Model-based Data Analysis Research Articles

An approach to modeling production decline in unconventional reservoirs

Most decline curve methods have two main limitations; the model parameters as a rule are not functions of reservoir parameters and may yield unrealistic (nonphysical) values of expected ultimate recovery (EUR) because boundary-dominated flow may not develop in unconventional reservoirs. Over the past few years, several empirical models have emerged to address the second limitation, but they are challenged by the time to transition from infinite-acting flow period to the boundary-dominated flow. In this study, we performed statistical and model-based analysis of production data from hydraulically fractured horizontal oil wells and present a method to mitigate some of the limitations highlighted above. The production data were carefully analyzed to identify the flow regimes and understand the overall decline behavior. Following this step, we performed model-based analysis using the parallel flow model (sum of exponential terms), and the logistic growth model. After the model-based analysis, the model parameters were analyzed statistically and cross-plotted against available reservoir and well completion parameters. Based on the conclusion from the cross-plots and statistical analysis, we used design of experiments (DoE) and numerical reservoir simulations to develop functions that relate the model parameters and reservoir/well completion properties. Results from this work indicate that the production characteristics from these wells are highly variable. In addition, the parallel flow model indicates that there are at least two to three different time domains in the production behavior and that they are not the result of operational changes, such as well shut-in or operating pressure changes at the surface. All the models used in this study provide very good fits to the data, and all provide realistic estimates of EUR. The cross-plots of model parameters and some reservoir/well completion properties indicate that there is some relationship between them, which we developed using DoE and flow simulations. We have also shown how these models can be applied to obtain realistic estimates of EUR from early-time production data in unconventional oil reservoirs.

Linear baseline interpolation for single-process DSC data—Yes or no?

Theoretical simulations were used to evaluate performance of the linear baseline interpolation for the consequent kinetic analysis of single processes under different circumstances including the effects of varying height of the heat capacity step between the reactants and products, or varying asymmetry of the interpolated kinetic peak. In general, height of the Cp step was found to have larger distorting influence than the peak asymmetry. The model-free kinetic methods were found to be robust enough not to be significantly affected by the distortions caused by using linear interpolation (the performance was compared to that of the most accurate and physically meaningful tangential area-proportional interpolation) – the largest errors in activation energy did not exceed approx. 3%. On the other hand, model-based analysis has shown that usage of the linear interpolation leads to a significant distortion of the shape of the kinetic peaks and large deviations in the integrated area of the kinetic peak (corresponding e.g. to specific heat associated with the process studied by differential scanning calorimetry). Distortions of the peak shape consequently led to often largely deviated values of kinetic exponents determined for the correctly preselected known kinetic models. Model-based kinetic analysis of real-life experimental data treated via linear interpolation could in such case lead even to a selection of an incorrect kinetic model.

LASSIM-A network inference toolbox for genome-wide mechanistic modeling.

Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naïve Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases.

A non-destructive non-contact method of the sea-bottom structure investigation

The paper describes a nondestructive noncontact method for investigating the sea-bottom structure using hydroacoustic and laser radiation. A practical implementation of this method is also presented. The research complex incorporates a state-of-the-art low-frequency hydroacoustic radiating system and a system of coastal laser strainmeters arranged in the specially selected sea waters and coastal grounds. The hydroacoustic radiators are used to generate a seismic signal. Seismic superficial waves are recorded by the coastal laser strainmeters. Optical parts of the strainmeters are constructed as the unequal-path Michelson interferometer where the frequency-stable helium-neon lasers serve as emission sources. A preliminary model of the sea-bottom testing ground has been developed by application of geologic-geophysical procedures. The model-based analysis of the timing data of the recorded seismic waves was carried out. The prospectivity of the used method was proved.

