Electrocatalysis of Lithium (Poly-) Sulfides in Organic Ether-Based Electrolytes

This work aims at identifying an effective electrocatalyst for polysulfide reactions to improve the electrode kinetics of the sulfur half-cell in liquid organic electrolytes for alkali-sulfur cells. To increase the charge and discharge rates and energy efficiency of the cell, functionalized electrocatalytic coatings have been prepared and their electrode kinetics have been measured. To the best of our knowledge, there is no extensive screening of electrocatalysts for the sulfur electrode in dimethoxyethane:1,3-dioxolane (DME:DOL) electrolytes. In order to identify a suitable electrocatalyst, apparent exchange current densities at various materials (Al, Co, Cr, Cu, Fe, Steel, glassy carbon, ITO, Ni, Pt, Ti, TiN, Zn) are evaluated in a polysulfide electrolyte using potentiodynamic measurements with a Butler-Volmer fit. The chemical stability and surface morphology changes after electrochemical measurements are assessed with X-ray diffraction (XRD) and Scanning Electron Microscopy (SEM). The results show that cobalt is a promising candidate with appropriate electrocatalytic properties for polysulfide reactions while being stable in the electrochemical environment, followed by chromium in terms of catalytic activity and stability. Sputtered TiN was found to be a very stable material with very low catalytic activity, a possible current collector for the cell

Matching persistent scatterers to buildings

Persistent Scatterer Interferometry (PSI) is by now a mature technique for the estimation of surface deformation in urban areas. In contrast to the classical interferometry a stack of interferograms is used to minimize the influence of atmospheric disturbances and to select a set of temporarily stable radar targets, the so called Persistent Scatterers (PS). As a result the deformation time series and the height for all identified PS are obtained with high accuracy. The achievable PS density depends thereby on the characteristics of the scene at hand and on the spatial resolution of the used SAR data. This means especially that the location of PS cannot be chosen by the operator and consequently deformation processes of interest may be spatially undersampled and not retrievable from the data. In case of the newly available high resolution SAR data, offering a ground resolution around one metre, the sampling is potentially dense enough to enable a monitoring of single buildings. However, the number of PS to be found on a single building highly depends on its orientation to the viewing direction of the sensor, its facade and roof structure, and also the surrounding buildings. It is thus of major importance to assess the PS density for the buildings in a scene for real world monitoring scenarios. Besides that it is interesting from a scientific point of view to investigate the factors influencing the PS density. In this work, we fuse building outlines (i.e. 2D GIS data) with a geocoded PS point cloud, which consists mainly in estimating and removing a shift between both datasets. After alignment of both datasets, the PS are assigned to buildings, which is in turn used to determine the PS density per building. The resulting map is a helpful tool to investigate the factors influencing PS density at buildings

Multi-source hierarchical conditional random field model for feature fusion of remote sensing images and LiDAR data

Feature fusion of remote sensing images and LiDAR points cloud data, which have strong complementarity, can effectively play the advantages of multi-class features to provide more reliable information support for the remote sensing applications, such as object classification and recognition. In this paper, we introduce a novel multi-source hierarchical conditional random field (MSHCRF) model to fuse features extracted from remote sensing images and LiDAR data for image classification. Firstly, typical features are selected to obtain the interest regions from multi-source data, then MSHCRF model is constructed to exploit up the features, category compatibility of images and the category consistency of multi-source data based on the regions, and the outputs of the model represents the optimal results of the image classification. Competitive results demonstrate the precision and robustness of the proposed method

FROM MULTIPLE POLYGONS TO SINGLE GEOMETRY: OPTIMIZATION OF POLYGON INTEGRATION FOR CROWDSOURCED DATA

Paid crowdsourcing is a popular approach for creating training data in machine learning, but output quality can suffer from various drawbacks, such as noisy data. One solution is to obtain multiple acquisitions of the same dataset and perform integration steps, which can be challenging for geometries such as polygons. In this paper, we propose a raster-based polygon integration approach for the use of crowdsourced data, providing a solution for integrating multiple geometric shapes into single geometries. We analyze the effects of the choice of the integration threshold parameter for different sample sizes on the quality measures intersection over union (IoU) and Hausdorff distance, and provide a recommendation for its optimal selection based on empirical analysis. Additionally, further possibilities to improve integration results are explored, i.e., methods of filtering data before integration by outlier detection

