scholarly journals Internal trophic pressure, a regulator of plant development? Insights from a stochastic functional–structural plant growth model applied to Coffea trees

2020 ◽  
Vol 126 (4) ◽  
pp. 687-699 ◽  
Author(s):  
Véronique Letort ◽  
Sylvie Sabatier ◽  
Michelle Pamelas Okoma ◽  
Marc Jaeger ◽  
Philippe de Reffye
Keyword(s):  
Parameter Estimation ◽  
Plant Growth ◽  
Real Data ◽  
Coffea Canephora ◽  
Parametric Estimation ◽  
Trophic Dynamics ◽  
Original Procedure ◽  
Effect Parameter ◽  
Set Up ◽  
Establishment Phase

Abstract Background and Aims Using internal trophic pressure as a regulating variable to model the complex interaction loops between organogenesis, production of assimilates and partitioning in functional–structural models of plant growth has attracted increasing interest in recent years. However, this approach is hampered by the fact that internal trophic pressure is a non-measurable quantity that can be assessed only through model parametric estimation, for which the methodology is not straightforward, especially when the model is stochastic. Methods A stochastic GreenLab model of plant growth (called ‘GL4’) is developed with a feedback effect of internal trophic competition, represented by the ratio of biomass supply to demand (Q/D), on organogenesis. A methodology for its parameter estimation is presented and applied to a dataset of 15 two-year-old Coffea canephora trees. Based on the fitting results, variations in Q/D are reconstructed and analysed in relation to the estimated variations in organogenesis parameters. Key Results Our stochastic retroactive model was able to simulate realistically the progressive set-up of young plant architecture and the branch pruning effect. Parameter estimation using real data for Coffea trees provided access to the internal trophic dynamics. These dynamics correlated with the organogenesis probabilities during the establishment phase. Conclusions The model can satisfactorily reproduce the measured data, thus opening up promising avenues for further applying this original procedure to other experimental data. The framework developed can serve as a model-based toolkit to reconstruct the hidden internal trophic dynamics of plant growth.

scholarly journals Probabilistic moveout analysis by time warping

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. U1-U20
Author(s):  
Yanadet Sripanich ◽  
Sergey Fomel ◽  
Jeannot Trampert ◽  
William Burnett ◽  
Thomas Hess
Keyword(s):  
Parameter Estimation ◽  
Posterior Probability ◽  
Model Building ◽  
Probability Distributions ◽  
Real Data ◽  
Inversion Method ◽  
Time Warping ◽  
Anisotropic Models ◽  
Trade Offs ◽  
Set Up

Parameter estimation from reflection moveout analysis represents one of the most fundamental problems in subsurface model building. We have developed an efficient moveout inversion method based on the process of automatic flattening of common-midpoint (CMP) gathers using local slopes. We find that as a by-product of this flattening process, we can also estimate reflection traveltimes corresponding to the flattened CMP gathers. This traveltime information allows us to construct a highly overdetermined system and subsequently invert for moveout parameters including normal-moveout velocities and quartic coefficients related to anisotropy. We use the 3D generalized moveout approximation (GMA), which can accurately capture the effects of complex anisotropy on reflection traveltimes as the basis for our moveout inversion. Due to the cheap forward traveltime computations by GMA, we use a Monte Carlo inversion scheme for improved handling of the nonlinearity between the reflection traveltimes and moveout parameters. This choice also allows us to set up a probabilistic inversion workflow within a Bayesian framework, in which we can obtain the posterior probability distributions that contain valuable statistical information on estimated parameters such as uncertainty and correlations. We use synthetic and real data examples including the data from the SEAM Phase II unconventional reservoir model to demonstrate the performance of our method and discuss insights into the problem of moveout inversion gained from analyzing the posterior probability distributions. Our results suggest that the solutions to the problem of traveltime-only moveout inversion from 2D CMP gathers are relatively well constrained by the data. However, parameter estimation from 3D CMP gathers associated with more moveout parameters and complex anisotropic models are generally nonunique, and there are trade-offs among inverted parameters, especially the quartic coefficients.

