Multi-site Models Research Articles

Does your species have memory? Analyzing capture-recapture data with memory models.

We examine memory models for multisite capture–recapture data. This is an important topic, as animals may exhibit behavior that is more complex than simple first-order Markov movement between sites, when it is necessary to devise and fit appropriate models to data. We consider the Arnason–Schwarz model for multisite capture–recapture data, which incorporates just first-order Markov movement, and also two alternative models that allow for memory, the Brownie model and the Pradel model. We use simulation to compare two alternative tests which may be undertaken to determine whether models for multisite capture–recapture data need to incorporate memory. Increasing the complexity of models runs the risk of introducing parameters that cannot be estimated, irrespective of how much data are collected, a feature which is known as parameter redundancy. Rouan et al. (JABES, 2009, pp 338–355) suggest a constraint that may be applied to overcome parameter redundancy when it is present in multisite memory models. For this case, we apply symbolic methods to derive a simpler constraint, which allows more parameters to be estimated, and give general results not limited to a particular configuration. We also consider the effect sparse data can have on parameter redundancy and recommend minimum sample sizes. Memory models for multisite capture–recapture data can be highly complex and difficult to fit to data. We emphasize the importance of a structured approach to modeling such data, by considering a priori which parameters can be estimated, which constraints are needed in order for estimation to take place, and how much data need to be collected. We also give guidance on the amount of data needed to use two alternative families of tests for whether models for multisite capture–recapture data need to incorporate memory.

Long-range correction for multi-site Lennard-Jones models and planar interfaces

A slab-based long-range correction (LRC) approach for multi-site Lennard-Jones models is presented for systems with a planar film geometry that is based on the work by Janeček [J. Phys. Chem. B 110: 6264 (2006)]. It is efficient because it relies on a centre-of-mass cut-off scheme and scales in terms of numerics almost perfectly with the molecule number. For validation, a series of simulations with the two-centre Lennard-Jones model fluid, carbon dioxide and cyclohexane is carried out. The results of the present approach, a site-based LRC and simulations without any LRC are compared with respect to the saturated liquid density and the surface tension. The present simulation results exhibit only a weak dependence on the cut-off radius, indicating a high accuracy of the implemented LRC.

A new framework for multi-site weather generator: a two-stage model combining a parametric method with a distribution-free shuffle procedure

Keeping the spatial correlation of synthetic precipitation data is of utmost importance for hydrological modeling; however, most present weather generators are single-site models and ignore the spatial dependence in daily weather data. Multi-site weather generator is an effective method to solve this problem. This study proposes a new framework for multi-site weather generator denoted as two-stage weather generator (TSWG), in which the first stage generates the single-site precipitation occurrence and amount with a parametric chain-dependent process, and the second stage rebuilds the spatial correlation of the synthetic data using a post-processing, distribution-free shuffle procedure. Results show that TSWG reproduces the statistical parameters of the parametric stage quite well, such as wet days and precipitation amount, and it almost perfectly preserves the inter-station correlations of precipitation occurrence and amount as well as their dependences. Most important, it matches the input requirement of hydrological model and gives satisfactory hydrological simulations. There are several advantages for this new framework: (1) only one correlation matrix and two simple steps, no more input variables or iterative optimizations, are needed to rebuild the spatial correlation; (2) the statistical parameters of the observed data can be easily preserved; (3) the inter-station correlations can be satisfactorily rebuilt. As a post-processing method, the shuffle procedure used to reconstruct the spatial correlation has some potential extensions, such as turning current single-site weather generator into multi-site models and generating future multi-site climate scenarios.

Multisite Ion Models That Improve Coordination and Free Energy Calculations in Molecular Dynamics Simulations.

Current ion models in molecular mechanics are simple spheres, and their interactions are solely determined from the van der Waals radius of the sphere and the total charge. Here, we introduce a model where we distribute the total charge of the ion into n-dummy centers that are placed in the direction of the coordinating atoms. We have parametrized this model for two divalent cations, Ca(2+) and Mg(2+), and have tested the model's accuracy in a variety of simulations. With this model we are not only able to correctly predict the free energies and selectivity for cation binding sites in proteins and nucleic acids, but we achieve better coordination geometries and can capture more subtle effects such as the exchange of inner shell waters. Additionally, this model does not employ higher-order electrostatics and thus can be easily used with standard force fields.

