANN model provided the highest prediction precision of the three models considered. The present work makes a positive contribution to expanding the applications of ANNs and can be considered as a guide for modelling complex problems of general machining.

  1. [Application of wavelet transform and neural network in the near-infrared spectrum analysis of oil shale].

    Science.gov (United States)

    Li, Su-Yi; Ji, Yan-Ju; Liu, Wei-Yu; Wang, Zhi-Hong

    2013-04-01

    In the present study, an innovative method is proposed, employing both wavelet transform and neural network, to analyze the near-infrared spectrum data in oil shale survey. The method entails using db8 wavelet at 3 levels decomposition to process raw data, using the transformed data as the input matrix, and creating the model through neural network. To verify the validity of the method, this study analyzes 30 synthesized oil shale samples, in which 20 samples are randomly selected for network training, the other 10 for model prediction, and uses the full spectrum and the wavelet transformed spectrum to carry out 10 network models, respectively. Results show that the mean speed of the full spectrum neural network modeling is 570.33 seconds, and the predicted residual sum of squares (PRESS) and correlation coefficient of prediction are 0.006 012 and 0.843 75, respectively. In contrast, the mean speed of the wavelet network modeling method is 3.15 seconds, and the mean PRESS and correlation coefficient of prediction are 0.002 048 and 0.953 19, respectively. These results demonstrate that the wavelet neural network modeling method is significantly superior to the full spectrum neural network modeling method. This study not only provides a new method for more efficient and accurate detection of the oil content of oil shale, but also indicates the potential for applying wavelet transform and neutral network in broad near-infrared spectrum analysis.

  2. Artificial neural networks a practical course

    CERN Document Server

    da Silva, Ivan Nunes; Andrade Flauzino, Rogerio; Liboni, Luisa Helena Bartocci; dos Reis Alves, Silas Franco

    2017-01-01

    This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

  3. Prediction of some physical and drying properties of terebinth fruit (Pistacia atlantica L.) using Artificial Neural Networks.

    Science.gov (United States)

    Kaveh, Mohammad; Chayjan, Reza Amiri

    2014-01-01

    Drying of terebinth fruit was conducted to provide microbiological stability, reduce product deterioration due to chemical reactions, facilitate storage and lower transportation costs. Because terebinth fruit is susceptible to heat, the selection of a suitable drying technology is a challenging task. Artificial neural networks (ANNs) are used as a nonlinear mapping structures for modelling and prediction of some physical and drying properties of terebinth fruit. Drying characteristics of terebinth fruit with an initial moisture content of 1.16 (d.b.) was studied in an infrared fluidized bed dryer. Different levels of air temperatures (40, 55 and 70°C), air velocities (0.93, 1.76 and 2.6 m/s) and infrared (IR) radiation powers (500, 1000 and 1500 W) were applied. In the present study, the application of Artificial Neural Network (ANN) for predicting the drying moisture diffusivity, energy consumption, shrinkage, drying rate and moisture ratio (output parameter for ANN modelling) was investigated. Air temperature, air velocity, IR radiation and drying time were considered as input parameters. The results revealed that to predict drying rate and moisture ratio a network with the TANSIG-LOGSIG-TANSIG transfer function and Levenberg-Marquardt (LM) training algorithm made the most accurate predictions for the terebinth fruit drying. The best results for ANN at predications were R2 = 0.9678 for drying rate, R2 = 0.9945 for moisture ratio, R2 = 0.9857 for moisture diffusivity and R2 = 0.9893 for energy consumption. Results indicated that artificial neural network can be used as an alternative approach for modelling and predicting of terebinth fruit drying parameters with high correlation. Also ANN can be used in optimization of the process.

  4. Artificial neural network model for prediction of safety performance indicators goals in nuclear plants

    Energy Technology Data Exchange (ETDEWEB)

    Souto, Kelling C.; Nunes, Wallace W. [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro, Nilopolis, RJ (Brazil). Lab. de Aplicacoes Computacionais; Machado, Marcelo D., E-mail: dornemd@eletronuclear.gov.b [ELETROBRAS Termonuclear S.A. (ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear - GCN.T

    2011-07-01

    Safety performance indicators have been developed to provide a quantitative indication of the performance and safety in various industry sectors. These indexes can provide assess to aspects ranging from production, design, and human performance up to management issues in accordance with policy, objectives and goals of the company. The use of safety performance indicators in nuclear power plants around the world is a reality. However, it is necessary to periodically set goal values. Such goals are targets relating to each of the indicators to be achieved by the plant over a predetermined period of operation. The current process of defining these goals is carried out by experts in a subjective way, based on actual data from the plant, and comparison with global indices. Artificial neural networks are computational techniques that present a mathematical model inspired by the neural structure of intelligent organisms that acquire knowledge through experience. This paper proposes an artificial neural network model aimed at predicting values of goals to be used in the evaluation of safety performance indicators for nuclear power plants. (author)

  5. Artificial neural network model for prediction of safety performance indicators goals in nuclear plants

    International Nuclear Information System (INIS)

    Souto, Kelling C.; Nunes, Wallace W.; Machado, Marcelo D.

    2011-01-01

    Safety performance indicators have been developed to provide a quantitative indication of the performance and safety in various industry sectors. These indexes can provide assess to aspects ranging from production, design, and human performance up to management issues in accordance with policy, objectives and goals of the company. The use of safety performance indicators in nuclear power plants around the world is a reality. However, it is necessary to periodically set goal values. Such goals are targets relating to each of the indicators to be achieved by the plant over a predetermined period of operation. The current process of defining these goals is carried out by experts in a subjective way, based on actual data from the plant, and comparison with global indices. Artificial neural networks are computational techniques that present a mathematical model inspired by the neural structure of intelligent organisms that acquire knowledge through experience. This paper proposes an artificial neural network model aimed at predicting values of goals to be used in the evaluation of safety performance indicators for nuclear power plants. (author)

  6. Artificial neural network approach to predicting engine-out emissions and performance parameters of a turbo charged diesel engine

    Directory of Open Access Journals (Sweden)

    Özener Orkun

    2013-01-01

    Full Text Available This study details the artificial neural network (ANN modelling of a diesel engine to predict the torque, power, brake-specific fuel consumption and pollutant emissions, including carbon dioxide, carbon monoxide, nitrogen oxides, total hydrocarbons and filter smoke number. To collect data for training and testing the neural network, experiments were performed on a four cylinder, four stroke compression ignition engine. A total of 108 test points were run on a dynamometer. For the first part of this work, a parameter packet was used as the inputs for the neural network, and satisfactory regression was found with the outputs (over ~95%, excluding total hydrocarbons. The second stage of this work addressed developing new networks with additional inputs for predicting the total hydrocarbons, and the regression was raised from 75 % to 90 %. This study shows that the ANN approach can be used for accurately predicting characteristic values of an internal combustion engine and that the neural network performance can be increased using additional related input data.

  7. Accurate prediction of the dew points of acidic combustion gases by using an artificial neural network model

    International Nuclear Information System (INIS)

    ZareNezhad, Bahman; Aminian, Ali

    2011-01-01

    This paper presents a new approach based on using an artificial neural network (ANN) model for predicting the acid dew points of the combustion gases in process and power plants. The most important acidic combustion gases namely, SO 3 , SO 2 , NO 2 , HCl and HBr are considered in this investigation. Proposed Network is trained using the Levenberg-Marquardt back propagation algorithm and the hyperbolic tangent sigmoid activation function is applied to calculate the output values of the neurons of the hidden layer. According to the network's training, validation and testing results, a three layer neural network with nine neurons in the hidden layer is selected as the best architecture for accurate prediction of the acidic combustion gases dew points over wide ranges of acid and moisture concentrations. The proposed neural network model can have significant application in predicting the condensation temperatures of different acid gases to mitigate the corrosion problems in stacks, pollution control devices and energy recovery systems.

  8. Advances in Artificial Neural Networks – Methodological Development and Application

    Directory of Open Access Journals (Sweden)

    Yanbo Huang

    2009-08-01

    Full Text Available Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological

  9. A neutron spectrum unfolding computer code based on artificial neural networks

    International Nuclear Information System (INIS)

    Ortiz-Rodríguez, J.M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J.M.; Vega-Carrillo, H.R.

    2014-01-01

    in the HTML format. NSDann unfolding code is freely available, upon request to the authors. - Highlights: • An optimized artificial neural network was designed using robust design of neural nets. • Neural network was trained to solve neutron spectrometry and dosimetry problems. • Knowledge stored at synaptic weights after training neural net was extracted. • Using knowledge extracted, a neutron spectrum unfolding code was designed. • Neutron spectrum of a 241AmBe neutron source was unfolded using the code based on neural nets technology

  10. Antioxidant Activity of Essential Oil Extracted by SC-CO₂ from Seeds of Trachyspermum ammi.

    Science.gov (United States)

    Singh, Aarti; Ahmad, Anees

    2017-07-11

    Bcakground: Extracts obtained from natural sources such as plants are of immense importance for humans. Methods: Therefore this study was conducted to obtain essential oil from the seeds of T. ammi by conventional and non-conventional methods. Hydrodistillation (HD), Solvent Extraction (SE), Ultrasonication (US), and Supercritical Carbon-dioxide (SC-CO₂) extraction techniques were used to extract essential oil from the powdered seeds of T. ammi . A quality control method for each extracted oil was developed using HPTLC, FTIR, and GC-MS. The optimization process was carried out using fractional factorial design (FFD) under which three parameters were considered: pressure (150, 175, and 300 bar), temperature (25, 30, and 40 °C), and CO₂ flow rate (5, 10, 15 g/min). Results: The yield of essential oil obtained from the HD, SE, US, and SC-CO₂ methods were 1.20%, 1.82%, 2.30%, and 2.64% v/w , respectively. Antioxidant activity was determined by the DPPH and superoxide scavenging methods and the IC 50 (Inhibition Concentration) values of the T. ammi oil sample were found to be 36.41 and 20.55 µg mL -1 , respectively. Conclusion: The present paper reported that different extraction methods lead to different yields of essential oils and the choice of a suitable method is extremely important to obtain more preferred compounds. The yield was higher in the SC-CO₂ method and it is a sustainable and green extraction technique. Many important constituents were detected in analytical techniques. Antioxidant activities carried out showed that essential oil extracted from T. ammi seeds possess significant antioxidant activity.

  11. Chemical composition of the essential oil and supercritical CO2 extract of Commiphora myrrha (Nees) Engl. and of Acorus calamus L.

    Science.gov (United States)

    Marongiu, Bruno; Piras, Alessandra; Porcedda, Silvia; Scorciapino, Andrea

    2005-10-05

    Volatile concentrates from the oleo-gum resin of Commiphora myrrha (Nees) Engl. and from the rhizomes of Acorus calamus were isolated by supercritical extraction with carbon dioxide. The volatile oil of myrrh was obtained at 9.0 MPa and 50 degrees C and at a CO2 flow of 1.5 kg/h. Acorus calamus was extracted at 9.0 MPa and 45 degrees C and at a CO2 flow of 1.6 kg/h. In both cases, an oil devoid of cuticular waxes was obtained with a single depressurization stage. The SFE myrrh oil had a yield, Y, of 3.2%. Its main components, identified and quantified by GC/MS, were furanoeudesma-1,3-diene, 34.9%; lindestrene, 12.9%; curzerene, 8.5%; and germacrone, 5.8%. The essential oils from the same starting material by hydrodistillation, HD, (Y = 2.8%) and by steam distillation, SD, (Y = 0.4%) were quite similar to the SFE extract. The main components of the SFE oil of A. calamus (Y = 3.5%) were acorenone, 13.4%; iso-acorone, 11.6%; (Z)-sesquilavandulol, 11.0%; dehydroxy isocalamendiol, 7.7%; and beta-asarone, 5.5%. The comparison with hydrodistilled (Y = 1.8%) and steam distilled (Y = 1.0%) oils revealed large differences in the content of iso-acorone and crypto-acorone.

  12. Antimicrobial Activity of Individual and Combined Essential Oils against Foodborne Pathogenic Bacteria.

    Science.gov (United States)

    Reyes-Jurado, Fatima; López-Malo, Aurelio; Palou, Enrique

    2016-02-01

    The antimicrobial activities of essential oils from Mexican oregano (Lippia berlandieri Schauer), mustard (Brassica nigra), and thyme (Thymus vulgaris) were evaluated alone and in binary combinations against Listeria monocytogenes, Staphylococcus aureus, or Salmonella Enteritidis. Chemical compositions of the essential oils were analyzed by gas chromatography-mass spectrometry. The MICs of the evaluated essential oils ranged from 0.05 to 0.50% (vol/vol). Mustard essential oil was the most effective, likely due to the presence of allyl isothiocyanate, identified as its major component. Furthermore, mustard essential oil exhibited synergistic effects when combined with either Mexican oregano or thyme essential oils (fractional inhibitory concentration indices of 0.75); an additive effect was obtained by combining thyme and Mexican oregano essential oils (fractional inhibitory concentration index = 1.00). These results suggest the potential of studied essential oil mixtures to inhibit microbial growth and preserve foods; however, their effect on sensory quality in selected foods compatible with their flavor needs to be assessed.

  13. Prediction of enthalpy of fusion of pure compounds using an Artificial Neural Network-Group Contribution method

    International Nuclear Information System (INIS)

    Gharagheizi, Farhad; Salehi, Gholam Reza

    2011-01-01

    Highlights: → An Artificial Neural Network-Group Contribution method is presented for prediction of enthalpy of fusion of pure compounds at their normal melting point. → Validity of the model is confirmed using a large evaluated data set containing 4157 pure compounds. → The average percent error of the model is equal to 2.65% in comparison with the experimental data. - Abstract: In this work, the Artificial Neural Network-Group Contribution (ANN-GC) method is applied to estimate the enthalpy of fusion of pure chemical compounds at their normal melting point. 4157 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the Squared Correlation Coefficient (R 2 ) of 0.999, Root Mean Square Error of 0.82 kJ/mol, and average absolute deviation lower than 2.65% for the estimated properties from existing experimental values.

  14. Artificial Neural Network Based Model of Photovoltaic Cell

    Directory of Open Access Journals (Sweden)

    Messaouda Azzouzi

    2017-03-01

    Full Text Available This work concerns the modeling of a photovoltaic system and the prediction of the sensitivity of electrical parameters (current, power of the six types of photovoltaic cells based on voltage applied between terminals using one of the best known artificial intelligence technique which is the Artificial Neural Networks. The results of the modeling and prediction have been well shown as a function of number of iterations and using different learning algorithms to obtain the best results. 

  15. Ground Motion Prediction Model Using Artificial Neural Network

    Science.gov (United States)

    Dhanya, J.; Raghukanth, S. T. G.

    2018-03-01

    This article focuses on developing a ground motion prediction equation based on artificial neural network (ANN) technique for shallow crustal earthquakes. A hybrid technique combining genetic algorithm and Levenberg-Marquardt technique is used for training the model. The present model is developed to predict peak ground velocity, and 5% damped spectral acceleration. The input parameters for the prediction are moment magnitude ( M w), closest distance to rupture plane ( R rup), shear wave velocity in the region ( V s30) and focal mechanism ( F). A total of 13,552 ground motion records from 288 earthquakes provided by the updated NGA-West2 database released by Pacific Engineering Research Center are utilized to develop the model. The ANN architecture considered for the model consists of 192 unknowns including weights and biases of all the interconnected nodes. The performance of the model is observed to be within the prescribed error limits. In addition, the results from the study are found to be comparable with the existing relations in the global database. The developed model is further demonstrated by estimating site-specific response spectra for Shimla city located in Himalayan region.

  16. Application of artificial neural network in radiographic diagnosis

    International Nuclear Information System (INIS)

    Piraino, D.; Amartur, S.; Richmond, B.; Schils, J.; Belhobek, G.

    1990-01-01

    This paper reports on an artificial neural network trained to rate the likelihood of different bone neoplasms when given a standard description of a radiograph. A three-layer back propagation algorithm was trained with descriptions of examples of bone neoplasms obtained from standard radiographic textbooks. Fifteen bone neoplasms obtained from clinical material were used as unknowns to test the trained artificial neural network. The artificial neural network correctly rated the pathologic diagnosis as the most likely diagnosis in 10 of the 15 unknown cases

  17. Real-space mapping of topological invariants using artificial neural networks

    Science.gov (United States)

    Carvalho, D.; García-Martínez, N. A.; Lado, J. L.; Fernández-Rossier, J.

    2018-03-01

    Topological invariants allow one to characterize Hamiltonians, predicting the existence of topologically protected in-gap modes. Those invariants can be computed by tracing the evolution of the occupied wave functions under twisted boundary conditions. However, those procedures do not allow one to calculate a topological invariant by evaluating the system locally, and thus require information about the wave functions in the whole system. Here we show that artificial neural networks can be trained to identify the topological order by evaluating a local projection of the density matrix. We demonstrate this for two different models, a one-dimensional topological superconductor and a two-dimensional quantum anomalous Hall state, both with spatially modulated parameters. Our neural network correctly identifies the different topological domains in real space, predicting the location of in-gap states. By combining a neural network with a calculation of the electronic states that uses the kernel polynomial method, we show that the local evaluation of the invariant can be carried out by evaluating a local quantity, in particular for systems without translational symmetry consisting of tens of thousands of atoms. Our results show that supervised learning is an efficient methodology to characterize the local topology of a system.

  18. Antioxidant, Anti-microbial Properties and Chemical Composition of Cumin Essential Oils Extracted by Three Methods

    OpenAIRE

    Fang Lianying; Wang Xiangxing; Guo Limin; Liu Qiang

    2018-01-01

    The purpose of this study is to evaluate the chemical composition, antioxidant and anti-bacterial activity of cumin essential oils (CEOs) extracted by different techniques, including supercritical carbon dioxide extraction (SCE), subcritical butane extraction (SBE) and traditional solvent extraction (SE). Our results indicated that CEOs are a valuable source of bioactive compounds, including cumin aldehyde, γ-terpinene and β-pinene. The most abundant components found in CEOs obtained by SCE a...

  19. Knowledge extraction from evolving spiking neural networks with rank order population coding.

    Science.gov (United States)

    Soltic, Snjezana; Kasabov, Nikola

    2010-12-01

    This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.

  20. Prediction of thermophysical properties of mixed refrigerants using artificial neural network

    International Nuclear Information System (INIS)

    Sencan, Arzu; Koese, Ismail Ilke; Selbas, Resat

    2011-01-01

    The determination of thermophysical properties of the refrigerants is very important for thermodynamic analysis of vapor compression refrigeration systems. In this paper, an artificial neural network (ANN) is proposed to determine properties as heat conduction coefficient, dynamic viscosity, kinematic viscosity, thermal diffusivity, density, specific heat capacity of refrigerants. Five alternative refrigerants are considered: R413A, R417A, R422A, R422D and R423A. The training and validation were performed with good accuracy. The thermophysical properties of the refrigerants are formulated using artificial neural network (ANN) methodology. Liquid and vapor thermophysical properties of refrigerants with new formulation obtained from ANN can be easily estimated. The method proposed offers more flexibility and therefore thermodynamic analysis of vapor compression refrigeration systems is fairly simplified.

  1. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

    Science.gov (United States)

    Saro, Lee; Woo, Jeon Seong; Kwan-Young, Oh; Moung-Jin, Lee

    2016-02-01

    The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using artificial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the artificial neural network model, `slope' yielded the highest weight value (1.330), and `aspect' yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

  2. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

    Directory of Open Access Journals (Sweden)

    Saro Lee

    2016-02-01

    Full Text Available The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS. These factors were analysed using artificial neural network (ANN and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50% and a test set (50%. A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10% was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%. Of the weights used in the artificial neural network model, ‘slope’ yielded the highest weight value (1.330, and ‘aspect’ yielded the lowest value (1.000. This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

  3. Supercritical carbon dioxide extraction of oil from Clanis bilineata ...

    African Journals Online (AJOL)

    AJL

    2012-02-16

    Feb 16, 2012 ... temperature, 35°C; pressure, 25 MPa; supercritical CO2 flow rate, 20 L/min and time, 60 min. ... methyl esters were recovered after solvent evaporation in vacuum ... Effect of time on extraction of the oil from C. bilineata larvae.

  4. DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY AUTHENTICATION USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Timur Kartbayev

    2017-03-01

    Full Text Available The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics’ changes over time (e.g., due to aging. The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.

