Real-time observation of the charge-transfer-to-solvent dynamics

Intermolecular electron-transfer reactions have a crucial role in biology, solution chemistry and electrochemistry. The first step of such reactions is the expulsion of the electron to the solvent, whose mechanism is determined by the structure and dynamical response of the latter. Here we visualize the electron transfer to water using ultrafast fluorescence spectroscopy with polychromatic detection from the ultraviolet to the visible region, upon photo-excitation of the so-called charge transfer to solvent states of aqueous iodide. The initial emission is short lived (~60 fs) and it relaxes to a broad distribution of lower-energy charge transfer to solvent states upon rearrangement of the solvent cage. This distribution reflects the inhomogeneous character of the solvent cage around iodide. Electron ejection occurs from the relaxed charge transfer to solvent states with lifetimes of 100–400 fs that increase with decreasing emission energy.

Density-dependent dynamics of a dominant rain forest tree change with juvenile stage and time of masting

Although negative density dependence (NDD) can facilitate tree species coexistence in forests, the underlying mechanisms can differ, and rarely are the dynamics of seedlings and saplings studied together. Herein we present and discuss a novel mechanism based on our investigation of NDD predictions for the large, grove-forming ectomycorrhizal mast fruiting tree, Microberlinia bisulcata (Caesalpiniaceae), in an 82.5-ha plot at Korup, Cameroon. We tested whether juvenile density, size, growth and survival decreases with increasing conspecific adult basal area for 3245 ‘new’ seedlings and 540 ‘old’ seedlings (< 75-cm tall) during an approximately 4-year study period (2008–2012) and for 234 ‘saplings’ (≥ 75-cm tall) during an approximately 6-year study period (2008–2014). We found that the respective densities of new seedlings, old seedlings and saplings were positively, not and negatively related to increasing BA. Maximum leaf numbers and heights of old seedlings were negatively correlated with increasing basal areas, as were sapling heights and stem diameters. Whereas survivorship of new seedlings decreased by more than one-half with increasing basal area over its range in 2010–2012, that of old seedlings decreased by almost two-thirds, but only in 2008–2010, and was generally unrelated to conspecific seedling density. In 2010–2012 relative growth rates in new seedlings’ heights decreased with increasing basal area, as well as with increasing seedling density, together with increasing leaf numbers, whereas old seedlings’ growth was unrelated to either conspecific density or basal area. Saplings of below-average height had reduced survivorship with increasing basal area (probability decreasing from approx. 0.4 to 0.05 over the basal area range tested), but only sapling growth in terms of leaf numbers decreased with increasing basal area. These static and dynamic results indicate that NDD is operating within this system, possibly stabilizing the M. bisulcata population. However, these NDD patterns are unlikely to be caused by symmetric competition or by consumers. Instead, an alternative mechanism for conspecific adult–juvenile negative feedback is proposed, one which involves the interaction between tree phenology and ectomycorrhizal linkages.

From quantum to classical dynamics: The relativistic O ( N ) model in the framework of the real-time functional renormalization group

We investigate the transition from unitary to dissipative dynamics in the relativistic O(N) vector model with the λ(φ2)2 interaction using the nonperturbative functional renormalization group in the real-time formalism. In thermal equilibrium, the theory is characterized by two scales, the interaction range for coherent scattering of particles and the mean free path determined by the rate of incoherent collisions with excitations in the thermal medium. Their competition determines the renormalization group flow and the effective dynamics of the model. Here we quantify the dynamic properties of the model in terms of the scale-dependent dynamic critical exponent z in the limit of large temperatures and in 2≤d≤4 spatial dimensions. We contrast our results to the behavior expected at vanishing temperature and address the question of the appropriate dynamic universality class for the given microscopic theory.

NONLINEAR TIME SERIES/SYSTEM ANALYSIS (STATE-SPACE, DYNAMICS, INTERVENTION, RESILIENCE, KALMAN)

