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  • 1
    Electronic Resource
    Electronic Resource
    Silent Pituitary Tuberculoma Associated with Pituitary Adenoma (1999)
    Springer
    Acta neurochirurgica 141 (1999), S. 785-786 
    Details
    ISSN: 0942-0940
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s007010050378
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Idiopathic granulomatous hypophysitis: are there reliable, constant radiological and clinical diagnostic criterias? (2000)
    Springer
    Neuroradiology 42 (2000), S. 890-894 
    Details
    ISSN: 1432-1920
    Keywords: Key words Pituitary ; Hypophysitis, granulomatous ; Magnetic resonance imaging
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract Idiopathic granulomatous hypophysitis is a rare inflammatory disease of unknown aetiology; few cases are reported. We review the clinical presentation and radiological characteristics of these cases and our own experience with three new surgical cases, to determine diagnostic criteria. MRI of three cases revealed sellar lesions extending into the chiasmatic cistern. Their shape varied, from dumbbell to spherical and elliptical. All were isointense with the brain on T1-weighted images and gave heterogeneously high signal on T2-weighted images. Contrast enhancement was homogeneous in one case and heterogeneous in another. The pituitary stalk could not be identified. There was no dural enhancement. The sphenoid sinus mucosa was thickened in two cases and normal in one.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s002340000481
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    Paper (German National Licenses)
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  • 3
    Electronic Resource
    Electronic Resource
    The Application of Artificial Neural Networks for the Prediction of Water Quality of Polluted Aquifer (2000)
    Springer
    Water, air & soil pollution 119 (2000), S. 275-294 
    Details
    ISSN: 1573-2932
    Keywords: Artificial Neural Network ; chlorine concentration ; groundwater simulation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Abstract From hydrocarbon reservoirs, beside of oil and natural gas, thebrine is also produced as a waste material, which may bedischarged at the surface or re-injected into the ground. Whenthe wastewater is injected into the ground, it may be mixed withfresh water source due to to several reasons. Forecastingthe pollutant concentrations by knowing the historical data atseveral locations on a field has a great importance to take thenecessary precautions before the undesired situations arehappened.The aim of this study is to describe Artificial Neural Network(ANN) approach that can be used to forecast the future pollutantconcentrations and hydraulic heads of a groundwater source. Inorder to check the validity of the approach, a hypotheticalfield data as a case study were produced by using groundwatersimulator (MOC). Hydraulic heads and chlorine concentrationswere obtained from groundwater simulations. ANN was trained byusing the historical data of last two years. The future chlorineconcentrations and hydraulic heads were estimated by applyingboth the long-term and the short-term ANN predictions. Anapproach to overcome the effects of using the data of a singlewell was proposed by favouring the use of data set for aneighbour well. The higher errors for the long-term ANNpredictions were obtained at the observation wells, which wereaway from an injection well. In order to minimise the differencebetween the results of long-term ANN approach and flowsimulation runs; the short-term prediction was applied. The useof short-term prediction for the wells away from an injectionwell was found to give highly acceptable results when thelong-term prediction fails. The average absolute error obtainedfrom the shortterm forecasting study was 3.5% when compared to18.5% for the long-term forecasting.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1005165315197
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  • 4
    Electronic Resource
    Electronic Resource
    Genetic Algorithms for Optimizing the Remediation of Contaminated Aquifer (2000)
    Springer
    Transport in porous media 41 (2000), S. 149-171 
    Details
    ISSN: 1573-1634
    Keywords: hydrodynamic isolation ; plume ; remediation ; genetic algorithms ; optimization ; aquifer
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Technology
    Notes: Abstract Produced water constitutes a large amount of waste fluids during the production operation of an oil field. Underground injection for disposing the wastewater from hydrocarbon production is an engineering problem due to the possibility of leakage of injected pollutant material from receiving medium to a drinking water source. This paper describes a method for optimization of polluted aquifer remediation design using one of the artificial intelligence optimization methods, namely Genetic Algorithms (GAs). As a case study, the contaminated area was created by using a groundwater transport simulator, which is based on Method of Characteristics (MOC). Then, the developed computer program was run to find the optimum solution for remediation, and the solution yielded from the program was verified by using a groundwater simulator. The plume was captured and the concentration level of chloride ion within the aquifer was diminished by using extraction wells. The analytical model approach provided different alternatives for appropriate isolation of plume. GAs were used as an optimization technique for making a decision among the alternatives, by considering operation time, number of wells, pumping rate and drawdown as decision variables and constraints.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1006774101991
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    Paper (German National Licenses)
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