  5. The cluster analysis based on non-teacher artificial neural network for the danger prediction of coal spontaneous fire

    Energy Technology Data Exchange (ETDEWEB)

    Wang, D.; Wang, J. [China University of Mining and Technology (China)

    1999-04-01

    This paper focuses on the problem of predicting the danger level of spontaneous fire in coal mines. Firstly, the inadequacy of the present artificial neural networks prediction model is analysed. Then a new cluster model based on non-teacher neural network is constructed according to the danger judgement standards given by experts. On this basis, by adopting the error square sum criterion and its algorithm, the corresponding prediction software is developed and applied in two working faces of Chaili Coal Mine. The forecasting result is importantly significant for the prevention of spontaneous fire. 4 refs., 1 fig., 1 tab.

  6. Wave transmission prediction of multilayer floating breakwater using neural network

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Patil, S.G.; Hegde, A.V.

    In the present study, an artificial neural network method has been applied for wave transmission prediction of multilayer floating breakwater. Two neural network models are constructed based on the parameters which influence the wave transmission...

  7. Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Shen Yang; Bax, Ad, E-mail: bax@nih.gov [National Institutes of Health, Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases (United States)

    2013-07-15

    A new program, TALOS-N, is introduced for predicting protein backbone torsion angles from NMR chemical shifts. The program relies far more extensively on the use of trained artificial neural networks than its predecessor, TALOS+. Validation on an independent set of proteins indicates that backbone torsion angles can be predicted for a larger, {>=}90 % fraction of the residues, with an error rate smaller than ca 3.5 %, using an acceptance criterion that is nearly two-fold tighter than that used previously, and a root mean square difference between predicted and crystallographically observed ({phi}, {psi}) torsion angles of ca 12 Masculine-Ordinal-Indicator . TALOS-N also reports sidechain {chi}{sup 1} rotameric states for about 50 % of the residues, and a consistency with reference structures of 89 %. The program includes a neural network trained to identify secondary structure from residue sequence and chemical shifts.

  8. Applications of Artificial Neural Network for the Prediction of Pool Boiling Curves

    International Nuclear Information System (INIS)

    Su, Guanghui; Fukuda, K.; Morita, K.

    2002-01-01

    Artificial neural network (ANN) has the advantage that the best-fit correlations of experimental data will no longer be necessary for predicting unknowns from the known parameters. The ANN was applied to predict the pool boiling curves in this paper. The database of experimental data presented by Berenson, Dhuga et al., and Bui and Dhir etc. were used in the analysis. The database is subdivided in two subsets. The first subset is used to train the network and the second one is used to test the network after the training process. The input parameters of the ANN are: wall superheat ΔT w , surface roughness, steady/transient heating/transient cooling, subcooling, Surface inclination and pressure. The output parameter is heat flux q. The proposed methodology allows us to achieve the accuracy that satisfies the user's convergence criterion and it is suitable for pool boiling curve data processing. (authors)

  9. Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach

    DEFF Research Database (Denmark)

    Buus, S.; Lauemoller, S.L.; Worning, Peder

    2003-01-01

    We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict bind...... of an iterative feedback loop whereby advanced, computational bioinformatics optimize experimental strategy, and vice versa....

  10. Antimicrobial activity of essential oils of cultivated oregano (Origanum vulgare), sage (Salvia officinalis), and thyme (Thymus vulgaris) against clinical isolates of Escherichia coli, Klebsiella oxytoca, and Klebsiella pneumoniae.

    Science.gov (United States)

    Fournomiti, Maria; Kimbaris, Athanasios; Mantzourani, Ioanna; Plessas, Stavros; Theodoridou, Irene; Papaemmanouil, Virginia; Kapsiotis, Ioannis; Panopoulou, Maria; Stavropoulou, Elisavet; Bezirtzoglou, Eugenia E; Alexopoulos, Athanasios

    2015-01-01

    Oregano (Origanum vulgare), sage (Salvia officinalis), and thyme (Thymus vulgaris) are aromatic plants with ornamental, culinary, and phytotherapeutic use all over the world. In Europe, they are traditionally used in the southern countries, particularly in the Mediterranean region. The antimicrobial activities of the essential oils (EOs) derived from those plants have captured the attention of scientists as they could be used as alternatives to the increasing resistance of traditional antibiotics against pathogen infections. Therefore, significant interest in the cultivation of various aromatic and medicinal plants is recorded during the last years. However, to gain a proper and marketable chemotype various factors during the cultivation should be considered as the geographical morphology, climatic, and farming conditions. In this frame, we have studied the antimicrobial efficiency of the EOs from oregano, sage, and thyme cultivated under different conditions in a region of NE Greece in comparison to the data available in literature. Plants were purchased from a certified supplier, planted, and cultivated in an experimental field under different conditions and harvested after 9 months. EOs were extracted by using a Clevenger apparatus and tested for their antibacterial properties (Minimum inhibitory concentration - MIC) against clinical isolates of multidrug resistant Escherichia coli (n=27), Klebsiella oxytoca (n=7), and Klebsiella pneumoniae (n=16) strains by using the broth microdilution assay. Our results showed that the most sensitive organism was K. oxytoca with a mean value of MIC of 0.9 µg/mL for oregano EOs and 8.1 µg/mL for thyme. The second most sensitive strain was K. pneumoniae with mean MIC values of 9.5 µg/mL for thyme and 73.5 µg/mL for oregano EOs. E. coli strains were among the most resistant to EOs antimicrobial action as the observed MICs were 24.8-28.6 µg/mL for thyme and above 125 µg/mL for thyme and sage. Most efficient were the EOs

  11. Artificial neural networks in prediction of mechanical behavior of concrete at high temperature

    International Nuclear Information System (INIS)

    Mukherjee, A.; Nag Biswas, S.

    1997-01-01

    The behavior of concrete structures that are exposed to extreme thermo-mechanical loading is an issue of great importance in nuclear engineering. The mechanical behavior of concrete at high temperature is non-linear. The properties that regulate its response are highly temperature dependent and extremely complex. In addition, the constituent materials, e.g. aggregates, influence the response significantly. Attempts have been made to trace the stress-strain curve through mathematical models and rheological models. However, it has been difficult to include all the contributing factors in the mathematical model. This paper examines a new programming paradigm, artificial neural networks, for the problem. Implementing a feedforward network and backpropagation algorithm the stress-strain relationship of the material is captured. The neural networks for the prediction of uniaxial behavior of concrete at high temperature has been presented here. The results of the present investigation are very encouraging. (orig.)

  12. A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

    Science.gov (United States)

    Huang, Darong; Bai, Xing-Rong

    Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

  13. Influence of Food Characteristics and Food Additives on the Antimicrobial Effect of Garlic and Oregano Essential Oils.

    Science.gov (United States)

    García-Díez, Juan; Alheiro, Joana; Pinto, Ana Luisa; Soares, Luciana; Falco, Virgilio; Fraqueza, Maria João; Patarata, Luis

    2017-06-10

    Utilization of essential oils (EOs) as antimicrobial agents against foodborne disease has gained importance, for their use as natural preservatives. Since potential interactions between EOs and food characteristics may affect their antimicrobial properties, the present work studies the influence of fat, protein, pH, a w and food additives on the antimicrobial effect of oregano and garlic EOs against Salmonella spp. and Listeria monocytogenes. Results showed that protein, pH, a w , presence of beef extract, sodium lactate and nitrates did not influence their antimicrobial effect. In contrast, the presence of pork fat had a negative effect against both EOs associated with their dilution of the lipid content. The addition of food phosphates also exerts a negative effect against EOs probably associated with their emulsification properties as observed with the addition of fat. The results may help the food industry to select more appropriate challenges to guarantee the food safety of foodstuffs.

  14. Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Lei Feng

    2018-06-01

    Full Text Available Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874–1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN, evolutionary neural network (ENN, extreme learning machine (ELM, general regression neural network (GRNN and radial basis neural network (RBNN were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts.

  15. Cotton genotypes selection through artificial neural networks.

    Science.gov (United States)

    Júnior, E G Silva; Cardoso, D B O; Reis, M C; Nascimento, A F O; Bortolin, D I; Martins, M R; Sousa, L B

    2017-09-27

    Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explored in genetic improvement of cotton. Thus, this study was carried out with the objective of presenting the use of artificial neural networks as auxiliary tools in the improvement of the cotton to improve fiber quality. To demonstrate the applicability of this approach, this research was carried out using the evaluation data of 40 genotypes. In order to classify the genotypes for fiber quality, the artificial neural networks were trained with replicate data of 20 genotypes of cotton evaluated in the harvests of 2013/14 and 2014/15, regarding fiber length, uniformity of length, fiber strength, micronaire index, elongation, short fiber index, maturity index, reflectance degree, and fiber quality index. This quality index was estimated by means of a weighted average on the determined score (1 to 5) of each characteristic of the HVI evaluated, according to its industry standards. The artificial neural networks presented a high capacity of correct classification of the 20 selected genotypes based on the fiber quality index, so that when using fiber length associated with the short fiber index, fiber maturation, and micronaire index, the artificial neural networks presented better results than using only fiber length and previous associations. It was also observed that to submit data of means of new genotypes to the neural networks trained with data of repetition, provides better results of classification of the genotypes. When observing the results obtained in the present study, it was verified that the artificial neural networks present great potential to be used in the different stages of a genetic improvement program of the cotton, aiming at the improvement of the fiber quality of the future cultivars.

  16. Ethanol production from steam exploded rapeseed straw and the process simulation using artificial neural networks

    DEFF Research Database (Denmark)

    Talebnia, Farid; Mighani, Moein; Rahimnejad, Mostafa

    2015-01-01

    and 67% of maximum theoretical value. Next, data of the experimental runs were exploited for modeling the processes by artificial neural networks (ANNs) and performance of the developed models was evaluated. The ANN-based models showed a great potential for time-course prediction of the studied processes....... Efficiency of the joint network for simulating the whole process was also determined and promising results were obtained....

  17. Kinetic models for supercritical CO2 extraction of oilseeds - a review

    Directory of Open Access Journals (Sweden)

    B. Nagy

    2011-01-01

    Full Text Available The supercritical fluid extraction of oilseeds is gaining increasing interest in commercial application for the last few decades, most particularly thanks to technical and environmental advantages of supercritical fluid extraction technology compared to current extraction methods with organic solvents. Furthermore, CO2 as a solvent is generally recognized as safe (GRAS. At present moment, supercritical fluid extractions on a commercial scale are limited to decaffeination, production of soluble hops extracts, sesame seed oil production and extraction of certain petroleum products. When considering industrial application, it is essential to test the applicability of the appropriate model for supercritical fluid extraction of oilseeds used for scale up of laboratory data to industrial design purposes. The aim of this paper is to review the most significant kinetic models reported in the literature for supercritical fluid extraction.

  18. Artificial Neural Networks and Instructional Technology.

    Science.gov (United States)

    Carlson, Patricia A.

    1991-01-01

    Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

  19. The Prediction of Bandwidth On Need Computer Network Through Artificial Neural Network Method of Backpropagation

    Directory of Open Access Journals (Sweden)

    Ikhthison Mekongga

    2014-02-01

    Full Text Available The need for bandwidth has been increasing recently. This is because the development of internet infrastructure is also increasing so that we need an economic and efficient provider system. This can be achieved through good planning and a proper system. The prediction of the bandwidth consumption is one of the factors that support the planning for an efficient internet service provider system. Bandwidth consumption is predicted using ANN. ANN is an information processing system which has similar characteristics as the biologic al neural network.  ANN  is  chosen  to  predict  the  consumption  of  the  bandwidth  because  ANN  has  good  approachability  to  non-linearity.  The variable used in ANN is the historical load data. A bandwidth consumption information system was built using neural networks  with a backpropagation algorithm to make the use of bandwidth more efficient in the future both in the rental rate of the bandwidth and in the usage of the bandwidth.Keywords: Forecasting, Bandwidth, Backpropagation

  20. Determination of volume fraction in biphasic flows oil-gas and water-gas using artificial neural network and gamma densitometry

    International Nuclear Information System (INIS)

    Peixoto, Philippe Netto Belache

    2016-01-01

    This study presents a methodology based on the principles of gamma ray attenuation to identify volume fractions in biphasic systems composed of oil-gas-water and gas which are found in the offshore oil industry. This methodology is based on the acknowledgment counts per second on the photopeak energy using a detection system composed of a NaI (Tl) detector, a source of 137 Cs without collimation positioned at 180 ° relative to the detector on a smooth stratified flow regime. The mathematical modeling for computational simulation using the code MCNP-X was performed using the experimental measurements of the detector characteristics (energy resolution and efficiency), characteristics of the material water and oil (density and coefficient attenuation) and measurement of the volume fractions. To predict these fractions were used artificial neural networks (ANNs), and to obtain an adequate training the ANNs for the prediction of volume fractions were simulated a larger number of volume fractions in MCNP-X. The experimental data were used in the set data necessary for validation of ANNs and the data generated using the computer code MCNP-X were used in training and test sets of the ANNs. Were used ANNs of type feed-forward Multilayer Perceptron (MLP) and analyzed two functions of training, Levenberg-Marquardt (LM) and gradient descent with momentum (GDM), both using the Backpropagation training algorithm. The ANNs identified correctly the volume fractions of the multiphase system with mean relative errors lower than 1.21 %, enabling the application of this methodology for this purpose. (author)

  1. Generation of artificial accelerograms using neural networks for data of Iran

    International Nuclear Information System (INIS)

    Bargi, Kh.; Loux, C.; Rohani, H.

    2002-01-01

    A new method for generation of artificial earthquake accelerograms from response spectra is proposed by Ghaboussi and Lin in 1997 using neural networks. In this paper the methodology has been extended and enhanced for data of Iran. For this purpose, first 40 records of Iran acceleration is chosen, then an RBF neural network which called generalized regression neural network learn the inverse mapping directly from the response spectrum to the Discrete Cosine Transform of accelerograms. Discrete Cosine Transform has been used as an assisting device to extract the content of frequency domain. Learning of network is reasonable and a generalized regression neural network learns it in a few second. Outputs are presented to demonstrate the performance of this method and show its capabilities

  2. Performance Evaluation of 14 Neural Network Architectures Used for Predicting Heat Transfer Characteristics of Engine Oils

    Science.gov (United States)

    Al-Ajmi, R. M.; Abou-Ziyan, H. Z.; Mahmoud, M. A.

    2012-01-01

    This paper reports the results of a comprehensive study that aimed at identifying best neural network architecture and parameters to predict subcooled boiling characteristics of engine oils. A total of 57 different neural networks (NNs) that were derived from 14 different NN architectures were evaluated for four different prediction cases. The NNs were trained on experimental datasets performed on five engine oils of different chemical compositions. The performance of each NN was evaluated using a rigorous statistical analysis as well as careful examination of smoothness of predicted boiling curves. One NN, out of the 57 evaluated, correctly predicted the boiling curves for all cases considered either for individual oils or for all oils taken together. It was found that the pattern selection and weight update techniques strongly affect the performance of the NNs. It was also revealed that the use of descriptive statistical analysis such as R2, mean error, standard deviation, and T and slope tests, is a necessary but not sufficient condition for evaluating NN performance. The performance criteria should also include inspection of the smoothness of the predicted curves either visually or by plotting the slopes of these curves.

  3. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

    Science.gov (United States)

    Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

    2017-04-10

    This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

  4. The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents

    Science.gov (United States)

    Valizadeh, Maryam; Sohrabi, Mahmoud Reza

    2018-03-01

    In the present study, artificial neural networks (ANNs) and support vector regression (SVR) as intelligent methods coupled with UV spectroscopy for simultaneous quantitative determination of Dorzolamide (DOR) and Timolol (TIM) in eye drop. Several synthetic mixtures were analyzed for validating the proposed methods. At first, neural network time series, which one type of network from the artificial neural network was employed and its efficiency was evaluated. Afterwards, the radial basis network was applied as another neural network. Results showed that the performance of this method is suitable for predicting. Finally, support vector regression was proposed to construct the Zilomole prediction model. Also, root mean square error (RMSE) and mean recovery (%) were calculated for SVR method. Moreover, the proposed methods were compared to the high-performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them. Also, the effect of interferences was investigated in spike solutions.

  5. A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator

    International Nuclear Information System (INIS)

    Almonacid, F.; Pérez-Higueras, P.J.; Fernández, Eduardo F.; Hontoria, L.

    2014-01-01

    Highlights: • The output of the majority of renewables energies depends on the variability of the weather conditions. • The short-term forecast is going to be essential for effectively integrating solar energy sources. • A new method based on artificial neural network to predict the power output of a PV generator one hour ahead is proposed. • This new method is based on dynamic artificial neural network to predict global solar irradiance and the air temperature. • The methodology developed can be used to estimate the power output of a PV generator with a satisfactory margin of error. - Abstract: One of the problems of some renewables energies is that the output of these kinds of systems is non-dispatchable depending on variability of weather conditions that cannot be predicted and controlled. From this point of view, the short-term forecast is going to be essential for effectively integrating solar energy sources, being a very useful tool for the reliability and stability of the grid ensuring that an adequate supply is present. In this paper a new methodology for forecasting the output of a PV generator one hour ahead based on dynamic artificial neural network is presented. The results of this study show that the proposed methodology could be used to forecast the power output of PV systems one hour ahead with an acceptable degree of accuracy

  6. Financial time series prediction using spiking neural networks.

    Science.gov (United States)

    Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

    2014-01-01

    In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

  7. Artificial Neural Networks for SCADA Data based Load Reconstruction (poster)

    NARCIS (Netherlands)

    Hofemann, C.; Van Bussel, G.J.W.; Veldkamp, H.

    2011-01-01

    If at least one reference wind turbine is available, which provides sufficient information about the wind turbine loads, the loads acting on the neighbouring wind turbines can be predicted via an artificial neural network (ANN). This research explores the possibilities to apply such a network not

  8. Using neural networks for prediction of nuclear parameters

    Energy Technology Data Exchange (ETDEWEB)

    Pereira Filho, Leonidas; Souto, Kelling Cabral, E-mail: leonidasmilenium@hotmail.com, E-mail: kcsouto@bol.com.br [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro (IFRJ), Rio de Janeiro, RJ (Brazil); Machado, Marcelo Dornellas, E-mail: dornemd@eletronuclear.gov.br [Eletrobras Termonuclear S.A. (GCN.T/ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear

    2013-07-01

    Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)

  9. Using neural networks for prediction of nuclear parameters

    International Nuclear Information System (INIS)

    Pereira Filho, Leonidas; Souto, Kelling Cabral; Machado, Marcelo Dornellas

    2013-01-01

    Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)

  10. Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    El Haddad, J. [Univ. Bordeaux, LOMA, UMR 5798, F-33400 Talence (France); CNRS, LOMA, UMR 5798, F-33400 Talence (France); Villot-Kadri, M.; Ismaël, A.; Gallou, G. [IVEA Solution, Centre Scientifique d' Orsay, Bât 503, 91400 Orsay (France); Michel, K.; Bruyère, D.; Laperche, V. [BRGM, Service Métrologie, Monitoring et Analyse, 3 avenue Claude Guillemin, B.P 36009, 45060 Orléans Cedex (France); Canioni, L. [Univ. Bordeaux, LOMA, UMR 5798, F-33400 Talence (France); CNRS, LOMA, UMR 5798, F-33400 Talence (France); Bousquet, B., E-mail: bruno.bousquet@u-bordeaux1.fr [Univ. Bordeaux, LOMA, UMR 5798, F-33400 Talence (France); CNRS, LOMA, UMR 5798, F-33400 Talence (France)

    2013-01-01

    Nowadays, due to environmental concerns, fast on-site quantitative analyses of soils are required. Laser induced breakdown spectroscopy is a serious candidate to address this challenge and is especially well suited for multi-elemental analysis of heavy metals. However, saturation and matrix effects prevent from a simple treatment of the LIBS data, namely through a regular calibration curve. This paper details the limits of this approach and consequently emphasizes the advantage of using artificial neural networks well suited for non-linear and multi-variate calibration. This advanced method of data analysis is evaluated in the case of real soil samples and on-site LIBS measurements. The selection of the LIBS data as input data of the network is particularly detailed and finally, resulting errors of prediction lower than 20% for aluminum, calcium, copper and iron demonstrate the good efficiency of the artificial neural networks for on-site quantitative LIBS of soils. - Highlights: ► We perform on-site quantitative LIBS analysis of soil samples. ► We demonstrate that univariate analysis is not convenient. ► We exploit artificial neural networks for LIBS analysis. ► Spectral lines other than the ones from the analyte must be introduced.

  11. Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy

    International Nuclear Information System (INIS)

    El Haddad, J.; Villot-Kadri, M.; Ismaël, A.; Gallou, G.; Michel, K.; Bruyère, D.; Laperche, V.; Canioni, L.; Bousquet, B.