Population pharmacokinetics at two dose levels and pharmacodynamic profiling of flucloxacillin

Flucloxacillin is often used for the treatment of serious infections due to sensitive staphylococci. The pharmacokinetic (PK)-pharmacodynamic (PD) breakpoint of flucloxacillin has not been determined by the use of population PK. Targets based on the duration of non-protein-bound concentrations above the MIC (fT(> MIC)) best correlate with clinical cure rates for beta-lactams. We compared the breakpoints for flucloxacillin between several dosage regimens. In a randomized, two-way crossover study, 10 healthy volunteers received 500 mg and 1,000 mg flucloxacillin as 5-min intravenous infusions. Drug concentrations were determined by high-pressure liquid chromatography. We used the programs WinNonlin for noncompartmental analysis and statistics and NONMEM for population PK and Monte Carlo simulation. We compared the probability of target attainment (PTA) for intermittent- and continuous-dosage regimens based on the targets of fT(> MIS)s of >= 50% and >= 30% of the dosing interval. The clearance and the volume of distribution were very similar after the administration of 500 mg and 1,000 mg flucloxacillin. We estimated renal and nonrenal clearances of 5.37 liters/h (coefficient of variation, 19%) and 2.73 liters/h (33%). For near maximal killing (target, fT(> MIC) of >= 50%) flucloxacillin showed a robust (>= 90%) PTA up to MICs of 0.75 to 1 mg/liter (PTA of 860/v at 1 mg/liter) for a continuous or a prolonged infusion of 6 g/day. Short-term infusions of 6 g/day had a lower breakpoint of 0.25 to 0.375 mg/liter. The flucloxacillin PK was linear for doses of 500 mg and 1,000 mg. Prolonged and continuous infusion at a 66% lower daily dose achieved the same PK-PD breakpoints as short-term infusions. Prolonged infusion and continuous infusion are appealing options for the treatment of serious infections caused by sensitive staphylococci

Scalar turbulent behavior in the roughness sublayer of an Amazonian forest.

An important current problem in micrometeorology is the characterization of turbulence in the roughness sublayer (RSL), where most of the measurements above tall forests are made. There, scalar turbulent fluctuations display significant departures from the predictions of Monin?Obukhov similarity theory (MOST). In this work, we analyze turbulence data of virtual temperature, carbon dioxide, and water vapor in the RSL above an Amazonian forest (with a canopy height of 40&#8239;m), measured at 39.4 and 81.6&#8239;m above the ground under unstable conditions. We found that dimensionless statistics related to the rate of dissipation of turbulence kinetic energy (TKE) and the scalar variance display significant departures from MOST as expected, whereas the vertical velocity variance follows MOST much more closely. Much better agreement between the dimensionless statistics with the Obukhov similarity variable, however, was found for the subset of measurements made at a low zenith angle Z, in the range 0°&#8239;&#8201;<&#8201;&#8239;|Z|&#8239;&#8201;<&#8201;&#8239;20°. We conjecture that this improvement is due to the relationship between sunlight incidence and the ?activation?deactivation? of scalar sinks and sources vertically distributed in the forest. Finally, we evaluated the relaxation coefficient of relaxed eddy accumulation: it is also affected by zenith angle, with considerable improvement in the range 0°&#8239;&#8201;<&#8201;&#8239;|Z|&#8239;&#8201;<&#8201;&#8239;20°, and its values fall within the range reported in the literature for the unstable surface layer. In general, our results indicate the possibility of better stability-derived flux estimates for low zenith angle ranges

A new straight line reconstruction methodology from multi-spectral stereo aerial images