Effect of nonmonotonic changing of surface coverage in multisite adsorption models with possibility of different orientations of molecules with respect to solid surface

The reasons for nonmonotonous changing of surface coverage as function of chemical potential in the multisite adsorption models (with allowance for the possibility that molecules can be oriented in a different manner with respect to the solid surface) are revealed in this work. It is demonstrated that this behavior of the surface coverage as function of chemical potential is determined by either the sequence of ordered structures being formed or by the emergence of a stable interface between the ordered phases at temperatures above the tricritical point.

Stochastic generation of multi-site daily precipitation for applications in risk management

Unlike single-site precipitation generators, multi-site precipitation generators make it possible to reproduce the space–time variation of precipitation at several sites. The extension of single-site approaches to multiple sites is a challenging task, and has led to a large variety of different model philosophies for multi-site models. This paper presents an alternative semi-parametric multi-site model for daily precipitation that is straightforward and easy to implement. Multi-site precipitation occurrences are simulated with a univariate Markov process, removing the need for individual Markov models at each site. Precipitation amounts are generated by first resampling observed values, followed by sampling synthetic precipitation amounts from parametric distribution functions. These synthetic precipitation amounts are subsequently reshuffled according to the ranks of the resampled observations in order to maintain important statistical properties of the observation network. The proposed method successfully combines the advantages of non-parametric bootstrapping and parametric modeling techniques. It is applied to two small rain gauge networks in France (Ubaye catchment) and Austria/Germany (Salzach catchment) and is shown to well reproduce the observations. Limitations of the model relate to the bias of the reproduced seasonal standard deviation of precipitation and the underestimation of maximum dry spells. While the lag-1 autocorrelation is well reproduced for precipitation occurrences, it tends to be underestimated for precipitation amounts. The model can generate daily precipitation amounts exceeding the ones in the observations, which can be crucial for risk management related applications. Moreover, the model deals particularly well with the spatial variability of precipitation. Despite its straightforwardness, the new concept makes a good alternative for risk management related studies concerned with producing daily synthetic multi-site precipitation time series.

The Weather Generator Used in the Empirical Statistical Downscaling Method, WETTREG

In this paper, the weather generator (WG) used by the empirical statistical downscaling method, weather situation-based regionalization method (in German: WETTerlagen-basierte REGionalisierungsmethode, WETTREG), is described. It belongs to the class of multi-site parametric models that aim at the representation of the spatial dependence among weather variables with conditioning on exogenous atmospheric predictors. The development of the WETTREG WG was motivated by (i) the requirement of climate impact modelers to obtain input data sets that are consistent and can be produced in a relatively economic way and (ii) the well-sustained hypothesis that large scale atmospheric features are well reproduced by climate models and can be used as a link to regional climate. The WG operates at daily temporal resolution. The conditioning factor is the temporal development of the frequency distribution of circulation patterns. Following a brief description of the strategy of classifying circulation patterns that have a strong link to regional climate, the bulk of this paper is devoted to a description of the WG itself. This includes aspects, such as the utilized building blocks, seasonality or the methodology with which a signature of climate change is imprinted onto the generated time series. Further attention is given to particularities of the WG’s conditioning processes, as well as to extremes, areal representativity and the interface of WGs and user requirements.