    2013-01-01

    Nowadays, due to environmental concerns, fast on-site quantitative analyses of soils are required. Laser induced breakdown spectroscopy is a serious candidate to address this challenge and is especially well suited for multi-elemental analysis of heavy metals. However, saturation and matrix effects prevent from a simple treatment of the LIBS data, namely through a regular calibration curve. This paper details the limits of this approach and consequently emphasizes the advantage of using artificial neural networks well suited for non-linear and multi-variate calibration. This advanced method of data analysis is evaluated in the case of real soil samples and on-site LIBS measurements. The selection of the LIBS data as input data of the network is particularly detailed and finally, resulting errors of prediction lower than 20% for aluminum, calcium, copper and iron demonstrate the good efficiency of the artificial neural networks for on-site quantitative LIBS of soils. - Highlights: ► We perform on-site quantitative LIBS analysis of soil samples. ► We demonstrate that univariate analysis is not convenient. ► We exploit artificial neural networks for LIBS analysis. ► Spectral lines other than the ones from the analyte must be introduced

  12. Modelling and Predicting the Breaking Strength and Mass Irregularity of Cotton Rotor-Spun Yarns Containing Cotton Fiber Recovered from Ginning Process by Using Artificial Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Mohsen Shanbeh

    2011-01-01

    Full Text Available One of the main methods to reduce the production costs is waste recycling which is the most important challenge for the future. Cotton wastes collected from ginning process have desirable properties which could be used during spinning process. The purpose of this study was to develop predictive models of breaking strength and mass irregularity (CV% of cotton waste rotor-spun yarns containing cotton waste collected from ginning process by using the artificial neural network trained with backpropagation algorithm. Artificial neural network models have been developed based on rotor diameter, rotor speed, navel type, opener roller speed, ginning waste proportion and yarn linear density as input parameters. The parameters of artificial neural network model, namely, learning, and momentum rate, number of hidden layers and number of hidden processing elements (neurons were optimized to get the best predictive models. The findings showed that the breaking strength and mass irregularity of rotor spun yarns could be predicted satisfactorily by artificial neural network. The maximum error in predicting the breaking strength and mass irregularity of testing data was 8.34% and 6.65%, respectively.

  13. Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran)

    International Nuclear Information System (INIS)

    Choobbasti, A J; Farrokhzad, F; Barari, A

    2009-01-01

    Investigations of failures of soil masses are subjects touching both geology and engineering. These investigations call the joint efforts of engineering geologists and geotechnical engineers. Geotechnical engineers have to pay particular attention to geology, ground water, and shear strength of soils in assessing slope stability. Artificial neural networks (ANNs) are very sophisticated modeling techniques, capable of modeling extremely complex functions. In particular, neural networks are nonlinear. In this research, with respect to the above advantages, ANN systems consisting of multilayer perceptron networks are developed to predict slope stability in a specified location, based on the available site investigation data from Noabad, Mazandaran, Iran. Several important parameters, including total stress, effective stress, angle of slope, coefficient of cohesion, internal friction angle, and horizontal coefficient of earthquake, were used as the input parameters, while the slope stability was the output parameter. The results are compared with the classical methods of limit equilibrium to check the ANN model's validity. (author)

  14. Quality of Cosmetic Argan Oil Extracted by Supercritical Fluid Extraction from Argania spinosa L.

    Directory of Open Access Journals (Sweden)

    Chouaa Taribak

    2013-01-01

    Full Text Available Argan oil has been extracted using supercritical CO2. The influence of the variables pressure (100, 200, 300, and 400 bar and temperature (35, 45, 55°C was investigated. The best extraction yields were achieved at a temperature of 45°C and a pressure of 400 bar. The argan oil extracts were characterized in terms of acid, peroxide and iodine values, total tocopherol, carotene, and fatty acids content. Significant compositional differences were not observed between the oil samples obtained using different pressures and temperatures. The antioxidant capacity of the argan oil samples was high in comparison to those of walnut, almond, hazelnut, and peanut oils and comparable to that of pistachio oil. The physicochemical parameters of the extracted oils obtained by SFE, Soxhlet, and traditional methods are comparable. The technique used for oil processing does not therefore markedly alter the quality of argan oil.

  15. Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Federico Nuñez-Piña

    2018-01-01

    Full Text Available The problem of assigning buffers in a production line to obtain an optimum production rate is a combinatorial problem of type NP-Hard and it is known as Buffer Allocation Problem. It is of great importance for designers of production systems due to the costs involved in terms of space requirements. In this work, the relationship among the number of buffer slots, the number of work stations, and the production rate is studied. Response surface methodology and artificial neural network were used to develop predictive models to find optimal throughput values. 360 production rate values for different number of buffer slots and workstations were used to obtain a fourth-order mathematical model and four hidden layers’ artificial neural network. Both models have a good performance in predicting the throughput, although the artificial neural network model shows a better fit (R=1.0000 against the response surface methodology (R=0.9996. Moreover, the artificial neural network produces better predictions for data not utilized in the models construction. Finally, this study can be used as a guide to forecast the maximum or near maximum throughput of production lines taking into account the buffer size and the number of machines in the line.

  16. Chemical Composition of Herbal Macerates and Corresponding Commercial Essential Oils and Their Effect on Bacteria Escherichia coli

    Directory of Open Access Journals (Sweden)

    Marietta Białoń

    2017-11-01

    Full Text Available This study addresses the chemical composition of some commercial essential oils (clove, juniper, oregano, and marjoram oils, as well as appropriate herbal extracts obtained in the process of cold maceration and their biological activity against selected Escherichia coli strains: E. coli ATTC 25922, E. coli ATTC 10536, and E. coli 127 isolated from poultry waste. On the basis of the gas chromatography-mass spectrometry (GCMS analysis, it was found that the commercial essential oils revealed considerable differences in terms of the composition and diversity of terpenes, terpenoids and sesquiterpenes as compared with the extracts obtained from plant material. The commercial clove, oregano, and marjoram oils showed antibacterial properties against all the tested strains of E. coli. However, these strains were not sensitive to essential oils obtained from the plant material in the process of maceration. The tested strains of E. coli show a high sensitivity, mainly against monoterpenes (α-pinene, β-pinene, α,β,γ-terpinene, limonene and some terpenoids (thymol, carvacrol. The commercial juniper oil contained mainly monoterpenes and monoterpenoids, while the extracts contained lower amounts of monoterpenes and high amounts of sesquiterpenes—the anti-microbiotic properties of the juniper herbal extract seem to be caused by the synergistic activity of mono- and sesquiterpenes.

  17. SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network

    International Nuclear Information System (INIS)

    Shen Yang; Bax, Ad

    2010-01-01

    NMR chemical shifts provide important local structural information for proteins and are key in recently described protein structure generation protocols. We describe a new chemical shift prediction program, SPARTA+, which is based on artificial neural networking. The neural network is trained on a large carefully pruned database, containing 580 proteins for which high-resolution X-ray structures and nearly complete backbone and 13 C β chemical shifts are available. The neural network is trained to establish quantitative relations between chemical shifts and protein structures, including backbone and side-chain conformation, H-bonding, electric fields and ring-current effects. The trained neural network yields rapid chemical shift prediction for backbone and 13 C β atoms, with standard deviations of 2.45, 1.09, 0.94, 1.14, 0.25 and 0.49 ppm for δ 15 N, δ 13 C', δ 13 C α , δ 13 C β , δ 1 H α and δ 1 H N , respectively, between the SPARTA+ predicted and experimental shifts for a set of eleven validation proteins. These results represent a modest but consistent improvement (2-10%) over the best programs available to date, and appear to be approaching the limit at which empirical approaches can predict chemical shifts.

  18. Artificial neural networks in variable process control: application in particleboard manufacture

    Energy Technology Data Exchange (ETDEWEB)

    Esteban, L. G.; Garcia Fernandez, F.; Palacios, P. de; Conde, M.

    2009-07-01

    Artificial neural networks are an efficient tool for modelling production control processes using data from the actual production as well as simulated or design of experiments data. In this study two artificial neural networks were combined with the control process charts and it was checked whether the data obtained by the networks were valid for variable process control in particleboard manufacture. The networks made it possible to obtain the mean and standard deviation of the internal bond strength of the particleboard within acceptable margins using known data of thickness, density, moisture content, swelling and absorption. The networks obtained met the acceptance criteria for test values from non-standard test methods, as well as the criteria for using these values in statistical process control. (Author) 47 refs.

  19. Fault diagnosis in nuclear power plants using an artificial neural network technique

    International Nuclear Information System (INIS)

    Chou, H.P.; Prock, J.; Bonfert, J.P.

    1993-01-01

    Application of artificial intelligence (AI) computational techniques, such as expert systems, fuzzy logic, and neural networks in diverse areas has taken place extensively. In the nuclear industry, the intended goal for these AI techniques is to improve power plant operational safety and reliability. As a computerized operator support tool, the artificial neural network (ANN) approach is an emerging technology that currently attracts a large amount of interest. The ability of ANNs to extract the input/output relation of a complicated process and the superior execution speed of a trained ANN motivated this study. The goal was to develop neural networks for sensor and process faults diagnosis with the potential of implementing as a component of a real-time operator support system LYDIA, early sensor and process fault detection and diagnosis

  20. Prediction of small hydropower plant power production in Himreen Lake dam (HLD using artificial neural network

    Directory of Open Access Journals (Sweden)

    Ali Thaeer Hammid

    2018-03-01

    Full Text Available In developing countries, the power production is properly less than the request of power or load, and sustaining a system stability of power production is a trouble quietly. Sometimes, there is a necessary development to the correct quantity of load demand to retain a system of power production steadily. Thus, Small Hydropower Plant (SHP includes a Kaplan turbine was verified to explore its applicability. This paper concentrates on applying on Artificial Neural Networks (ANNs by approaching of Feed-Forward, Back-Propagation to make performance predictions of the hydropower plant at the Himreen lake dam-Diyala in terms of net turbine head, flow rate of water and power production that data gathered during a research over a 10 year period. The model studies the uncertainties of inputs and output operation and there's a designing to network structure and then trained by means of the entire of 3570 experimental and observed data. Furthermore, ANN offers an analyzing and diagnosing instrument effectively to model performance of the nonlinear plant. The study suggests that the ANN may predict the performance of the plant with a correlation coefficient (R between the variables of predicted and observed output that would be higher than 0.96. Keywords: Himreen Lake Dam, Small Hydropower plants, Artificial Neural Networks, Feed forward-back propagation model, Generation system's prediction

  1. IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK FOR FACE RECOGNITION USING GABOR FEATURE EXTRACTION

    Directory of Open Access Journals (Sweden)

    Muthukannan K

    2013-11-01

    Full Text Available Face detection and recognition is the first step for many applications in various fields such as identification and is used as a key to enter into the various electronic devices, video surveillance, and human computer interface and image database management. This paper focuses on feature extraction in an image using Gabor filter and the extracted image feature vector is then given as an input to the neural network. The neural network is trained with the input data. The Gabor wavelet concentrates on the important components of the face including eye, mouth, nose, cheeks. The main requirement of this technique is the threshold, which gives privileged sensitivity. The threshold values are the feature vectors taken from the faces. These feature vectors are given into the feed forward neural network to train the network. Using the feed forward neural network as a classifier, the recognized and unrecognized faces are classified. This classifier attains a higher face deduction rate. By training more input vectors the system proves to be effective. The effectiveness of the proposed method is demonstrated by the experimental results.

  2. Antioxidant Activity of Essential Oil Extracted by SC-CO2 from Seeds of Trachyspermum ammi

    Directory of Open Access Journals (Sweden)

    Aarti Singh

    2017-07-01

    Full Text Available Bcakground: Extracts obtained from natural sources such as plants are of immense importance for humans. Methods: Therefore this study was conducted to obtain essential oil from the seeds of T. ammi by conventional and non-conventional methods. Hydrodistillation (HD, Solvent Extraction (SE, Ultrasonication (US, and Supercritical Carbon-dioxide (SC-CO2 extraction techniques were used to extract essential oil from the powdered seeds of T. ammi. A quality control method for each extracted oil was developed using HPTLC, FTIR, and GC-MS. The optimization process was carried out using fractional factorial design (FFD under which three parameters were considered: pressure (150, 175, and 300 bar, temperature (25, 30, and 40 °C, and CO2 flow rate (5, 10, 15 g/min. Results: The yield of essential oil obtained from the HD, SE, US, and SC-CO2 methods were 1.20%, 1.82%, 2.30%, and 2.64% v/w, respectively. Antioxidant activity was determined by the DPPH and superoxide scavenging methods and the IC50 (Inhibition Concentration values of the T. ammi oil sample were found to be 36.41 and 20.55 µg mL−1, respectively. Conclusion: The present paper reported that different extraction methods lead to different yields of essential oils and the choice of a suitable method is extremely important to obtain more preferred compounds. The yield was higher in the SC-CO2 method and it is a sustainable and green extraction technique. Many important constituents were detected in analytical techniques. Antioxidant activities carried out showed that essential oil extracted from T. ammi seeds possess significant antioxidant activity.

  3. Antioxidant Activity of Essential Oil Extracted by SC-CO2 from Seeds of Trachyspermum ammi

    Science.gov (United States)

    Singh, Aarti; Ahmad, Anees

    2017-01-01

    Bcakground: Extracts obtained from natural sources such as plants are of immense importance for humans. Methods: Therefore this study was conducted to obtain essential oil from the seeds of T. ammi by conventional and non-conventional methods. Hydrodistillation (HD), Solvent Extraction (SE), Ultrasonication (US), and Supercritical Carbon-dioxide (SC-CO2) extraction techniques were used to extract essential oil from the powdered seeds of T. ammi. A quality control method for each extracted oil was developed using HPTLC, FTIR, and GC-MS. The optimization process was carried out using fractional factorial design (FFD) under which three parameters were considered: pressure (150, 175, and 300 bar), temperature (25, 30, and 40 °C), and CO2 flow rate (5, 10, 15 g/min). Results: The yield of essential oil obtained from the HD, SE, US, and SC-CO2 methods were 1.20%, 1.82%, 2.30%, and 2.64% v/w, respectively. Antioxidant activity was determined by the DPPH and superoxide scavenging methods and the IC50 (Inhibition Concentration) values of the T. ammi oil sample were found to be 36.41 and 20.55 µg mL−1, respectively. Conclusion: The present paper reported that different extraction methods lead to different yields of essential oils and the choice of a suitable method is extremely important to obtain more preferred compounds. The yield was higher in the SC-CO2 method and it is a sustainable and green extraction technique. Many important constituents were detected in analytical techniques. Antioxidant activities carried out showed that essential oil extracted from T. ammi seeds possess significant antioxidant activity. PMID:28930268

  4. Advanced approach to numerical forecasting using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Michael Štencl

    2009-01-01

    Full Text Available Current global market is driven by many factors, such as the information age, the time and amount of information distributed by many data channels it is practically impossible analyze all kinds of incoming information flows and transform them to data with classical methods. New requirements could be met by using other methods. Once trained on patterns artificial neural networks can be used for forecasting and they are able to work with extremely big data sets in reasonable time. The patterns used for learning process are samples of past data. This paper uses Radial Basis Functions neural network in comparison with Multi Layer Perceptron network with Back-propagation learning algorithm on prediction task. The task works with simplified numerical time series and includes forty observations with prediction for next five observations. The main topic of the article is the identification of the main differences between used neural networks architectures together with numerical forecasting. Detected differences then verify on practical comparative example.

  5. Supercritical CO2 extract and essential oil of bay (Laurus nobilis L. – chemical composition and antibacterial activity

    Directory of Open Access Journals (Sweden)

    JASNA IVANOVIĆ

    2010-03-01

    Full Text Available The present study deals with the supercritical carbon dioxide (SC-CO2 extraction and hydrodistillation (HD of dried bay leaves (Laurus nobilis L.. The chemical composition and antibacterial activity of the SC-CO2 extract and essential oil (EO from dried leaves of bay were compared to each other and literature data. Qualitative and quantitative analyses of the SC-CO2 extract and EO were performed using GC–FID and GC–MS analytical methods. A significant difference in the chemical composition of the SC-CO2 extract and EO was observed. The EO comprised high contents of monoterpenes and their oxygenated derivates (98.4 %, principally 1,8-cineole (33.4 %, linalool (16.0 % and α-terpinyl acetate (13.8 %, sabinene (6.91 % and methyl eugenol (5.32 %. The SC-CO2 extract comprised twice less monoterpenes and their oxygenated derivates (43.89 %, together with sesquiterpenes (12.43 %, diterpenes (1.33 % and esters (31.13 %. The major components were methyl linoleate (16.18 %, α-terpinyl acetate (12.88 %, linalool (9.00 %, methyl eugenol (8.67 %, methyl arachidonate (6.28 % and eugenol (6.14 %. An investigation of the antibacterial activity of bay SC-CO2 extract and EO was completed on different Staphylococcus strains using the broth macrodilution method. Staphylococcus intermedius strains were the most susceptible to both the SC-CO2 extract and EO (MIC = 640 µg/ml.

  6. An artificial neural network to predict resting energy expenditure in obesity.

    Science.gov (United States)

    Disse, Emmanuel; Ledoux, Séverine; Bétry, Cécile; Caussy, Cyrielle; Maitrepierre, Christine; Coupaye, Muriel; Laville, Martine; Simon, Chantal

    2017-09-01

    The resting energy expenditure (REE) determination is important in nutrition for adequate dietary prescription. The gold standard i.e. indirect calorimetry is not available in clinical settings. Thus, several predictive equations have been developed, but they lack of accuracy in subjects with extreme weight including obese populations. Artificial neural networks (ANN) are useful predictive tools in the area of artificial intelligence, used in numerous clinical fields. The aim of this study was to determine the relevance of ANN in predicting REE in obesity. A Multi-Layer Perceptron (MLP) feed-forward neural network with a back propagation algorithm was created and cross-validated in a cohort of 565 obese subjects (BMI within 30-50 kg m -2 ) with weight, height, sex and age as clinical inputs and REE measured by indirect calorimetry as output. The predictive performances of ANN were compared to those of 23 predictive REE equations in the training set and in two independent sets of 100 and 237 obese subjects for external validation. Among the 23 established prediction equations for REE evaluated, the Harris & Benedict equations recalculated by Roza were the most accurate for the obese population, followed by the USA DRI, Müller and the original Harris & Benedict equations. The final 5-fold cross-validated three-layer 4-3-1 feed-forward back propagation ANN model developed in that study improved precision and accuracy of REE prediction over linear equations (precision = 68.1%, MAPE = 8.6% and RMSPE = 210 kcal/d), independently from BMI subgroups within 30-50 kg m -2 . External validation confirmed the better predictive performances of ANN model (precision = 73% and 65%, MAPE = 7.7% and 8.6%, RMSPE = 187 kcal/d and 200 kcal/d in the 2 independent datasets) for the prediction of REE in obese subjects. We developed and validated an ANN model for the prediction of REE in obese subjects that is more precise and accurate than established REE predictive

  7. Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA-ANN model

    International Nuclear Information System (INIS)

    Koutroumanidis, Theodoros; Ioannou, Konstantinos; Arabatzis, Garyfallos

    2009-01-01

    Throughout history, energy resources have acquired a strategic significance for the economic growth and social welfare of any country. The large-scale oil crisis of 1973 coupled with various environmental protection issues, have led many countries to look for new, alternative energy sources. Biomass and fuelwood in particular, constitutes a major renewable energy source (RES) that can make a significant contribution, as a substitute for oil. This paper initially provides a description of the contribution of renewable energy sources to the production of electricity, and also examines the role of forests in the production of fuelwood in Greece. Following this, autoregressive integrated moving average (ARIMA) models, artificial neural networks (ANN) and a hybrid model are used to predict the future selling prices of the fuelwood (from broadleaved and coniferous species) produced by Greek state forest farms. The use of the ARIMA-ANN hybrid model provided the optimum prediction results, thus enabling decision-makers to proceed with a more rational planning for the production and fuelwood market. (author)

  8. Neutron spectrometry with artificial neural networks

    International Nuclear Information System (INIS)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Rodriguez, J.M.; Mercado S, G.A.; Iniguez de la Torre Bayo, M.P.; Barquero, R.; Arteaga A, T.

    2005-01-01

    An artificial neural network has been designed to obtain the neutron spectra from the Bonner spheres spectrometer's count rates. The neural network was trained using 129 neutron spectra. These include isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra from mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-bin ned to 31 energy groups using the MCNP 4C code. Re-binned spectra and UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and the respective spectrum was used as output during neural network training. After training the network was tested with the Bonner spheres count rates produced by a set of neutron spectra. This set contains data used during network training as well as data not used. Training and testing was carried out in the Mat lab program. To verify the network unfolding performance the original and unfolded spectra were compared using the χ 2 -test and the total fluence ratios. The use of Artificial Neural Networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  9. Neural networks for predicting breeding values and genetic gains

    Directory of Open Access Journals (Sweden)

    Gabi Nunes Silva

    2014-12-01

    Full Text Available Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.

