Search Results
Temporal Role Annotation for Named Entities
2018, Koutraki, Maria, Bakhshandegan-Moghaddam, Farshad, Sack, Harald, Fensel, Anna, de Boer, Victor, Pellegrini, Tassilo, Kiesling, Elmar, Haslhofer, Bernhard, Hollink, Laura, Schindler, Alexander
Natural language understanding tasks are key to extracting structured and semantic information from text. One of the most challenging problems in natural language is ambiguity and resolving such ambiguity based on context including temporal information. This paper, focuses on the task of extracting temporal roles from text, e.g. CEO of an organization or head of a state. A temporal role has a domain, which may resolve to different entities depending on the context and especially on temporal information, e.g. CEO of Microsoft in 2000. We focus on the temporal role extraction, as a precursor for temporal role disambiguation. We propose a structured prediction approach based on Conditional Random Fields (CRF) to annotate temporal roles in text and rely on a rich feature set, which extracts syntactic and semantic information from text. We perform an extensive evaluation of our approach based on two datasets. In the first dataset, we extract nearly 400k instances from Wikipedia through distant supervision, whereas in the second dataset, a manually curated ground-truth consisting of 200 instances is extracted from a sample of The New York Times (NYT) articles. Last, the proposed approach is compared against baselines where significant improvements are shown for both datasets.
Nitrous oxide emissions from winter oilseed rape cultivation
2017, Ruser, Reiner, Fuß, Roland, Andres, Monique, Hegewald, Hannes, Kesenheimer, Katharina, Köbke, Sarah, Räbiger, Thomas, Quinones, Teresa Suarez, Augustin, Jürgen, Christen, Olaf, Dittert, Klaus, Kage, Henning, Lewandowski, Iris, Prochnow, Annette, Stichnothe, Heinz, Flessa, Heinz
Winter oilseed rape (Brassica napus L., WOSR) is the major oil crop cultivated in Europe. Rapeseed oil is predominantly used for production of biodiesel. The framework of the European Renewable Energy Directive requires that use of biofuels achieves GHG savings of at least 50% compared to use of fossil fuel starting in 2018. However, N2O field emissions are estimated using emission factors that are not specific for the crop and associated with strong uncertainty. N2O field emissions are controlled by N fertilization and dominate the GHG balance of WOSR cropping due to the high global warming potential of N2O. Thus, field experiments were conducted to increase the data basis and subsequently derive a new WOSR-specific emission factor. N2O emissions and crop yields were monitored for three years over a range of N fertilization intensities at five study sites representative of German WOSR production. N2O fluxes exhibited the typical high spatial and temporal variability in dependence on soil texture, weather and nitrogen availability. The annual N2O emissions ranged between 0.24 kg and 5.48 kg N2O-N ha−1 a−1. N fertilization increased N2O emissions, particularly with the highest N treatment (240 kg N ha−1). Oil yield increased up to a fertilizer amount of 120 kg N ha−1, higher N-doses increased grain yield but decreased oil concentrations in the seeds. Consequently oil yield remained constant at higher N fertilization. Since, yield-related emission also increased exponentially with N surpluses, there is potential for reduction of the N fertilizer rate, which offers perspectives for the mitigation of GHG emissions. Our measurements double the published data basis of annual N2O flux measurements in WOSR. Based on this extended dataset we modeled the relationship between N2O emissions and fertilizer N input using an exponential model. The corresponding new N2O emission factor was 0.6% of applied fertilizer N for a common N fertilizer amount under best management practice in WOSR production (200 kg N ha−1 a−1). This factor is substantially lower than the linear IPCC Tier 1 factor (EF1) of 1.0% and other models that have been proposed. © 2017
Deep Geothermal Energy for Lower Saxony (North Germany) – Combined Investigations of Geothermal Reservoir Characteristics
2014, Hahne, Barbara, Thomas, Rüdiger, Bruckman, Viktor J., Hangx, Suzanne, Ask, Maria
For the economic success of a geothermal project the hydraulic properties and temperature of the geothermal reservoir are crucial. New methodologies in seismics, geoelectrics and reservoir geology are tested within the frame of the collaborative research programme “Geothermal Energy and High-Performance Drilling” (gebo). Within nine geoscientific projects, tools were developed that help in the evaluation and interpretation of acquired data. Special emphasis is placed on the investigation of rock properties, on the development of early reservoir assessment even during drilling, and on the interaction between the drilling devices and the reservoir formation. The propagation of fractures and the transport of fluid and heat within the regional stress field are investigated using different approaches (field studies, seismic monitoring, multi-parameter modelling). Geologic structural models have been created for simulation of the local stress field and hydromechanical processes. Furthermore, a comprehensive dataset of hydrogeochemical environments was collected allowing characterisation and hydrogeochemical modelling of the reservoir.
