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Simultaneous dynamic characterization of charge and structural motion during ferroelectric switching
(2017)
Monitoring structural changes in ferroelectric thin films during electric field induced polarization switching is important for a full microscopic understanding of the coupled motion of charges, atoms, and domainwalls in ferroelectric nanostructures. We combine standard ferroelectric test sequences of switching and nonswitching electrical pulses with time-resolved x-ray diffraction to investigate the structural response of a nanoscale Pb(Zr0.2Ti0.8)O-3 ferroelectric oxide capacitor upon charging, discharging, and polarization reversal. We observe that a nonlinear piezoelectric response of the ferroelectric layer develops on a much longer time scale than the RC time constant of the device. The complex atomic motion during the ferroelectric polarization reversal starts with a contraction of the lattice, whereas the expansive piezoelectric response sets in after considerable charge flow due to the applied voltage pulses on the electrodes of the capacitor. Our simultaneous measurements on a working device elucidate and visualize the complex interplay of charge flow and structural motion and challenges theoretical modeling.
Since COVID-19 became a pandemic, many studies are being conducted to get a better understanding of the disease itself and its spread. One crucial indicator is the prevalence of SARS-CoV-2 infections. Since this measure is an important foundation for political decisions, its estimate must be reliable and unbiased. This paper presents reasons for biases in prevalence estimates due to unit nonresponse in typical studies. Since it is difficult to avoid bias in situations with mostly unknown nonresponse mechanisms, we propose the maximum amount of bias as one measure to assess the uncertainty due to nonresponse. An interactive web application is presented that calculates the limits of such a conservative unit nonresponse confidence interval (CUNCI).
A new method is proposed for tracking individual motor units (MUs) across multiple experimental sessions on different days. The technique is based on a novel decomposition approach for high-density surface electromyography and was tested with two experimental studies for reliability and sensitivity. Experiment I (reliability): ten participants performed isometric knee extensions at 10, 30, 50 and 70% of their maximum voluntary contraction (MVC) force in three sessions, each separated by 1 week. Experiment II (sensitivity): seven participants performed 2 weeks of endurance training (cycling) and were tested pre-post intervention during isometric knee extensions at 10 and 30% MVC. The reliability (Experiment I) and sensitivity (Experiment II) of the measured MU properties were compared for the MUs tracked across sessions, with respect to all MUs identified in each session. In Experiment I, on average 38.3% and 40.1% of the identified MUs could be tracked across two sessions (1 and 2 weeks apart), for the vastus medialis and vastus lateralis, respectively. Moreover, the properties of the tracked MUs were more reliable across sessions than those of the full set of identified MUs (intra-class correlation coefficients ranged between 0.63-0.99 and 0.39-0.95, respectively). In Experiment II, similar to 40% of the MUs could be tracked before and after the training intervention and training-induced changes in MU conduction velocity had an effect size of 2.1 (tracked MUs) and 1.5 (group of all identified motor units). These results show the possibility of monitoring MU properties longitudinally to document the effect of interventions or the progression of neuromuscular disorders.
The chemical master equation (CME) is the fundamental evolution equation of the stochastic description of biochemical reaction kinetics. In most applications it is impossible to solve the CME directly due to its high dimensionality. Instead, indirect approaches based on realizations of the underlying Markov jump process are used, such as the stochastic simulation algorithm (SSA). In the SSA, however, every reaction event has to be resolved explicitly such that it becomes numerically inefficient when the system's dynamics include fast reaction processes or species with high population levels. In many hybrid approaches, such fast reactions are approximated as continuous processes or replaced by quasi-stationary distributions in either a stochastic or a deterministic context. Current hybrid approaches, however, almost exclusively rely on the computation of ensembles of stochastic realizations. We present a novel hybrid stochastic-deterministic approach to solve the CME directly. Our starting point is a partitioning of the molecular species into discrete and continuous species that induces a partitioning of the reactions into discrete-stochastic and continuous-deterministic processes. The approach is based on a WKB (Wentzel-Kramers-Brillouin) ansatz for the conditional probability distribution function (PDF) of the continuous species (given a discrete state) in combination with Laplace's method of integral approximation. The resulting hybrid stochastic-deterministic evolution equations comprise a CME with averaged propensities for the PDF of the discrete species that is coupled to an evolution equation of the related expected levels of the continuous species for each discrete state. In contrast to indirect hybrid methods, the impact of the evolution of discrete species on the dynamics of the continuous species has to be taken into account explicitly. The proposed approach is efficient whenever the number of discrete molecular species is small. We illustrate the performance of the new hybrid stochastic-deterministic approach in an application to model systems of biological interest.