  10. Artificial neural networks to predict presence of significant pathology in patients presenting to routine colorectal clinics.

    Science.gov (United States)

    Maslekar, S; Gardiner, A B; Monson, J R T; Duthie, G S

    2010-12-01

    Artificial neural networks (ANNs) are computer programs used to identify complex relations within data. Routine predictions of presence of colorectal pathology based on population statistics have little meaning for individual patient. This results in large number of unnecessary lower gastrointestinal endoscopies (LGEs - colonoscopies and flexible sigmoidoscopies). We aimed to develop a neural network algorithm that can accurately predict presence of significant pathology in patients attending routine outpatient clinics for gastrointestinal symptoms. Ethics approval was obtained and the study was monitored according to International Committee on Harmonisation - Good Clinical Practice (ICH-GCP) standards. Three-hundred patients undergoing LGE prospectively completed a specifically developed questionnaire, which included 40 variables based on clinical symptoms, signs, past- and family history. Complete data sets of 100 patients were used to train the ANN; the remaining data was used for internal validation. The primary output used was positive finding on LGE, including polyps, cancer, diverticular disease or colitis. For external validation, the ANN was applied to data from 50 patients in primary care and also compared with the predictions of four clinicians. Clear correlation between actual data value and ANN predictions were found (r = 0.931; P = 0.0001). The predictive accuracy of ANN was 95% in training group and 90% (95% CI 84-96) in the internal validation set and this was significantly higher than the clinical accuracy (75%). ANN also showed high accuracy in the external validation group (89%). Artificial neural networks offer the possibility of personal prediction of outcome for individual patients presenting in clinics with colorectal symptoms, making it possible to make more appropriate requests for lower gastrointestinal endoscopy. © 2010 The Authors. Colorectal Disease © 2010 The Association of Coloproctology of Great Britain and Ireland.

  11. DELAMINATION PREDICTION IN DRILLING OF CFRP COMPOSITES USING ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    K. PALANIKUMAR

    2011-04-01

    Full Text Available Carbon fibre reinforced plastic (CFRP materials play a major role in the applications of aeronautic, aerospace, sporting and transportation industries. Machining is indispensible and hence drilling of CFRP materials is considered in this present study with respect to spindle speed in rpm, drill size in mm and feed in mm/min. Delamination is one of the major defects to be dealt with. The experiments are carried out using computer numerical control machine and the results are applied to an artificial neural network (ANN for the prediction of delamination factor at the exit plane of the CFRP material. It is found that ANN model predicts the delamination for any given set of machining parameters with a maximum error of 0.81% and a minimum error of 0.03%. Thus an ANN model is highly suitable for the prediction of delamination in CFRP materials.

  12. Antimicrobial activity of essential oils from Mediterranean aromatic plants against several foodborne and spoilage bacteria.

    Science.gov (United States)

    Silva, Nuno; Alves, Sofia; Gonçalves, Alexandre; Amaral, Joana S; Poeta, Patrícia

    2013-12-01

    The antimicrobial activity of essential oils extracted from a variety of aromatic plants, often used in the Portuguese gastronomy was studied in vitro by the agar diffusion method. The essential oils of thyme, oregano, rosemary, verbena, basil, peppermint, pennyroyal and mint were tested against Gram-positive (Listeria monocytogenes, Clostridium perfringens, Bacillus cereus, Staphylococcus aureus, Enterococcus faecium, Enterococcus faecalis, and Staphylococcus epidermidis) and Gram-negative strains (Salmonella enterica, Escherichia coli, and Pseudomonas aeruginosa). For most essential oils examined, S. aureus, was the most susceptible bacteria, while P. aeruginosa showed, in general, least susceptibility. Among the eight essential oils evaluated, thyme, oregano and pennyroyal oils showed the greatest antimicrobial activity, followed by rosemary, peppermint and verbena, while basil and mint showed the weakest antimicrobial activity. Most of the essential oils considered in this study exhibited a significant inhibitory effect. Thyme oil showed a promising inhibitory activity even at low concentration, thus revealing its potential as a natural preservative in food products against several causal agents of foodborne diseases and food spoilage. In general, the results demonstrate that, besides flavoring the food, the use of aromatic herbs in gastronomy can also contribute to a bacteriostatic effect against pathogens.

  13. Total phenolic content, radical scavenging properties, and essential oil composition of Origanum species from different populations.

    Science.gov (United States)

    Dambolena, José S; Zunino, María P; Lucini, Enrique I; Olmedo, Rubén; Banchio, Erika; Bima, Paula J; Zygadlo, Julio A

    2010-01-27

    The aim of this work was to compare the antiradical activity, total phenol content (TPC), and essential oil composition of Origanum vulgare spp. virens, Origanum x applii, Origanum x majoricum, and O. vulgare spp. vulgare cultivated in Argentina in different localities. The experiment was conducted in the research station of La Consulta (INTA-Mendoza), the research station of Santa Lucia (INTA-San Juan), and Agronomy Faculty of National University of La Pampa, from 2007 to 2008. The composition of the essential oils of oregano populations was independent of cultivation conditions. In total, 39 compounds were identified in essential oils of oregano from Argentina by means of GC-MS. Thymol and trans-sabinene hydrate were the most prominent compounds, followed by gamma-terpinene, terpinen-4-ol, and alpha-terpinene. O. vulgare vulgare is the only Origanum studied which is rich in gamma-terpinene. Among tested oregano, O. x majoricum showed the highest essential oil content, 3.9 mg g(-1) dry matter. The plant extract of O. x majoricum had greater total phenol content values, 19.36 mg/g dry weight, than the rest of oregano studied. To find relationships among TPC, free radical scavenging activity (FRSA), and climate variables, canonical correlations were calculated. The results obtained allow us to conclude that 70% of the TPC and FRSA variability can be explained by the climate variables (R(2) = 0.70; p = 8.3 x 10(-6)), the temperature being the most important climatic variable.

  14. THE COMPUTATIONAL INTELLIGENCE TECHNIQUES FOR PREDICTIONS - ARTIFICIAL NEURAL NETWORKS

    OpenAIRE

    Mary Violeta Bar

    2014-01-01

    The computational intelligence techniques are used in problems which can not be solved by traditional techniques when there is insufficient data to develop a model problem or when they have errors.Computational intelligence, as he called Bezdek (Bezdek, 1992) aims at modeling of biological intelligence. Artificial Neural Networks( ANNs) have been applied to an increasing number of real world problems of considerable complexity. Their most important advantage is solving problems that are too c...

  15. Comparative study of the chemical composition of the essential oils ...

    African Journals Online (AJOL)

    USER

    2010-02-08

    Feb 8, 2010 ... essential oils from organs of Annona senegalensis ..... rosemary, oregano and coriander essential oils, J. Essent. Oil Res. 10 : 618-27. Bouquet A ... antibacterial activity of plant volatile oil, J. Appl. Microbiol. 88: 308-. 316.

  16. Internal-state analysis in layered artificial neural network trained to categorize lung sounds

    NARCIS (Netherlands)

    Oud, M

    2002-01-01

    In regular use of artificial neural networks, only input and output states of the network are known to the user. Weight and bias values can be extracted but are difficult to interpret. We analyzed internal states of networks trained to map asthmatic lung sound spectra onto lung function parameters.

  17. Application of Artificial Neural Networks for Efficient High-Resolution 2D DOA Estimation

    Directory of Open Access Journals (Sweden)

    M. Agatonović

    2012-12-01

    Full Text Available A novel method to provide high-resolution Two-Dimensional Direction of Arrival (2D DOA estimation employing Artificial Neural Networks (ANNs is presented in this paper. The observed space is divided into azimuth and elevation sectors. Multilayer Perceptron (MLP neural networks are employed to detect the presence of a source in a sector while Radial Basis Function (RBF neural networks are utilized for DOA estimation. It is shown that a number of appropriately trained neural networks can be successfully used for the high-resolution DOA estimation of narrowband sources in both azimuth and elevation. The training time of each smaller network is significantly re¬duced as different training sets are used for networks in detection and estimation stage. By avoiding the spectral search, the proposed method is suitable for real-time ap¬plications as it provides DOA estimates in a matter of seconds. At the same time, it demonstrates the accuracy comparable to that of the super-resolution 2D MUSIC algorithm.

  18. Experiment and Artificial Neural Network Prediction of Thermal Conductivity and Viscosity for Alumina-Water Nanofluids.

    Science.gov (United States)

    Zhao, Ningbo; Li, Zhiming

    2017-05-19

    To effectively predict the thermal conductivity and viscosity of alumina (Al₂O₃)-water nanofluids, an artificial neural network (ANN) approach was investigated in the present study. Firstly, using a two-step method, four Al₂O₃-water nanofluids were prepared respectively by dispersing different volume fractions (1.31%, 2.72%, 4.25%, and 5.92%) of nanoparticles with the average diameter of 30 nm. On this basis, the thermal conductivity and viscosity of the above nanofluids were analyzed experimentally under various temperatures ranging from 296 to 313 K. Then a radial basis function (RBF) neural network was constructed to predict the thermal conductivity and viscosity of Al₂O₃-water nanofluids as a function of nanoparticle volume fraction and temperature. The experimental results showed that both nanoparticle volume fraction and temperature could enhance the thermal conductivity of Al₂O₃-water nanofluids. However, the viscosity only depended strongly on Al₂O₃ nanoparticle volume fraction and was increased slightly by changing temperature. In addition, the comparative analysis revealed that the RBF neural network had an excellent ability to predict the thermal conductivity and viscosity of Al₂O₃-water nanofluids with the mean absolute percent errors of 0.5177% and 0.5618%, respectively. This demonstrated that the ANN provided an effective way to predict the thermophysical properties of nanofluids with limited experimental data.

  19. Artificial neural networks and neuro-fuzzy inference systems as virtual sensors for hydrogen safety prediction

    Energy Technology Data Exchange (ETDEWEB)

    Karri, Vishy; Ho, Tien [School of Engineering, University of Tasmania, GPO Box 252-65, Hobart, Tasmania 7001 (Australia); Madsen, Ole [Department of Production, Aalborg University, Fibigerstraede 16, DK-9220 Aalborg (Denmark)

    2008-06-15

    Hydrogen is increasingly investigated as an alternative fuel to petroleum products in running internal combustion engines and as powering remote area power systems using generators. The safety issues related to hydrogen gas are further exasperated by expensive instrumentation required to measure the percentage of explosive limits, flow rates and production pressure. This paper investigates the use of model based virtual sensors (rather than expensive physical sensors) in connection with hydrogen production with a Hogen 20 electrolyzer system. The virtual sensors are used to predict relevant hydrogen safety parameters, such as the percentage of lower explosive limit, hydrogen pressure and hydrogen flow rate as a function of different input conditions of power supplied (voltage and current), the feed of de-ionized water and Hogen 20 electrolyzer system parameters. The virtual sensors are developed by means of the application of various Artificial Intelligent techniques. To train and appraise the neural network models as virtual sensors, the Hogen 20 electrolyzer is instrumented with necessary sensors to gather experimental data which together with MATLAB neural networks toolbox and tailor made adaptive neuro-fuzzy inference systems (ANFIS) were used as predictive tools to estimate hydrogen safety parameters. It was shown that using the neural networks hydrogen safety parameters were predicted to less than 3% of percentage average root mean square error. The most accurate prediction was achieved by using ANFIS. (author)

  20. Supercritical Carbon Dioxide Extraction of the Oak Silkworm (Antheraea pernyi Pupal Oil: Process Optimization and Composition Determination

    Directory of Open Access Journals (Sweden)

    Zhao-Jun Wei

    2012-02-01

    Full Text Available Supercritical carbon dioxide (SC-CO2 extraction of oil from oak silkworm pupae was performed in the present research. Response surface methodology (RSM was applied to optimize the parameters of SC-CO2 extraction, including extraction pressure, temperature, time and CO2 flow rate on the yield of oak silkworm pupal oil (OSPO. The optimal extraction condition for oil yield within the experimental range of the variables researched was at 28.03 MPa, 1.83 h, 35.31 °C and 20.26 L/h as flow rate of CO2. Under this condition, the oil yield was predicted to be 26.18%. The oak silkworm pupal oil contains eight fatty acids, and is rich in unsaturated fatty acids and α-linolenic acid (ALA, accounting for 77.29% and 34.27% in the total oil respectively.

  1. Neural Network Modeling for the Extraction of Rare Earth Elements from Eudialyte Concentrate by Dry Digestion and Leaching

    Directory of Open Access Journals (Sweden)

    Yiqian Ma

    2018-04-01

    Full Text Available Eudialyte is a promising mineral for rare earth elements (REE extraction due to its good solubility in acid, low radioactive, and relatively high content of REE. In this paper, a two stage hydrometallurgical treatment of eudialyte concentrate was studied: dry digestion with hydrochloric acid and leaching with water. The hydrochloric acid for dry digestion to eudialyte concentrate ratio, mass of water for leaching to mass of eudialyte concentrate ratio, leaching temperature and leaching time as the predictor variables, and the total rare earth elements (TREE extraction efficiency as the response were considered. After experimental work in laboratory conditions, according to design of experiment theory (DoE, the modeling process was performed using Multiple Linear Regression (MLR, Stepwise Regression (SWR, and Artificial Neural Network (ANN. The ANN model of REE extraction was adopted. Additional tests showed that values predicted by the neural network model were in very good agreement with the experimental results. Finally, the experiments were performed on a scaled up system under optimal conditions that were predicted by the adopted ANN model. Results at the scale-up plant confirmed the results that were obtained in the laboratory.

  2. Artificial Neural Network Analysis of Xinhui Pericarpium Citri ...

    African Journals Online (AJOL)

    Methods: Artificial neural networks (ANN) models, including general regression neural network (GRNN) and multi-layer ... N-hexane (HPLC grade) was purchased from. Fisher Scientific. ..... Simultaneous Quantification of Seven Flavonoids in.

  3. SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Shen Yang; Bax, Ad, E-mail: bax@nih.go [National Institutes of Health, Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases (United States)

    2010-09-15

    NMR chemical shifts provide important local structural information for proteins and are key in recently described protein structure generation protocols. We describe a new chemical shift prediction program, SPARTA+, which is based on artificial neural networking. The neural network is trained on a large carefully pruned database, containing 580 proteins for which high-resolution X-ray structures and nearly complete backbone and {sup 13}C{sup {beta}} chemical shifts are available. The neural network is trained to establish quantitative relations between chemical shifts and protein structures, including backbone and side-chain conformation, H-bonding, electric fields and ring-current effects. The trained neural network yields rapid chemical shift prediction for backbone and {sup 13}C{sup {beta}} atoms, with standard deviations of 2.45, 1.09, 0.94, 1.14, 0.25 and 0.49 ppm for {delta}{sup 15}N, {delta}{sup 13}C', {delta}{sup 13}C{sup {alpha}}, {delta}{sup 13}C{sup {beta}}, {delta}{sup 1}H{sup {alpha}} and {delta}{sup 1}H{sup N}, respectively, between the SPARTA+ predicted and experimental shifts for a set of eleven validation proteins. These results represent a modest but consistent improvement (2-10%) over the best programs available to date, and appear to be approaching the limit at which empirical approaches can predict chemical shifts.

  4. Artificial Neural Networks to Predict the Power Output of a PV Panel

    Directory of Open Access Journals (Sweden)

    Valerio Lo Brano

    2014-01-01

    Full Text Available The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs for the power energy output forecasting of photovoltaic (PV modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP, a recursive neural network (RNN, and a gamma memory (GM trained with the back propagation. In order to investigate the influence of climate variability on the electricity production, the ANNs were trained using weather data (air temperature, solar irradiance, and wind speed along with historical power output data available for the two test modules. The model validation was performed by comparing model predictions with power output data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short-term power output forecasting problem and identified the best topology.

  5. A study on the optimal fuel loading pattern design in pressurized water reactor using the artificial neural network and the fuzzy rule based system

    International Nuclear Information System (INIS)

    Kim, Han Gon; Chang, Soon Heung; Lee, Byung

    2004-01-01

    The Optimal Fuel Shuffling System (OFSS) is developed for optimal design of PWR fuel loading pattern. In this paper, an optimal loading pattern is defined that the local power peaking factor is lower than predetermined value during one cycle and the effective multiplication factor is maximized in order to extract maximum energy. OFSS is a hybrid system that a rule based system, a fuzzy logic, and an artificial neural network are connected each other. The rule based system classifies loading patterns into two classes using several heuristic rules and a fuzzy rule. A fuzzy rule is introduced to achieve more effective and fast searching. Its membership function is automatically updated in accordance with the prediction results. The artificial neural network predicts core parameters for the patterns generated from the rule based system. The back-propagation network is used for fast prediction of core parameters. The artificial neural network and the fuzzy logic can be used as the tool for improvement of existing algorithm's capabilities. OFSS was demonstrated and validated for cycle 1 of Kori unit 1 PWR. (author)

  6. A study on the optimal fuel loading pattern design in pressurized water reactor using the artificial neural network and the fuzzy rule based system

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Han Gon; Chang, Soon Heung; Lee, Byung [Department of Nuclear Engineering, Korea Advanced Institute of Science and Technology, Yusong-gu, Taejon (Korea, Republic of)

    2004-07-01

    The Optimal Fuel Shuffling System (OFSS) is developed for optimal design of PWR fuel loading pattern. In this paper, an optimal loading pattern is defined that the local power peaking factor is lower than predetermined value during one cycle and the effective multiplication factor is maximized in order to extract maximum energy. OFSS is a hybrid system that a rule based system, a fuzzy logic, and an artificial neural network are connected each other. The rule based system classifies loading patterns into two classes using several heuristic rules and a fuzzy rule. A fuzzy rule is introduced to achieve more effective and fast searching. Its membership function is automatically updated in accordance with the prediction results. The artificial neural network predicts core parameters for the patterns generated from the rule based system. The back-propagation network is used for fast prediction of core parameters. The artificial neural network and the fuzzy logic can be used as the tool for improvement of existing algorithm's capabilities. OFSS was demonstrated and validated for cycle 1 of Kori unit 1 PWR. (author)

  7. Effects of different nitrogen levels and plant density on flower, essential oils and extract production and nitrogen use efficiency of Marigold (Calendula officinalis.

    Directory of Open Access Journals (Sweden)

    ali akbar ameri

    2009-06-01

    Full Text Available Efficient use of nitrogen for medicinal plants production, might increase flower dry matter, essential oil and extract yield and reduce cost of yield production. A two year (2005 and 2006 field study was conducted in Torogh region(36,10° N,59.33° E and 1300 m altitude of Mashhad, Iran, to observe the effects of different nitrogen and densities on flower dry matter, essential oil and extract production and nitrogen use efficiency (NUE in a multi-harvested Marigold (Calendula officinalis. The levels of Nitrogen fertilizer (N were 0, 50, 100 and 150 kg ha-1 and levels of density were 20, 40, 60 and 80 plant m-2. The combined analysis results revealed significant effects of N and density levels on flower dry matter, essential oil and extract production and NUE of Marigold. The highest dry flower production obtained by 150 kg ha-1 N and 80 plant m-2 plant population (102.86 g m-2. The higher flower dry matter production caused more essential oil and extract production in high nitrogen and density levels. Agronomic N-use efficiency (kg flower dry matter yield per kg N applied, physiological efficiency (kg flower dry matter yield per kg N absorbed and fertilizer N-recovery efficiency (kg N absorbed per kg N applied, expressed as % for marigold across treatments ranged from 6.8 to14.9, 12.3 to 33.6 and 55.5 to 77.6, respectively and all were greater for N application at 50 compared with150 kg N ha-1, and under high density than low density. The amount of essential oil and extract per 100g flower dry matter decreased during the flower harvesting period. The higher amount of essential oil and extract obtained at early flowering season. The essential oil and extract ranged from 0.22 to 0.12 (ml. per 100g flower dry matter and 2.74 to 2.13 (g per 100g flower dry matter respectively.