A data–model synthesis to explain variability in calcification observed during a CO<sub>2</sub> perturbation mesocosm experiment

Abstract. The effect of ocean acidification on growth and calcification of the marine algae Emiliania huxleyi was investigated in a series of mesocosm experiments where enclosed water volumes that comprised a natural plankton community were exposed to different carbon dioxide (CO2) concentrations. Calcification rates observed during those experiments were found to be highly variable, even among replicate mesocosms that were subject to similar CO2 perturbations. Here, data from an ocean acidification mesocosm experiment are reanalysed with an optimality-based dynamical plankton model. According to our model approach, cellular calcite formation is sensitive to variations in CO2 at the organism level. We investigate the temporal changes and variability in observations, with a focus on resolving observed differences in total alkalinity and particulate inorganic carbon (PIC). We explore how much of the variability in the data can be explained by variations of the initial conditions and by the level of CO2 perturbation. Nine mesocosms of one experiment were sorted into three groups of high, medium, and low calcification rates and analysed separately. The spread of the three optimised ensemble model solutions captures most of the observed variability. Our results show that small variations in initial abundance of coccolithophores and the prevailing physiological acclimation states generate differences in calcification that are larger than those induced by ocean acidification. Accordingly, large deviations between optimal mass flux estimates of carbon and of nitrogen are identified even between mesocosms that were subject to similar ocean acidification conditions. With our model-based data analysis we document how an ocean acidification response signal in calcification can be disentangled from the observed variability in PIC.

Working Memory Load Strengthens Reward Prediction Errors.

Reinforcement learning (RL) in simple instrumental tasks is usually modeled as a monolithic process in which reward prediction errors (RPEs) are used to update expected values of choice options. This modeling ignores the different contributions of different memory and decision-making systems thought to contribute even to simple learning. In an fMRI experiment, we investigated how working memory (WM) and incremental RL processes interact to guide human learning. WM load was manipulated by varying the number of stimuli to be learned across blocks. Behavioral results and computational modeling confirmed that learning was best explained as a mixture of two mechanisms: a fast, capacity-limited, and delay-sensitive WM process together with slower RL. Model-based analysis of fMRI data showed that striatum and lateral prefrontal cortex were sensitive to RPE, as shown previously, but, critically, these signals were reduced when the learning problem was within capacity of WM. The degree of this neural interaction related to individual differences in the use of WM to guide behavioral learning. These results indicate that the two systems do not process information independently, but rather interact during learning.SIGNIFICANCE STATEMENT Reinforcement learning (RL) theory has been remarkably productive at improving our understanding of instrumental learning as well as dopaminergic and striatal network function across many mammalian species. However, this neural network is only one contributor to human learning and other mechanisms such as prefrontal cortex working memory also play a key role. Our results also show that these other players interact with the dopaminergic RL system, interfering with its key computation of reward prediction errors.

The impact of land use and climate on tabanid assemblages in Europe

Land use and climate are important drivers of the diversity and abundance of insects. Tabanids (Diptera: Tabanidae) are haematophagous cosmopolitan flies that are pests to humans and livestock as well as mechanical vectors of animal disease agents such as Besnoitia besnoiti and the equine infectious anaemia virus. Given growing concerns about tabanid-borne diseases in Europe, there is a need to assess how land use and climate affect tabanid assemblages on the European scale. To investigate this, we used a dataset including 38 tabanid samples from 7 European countries to determine the geographical distribution of 79 tabanid species, and a dataset restricted to 32 samples from 4 countries to examine the relationship between tabanid assemblages and climatic and land-use variables. We also investigated the impact of the sampling method on findings. On the European scale, a non-metric multidimensional scaling (NMDS) ordination revealed that tabanid assemblages were spatially ordered along a marked gradient from Eastern to Western Europe. A model-based analysis of the multivariate tabanid abundance data showed that both climatic and landscape variables, but not sampling method, had a significant additive effect on mean abundance. Using linear mixed-effects models, we found that the Shannon diversity index decreased from 2.5 to 0.5 when the percentage of pastureland in a 2-km buffer around the sampling site increased from 0 to 80% and the annual mean temperature decreased from 16 to 4°C, while Pielou’s measure of species evenness decreased from 0.94 to 0.18 with an increase in the percentage of pastureland. We found that the community proportion of the two most common European species, Tabanus bromius and Haematopota pluvialis, was significantly associated with the sampling method. Our results indicate that the distribution and incidence of tabanid-borne diseases may be heavily influenced by changes in land use and climate and highlight the importance of environmental changes on insect vector populations.

