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Ambitious climate policies, as well as economic development, education, technological progress and less resource-intensive lifestyles, are crucial elements for progress towards the UN Sustainable Development Goals (SDGs). However, using an integrated modelling framework covering 56 indicators or proxies across all 17 SDGs, we show that they are insufficient to reach the targets. An additional sustainable development package, including international climate finance, progressive redistribution of carbon pricing revenues, sufficient and healthy nutrition and improved access to modern energy, enables a more comprehensive sustainable development pathway. We quantify climate and SDG outcomes, showing that these interventions substantially boost progress towards many aspects of the UN Agenda 2030 and simultaneously facilitate reaching ambitious climate targets. Nonetheless, several important gaps remain; for example, with respect to the eradication of extreme poverty (180 million people remaining in 2030). These gaps can be closed by 2050 for many SDGs while also respecting the 1.5 °C target and several other planetary boundaries.
Researchers and developers worldwide have put their efforts into the design, development and use of information and communication technology to support teaching and learning. This research is driven by pedagogical as well as technological disciplines. The most challenging ideas are currently found in the application of mobile, ubiquitous, pervasive, contextualized and seamless technologies for education, which we shall refer to as pervasive education. This article provides a comprehensive overview of the existing work in this field and categorizes it with respect to educational settings. Using this approach, best practice solutions for certain educational settings and open questions for pervasive education are highlighted in order to inspire interested developers and educators. The work is assigned to different fields, identified by the main pervasive technologies used and the educational settings. Based on these assignments we identify areas within pervasive education that are currently disregarded or deemed challenging so that further research and development in these fields are stimulated in a trans-disciplinary approach. (C) 2013 Elsevier B.V. All rights reserved.
Patent document collections are an immense source of knowledge for research and innovation communities worldwide. The rapid growth of the number of patent documents poses an enormous challenge for retrieving and analyzing information from this source in an effective manner. Based on deep learning methods for natural language processing, novel approaches have been developed in the field of patent analysis. The goal of these approaches is to reduce costs by automating tasks that previously only domain experts could solve. In this article, we provide a comprehensive survey of the application of deep learning for patent analysis. We summarize the state-of-the-art techniques and describe how they are applied to various tasks in the patent domain. In a detailed discussion, we categorize 40 papers based on the dataset, the representation, and the deep learning architecture that were used, as well as the patent analysis task that was targeted. With our survey, we aim to foster future research at the intersection of patent analysis and deep learning and we conclude by listing promising paths for future work.
Model transformation is one of the key tasks in model-driven engineering and relies on the efficient matching and modification of graph-based data structures; its sibling graph rewriting has been used to successfully model problems in a variety of domains. Over the last years, a wide range of graph and model transformation tools have been developed all of them with their own particular strengths and typical application domains. In this paper, we give a survey and a comparison of the model and graph transformation tools that participated at the Transformation Tool Contest 2011. The reader gains an overview of the field and its tools, based on the illustrative solutions submitted to a Hello World task, and a comparison alongside a detailed taxonomy. The article is of interest to researchers in the field of model and graph transformation, as well as to software engineers with a transformation task at hand who have to choose a tool fitting to their needs. All solutions referenced in this article provide a SHARE demo. It supported the peer-review process for the contest, and now allows the reader to test the tools online.
As meta-analyses demonstrate feedback effects on performance, our study examined possible mediators. Based on our cognitive-motivational model [Vollmeyer, R., & Rhemberg, F. (1998). Motivationale Einflusse auf Erwerb und Anwendung von Wissen in einem computersimulierten System [Motivational influences on the acquisition and application of knowledge in a simulated system]. Zeitschrift fur Padagogische Psychologie, 12, 11-23] we examined how feedback changed (1) strategies, and (2) motivation during learning, and by doing so improved (3) final performance. Students (N = 211) learned how a dynamic system works and how to reach given goal states for the system. One group received feedback (i.e., knowledge of performance) the other one did not. We expected learners to improve after they received the first feedback. However, we found that learners expecting feedback used better strategies right from the start. Thus, they acquired more knowledge over fewer trials. Although we had also expected effects of feedback on motivation during learning, we could not support this hypothesis. (c) 2005 Elsevier Ltd. All rights reserved