  8. Prediction of Global Solar Radiation in India Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Rajiv Gupta

    2016-06-01

    Full Text Available Increasing global warming and decreasing fossil fuel reserves has necessitated the use of renewable energy resources like solar energy in India. To maximize return on a solar farm, it had to be set up at a place with high solar radiation. The solar radiation values are available only for a small number of places and must be interpolated for the rest. This paper utilizes Artificial Neural Network in interpolation, by obtaining a function with input as combinations of 7 geographical and meteorological parameters affecting radiation, and output as global solar radiation. Data considered was of past 9 years for 13 Indian cities. Low error values and high coefficient of determination values thus obtained, verified that the results were accurate in terms of the original solar radiation data known. Thus, artificial neural network can be used to interpolate the solar radiation for the places of interest depending on the availability of the data.

  9. ECO INVESTMENT PROJECT MANAGEMENT THROUGH TIME APPLYING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Tamara Gvozdenović

    2007-06-01

    Full Text Available he concept of project management expresses an indispensable approach to investment projects. Time is often the most important factor in these projects. The artificial neural network is the paradigm of data processing, which is inspired by the one used by the biological brain, and it is used in numerous, different fields, among which is the project management. This research is oriented to application of artificial neural networks in managing time of investment project. The artificial neural networks are used to define the optimistic, the most probable and the pessimistic time in PERT method. The program package Matlab: Neural Network Toolbox is used in data simulation. The feed-forward back propagation network is chosen.

  10. Application of PLE for the determination of essential oil components from Thymus vulgaris L.

    Science.gov (United States)

    Dawidowicz, Andrzej L; Rado, Ewelina; Wianowska, Dorota; Mardarowicz, Marek; Gawdzik, Jan

    2008-08-15

    Essential plants, due to their long presence in human history, their status in culinary arts, their use in medicine and perfume manufacture, belong to frequently examined stock materials in scientific and industrial laboratories. Because of a large number of freshly cut, dried or frozen plant samples requiring the determination of essential oil amount and composition, a fast, safe, simple, efficient and highly automatic sample preparation method is needed. Five sample preparation methods (steam distillation, extraction in the Soxhlet apparatus, supercritical fluid extraction, solid phase microextraction and pressurized liquid extraction) used for the isolation of aroma-active components from Thymus vulgaris L. are compared in the paper. The methods are mainly discussed with regard to the recovery of components which typically exist in essential oil isolated by steam distillation. According to the obtained data, PLE is the most efficient sample preparation method in determining the essential oil from the thyme herb. Although co-extraction of non-volatile ingredients is the main drawback of this method, it is characterized by the highest yield of essential oil components and the shortest extraction time required. Moreover, the relative peak amounts of essential components revealed by PLE are comparable with those obtained by steam distillation, which is recognized as standard sample preparation method for the analysis of essential oils in aromatic plants.

  11. Antimicrobial activity of essential oils of cultivated oregano (Origanum vulgare, sage (Salvia officinalis, and thyme (Thymus vulgaris against clinical isolates of Escherichia coli, Klebsiella oxytoca, and Klebsiella pneumoniae

    Directory of Open Access Journals (Sweden)

    Maria Fournomiti

    2015-04-01

    Full Text Available Background: Oregano (Origanum vulgare, sage (Salvia officinalis, and thyme (Thymus vulgaris are aromatic plants with ornamental, culinary, and phytotherapeutic use all over the world. In Europe, they are traditionally used in the southern countries, particularly in the Mediterranean region. The antimicrobial activities of the essential oils (EOs derived from those plants have captured the attention of scientists as they could be used as alternatives to the increasing resistance of traditional antibiotics against pathogen infections. Therefore, significant interest in the cultivation of various aromatic and medicinal plants is recorded during the last years. However, to gain a proper and marketable chemotype various factors during the cultivation should be considered as the geographical morphology, climatic, and farming conditions. In this frame, we have studied the antimicrobial efficiency of the EOs from oregano, sage, and thyme cultivated under different conditions in a region of NE Greece in comparison to the data available in literature. Methods: Plants were purchased from a certified supplier, planted, and cultivated in an experimental field under different conditions and harvested after 9 months. EOs were extracted by using a Clevenger apparatus and tested for their antibacterial properties (Minimum inhibitory concentration – MIC against clinical isolates of multidrug resistant Escherichia coli (n=27, Klebsiella oxytoca (n=7, and Klebsiella pneumoniae (n=16 strains by using the broth microdilution assay. Results: Our results showed that the most sensitive organism was K. oxytoca with a mean value of MIC of 0.9 µg/mL for oregano EOs and 8.1 µg/mL for thyme. The second most sensitive strain was K. pneumoniae with mean MIC values of 9.5 µg/mL for thyme and 73.5 µg/mL for oregano EOs. E. coli strains were among the most resistant to EOs antimicrobial action as the observed MICs were 24.8–28.6 µg/mL for thyme and above 125 µg/mL for

  12. Antimicrobial activity of essential oils of cultivated oregano (Origanum vulgare), sage (Salvia officinalis), and thyme (Thymus vulgaris) against clinical isolates of Escherichia coli, Klebsiella oxytoca, and Klebsiella pneumoniae

    Science.gov (United States)

    Fournomiti, Maria; Kimbaris, Athanasios; Mantzourani, Ioanna; Plessas, Stavros; Theodoridou, Irene; Papaemmanouil, Virginia; Kapsiotis, Ioannis; Panopoulou, Maria; Stavropoulou, Elisavet; Bezirtzoglou, Eugenia E.; Alexopoulos, Athanasios

    2015-01-01

    Background Oregano (Origanum vulgare), sage (Salvia officinalis), and thyme (Thymus vulgaris) are aromatic plants with ornamental, culinary, and phytotherapeutic use all over the world. In Europe, they are traditionally used in the southern countries, particularly in the Mediterranean region. The antimicrobial activities of the essential oils (EOs) derived from those plants have captured the attention of scientists as they could be used as alternatives to the increasing resistance of traditional antibiotics against pathogen infections. Therefore, significant interest in the cultivation of various aromatic and medicinal plants is recorded during the last years. However, to gain a proper and marketable chemotype various factors during the cultivation should be considered as the geographical morphology, climatic, and farming conditions. In this frame, we have studied the antimicrobial efficiency of the EOs from oregano, sage, and thyme cultivated under different conditions in a region of NE Greece in comparison to the data available in literature. Methods Plants were purchased from a certified supplier, planted, and cultivated in an experimental field under different conditions and harvested after 9 months. EOs were extracted by using a Clevenger apparatus and tested for their antibacterial properties (Minimum inhibitory concentration – MIC) against clinical isolates of multidrug resistant Escherichia coli (n=27), Klebsiella oxytoca (n=7), and Klebsiella pneumoniae (n=16) strains by using the broth microdilution assay. Results Our results showed that the most sensitive organism was K. oxytoca with a mean value of MIC of 0.9 µg/mL for oregano EOs and 8.1 µg/mL for thyme. The second most sensitive strain was K. pneumoniae with mean MIC values of 9.5 µg/mL for thyme and 73.5 µg/mL for oregano EOs. E. coli strains were among the most resistant to EOs antimicrobial action as the observed MICs were 24.8–28.6 µg/mL for thyme and above 125 µg/mL for thyme and sage

  13. Spatial prediction of ground subsidence susceptibility using an artificial neural network.

    Science.gov (United States)

    Lee, Saro; Park, Inhye; Choi, Jong-Kuk

    2012-02-01

    Ground subsidence in abandoned underground coal mine areas can result in loss of life and property. We analyzed ground subsidence susceptibility (GSS) around abandoned coal mines in Jeong-am, Gangwon-do, South Korea, using artificial neural network (ANN) and geographic information system approaches. Spatial data of subsidence area, topography, and geology, as well as various ground-engineering data, were collected and used to create a raster database of relevant factors for a GSS map. Eight major factors causing ground subsidence were extracted from the existing ground subsidence area: slope, depth of coal mine, distance from pit, groundwater depth, rock-mass rating, distance from fault, geology, and land use. Areas of ground subsidence were randomly divided into a training set to analyze GSS using the ANN and a test set to validate the predicted GSS map. Weights of each factor's relative importance were determined by the back-propagation training algorithms and applied to the input factor. The GSS was then calculated using the weights, and GSS maps were created. The process was repeated ten times to check the stability of analysis model using a different training data set. The map was validated using area-under-the-curve analysis with the ground subsidence areas that had not been used to train the model. The validation showed prediction accuracies between 94.84 and 95.98%, representing overall satisfactory agreement. Among the input factors, "distance from fault" had the highest average weight (i.e., 1.5477), indicating that this factor was most important. The generated maps can be used to estimate hazards to people, property, and existing infrastructure, such as the transportation network, and as part of land-use and infrastructure planning.

  14. Balancing of a rigid rotor using artificial neural network to predict the correction masses - DOI: 10.4025/actascitechnol.v31i2.3912

    Directory of Open Access Journals (Sweden)

    Fábio Lúcio Santos

    2009-06-01

    Full Text Available This paper deals with an analytical model of a rigid rotor supported by hydrodynamic journal bearings where the plane separation technique together with the Artificial Neural Network (ANN is used to predict the location and magnitude of the correction masses for balancing the rotor bearing system. The rotating system is modeled by applying the rigid shaft Stodola-Green model, in which the shaft gyroscopic moments and rotatory inertia are accounted for, in conjunction with the hydrodynamic cylindrical journal bearing model based on the classical Reynolds equation. A linearized perturbation procedure is employed to render the lubrication equations from the Reynolds equation, which allows predicting the eight linear force coefficients associated with the bearing direct and cross-coupled stiffness and damping coefficients. The results show that the methodology presented is efficient for balancing rotor systems. This paper gives a step further in the monitoring process, since Artificial Neural Network is normally used to predict, not to correct the mass unbalance. The procedure presented can be used in turbo machinery industry to balance rotating machinery that require continuous inspections. Some simulated results will be used in order to clarify the methodology presented.

  15. An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

    Science.gov (United States)

    1996-01-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...

  16. Mode Choice Modeling Using Artificial Neural Networks

    OpenAIRE

    Edara, Praveen Kumar

    2003-01-01

    Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data becom...

  17. Neutron spectrometry using artificial neural networks

    International Nuclear Information System (INIS)

    Vega-Carrillo, Hector Rene; Martin Hernandez-Davila, Victor; Manzanares-Acuna, Eduardo; Mercado Sanchez, Gema A.; Pilar Iniguez de la Torre, Maria; Barquero, Raquel; Palacios, Francisco; Mendez Villafane, Roberto; Arteaga Arteaga, Tarcicio; Manuel Ortiz Rodriguez, Jose

    2006-01-01

    An artificial neural network has been designed to obtain neutron spectra from Bonner spheres spectrometer count rates. The neural network was trained using 129 neutron spectra. These include spectra from isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra based on mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. The re-binned spectra and the UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and their respective spectra were used as output during the neural network training. After training, the network was tested with the Bonner spheres count rates produced by folding a set of neutron spectra with the response matrix. This set contains data used during network training as well as data not used. Training and testing was carried out using the Matlab ( R) program. To verify the network unfolding performance, the original and unfolded spectra were compared using the root mean square error. The use of artificial neural networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated with this ill-conditioned problem

  18. Modeling the supercritical desorption of orange essential oil from a silica-gel bed

    Directory of Open Access Journals (Sweden)

    Silva E.A.

    2000-01-01

    Full Text Available One of the most important byproducts of the orange juice industry is the oil phase. This is a mixture of terpenes, alcohols, and aldehydes, dissolved in approximately 96% limonene. To satisfactorily use oil phase as an ingredient in the food and cosmetics industries separation of the limonene is required. One possibility is to use a fixed bed of silica gel to remove the light or aroma compounds from the limonene. The aroma substances are then extracted from the bed of silica gel using supercritical carbon dioxide. This work deals with the modeling of the desorption step of the process using mass balance equations coupled with the Langmuir equilibrium isotherm. Data taken from the literature for the overall extraction curves were used together with empirical correlations to calculate the concentration profile of solute in the supercritical phase at the bed outlet. The system of equations was solved by the finite volume technique. The overall extraction curves calculated were in good agreement with the experimental ones.

  19. Prediction of metal corrosion using feed-forward neural networks

    International Nuclear Information System (INIS)

    Mahjani, M.G.; Jalili, S.; Jafarian, M.; Jaberi, A.

    2004-01-01

    The reliable prediction of corrosion behavior for the effective control of corrosion is a fundamental requirement. Since real world corrosion never seems to involve quite the same conditions that have previously been tested, using corrosion literature does not provide the necessary answers. In order to provide a methodology for predicting corrosion in real and complex situations, artificial neural networks can be utilized. Feed-forward artificial neural network (FFANN) is an information-processing paradigm inspired by the way the densely interconnected, parallel structure of the human brain process information.The aim of the present work is to predict corrosion behavior in critical conditions, such as industrial applications, based on some laboratory experimental data. Electrochemical behavior of stainless steel in different conditions were studied, using polarization technique and Tafel curves. Back-propagation neural networks models were developed to predict the corrosion behavior. The trained networks result in predicted value in good comparison to the experimental data. They have generally been claimed to be successful in modeling the corrosion behavior. The results are presented in two tables. Table 1 gives corrosion behavior of stainless-steel as a function of pH and CuSO 4 concentration and table 2 gives corrosion behavior of stainless - steel as a function of electrode surface area and CuSO 4 concentration. (authors)

  20. Combination of counterpropagation artificial neural networks and antioxidant activities for comprehensive evaluation of associated-extraction efficiency of various cyclodextrins in the traditional Chinese formula Xue-Zhi-Ning.

    Science.gov (United States)

    Sun, Lili; Yang, Jianwen; Wang, Meng; Zhang, Huijie; Liu, Yanan; Ren, Xiaoliang; Qi, Aidi

    2015-11-10

    Xue-Zhi-Ning (XZN) is a widely used traditional Chinese medicine formula to treat hyperlipidemia. Recently, cyclodextrins (CDs) have been extensively used to minimize problems relative to medicine bioavailability, such as low solubility and poor stability. The objective of this study was to determine the associated-extraction efficiency of various CDs in XZN. Three various type CDs were evaluated, including native CDs (α-CD, β-CD), hydrophilic CD derivatives (HP-β-CD and Me-β-CD), and ionic CD derivatives (SBE-β-CD and CM-β-CD). An ultra high-performance liquid chromatography (UHPLC) fingerprint was applied to determine the components in CD extracts and original aqueous extract (OAE). A counterpropagation artificial neural network (CP-ANN) was used to analyze the components in different extracts and compare the selective extraction of various CDs. Extraction efficiencies of the various CDs in terms of extracted components follow the ranking, ionic CD derivatives>hydrophilic CD derivatives>native CDs>OAE. Besides, different types of CDs have their own selective extraction and ionic CD derivatives present the strongest associated-extraction efficiency. Antioxidant potentials of various extracts were evaluated by determining the inhibition of spontaneous, H2O2-induced, CCl4-induced and Fe(2+)/ascorbic acid-induced lipid peroxidation (LPO) and analyzing the scavenging capacity for DPPH and hydroxyl radicals. The order of extraction efficiencies of the various CDs relative to antioxidant activities is as follows: SBE-β-CD>CM-β-CD>HP-β-CD>Me-β-CD>β-CD>α-CD. It can be demonstrated that all of the CDs studied increase the extraction efficiency and that ionic CD derivatives (SBE-β-CD and CM-β-CD) present the highest extraction capability in terms of amount extracted and antioxidant activities of extracts. Copyright © 2015 Elsevier B.V. All rights reserved.

  1. Solvent-free microwave extraction of essential oil from Melaleuca leucadendra L.

    Directory of Open Access Journals (Sweden)

    Widya Ismanto Aviarina

    2018-01-01

    Full Text Available Cajuput (Melaleuca leucadendra L. oil is one of potential commodity that provides an important role for the country’s foreign exchange but the extraction of these essential oil is still using conventional method such as hydrodistillation which takes a long time to produce essential oil with good quality. Therefore it is necessary to optimize the extraction process using a more effective and efficient method. So in this study the extraction is done using solvent-free microwave extraction method that are considered more effective and efficient than conventional methods. The optimum yield in the extraction of cajuput oil using solvent-free microwave extraction method is 1.0674%. The optimum yield is obtained on the feed to distiller (F/D ratio of 0.12 g/mL with microwave power of 400 W. In the extraction of cajuput oil using solvent-free microwave extraction method is performed first-order and second-order kinetics modelling. Based on kinetics modelling that has been done, it can be said that the second-order kinetic model (R2 = 0.9901 can be better represent experimental results of extraction of cajuput oil that using solvent-free microwave extraction method when compared with the first-order kinetic model (R2 = 0.9854.

  2. Artificial Neural Network Model for Alkali-Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application

    Directory of Open Access Journals (Sweden)

    Si Le Van

    2016-12-01

    Full Text Available Chemical flooding has been widely utilized to recover a large portion of the oil remaining in light and viscous oil reservoirs after the primary and secondary production processes. As core-flood tests and reservoir simulations take time to accurately estimate the recovery performances as well as analyzing the feasibility of an injection project, it is necessary to find a powerful tool to quickly predict the results with a level of acceptable accuracy. An approach involving the use of an artificial neural network to generate a representative model for estimating the alkali-surfactant-polymer flooding performance and evaluating the economic feasibility of viscous oil reservoirs from simulation is proposed in this study. A typical chemical flooding project was referenced for this numerical study. A number of simulations have been made for training on the basis of a base case from the design of 13 parameters. After training, the network scheme generated from a ratio data set of 50%-20%-30% corresponding to the number of samples used for training-validation-testing was selected for estimation with the total coefficient of determination of 0.986 and a root mean square error of 1.63%. In terms of model application, the chemical concentration and injection strategy were optimized to maximize the net present value (NPV of the project at a specific oil price from the just created ANN model. To evaluate the feasibility of the project comprehensively in terms of market variations, a range of oil prices from 30 $/bbl to 60 $/bbl referenced from a real market situation was considered in conjunction with its probability following a statistical distribution on the NPV computation. Feasibility analysis of the optimal chemical injection scheme revealed a variation of profit from 0.42 $MM to 1.0 $MM, corresponding to the changes in oil price. In particular, at the highest possible oil prices, the project can earn approximately 0.61 $MM to 0.87 $MM for a quarter

  3. Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization

    Science.gov (United States)

    Qiu, Sihang; Chen, Bin; Wang, Rongxiao; Zhu, Zhengqiu; Wang, Yuan; Qiu, Xiaogang

    2018-04-01

    Hazardous gas leak accident has posed a potential threat to human beings. Predicting atmospheric dispersion and estimating its source become increasingly important in emergency management. Current dispersion prediction and source estimation models cannot satisfy the requirement of emergency management because they are not equipped with high efficiency and accuracy at the same time. In this paper, we develop a fast and accurate dispersion prediction and source estimation method based on artificial neural network (ANN), particle swarm optimization (PSO) and expectation maximization (EM). The novel method uses a large amount of pre-determined scenarios to train the ANN for dispersion prediction, so that the ANN can predict concentration distribution accurately and efficiently. PSO and EM are applied for estimating the source parameters, which can effectively accelerate the process of convergence. The method is verified by the Indianapolis field study with a SF6 release source. The results demonstrate the effectiveness of the method.

  4. Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil

    Directory of Open Access Journals (Sweden)

    Reginald B. Silva

    2010-01-01

    Full Text Available Soil loss is one of the main causes of pauperization and alteration of agricultural soil properties. Various empirical models (e.g., USLE are used to predict soil losses from climate variables which in general have to be derived from spatial interpolation of point measurements. Alternatively, Artificial Neural Networks may be used as a powerful option to obtain site-specific climate data from independent factors. This study aimed to develop an artificial neural network to estimate rainfall erosivity in the Ribeira Valley and Coastal region of the State of São Paulo. In the development of the Artificial Neural Networks the input variables were latitude, longitude, and annual rainfall and a mathematical equation of the activation function for use in the study area as the output variable. It was found among other things that the Artificial Neural Networks can be used in the interpolation of rainfall erosivity values for the Ribeira Valley and Coastal region of the State of São Paulo to a satisfactory degree of precision in the estimation of erosion. The equation performance has been demonstrated by comparison with the mathematical equation of the activation function adjusted to the specific conditions of the study area.

  5. Greek long-term energy consumption prediction using artificial neural networks

    International Nuclear Information System (INIS)

    Ekonomou, L.

    2010-01-01

    In this paper artificial neural networks (ANN) are addressed in order the Greek long-term energy consumption to be predicted. The multilayer perceptron model (MLP) has been used for this purpose by testing several possible architectures in order to be selected the one with the best generalizing ability. Actual recorded input and output data that influence long-term energy consumption were used in the training, validation and testing process. The developed ANN model is used for the prediction of 2005-2008, 2010, 2012 and 2015 Greek energy consumption. The produced ANN results for years 2005-2008 were compared with the results produced by a linear regression method, a support vector machine method and with real energy consumption records showing a great accuracy. The proposed approach can be useful in the effective implementation of energy policies, since accurate predictions of energy consumption affect the capital investment, the environmental quality, the revenue analysis, the market research management, while conserve at the same time the supply security. Furthermore it constitutes an accurate tool for the Greek long-term energy consumption prediction problem, which up today has not been faced effectively.

