scholarly journals Multiple isotopic models using various kinetic fractionation coefficients to estimateδ18O of leaf water

2018 ◽  
Author(s):  
Yonge Zhang ◽  
Xinxiao Yu ◽  
Lihua Chen ◽  
Guodong Jia
Keyword(s):  
Steady State ◽  
Time Scale ◽  
Model Performance ◽  
Special Care ◽  
Leaf Water ◽  
Data Sets ◽  
Kinetic Fractionation ◽  
Improved Model ◽  
Material Exchange ◽  
Good Agreement

AbstractInvestigation ofδ18O of leaf water may improve our understanding of the evapotranspiration partitioning and material exchange between the inside and outside of leaves. In this study,δ18O of bulk leaf water (δL,b) was estimated by both isotopic–steady–state (ISS) and non–steady–state (NSS) assumptions considering the Péclet effect. Specifically, we carefully modified kinetic fractionation coefficients (αk). The results showed that the Péclet effect is required to predictδL,b. On the diel time scale, both NSS assumption + Péclet effect (NSS + P) and ISS assumption + Péclet effect (ISS + P) using modifiedαk(αk–modified) forδL,bshowed a good agreement with observedδL,b(p> 0.05). When using previously proposedαk, however, both NSS + P and ISS + P were not reliable estimators ofδL,b(p< 0.05). On a longer time scale (days), estimates of daily meanδL,bfrom ISS + P outperformed the estimates from NSS + P when using the sameαkvalues. Also, the employment ofαk–modifiedimproved model performance in predicting daily meanδL,bcompared to the use of previously proposedαk. Clearly, special care must be taken concerningαkwhen using isotopic models to estimateδL,b.HighlightFor hourly and daily mean data sets, the employment of modified kinetic fractionation coefficients significantly improved model performance forδ18O of bulk leaf water.

Predicting Flowering of Rhizome Johnsongrass (Sorghum halepense) Populations Using a Temperature-dependent Model

Weed Science ◽  
1996 ◽  
Vol 44 (2) ◽  
pp. 266-272 ◽  
Author(s):  
David L. Holshouser ◽  
James M. Chandler
Keyword(s):  
Rate Equation ◽  
Model Performance ◽  
Population Level ◽  
Weibull Function ◽  
Data Sets ◽  
Temperature Dependent ◽  
Independent Field ◽  
Dependent Model ◽  
Improved Model ◽  
Temperature Dependent Model

Research was conducted to formulate a temperature-dependent population-level model for rhizome johnsongrass flowering. A nonlinear poikilotherm rate equation was used to describe development as a function of temperature and a temperature-independent Weibull function was used to distribute development times for the population. Johnsongrass flowering data were collected under constant temperature conditions to parameterize the poikilotherm rate equation and Weibull function. Coupling the poikilotherm rate equation with the Weibull function resulted in a population level temperature-dependent model. The model was validated against independent field data sets. The model accurately predicted rhizome johnsongrass flowering from plants emerging in the spring. The model performed poorly for plants emerging in summer. Adjustments to the high-temperature inhibition parameter of the poikilotherm rate equation improved model performance in the summer without affecting spring predictions.

Selection of one-dimensional sedimentation: models for on-line use

1995 ◽  
Vol 31 (2) ◽  
pp. 193-204 ◽  
Author(s):  
Koen Grijspeerdt ◽  
Peter Vanrolleghem ◽  
Willy Verstraete
Keyword(s):  
Steady State ◽  
Selection Criteria ◽  
Data Sets ◽  
Concentration Profiles ◽  
A Posteriori ◽  
One Dimensional ◽  
On Line ◽  
Dynamic Concentration ◽  
Selection Of ◽  
Modelling Task

A comparative study of several recently proposed one-dimensional sedimentation models has been made. This has been achieved by fitting these models to steady-state and dynamic concentration profiles obtained in a down-scaled secondary decanter. The models were evaluated with several a posteriori model selection criteria. Since the purpose of the modelling task is to do on-line simulations, the calculation time was used as one of the selection criteria. Finally, the practical identifiability of the models for the available data sets was also investigated. It could be concluded that the model of Takács et al. (1991) gave the most reliable results.

Systematic calculations of energy levels and transitions rates in Mo XXVIII

2020 ◽  
Vol 75 (8) ◽  
pp. 739-747
Author(s):  
Feng Hu ◽  
Yan Sun ◽  
Maofei Mei
Keyword(s):  
Energy Levels ◽  
Oscillator Strengths ◽  
Atomic Data ◽  
Data Sets ◽  
Electron Systems ◽  
Excitation Energies ◽  
Experimental Values ◽  
Qed Effects ◽  
Hyperfine Structures ◽  
Good Agreement

