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The minima on the potential energy surface of 1,2-bis(o-carboxyphenoxy) ethane (CPE) molecule in its electronic ground state were searched by a molecular dynamics simulation performed with MM2 force field. For each of the found minimum-energy conformers, the corresponding equilibrium geometry, charge distribution, HOMO-LUMO energy gap, force field, vibrational normal modes and associated IR and Raman spectral data were determined by means of the density functional theory (DFT) based electronic structure calculations carried out by using B3LYP method and various Pople- style basis sets. The obtained theoretical data confirmed the significant effects of the intra- and inter-molecular hydrogen bonding interactions on the conformational structure, force field, and group vibrations of the molecule. The same data have also revealed that two of the determined stable conformers, both of which exhibit pseudo-crown structure, are considerably more favorable in energy to the others and accordingly provide the major c ntribution to the experimental spectra of CPE. In the light of the improved vibrational spectral data obtained within the "SQM FF" methodology and "Dual Scale Factors" approach for the monomer and dimer forms of these two conformers, a reliable assignment of the fundamental bands observed in the experimental room-temperature IR and Raman spectra of the molecule was given, and the sensitivities of its group vibratb20s to conformation, substitution and dimerization were discussed.
The minima on the potential energy surface of 1,2-bis(o-carboxyphenoxy)ethane (CPE) molecule in its electronic ground state were searched by a molecular dynamics simulation performed with MM2 force field. For each of the found minimum-energy conformers, the corresponding equilibrium geometry, charge distribution, HOMO-LUMO energy gap, force field, vibrational normal modes and associated IR and Raman spectral data were determined by means of the density functional theory (DFT) based electronic structure calculations carried out by using B3LYP method and various Pople-style basis sets. The obtained theoretical data confirmed the significant effects of the intra- and inter-molecular hydrogen bonding interactions on the conformational structure, force field, and group vibrations of the molecule. The same data have also revealed that two of the determined stable conformers, both of which exhibit pseudo-crown structure, are considerably more favorable in energy to the others and accordingly provide the major contribution to the experimental spectra of CPE. In the light of the improved vibrational spectral data obtained within the "SQM FF" methodology and "Dual Scale Factors" approach for the monomer and dimer forms of these two conformers, a reliable assignment of the fundamental bands observed in the experimental room-temperature IR and Raman spectra of the molecule was given, and the sensitivities of its group vibrations to conformation, substitution and dimerization were discussed.
A confocal set-up is presented that improves micro-XRF and XAFS experiment with high-pressure e diamond-anvil cells (DACs) In this experiment a probing volume is defined by the focus of the incoming synchrotron radiation beam and that of a polycapillary X-ray half-lens with a very long working distance, which is placed in front of the fluorescence detector This set-up enhances the quality of the fluorescence and XAFS spectra, and thus the sensitivity for detecting elements at low concentrations. It efficiently suppresses signal from outside the sample chamber, which stems from elastic and inelastic scattering of the incoming beam by the diamond anvils as well as from excitation of fluorescence from the body of the DAC
The Net Reclassification Improvement (NRI) has become a popular metric for evaluating improvement in disease prediction models through the past years. The concept is relatively straightforward but usage and interpretation has been different across studies. While no thresholds exist for evaluating the degree of improvement, many studies have relied solely on the significance of the NRI estimate. However, recent studies recommend that statistical testing with the NRI should be avoided. We propose using confidence ellipses around the estimated values of event and non-event NRIs which might provide the best measure of variability around the point estimates. Our developments are illustrated using practical examples from EPIC-Potsdam study.
Time-delayed collection field (TDCF) and bias-amplified charge extraction (BACE) are applied to as-prepared and annealed poly(3-hexylthiophene):[6,6]-phenyl C-71 butyric acid methyl ester (P3HT:PCBM) blends coated from chloroform. Despite large differences in fill factor, short-circuit current, and power conversion efficiency, both blends exhibit a negligible dependence of photogeneration on the electric field and strictly bimolecular recombination (BMR) with a weak dependence of the BMR coefficient on charge density. Drift-diffusion simulations are performed using the measured coefficients and mobilities, taking into account bimolecular recombination and the possible effects of surface recombination. The excellent agreement between the simulation and the experimental data for an intensity range covering two orders of magnitude indicates that a field-independent generation rate and a density-independent recombination coefficient describe the current-voltage characteristics of the annealed P3HT: PCBM devices, while the performance of the as-prepared blend is shown to be limited by space charge effects due to a low hole mobility. Finally, even though the bimolecular recombination coefficient is small, surface recombination is found to be a negligible loss mechanism in these solar cells.