  6. Comparison of chemical composition and antibacterial activity of Nigella sativa seed essential oils obtained by different extraction methods

    Czech Academy of Sciences Publication Activity Database

    Kokoška, L.; Havlík, J.; Valterová, Irena; Sovová, Helena; Sajfrtová, Marie; Jankovská, I.

    2008-01-01

    Roč. 71, č. 12 (2008), s. 2475-2480 ISSN 0362-028X Institutional research plan: CEZ:AV0Z40550506; CEZ:AV0Z40720504 Keywords : Nigella * essential oil * supercritical fluid extraction Subject RIV: CC - Organic Chemistry Impact factor: 1.763, year: 2008

  7. Predictions on the Development Dimensions of Provincial Tourism Discipline Based on the Artificial Neural Network BP Model

    Science.gov (United States)

    Yang, Yang; Hu, Jun; Lv, Yingchun; Zhang, Mu

    2013-01-01

    As the tourism industry has gradually become the strategic mainstay industry of the national economy, the scope of the tourism discipline has developed rigorously. This paper makes a predictive study on the development of the scope of Guangdong provincial tourism discipline based on the artificial neural network BP model in order to find out how…

  8. Investigation and modeling on protective textiles using artificial neural networks for defense applications

    International Nuclear Information System (INIS)

    Ramaiah, Gurumurthy B.; Chennaiah, Radhalakshmi Y.; Satyanarayanarao, Gurumurthy K.

    2010-01-01

    Kevlar 29 is a class of Kevlar fiber used for protective applications primarily by the military and law enforcement agencies for bullet resistant vests, hence for these reasons military has found that armors reinforced with Kevlar 29 multilayer fabrics which offer 25-40% better fragmentation resistance and provide better fit with greater comfort. The objective of this study is to investigate and develop an artificial neural network model for analyzing the performance of ballistic fabrics made from Kevlar 29 single layer fabrics using their material properties as inputs. Data from fragment simulation projectile (FSP) ballistic penetration measurements at 244 m/s has been used to demonstrate the modeling aspects of artificial neural networks. The neural network models demonstrated in this paper is based on back propagation (BP) algorithm which is inbuilt in MATLAB 7.1 software and is used for studies in science, technology and engineering. In the present research, comparisons are also made between the measured values of samples selected for building the neural network model and network predicted results. The analysis of the results for network predicted and experimental samples used in this study showed similarity.

  9. Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique

    International Nuclear Information System (INIS)

    Hou Zhijian; Lian Zhiwei; Yao Ye; Yuan Xinjian

    2006-01-01

    A novel method integrating rough sets (RS) theory and an artificial neural network (ANN) based on data-fusion technique is presented to forecast an air-conditioning load. Data-fusion technique is the process of combining multiple sensors data or related information to estimate or predict entity states. In this paper, RS theory is applied to find relevant factors to the load, which are used as inputs of an artificial neural-network to predict the cooling load. To improve the accuracy and enhance the robustness of load forecasting results, a general load-prediction model, by synthesizing multi-RSAN (MRAN), is presented so as to make full use of redundant information. The optimum principle is employed to deduce the weights of each RSAN model. Actual prediction results from a real air-conditioning system show that, the MRAN forecasting model is better than the individual RSAN and moving average (AMIMA) ones, whose relative error is within 4%. In addition, individual RSAN forecasting results are better than that of ARIMA

  10. Back propagation artificial neural network for community Alzheimer's disease screening in China.

    Science.gov (United States)

    Tang, Jun; Wu, Lei; Huang, Helang; Feng, Jiang; Yuan, Yefeng; Zhou, Yueping; Huang, Peng; Xu, Yan; Yu, Chao

    2013-01-25

    Alzheimer's disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer's disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 (95% confidence interval: 0.868-0.968), sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer's disease in patients selected from the community.

  11. Back propagation artificial neural network for community Alzheimer's disease screening in China★

    Science.gov (United States)

    Tang, Jun; Wu, Lei; Huang, Helang; Feng, Jiang; Yuan, Yefeng; Zhou, Yueping; Huang, Peng; Xu, Yan; Yu, Chao

    2013-01-01

    Alzheimer's disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer's disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 (95% confidence interval: 0.868–0.968), sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer's disease in patients selected from the community. PMID:25206598

  12. Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

    Science.gov (United States)

    Boonjing, Veera; Intakosum, Sarun

    2016-01-01

    This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span. PMID:27974883

  13. Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

    Directory of Open Access Journals (Sweden)

    Montri Inthachot

    2016-01-01

    Full Text Available This study investigated the use of Artificial Neural Network (ANN and Genetic Algorithm (GA for prediction of Thailand’s SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid’s prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

  14. Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran

    Directory of Open Access Journals (Sweden)

    Zahra Pezeshki

    2016-02-01

    Full Text Available Background: Cholera as an endemic disease remains a health issue in Iran despite decrease in incidence. Since forecasting epidemic diseases provides appropriate preventive actions in disease spread, different forecasting methods including artificial neural networks have been developed to study parameters involved in incidence and spread of epidemic diseases such as cholera. Objectives: In this study, cholera in rural area of Chabahar, Iran was investigated to achieve a proper forecasting model. Materials and Methods: Data of cholera was gathered from 465 villages, of which 104 reported cholera during ten years period of study. Logistic regression modeling and correlate bivariate were used to determine risk factors and achieve possible predictive model one-hidden-layer perception neural network with backpropagation training algorithm and the sigmoid activation function was trained and tested between the two groups of infected and non-infected villages after preprocessing. For determining validity of prediction, the ROC diagram was used. The study variables included climate conditions and geographical parameters. Results: After determining significant variables of cholera incidence, the described artificial neural network model was capable of forecasting cholera event among villages of test group with accuracy up to 80%. The highest accuracy was achieved when model was trained with variables that were significant in statistical analysis describing that the two methods confirm the result of each other. Conclusions: Application of artificial neural networking assists forecasting cholera for adopting protective measures. For a more accurate prediction, comprehensive information is required including data on hygienic, social and demographic parameters.

  15. Steps of Supercritical Fluid Extraction of Natural Products and Their Characteristic Times

    Czech Academy of Sciences Publication Activity Database

    Sovová, Helena

    2012-01-01

    Roč. 66, SI (2012), s. 73-79 ISSN 0896-8446 R&D Projects: GA MŠk 2B06049 Institutional support: RVO:67985858 Keywords : supercritical fluid extraction * vegetable oils * essential oils Subject RIV: CI - Industrial Chemistry, Chemical Engineering Impact factor: 2.732, year: 2012

  16. Prediction of phenotypic susceptibility to antiretroviral drugs using physiochemical properties of the primary enzymatic structure combined with artificial neural networks

    DEFF Research Database (Denmark)

    Kjaer, J; Høj, L; Fox, Z

    2008-01-01

    OBJECTIVES: Genotypic interpretation systems extrapolate observed associations in datasets to predict viral susceptibility to antiretroviral drugs (ARVs) for given isolates. We aimed to develop and validate an approach using artificial neural networks (ANNs) that employ descriptors...

  17. Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study

    Directory of Open Access Journals (Sweden)

    Jair Minoro Abe

    Full Text Available Abstract EEG visual analysis has proved useful in aiding AD diagnosis, being indicated in some clinical protocols. However, such analysis is subject to the inherent imprecision of equipment, patient movements, electric registers, and individual variability of physician visual analysis. Objectives: To employ the Paraconsistent Artificial Neural Network to ascertain how to determine the degree of certainty of probable dementia diagnosis. Methods: Ten EEG records from patients with probable Alzheimer disease and ten controls were obtained during the awake state at rest. An EEG background between 8 Hz and 12 Hz was considered the normal pattern for patients, allowing a variance of 0.5 Hz. Results: The PANN was capable of accurately recognizing waves belonging to Alpha band with favorable evidence of 0.30 and contrary evidence of 0.19, while for waves not belonging to the Alpha pattern, an average favorable evidence of 0.19 and contrary evidence of 0.32 was obtained, indicating that PANN was efficient in recognizing Alpha waves in 80% of the cases evaluated in this study. Artificial Neural Networks - ANN - are well suited to tackle problems such as prediction and pattern recognition. The aim of this work was to recognize predetermined EEG patterns by using a new class of ANN, namely the Paraconsistent Artificial Neural Network - PANN, which is capable of handling uncertain, inconsistent and paracomplete information. An architecture is presented to serve as an auxiliary method in diagnosing Alzheimer disease. Conclusions: We believe the results show PANN to be a promising tool to handle EEG analysis, bearing in mind two considerations: the growing interest of experts in visual analysis of EEG, and the ability of PANN to deal directly with imprecise, inconsistent, and paracomplete data, thereby providing a valuable quantitative analysis.

  18. Alpha spectral analysis via artificial neural networks

    International Nuclear Information System (INIS)

    Kangas, L.J.; Hashem, S.; Keller, P.E.; Kouzes, R.T.; Troyer, G.L.

    1994-10-01

    An artificial neural network system that assigns quality factors to alpha particle energy spectra is discussed. The alpha energy spectra are used to detect plutonium contamination in the work environment. The quality factors represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with a quality factor by an expert and used in training the artificial neural network expert system. The investigation shows that the expert knowledge of alpha spectra quality factors can be transferred to an ANN system

  19. Prediction of Asphalt Creep Compliance Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Zofka A.

    2012-06-01

    Full Text Available Creep compliance of the hot-mix asphalt (HMA is a primary input of the pavement thermal cracking prediction model in the recently developed Mechanistic-Empirical Pavement Design Guide (M-EPDG in the US. The HMA creep compliance is typically determined from the Indirect Tension (IDT tests and requires complex experimental setup. On the other hand, creep compliance of asphalt binders is determined from a relatively simple three- point bending test performed in the Bending Beam Rheometer (BBR device. This paper discusses a process of training an Artificial Neural Network (ANN to correlate the creep compliance values obtained from the IDT with those from an innovative approach of testing HMA beams in the BBR. In addition, ANNs are also trained to predict HMA creep compliance from the creep compliance of asphalt binder and vice versa using the BBR setup. All trained ANNs exhibited a very high correlation of 97 to 99 percent between predicted and measured values. The binder creep compliance curves built on the ANN-predicted values also exhibited good correlation with those obtained from laboratory experiments. However, the simulation of trained ANNs on the independent dataset produced a significant deviation from the expected values which was most likely caused by the differences in material composition, such as aggregate type and gradation, presence of recycled additives, and binder type.

  20. Hardware Prototyping of Neural Network based Fetal Electrocardiogram Extraction

    Science.gov (United States)

    Hasan, M. A.; Reaz, M. B. I.

    2012-01-01

    The aim of this paper is to model the algorithm for Fetal ECG (FECG) extraction from composite abdominal ECG (AECG) using VHDL (Very High Speed Integrated Circuit Hardware Description Language) for FPGA (Field Programmable Gate Array) implementation. Artificial Neural Network that provides efficient and effective ways of separating FECG signal from composite AECG signal has been designed. The proposed method gives an accuracy of 93.7% for R-peak detection in FHR monitoring. The designed VHDL model is synthesized and fitted into Altera's Stratix II EP2S15F484C3 using the Quartus II version 8.0 Web Edition for FPGA implementation.

  1. Precursors predicted by artificial neural networks for mass balance calculations: Quantifying hydrothermal alteration in volcanic rocks

    Science.gov (United States)

    Trépanier, Sylvain; Mathieu, Lucie; Daigneault, Réal; Faure, Stéphane

    2016-04-01

    This study proposes an artificial neural networks-based method for predicting the unaltered (precursor) chemical compositions of hydrothermally altered volcanic rock. The method aims at predicting precursor's major components contents (SiO2, FeOT, MgO, CaO, Na2O, and K2O). The prediction is based on ratios of elements generally immobile during alteration processes; i.e. Zr, TiO2, Al2O3, Y, Nb, Th, and Cr, which are provided as inputs to the neural networks. Multi-layer perceptron neural networks were trained on a large dataset of least-altered volcanic rock samples that document a wide range of volcanic rock types, tectonic settings and ages. The precursors thus predicted are then used to perform mass balance calculations. Various statistics were calculated to validate the predictions of precursors' major components, which indicate that, overall, the predictions are precise and accurate. For example, rank-based correlation coefficients were calculated to compare predicted and analysed values from a least-altered test dataset that had not been used to train the networks. Coefficients over 0.87 were obtained for all components, except for Na2O (0.77), indicating that predictions for alkali might be less performant. Also, predictions are performant for most volcanic rock compositions, except for ultra-K rocks. The proposed method provides an easy and rapid solution to the often difficult task of determining appropriate volcanic precursor compositions to rocks modified by hydrothermal alteration. It is intended for large volcanic rock databases and is most useful, for example, to mineral exploration performed in complex or poorly known volcanic settings. The method is implemented as a simple C++ console program.

  2. Prediction of Polymer Flooding Performance with an Artificial Neural Network: A Two-Polymer-Slug Case

    Directory of Open Access Journals (Sweden)

    Jestril Ebaga-Ololo

    2017-07-01

    Full Text Available Many previous contributions to methods of forecasting the performance of polymer flooding using artificial neural networks (ANNs have been made by numerous researchers previously. In most of those forecasting cases, only a single polymer slug was employed to meet the objective of the study. The intent of this manuscript is to propose an efficient recovery factor prediction tool at different injection stages of two polymer slugs during polymer flooding using an ANN. In this regard, a back-propagation algorithm was coupled with six input parameters to predict three output parameters via a hidden layer composed of 10 neurons. Evaluation of the ANN model performance was made with multiple linear regression. With an acceptable correlation coefficient, the proposed ANN tool was able to predict the recovery factor with errors of <1%. In addition, to understand the influence of each parameter on the output parameters, a sensitivity analysis was applied to the input parameters. The results showed less impact from the second polymer concentration, owing to changes in permeability after the injection of the first polymer slug.

  3. Characterization of Starch Edible Films with Different Essential Oils Addition

    Directory of Open Access Journals (Sweden)

    Šuput Danijela

    2016-12-01

    Full Text Available This study investigated properties of starch-based edible films with oregano and black cumin essential oil addition. Essential oils addition positively affected film swelling (decreased due to essential oil addition, mechanical properties (tensile strength decreased while elongation at break increased, and water vapor barrier properties (decreased along with essential oils addition. Control film did not have any biological activity, which proves the need for essential oils addition in order to obtain active packaging. Oregano oil was more effective in terms of biological activity. Endothermal peak, above 200°C, represents total thermal degradation of edible films. Diffraction pattern of control film showed significant destruction of A-type crystal structure. Addition of essential oils resulted in peak shape change: diffraction peaks became narrower. Principal Component Analysis has been used to assess the effect of essential oils addition on final starch-based edible films characteristics with the aim to reveal directions for the film characteristics improvement, since the next phase will be optimal film application for food packaging.

  4. CONSTRUCTION COST PREDICTION USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Smita K Magdum

    2017-10-01

    Full Text Available Construction cost prediction is important for construction firms to compete and grow in the industry. Accurate construction cost prediction in the early stage of project is important for project feasibility studies and successful completion. There are many factors that affect the cost prediction. This paper presents construction cost prediction as multiple regression model with cost of six materials as independent variables. The objective of this paper is to develop neural networks and multilayer perceptron based model for construction cost prediction. Different models of NN and MLP are developed with varying hidden layer size and hidden nodes. Four artificial neural network models and twelve multilayer perceptron models are compared. MLP and NN give better results than statistical regression method. As compared to NN, MLP works better on training dataset but fails on testing dataset. Five activation functions are tested to identify suitable function for the problem. ‘elu' transfer function gives better results than other transfer function.

  5. Modelling of word usage frequency dynamics using artificial neural network

    International Nuclear Information System (INIS)

    Maslennikova, Yu S; Bochkarev, V V; Voloskov, D S

    2014-01-01

    In this paper the method for modelling of word usage frequency time series is proposed. An artificial feedforward neural network was used to predict word usage frequencies. The neural network was trained using the maximum likelihood criterion. The Google Books Ngram corpus was used for the analysis. This database provides a large amount of data on frequency of specific word forms for 7 languages. Statistical modelling of word usage frequency time series allows finding optimal fitting and filtering algorithm for subsequent lexicographic analysis and verification of frequency trend models

  6. The artificial neural networks: An approach to artificial intelligence; Un approccio ``biologico`` all`intelligenza artificiale

    Energy Technology Data Exchange (ETDEWEB)

    Taraglio, Sergio; Zanela, Andrea [ENEA, Casaccia (Italy). Dipt. Innovazione

    1997-05-01

    The artificial neural networks try to simulate the functionalities of the nervous system through a complex network of simple computing elements. In this work is presented an introduction to the neural networks and some of their possible applications, especially in the field of Artificial Intelligence.

  7. using Supercritical Fluid Extraction

    African Journals Online (AJOL)

    Methods: Supercritical CO2 extraction technology was adopted in this experiment to study the process of extraction of volatile oil from Polygonatum odoratum while gas chromatograph-mass spectrometer ..... Saponin rich fractions from.

  8. Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

    Science.gov (United States)

    Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

    2017-01-01

    Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  9. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  10. Development of Burdock Root Inulin/Chitosan Blend Films Containing Oregano and Thyme Essential Oils

    Science.gov (United States)

    Cao, Thi Luyen; Yang, So-Young; Song, Kyung Bin

    2018-01-01

    In this study, inulin (INU) extracted from burdock root was utilized as a new film base material and combined with chitosan (CHI) to prepare composite films. Oregano and thyme essential oils (OT) were incorporated into the INU-CHI film to confer the films with bioactivities. The physical and optical properties as well as antioxidant and antimicrobial activities of the films were evaluated. INU film alone showed poor physical properties. In contrast, the compatibility of INU and CHI demonstrated by the changes in attenuated total reflectance-Fourier transformation infrared spectrum of the INU-CHI film increased tensile strength and elongation at break of the INU film by 8.2- and 3.9-fold, respectively. In addition, water vapor permeability, water solubility, and moisture content of the films decreased proportionally with increasing OT concentration in the INU-CHI film. Incorporation of OT also increased the opacity of a and b values and decreased the L value of the INU-CHI films. All INU-CHI films containing OT exhibited antioxidant and antimicrobial properties. Particularly, the INU-CHI film with 2.0% OT exhibited the highest 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid), 2,2-diphenyl-1-picrylhydrazyl radical scavenging, and antimicrobial activities against four pathogens. Thus, the INU-CHI film containing OT developed in this study might be utilized as an active packaging material in the food industry. PMID:29301339

  11. Development of Burdock Root Inulin/Chitosan Blend Films Containing Oregano and Thyme Essential Oils.

    Science.gov (United States)

    Cao, Thi Luyen; Yang, So-Young; Song, Kyung Bin

    2018-01-03

    In this study, inulin (INU) extracted from burdock root was utilized as a new film base material and combined with chitosan (CHI) to prepare composite films. Oregano and thyme essential oils (OT) were incorporated into the INU-CHI film to confer the films with bioactivities. The physical and optical properties as well as antioxidant and antimicrobial activities of the films were evaluated. INU film alone showed poor physical properties. In contrast, the compatibility of INU and CHI demonstrated by the changes in attenuated total reflectance-Fourier transformation infrared spectrum of the INU-CHI film increased tensile strength and elongation at break of the INU film by 8.2- and 3.9-fold, respectively. In addition, water vapor permeability, water solubility, and moisture content of the films decreased proportionally with increasing OT concentration in the INU-CHI film. Incorporation of OT also increased the opacity of a and b values and decreased the L value of the INU-CHI films. All INU-CHI films containing OT exhibited antioxidant and antimicrobial properties. Particularly, the INU-CHI film with 2.0% OT exhibited the highest 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid), 2,2-diphenyl-1-picrylhydrazyl radical scavenging, and antimicrobial activities against four pathogens. Thus, the INU-CHI film containing OT developed in this study might be utilized as an active packaging material in the food industry.