Sulfamethazine Transport in Agroforestry and Cropland Soils

Knowledge of veterinary antibiotic (VA) transport and persistence is critical to understanding environmental risks associated with these potential contaminants. To understand mobility of sulfamethazine (SMZ) and sorption processes involved during SMZ transport in soil, batch sorption experiments were conducted and column leaching experiments were performed with repacked soil columns containing silt loam soils collected from cropland (Crop) and an agroforestry (AGF) vegetative buffer system. Bromide (Br−) was applied as a nonreactive tracer to characterize the flow of water. Radio‐labeled (14C) SMZ with or without 150 mg L−1 of manure‐derived dissolved organic carbon (DOC), was added as a pulse and leaching of the compounds was monitored for 21 d. Sulfamethazine concentration in the leachate was monitored using a liquid scintillation counter and high performance liquid chromatography analysis confirmed absence of SMZ degradation product in the leachate. Breakthrough curves of SMZ and Br− were constructed for each column. Bromide breakthrough curves were fitted with a convection/dispersion based transport equilibrium model with no sorption, whereas SMZ breakthrough curves were fitted with multisite sorption chemical nonequilibrium transport models with linear or Freundlich sorption components. Results indicated that the three‐site model containing two reversible sites (one instantaneous and one kinetic) and one irreversible site coupled with the Freundlich sorption component (3S2R‐Freu‐irrev model) best described SMZ transport through the columns with model efficiencies of 0.998, 0.994, and 0.991 for AGF, AGF + DOC and Crop soils, respectively. No difference or small difference between fitted sorption parameters, such as the linear sorption coefficient (Kd), Freundlich sorption coefficient (Kf), and Freundlich nonlinearity parameter (N), and those obtained from equilibrium sorption experiments was observed. Data from equilibrium sorption experiments and column transport experiments suggested that the AGF soil was able to retain a larger quantity of SMZ than the Crop soil, and DOC had little effect on SMZ sorption or leaching. Greater SMZ retention by the AGF soil compared to the Crop soil suggests that vegetative buffers may be a viable means to mitigate VA loss from agroecosystems.

A nonparametric kernel regression model for downscaling multisite daily precipitation in the Mahanadi basin

Key PointsMulti‐site statistical downscaling to preserve spatio‐temporal variabilitySimulations with kernel regressionClimate Change Impacts on rainfall in Mahanadi Basin, India

Multi-site doubly stochastic Poisson process models for fine-scale rainfall

We consider a class of doubly stochastic Poisson process models in the modelling of fine-scale rainfall at multiple gauges in a dense network. Multi-site stochastic point process models are constructed and their likelihood functions are derived. The application of this class of multi-site models, a useful alternative to the widely-known Poisson cluster models, is explored to make inferences about the properties of fine time-scale rainfall. The proposed models, which incorporate covariate information about the catchment area, are used to analyse tipping-bucket raingauge data from multiple sites. The results show the potential of this class of models to reproduce temporal and spatial variability of fine time-scale rainfall characteristics.

A comparison of three multi-site statistical downscaling models for daily rainfall in the North China Plain

Three statistical downscaling methods (conditional resampling statistical downscaling model: CR-SDSM, the generalised linear model for daily climate time series: GLIMCLIM, and the non-homogeneous hidden Markov model: NHMM) for multi-site daily rainfall were evaluated and compared in the North China Plain (NCP). The comparison focused on a range of statistics important for hydrological studies including rainfall amount, extreme rainfall, intra-annual variability, and spatial coherency. The results showed that no single model performed well over all statistics/timescales, suggesting that the user should chose appropriate methods after assessing their advantages and limitations when applying downscaling methods for particular purposes. Specifically, the CR-SDSM provided relatively robust results for annual/monthly statistics and extreme characteristics, but exhibited weakness for some daily statistics, such as daily rainfall amount, dry-spell length, and annual wet/dry days. GLIMCLIM performed well for annual dry/wet days, dry/wet spell length, and spatial coherency, but slightly overestimated the daily rainfall. Additionally, NHMM performed better for daily rainfall and annual wet/dry days, but slightly underestimated dry/wet spell length and overestimated the daily extremes. The results of this study could be applied when investigating climate change impact on hydrology and water availability for the NCP, which suffers from intense water shortages due to climate change and human activities in recent years.