Background:
Contamination detection is a important step that should be carefully considered in early stages when designing and performing microbiome studies to avoid biased outcomes. Detecting and removing true contaminants is challenging, especially in low-biomass samples or in studies lacking proper controls. Interactive visualizations and analysis platforms are crucial to better guide this step, to help to identify and detect noisy patterns that could potentially be contamination. Additionally, external evidence, like aggregation of several contamination detection methods and the use of common contaminants reported in the literature, could help to discover and mitigate contamination.
Results:
We propose GRIMER, a tool that performs automated analyses and generates a portable and interactive dashboard integrating annotation, taxonomy, and metadata. It unifies several sources of evidence to help detect contamination. GRIMER is independent of quantification methods and directly analyzes contingency tables to create an interactive and offline report. Reports can be created in seconds and are accessible for nonspecialists, providing an intuitive set of charts to explore data distribution among observations and samples and its connections with external sources. Further, we compiled and used an extensive list of possible external contaminant taxa and common contaminants with 210 genera and 627 species reported in 22 published articles.
Conclusion:
GRIMER enables visual data exploration and analysis, supporting contamination detection in microbiome studies. The tool and data presented are open source and available at https://gitlab.com/dacs-hpi/grimer.
Background: Dementia is a psychiatric condition the development of which is associated with numerous aspects of life. Our aim was to estimate dementia risk factors in German primary care patients.
Methods: The case-control study included primary care patients (70-90 years) with first diagnosis of dementia (all-cause) during the index period (01/2010-12/2014) (Disease Analyzer, Germany), and controls without dementia matched (1:1) to cases on the basis of age, sex, type of health insurance, and physician. Practice visit records were used to verify that there had been 10 years of continuous follow-up prior to the index date. Multivariate logistic regression models were fitted with dementia as a dependent variable and the potential predictors.
Results: The mean age for the 11,956 cases and the 11,956 controls was 80.4 (SD: 5.3) years. 39.0% of them were male and 1.9% had private health insurance. In the multivariate regression model, the following variables were linked to a significant extent with an increased risk of dementia: diabetes (OR: 1.17; 95% CI: 1.10-1.24), lipid metabolism (1.07; 1.00-1.14), stroke incl. TIA (1.68; 1.57-1.80), Parkinson's disease (PD) (1.89; 1.64-2.19), intracranial injury (1.30; 1.00-1.70), coronary heart disease (1.06; 1.00-1.13), mild cognitive impairment (MCI) (2.12; 1.82-2.48), mental and behavioral disorders due to alcohol use (1.96; 1.50-2.57). The use of statins (OR: 0.94; 0.90-0.99), proton-pump inhibitors (PPI) (0.93; 0.90-0.97), and antihypertensive drugs (0.96, 0.94-0.99) were associated with a decreased risk of developing dementia.
Conclusions: Risk factors for dementia found in this study are consistent with the literature. Nevertheless, the associations between statin, PPI and antihypertensive drug use, and decreased risk of dementia need further investigations.
Siberian larch forests and the ion content of thaw lakes form a geochemically functional entity
(2013)
Siberian larch forests growing on shallow permafrost soils have not, until now, been considered to be controlling the abiotic and biotic characteristics of the vast number of thaw-lake ecosystems. Here we show, using four independent data sets (a modern data set from 201 lakes from the tundra to taiga, and three lake-core records), that lake-water geochemistry in Yakutia is highly correlated with vegetation. Alkalinity increases with catchment forest density. We postulate that in this arid area, higher evapotranspiration in larch forests compared with that in the tundra vegetation leads to local salt accumulation in soils. Solutes are transported to nearby thaw lakes during rain events and snow melt, but are not fully transported into rivers, because there is no continuous groundwater flow within permafrost soils. This implies that potentially large shifts in the chemical characteristics of aquatic ecosystems to known warming are absent because of the slow response of catchment forests to climate change.
Mapping hydrological parameter distributions in high resolution is essential to understand and simulate groundwater flow and contaminant transport. Of particular interest is surface-based ground-penetrating radar (GPR) reflection imaging in electrically resistive sediments because of the expected close link between the subsurface water content and the dielectric permittivity, which controls GPR wave velocity and reflectivity. Conventional tools like common midpoint (CMP) velocity analysis provide physical parameter models of limited resolution only. We present a novel reflection amplitude inversion workflow for surface-based GPR data capable of resolving the subsurface dielectric permittivity and related water content distribution with markedly improved resolution. Our scheme is an adaptation of a seismic reflection impedance inversion scheme to surface-based GPR data. Key is relative-amplitude-preserving data preconditioning including GPR deconvolution, which results in traces with the source-wavelet distortions and propagation effects largely removed. The subsequent inversion for the underlying dielectric permittivity and water content structure is constrained by in situ dielectric permittivity data obtained by direct-push logging. After demonstrating the potential of our novel scheme on a realistic synthetic data set, we apply it to two 2-D 100 MHz GPR profiles acquired over a shallow sedimentary aquifer resulting in water content images of the shallow (3-7 m depth) saturated zone having decimeter resolution.