A novel multilayer cytochrome c electrode for the quantification of superoxide radical concentrations is introduced. The electrode consists of alternating layers of cytochrome c and poly(aniline(sulfonic acid)) on a gold wire electrode. The formation of multilayer structures was proven by SPR experiments. Assemblies with 2-15 protein layers showed electrochemical communication with the gold electrode. For every additional layer, a substantial increase in electrochemically active cytochrome c (cyt. c) was found. For electrodes of more than 10 layers, the increase was more than 1 order of magnitude as compared to monolayer electrode systems. Thermodynamic and kinetic parameters of the electrodes were characterized. The mechanism of electron transfer within the multilayer assembly was studied, with results suggesting a protein-protein electron-transfer model. Electrodes of 2-15 layers were applied to the in vitro quantification of enzymatically generated superoxide, showing superior sensitivity as compared to a monolayer-based sensor. An electrode with 6 cyt. c/PASA layers showed the highest sensitivity of the systems studied, giving an increase in sensitivity of half an order of magnitude versus the that of the monolayer electrode. The stability of the system was optimized using thermal treatment, resulting in no loss in sensor signal or protein loading after 10 successive measurements or 2 days of storage
A submerged pine forest from the early Holocene in the Mecklenburg Lake District, northern Germany
(2018)
For the first time, evidence of a submerged pine forest from the early Holocene can be documented in a central European lake. Subaquatic tree stumps were discovered in Lake Giesenschlagsee at a depth of between 2 and 5m using scuba divers, side-scan sonar and a remotely operated vehicle. Several erect stumps, anchored to the ground by roots, represent an insitu record of this former forest. Botanical determination revealed the stumps to be Scots pine (Pinus sylvestris) with an individual tree age of about 80years. The trees could not be dated by means of dendrochronology, as they are older than the regional reference chronology for pine. Radiocarbon ages from the wood range from 10880 +/- 210 to 10370 +/- 130cal. a BP, which is equivalent to the mid-Preboreal to early Boreal biozones. The trees are rooted in sedge peat, which can be dated to this period as well, using pollen stratigraphical analysis. Tilting of the peat bed by 4m indicates subsidence of the ground due to local dead ice melting, causing the trees to become submerged and preserved for millennia. Together with recently detected Lateglacial insitu tree occurrences in nearby lakes, the submerged pine forest at Giesenschlagsee represents a new and highly promising type of geo-bio-archive for the wider region. Comparable insitu pine remnants occur at some terrestrial (buried setting) and marine (submerged setting) sites in northern central Europe and beyond, but they partly differ in age. In general, the insitu pine finds document shifts of the zonal boreal forest ecosystem during the late Quaternary.
In this combined theoretical and experimental study we report a full analysis of the resonant inelastic X-ray scattering (RIXS) spectra of H2O, D2O and HDO. We demonstrate that electronically-elastic RIXS has an inherent capability to map the potential energy surface and to perform vibrational analysis of the electronic ground state in multimode systems. We show that the control and selection of vibrational excitation can be performed by tuning the X-ray frequency across core-excited molecular bands and that this is clearly reflected in the RIXS spectra. Using high level ab initio electronic structure and quantum nuclear wave packet calculations together with high resolution RIXS measurements, we discuss in detail the mode coupling, mode localization and anharmonicity in the studied systems.
Response spectra are of fundamental importance in earthquake engineering and represent a standard measure in seismic design for the assessment of structural performance. However, unlike Fourier spectral amplitudes, the relationship of response spectral amplitudes to seismological source, path, and site characteristics is not immediately obvious and might even be considered counterintuitive for high oscillator frequencies. The understanding of this relationship is nevertheless important for seismic-hazard analysis. The purpose of the present study is to comprehensively characterize the variation of response spectral amplitudes due to perturbations of the causative seismological parameters. This is done by calculating the absolute parameter sensitivities (sensitivity coefficients) defined as the partial derivatives of the model output with respect to its input parameters. To derive sensitivities, we apply algorithmic differentiation (AD). This powerful approach is extensively used for sensitivity analysis of complex models in meteorology or aerodynamics. To the best of our knowledge, AD has not been explored yet in the seismic-hazard context. Within the present study, AD was successfully implemented for a proven and extensively applied simulation program for response spectra (Stochastic Method SIMulation [SMSIM]) using the TAPENADE AD tool. We assess the effects and importance of input parameter perturbations on the shape of response spectra for different regional stochastic models in a quantitative way. Additionally, we perform sensitivity analysis regarding adjustment issues of groundmotion prediction equations.
Large-scale biochemical models are of increasing sizes due to the consideration of interacting organisms and tissues. Model reduction approaches that preserve the flux phenotypes can simplify the analysis and predictions of steady-state metabolic phenotypes. However, existing approaches either restrict functionality of reduced models or do not lead to significant decreases in the number of modelled metabolites. Here, we introduce an approach for model reduction based on the structural property of balancing of complexes that preserves the steady-state fluxes supported by the network and can be efficiently determined at genome scale. Using two large-scale mass-action kinetic models of Escherichia coli, we show that our approach results in a substantial reduction of 99% of metabolites. Applications to genome-scale metabolic models across kingdoms of life result in up to 55% and 85% reduction in the number of metabolites when arbitrary and mass-action kinetics is assumed, respectively. We also show that predictions of the specific growth rate from the reduced models match those based on the original models. Since steady-state flux phenotypes from the original model are preserved in the reduced, the approach paves the way for analysing other metabolic phenotypes in large-scale biochemical networks.