On finite mixture models

ABSTRACTFinite mixture models are widely used in scientific investigations. Due to their non-regularity, there are many technical challenges concerning inference problems on various aspects of the finite mixture models. After decades of effort by statisticians, substantial progresses are recorded recently in characterising large sample properties of some classical inference methods when applied to finite mixture models, providing effective numerical solutions for mixture model-based data analysis, and the invention of novel inference approaches. This paper aims to provide a comprehensive summary on large sample properties of some classical statistical methods and recently developed modified likelihood ratio test and EM-test for the order of the finite mixture model. The presentation de-emphasises the rigour in order to gain some insights behind some complex technical issues. The paper wishes to recommend the EM-test as the most promising approach to data analysis problems from all models with mixture structures.

Selecting the model that best fits the data.

Leibovich et al. argue that that none of the experiments they review really establishes that human adults, infants, or nonhuman animals are sensitive to numerosity independent of a range of continuous quantities. We do not dispute their claim that the empirical record is inconclusive but argue that model-based data analysis does offer a way to make progress.

BAYESIAN CLUSTERING OF INDONESIAN RICE GERMPLASM

Model-based clustering where the inference on the parameters follow the Bayesian principle has been used to cluster 467 accessions of Indonesian rice germplasm which consist of released varieties, landraces, introduction lines, improved lines and wild species. A model-based Bayesian cluster analysis of genotype data can be used to evaluate the genetic backgrounds of rice populations of interest. Such analyses can be used to infer population structure, assign individuals to sub populations, and to study hybrid populations. Thus, the goal of this research was to examine the genotype data of numerous accession of rice germplasm using the model bayesian cluster analysis. The 1536 SNP-chip design was performed for genome scanning of the accession using the high throughput genotyping platform, the data of which were used for clustering. The result indicated that the germplasm can be clustered into five cluster based on similarities on genetic profile, i.e. similarities in gene frequencies across genome among individuals. Each cluster can be identified by reference lines, i.e. the lines or varieties that their genetic profile uniquely belong to one cluster and do not have or very rare introgression from lines or varieties of other clusters. Many introgressions have been identified among lines in all clusters which indicated that most of Indonesia rice germplasm, including local and introduced varieties were the results of crosses that occurred either in naturally fixation or breeding program activities that crossed one line/varieties to the others. There is also cluster in which no reference line and almost all lines/varieties in that cluster are known to have same common specific phenotype, e.g. aromatic.

Point-based and model-based geolocation analysis of airborne laser scanning data

Airborne laser scanning (ALS) is one of the most effective remote sensing technologies providing precise three-dimensional (3-D) dense point clouds. A large-size ALS digital surface model (DSM) covering the whole Istanbul province was analyzed by point-based and model-based comprehensive statistical approaches. Point-based analysis was performed using checkpoints on flat areas. Model-based approaches were implemented in two steps as strip to strip comparing overlapping ALS DSMs individually in three subareas and comparing the merged ALS DSMs with terrestrial laser scanning (TLS) DSMs in four other subareas. In the model-based approach, the standard deviation of height and normalized median absolute deviation were used as the accuracy indicators combined with the dependency of terrain inclination. The results demonstrate that terrain roughness has a strong impact on the vertical accuracy of ALS DSMs. From the relative horizontal shifts determined and partially improved by merging the overlapping strips and comparison of the ALS, and the TLS, data were found not to be negligible. The analysis of ALS DSM in relation to TLS DSM allowed us to determine the characteristics of the DSM in detail.

Statistical Performance Analysis of Data-Driven Neural Models

Data-driven model-based analysis of electrophysiological data is an emerging technique for understanding the mechanisms of seizures. Model-based analysis enables tracking of hidden brain states that are represented by the dynamics of neural mass models. Neural mass models describe the mean firing rates and mean membrane potentials of populations of neurons. Various neural mass models exist with different levels of complexity and realism. An ideal data-driven model-based analysis framework will incorporate the most realistic model possible, enabling accurate imaging of the physiological variables. However, models must be sufficiently parsimonious to enable tracking of important variables using data. This paper provides tools to inform the realism versus parsimony trade-off, the Bayesian Cramer-Rao (lower) Bound (BCRB). We demonstrate how the BCRB can be used to assess the feasibility of using various popular neural mass models to track epilepsy-related dynamics via stochastic filtering methods. A series of simulations show how optimal state estimates relate to measurement noise, model error and initial state uncertainty. We also demonstrate that state estimation accuracy will vary between seizure-like and normal rhythms. The performance of the extended Kalman filter (EKF) is assessed against the BCRB. This work lays a foundation for assessing feasibility of model-based analysis. We discuss how the framework can be used to design experiments to better understand epilepsy.