Enhancing the resolution and accuracy of surface ground-penetrating radar (GPR) reflection data by inverse filtering to recover a zero-phased band-limited reflectivity image requires a deconvolution technique that takes the mixed-phase character of the embedded wavelet into account. In contrast, standard stochastic deconvolution techniques assume that the wavelet is minimum phase and, hence, often meet with limited success when applied to GPR data. We present a new general-purpose blind deconvolution algorithm for mixed-phase wavelet estimation and deconvolution that (1) uses the parametrization of a mixed-phase wavelet as the convolution of the wavelet's minimum-phase equivalent with a dispersive all-pass filter, (2) includes prior information about the wavelet to be estimated in a Bayesian framework, and (3) relies on the assumption of a sparse reflectivity. Solving the normal equations using the data autocorrelation function provides an inverse filter that optimally removes the minimum-phase equivalent of the wavelet from the data, which leaves traces with a balanced amplitude spectrum but distorted phase. To compensate for the remaining phase errors, we invert in the frequency domain for an all-pass filter thereby taking advantage of the fact that the action of the all-pass filter is exclusively contained in its phase spectrum. A key element of our algorithm and a novelty in blind deconvolution is the inclusion of prior information that allows resolving ambiguities in polarity and timing that cannot be resolved using the sparseness measure alone. We employ a global inversion approach for non-linear optimization to find the all-pass filter phase values for each signal frequency. We tested the robustness and reliability of our algorithm on synthetic data with different wavelets, 1-D reflectivity models of different complexity, varying levels of added noise, and different types of prior information. When applied to realistic synthetic 2-D data and 2-D field data, we obtain images with increased temporal resolution compared to the results of standard processing.
Three-dimensional hydrostratigraphic models from ground-penetrating radar and direct-push data
(2011)
Three-dimensional models of hydraulic conductivity and porosity are essential to understand and simulate groundwater flow in heterogeneous geological environments. However, considering the inherent limitations of traditional hydrogeological field methods in terms of resolution, alternative field approaches are needed to establish such 3-D models with sufficient accuracy. In this study, we developed a workflow combining 3-D structural information extracted from ground penetrating radar (GPR) images with 1-D in situ physical-property estimates from direct-push (DP) logging to construct a 3-D hydrostratigraphic model. To illustrate this workflow, we collected an similar to 70 m x 90 m 100 MHz 3-D GPR data set over a shallow sedimentary aquifer system resolving six different GPR facies down to similar to 15 m depth. DP logs of the relative dielectric permittivity, the relative hydraulic conductivity, the cone resistance, the sleeve friction and the pore pressure provided crucial data (1) to establish a GPR velocity model for 3-D depth migration and to check the time-to-depth conversion of the GPR data, and (2) to construct a 3-D hydrostratigraphic model. This model was built by assigning porosity values, which were computed from the DP relative dielectric permittivity logs, and DP relative hydraulic conductivity estimates to the identified GPR facies. We conclude that the integration of 3-D GPR structural images and 1-D DP logs of target physical parameters provides an efficient way for detailed 3-D subsurface characterization as needed, for example, for groundwater flow simulations.