scholarly journals Enhanced Language Model with Hybrid Knowledge Graph for Mathematical Topic Prediction

2021 ◽  
Author(s):  
Minghui Wu ◽  
Canghong Jin ◽  
Wenkang Hu ◽  
Yabo Chen
Keyword(s):  
Language Model ◽  
Real Data ◽  
Mathematical Concept ◽  
Fine Tuning ◽  
Knowledge Graph ◽  
Mathematics Knowledge ◽  
Hybrid Knowledge ◽  
Model Set ◽  
Set Up ◽  
Mathematical Topic

Understanding mathematical topics is important for both educators and students to capture latent concepts of questions, evaluate study performance, and recommend content in online learning systems. Compared to traditional text classification, mathematical topic classification has several main challenges: (1) the length of mathematical questions is relatively short; (2) there are various representations of the same mathematical concept(i.e., calculations and application); (3) the content of question is complex including algebra, geometry, and calculus. In order to overcome these problems, we propose a framework that combines content tokens and mathematical knowledge concepts in whole procedures. We embed entities from mathematics knowledge graphs, integrate entities into tokens in a masked language model, set up semantic similarity-based tasks for next-sentence prediction, and fuse knowledge vectors and token vectors during the fine-tuning procedure. We also build a Chinese mathematical topic prediction dataset consisting of more than 70,000 mathematical questions with topics. Our experiments using real data demonstrate that our knowledge graph-based mathematical topic prediction model outperforms other state-of-the-art methods.

scholarly journals Screen recordings as a tool to document computer assisted datacollection procedures

2019 ◽  
Author(s):  
Tobias Heycke ◽  
Lisa Spitzer
Keyword(s):  
Data Collection ◽  
False Positive ◽  
Computer Assisted ◽  
Public Repository ◽  
Specific Detail ◽  
Experimental Procedure ◽  
Psychological Science ◽  
Screen Capture ◽  
Original Procedure ◽  
Set Up

Recently in psychological science and many related fields, a surprisingly large amount of experiments could not be replicated by independent researchers. A non-replication could indicate that a previous finding might have been a false positive statistical result and the effect does not exist. However, it could also mean that a specific detail of the experimental procedure is essential for the effect to emerge, which might not have been included in the replication attempt. Therefore any replication attempt that does not replicate the original effect is most informative when the original procedure is closely adhered to. One proposed solution to facilitate the empirical reproducibility of the experimental procedures in psychology is to upload the experimental script and materials to a public repository. However, we believe that merely providing the materials of an experimental procedure is not sufficient, as many software solutions are not freely available, software solutions might change, and it is time consuming to set up the procedure. We argue that there is a simple solution to these problems when an experiment is conducted using computers: recording an example procedure with a screen capture software and providing the video in an online repository. We therefore provide a brief tutorial on screen recordings using an open source screen recording software. With this information, individual researchers should be able to record their experimental procedures and we hope to facilitate the use of screen recordings in computer assisted data collection procedures.

Continuous Autoregressive Moving Average Models

2021 ◽  
pp. 118-141
Author(s):  
Yakup Ari
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Discrete Time ◽  
Moving Average ◽  
Likelihood Estimation ◽  
Real Data ◽  
Autoregressive Moving Average ◽  
The Difference

The financial time series have a high frequency and the difference between their observations is not regular. Therefore, continuous models can be used instead of discrete-time series models. The purpose of this chapter is to define Lévy-driven continuous autoregressive moving average (CARMA) models and their applications. The CARMA model is an explicit solution to stochastic differential equations, and also, it is analogue to the discrete ARMA models. In order to form a basis for CARMA processes, the structures of discrete-time processes models are examined. Then stochastic differential equations, Lévy processes, compound Poisson processes, and variance gamma processes are defined. Finally, the parameter estimation of CARMA(2,1) is discussed as an example. The most common method for the parameter estimation of the CARMA process is the pseudo maximum likelihood estimation (PMLE) method by mapping the ARMA coefficients to the corresponding estimates of the CARMA coefficients. Furthermore, a simulation study and a real data application are given as examples.