A discussion of nonlinear dynamics, demonstrated by the familiar automobile, is followed by the development of a systematic method of analysis of a possibly nonlinear time series using difference equations in the general state-space format. This format allows recursive state-dependent parameter estimation after each observation thereby revealing the dynamics inherent in the system in combination with random external perturbations.^ The one-step ahead prediction errors at each time period, transformed to have constant variance, and the estimated parametric sequences provide the information to (1) formally test whether time series observations y(,t) are some linear function of random errors (ELEM)(,s), for some t and s, or whether the series would more appropriately be described by a nonlinear model such as bilinear, exponential, threshold, etc., (2) formally test whether a statistically significant change has occurred in structure/level either historically or as it occurs, (3) forecast nonlinear system with a new and innovative (but very old numerical) technique utilizing rational functions to extrapolate individual parameters as smooth functions of time which are then combined to obtain the forecast of y and (4) suggest a measure of resilience, i.e. how much perturbation a structure/level can tolerate, whether internal or external to the system, and remain statistically unchanged. Although similar to one-step control, this provides a less rigid way to think about changes affecting social systems.^ Applications consisting of the analysis of some familiar and some simulated series demonstrate the procedure. Empirical results suggest that this state-space or modified augmented Kalman filter may provide interesting ways to identify particular kinds of nonlinearities as they occur in structural change via the state trajectory.^ A computational flow-chart detailing computations and software input and output is provided in the body of the text. IBM Advanced BASIC program listings to accomplish most of the analysis are provided in the appendix. ^

Robust trends of landscape dynamics in the Arctic Lena Delta with temporally dense Landsat time-series stacks, with links to GeoTIFFs

Arctic permafrost landscapes are among the most vulnerable and dynamic landscapes globally, but due to their extent and remoteness most of the landscape changes remain unnoticed. In order to detect disturbances in these areas we developed an automated processing chain for the calculation and analysis of robust trends of key land surface indicators based on the full record of available Landsat TM, ETM +, and OLI data. The methodology was applied to the ~ 29,000 km**2 Lena Delta in Northeast Siberia, where robust trend parameters (slope, confidence intervals of the slope, and intercept) were calculated for Tasseled Cap Greenness, Wetness and Brightness, NDVI, and NDWI, and NDMI based on 204 Landsat scenes for the observation period between 1999 and 2014. The resulting datasets revealed regional greening trends within the Lena Delta with several localized hot-spots of change, particularly in the vicinity of the main river channels. With a 30-m spatial resolution various permafrost-thaw related processes and disturbances, such as thermokarst lake expansion and drainage, fluvial erosion, and coastal changes were detected within the Lena Delta region, many of which have not been noticed or described before. Such hotspots of permafrost change exhibit significantly different trend parameters compared to non-disturbed areas. The processed dataset, which is made freely available through the data archive PANGAEA, will be a useful resource for further process specific analysis by researchers and land managers. With the high level of automation and the use of the freely available Landsat archive data, the workflow is scalable and transferrable to other regions, which should enable the comparison of land surface changes in different permafrost affected regions and help to understand and quantify permafrost landscape dynamics.

Brownian dynamics simulations of protein folding: Access to milliseconds time scale and beyond

Protein folding occurs on a time scale ranging from milliseconds to minutes for a majority of proteins. Computer simulation of protein folding, from a random configuration to the native structure, is nontrivial owing to the large disparity between the simulation and folding time scales. As an effort to overcome this limitation, simple models with idealized protein subdomains, e.g., the diffusion–collision model of Karplus and Weaver, have gained some popularity. We present here new results for the folding of a four-helix bundle within the framework of the diffusion–collision model. Even with such simplifying assumptions, a direct application of standard Brownian dynamics methods would consume 10,000 processor-years on current supercomputers. We circumvent this difficulty by invoking a special Brownian dynamics simulation. The method features the calculation of the mean passage time of an event from the flux overpopulation method and the sampling of events that lead to productive collisions even if their probability is extremely small (because of large free-energy barriers that separate them from the higher probability events). Using these developments, we demonstrate that a coarse-grained model of the four-helix bundle can be simulated in several days on current supercomputers. Furthermore, such simulations yield folding times that are in the range of time scales observed in experiments.

Vibrational population relaxation of carbon monoxide in the heme pocket of photolyzed carbonmonoxy myoglobin: Comparison of time-resolved mid-IR absorbance experiments and molecular dynamics simulations

The vibrational energy relaxation of carbon monoxide in the heme pocket of sperm whale myoglobin was studied by using molecular dynamics simulation and normal mode analysis methods. Molecular dynamics trajectories of solvated myoglobin were run at 300 K for both the δ- and ɛ-tautomers of the distal His-64. Vibrational population relaxation times of 335 ± 115 ps for the δ-tautomer and 640 ± 185 ps for the ɛ-tautomer were estimated by using the Landau–Teller model. Normal mode analysis was used to identify those protein residues that act as the primary “doorway” modes in the vibrational relaxation of the oscillator. Although the CO relaxation rates in both the ɛ- and δ-tautomers are similar in magnitude, the simulations predict that the vibrational relaxation of the CO is faster in the δ-tautomer with the distal His playing an important role in the energy relaxation mechanism. Time-resolved mid-IR absorbance measurements were performed on photolyzed carbonmonoxy hemoglobin (Hb13CO). From these measurements, a T1 time of 600 ± 150 ps was determined. The simulation and experimental estimates are compared and discussed.