According to Dooge (1986) intermediate-scale catchments are systems of organized complexity, being too organized and yet too small to be characterized on a statistical/conceptual basis, but too large and too heterogeneous to be characterized in a deterministic manner. A key requirement for building structurally adequate models precisely for this intermediate scale is a better understanding of how different forms of spatial organization affect storage and release of water and energy. Here, we propose that a combination of the concept of hydrological response units (HRUs) and thermodynamics offers several helpful and partly novel perspectives for gaining this improved understanding. Our key idea is to define functional similarity based on similarity of the terrestrial controls of gradients and resistance terms controlling the land surface energy balance, rainfall runoff transformation, and groundwater storage and release. This might imply that functional similarity with respect to these specific forms of water release emerges at different scales, namely the small field scale, the hillslope, and the catchment scale. We thus propose three different types of "functional units" - specialized HRUs, so to speak - which behave similarly with respect to one specific form of water release and with a characteristic extent equal to one of those three scale levels. We furthermore discuss an experimental strategy based on exemplary learning and replicate experiments to identify and delineate these functional units, and as a promising strategy for characterizing the interplay and organization of water and energy fluxes across scales. We believe the thermodynamic perspective to be well suited to unmask equifinality as inherent in the equations governing water, momentum, and energy fluxes: this is because several combinations of gradients and resistance terms yield the same mass or energy flux and the terrestrial controls of gradients and resistance terms are largely independent. We propose that structurally adequate models at this scale should consequently disentangle driving gradients and resistance terms, because this optionally allow sequifinality to be partly reduced by including available observations, e. g., on driving gradients. Most importantly, the thermodynamic perspective yields an energy-centered perspective on rainfall-runoff transformation and evapotranspiration, including fundamental limits for energy fluxes associated with these processes. This might additionally reduce equifinality and opens up opportunities for testing thermodynamic optimality principles within independent predictions of rainfall-runoff or land surface energy exchange. This is pivotal to finding out whether or not spatial organization in catchments is in accordance with a fundamental organizing principle.
Multi-scale analysis of electrical conductivity of peatlands for the assessment of peat properties
(2015)
Peatlands store large amounts of carbon. This storage function has been reduced through intensive drainage, which leads to the decomposition of peat, resulting in a loss of carbon. Measurements of the real (sigma) and imaginary part (sigma) of electrical conductivity can deliver information on peat properties, such as the pore fluid conductivity (sigma(w)), cation exchange capacity (CEC), bulk density ((b)), water content (WC) and soil organic matter (SOM) content. These properties change with the peat's degree of decomposition (DD). To explore the relationships between the peat properties, sigma, sigma and DD, we focused on three different types of survey and scales. First, point measurements were made with a conductivity probe at various locations over a large area of northeast Germany to determine the degree of correlation between sigma and DD. Second, nine of these locations were selected for sampling to determine which of the properties sigma(w), CEC, (b), WC and SOM predominantly influence sigma and sigma. This multisite dataset includes the entire range of DD and was analysed in the laboratory. Third, one site was selected for a survey of sigma including sampling, to identify which properties mainly control sigma in a single-site approach. Statistical analysis revealed that for the multisite laboratory dataset, sigma(w) has the strongest effect on sigma, followed by CEC, whereas sigma is mainly determined by CEC. In a single-site approach, WC followed by CEC had a dominant effect on sigma. No clear correlation could be observed between (i) DD and peat properties and (ii) DD and sigma or sigma. This is because of the complex changes in properties with increasing DD.
Precision fruticulture addresses site or tree-adapted crop management. In the present study, soil and tree status, as well as fruit quality at harvest were analysed in a commercial apple (Malus × domestica 'Gala Brookfield'/Pajam1) orchard in a temperate climate. Trees were irrigated in addition to precipitation. Three irrigation levels (0, 50 and 100%) were applied. Measurements included readings of apparent electrical conductivity of soil (ECa), stem water potential, canopy temperature obtained by infrared camera, and canopy volume estimated by LiDAR and RGB colour imaging. Laboratory analyses of 6 trees per treatment were done on fruit considering the pigment contents and quality parameters. Midday stem water potential (SWP), normalized crop water stress index (CWSI) calculated from thermal data, and fruit yield and quality at harvest were analysed. Spatial patterns of the variability of tree water status were estimated by CWSI imaging supported by SWP readings. CWSI ranged from 0.1 to 0.7 indicating high variability due to irrigation and precipitation. Canopy volume data were less variable. Soil ECa appeared homogeneous in the range of 0 to 4 mS m-1. Fruit harvested in a drought stress zone showed enhanced portion of pheophytin in the chlorophyll pool. Irrigation affected soluble solids content and, hence, the quality of fruit. Overall, results highlighted that spatial variation in orchards can be found even if marginal variability of soil properties can be assumed.
Confronted with a new wave of criticism on the in effectiveness of its development programs, the World Bank embarked on a revitalization process, turning to private investors to finance International Development Association projects and widening its mandate. To explain these adaptation strategies of the World Bank to regain relevance, this piece draws on organizational ecology and orchestration scholarship. We contend that international organizations rely on two adaptation mechanisms, orchestration and scope expansion, when they lose their role as focal actors in an issue area. We find that the World Bank has indeed lost market share and has relied on these two mechanisms to revitalize itself. We show that the World Bank responded to changes in the environment by orchestrating a private sector-oriented capital increase, prioritizing private funding for development through a “cascade approach,” and expanding the scope of its mandate into adjacent domains of transnational governance, including climate change and global health.