Livelihood and climate trade-offs in Kenyan peri-urban vegetable production
2017, Kurgat, Barnabas K., Stöber, Silke, Mwonga, Samuel, Lotze-Campen, Hermann, Rosenstock, Todd S.
Trade-offs between livelihood and environmental outcomes due to agricultural intensification in sub-Saharan Africa are uncertain. The present study measured yield, economic performance and nitrous oxide (N2O) emissions in African indigenous vegetable (AIV) production to investigate the optimal nutrient management strategies. In order to achieve this, an on-farm experiment with four treatments – (1) 40 kg N/ha diammonium phosphate (DAP), (2) 10 t/ha cattle manure, (3) 20 kg N/ha DAP and 5 t/ha cattle manure and (4) a no-N input control – was performed for two seasons. Yields and N2O emissions were directly measured with subsampling and static chambers/gas chromatography, respectively. Economic outcomes were estimated from semi-structured interviews (N = 12). Trade-offs were quantified by calculating N2O emissions intensity (N2OI) and N2O emissions economic intensity (N2OEI). The results indicate that, DAP alone resulted at least 14% greater yields, gross margin and returns to labour in absolute terms but had the highest emissions (p = 0.003). Productivity-climate trade-offs, expressed as N2OI, were statistically similar for DAP and mixed treatments. However, N2OEI was minimized under mixed management (p = 0.0004) while maintaining productivity and gross margins. We therefore conclude that soil fertility management strategies that mix inorganic and organic source present a pathway to sustainable intensification in AIV production. Future studies of GHG emissions in crop production need to consider not only productivity but economic performance when considering trade-offs.
The Research Core Dataset (KDSF) in the Linked Data context
2019, Walther, Tatiana, Hauschke, Christian, Kasprzik, Anna, Sicilia, Miguel-Angel, Simons, Ed, Clements, Anna, de Castro, Pablo, Bergström, Johan
This paper describes our efforts to implement the Research Core Dataset (“Kerndatensatz Forschung”; KDSF) as an ontology in VIVO. KDSF is used in VIVO to record the required metadata on incoming data and to produce reports as an output. While both processes need an elaborate adaptation of the KDSF specification, this paper focusses on the adaptation of the KDSF basic data model for recording data in VIVO. In this context, the VIVO and KDSF ontologies were compared with respect to domain, syntax, structure, and granularity in order to identify correspondences and mismatches. To produce an alignment, different matching approaches have been applied. Furthermore, we made necessary modifications and extensions on KDSF classes and properties.
Towards hybrid one-pot/one-electrode Pd-NPs-based nanoreactors for modular biocatalysis
2021, Koch, M., Apushkinskaya, N., Zolotukhina, E.V., Silina, Y.E.
Here, fundamental aspects affecting template-assisted engineering of oxidase-associated peroxide oxidation co-catalysis of the modeled microanalytical system based on the hybrid palladium nanoparticles (Pd-NPs) with tailored functional properties were studied. By an accurate tuning and validation of the experimental setup, a modular Pd-NPs-doped one-pot/one-electrode amperometric nanobiosensor for advanced multiplex analyte detection was constructed. The specific operational conditions (electrochemical read-out mode, pH, regeneration procedure) of the modular one-pot/one-electrode nanobiosensor allowed a reliable sensing of L-lactate (with linear dynamic range, LDR = 500 µM – 2 mM, R2 = 0.977), D-glucose (with LDR = 200 µM – 50 mM, R2 = 0.987), hydrogen peroxide (with LDR = 20 µM – 100 mM, R2 = 0.998) and glutaraldehyde (with LDR = 1 – 100 mM, R2 = 0.971). In addition, mechanistic aspects influencing the performance of Pd-NPs-doped one-pot/one-electrode for multiplex analyte sensing were studied in detail. The designed one-pot/one-electrode amperometric nanobiosensor showed a thin layer electrochemical behavior that greatly enhanced electron transfer between the functional hybrid layer and the electrode. Finally, a specific regeneration procedure of the hybrid one-pot/one-electrode and algorithm towards its usage for modular biocatalysis were developed. The reported strategy can readily be considered as a guideline towards the fabrication of commercialized nanobiosensors with tailored properties for advanced modular biocatalysis.
