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Das deutsche Strafgesetzbuch stellt nur die Fremdtötung und die Tötung auf Verlangen in den §§ 211ff. StGB unter Strafe. Der Suizid hingegen stellt keine Straftat dar, weshalb auch die Teilnahme daran straflos bleibt. Vor dem Hintergrund der sich hieraus ergebenden Abgrenzungsschwierigkeiten gibt es seit 2006 verschiedene Gesetzesvorhaben, die Suizidmitwirkung, insbesondere durch Sterbehilfeorganisationen, strafrechtlich zu erfassen. Die vorliegende Arbeit erörtert die in diesem Zusammenhang entstehende Fragestellung, ob die Teilnahme am Suizid ein strafwürdiges Verhalten darstellt, welches strafrechtlich geahndet werden muss. Hierzu werden auch die vorgenannten Gesetzesvorhaben einer kritischen Stellungnahme unterzogen und entsprechende ausländische Regelungen betrachtet. Neben den Entwürfen eines solchen Straftatbestandes geht die Arbeit außerdem auf rechtliche Lösungen außerhalb des Strafrechts ein und stellt abschließend fest, ob ein Erfordernis zur Änderung der bestehenden Rechtslage im Strafgesetzbuch besteht.
Am 20. November 2010 fand an der Universität Potsdam das 4. Herbsttreffen Patholinguistik statt. Die Konferenzreihe wird regelmäßig seit 2007 vom Verband für Patholinguistik e.V. (vpl) durchgeführt. Der vorliegende Tagungsband veröffentlicht die Hauptvorträge des Herbsttreffens zum Thema "Lesen lernen: Diagnostik und Therapie bei Störungen des Leseerwerbs". Des Weiteren sind die Beiträge promovierender bzw. promovierter PatholinguistInnen sowie der Posterpräsentationen enthalten.
The sensing potential of CuO nanoparticles synthesized via. precipitation from a water/ionic liquid precursor (ILP) mixture was investigated. The particles have a moderate surface area of 66 m(2)/g after synthesis, which decreases upon thermal treatment to below 5 m(2)/g. Transmission electron microscopy confirms crystal growth upon annealing, likely due to sintering effects. The as-synthesized particles can be used for ethanol sensing. The respective sensors show fast response and recovery times of below 10 s and responses greater than 2.3 at 100 ppm of ethanol at 200 degrees C, which is higher than any CuO-based ethanol sensor described so far.
Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making.
Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop-and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.
Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making.
Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop-and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.