Multisite models to determine the distribution of kink sites adjacent to low-energy edges

Kink sites play a critical role in crystal growth. The incorporation of a growth unit into a kink site (1) maintains the free energy of the edge constant and (2) creates another site with the same properties. These properties allow growth through successive incorporation events to proceed in a self-sustaining manner such that the equilibrium spacing between kinks is maintained on average. Traditionally the distributions of kink sites have been determined using a single-site model whereby the probabilities of encountering a kink site adjacent to an edge and encountering a disturbance along an edge are assumed equivalent. In this paper, we develop multisite models that determine the probabilities of encountering kink sites; with the requirement that they obey both properties necessary for growth through successive self-sustaining incorporation events. The probabilities determined using the multisite models diverge significantly from the single-site model for edges with intermolecular interactions ≲6k(b)T between successive molecules. The implications of these findings for the development of predictive crystal shape models and experimental analysis are discussed.

Magnetic-Field Dependence of the Electroluminescence of Organic Light-Emitting Diodes: A Competition between Exciton Formation and Spin Mixing

We explore the magnetoelectroluminescence (MEL) of organic light-emitting diodes by evaluating the magnetic-field dependent fraction of singlet excitons formed. We use two- and multisite polaron-hopping models with spin mixing by hyperfine fields and different singlet and triplet exciton formation rates k(S) and k(T). A huge MEL is predicted when exciton formation is in competition with spin mixing and when k(T) is significantly larger than k(S). This competition also leads to a low-field structure in the MEL that is in agreement with recent experiments.

Optimization of the separation of sulfur hexafluoride and nitrogen by selective adsorption using monte carlo simulations

AbstractWe present molecular simulations to find the optimal conditions for the separation by adsorption of SF6 from a gaseous mixture with N2, a mixture of key interest in electrical applications. The effect of pore size, pressure, and mixture compositions on the selective adsorption of SF6 was investigated by using Grand Canonical Monte Carlo simulations with simple fluid models and a simplified model of MCM‐41. Simulations performed with multisite fluid models confirm that general trends are predicted using simple models, including a maximum in SF6 selectivity for pore diameters around 1.1 nm. Simulations were also performed using two atomistic models of zeolite‐templated carbon (ZTC), FAU‐ZTC, and EMT‐ZTC, materials with average pore sizes close to 1.1 nm, obtaining high selectivities for both materials. Selectivities for FAU‐ZTC are approximately four times higher than the best materials published for this mixture separation, opening excellent opportunities to use it for recovering SF6 from SF6/N2 mixtures. © 2010 American Institute of Chemical Engineers AIChE J, 2011

Transient molecular dynamics simulations of liquid viscosity for nonpolar and polar fluids

A transient molecular dynamics (TMD) method for obtaining fluid viscosity is extended to multisite, force-field models of both nonpolar and polar liquids. The method overlays a sinusoidal velocity profile over the peculiar particle velocities and then records the transient decay of the velocity profile. The viscosity is obtained by regression of the solution of the momentum equation with an appropriate constitutive equation and initial and boundary conditions corresponding to those used in the simulation. The transient velocity decays observed appeared to include both relaxation and retardation effects. The Jeffreys viscoelastic model was found to model accurately the transient responses obtained for multisite models for n-butane, isobutane, n-hexane, water, methanol, and 1-hexanol. TMD viscosities obtained for saturated liquids over a wide range of densities agreed well for the polar fluids, both with nonequilibrium molecular dynamics (NEMD) results using the same force-field models and with correlations based on experimental data. Viscosities obtained for the nonpolar fluids agreed well with the experimental and NEMD results at low to moderate densities, but underpredicted experimental values at higher densities where shear-thinning effects and viscous heating may impact the TMD simulations.

The use of two-phase molecular dynamics simulations to determine the phase behavior and critical point of propane molecular models

Molecular dynamics simulations were performed to determine two-phase configurations of model propane molecules below the critical point and in the near-critical, two-phase region. A postprocessor that uses a Monte Carlo method for determination of volumes attributable to each molecule was used to obtain density histograms of the particles from which the bulk coexisting equilibrium vapor and liquid densities were determined. This method of analyzing coexisting densities in a two-phase simulation is straightforward and can be easily implemented for complex, multisite models. Various degrees of internal flexibility in the propane models have little effect on the coexisting densities at temperatures 40 K or more below the critical point, but internal flexibility (angle bending and bond vibrations) does affect the saturated liquid densities in the near-critical region, changing the critical temperature by approximately 20 K. Shorter cutoffs were also found to affect the phase dome and the location of the critical point.

