Exploiting Heterogeneous Compute Resources for Optimizing Lightweight Structures

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015.Optimizing lightweight structures with numerical simulations leads to the development of complex simulation codes with high computational demands. The optimization approach for lightweight structures consisting of fiberreinforced plastics is considered. During the simulated optimization, independent simulation tasks have to be executed efficiently on the heterogeneous computing resources. In this article, several scheduling methods for distributing parallel simulation tasks among compute nodes are presented. Performance results are shown for the scheduling and execution of synthetic benchmark tasks, matrix multiplication tasks, as well as FEM simulation tasks on a heterogeneous compute cluster.This work was performed within the Federal Cluster of Excellence EXC 1075 “MERGE Technologies for Multifunctional Lightweight Structures” and supported by the German Research Foundation (DFG)

Sandbox:Creating and analysing synthetic sediment sections with R

Past environmental information is typically inferred from proxy data contained in accretionary sediments. The validity of proxy data and analysis workflows are usually assumed implicitly, with systematic tests and uncertainty estimates restricted to modern analogue studies or reduced-complexity case studies. However, a more generic and consistent approach to exploring the validity and variability of proxy functions would be to translate a sediment section into a model scenario: a “virtual twin”. Here, we introduce a conceptual framework and numerical tool set that allows the definition and analysis of synthetic sediment sections. The R package sandbox describes arbitrary stratigraphically consistent deposits by depth-dependent rules and grain-specific parameters, allowing full scalability and flexibility. Virtual samples can be taken, resulting in discrete grain mixtures with defined parameters. These samples can be virtually prepared and analysed, for example, to test hypotheses. We illustrate the concept of sandbox, explain how a sediment section can be mapped into the model and explore geochronological research questions related to the effects of sample geometry and grain-size-specific age inheritance. We summarise further application scenarios of the model framework, relevant for but not restricted to the broader geochronological community.CREDit - Chronological REference Datasets and Sites (CREDit) towards improved accuracy and precision in luminescence-based chronologie

Improving the monitoring of deciduous broadleaf phenology using the Geostationary Operational Environmental Satellite (GOES) 16 and 17

Monitoring leaf phenology allows for tracking the progression of climate change and seasonal variations in a variety of organismal and ecosystem processes. Networks of finite-scale remote sensing, such as the PhenoCam Network, provide valuable information on phenological state at high temporal resolution, but have limited coverage. To more broadly remotely sense phenology, satellite-based data that has lower temporal resolution has primarily been used (e.g., 16-day MODIS NDVI 10 product). Recent versions of the Geostationary Operational Environmental Satellites (GOES-16 and -17) allow the monitoring of NDVI at temporal scales comparable to that of PhenoCam throughout most of the western hemisphere. Here we examine the current capacity of this new data to measure the phenology of deciduous broadleaf forests for the first two full calendar years of data (2018 and 2019) by fitting double-logistic Bayesian models and comparing the start, middle, and end of season transition dates to those obtained from PhenoCam and MODIS 16-day NDVI and EVI products. Compared to the MODIS 15 indices, GOES was more correlated with PhenoCam at the start and middle of spring, but had a larger bias (3.35 ± 0.03 days later than PhenoCam) at the end of spring. Satellite-based autumn transition dates were mostly uncorrelated with those of PhenoCam. PhenoCam data produced significantly more certain (all p-values £ 0.013) estimates of all transition dates than any of the satellite sources did. GOES transition date uncertainties were significantly smaller than those of MODIS EVI for all transition dates (all p-values £ 0.026), but were only smaller (based on p-value < 0.05) than those from MODIS NDVI for 20 the beginning and middle of spring estimates. GOES will improve the monitoring of phenology at large spatial coverages and is able to provide real-time indicators of phenological change even for spring transitions that might occur within the 16-day resolution of these MODIS products.https://doi.org/10.5194/bg-2020-30

Forecasting a bright future for ecology

Published versio

A Maximum-Entropy approach for accurate document annotation in the biomedical domain

The increasing number of scientific literature on the Web and the absence of efficient tools used for classifying and searching the documents are the two most important factors that influence the speed of the search and the quality of the results. Previous studies have shown that the usage of ontologies makes it possible to process document and query information at the semantic level, which greatly improves the search for the relevant information and makes one step further towards the Semantic Web. A fundamental step in these approaches is the annotation of documents with ontology concepts, which can also be seen as a classification task. In this paper we address this issue for the biomedical domain and present a new automated and robust method, based on a Maximum Entropy approach, for annotating biomedical literature documents with terms from the Medical Subject Headings (MeSH)

Suspended sediment load and bedload flux from the Glacier d'Otemma proglacial forefield (summers 2020 and 2021)