AbstractComplete and consistent atomic data, including excitation energies, lifetimes, wavelengths, hyperfine structures, Landé gJ-factors and E1, E2, M1, and M2 line strengths, oscillator strengths, transitions rates are reported for the low-lying 41 levels of Mo XXVIII, belonging to the n = 3 states (1s22s22p6)3s23p3, 3s3p4, and 3s23p23d. High-accuracy calculations have been performed as benchmarks in the request for accurate treatments of relativity, electron correlation, and quantum electrodynamic (QED) effects in multi-valence-electron systems. Comparisons are made between the present two data sets, as well as with the experimental results and the experimentally compiled energy values of the National Institute for Standards and Technology wherever available. The calculated values including core-valence correction are found to be in a good agreement with other theoretical and experimental values. The present results are accurate enough for identification and deblending of emission lines involving the n = 3 levels, and are also useful for modeling and diagnosing plasmas.

scholarly journals An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

2020 ◽  
Vol 12 (1) ◽  
pp. 580-597
Author(s):  
Mohamad Hamzeh ◽  
Farid Karimipour
Keyword(s):  
Spatial Data ◽  
Spatial Models ◽  
Model Performance ◽  
Essential Elements ◽  
Petroleum System ◽  
Petroleum Exploration ◽  
System Level ◽  
Data Sets ◽  
Petroleum Potential ◽  
Spatial Data Sets

AbstractAn inevitable aspect of modern petroleum exploration is the simultaneous consideration of large, complex, and disparate spatial data sets. In this context, the present article proposes the optimized fuzzy ELECTRE (OFE) approach based on combining the artificial bee colony (ABC) optimization algorithm, fuzzy logic, and an outranking method to assess petroleum potential at the petroleum system level in a spatial framework using experts’ knowledge and the information available in the discovered petroleum accumulations simultaneously. It uses the characteristics of the essential elements of a petroleum system as key criteria. To demonstrate the approach, a case study was conducted on the Red River petroleum system of the Williston Basin. Having completed the assorted preprocessing steps, eight spatial data sets associated with the criteria were integrated using the OFE to produce a map that makes it possible to delineate the areas with the highest petroleum potential and the lowest risk for further exploratory investigations. The success and prediction rate curves were used to measure the performance of the model. Both success and prediction accuracies lie in the range of 80–90%, indicating an excellent model performance. Considering the five-class petroleum potential, the proposed approach outperforms the spatial models used in the previous studies. In addition, comparing the results of the FE and OFE indicated that the optimization of the weights by the ABC algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration.

scholarly journals Influence of Random Forest Hyperparameterization on Short-Term Runoff Forecasting in an Andean Mountain Catchment

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 238
Author(s):  
Pablo Contreras ◽  
Johanna Orellana-Alvear ◽  
Paul Muñoz ◽  
Jörg Bendix ◽  
Rolando Célleri
Keyword(s):  
Random Forest ◽  
Model Performance ◽  
Early Warning Systems ◽  
Point Of View ◽  
Lead Times ◽  
Physical Parameters ◽  
Runoff Forecasting ◽  
Spatio Temporal ◽  
Improved Model ◽  
Search Approach

The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach for applications addressing runoff forecasting in remote areas. This machine learning approach can overcome the limitations of scarce spatio-temporal data and physical parameters needed for process-based hydrological models. However, the influence of RF hyperparameters is still uncertain and needs to be explored. Therefore, the aim of this study is to analyze the sensitivity of RF runoff forecasting models of varying lead time to the hyperparameters of the algorithm. For this, models were trained by using (a) default and (b) extensive hyperparameter combinations through a grid-search approach that allow reaching the optimal set. Model performances were assessed based on the R2, %Bias, and RMSE metrics. We found that: (i) The most influencing hyperparameter is the number of trees in the forest, however the combination of the depth of the tree and the number of features hyperparameters produced the highest variability-instability on the models. (ii) Hyperparameter optimization significantly improved model performance for higher lead times (12- and 24-h). For instance, the performance of the 12-h forecasting model under default RF hyperparameters improved to R2 = 0.41 after optimization (gain of 0.17). However, for short lead times (4-h) there was no significant model improvement (0.69 < R2 < 0.70). (iii) There is a range of values for each hyperparameter in which the performance of the model is not significantly affected but remains close to the optimal. Thus, a compromise between hyperparameter interactions (i.e., their values) can produce similar high model performances. Model improvements after optimization can be explained from a hydrological point of view, the generalization ability for lead times larger than the concentration time of the catchment tend to rely more on hyperparameterization than in what they can learn from the input data. This insight can help in the development of operational early warning systems.

scholarly journals The frequency of Kozai–Lidov disc oscillation driven giant outbursts in Be/X-ray binaries

2019 ◽  
Vol 489 (2) ◽  
pp. 1797-1804 ◽  
Author(s):  
Rebecca G Martin ◽  
Alessia Franchini
Keyword(s):  
Orbital Period ◽  
Time Scale ◽  
Orbital Plane ◽  
Material Flows ◽  
X Ray ◽  
System Parameters ◽  
Be Star ◽  
Chaotic Nature ◽  
X Ray Binaries ◽  
Good Agreement