Photocatalysis: Light energy conversion for the oxidation, disinfection, and decomposition of water

The results of many-year studies of the relationship between the physical properties and photocatalytic activity of TiO2 and Pt/TiO2 in photocatalytic purification and disinfection of air and water and water photodecomposition with oxygen evolution are presented. Recommendations are given as to finding the optimal method for platinum supporting on TiO2 to achieve the highest possible catalytic activity. Multisite kinetic models of the gas-phase oxidation of simple organic substances are considered. Methods for regenerating the photocatalyst after its deactivation in the oxidation of sulfur-containing organic substances are suggested. New data are discussed on the acceleration of air purification by the combination of photocatalytic oxidation with atmospheric electric discharges, the addition of gaseous hydrogen peroxide, and oxidation on photocatalysts existing in the aerosol state. As compared to pure TiO2, platinated titanium dioxide has a higher capability for disinfection and complete mineralization of microorganisms. Two promising methods for production of hydrogen from water using solar light are presented.

Anti-Zeno effect for quantum transport in disordered systems

We demonstrate that repeated measurements in disordered systems can induce a quantum anti-Zeno effect under certain conditions to enhance quantum transport. The enhancement of energy transfer is really exhibited in multisite models under repeated measurements. The optimal measurement interval for the anti-Zeno effect and the maximal efficiency of energy transfer are specified in terms of the relevant physical parameters. Since the environment acts as frequent measurements on the system, the decoherence-induced energy transfer, which has been discussed recently for photosynthetic complexes, may be interpreted in terms of the anti-Zeno effect. We further find an interesting phenomenon in a specific three-site case, where local decoherence or repeated measurements may even promote entanglement generation between the nonlocal sites.

Understanding structural effects of multipole moments on aqueous solvation of ions using the soft-sticky dipole–quadrupole–octupole water model

The effects of water multipole moments on the aqueous solvation of ions were determined in Monte Carlo simulations using soft-sticky dipole–quadrupole–octupole (SSDQO) water. Water molecules formed linear hydrogen bonds to Cl − using the new SSDQO1 parameters, similar to multi-site models. However, the dipole vector was tilted rather than parallel to the oxygen–Na + internuclear vector as in most multi-site model, while experiment and ab initio molecular dynamics simulations generally indicate a range of values between tilted and parallel. By varying the multipoles in SSDQO, the octupole was found to determine the orientation around Na +. Moreover, analysis of the multipoles of more conventional models is predictive of their performance as solvents.

Application of Integrated Back-Propagation Network and Self-Organizing Map for Groundwater Level Forecasting

In this paper, based on the combination of the back-propagation network (BPN) and the self-organizing map (SOM), a groundwater level forecasting model is proposed, named improved multisite SOM-BPN model. In the proposed model, the SOM is used to determine the number of hidden layer neurons, and the autoregressive integrated moving-average (ARIMA) model and semivariogram are used to determine the number of input neurons. To evaluate the forecast accuracy of the proposed model, the improved multisite SOM-BPN model as well as five other models (ARIMA model, single-site BPN model, single-site SOM-BPN model, multisite BPN model, and multisite SOM-BPN model) are applied to actual groundwater level data in southern Taiwan. According to the results, it is found that the single-site and multisite BPN models can forecast more precisely than the ARIMA model. Moreover, the results show that the multisite model is more competent in forecasting groundwater level as compared to the single-site model. Finally, among the six models, the improved multisite SOM-BPN model has the highest accuracy. The improved multisite SOM-BPN model is recommended as an alternative to groundwater level forecasting because it cannot only produce reasonable forecasts, but also objectively determine the suitable number of hidden layer neurons of BPN.