The Glacier d’Otemma proglacial margin, located in the Swiss Alps at an altitude of about 2450 m a.s.l. (45.93423 N, 7.41160 E), is characterized by a ca. 1 km long by 200 m wide active braided forefield. In this setting we installed two gauging stations for the monitoring of both suspended sediment and bedload transport within the proglacial margin: GS1 at about 350 m from the glacier terminus and GS2 at the forefield outlet. Monitoring stations were equipped with water pressure sensors (CS451 from Cambell Scientific), turbidity probes (OBS300+ from Cambell Scientific) and geophones (3-components PE-6/B from Sensor Nederland connected to a DiGOS DATA-CUBE type 2 logger). Water discharge were determined following modalities described in Müller and Miesen (2022). Suspended loads were quantified using a conventional turbidity-suspended sediment concentration relationship, while bedload transport was derived seismically using the geophysical Fluvial model inversion (FMI) algorithm developed in Dietze et al. (2018). The dataset covers summers 2020 and 2021. Further details on data aquisition and post-processing techniques are available in Mancini et al. (2023)

Sub-daily Statistical Downscaling of Meteorological Variables Using Neural Networks

AbstractA new open source neural network temporal downscaling model is described and tested using CRU-NCEP reanal ysis and CCSM3 climate model output. We downscaled multiple meteorological variables in tandem from monthly to sub-daily time steps while also retaining consistent correlations between variables. We found that our feed forward, error backpropagation approach produced synthetic 6 hourly meteorology with biases no greater than 0.6% across all variables and variance that was accurate within 1% for all variables except atmospheric pressure, wind speed, and precipitation. Correlations between downscaled output and the expected (original) monthly means exceeded 0.99 for all variables, which indicates that this approach would work well for generating atmospheric forcing data consistent with mass and energy conserved GCM output. Our neural network approach performed well for variables that had correlations to other variables of about 0.3 and better and its skill was increased by downscaling multiple correlated variables together. Poor replication of precipitation intensity however required further post-processing in order to obtain the expected probability distribution. The concurrence of precipitation events with expected changes in sub ordinate variables (e.g., less incident shortwave radiation during precipitation events) were nearly as consistent in the downscaled data as in the training data with probabilities that differed by no more than 6%. Our downscaling approach requires training data at the target time step and relies on a weak assumption that climate variability in the extrapolated data is similar to variability in the training data

Left atrial appendage closure with the Amplatzer™ Cardiac Plug: Rationale for a higher degree of device oversizing at implantation

Background: In left atrial appendage (LAA) closure, the correct sizing of the implantable devices is crucial. Data on the time-dependent changes in the shape and positioning of LAA occlusion devices are missing. We analyzed the results of 33 consecutive patients after implantation of an Amplatzer™ Cardiac Plug (ACP) LAA closure device to get more information on the optimal device sizing during implantation. Methods and results: Thirty-three consecutive patients were enrolled in this observational study. ACP implantation was guided by fluoroscopy and three dimensional transesophageal echocardiography (3-D TEE). Device sizing was based on the largest measured diameter of the intended landing zone adding 2–4 mm of device oversizing. Fluoroscopies were performed at 1 day after, and after 3 months, control 3-D TEE was performed 3 months after implantation. The stability of device positioning and shape was matched with the results of 3-D TEE. Patients’ mean age was 70.2 ± 8 years; mean CHA2DS2VASc score was 3.8 ± 1.1. According to the manufacture’s classification, the post-implant degree of compression of the device-lobe was classified in three categories 1) undercompression “square-like shape” (1 patient); 2) op­timal compression “tire-like shape” (20 patients), 3) overcompression “strawberry-like shape” (12 patients). Changes in the degree of device compression by more than one classification class occurred in 18/33 of our patients. A complete loss of device compression (“square-like shape”) was observed in 9 patients. Despite the changes in device compression, a complete closure of the LAA was achieved in 32/33 patients. Conclusions: There is a temporal change in shape and positioning of the ACP within 3 months after implantation. A late decompression of the ACP lobe was observed in 61% of our patients, leading to a complete loss in device compression in 27%. This observation may be the rationale for a higher degree of ACP oversizing during implantation

Scaling Contagious Disturbance: A Spatially-Implicit Dynamic Model

Spatial processes often drive ecosystem processes, biogeochemical cycles, and land-atmosphere feedbacks at the landscape-scale. Climate-sensitive disturbances, such as fire, land-use change, pests, and pathogens, often spread contagiously across the landscape. While the climate-change implications of these factors are often discussed, none of these processes are incorporated into earth system models as contagious disturbances because they occur at a spatial scale well below model resolution. Here we present a novel second-order spatially-implicit scheme for representing the size distribution of spatially contagious disturbances. We demonstrate a means for dynamically evolving spatial adjacency through time in response to disturbance. Our scheme shows that contagious disturbance types can be characterized as a function of their size and edge-to-interior ratio. This emergent disturbance characterization allows for description of disturbance across scales. This scheme lays the ground for a more realistic global-scale exploration of how spatially-complex disturbances interact with climate-change drivers, and forwards theoretical understanding of spatial and temporal evolution of disturbance