Technological advancements are giving rise to the fourth industrial revolution - Industry 4.0 -characterized by the mass employment of smart objects in highly reconfigurable and thoroughly connected industrialproduct-service systems. The purpose of this paper is to propose a theory-based knowledgedynamics model in the smart grid scenario that would provide a holistic view on the knowledge-based interactions among smart objects, humans, and other actors as an underlyingmechanism of value co-creation in Industry 4.0. A multi-loop and three-layer - physical, virtual, and interface - model of knowledge dynamics is developedby building on the concept of ba - an enabling space for interactions and theemergence of knowledge. The model depicts how big data analytics are just one component inunlocking the value of big data, whereas the tacit engagement of humans-in-the-loop - theirsense-making and decision-making - is needed for insights to be evoked fromanalytics reports and customer needs to be met.
Environmental heterogeneity is a major determinant of plant population dynamics. In semi-arid Kalahari savannas, heterogeneity is created by savanna structure, i.e. by the spatial arrangement and temporal dynamics of woody plant and open grassland microsites. We formulate a conceptual model describing the effects of savanna dynamics on the population dynamics of the animal-dispersed shrub Grewia flava. From empirical results we derive model rules describing effects of savanna structure on several processes in Grewia's life cycle. By formulating the model, we summarise existing information on Grewia demography and identify gaps in this knowledge. Despite a number of such gaps, the model can be used to make certain quantitative predictions. As an example, we apply the model to investigate the role of seed dispersal in Grewia encroachment on rangelands. Model results show that cattle promote encroachment by depositing substantial numbers of seeds in open areas, where Grewia is otherwise dispersal-limited. Finally, we draw some general conclusions about Grewia's life history and population dynamics. Under natural conditions, concentrated seed deposition under woody plants appears to be a key process causing the observed association between Grewia and other woody plants. Furthermore, low rates of recruitment and high adult survival result in slow-motion dynamics of Grewia populations. As a consequence, Grewia populations interact with savanna dynamics on long temporal and short to intermediate spatial scales.
Land-use concepts provide decision support for the most efficient usage options according to sustainable development and multifunctionality requirements. However, developments in landscape-related, agricultural production schemes are primarily driven by economic benefits. Therefore, most agricultural land-use concepts tackle particular problems or interests and lack a systemic perspective. As a result, we discuss a conceptual model for future site-specific agricultural land-use with an inbuilt requirement for adequate experimental sites to enable monitoring systems for a new generation of ecosystem models and for new approaches to address science-stakeholder interactions.
BACKGROUND: Work capacity demands are a concept to describe which psychological capacities are required in a job. Assessing psychological work capacity demands is of specific importance when mental health problems at work endanger work ability. Exploring psychological work capacity demands is the basis for mental hazard analysis or rehabilitative action, e.g. in terms of work adjustment. OBJECTIVE: This is the first study investigating psychological work capacity demands in rehabilitation patients with and without mental disorders. METHODS: A structured interview on psychological work capacity demands (Mini-ICF-Work; Muschalla, 2015; Linden et al., 2015) was done with 166 rehabilitation patients of working age. All interviews were done by a state-licensed socio-medically trained psychotherapist. Inter-rater-reliability was assessed by determining agreement in independent co-rating in 65 interviews. For discriminant validity purposes, participants filled in the Short Questionnaire for Work Analysis (KFZA, Prumper et al., 1994). RESULTS: In different professional fields, different psychological work capacity demands were of importance. The Mini-ICF-Work capacity dimensions reflect different aspects than the KFZA. Patients with mental disorders were longer on sick leave and had worse work ability prognosis than patients without mental disorders, although both groups reported similar work capacity demands. CONCLUSIONS: Psychological work demands - which are highly relevant for work ability prognosis and work adjustment processes - can be explored and differentiated in terms of psychological capacity demands.
The past three decades of policy process studies have seen the emergence of a clear intellectual lineage with regard to complexity. Implicitly or explicitly, scholars have employed complexity theory to examine the intricate dynamics of collective action in political contexts. However, the methodological counterparts to complexity theory, such as computational methods, are rarely used and, even if they are, they are often detached from established policy process theory. Building on a critical review of the application of complexity theory to policy process studies, we present and implement a baseline model of policy processes using the logic of coevolving networks. Our model suggests that an actor's influence depends on their environment and on exogenous events facilitating dialogue and consensus-building. Our results validate previous opinion dynamics models and generate novel patterns. Our discussion provides ground for further research and outlines the path for the field to achieve a computational turn.