scholarly journals Utilization of microbial inoculation and compost for revitalization of soils

2009 ◽  
Vol 4 (No. 3) ◽  
pp. 126-130 ◽  
Author(s):  
O. Mikanová ◽  
S. Usťak ◽  
A. Czakó
Keyword(s):  
Plant Growth ◽  
Ammonium Sulphate ◽  
Nitrogenase Activity ◽  
Mineral Fertilizer ◽  
Pot Experiment ◽  
Mineral Fertilizers ◽  
Microbial Inoculation ◽  
Active Population ◽  
Rhizobium Spp ◽  
Set Up

Improving the quality of reclaimed soils requires an active population of microorganisms which can promote plant growth. Increasing the activity of microorganisms can be done by adding nutrients, making agrotechnical soil improvements and by the inoculation of beneficial microorganisms. We investigated the role of fertilizer treatments on plant growth and nitrogen fixation in a pot experiment conducted under green house conditions. Influence of the fertilizer type on numbers of bacteria was also investigated. The seeds were inoculated with the mixture of Azotobacter spp. and Rhizobium spp. The pot experiment was set up with the substrate from the mine spoil (North Bohemia coal basin, the Czech Republic) using Medicago sativa as test plants. The following treatments were used: compost 0, 20, 40, 120, 400, 800 t/ha and mineral fertilizer – ammonium sulphate. The doses of ammonium sulphate were calculated to be equivalent (in nitrogen content) to those doses of compost. Control variants without bacteria inoculation and fertilizers were also included. Inoculation with the mixture of AzotobacterRhizobium spp. significantly increased plant growth and nitrogenase activity. The nitrogenase activity was inhibited by mineral fertilizers in all doses used. The results of the study have proved that compost application stimulated the growth of Azotobacter spp. and Rhizobium spp.

scholarly journals Regularized Parameter Estimation in High-Dimensional Gaussian Mixture Models

2011 ◽  
Vol 23 (6) ◽  
pp. 1605-1622 ◽  
Author(s):  
Lingyan Ruan ◽  
Ming Yuan ◽  
Hui Zou
Keyword(s):  
Parameter Estimation ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Expectation Maximization Algorithm ◽  
Real Data ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Model Based Clustering ◽  
Text Type ◽  
Effective Dimensionality

Finite gaussian mixture models are widely used in statistics thanks to their great flexibility. However, parameter estimation for gaussian mixture models with high dimensionality can be challenging because of the large number of parameters that need to be estimated. In this letter, we propose a penalized likelihood estimator to address this difficulty. The [Formula: see text]-type penalty we impose on the inverse covariance matrices encourages sparsity on its entries and therefore helps to reduce the effective dimensionality of the problem. We show that the proposed estimate can be efficiently computed using an expectation-maximization algorithm. To illustrate the practical merits of the proposed method, we consider its applications in model-based clustering and mixture discriminant analysis. Numerical experiments with both simulated and real data show that the new method is a valuable tool for high-dimensional data analysis.

Parameter estimation using relay feedback

2019 ◽  
Vol 35 (4) ◽  
pp. 505-529
Author(s):  
Kalpana Dharmalingam ◽  
Thyagarajan Thangavelu
Keyword(s):  
Parameter Estimation ◽  
Closed Loop ◽  
Parametric Estimation ◽  
Estimation Methods ◽  
Model Parameters ◽  
Relay Feedback ◽  
Research Activity ◽  
Process Industries ◽  
Single Input Single Output ◽  
Estimation Techniques

