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33 results for “partition model”
Structural constraints in current stomatal conductance models preclude accurate estimation of evapotranspiration and its partitions
<p>This archive includes the scripts and related input data to produce results for the paper entitled - "Structural constraints in current stomatal conductance models preclude accurate estimation of evapotranspiration and its partitions". Following is the description of files/folders:</p> <p>1. Input_Data: This folder contains all the required input data including FluxNet data, soil properties, quality controlled training-validation data, and metadata & other supporting information of the sites. </p> <p>2. Model_EMP: This folder contains all the scripts for empirical model of stomatal conductance. (Note: Scripts have been written in MATLAB"). No need to change anything except the MATLAB executive path in two files "run_all_tasks_to_optimize_params.sh" and "prediction.sh". Read "ReadMe.txt" file in the folder "Model_EMP" for more instructions on running the model. </p> <p>3. Model_ML: This folder contains all the scripts for pure machine learning model of stomatal conductance. It contains four sub-folders: 1. Model_Config_1 (Model with configuration-1); 2. Model_Config_2_TEA (Model with Configuration-2 & TEA-based T estimates); 3. Model_Config_2_uWUE (Model with Configuration-2 & uWUE-based T estimates); 4. Model_Config_2_Yu22 (Model with Configuration-2 & Yu22-based T estimates). Further instructions have been given in each jupyter notebooks. Briefly, in folder "Model_Config_1", the notebook "train_ML_config_1.ipynb" trains the model parameters and notebook "Predictions_ML_config_1" is used to do predictions. Similar instructions apply for other subfolders. (Note: Scripts have been written in Python Language"). All the scripts are fully functional as long as all the required modules are installed.</p> <p>4. Model_PH_exp: This folder contains all the scripts for plant hydraulics model with explicit representation. All the scripts are self explanatory and further instructions are provided in the scripts as needed. (Note: Scripts have been written in Python Language"). All the scripts are fully functional as long as all the required modules are installed.</p> <p>5. Model_PN_imp: This folder contains all the scripts for plant hydraulics model with implicit representation. Instructions given for "Model_ML" are applicable here. (Note: Scripts have been written in Python Language"). All the scripts are fully functional as long as all the required modules are installed.<br> </p> <p>Versions: Tensorflow 2.11.0, MATLAB_R2022a, Python 3.10.9</p>
Performance of akaike information criterion and bayesian information criterion in selecting partition models and mixture models
<p>In molecular phylogenetics, partition models and mixture models provide different approaches to accommodating heterogeneity in genomic sequencing data. Both types of models generally give a superior fit to data than models that assume the process of sequence evolution is homogeneous across sites and lineages. The Akaike Information Criterion (AIC), an estimator of Kullback-Leibler divergence, and the Bayesian Information Criterion (BIC) are popular tools to select models in phylogenetics. Recent work suggests AIC should not be used for comparing mixture and partition models. In this work, we clarify that this difficulty is not fully explained by AIC misestimating the Kullback-Leibler divergence. We also investigate the performance of the AIC and BIC by comparing amongst mixture models and amongst partition models. We find that under non-standard conditions (i.e. when some edges have a small expected number of changes), AIC underestimates the expected Kullback-Leibler divergence. Under such conditions, AIC preferred the complex mixture models and BIC preferred the simpler mixture models. The mixture models selected by AIC had a better performance in estimating the edge length, while the simpler models selected by BIC performed better in estimating the base frequencies and substitution rate parameters. In contrast, AIC and BIC both prefer simpler partition models over more complex partition models under non-standard conditions, despite the fact that the more complex partition model was the generating model. We also investigated how mispartitioning (i.e. grouping sites that have not evolved under the same process) affects both the performance of partition models compared to mixture models and the model selection process. We found that as the level of mispartitioning increases, the bias of AIC in estimating the expected Kullback-Leibler divergence remains the same, and the branch lengths and evolutionary parameters estimated by partition models become less accurate. We recommend that researchers be cautious when using AIC and BIC to select among partition and mixture models; other alternatives, such as cross-validation and bootstrapping should be explored, but may suffer similar limitations.</p>
Figure 6. One chromosome from the population and the five chromosomes existing in the evaluation partition.-Genetic Algorithms Principles Towards Hidden Markov Model
<p>For example comparing the<br> chromosome given in Figure 6 with the first chromosome in the evaluation partition, the<br> difference between the relation Med-Med and Med-High as a pair is 0.0 and the difference<br> between the relation High-High and High-Med as a pair is 0.1. Similarly the difference between<br> the relation Med-Cold and Med-Hot as a pair is 0.1 and the difference between the relation<br> High-Cold and High-Hot as a pair is 0.2. We sum all these differences to get the value of<br> compare(i,j), the sum value is 0+0.1+0.1+0.2 = 0.4. Using the same approach we compute the<br> compare function with the other four chromosomes and we get values 0.4, 0.5,0.4 and 0.6. Now<br> we sum the five values 0.4 + 0.4 + 0.5+ 0.4 +0.6 = 2.3. The fitness value is then 1/ 2.3 = 0.434.<br> The highest is the fitness value, the better is the performance of the chromosome.</p>
Validation of an Idealized Aorta Model Analysed through Fluid-Structure Interaction Simulation with Robin-Neumann Partitioned Approach