  12. Optimizing sliver quality using Artificial Neural Networks in ring spinning

    Directory of Open Access Journals (Sweden)

    Samar Ahmed Mohsen Abd-Ellatif

    2013-12-01

    Full Text Available Sliver evenness is a very important parameter affecting the quality of the yarn produced. Therefore, controlling the sliver evenness is of major importance. Auto-levelers mounted on modern Drawing Frames should be accurately adjusted to help to achieve this task. The Leveling Action Point (LAP is one of the important auto-leveling parameters which highly influence the evenness of the slivers produced. Its adjustment is therefore of a crucial importance. In this research work, Artificial Neural Networks are applied to predict the optimum value of the LAP under different productions and material conditions. Five models are developed and tested for their ability to predict the optimum value of the LAP from the most influencing fiber and process parameters. As a final step, a statistical multiple regression model was developed to conduct a comparison between the performance of the developed Artificial Neural Network model and the currently applied statistical techniques.

  13. Prediction of deformations of steel plate by artificial neural network in forming process with induction heating

    International Nuclear Information System (INIS)

    Nguyen, Truong Thinh; Yang, Young Soo; Bae, Kang Yul; Choi, Sung Nam

    2009-01-01

    To control a heat source easily in the forming process of steel plate with heating, the electro-magnetic induction process has been used as a substitute of the flame heating process. However, only few studies have analyzed the deformation of a workpiece in the induction heating process by using a mathematical model. This is mainly due to the difficulty of modeling the heat flux from the inductor traveling on the conductive plate during the induction process. In this study, the heat flux distribution over a steel plate during the induction process is first analyzed by a numerical method with the assumption that the process is in a quasi-stationary state around the inductor and also that the heat flux itself greatly depends on the temperature of the workpiece. With the heat flux, heat flow and thermo-mechanical analyses on the plate to obtain deformations during the heating process are then performed with a commercial FEM program for 34 combinations of heating parameters. An artificial neural network is proposed to build a simplified relationship between deformations and heating parameters that can be easily utilized to predict deformations of steel plate with a wide range of heating parameters in the heating process. After its architecture is optimized, the artificial neural network is trained with the deformations obtained from the FEM analyses as outputs and the related heating parameters as inputs. The predicted outputs from the neural network are compared with those of the experiments and the numerical results. They are in good agreement

  14. Artificial Neural Network Modelling of the Energy Content of Municipal Solid Wastes in Northern Nigeria

    Directory of Open Access Journals (Sweden)

    M. B. Oumarou

    2017-12-01

    Full Text Available The study presents an application of the artificial neural network model using the back propagation learning algorithm to predict the actual calorific value of the municipal solid waste in major cities of the northern part of Nigeria, with high population densities and intense industrial activities. These cities are: Kano, Damaturu, Dutse, Bauchi, Birnin Kebbi, Gusau, Maiduguri, Katsina and Sokoto. Experimental data of the energy content and the physical characterization of the municipal solid waste serve as the input parameter in nature of wood, grass, metal, plastic, food remnants, leaves, glass and paper. Comparative studies were made by using the developed model, the experimental results and a correlation which was earlier developed by the authors to predict the energy content. While predicting the actual calorific value, the maximum error was 0.94% for the artificial neural network model and 5.20% by the statistical correlation. The network with eight neurons and an R2 = 0.96881 in the hidden layer results in a stable and optimum network. This study showed that the artificial neural network approach could successfully be used for energy content predictions from the municipal solid wastes in Northern Nigeria and other areas of similar waste stream and composition.

  15. Antimicrobial Susceptibility of Escherichia coli Strains Isolated from Alouatta spp. Feces to Essential Oils

    Directory of Open Access Journals (Sweden)

    Valéria Maria Lara

    2016-01-01

    Full Text Available This study evaluated the in vitro antibacterial activity of essential oils from Lippia graveolens (Mexican oregano, Origanum vulgaris (oregano, Thymus vulgaris (thyme, Rosmarinus officinalis (rosemary, Cymbopogon nardus (citronella, Cymbopogon citratus (lemongrass, and Eucalyptus citriodora (eucalyptus against Escherichia coli (n=22 strains isolated from Alouatta spp. feces. Minimum inhibitory concentration (MIC and minimum bactericidal concentration (MBC were determined for each isolate using the broth microdilution technique. Essential oils of Mexican oregano (MIC mean = 1818 μg mL−1; MBC mean = 2618 μg mL−1, thyme (MIC mean = 2618 μg mL−1; MBC mean = 2909 μg mL−1, and oregano (MIC mean = 3418 μg mL−1; MBC mean = 4800 μg mL−1 showed the best antibacterial activity, while essential oils of eucalyptus, rosemary, citronella, and lemongrass displayed no antibacterial activity at concentrations greater than or equal to 6400 μg mL−1. Our results confirm the antimicrobial potential of some essential oils, which deserve further research.

  16. Antimicrobial Susceptibility of Escherichia coli Strains Isolated from Alouatta spp. Feces to Essential Oils

    Science.gov (United States)

    Carregaro, Adriano Bonfim; Santurio, Deise Flores; de Sá, Mariangela Facco; Santurio, Janio Moraes; Alves, Sydney Hartz

    2016-01-01

    This study evaluated the in vitro antibacterial activity of essential oils from Lippia graveolens (Mexican oregano), Origanum vulgaris (oregano), Thymus vulgaris (thyme), Rosmarinus officinalis (rosemary), Cymbopogon nardus (citronella), Cymbopogon citratus (lemongrass), and Eucalyptus citriodora (eucalyptus) against Escherichia coli (n = 22) strains isolated from Alouatta spp. feces. Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were determined for each isolate using the broth microdilution technique. Essential oils of Mexican oregano (MIC mean = 1818 μg mL−1; MBC mean = 2618 μg mL−1), thyme (MIC mean = 2618 μg mL−1; MBC mean = 2909 μg mL−1), and oregano (MIC mean = 3418 μg mL−1; MBC mean = 4800 μg mL−1) showed the best antibacterial activity, while essential oils of eucalyptus, rosemary, citronella, and lemongrass displayed no antibacterial activity at concentrations greater than or equal to 6400 μg mL−1. Our results confirm the antimicrobial potential of some essential oils, which deserve further research. PMID:27313638

  17. Artificial neural networks application for horizontal and vertical forecasting radionuclides transport

    International Nuclear Information System (INIS)

    Khil'ko, O.S.; Kovalenko, V.I.; Kundas, S.P.

    2010-01-01

    Artificial neural networks approach for horizontal and vertical radionuclide transport forecasting was proposed. Runoff factors analysis was considered. Additional artificial neural network structures for physical-chemical properties recognition were used. (authors)

  18. An artificial neural network approach to reconstruct the source term of a nuclear accident

    International Nuclear Information System (INIS)

    Giles, J.; Palma, C. R.; Weller, P.

    1997-01-01

    This work makes use of one of the main features of artificial neural networks, which is their ability to 'learn' from sets of known input and output data. Indeed, a trained artificial neural network can be used to make predictions on the input data when the output is known, and this feedback process enables one to reconstruct the source term from field observations. With this aim, an artificial neural networks has been trained, using the projections of a segmented plume atmospheric dispersion model at fixed points, simulating a set of gamma detectors located outside the perimeter of a nuclear facility. The resulting set of artificial neural networks was used to determine the release fraction and rate for each of the noble gases, iodines and particulate fission products that could originate from a nuclear accident. Model projections were made using a large data set consisting of effective release height, release fraction of noble gases, iodines and particulate fission products, atmospheric stability, wind speed and wind direction. The model computed nuclide-specific gamma dose rates. The locations of the detectors were chosen taking into account both building shine and wake effects, and varied in distance between 800 and 1200 m from the reactor.The inputs to the artificial neural networks consisted of the measurements from the detector array, atmospheric stability, wind speed and wind direction; the outputs comprised a set of release fractions and heights. Once trained, the artificial neural networks was used to reconstruct the source term from the detector responses for data sets not used in training. The preliminary results are encouraging and show that the noble gases and particulate fission product release fractions are well determined

  19. Patterns recognition of electric brain activity using artificial neural networks

    Science.gov (United States)

    Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

    2017-04-01

    An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

  20. Artificial neural networks as a tool in urban storm drainage

    DEFF Research Database (Denmark)

    Loke, E.; Warnaars, E.A.; Jacobsen, P.

    1997-01-01

    The introduction of Artificial Neural Networks (ANNs) as a tool in the field of urban storm drainage is discussed. Besides some basic theory on the mechanics of ANNs and a general classification of the different types of ANNs, two ANN application examples are presented: The prediction of runoff...

  1. Prediction of friction factor of pure water flowing inside vertical smooth and microfin tubes by using artificial neural networks

    Science.gov (United States)

    Çebi, A.; Akdoğan, E.; Celen, A.; Dalkilic, A. S.

    2017-02-01

    An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.

  2. Modeling a full-scale primary sedimentation tank using artificial neural networks.

    Science.gov (United States)

    Gamal El-Din, A; Smith, D W

    2002-05-01

    Modeling the performance of full-scale primary sedimentation tanks has been commonly done using regression-based models, which are empirical relationships derived strictly from observed daily average influent and effluent data. Another approach to model a sedimentation tank is using a hydraulic efficiency model that utilizes tracer studies to characterize the performance of model sedimentation tanks based on eddy diffusion. However, the use of hydraulic efficiency models to predict the dynamic behavior of a full-scale sedimentation tank is very difficult as the development of such models has been done using controlled studies of model tanks. In this paper, another type of model, namely artificial neural network modeling approach, is used to predict the dynamic response of a full-scale primary sedimentation tank. The neuralmodel consists of two separate networks, one uses flow and influent total suspended solids data in order to predict the effluent total suspended solids from the tank, and the other makes predictions of the effluent chemical oxygen demand using data of the flow and influent chemical oxygen demand as inputs. An extensive sampling program was conducted in order to collect a data set to be used in training and validating the networks. A systematic approach was used in the building process of the model which allowed the identification of a parsimonious neural model that is able to learn (and not memorize) from past data and generalize very well to unseen data that were used to validate the model. Theresults seem very promising. The potential of using the model as part of a real-time process control system isalso discussed.

  3. Artificial neural network modelling of a large-scale wastewater treatment plant operation.

    Science.gov (United States)

    Güçlü, Dünyamin; Dursun, Sükrü

    2010-11-01

    Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

  4. Scaling of counter-current imbibition recovery curves using artificial neural networks

    Science.gov (United States)

    Jafari, Iman; Masihi, Mohsen; Nasiri Zarandi, Masoud

    2018-06-01

    Scaling imbibition curves are of great importance in the characterization and simulation of oil production from naturally fractured reservoirs. Different parameters such as matrix porosity and permeability, oil and water viscosities, matrix dimensions, and oil/water interfacial tensions have an effective on the imbibition process. Studies on the scaling imbibition curves along with the consideration of different assumptions have resulted in various scaling equations. In this work, using an artificial neural network (ANN) method, a novel technique is presented for scaling imbibition recovery curves, which can be used for scaling the experimental and field-scale imbibition cases. The imbibition recovery curves for training and testing the neural network were gathered through the simulation of different scenarios using a commercial reservoir simulator. In this ANN-based method, six parameters were assumed to have an effect on the imbibition process and were considered as the inputs for training the network. Using the ‘Bayesian regularization’ training algorithm, the network was trained and tested. Training and testing phases showed superior results in comparison with the other scaling methods. It is concluded that using the new technique is useful for scaling imbibition recovery curves, especially for complex cases, for which the common scaling methods are not designed.

  5. Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network

    International Nuclear Information System (INIS)

    Pendashteh, Ali Reza; Fakhru'l-Razi, A.; Chaibakhsh, Naz; Abdullah, Luqman Chuah; Madaeni, Sayed Siavash; Abidin, Zurina Zainal

    2011-01-01

    Highlights: → Hypersaline oily wastewater was treated in a membrane bioreactor. → The effects of salinity and organic loading rate were evaluated. → The system was modeled by neural network and optimized by genetic algorithm. → The model prediction agrees well with experimental values. → The model can be used to obtain effluent characteristics less than discharge limits. - Abstract: A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000 mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372 kg COD/(m 3 day)) and cyclic time (12, 24, and 48 h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O and G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44 kg COD/(m 3 day), TDS of 78,000 mg/L and reaction time (RT) of 40 h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100 mg/L and met the discharge limits.

  6. Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Pendashteh, Ali Reza [Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. (Malaysia); Environmental Research Institute, Iranian Academic Center for Education, Culture and Research (ACECR), Rasht (Iran, Islamic Republic of); Fakhru' l-Razi, A., E-mail: fakhrul@eng.upm.edu.my [Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. (Malaysia); Chaibakhsh, Naz [Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. (Malaysia); Abdullah, Luqman Chuah [Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. (Malaysia); Madaeni, Sayed Siavash [Chemical Engineering Department, Razi University, Kermanshah (Iran, Islamic Republic of); Abidin, Zurina Zainal [Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. (Malaysia)

    2011-08-30

    Highlights: {yields} Hypersaline oily wastewater was treated in a membrane bioreactor. {yields} The effects of salinity and organic loading rate were evaluated. {yields} The system was modeled by neural network and optimized by genetic algorithm. {yields} The model prediction agrees well with experimental values. {yields} The model can be used to obtain effluent characteristics less than discharge limits. - Abstract: A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000 mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372 kg COD/(m{sup 3} day)) and cyclic time (12, 24, and 48 h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O and G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44 kg COD/(m{sup 3} day), TDS of 78,000 mg/L and reaction time (RT) of 40 h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100 mg/L and met the discharge limits.

  7. essential oil as hatching egg disinfectant

    African Journals Online (AJOL)

    STORAGESEVER

    2010-04-26

    Apr 26, 2010 ... disinfectant for hatching egg obtained from broiler breeder flock. Oregano essential ... contamination rate, hatchability of fertile egg, body weight at 21 and 42 days, body weight gain and total feed ... successful healthy hatchlings. Several ...... Insecticidal properties of essential plant oils against the mosquito.

  8. Predicting all-cause risk of 30-day hospital readmission using artificial neural networks.

    Science.gov (United States)

    Jamei, Mehdi; Nisnevich, Aleksandr; Wetchler, Everett; Sudat, Sylvia; Liu, Eric

    2017-01-01

    Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health's EHR system, we built and tested an artificial neural network (NN) model based on Google's TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions.

  9. The Application of Neural Networks in Balancing Production of Crude Sunflower Oil and Meal

    Directory of Open Access Journals (Sweden)

    Bojan Ivetic

    2014-08-01

    Full Text Available The aim of the research is to predict specific output characteristics of half finished goods (crude sunflower oil and meal on the basis of specific input variables (quality and composition of sunflower seeds, with the help of artificial neural networks. This is an attempt to predict the amount much more precisely than is the case with technological calculations commonly used in the oil industry. All input variables are representing the data received by the laboratory, and the output variables except category % of oil which is obtained by measuring the physical quantity of produced crude sunflower oil and sunflower consumed quantity of the processing quality. The correct prediction of the output variables contributes to better sales planning, production of sunflower oil, and better use of storage. Also, the correct prediction of technological results of the quality of crude oil and meal provides timely response and also preventing getting rancid and poor-quality oil, timely categorizing meal, which leads to proper planning and sales to the rational utilization of storage space, allows timely response technologists and prevents the growth of microorganisms in the meal.

  10. Comparison of multiple linear regression, partial least squares and artificial neural networks for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids.

    Science.gov (United States)

    Fragkaki, A G; Farmaki, E; Thomaidis, N; Tsantili-Kakoulidou, A; Angelis, Y S; Koupparis, M; Georgakopoulos, C

    2012-09-21

    The comparison among different modelling techniques, such as multiple linear regression, partial least squares and artificial neural networks, has been performed in order to construct and evaluate models for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids. The performance of the quantitative structure-retention relationship study, using the multiple linear regression and partial least squares techniques, has been previously conducted. In the present study, artificial neural networks models were constructed and used for the prediction of relative retention times of anabolic androgenic steroids, while their efficiency is compared with that of the models derived from the multiple linear regression and partial least squares techniques. For overall ranking of the models, a novel procedure [Trends Anal. Chem. 29 (2010) 101-109] based on sum of ranking differences was applied, which permits the best model to be selected. The suggested models are considered useful for the estimation of relative retention times of designer steroids for which no analytical data are available. Copyright © 2012 Elsevier B.V. All rights reserved.

  11. Advances in Artificial Neural Networks - Methodological Development and Application

    Science.gov (United States)

    Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other ne...

  12. Heat transfer prediction in a square porous medium using artificial neural network

    Science.gov (United States)

    Ahamad, N. Ameer; Athani, Abdulgaphur; Badruddin, Irfan Anjum

    2018-05-01

    Heat transfer in porous media has been investigated extensively because of its applications in various important fields. Neural network approach is applied to analyze steady two dimensional free convection flows through a porous medium fixed in a square cavity. The backpropagation neural network is trained and used to predict the heat transfer. The results are compared with available information in the literature. It is found that the heat transfer increases with increase in Rayleigh number. It is further found that the local Nusselt number decreases along the height of cavity. The neural network is found to predict the heat transfer behavior accurately for given parameters.

  13. Vitamin D and ferritin correlation with chronic neck pain using standard statistics and a novel artificial neural network prediction model.

    Science.gov (United States)

    Eloqayli, Haytham; Al-Yousef, Ali; Jaradat, Raid

    2018-02-15

    Despite the high prevalence of chronic neck pain, there is limited consensus about the primary etiology, risk factors, diagnostic criteria and therapeutic outcome. Here, we aimed to determine if Ferritin and Vitamin D are modifiable risk factors with chronic neck pain using slandered statistics and artificial intelligence neural network (ANN). Fifty-four patients with chronic neck pain treated between February 2016 and August 2016 in King Abdullah University Hospital and 54 patients age matched controls undergoing outpatient or minor procedures were enrolled. Patients and control demographic parameters, height, weight and single measurement of serum vitamin D, Vitamin B12, ferritin, calcium, phosphorus, zinc were obtained. An ANN prediction model was developed. The statistical analysis reveals that patients with chronic neck pain have significantly lower serum Vitamin D and Ferritin (p-value artificial neural network can be of future benefit in classification and prediction models for chronic neck pain. We hope this initial work will encourage a future larger cohort study addressing vitamin D and iron correction as modifiable factors and the application of artificial intelligence models in clinical practice.

  14. Optimal Brain Surgeon on Artificial Neural Networks in

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine

    2012-01-01

    It is shown how the procedure know as optimal brain surgeon can be used to trim and optimize artificial neural networks in nonlinear structural dynamics. Beside optimizing the neural network, and thereby minimizing computational cost in simulation, the surgery procedure can also serve as a quick...

  15. Predicting the effects of magnesium oxide nanoparticles and temperature on the thermal conductivity of water using artificial neural network and experimental data

    Science.gov (United States)

    Afrand, Masoud; Hemmat Esfe, Mohammad; Abedini, Ehsan; Teimouri, Hamid

    2017-03-01

    The current paper first presents an empirical correlation based on experimental results for estimating thermal conductivity enhancement of MgO-water nanofluid using curve fitting method. Then, artificial neural networks (ANNs) with various numbers of neurons have been assessed by considering temperature and MgO volume fraction as the inputs variables and thermal conductivity enhancement as the output variable to select the most appropriate and optimized network. Results indicated that the network with 7 neurons had minimum error. Eventually, the output of artificial neural network was compared with the results of the proposed empirical correlation and those of the experiments. Comparisons revealed that ANN modeling was more accurate than curve-fitting method in the predicting the thermal conductivity enhancement of the nanofluid.

  16. Predicting concrete corrosion of sewers using artificial neural network.

    Science.gov (United States)

    Jiang, Guangming; Keller, Jurg; Bond, Philip L; Yuan, Zhiguo

    2016-04-01

    Corrosion is often a major failure mechanism for concrete sewers and under such circumstances the sewer service life is largely determined by the progression of microbially induced concrete corrosion. The modelling of sewer processes has become possible due to the improved understanding of in-sewer transformation. Recent systematic studies about the correlation between the corrosion processes and sewer environment factors should be utilized to improve the prediction capability of service life by sewer models. This paper presents an artificial neural network (ANN)-based approach for modelling the concrete corrosion processes in sewers. The approach included predicting the time for the corrosion to initiate and then predicting the corrosion rate after the initiation period. The ANN model was trained and validated with long-term (4.5 years) corrosion data obtained in laboratory corrosion chambers, and further verified with field measurements in real sewers across Australia. The trained model estimated the corrosion initiation time and corrosion rates very close to those measured in Australian sewers. The ANN model performed better than a multiple regression model also developed on the same dataset. Additionally, the ANN model can serve as a prediction framework for sewer service life, which can be progressively improved and expanded by including corrosion rates measured in different sewer conditions. Furthermore, the proposed methodology holds promise to facilitate the construction of analytical models associated with corrosion processes of concrete sewers. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. Fouling resistance prediction using artificial neural network nonlinear auto-regressive with exogenous input model based on operating conditions and fluid properties correlations

    Energy Technology Data Exchange (ETDEWEB)

    Biyanto, Totok R. [Department of Engineering Physics, Institute Technology of Sepuluh Nopember Surabaya, Surabaya, Indonesia 60111 (Indonesia)

    2016-06-03

    Fouling in a heat exchanger in Crude Preheat Train (CPT) refinery is an unsolved problem that reduces the plant efficiency, increases fuel consumption and CO{sub 2} emission. The fouling resistance behavior is very complex. It is difficult to develop a model using first principle equation to predict the fouling resistance due to different operating conditions and different crude blends. In this paper, Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) with input structure using Nonlinear Auto-Regressive with eXogenous (NARX) is utilized to build the fouling resistance model in shell and tube heat exchanger (STHX). The input data of the model are flow rates and temperatures of the streams of the heat exchanger, physical properties of product and crude blend data. This model serves as a predicting tool to optimize operating conditions and preventive maintenance of STHX. The results show that the model can capture the complexity of fouling characteristics in heat exchanger due to thermodynamic conditions and variations in crude oil properties (blends). It was found that the Root Mean Square Error (RMSE) are suitable to capture the nonlinearity and complexity of the STHX fouling resistance during phases of training and validation.