We present a computational evaluation of three hypotheses about sources of deficit in sentence comprehension in aphasia: slowed processing, intermittent deficiency, and resource reduction. The ACT-R based Lewis and Vasishth (2005) model is used to implement these three proposals. Slowed processing is implemented as slowed execution time of parse steps; intermittent deficiency as increased random noise in activation of elements in memory; and resource reduction as reduced spreading activation. As data, we considered subject vs. object relative sentences, presented in a self-paced listening modality to 56 individuals with aphasia (IWA) and 46 matched controls. The participants heard the sentences and carried out a picture verification task to decide on an interpretation of the sentence. These response accuracies are used to identify the best parameters (for each participant) that correspond to the three hypotheses mentioned above. We show that controls have more tightly clustered (less variable) parameter values than IWA; specifically, compared to controls, among IWA there are more individuals with slow parsing times, high noise, and low spreading activation. We find that (a) individual IWA show differential amounts of deficit along the three dimensions of slowed processing, intermittent deficiency, and resource reduction, (b) overall, there is evidence for all three sources of deficit playing a role, and (c) IWA have a more variable range of parameter values than controls. An important implication is that it may be meaningless to talk about sources of deficit with respect to an abstract verage IWA; the focus should be on the individual's differential degrees of deficit along different dimensions, and on understanding the causes of variability in deficit between participants.
To provide physically based wind modelling for wind erosion research at regional scale, a 3D computational fluid dynamics (CFD) wind model was developed. The model was programmed in C language based on the Navier-Stokes equations, and it is freely available as open source. Integrated with the spatial analysis and modelling tool (SAMT), the wind model has convenient input preparation and powerful output visualization. To validate the wind model, a series of experiments was conducted in a wind tunnel. A blocking inflow experiment was designed to test the performance of the model on simulation of basic fluid processes. A round obstacle experiment was designed to check if the model could simulate the influences of the obstacle on wind field. Results show that measured and simulated wind fields have high correlations, and the wind model can simulate both the basic processes of the wind and the influences of the obstacle on the wind field. These results show the high reliability of the wind model. A digital elevation model (DEM) of an area (3800 m long and 1700 m wide) in the Xilingele grassland in Inner Mongolia (autonomous region, China) was applied to the model, and a 3D wind field has been successfully generated. The clear implementation of the model and the adequate validation by wind tunnel experiments laid a solid foundation for the prediction and assessment of wind erosion at regional scale.
Individuals with agrammatic Broca's aphasia experience difficulty when processing reversible non-canonical sentences. Different accounts have been proposed to explain this phenomenon. The Trace Deletion account (Grodzinsky, 1995, 2000, 2006) attributes this deficit to an impairment in syntactic representations, whereas others (e.g., Caplan, Waters, Dede, Michaud, & Reddy, 2007; Haarmann, Just, & Carpenter, 1997) propose that the underlying structural representations are unimpaired, but sentence comprehension is affected by processing deficits, such as slow lexical activation, reduction in memory resources, slowed processing and/or intermittent deficiency, among others. We test the claims of two processing accounts, slowed processing and intermittent deficiency, and two versions of the Trace Deletion Hypothesis (TDH), in a computational framework for sentence processing (Lewis & Vasishth, 2005) implemented in ACT-R (Anderson, Byrne, Douglass, Lebiere, & Qin, 2004). The assumption of slowed processing is operationalized as slow procedural memory, so that each processing action is performed slower than normal, and intermittent deficiency as extra noise in the procedural memory, so that the parsing steps are more noisy than normal. We operationalize the TDH as an absence of trace information in the parse tree. To test the predictions of the models implementing these theories, we use the data from a German sentence—picture matching study reported in Hanne, Sekerina, Vasishth, Burchert, and De Bleser (2011). The data consist of offline (sentence-picture matching accuracies and response times) and online (eye fixation proportions) measures. From among the models considered, the model assuming that both slowed processing and intermittent deficiency are present emerges as the best model of sentence processing difficulty in aphasia. The modeling of individual differences suggests that, if we assume that patients have both slowed processing and intermittent deficiency, they have them in differing degrees.