Plasma Retinol Kinetics and β-Carotene Bioefficacy Are Quantified by Model-Based Compartmental Analysis in Healthy Young Adults with Low Vitamin A Stores

Background: Model-based compartmental analysis of data on plasma retinol kinetics after administration of labeled retinol provides unique information about whole-body vitamin A metabolism. If labeled β-carotene is coadministered, its bioefficacy relative to the retinol reference dose can also be estimated.Objectives: The objectives were to model plasma retinol kinetics after administration of labeled preformed vitamin A and provitamin A β-carotene and to determine relative β-carotene bioefficacy.Methods: We used the Simulation, Analysis and Modeling software (WinSAAM version 3.0.8; http://www.WinSAAM.org) to analyze previously collected data on plasma [13C10]- and [13C5]retinol kinetics for 14 d after oral administration of 1 mg [13C10]retinyl acetate and 2 mg [13C10]β-carotene in oil to 30 healthy young adults of European ancestry [13 men, 17 women; mean ± SD age: 24.5 ± 4.2 y; mean ± SD body weight: 65.2 ± 10 kg; mean ± SD body mass index (in kg/m2): 22.5 ± 1.9] with moderate vitamin A intakes.Results: A 6-component model provided the best fit to the data, including compartments for initial metabolism of vitamin A, plasma retinol, and extravascular vitamin A storage. The disposal rate was 6.7 ± 3.1 μmol/d, fractional catabolic rate was 6.0% ± 2.3%/d, and vitamin A stores were 123 ± 71 μmol. Relative β-carotene bioefficacy, based on the ratio of the areas under the fraction of dose curves calculated by WinSAAM, averaged 13.5% ± 6.02% (retinol activity equivalents = 7.7:1.0 μg). Interindividual variation in relative β-carotene bioefficacy was high (CV: 44%).Conclusions: Vitamin A kinetics in these young adults were best described by essentially the same model that had been previously developed by using data for older adults with higher vitamin A stores; differences in parameter values reflected differences in vitamin A status. Estimated β-carotene bioefficacy was relatively low but similar to previously reported estimates obtained by graphical methods. This trial was registered at the UK Clinical Research Network as UKCRN 7413.

Origin of a cryptic lineage in a threatened reptile through isolation and historical hybridization.

Identifying phylogenetically distinct lineages and understanding the evolutionary processes by which they have arisen are important goals of phylogeography. This information can also help define conservation units in endangered species. Such analyses are being transformed by the availability of genomic-scale data sets and novel analytical approaches for statistically comparing different historical scenarios as causes of phylogeographic patterns. Here, we use genomic-scale restriction-site-associated DNA sequencing (RADseq) data to test for distinct lineages in the endangered Eastern Massasauga Rattlesnake (Sistrurus catenatus). We then use coalescent-based modeling techniques to identify the evolutionary mechanisms responsible for the origin of the lineages in this species. We find equivocal evidence for distinct phylogenetic lineages within S. catenatus east of the Mississippi River, but strong support for a previously unrecognized lineage on the western edge of the range of this snake, represented by populations from Iowa, USA. Snakes from these populations show patterns of genetic admixture with a nearby non-threatened sister species (Sistrurus tergeminus). Tests of historical demographic models support the hypothesis that the genetic distinctiveness of Iowa snakes is due to a combination of isolation and historical introgression between S. catenatus and S. tergeminus. Our work provides an example of how model-based analysis of genomic-scale data can help identify conservation units in rare species.

