Search results for "Gaussia"

showing 10 items of 653 documents

Ab initio study of rotational isomerism and electronic structure of isomeric bipyrroles

1985

Abstract Ab initio calculations using STO-3G and 4-31G basis sets have been performed on the internal rotation barriers and conformational stabilities for 2,3′- and 3,3′-bipyrrole. The twofold rotation potential predicted for both isomers at minimal basis level becomes a more involved fourfold potential when the split-valence basis set is employed, because it takes into account more properly the nonbonded interannular interactions. A transoid-gauche minimum is predicted to have the minimal absolute conformational energy in both isomers. The electronic structure of the highest occupied MOs of 2,2′-, 2,3′- and 3,3′-bipyrrole are analyzed in terms of the single pyrrole MO pattern and a similar…

ChemistryGaussian orbitalAb initioElectronElectronic structureCondensed Matter PhysicsRotationBiochemistryMolecular physicsPlanarity testingComputational chemistryAb initio quantum chemistry methodsPhysical and Theoretical ChemistryBasis setJournal of Molecular Structure: THEOCHEM
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Gaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence dataset.

2013

Abstract: Biochemical and structural leaf properties such as chlorophyll content (Chl), nitrogen content (N), leaf water content (LWC), and specific leaf area (SLA) have the benefit to be estimated through nondestructive spectral measurements. Current practices, however, mainly focus on a limited amount of wavelength bands while more information could be extracted from other wavelengths in the full range (400-2500 nm) spectrum. In this research, leaf characteristics were estimated from a field-based multi-species dataset, covering a wide range in leaf structures and Chl concentrations. The dataset contains leaves with extremely high Chl concentrations (>100 mu g cm(-2)), which are seldom es…

ChlorophyllSpecific leaf areaNitrogenBiophysicsRed edgeTreesAbsorbancesymbols.namesakeRadiology Nuclear Medicine and imagingGaussian processWater contentBiologyRemote sensingMathematicsRadiationRadiological and Ultrasound TechnologyPhysicsHyperspectral imagingWaterRegression analysisPlant LeavesChemistrySpectrometry FluorescencesymbolsCurve fittingAlgorithmsJournal of photochemistry and photobiology. B, Biology
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Approaches to estimate the time and height at the peak maximum in liquid chromatography based on a modified Gaussian model

2011

The time and height at the peak maximum are key parameters to describe a chromatographic peak with prediction or optimization purposes, or in the qualitative/quantitative analysis of samples. Three different approaches to estimate these parameters, using the experimental points in the peak maximum region, are here described and compared. The approaches are based on the reliable description of the peak profile using a modified Gaussian model with a parabolic variance (PVMG). In the first approach, non-linear fitting of the chromatographic data to the PVMG model is carried out to obtain the time and height at the peak maximum (Approach I). In the other two approaches, the PVMG model is linear…

Chromatography Reverse-PhaseSulfonamidesChromatographyLinear fittingChemistryElutionOrganic ChemistryNormal DistributionGeneral MedicineBiochemistryNoise (electronics)Analytical Chemistrysymbols.namesakeModels ChemicalRobustness (computer science)symbolsAlprenololGaussian network modelAlgorithmsJournal of Chromatography A
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Characterization of chromatographic peaks using the linearly modified Gaussian model. Comparison with the bi-Gaussian and the Foley and Dorsey approa…

2017

To characterize column performance in liquid chromatography, several parameters must be obtained from experimental data. These parameters can be computed through the numerical integration of the net signal to calculate the moments after subtraction of the baseline. This requires the establishment of the peak integration limits. The whole process introduces significant uncertainty. For this reason, several alternative procedures have been proposed to measure the area, mean time and variance, based on the assumption that the chromatographic peak can be described with a mathematical function. This allows the calculation of the peak position and variance making use of the values of the experime…

ChromatographyChemistryGaussianmedia_common.quotation_subject010401 analytical chemistryOrganic ChemistryGeneral MedicineVariance (accounting)010402 general chemistry01 natural sciencesBiochemistryMeasure (mathematics)Asymmetry0104 chemical sciencesAnalytical ChemistryNumerical integrationsymbols.namesakePosition (vector)Linear ModelssymbolsRange (statistics)Gaussian network modelChromatography Liquidmedia_commonJournal of Chromatography A
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Parabolic-Lorentzian modified Gaussian model for describing and deconvolving chromatographic peaks.

2002

Abstract A new mathematical model for characterising skewed chromatographic peaks, which improves the previously reported polynomially modified Gaussian (PMG) model, is proposed. The model is a Gaussian based equation whose variance is a combined parabolic-Lorentzian function. The parabola accounts for the non-Gaussian shaped peak, whereas the Lorentzian function cancels the variance growth out of the elution region, which gives rise to a problematic baseline increase in the PMG model. The proposed parabolic-Lorentzian modified Gaussian (PLMG) model makes a correct description of peaks showing a wide range of asymmetry with positive and/or negative skewness. The new model is shown to give b…

ChromatographyChromatographyModels StatisticalChemistryGaussianOrganic ChemistryCauchy distributionGeneral MedicineFunction (mathematics)BiochemistryAnalytical Chemistrysymbols.namesakeSkewnesssymbolsKurtosisDeconvolutionGaussian network modelAntibacterial agentJournal of chromatography. A
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Peak deconvolution in one-dimensional chromatography using a two-way data approach.

