The ATSAS software suite encompasses a number of programs for the processing, visualization, analysis and modelling of small‐angle scattering data, with a focus on the data measured from biological ...macromolecules. Here, new developments in the ATSAS 3.0 package are described. They include IMSIM, for simulating isotropic 2D scattering patterns; IMOP, to perform operations on 2D images and masks; DATRESAMPLE, a method for variance estimation of structural invariants through parametric resampling; DATFT, which computes the pair distance distribution function by a direct Fourier transform of the scattering data; PDDFFIT, to compute the scattering data from a pair distance distribution function, allowing comparison with the experimental data; a new module in DATMW for Bayesian consensus‐based concentration‐independent molecular weight estimation; DATMIF, an ab initio shape analysis method that optimizes the search model directly against the scattering data; DAMEMB, an application to set up the initial search volume for multiphase modelling of membrane proteins; ELLLIP, to perform quasi‐atomistic modelling of liposomes with elliptical shapes; NMATOR, which models conformational changes in nucleic acid structures through normal mode analysis in torsion angle space; DAMMIX, which reconstructs the shape of an unknown intermediate in an evolving system; and LIPMIX and BILMIX, for modelling multilamellar and asymmetric lipid vesicles, respectively. In addition, technical updates were deployed to facilitate maintainability of the package, which include porting the PRIMUS graphical interface to Qt5, updating SASpy – a PyMOL plugin to run a subset of ATSAS tools – to be both Python 2 and 3 compatible, and adding utilities to facilitate mmCIF compatibility in future ATSAS releases. All these features are implemented in ATSAS 3.0, freely available for academic users at https://www.embl‐hamburg.de/biosaxs/software.html.
ATSAS is a comprehensive software suite for the processing, visualization, analysis and modelling of small‐angle scattering data. This article describes developments in the ATSAS 3.0 release, including new programs for data simulation and for the structural modelling of lipids, nucleic acids and polydisperse systems.
Measuring seasonal productivity is difficult in multi‐brooded species without labour‐intensive ringing studies. Individual‐based (IB) models have been used to estimate seasonal productivity with no ...direct knowledge of number of nesting attempts, but they are often based on simplified re‐nesting probability (φR) step‐functions instead of observed or more biologically plausible ones. We present a new, open‐source IB seasonal productivity model parameterized from studies of Black Redstart Phoenicurus ochruros and Yellowhammer Emberiza citrinella. We examined how the φR function shape (empirical versus simplified) influenced (1) model performance, (2) re‐nesting compensation and (3) population‐level predictions of a simulated management intervention. Population‐level predictions were made only for Yellowhammer as we had more detailed demographic data, such as survival rates, available. Pattern‐oriented modelling revealed that IB models produced realistic within‐population distributions of breeding parameters, and those specified with an observed or empirically derived φR function generally outperformed those specified with simpler step functions. Strength of re‐nesting compensation differed depending on the φR function used. For Yellowhammers, type of φR function in IB models marginally influenced population‐level predictions of a simulated management intervention (potential population growth rate increased between 23% and 29% relative to no management intervention). In contrast, a simple deterministic productivity model, which did not simulate re‐nesting compensation, predicted a 41% increase in potential population growth. At a population level, choice of φR function may have less influence on IB model predictions, but choice of model itself (IB versus deterministic) may have substantial impact. We discuss how more biologically plausible φR functions might either be observed directly, derived from nest data, or estimated from proxy information such as moult or brood patch changes.
Abstract
The aim of this work is to describe method of modeling straw signal using Garfield++ interface to LTspice. Straw Tube Trackers will be use in the SPD experiment. When designing such large ...scale and complex detector it is of extreme importance to run precise simulations. The physical task of this research is to reliably predict drift time and shape signal, which is important for further modeling of electronics for SPD Straw Trackers
AIM: Ecological niche models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex models that show high predictive performance on ...current distribution data may do a poor job of predicting new data due to overfitting. In addition, model performance is often evaluated using techniques that are sensitive to spatial sampling bias. Here, we explore the effects of model complexity and spatial sampling bias on niche models for 90 vertebrate taxa of conservation concern. LOCATION: California, USA. METHODS: We used Akaike information criterion (AICc) to select variables and tune Maxent's built‐in regularization parameter (β) to constrain model complexity. In addition, we incorporated several estimates of spatial sampling bias based on interpolations of target group data. Ensemble forecasts were developed for future conditions from two emission scenarios and three climate change models for the year 2050. RESULTS: Reducing the number of predictors and tuning β resulted in a reduction in the number of parameters in models built with sample sizes greater than approximately 10 occurrence points. Reducing the number of predictors had a substantially higher impact on the relative prioritization of different grid cells than did increasing regularization. There was little difference in prioritization of habitat when comparing models built using different spatial sampling bias estimates. Over half of the taxa were predicted to experience >80% reductions in environmental suitability in currently occupied cells, and this pattern was consistent across taxonomic groups. MAIN CONCLUSIONS: Our results demonstrate that reducing the number of correlated predictor variables tends to decrease the breadth of models, while tuning regularization using AICc tends to increase it. These two strategies may provide a reasonable bracketing strategy for assessing climate change impacts.