Site-specific water quality criteria for lethal/sublethal protection of freshwater fish exposed to zinc in southern Taiwan

There were considerable concerns about the zinc (Zn) pollution caused by electroplating, chemical, and computer-related high-tech industrial discharges in Kaohsiung Rivers situated at south Taiwan. There is, however, a lack of site-specific water chemistry based toxicity assessment and little is known about the sublethal toxicity on freshwater fish. This study proposes an integrated framework to link experimental and mechanistic model-based data analysis of lethal and sublethal Zn toxicities for grass carp (Ctenopharyn odon idellus) populations for providing the site-specific Zn water quality threshold in Kaohsiung Rivers. A biotic ligand model (BLM) that relates toxicity impairment of physiological active sites impacted by Zn species was used to elucidate the site-specific water chemistry affecting the bioavailability and biological response of grass carp exposed to Zn. Results indicated that 96-h LC50 for mortality and 28-d EC50 for growth inhibition were 474.7 ± 1.3 (mean ± SE) and 149 ± 23.5 μg L−1, respectively. Here the BLM-based predicted steady-state LC50s for mortality were 2110.7, 818.2, 1303.6, 563.3, and 497.1 μg L−1, whereas measured steady-state EC50s for growth inhibition were 726.8, 326.2, 373.4, 193.9, and 170.5 μg L−1 for the Agongdian, Houling, Love, Fengshan, and Gaoping Rivers, respectively. A positive correlation between Mg2+ and EC50 values were found in both acute (r = 0.94, p < 0.01) and chronic (r = 0.97, p < 0.01) Zn exposures. This study suggests that the use of site-specific water chemistry data and ecophysiological traits would enhance the predictive capacities to assess the potential effect of metal toxicity posed to aquatic organisms.

Oxidation of copper at high temperature as an example for gas-solid reactions in a downer reactor – experiments and model-based analysis

The kinetics of a gas-solid reaction at high temperatures were studied in a downer reactor of 2.8m length. As an example, the oxidation of copper particles was carried out at different wall temperatures (1073K–1323K), oxygen concentrations (14 Vol.-%–67 Vol.-%), and particle diameters (51μm–156μm). Although the residence time of the particles was of the order of 1–4s only, considerable copper conversions could be achieved. XRD, EDX and SEM analysis of the formed products revealed that single layer formation of cuprous oxide prevails up to oxygen concentrations of about 25 Vol.-%. At higher oxygen concentrations, double layer oxidation occurs with a second cupric oxide layer forming on top of cuprous oxide. The remaining core radius of unreacted copper was determined through a combination of density measurements, analysis of TPR spectra, and direct SEM observation. Although significant scattering of the individual measurements was observed, it could be shown by independent elemental analysis of the products that the average values from all measurements are reliable. For quantitative evaluation of the measurements we developed a one-dimensional downer reactor model which allowed to calculate the residence time and particle temperature as a function of reactor length. Using this information we derived solid state diffusion coefficients, the activation energies of which agreed well with literature data. Overall it could be shown that the progress of a gas-solid reaction in a downer reactor can be successfully described with a combination of experimental methods and subsequent model-based data analysis.

Hepatic and Extrahepatic Insulin Clearance Are Differentially Regulated: Results From a Novel Model-Based Analysis of Intravenous Glucose Tolerance Data.

Insulin clearance is a highly variable and important factor that affects circulating insulin concentrations. We developed a novel model-based method to estimate both hepatic and extrahepatic insulin clearance using plasma insulin and C-peptide profiles obtained from the insulin-modified frequently sampled intravenous glucose tolerance test. Data from 100 African immigrants without diabetes (mean age 38 years, body weight 81.7 kg, fasting plasma glucose concentration 83 mg/dL, and fasting insulin concentration 37 pmol/L) were used. Endogenous insulin secretion (calculated by C-peptide deconvolution) and insulin infusion rates were used as inputs to a new two-compartment model of insulin kinetics and hepatic and extrahepatic clearance parameters were estimated. Good agreement between modeled and measured plasma insulin profiles was observed (mean normalized root mean square error 6.8%), and considerable intersubject variability in parameters of insulin clearance among individuals was identified (the mean [interquartile range] for hepatic extraction was 25.8% [32.7%], and for extrahepatic insulin clearance was 20.7 mL/kg/min [11.7 mL/kg/min]). Parameters of insulin clearance were correlated with measures of insulin sensitivity and acute insulin response to glucose. The method described appears promising for future research aimed at characterizing variability in insulin clearance and the mechanisms involved in the regulation of insulin clearance.