Abstract In process industries, closed-loop step and closed-loop relay feedback tests are popularly used for estimating model parameters. In this paper, different methods available in the literature for parameter estimation using conventional techniques and techniques based on relay feedback test are surveyed by reviewing around 152 research articles published during the past three decades. Through a comprehensive survey of available literature, the parameter estimation methods are classified into two broad groups, namely conventional techniques and relay-based parametric estimation techniques. These relay-based techniques are further classified into two subgroups, namely single-input-single-output (SISO) systems and multi-input-multi-output systems (both square and nonsquare), and are revealed in a lucid manner with the help of benchmark examples and case studies. For the above categorized methods, the procedural steps involved in relay-based parametric estimation methods are also presented. To facilitate the readers, comparison tables are included to comprehend the results of different parametric estimation techniques available in the literature. The incorporation of quantitative and qualitative analysis of papers published in various journals in the above area with the help of pie charts and graphs would enable the readers to grasp the overview of the research activity being carried out in the relay feedback domain. At the end, the challenging issues in relay-based parametric estimation methods and the directions for future investigations that can be explored are also highlighted.

scholarly journals Improving VANETs Connectivity with a Totally Ad Hoc Living Mobile Backbone

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Joilson Alves Junior ◽  
Emilio C. G. Wille
Keyword(s):  
Intelligent Transportation Systems ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Network Connectivity ◽  
Real Data ◽  
Transportation Systems ◽  
Network Backbone ◽  
Set Up ◽  
Fully Connected ◽  
Analytic Models

The vehicular ad hoc network (VANET) for intelligent transportation systems is an emerging concept to improve transportation security, reliability, and management. The network behavior can be totally different in topological aspects because of the mobility of vehicular nodes. The topology can be fully connected when the flow of vehicles is high and may have low connectivity or be invalid when the flow of vehicles is low or unbalanced. In big cities, the metropolitan buses that travel on exclusive lanes may be used to set up a metropolitan vehicular data network (backbone), raising the connectivity among the vehicles. Therefore, this paper proposes the implementation of a living mobile backbone, totally ad hoc (MOB-NET), which will provide infrastructure and raise the network connectivity. In order to show the viability of MOB-NET, statistical analyses were made with real data of express buses that travel through exclusive lanes, besides evaluations through simulations and analytic models. The statistic, analytic, and simulation results prove that the buses that travel through exclusive lanes can be used to build a communication network totally ad hoc and provide connectivity in more than 99% of the time, besides raising the delivery rate up to 95%.

scholarly journals Effect of Selected Plant Growth Regulators on Yield and Stem Height of Spring Wheat in Ontario

2017 ◽  
Vol 9 (12) ◽  
pp. 30 ◽  
Author(s):  
Yuxue Zhang ◽  
Shengnan Su ◽  
Mirko Tabori ◽  
Junjie Yu ◽  
Denise Chabot ◽  
...  
Keyword(s):  
Plant Growth ◽  
Plant Growth Regulators ◽  
Growth Regulators ◽  
Spring Wheat ◽  
Active Ingredient ◽  
Growth Yield ◽  
Limiting Factor ◽  
Stem Height ◽  
Stem Strength ◽  
Set Up

Lodging is an important limiting factor in wheat because it affects growth, yield and grain quality. Plant growth regulators (PGRs) are often used to restrain elongation of internodes, improve lodging traits, and protect yield potentials. An experiment was set up in the greenhouse at the Ottawa Research and Development Centre (ORDC) to study the effect of the selected PGRs (Manipulator, the active ingredient of which is chlormequat; and Palisade, the active ingredient of which is trinexapac-ethyl) on yield, stem height and morphological traits in six spring wheat cultivars (AC Carberry, AAC Scotia, Hoffman, Fuzion, FL62R1, and AW725). Both PGRs reduced plant height and caused a 6% to 48% reduction in the length of the second basal internode. The mixture of the two PGRs had a synergistic affect and made the stem shorter. The application of PGRs significantly reduced lodging, increased stem diameter, thickness, filling degree, and stem strength, and increased leaf relative chlorophyll content. However, application of PGRs significantly reduced grain yield, and the combination of the two PGRs (Manipulator and Palisade) had a synergistic effect and lowered the yield. In general, the effect of Palisade was more evident than that of Manipulator.

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