A comprehensive workflow to analyze ensembles of globally inverted 2D electrical resistivity models
(2022)
Electrical resistivity tomography (ERT) aims at imaging the subsurface resistivity distribution and provides valuable information for different geological, engineering, and hydrological applications. To obtain a subsurface resistivity model from measured apparent resistivities, stochastic or deterministic inversion procedures may be employed. Typically, the inversion of ERT data results in non-unique solutions; i.e., an ensemble of different models explains the measured data equally well. In this study, we perform inference analysis of model ensembles generated using a well-established global inversion approach to assess uncertainties related to the nonuniqueness of the inverse problem. Our interpretation strategy starts by establishing model selection criteria based on different statistical descriptors calculated from the data residuals. Then, we perform cluster analysis considering the inverted resistivity models and the corresponding data residuals. Finally, we evaluate model uncertainties and residual distributions for each cluster. To illustrate the potential of our approach, we use a particle swarm optimization (PSO) algorithm to obtain an ensemble of 2D layer-based resistivity models from a synthetic data example and a field data set collected in Loon-Plage, France. Our strategy performs well for both synthetic and field data and allows us to extract different plausible model scenarios with their associated uncertainties and data residual distributions. Although we demonstrate our workflow using 2D ERT data and a PSObased inversion approach, the proposed strategy is general and can be adapted to analyze model ensembles generated from other kinds of geophysical data and using different global inversion approaches.
The improvement of process representations in hydrological models is often only driven by the modelers' knowledge and data availability. We present a comprehensive comparison between two hydrological models of different complexity that is developed to support (1) the understanding of the differences between model structures and (2) the identification of the observations needed for model assessment and improvement. The comparison is conducted on both space and time and by aggregating the outputs at different spatiotemporal scales. In the present study, mHM, a process‐based hydrological model, and ParFlow‐CLM, an integrated subsurface‐surface hydrological model, are used. The models are applied in a mesoscale catchment in Germany. Both models agree in the simulated river discharge at the outlet and the surface soil moisture dynamics, lending their supports for some model applications (drought monitoring). Different model sensitivities are, however, found when comparing evapotranspiration and soil moisture at different soil depths. The analysis supports the need of observations within the catchment for model assessment, but it indicates that different strategies should be considered for the different variables. Evapotranspiration measurements are needed at daily resolution across several locations, while highly resolved spatially distributed observations with lower temporal frequency are required for soil moisture. Finally, the results show the impact of the shallow groundwater system simulated by ParFlow‐CLM and the need to account for the related soil moisture redistribution. Our comparison strategy can be applied to other models types and environmental conditions to strengthen the dialog between modelers and experimentalists for improving process representations in Earth system models.
Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated‐Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross‐validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half‐sample cross‐validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ( N̂ area
) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID‐based estimates by a mean factor of 2. The median number of cross‐validated locations included in the hold‐out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing N̂ area. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small N̂ area. While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an N̂ area >1,000, where 30% had an N̂ area <30. In this frequently encountered scenario of small N̂ area, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.
The quantification of spatial propagation of extreme precipitation events is vital in water resources planning and disaster mitigation. However, quantifying these extreme events has always been challenging as many traditional methods are insufficient to capture the nonlinear interrelationships between extreme event time series. Therefore, it is crucial to develop suitable methods for analyzing the dynamics of extreme events over a river basin with a diverse climate and complicated topography. Over the last decade, complex network analysis emerged as a powerful tool to study the intricate spatiotemporal relationship between many variables in a compact way. In this study, we employ two nonlinear concepts of event synchronization and edit distance to investigate the extreme precipitation pattern in the Ganga river basin. We use the network degree to understand the spatial synchronization pattern of extreme rainfall and identify essential sites in the river basin with respect to potential prediction skills. The study also attempts to quantify the influence of precipitation seasonality and topography on extreme events. The findings of the study reveal that (1) the network degree is decreased in the southwest to northwest direction, (2) the timing of 50th percentile precipitation within a year influences the spatial distribution of degree, (3) the timing is inversely related to elevation, and (4) the lower elevation greatly influences connectivity of the sites. The study highlights that edit distance could be a promising alternative to analyze event-like data by incorporating event time and amplitude and constructing complex networks of climate extremes.
A long-standing and profound problem in astronomy is the difficulty in obtaining deep near-infrared observations due to the extreme brightness and variability of the night sky at these wavelengths. A solution to this problem is crucial if we are to obtain the deepest possible observations of the early Universe, as redshifted starlight from distant galaxies appears at these wavelengths. The atmospheric emission between 1,000 and 1,800 nm arises almost entirely from a forest of extremely bright, very narrow hydroxyl emission lines that varies on timescales of minutes. The astronomical community has long envisaged the prospect of selectively removing these lines, while retaining high throughput between them. Here we demonstrate such a filter for the first time, presenting results from the first on-sky tests. Its use on current 8 m telescopes and future 30 m telescopes will open up many new research avenues in the years to come.