Can the Effects of Anthropogenic Pressures and Environmental Variability on Nekton Fauna Be Detected in Fishery Data? Insights from the Monitoring of the Artisanal Fishery Within the Venice Lagoon

Nekton communities in transitional ecosystems are naturally adapted to stressful conditions associated with high environmental variability. Human activities in these systems are likely to determine additional stress with a possible effect on fish fauna, hence on fisheries. In order to test the relative importance of natural and anthropogenic factors in determining changes in nekton community, catches (incl. bycatch) from artisanal fisheries (fyke nets) were monitored seasonally in different areas of the Venice lagoon (Italy) between 2001 and 2013. Changes in nekton community composition and in the biomass of target and non-target species/groups were analysed, and the results were related to temporal factors, environmental characteristics and to the variability in anthropogenic pressures. Statistical tests were carried out using a model-based analysis of both univariate and multivariate data. Results highlighted that temporal factors and environmental conditions (i.e. the main chemico-physical descriptors) are more relevant than anthropogenic pressures in explaining spatial and temporal changes in the lagoon nekton assemblage, but that several characteristics of the assemblage, in particular the biomass of some particular categories and of the whole assemblage, are sensitive to human impacts. A particularly negligible effect seemed to be associated with fishing effort, thus suggesting that the monitoring of the local artisanal fishery is suitable also to provide useful information on the evaluation of the status of nekton assemblage.

An Intermittent Model for Intracellular Motions of Gold Nanostars by k-Space Scattering Image Correlation

Anisotropic metallic nanoparticles have been devised as powerful potential tools for in vivo imaging, photothermal therapy, and drug delivery thanks to plasmon-enhanced absorption and scattering cross sections, ease in synthesis and functionalization, and controlled cytotoxicity. The rational design of all these applications requires the characterization of the nanoparticles intracellular trafficking pathways. In this work, we exploit live-cell time-lapse confocal reflectance microscopy and image correlation in both direct and reciprocal space to investigate the intracellular transport of branched gold nanostars (GNSs). Different transport mechanisms, spanning from pure Brownian diffusion to (sub-)ballistic superdiffusion, are revealed by temporal and spatio-temporal image correlation spectroscopy on the tens-of-seconds timescale. According to these findings, combined with numerical simulations and with a Bayesian (hidden Markov model-based) analysis of single particle tracking data, we ascribe the superdiffusive, subballistic behavior characterizing the GNSs dynamics to a two-state switching between Brownian diffusion in the cytoplasm and molecular motor-mediated active transport. For the investigation of intermittent-type transport phenomena, we derive an analytical theoretical framework for Fourier-space image correlation spectroscopy (kICS). At first, we evaluate the influence of all the dynamic and kinetic parameters (the diffusion coefficient, the drift velocity, and the transition rates between the diffusive and the active transport regimes) on simulated kICS correlation functions. Then we outline a protocol for data analysis and employ it to derive whole-cell maps for each parameter underlying the GNSs intracellular dynamics. Capable of identifying even simpler transport phenomena, whether purely diffusive or ballistic, our intermittent kICS approach allows an exhaustive investigation of the dynamics of GNSs and biological macromolecules.

The Optics and Alignment of the Divergent Beam Laboratory X-ray Powder Diffractometer and its Calibration Using NIST Standard Reference Materials.

The laboratory X-ray powder diffractometer is one of the primary analytical tools in materials science. It is applicable to nearly any crystalline material, and with advanced data analysis methods, it can provide a wealth of information concerning sample character. Data from these machines, however, are beset by a complex aberration function that can be addressed through calibration with the use of NIST Standard Reference Materials (SRMs). Laboratory diffractometers can be set up in a range of optical geometries; considered herein are those of Bragg-Brentano divergent beam configuration using both incident and diffracted beam monochromators. We review the origin of the various aberrations affecting instruments of this geometry and the methods developed at NIST to align these machines in a first principles context. Data analysis methods are considered as being in two distinct categories: those that use empirical methods to parameterize the nature of the data for subsequent analysis, and those that use model functions to link the observation directly to a specific aspect of the experiment. We consider a multifaceted approach to instrument calibration using both the empirical and model based data analysis methods. The particular benefits of the fundamental parameters approach are reviewed.