<p>The aorta is multiphysics system where hemodynamics and wall structural mechanic are mutually influenced. A fluid-structure interaction approach is appropriate to describe the mechanical alterations suffered by the aortic wall in response to altered hemodynamic patterns. This work demonstrates the validation of the simulated idealized aorta model with a fluid-structure interaction (FSI) model through modified PIMPLE solver to use Robin-Neumann partitioned approach for the strongly-coupled algorithm using solids4foam v2. The validation involves the comparison of streamlines, pressure, and displacements with in vivo measurements. The geometry is reconstructed from the healthy aorta presented in 10.5281/zenodo.5801938. Our analysis shows that the streamlines and pressure pattern are comparable with the literature data acquired using rich medical imaging data. The maximum diameter deformation at the level of abdominal aorta is comparable with measured data and the diameter deformation profile along the cardiac cycle correctly follow the velocity profile. According to this results, our work shows a high-performance simulation suitable for several future works.</p>
Data from: Partitioning variance in population growth for models with environmental and demographic stochasticity
<ol> <li>How demographic factors lead to variation or change in growth rates can be investigated using life table response experiments (LTRE) based on structured population models. Traditionally, LTREs focused on decomposing the asymptotic growth rate, but more recently decompositions of annual 'realized' growth rates have gained in popularity.</li> <li>Realized LTREs have been used particularly to understand how variation in vital rates translates into variation in growth for populations under long-term study. For these, complete population models may be constructed by combining data in an integrated population model (IPM). IPMs are also used to investigate how temporal variation in environmental drivers affect vital rates. Such investigations have usually come down to estimating covariate coefficients for the effects of environmental variables on vital rates, but formal ways of assessing how they lead to variation in growth rates have been lacking. </li> <li>We extend realized LTREs in two ways. First, we further partition the contributions from vital rates into contributions from temporally varying factors that affect them. The decomposition allows us to compare the resultant effect on the growth rate of different environmental factors that may each act via multiple vital rates. Second, we show how realized growth rates can be decomposed into separate components from environmental and demographic stochasticity. The latter is typically omitted in LTRE analyses.</li> <li>We illustrate how to use the approach in an IPM for data from a 26-year study on northern wheatears (Oenanthe oenanthe), a migratory passerine bird breeding in an agricultural landscape. For this population, consisting of around 50–120 breeding pairs per year, we partition variation in realized growth rates into environmental contributions from temperature, rainfall, population density, and unexplained random variation via multiple vital rates, and from demographic stochasticity.</li> <li>The case study suggests that variation in first-year survival via the random component, and adult survival via temperature are two main factors behind environmental variation in growth rates. More than half of the variation in growth rates is suggested to come from demographic stochasticity, demonstrating the importance of this factor for populations of moderate size.</li> </ol>
Performance of akaike information criterion and bayesian information criterion in selecting partition models and mixture models
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Data from: Partitioning variance in population growth for models with environmental and demographic stochasticity
Open the record for dataset details and reuse information.
Dataset for the SFmodel, applied in Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches
<p>Dataset used for the SFmodel, applied in the work "Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches".</p> <p>For units and nomenclature of the variables refer to Units_and_Nomenclature_for_SFmodel_in_Evapotranspiration_dynamics_and_partitioning_in_a_grassed_vineyard.pdf. </p>
Dataset for Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches
<p>Data sets of the work "Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches".</p> <p>You will find all data files needed for this work, organised by the figures of the paper. For the codes, refer to Flavio Bastos Campos. (2024). flaviobastoscampos/ET_dynamics_and_partitioning_vineyard: v2024.1 (v2024.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.10864169" target="_blank" rel="nofollow noopener">https://doi.org/10.5281/zenodo.10864169</a>. </p>
Spatial heterogeneity effects on land surface modeling of water and energy partitioning
<p>Related code and data used in the manuscript https://doi.org/10.5194/gmd-2022-4, <Spatial heterogeneity effects on land surface modeling of water and energy partitioning>. The latest source code of ELMv1 is available from https://github.com/E3SM-Project/E3SM (last access: September 2020). If you have any questions, please contact lingchengliwhu@gmail.com</p>
Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS
<p>InSAR Line-of-Sight (LOS) velocities and their associated uncertainties in the southeastern Tibetan Plateau, along with the strain rate fields.</p> <p><br>Citations:</p> <p><br>Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS. Geophysical Research Letters.</p> <p><br>Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13731812</p>
Dataset for Paper "An Event Model for Trace-Based Performance Analysis of MPI Partitioned Point-to-Point Communication"
<p>Test cases, measurement and processing scripts, and measurement data for the referenced paper. </p>
Model Partition of Three-Dimensional Space
* Model Relating to the Regular Partition of Three-Dimensional Space. This white plaster model has faces that are rectangles, pentagons, hexagons, and septagons. Four line segments are indicated. * *(метка Р127.3)* Source: Objaverse 1.0 / Sketchfab