Use of an Inverse Method for Time Series to Estimate the Dynamics of and Management Strategies for the Box Jellyfish Carybdea marsupialis

Frequently, population ecology of marine organisms uses a descriptive approach in which their sizes and densities are plotted over time. This approach has limited usefulness for design strategies in management or modelling different scenarios. Population projection matrix models are among the most widely used tools in ecology. Unfortunately, for the majority of pelagic marine organisms, it is difficult to mark individuals and follow them over time to determine their vital rates and built a population projection matrix model. Nevertheless, it is possible to get time-series data to calculate size structure and densities of each size, in order to determine the matrix parameters. This approach is known as a “demographic inverse problem” and it is based on quadratic programming methods, but it has rarely been used on aquatic organisms. We used unpublished field data of a population of cubomedusae Carybdea marsupialis to construct a population projection matrix model and compare two different management strategies to lower population to values before year 2008 when there was no significant interaction with bathers. Those strategies were by direct removal of medusae and by reducing prey. Our results showed that removal of jellyfish from all size classes was more effective than removing only juveniles or adults. When reducing prey, the highest efficiency to lower the C. marsupialis population occurred when prey depletion affected prey of all medusae sizes. Our model fit well with the field data and may serve to design an efficient management strategy or build hypothetical scenarios such as removal of individuals or reducing prey. TThis This sdfsdshis method is applicable to other marine or terrestrial species, for which density and population structure over time are available.

Quantifying ice-shelf basal melt and ice-stream dynamics using high-resolution DEM and GPS time series

Thesis (Ph.D.)--University of Washington, 2016-06

Complex temporal patterns in molecular dynamics:a direct measure of the phase-space exploration by the trajectory at macroscopic time scales

Computer simulated trajectories of bulk water molecules form complex spatiotemporal structures at the picosecond time scale. This intrinsic complexity, which underlies the formation of molecular structures at longer time scales, has been quantified using a measure of statistical complexity. The method estimates the information contained in the molecular trajectory by detecting and quantifying temporal patterns present in the simulated data (velocity time series). Two types of temporal patterns are found. The first, defined by the short-time correlations corresponding to the velocity autocorrelation decay times (â‰0.1â€ps), remains asymptotically stable for time intervals longer than several tens of nanoseconds. The second is caused by previously unknown longer-time correlations (found at longer than the nanoseconds time scales) leading to a value of statistical complexity that slowly increases with time. A direct measure based on the notion of statistical complexity that describes how the trajectory explores the phase space and independent from the particular molecular signal used as the observed time series is introduced. © 2008 The American Physical Society.

Computational mechanics reveals nanosecond time correlations in molecular dynamics of liquid systems

Statistical complexity, a measure introduced in computational mechanics has been applied to MD simulated liquid water and other molecular systems. It has been found that statistical complexity does not converge in these systems but grows logarithmically without a limit. The coefficient of the growth has been introduced as a new molecular parameter which is invariant for a given liquid system. Using this new parameter extremely long time correlations in the system undetectable by traditional methods are elucidated. The existence of hundreds of picosecond and even nanosecond long correlations in bulk water has been demonstrated. © 2008 Elsevier B.V. All rights reserved.

Modelling nonlinear stochastic dynamics in financial time series

For analysing financial time series two main opposing viewpoints exist, either capital markets are completely stochastic and therefore prices follow a random walk, or they are deterministic and consequently predictable. For each of these views a great variety of tools exist with which it can be tried to confirm the hypotheses. Unfortunately, these methods are not well suited for dealing with data characterised in part by both paradigms. This thesis investigates these two approaches in order to model the behaviour of financial time series. In the deterministic framework methods are used to characterise the dimensionality of embedded financial data. The stochastic approach includes here an estimation of the unconditioned and conditional return distributions using parametric, non- and semi-parametric density estimation techniques. Finally, it will be shown how elements from these two approaches could be combined to achieve a more realistic model for financial time series.