Modelling the Surface Heat Flow Distribution in the Area of Brandenburg (Northern Germany)
2013, Cacace, Mauro, Scheck-Wenderoth, Magdalena, Noack, Vera, Cherubini, Yvonne, Schellschmidt, Rüdiger, Kühn, Michael, Juhlin, Christopher, Held, Hermann, Bruckman, Viktor, Tambach, Tim, Kempka, Thomas
A lithosphere scale geological model has been used to determine the surface heat flow component due to conductive heat transport for the area of Brandenburg. The modelling results have been constrained by a direct comparison with available heat flow measurements. The calculated heat flow captures the regional trend in the surface heat flow distribution which can be related to existing thermal conductivity variations between the different sedimentary units. An additional advective component due to topography induced regional flow and focused flow within major fault zones should be considered to explain the spatial variation observed in the surface heat flow.
Evaluation of Expert Reports to Quantify the Exploration Risk for Geothermal Projects in Germany
2015, Ganz, Britta, Ask, Maria, Hangx, Suzanne, Bruckman, Viktor, Kühn, Michael
The development of deep geothermal energy sources in Germany still faces many uncertainties and high upfront investment costs. Methodical approaches to assess the exploration risk are thus of major importance for geothermal project development. Since 2002, expert reports to quantify the exploration risk for geothermal projects in Germany were carried out. These reports served as a basis for insurance contracts covering the exploration risk. Using data from wells drilled in the meantime, the reports were evaluated and the stated probabilities compared with values actually reached.
SemSur: A Core Ontology for the Semantic Representation of Research Findings
2018, Fathalla, Said, Vahdati, Sahar, Auer, Sören, Lange, Christoph, Fensel, Anna, de Boer, Victor, Pellegrini, Tassilo, Kiesling, Elmar, Haslhofer, Bernhard, Hollink, Laura, Schindler, Alexander
The way how research is communicated using text publications has not changed much over the past decades. We have the vision that ultimately researchers will work on a common structured knowledge base comprising comprehensive semantic and machine-comprehensible descriptions of their research, thus making research contributions more transparent and comparable. We present the SemSur ontology for semantically capturing the information commonly found in survey and review articles. SemSur is able to represent scientific results and to publish them in a comprehensive knowledge graph, which provides an efficient overview of a research field, and to compare research findings with related works in a structured way, thus saving researchers a significant amount of time and effort. The new release of SemSur covers more domains, defines better alignment with external ontologies and rules for eliciting implicit knowledge. We discuss possible applications and present an evaluation of our approach with the retrospective, exemplary semantification of a survey. We demonstrate the utility of the SemSur ontology to answer queries about the different research contributions covered by the survey. SemSur is currently used and maintained at OpenResearch.org.
Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change
2017, Fronzek, Stefan, Pirttioja, Nina, Carter, Timothy R., Bindi, Marco, Hoffmann, Holger, Palosuo, Taru, Ruiz-Ramos, Margarita, Tao, Fulu, Trnka, Miroslav, Acutis, Marco, Asseng, Senthold, Baranowski, Piotr, Basso, Bruno, Bodin, Per, Buis, Samuel, Cammarano, Davide, Deligios, Paola, Destain, Marie-France, Dumont, Benjamin, Ewert, Frank, Ferrise, Roberto, François, Louis, Gaiser, Thomas, Hlavinka, Petr, Jacquemin, Ingrid, Kersebaum, Kurt Christian, Kollas, Chris, Krzyszczak, Jaromir, Lorite, Ignacio J., Minet, Julien, Minguez, M. Ines, Montesino, Manuel, Moriondo, Marco, Müller, Christoph, Nendel, Claas, Öztürk, Isik, Perego, Alessia, Rodríguez, Alfredo, Ruane, Alex C., Ruget, Françoise, Sanna, Mattia, Semenov, Mikhail A., Slawinski, Cezary, Stratonovitch, Pierre, Supit, Iwan, Waha, Katharina, Wang, Enli, Wu, Lianhai, Zhao, Zhigan, Rötter, Reimund P.
Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.