Data from: Life table invasion models: spatial progression and species-specific partitioning
Biological invasions are increasingly being considered important spatial processes that drive global changes, threatening biodiversity, regional economies, and ecosystem functions. A unifying conceptual model of the invasion dynamics could serve as a useful tool for comparison and classification of invasion processes involving different species across large geographic ranges. By dividing these geographic ranges that are subject to invasions into discrete spatial units we here conceptualize the invasion process as the transition from pristine to invaded spatial units. We use California cities as the spatial units and a long-term database of invasive tropical tephritids to characterize the invasion patterns. A new life-table method based on insect demography, including the progression model of invasion stage transition and the species-specific partitioning model of multispecies invasions, was developed to analyze the invasion patterns. The progression model allows us to estimate the probability and rate of transition, for individual cities, from pristine to infested stages and subsequently differentiate first year of detection from detection recurrences. Importantly, we show that the interval of invasive tephritid recurrence in a city declines with increasing invasion stages of the city. The species-specific partitioning model revealed profound difference in invasion outcome depending on which tephritid species was first detected (and then locally eradicated) in the early stage of invasion. Taken together, we discuss how these two life-table invasion models can cast new light on existing invasion concepts; in particular, on formulating invasion dynamics as the state transition of cities and partitioning species-specific role during multispecies invasions. These models provide a new set of tools for predicting the spatiotemporal progression of invasion and providing early warnings of recurrent invasions for efficient management.
Electronic Supplement to: A Predictive Model for Divalent Element Partitioning between Clinopyroxene and Basaltic Melt and a Europium-in-Plagioclase-Clinopyroxene Oxybarometer for Cumulate Rocks
<p>Contents</p> <p>Supplementary Figures</p> <p>Supplementary Calculator Spreadsheet</p> <ul> <li>A calculator for divalent element partitioning between the clinopyroxene M2 site and silicate melt</li> <li>An fO2-, temperature- and composition- dependent clinopyroxene-melt Eu partition coefficient calculator for many samples, each at a single fO2</li> <li>An fO2-, temperature- and composition- dependent clinopyroxene-melt Eu partition coefficient calculator for a single sample at many fO2s</li> <li>A Eu-in-clinopyroxene-melt oxybarometer</li> <li>A Eu-in-plagioclase-clinopyroxene oxybarometer</li> </ul> <p>Supplementary Code</p> <ul> <li>A Eu-in-plagioclase-clinopyroxene oxybarometer</li> <li>A Monte Carlo-based fO2 uncertainty calculator</li> </ul>
Strain partitioning and localization due to heterogeneities in the fold - and - thrust belt 2 detachment: Analogue models of progressive arcs
<p>Although most arcuate orogens are deformed as progressive arcs–curvature is acquired during12shortening-, they have been scarcely simulated by analogue modelling. Our team work designed13a backstop that deformed in map view building up fold-and-thrust belts (FTBs) that acquire14progressively its curved shape (Jiménez-Bonilla et al., 2020); silicone and sand layers15reproduced the brittle-ductile conditions expected to be common in external tectonic wedges.16However, natural cases usually includeheterogeneities in the detachment such as diapirs,17thickness variations of the viscous layer or pinch outs. Based on the same progressive arc model18setup, we present here seven new experiments including these three types of heterogeneities.19Our results show that strain was partitioned between shortening structures whose transport20directions draw a radial pattern and normal faults and oblique strike-slip faults that21accommodate arc-lengthening. Moreover, any heterogeneity conditions the wedge evolution and22the nucleation of structures. Both diapirs and the presence of a silicone pinch-out perpendicular23to the apex movement favour that frontal deformation slows down and the wedge thickens up to24reach the supercritical angle. Interestingly, the presence of diapirs or silicone thickness25variations favour the arc-parallel stretching localization close to these heterogeneities. In26addition, silicone bands parallel to the apex movement generate different structural styles along27the FTB. More frictional detachments favour thicker wedges and less frontal propagation.28Transfer zones accommodate the differential displacement between FTB segments. These29results may be useful to investigate geometric and kinematic changes along natural progressive30arcs such as the Gibraltar, Sulaiman and Zagros cases</p>
Figure 8 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 8. Divergence ages (median and 95% HPD) for all dating models, shown for the main groups of Folivora of the present classification. Time scale in million years ago.
Figure 5 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 5. Estimated rate multipliers for anatomical partitions in each model. Partition colours as in Figure 1.
Figure 2 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 2. Diversity through time for sloth genera sampled and its association with geological epochs. Time scale in million years ago.
Figure 1 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 1. Anatomical partitions and partitioning schemes. Coloured anatomical regions in the skeleton of Paramylodon harlani (modified from Stock, 1925) correspond to the maximally partitioned data subsets, as used in model A7, whereas their combinations into composite partitions used in schemes A1 to A6 are indicated by other colours in the table. UN, unpartitioned model.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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