On 2015 March 23, the Very Energetic Radiation Imaging Telescope Array System (VERITAS) responded to a Swift-Burst Alert Telescope (BAT) detection of a gamma-ray burst, with observations beginning 270 s after the onset of BAT emission, and only 135 s after the main BAT emission peak. No statistically significant signal is detected above 140 GeV. The VERITAS upper limit on the fluence in a 40-minute integration corresponds to about 1% of the prompt fluence. Our limit is particularly significant because the very-high-energy (VHE) observation started only similar to 2 minutes after the prompt emission peaked, and Fermi-Large Area Telescope observations of numerous other bursts have revealed that the high-energy emission is typically delayed relative to the prompt radiation and lasts significantly longer. Also, the proximity of GRB 150323A (z = 0.593) limits the attenuation by the extragalactic background light to similar to 50% at 100-200 GeV. We conclude that GRB 150323A had an intrinsically very weak high-energy afterglow, or that the GeV spectrum had a turnover below similar to 100 GeV. If the GRB exploded into the stellar wind of a massive progenitor, the VHE non-detection constrains the wind density parameter to be A greater than or similar to 3 x 10(11) g . cm(-1), consistent with a standard Wolf-Rayet progenitor. Alternatively, the VHE emission from the blast wave would be weak in a very tenuous medium such as the interstellar medium, which therefore cannot be ruled out as the environment of GRB 150323A.
Context
The recently claimed discovery of a massive (M-BH = 68(-13)(+11) M-circle dot) black hole in the Galactic solar neighborhood has led to controversial discussions because it severely challenges our current view of stellar evolution.
Aims
A crucial aspect for the determination of the mass of the unseen black hole is the precise nature of its visible companion, the B-type star LSV +22 25. Because stars of different mass can exhibit B-type spectra during the course of their evolution, it is essential to obtain a comprehensive picture of the star to unravel its nature and, thus, its mass.
Methods
To this end, we study the spectral energy distribution of LSV +22 25 and perform a quantitative spectroscopic analysis that includes the determination of chemical abundances for He, C, N, O, Ne, Mg, Al, Si, S, Ar, and Fe.
Results
Our analysis clearly shows that LSV +22 25 is not an ordinary main sequence B-type star. The derived abundance pattern exhibits heavy imprints of the CNO bi-cycle of hydrogen burning, that is, He and N are strongly enriched at the expense of C and O. Moreover, the elements Mg, Al, Si, S, Ar, and Fe are systematically underabundant when compared to normal main-sequence B-type stars. We suggest that LSV +22 25 is a stripped helium star and discuss two possible formation scenarios. Combining our photometric and spectroscopic results with the Gaia parallax, we infer a stellar mass of 1.1 +/- 0.5 M-circle dot. Based on the binary system's mass function, this yields a minimum mass of 2-3 M-circle dot for the compact companion, which implies that it may not necessarily be a black hole but a massive neutron- or main sequence star.
Conclusions
The star LSV +22 25 has become famous for possibly having a very massive black hole companion. However, a closer look reveals that the star itself is a very intriguing object. Further investigations are necessary for complete characterization of this object.
Chemotaxis, the directed motion of a cell toward a chemical source, plays a key role in many essential biological processes. Here, we derive a statistical model that quantitatively describes the chemotactic motion of eukaryotic cells in a chemical gradient. Our model is based on observations of the chemotactic motion of the social ameba Dictyostelium discoideum, a model organism for eukaryotic chemotaxis. A large number of cell trajectories in stationary, linear chemoattractant gradients is measured, using microfluidic tools in combination with automated cell tracking. We describe the directional motion as the interplay between deterministic and stochastic contributions based on a Langevin equation. The functional form of this equation is directly extracted from experimental data by angle-resolved conditional averages. It contains quadratic deterministic damping and multiplicative noise. In the presence of an external gradient, the deterministic part shows a clear angular dependence that takes the form of a force pointing in gradient direction. With increasing gradient steepness, this force passes through a maximum that coincides with maxima in both speed and directionality of the cells. The stochastic part, on the other hand, does not depend on the orientation of the directional cue and remains independent of the gradient magnitude. Numerical simulations of our probabilistic model yield quantitative agreement with the experimental distribution functions. Thus our model captures well the dynamics of chemotactic cells and can serve to quantify differences and similarities of different chemotactic eukaryotes. Finally, on the basis of our model, we can characterize the heterogeneity within a population of chemotactic cells.
All stereoisomers of the natural product centrolobine are selectively synthesized, by starting from a common precursor. Key steps are an enantioselective allylation with enantiomerically pure allylsilanes, a tandem ring-closing metathesis-isomerization reaction, and a Heck reaction by using an arene diazonium salt. By choosing appropriate conditions for the final deprotection step, either the cis-configured centrolobines or their epimers are selectively obtained.