  18. Unstable Simple Volatiles and Gas Chromatography-Tandem Mass Spectrometry Analysis of Essential Oil from the Roots Bark of Oplopanax Horridus Extracted by Supercritical Fluid Extraction

    Directory of Open Access Journals (Sweden)

    Li Shao

    2014-11-01

    Full Text Available Volatile oil from the root bark of Oplopanax horridus is regarded to be responsible for the clinical uses of the title plant as a respiratory stimulant and expectorant. Therefore, a supercritical fluid extraction method was first employed to extract the volatile oil from the roots bark of O. horridus, which was subsequently analyzed by GC/MS. Forty-eight volatile compounds were identified by GC/MS analysis, including (S,E-nerolidol (52.5%, τ-cadinol (21.6% and S-falcarinol (3.6%. Accordingly, the volatile oil (100 g was subjected to chromatographic separation and purification. As a result, the three compounds, (E-nerolidol (2 g, τ-cadinol (62 mg and S-falcarinol (21 mg, were isolated and purified from the volatile oil, the structures of which were unambiguously elucidated by detailed spectroscopic analysis including 1D- and 2D-NMR techniques.

  19. Application of artificial neural network to search for gravitational-wave signals associated with short gamma-ray bursts

    International Nuclear Information System (INIS)

    Kim, Kyungmin; Lee, Hyun Kyu; Harry, Ian W; Hodge, Kari A; Kim, Young-Min; Lee, Chang-Hwan; Oh, John J; Oh, Sang Hoon; Son, Edwin J

    2015-01-01

    We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts (GRBs). The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability (FAP) is improved by the artificial neural network in comparison to the conventional detection statistic. Specifically, the distance at 50% detection probability at a fixed false positive rate is increased about 8%–14% for the considered waveform models. We also evaluate a few seconds of the gravitational-wave data segment using the trained networks and obtain the FAP. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short GRBs. (paper)

  20. The effect of essential oils on microbial composition and quality of grass carp (Ctenopharyngodon idellus) fillets during chilled storage.

    Science.gov (United States)

    Huang, Zhan; Liu, Xiaochang; Jia, Shiliang; Zhang, Longteng; Luo, Yongkang

    2018-02-02

    Antimicrobial and antioxidant effects of essential oils (oregano, thyme, and star anise) on microbial composition and quality of grass carp fillets were investigated. Essential oils treatment was found to be effective in inhibiting microbial growth, delaying lipid oxidation, and retarding the increase of TVB-N, putrescine, hypoxanthine, and K-value. Based on sensory analysis, shelf-life of grass carp fillets was 6days for control and 8days for treatment groups. Among the essential oils, oregano essential oil exhibited the highest antimicrobial and antioxidant activities. GC-MS analysis of essential oils components revealed that carvacrol (88.64%) was the major component of oregano essential oil. According to the results of high-throughput sequencing, Aeromonas, Glutamicibacter, and Aequorivita were the predominant microbiota in fresh control samples. However, oregano essential oil decreased the relative abundance of Aeromonas, while thyme and star anise essential oils decreased the relative abundance of Glutamicibacter and Aequorivita in fresh treated samples. The microbial composition of both control and treatment groups became less diverse as storage time increased. Aeromonas and Pseudomonas were dominant in spoiled samples and contributed to fish spoilage. Compared to the control, essential oils effectively inhibited the growth of Aeromonas and Shewanella in grass carp fillets during chilled storage. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Application of self-organizing competition artificial neural network to logging data explanation of sandstone-hosted uranium deposits

    International Nuclear Information System (INIS)

    Xu Jianguo; Xu Xianli; Wang Weiguo

    2008-01-01

    The article describes the model construction of self-organizing competition artificial neural network, its principle and automatic recognition process of borehole lithology in detail, and then proves the efficiency of the neural network model for automatically recognizing the borehole lithology with some cases. The self-organizing competition artificial neural network has the ability of self- organization, self-adjustment and high permitting errors. Compared with the BP algorithm, it takes less calculation quantity and more rapidly converges. Furthermore, it can automatically confirm the category without the known sample information. Trial results based on contrasting the identification results of the borehole lithology with geological documentations, indicate that self-organizing artificial neural network can be well applied to automatically performing the category of borehole lithology, during the logging data explanation of sandstone-hosted uranium deposits. (authors)

  2. [Identification of spill oil species based on low concentration synchronous fluorescence spectra and RBF neural network].

    Science.gov (United States)

    Liu, Qian-qian; Wang, Chun-yan; Shi, Xiao-feng; Li, Wen-dong; Luan, Xiao-ning; Hou, Shi-lin; Zhang, Jin-liang; Zheng, Rong-er

    2012-04-01

    In this paper, a new method was developed to differentiate the spill oil samples. The synchronous fluorescence spectra in the lower nonlinear concentration range of 10(-2) - 10(-1) g x L(-1) were collected to get training data base. Radial basis function artificial neural network (RBF-ANN) was used to identify the samples sets, along with principal component analysis (PCA) as the feature extraction method. The recognition rate of the closely-related oil source samples is 92%. All the results demonstrated that the proposed method could identify the crude oil samples effectively by just one synchronous spectrum of the spill oil sample. The method was supposed to be very suitable to the real-time spill oil identification, and can also be easily applied to the oil logging and the analysis of other multi-PAHs or multi-fluorescent mixtures.

  3. Surface roughness prediction of particulate composites using artificial neural networks in turning operation

    Directory of Open Access Journals (Sweden)

    Mohammad Ramezani

    2015-07-01

    Full Text Available A number of factors, e.g. cutting speed and feed rate, affect the surface roughness in machining process. In this paper, an Artificial Neural Network model was used to forecast surface roughness with related inputs, including cutting speed and feed rate. The output of the ANN model input parameters related to the machined surface roughness parameters. In this research, twelve samples of experimental data were used to train the network. Moreover, four other experimental tests were implemented to test the network. The study concludes that ANN was a reliable and accurate method for predicting machining parameters in CNC turning operation of Particulate Reinforced Aluminum Matrix Composites (PAMCs specimens with 0%, 5%, 10% and 15% filler. The aim of this work is to decrease the production cost and consequently increase the production rate of these materials for industry without any trial and error method procedure.

  4. Detection of directional eye movements based on the electrooculogram signals through an artificial neural network

    International Nuclear Information System (INIS)

    Erkaymaz, Hande; Ozer, Mahmut; Orak, İlhami Muharrem

    2015-01-01

    The electrooculogram signals are very important at extracting information about detection of directional eye movements. Therefore, in this study, we propose a new intelligent detection model involving an artificial neural network for the eye movements based on the electrooculogram signals. In addition to conventional eye movements, our model also involves the detection of tic and blinking of an eye. We extract only two features from the electrooculogram signals, and use them as inputs for a feed-forwarded artificial neural network. We develop a new approach to compute these two features, which we call it as a movement range. The results suggest that the proposed model have a potential to become a new tool to determine the directional eye movements accurately

  5. Prediction of the hardness profile of an AISI 4340 steel cylinder heat-treated by laser - 3D and artificial neural networks modelling and experimental validation

    Energy Technology Data Exchange (ETDEWEB)

    Hadhri, Mahdi; Ouafi, Abderazzak El; Barka, Noureddine [University of Quebec, Rimouski (Canada)

    2017-02-15

    This paper presents a comprehensive approach developed to design an effective prediction model for hardness profile in laser surface transformation hardening process. Based on finite element method and Artificial neural networks, the proposed approach is built progressively by (i) examining the laser hardening parameters and conditions known to have an influence on the hardened surface attributes through a structured experimental investigation, (ii) investigating the laser hardening parameters effects on the hardness profile through extensive 3D modeling and simulation efforts and (ii) integrating the hardening process parameters via neural network model for hardness profile prediction. The experimental validation conducted on AISI4340 steel using a commercial 3 kW Nd:Yag laser, confirm the feasibility and efficiency of the proposed approach leading to an accurate and reliable hardness profile prediction model. With a maximum relative error of about 10 % under various practical conditions, the predictive model can be considered as effective especially in the case of a relatively complex system such as laser surface transformation hardening process.

  6. Prediction of the hardness profile of an AISI 4340 steel cylinder heat-treated by laser - 3D and artificial neural networks modelling and experimental validation

    International Nuclear Information System (INIS)

    Hadhri, Mahdi; Ouafi, Abderazzak El; Barka, Noureddine

    2017-01-01

    This paper presents a comprehensive approach developed to design an effective prediction model for hardness profile in laser surface transformation hardening process. Based on finite element method and Artificial neural networks, the proposed approach is built progressively by (i) examining the laser hardening parameters and conditions known to have an influence on the hardened surface attributes through a structured experimental investigation, (ii) investigating the laser hardening parameters effects on the hardness profile through extensive 3D modeling and simulation efforts and (ii) integrating the hardening process parameters via neural network model for hardness profile prediction. The experimental validation conducted on AISI4340 steel using a commercial 3 kW Nd:Yag laser, confirm the feasibility and efficiency of the proposed approach leading to an accurate and reliable hardness profile prediction model. With a maximum relative error of about 10 % under various practical conditions, the predictive model can be considered as effective especially in the case of a relatively complex system such as laser surface transformation hardening process

  7. Optimization of Operation Parameters for Helical Flow Cleanout with Supercritical CO2 in Horizontal Wells Using Back-Propagation Artificial Neural Network.

    Science.gov (United States)

    Song, Xianzhi; Peng, Chi; Li, Gensheng; He, Zhenguo; Wang, Haizhu

    2016-01-01

    Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells.

  8. Optimization of Operation Parameters for Helical Flow Cleanout with Supercritical CO2 in Horizontal Wells Using Back-Propagation Artificial Neural Network.

    Directory of Open Access Journals (Sweden)

    Xianzhi Song

    Full Text Available Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2 as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in

  9. Artificial neural networks for plasma spectroscopy analysis

    International Nuclear Information System (INIS)

    Morgan, W.L.; Larsen, J.T.; Goldstein, W.H.

    1992-01-01

    Artificial neural networks have been applied to a variety of signal processing and image recognition problems. Of the several common neural models the feed-forward, back-propagation network is well suited for the analysis of scientific laboratory data, which can be viewed as a pattern recognition problem. The authors present a discussion of the basic neural network concepts and illustrate its potential for analysis of experiments by applying it to the spectra of laser produced plasmas in order to obtain estimates of electron temperatures and densities. Although these are high temperature and density plasmas, the neural network technique may be of interest in the analysis of the low temperature and density plasmas characteristic of experiments and devices in gaseous electronics

  10. Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Wenqian Ruan

    2017-12-01

    Full Text Available The commercially available nanoscale zerovalent zinc (nZVZ was used as an adsorbent for the removal of malachite green (MG from aqueous solutions. This material was characterized by X-ray diffraction and X-ray photoelectron spectroscopy. The advanced experimental design tools were adopted to study the effect of process parameters (viz. initial pH, temperature, contact time and initial concentration and to reduce number of trials and cost. Response surface methodology and rapidly developing artificial intelligence technologies, i.e., artificial neural network coupled with particle swarm optimization (ANN-PSO and artificial neural network coupled with genetic algorithm (ANN-GA were employed for predicting the optimum process variables and obtaining the maximum removal efficiency of MG. The results showed that the removal efficiency predicted by ANN-GA (94.12% was compatible with the experimental value (90.72%. Furthermore, the Langmuir isotherm was found to be the best model to describe the adsorption of MG onto nZVZ, while the maximum adsorption capacity was calculated to be 1000.00 mg/g. The kinetics for adsorption of MG onto nZVZ was found to follow the pseudo-second-order kinetic model. Thermodynamic parameters (ΔG0, ΔH0 and ΔS0 were calculated from the Van’t Hoff plot of lnKc vs. 1/T in order to discuss the removal mechanism of MG.

  11. Supercritical Fluid Extraction of Minor Components of Vegetable Oils: beta-Sitosterol

    Czech Academy of Sciences Publication Activity Database

    Sovová, Helena; Galushko, A.A.; Stateva, R.P.; Rochová, Kristina; Sajfrtová, Marie; Bártlová, Milena

    2010-01-01

    Roč. 101, č. 2 (2010), s. 201-209 ISSN 0260-8774 R&D Projects: GA MŠk 2B06024 Institutional research plan: CEZ:AV0Z40720504 Keywords : supercritical fluid extraction * sea buckthorn oil * beta-sitosterol Subject RIV: CI - Industrial Chemistry, Chemical Engineering Impact factor: 2.168, year: 2010

  12. Artificial Neural Network Maximum Power Point Tracker for Solar Electric Vehicle

    Institute of Scientific and Technical Information of China (English)

    Theodore Amissah OCRAN; CAO Junyi; CAO Binggang; SUN Xinghua

    2005-01-01

    This paper proposes an artificial neural network maximum power point tracker (MPPT) for solar electric vehicles. The MPPT is based on a highly efficient boost converter with insulated gate bipolar transistor (IGBT) power switch. The reference voltage for MPPT is obtained by artificial neural network (ANN) with gradient descent momentum algorithm. The tracking algorithm changes the duty-cycle of the converter so that the PV-module voltage equals the voltage corresponding to the MPPT at any given insolation, temperature, and load conditions. For fast response, the system is implemented using digital signal processor (DSP). The overall system stability is improved by including a proportional-integral-derivative (PID) controller, which is also used to match the reference and battery voltage levels. The controller, based on the information supplied by the ANN, generates the boost converter duty-cycle. The energy obtained is used to charge the lithium ion battery stack for the solar vehicle. The experimental and simulation results show that the proposed scheme is highly efficient.

  13. Hybrid response surface methodology-artificial neural network optimization of drying process of banana slices in a forced convective dryer.

    Science.gov (United States)

    Taheri-Garavand, Amin; Karimi, Fatemeh; Karimi, Mahmoud; Lotfi, Valiullah; Khoobbakht, Golmohammad

    2018-06-01

    The aim of the study is to fit models for predicting surfaces using the response surface methodology and the artificial neural network to optimize for obtaining the maximum acceptability using desirability functions methodology in a hot air drying process of banana slices. The drying air temperature, air velocity, and drying time were chosen as independent factors and moisture content, drying rate, energy efficiency, and exergy efficiency were dependent variables or responses in the mentioned drying process. A rotatable central composite design as an adequate method was used to develop models for the responses in the response surface methodology. Moreover, isoresponse contour plots were useful to predict the results by performing only a limited set of experiments. The optimum operating conditions obtained from the artificial neural network models were moisture content 0.14 g/g, drying rate 1.03 g water/g h, energy efficiency 0.61, and exergy efficiency 0.91, when the air temperature, air velocity, and drying time values were equal to -0.42 (74.2 ℃), 1.00 (1.50 m/s), and -0.17 (2.50 h) in the coded units, respectively.

  14. Improved Artificial Fish Algorithm for Parameters Optimization of PID Neural Network

    OpenAIRE

    Jing Wang; Yourui Huang

    2013-01-01

    In order to solve problems such as initial weights are difficult to be determined, training results are easy to trap in local minima in optimization process of PID neural network parameters by traditional BP algorithm, this paper proposed a new method based on improved artificial fish algorithm for parameters optimization of PID neural network. This improved artificial fish algorithm uses a composite adaptive artificial fish algorithm based on optimal artificial fish and nearest artificial fi...

  15. Artificial neural network with self-organizing mapping for reactor stability monitoring

    International Nuclear Information System (INIS)

    Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro; Narabayashi, Tadashi

    2008-01-01

    In BWR stability monitoring damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; In this method, measured fluctuating signal is decomposed into some independent components and the signal component directly related to stability is extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal component efficiently. The self-organizing map (SOM) is one of the artificial neural networks and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal component more quickly and more accurately, and the availability was confirmed through the feasibility study. (author)

  16. Supercritical fluid extraction of volatile and non-volatile compounds from Schinus molle L.

    Directory of Open Access Journals (Sweden)

    M. S. T. Barroso

    2011-06-01

    Full Text Available Schinus molle L., also known as pepper tree, has been reported to have antimicrobial, antifungal, anti-inflammatory, antispasmodic, antipyretic, antitumoural and cicatrizing properties. This work studies supercritical fluid extraction (SFE to obtain volatile and non-volatile compounds from the aerial parts of Schinus molle L. and the influence of the process on the composition of the extracts. Experiments were performed in a pilot-scale extractor with a capacity of 1 L at pressures of 9, 10, 12, 15 and 20 MPa at 323.15 K. The volatile compounds were obtained by CO2 supercritical extraction with moderate pressure (9 MPa, whereas the non-volatile compounds were extracted at higher pressure (12 to 20 MPa. The analysis of the essential oil was carried out by GC-MS and the main compounds identified were sabinene, limonene, D-germacrene, bicyclogermacrene, and spathulenol. For the non-volatile extracts, the total phenolic content was determined by the Folin-Ciocalteau method. Moreover, one of the goals of this study was to compare the experimental data with the simulated yields predicted by a mathematical model based on mass transfer. The model used requires three adjustable parameters to predict the experimental extraction yield curves.

  17. Artificial Neural Network L* from different magnetospheric field models

    Science.gov (United States)

    Yu, Y.; Koller, J.; Zaharia, S. G.; Jordanova, V. K.

    2011-12-01

    The third adiabatic invariant L* plays an important role in modeling and understanding the radiation belt dynamics. The popular way to numerically obtain the L* value follows the recipe described by Roederer [1970], which is, however, slow and computational expensive. This work focuses on a new technique, which can compute the L* value in microseconds without losing much accuracy: artificial neural networks. Since L* is related to the magnetic flux enclosed by a particle drift shell, global magnetic field information needed to trace the drift shell is required. A series of currently popular empirical magnetic field models are applied to create the L* data pool using 1 million data samples which are randomly selected within a solar cycle and within the global magnetosphere. The networks, trained from the above L* data pool, can thereby be used for fairly efficient L* calculation given input parameters valid within the trained temporal and spatial range. Besides the empirical magnetospheric models, a physics-based self-consistent inner magnetosphere model (RAM-SCB) developed at LANL is also utilized to calculate L* values and then to train the L* neural network. This model better predicts the magnetospheric configuration and therefore can significantly improve the L*. The above neural network L* technique will enable, for the first time, comprehensive solar-cycle long studies of radiation belt processes. However, neural networks trained from different magnetic field models can result in different L* values, which could cause mis-interpretation of radiation belt dynamics, such as where the source of the radiation belt charged particle is and which mechanism is dominant in accelerating the particles. Such a fact calls for attention to cautiously choose a magnetospheric field model for the L* calculation.

  18. NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY

    Directory of Open Access Journals (Sweden)

    Yao Yevenyo Ziggah

    Full Text Available Abstract: Geocentric translation model (GTM in recent times has not gained much popularity in coordinate transformation research due to its attainable accuracy. Accurate transformation of coordinate is a major goal and essential procedure for the solution of a number of important geodetic problems. Therefore, motivated by the successful application of Artificial Intelligence techniques in geodesy, this study developed, tested and compared a novel technique capable of improving the accuracy of GTM. First, GTM based on official parameters (OP and new parameters determined using the arithmetic mean (AM were applied to transform coordinate from global WGS84 datum to local Accra datum. On the basis of the results, the new parameters (AM attained a maximum horizontal position error of 1.99 m compared to the 2.75 m attained by OP. In line with this, artificial neural network technology of backpropagation neural network (BPNN, radial basis function neural network (RBFNN and generalized regression neural