In this study, a new methodology for the reconstruction of line features from multispectral stereo aerial images is presented. We take full advantage of the existing multispectral information in aerial images all over the steps of pre-processing and edge detection. To accurately describe the straight line segments, a principal component analysis technique is adapted. The line to line correspondences between the stereo images are established using a new pair-wise stereo matching approach. The approach involves new constraints, and the redundancy inherent in pair relations gives us a possibility to reduce the number of false matches in a probabilistic manner. The methodology is tested over three different urban test sites and provided good results for line matching and reconstruction

VEHICLE OCCLUSION REMOVAL FROM SINGLE AERIAL IMAGES USING GENERATIVE ADVERSARIAL NETWORKS

Removing occluding objects such as vehicles from drivable areas allows precise extraction of road boundaries and related semantic objects such as lane-markings, which is crucial for several applications such as generating high-definition maps for autonomous driving. Conventionally, multiple images of the same area taken at different times or from various perspectives are used to remove occlusions and to reconstruct the occluded areas. Nevertheless, these approaches require large amounts of data, which are not always available. Furthermore, they do not work for static occlusions caused by, among others, parked vehicles. In this paper, we address occlusion removal based on single aerial images using generative adversarial networks (GANs), which are able to deal with the mentioned challenges. To this end, we adapt several state-of-the-art GAN-based image inpainting algorithms to reconstruct the missing information. Results indicate that the StructureFlow algorithm outperforms the competitors and the restorations obtained are robust, with high visual fidelity in real-world applications. Furthermore, due to the lack of annotated aerial vehicle removal datasets, we generate a new dataset for training and validating the algorithms, the Aerial Vehicle Occlusion Removal (AVOR) dataset. To the best of our knowledge, our work is the first to address vehicle removal using deep learning algorithms to enhance maps

Simulation of the scalar transport above and within the Amazon forest canopy

The parallelized large-eddy simulation model (PALM) was used to understand better the turbulent exchanges of a passive scalar above and within a forested region located in the central Amazon. Weak (2 ms−1) and strong (6 ms−1) wind conditions were simulated. A passive scalar source was introduced to the forest floor for both simulations. The simulations reproduced the main characteristics of the turbulent flow and of the passive scalar transport between the forest and the atmosphere. Noteworthily, strong and weak wind conditions presented different turbulence structures that drove different patterns of scalar exchange both within and above the forest. These results show how passive scalar concentration is influenced by the wind speed at the canopy top. Additionally, higher wind speeds are related to stronger sweep and ejection regimes, generating more intense plumes that are able to reduce the passive scalar concentration inside the forest canopy. This work was the first that used PALM to investigate scalar transport between the Amazon rainforest and the atmosphere

Intercomparison of planetary boundary layer heights using remote sensing retrievals and ERA5 reanalysis over Central Amazonia

The atmospheric boundary layer height (zi) is a key parameter in the vertical transport of mass, energy, moisture, and chemical species between the surface and the free atmosphere. There is a lack of long-term and continuous observations of zi, however, particularly for remote regions, such as the Amazon forest. Reanalysis products, such as ERA5, can fill this gap by providing temporally and spatially resolved information on zi. In this work, we evaluate the ERA5 estimates of zi (zi-ERA5) for two locations in the Amazon and corrected them by means of ceilometer, radiosondes, and SODAR measurements (zi-experimental). The experimental data were obtained at the remote Amazon Tall Tower Observatory (ATTO) with its pristine tropical forest cover and the T3 site downwind of the city of Manaus with a mixture of forest (63%), pasture (17%), and rivers (20%). We focus on the rather typical year 2014 and the El Ni&ntilde;o year 2015. The comparison of the experimental vs. ERA5 zi data yielded the following results: (i) zi-ERA5 underestimates zi-experimental daytime at the T3 site for both years 2014 (30%, underestimate) and 2015 (15%, underestimate); (ii) zi-ERA5 overestimates zi-experimental daytime at ATTO site (12%, overestimate); (iii) during nighttime, no significant correlation between the zi-experimental and zi-ERA5 was observed. Based on these findings, we propose a correction for the daytime zi-ERA5, for both sites and for both years, which yields a better agreement between experimental and ERA5 data. These results and corrections are relevant for studies at ATTO and the T3 site and can likely also be applied at further locations in the Amazon