Low donor content solar cells are an intriguing class of photovoltaic device about which there is still considerable discussion with respect to their mode of operation. We have synthesized a series of triphenylamine-based materials for use in low donor content devices with the electron accepting [6,6]-phenyl-C71-butyric acid methyl ester (PC(7)0BM). The triphenylamine-based materials absorb light in the near UV enabling the PC(7)0BM to be be the main light absorbing organic semiconducting material in the solar cell. It was found that the devices did not operate as classical Schottky junctions but rather photocurrent was generated by hole transfer from the photo-excited PC(7)0BM to the triphenylamine-based donors. We found that replacing the methoxy surface groups with methyl groups on the donor material led to a decrease in hole mobility for the neat films, which was due to the methyl substituted materials having the propensity to aggregate. The thermodynamic drive to aggregate was advantageous for the performance of the low donor content (6 wt%) films. It was found that the 6 wt% donor devices generally gave higher performance than devices containing 50 wt% of the donor.
Monolithic perovskite silicon tandem solar cells can overcome the theoretical efficiency limit of silicon solar cells. This requires an optimum bandgap, high quantum efficiency, and high stability of the perovskite. Herein, a silicon heterojunction bottom cell is combined with a perovskite top cell, with an optimum bandgap of 1.68 eV in planar p-i-n tandem configuration. A methylammonium-free FA(0.75)Cs(0.25)Pb(I0.8Br0.2)(3) perovskite with high Cs content is investigated for improved stability. A 10% molarity increase to 1.1 m of the perovskite precursor solution results in approximate to 75 nm thicker absorber layers and 0.7 mA cm(-2) higher short-circuit current density. With the optimized absorber, tandem devices reach a high fill factor of 80% and up to 25.1% certified efficiency. The unencapsulated tandem device shows an efficiency improvement of 2.3% (absolute) over 5 months, showing the robustness of the absorber against degradation. Moreover, a photoluminescence quantum yield analysis reveals that with adapted charge transport materials and surface passivation, along with improved antireflection measures, the high bandgap perovskite absorber has the potential for 30% tandem efficiency in the near future.
Monolithic perovskite silicon tandem solar cells can overcome the theoretical efficiency limit of silicon solar cells. This requires an optimum bandgap, high quantum efficiency, and high stability of the perovskite. Herein, a silicon heterojunction bottom cell is combined with a perovskite top cell, with an optimum bandgap of 1.68 eV in planar p-i-n tandem configuration. A methylammonium-free FA(0.75)Cs(0.25)Pb(I0.8Br0.2)(3) perovskite with high Cs content is investigated for improved stability. A 10% molarity increase to 1.1 m of the perovskite precursor solution results in approximate to 75 nm thicker absorber layers and 0.7 mA cm(-2) higher short-circuit current density. With the optimized absorber, tandem devices reach a high fill factor of 80% and up to 25.1% certified efficiency. The unencapsulated tandem device shows an efficiency improvement of 2.3% (absolute) over 5 months, showing the robustness of the absorber against degradation. Moreover, a photoluminescence quantum yield analysis reveals that with adapted charge transport materials and surface passivation, along with improved antireflection measures, the high bandgap perovskite absorber has the potential for 30% tandem efficiency in the near future.
In this study, the stable conformers of neutral anserine were searched by molecular dynamics simulations and energy minimization calculations using the MM2 force field. Thermochemical calculations at B3LYP/6-31G(d) level of theory followed these preliminary calculations. The results confirmed that neutral anserine has quite a flexible structure and many stable gauche and trans conformers at room temperature. Nevertheless, two are considerably more favourable in energy than the others and expected to dominate the gas-phase and matrix IR spectra of the molecule. The corresponding structural and vibrational spectral data for these two conformers of neutral anserine, whose relative stabilities were also examined by high-accuracy energy calculations carried out using G3MP2B3 method, and for the most stable conformer of anserine in zwitterion form were calculated at B3LYP/6-311++G(d,p) level of theory. The calculated harmonic force constants were refined using the Scaled Quantum Mechanical Force Field (SQM-FF) method and then used to produce the refined wavenumbers, potential energy distributions (PEDs) and IR and Raman intensities. These refined data together with the scaled harmonic wavenumbers obtained using another method, Dual Scale factors (DS), enabled us to correctly analyse the observed IR and Raman spectra of anserine and revealed the effects of conformation and zwitterionic tautomerism on its structural and vibrational spectral data. (C) 2016 Elsevier B.V. All rights reserved.