2002

A deconvolution methodology for overlapped chromatographic signals is proposed. Several single-wavelength chromatograms of binary mixtures, obtained in different runs at diverse concentration ratios of the individual components, were simultaneously processed (multi-batch approach), after being arranged as two-way data. The chromatograms were modelled as linear combinations of forced peak profiles according to a polynomially modified Gaussian equation. The fitting was performed with a previously reported hybrid genetic algorithm with local search, leaving all model parameters free. The approach yielded more accurate solutions than those found when each experimental chromatogram was fitted in…

ChromatographyChromatographyResolution (mass spectrometry)Matching (graph theory)Chemistrybusiness.industryOrganic ChemistryBinary numberGeneral MedicineBiochemistryAnalytical Chemistrysymbols.namesakeData Interpretation StatisticalGaussian functionsymbolsFigure of meritLocal search (optimization)DeconvolutionbusinessLinear combinationJournal of chromatography. A
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New approaches based on modified Gaussian models for the prediction of chromatographic peaks

2012

Abstract The description of skewed chromatographic peaks has been discussed extensively and many functions have been proposed. Among these, the Polynomially Modified Gaussian (PMG) models interpret the deviations from ideality as a change in the standard deviation with time. This approach has shown a high accuracy in the fitting to tailing and fronting peaks. However, it has the drawback of the uncontrolled growth of the predicted signal outside the elution region, which departs from the experimental baseline. To solve this problem, the Parabolic-Lorentzian Modified Gaussian (PLMG) model was developed. This combines a parabola that describes the variance change in the peak region, and a Lor…

ChromatographyDegree (graph theory)Chemistrymedia_common.quotation_subjectGaussianParabolaCauchy distributionVariance (accounting)BiochemistrySignalAsymmetryStandard deviationAnalytical Chemistrysymbols.namesakesymbolsEnvironmental ChemistrySpectroscopymedia_commonAnalytica Chimica Acta
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Coherence resonance in Bonhoeffer-Van der Pol circuit

2009

International audience; A nonlinear electronic circuit simulating the neuronal activity in a noisy environment is proposed. This electronic circuit is exactly ruled by the set of Bonhoeffer-Van Der Pol equations and is excited with a Gaussian noise. Without external deterministic stimuli, it is shown that the circuit exhibits the so-called 'coherence resonance' phenomenon.

Circuit design[ NLIN.NLIN-CD ] Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]02 engineering and technology01 natural sciencesResonance (particle physics)symbols.namesakeComputer Science::Hardware ArchitectureComputer Science::Emerging TechnologiesControl theoryQuantum mechanics0103 physical sciences0202 electrical engineering electronic engineering information engineeringElectrical and Electronic Engineering010306 general physicsMathematicsElectronic circuitVan der Pol oscillatorAmplifier020208 electrical & electronic engineering[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/ElectronicsNonlinear systemGaussian noise[NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]symbolsRLC circuit
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A nonlinear electronic circuit mimicking the neuronal activity in presence of noise

2013

We propose a nonlinear electronic circuit simulating the neuronal activity in a noisy environment. This electronic circuit is ruled by the set of Bonhaeffer-Van der Pol equations and is excited with a white gaussian noise, that is without external deterministic stimuli. Under these conditions, our circuits reveals the Coherence Resonance signature, that is an optimum of regularity in the system response for a given noise intensity.

Coherence ResonanceStochastic resonanceneural network[PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph]02 engineering and technologyTopology01 natural sciencesNoise (electronics)symbols.namesakeComputer Science::Emerging TechnologiesNoise generator[NLIN.NLIN-PS]Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]Control theory[ PHYS.PHYS.PHYS-BIO-PH ] Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph]0103 physical sciences[NLIN.NLIN-PS] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]0202 electrical engineering electronic engineering information engineering[ NLIN.NLIN-PS ] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]Value noisestochastic resonance010306 general physicsComputingMilieux_MISCELLANEOUSPhysics[PHYS.PHYS.PHYS-BIO-PH] Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph]020208 electrical & electronic engineeringShot noiseWhite noiseNoise floor[SPI.TRON] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/ElectronicsGaussian noisesymbolsnonlinear circuit
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Covering and differentiation

1995

CombinatoricsEuclidean distanceDiscrete mathematicsConvex geometryEuclidean spaceEuclidean geometryAffine spaceBall (mathematics)Euclidean distance matrixGaussian measureMathematics
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