Purpose
– Research on international marketing usually involves comparing different groups of respondents. When using structural equation modeling (SEM), group comparisons can be misleading unless ...researchers establish the invariance of their measures. While methods have been proposed to analyze measurement invariance in common factor models, research lacks an approach in respect of composite models. The purpose of this paper is to present a novel three-step procedure to analyze the measurement invariance of composite models (MICOM) when using variance-based SEM, such as partial least squares (PLS) path modeling.
Design/methodology/approach
– A simulation study allows us to assess the suitability of the MICOM procedure to analyze the measurement invariance in PLS applications.
Findings
– The MICOM procedure appropriately identifies no, partial, and full measurement invariance.
Research limitations/implications
– The statistical power of the proposed tests requires further research, and researchers using the MICOM procedure should take potential type-II errors into account.
Originality/value
– The research presents a novel procedure to assess the measurement invariance in the context of composite models. Researchers in international marketing and other disciplines need to conduct this kind of assessment before undertaking multigroup analyses. They can use MICOM procedure as a standard means to assess the measurement invariance.
Average-value modeling (AVM) provides an efficient way to study power electronic systems in large- and small-signal senses. This paper presents a new reduced-order AVM for dual-active-bridge dc-dc ...converters. The proposed model considers the conduction and transformer power losses as well as the input/output filters, which may be very useful for system-level studies. Based on the large-signal AVM, the small-signal model and the control-to-output transfer function are also derived. The proposed AVM is compared with the full-order generalized average model and the detailed model in predicting large-signal transients and small-signal analysis in the frequency domain. The experimental results confirm that the proposed model yields a high accuracy, which represents an improvement over other existing models.
Spatial modelling techniques are increasingly used in species distribution modelling. However, the implemented techniques differ in their modelling performance, and some consensus methods are needed ...to reduce the uncertainty of predictions. In this study, we tested the predictive accuracies of five consensus methods, namely Weighted Average (WA), Mean(All), Median(All), Median(PCA), and Best, for 28 threatened plant species. North-eastern Finland, Europe. The spatial distributions of the plant species were forecasted using eight state-of-the-art single-modelling techniques providing an ensemble of predictions. The probability values of occurrence were then combined using five consensus algorithms. The predictive accuracies of the single-model and consensus methods were assessed by computing the area under the curve (AUC) of the receiver-operating characteristic plot. The mean AUC values varied between 0.697 (classification tree analysis) and 0.813 (random forest) for the single-models, and from 0.757 to 0.850 for the consensus methods. WA and Mean(All) consensus methods provided significantly more robust predictions than all the single-models and the other consensus methods. Consensus methods based on average function algorithms may increase significantly the accuracy of species distribution forecasts, and thus they show considerable promise for different conservation biological and biogeographical applications.
An increasing number of power-electronic-based distributed generation systems and loads generate not only characteristic harmonics but also unexpected harmonics. Several methods, such as ...impedance-based analysis, which are derived from the conventional average model, are introduced to perform research about the harmonic interaction. However, it is found that the linear-time-invariant-based model analysis makes it difficult to analyze these phenomena because of the time-varying properties of the power-electronic-based systems. This paper investigates a grid-connected converter by using the harmonic state-space (HSS) small-signal model, which is based on a linear time-varying periodically theory. The proposed model can include the switching behavior of the model, where it makes the model possible to analyze how harmonics are transferred into both the ac-side and dc-side circuits. Furthermore, a harmonic matrix of the grid-connected converter is developed to analyze the harmonic interaction at the steady-state behavior. Besides, the frequency-domain results are compared with time-domain simulation results by using the HSS modeling to verify the theoretical analysis. Experimental results are finally discussed to verify the proposed model and study.
AIM: The Hutchinsonian hypervolume is the conceptual foundation for many lines of ecological and evolutionary inquiry, including functional morphology, comparative biology, community ecology and ...niche theory. However, extant methods to sample from hypervolumes or measure their geometry perform poorly on high‐dimensional or holey datasets. INNOVATION: We first highlight the conceptual and computational issues that have prevented a more direct approach to measuring hypervolumes. Next, we present a new multivariate kernel density estimation method that resolves many of these problems in an arbitrary number of dimensions. MAIN CONCLUSIONS: We show that our method (implemented as the ‘hypervolume’ R package) can match several extant methods for hypervolume geometry and species distribution modelling. Tools to quantify high‐dimensional ecological hypervolumes will enable a wide range of fundamental descriptive, inferential and comparative questions to be addressed.
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