ABSTRACT Giant outbursts of Be/X-ray binaries may occur when a Be-star disc undergoes strong eccentricity growth due to the Kozai–Lidov (KL) mechanism. The KL effect acts on a disc that is highly inclined to the binary orbital plane provided that the disc aspect ratio is sufficiently small. The eccentric disc overflows its Roche lobe and material flows from the Be star disc over to the companion neutron star causing X-ray activity. With N-body simulations and steady state decretion disc models we explore system parameters for which a disc in the Be/X-ray binary 4U 0115+634 is KL unstable and the resulting time-scale for the oscillations. We find good agreement between predictions of the model and the observed giant outburst time-scale provided that the disc is not completely destroyed by the outburst. This allows the outer disc to be replenished between outbursts and a sufficiently short KL oscillation time-scale. An initially eccentric disc has a shorter KL oscillation time-scale compared to an initially circular orbit disc. We suggest that the chaotic nature of the outbursts is caused by the sensitivity of the mechanism to the distribution of material within the disc. The outbursts continue provided that the Be star supplies material that is sufficiently misaligned to the binary orbital plane. We generalize our results to Be/X-ray binaries with varying orbital period and find that if the Be star disc is flared, it is more likely to be unstable to KL oscillations in a smaller orbital period binary, in agreement with observations.

scholarly journals An Improved Approach for Reduction of Defect Density Using Optimal Module Sizes

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Dinesh Verma ◽  
Shishir Kumar
Keyword(s):  
Goodness Of Fit ◽  
Defect Density ◽  
Optimal Size ◽  
Data Sets ◽  
Density Parameter ◽  
Proposed Model ◽  
Module Size ◽  
Curve Fitting Method ◽  
Improved Model ◽  
Effective Distribution

Nowadays, software developers are facing challenges in minimizing the number of defects during the software development. Using defect density parameter, developers can identify the possibilities of improvements in the product. Since the total number of defects depends on module size, so there is need to calculate the optimal size of the module to minimize the defect density. In this paper, an improved model has been formulated that indicates the relationship between defect density and variable size of modules. This relationship could be used for optimization of overall defect density using an effective distribution of modules sizes. Three available data sets related to concern aspect have been examined with the proposed model by taking the distinct values of variables and parameter by putting some constraint on parameters. Curve fitting method has been used to obtain the size of module with minimum defect density. Goodness of fit measures has been performed to validate the proposed model for data sets. The defect density can be optimized by effective distribution of size of modules. The larger modules can be broken into smaller modules and smaller modules can be merged to minimize the overall defect density.

PWA OF πN SCATTERING WITH FIXED-t DISPERSION RELATIONS

2005 ◽  
Vol 20 (08n09) ◽  
pp. 1810-1813
Author(s):  
PEKKO PIIROLA ◽  
M. E. SAINIO
Keyword(s):  
Data Base ◽  
Forward Direction ◽  
Dispersion Relations ◽  
Data Sets ◽  
Coupling Constant ◽  
Partial Waves ◽  
Scattering Measurements ◽  
Pion Nucleon ◽  
Good Agreement

The πN scattering measurements from last couple of decades are not in very good agreement with each other. In fact, using the different data sets one finds different values for the pion-nucleon coupling constant. An analysis with theoretical constraints is the only way to produce accurate partial waves. In this analysis, the fixed-t dispersion relations are used to ensure analyticity in the invariant amplitudes and to decrease the effects of inaccuracies in the data base. Pietarinen's expansion is the method used to enforce the dispersion constraints. The strength of the analyticity constraints is illustrated with C± amplitudes in the forward direction.

Comparison of Small-Angle Neutron Scattering of δ' Precipitation in an Al–Li Alloy at a High-Flux Reactor and at a Pulsed-Neutron Source

1997 ◽  
Vol 30 (5) ◽  
pp. 602-606 ◽  
Author(s):  
G. Albertini ◽  
F. Carsughi ◽  
R. Coppola ◽  
R. K. Heenan ◽  
M. Stefanon
Keyword(s):  
Neutron Scattering ◽  
Small Angle ◽  
Small Angle Neutron Scattering ◽  
Size Distribution Function ◽  
Data Sets ◽  
Flux Reactor ◽  
Average Size ◽  
Pulsed Neutron ◽  
Pulsed Neutron Source ◽  
Good Agreement

Two different small-angle neutron scattering (SANS) facilities, the D11 camera at the Institut Laue–Langevin (ILL, Grenoble, France) and the LOQ time-of-flight diffractometer at the Rutherford Appleton Laboratory (RAL, Didcot, Oxon, England), were used in the investigations of δ′-Al3Li precipitation at 463 K in Al–Li 3% alloy. The results obtained from the steady-state reactor and from the pulsed source by using two different data-acquisition techniques and two different procedures for data analysis are compared. The SANS curves for the same set of samples investigated using the two different instruments are in good agreement within the experimental uncertainties. A check was also made on the metallurgically relevant quantities, namely the average size and the size-distribution function of the δ′ precipitates at the various stages of the ageing process, obtained from the two sets of SANS curves by applying the same numerical method. Good agreement was found between the results from the two data sets.

Sign in / Sign up

Export Citation Format

Share Document