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Our knowledge on tree responses to drought is mainly based on short-term manipulation experiments which do not capture any possible long-term adjustments in this response. Therefore, historical water channels in inner-Alpine dry valleys were used as century-long irrigation experiments to investigate adjustments in tree growth to contrasting water supply. This involved quantifying the tree-ring growth of irrigated and non-irrigated (control) Scots pine (Pinus sylvestris L.) in Valais (Switzerland), as well as European larch (Larix decidua Mill.) and black pine (Pinus nigra Arnold) in Vinschgau (Italy). Furthermore, the adjustments in radial growth of Scots pine and European larch to an abrupt stop in irrigation were analyzed.
Irrigation promoted the radial growth of all tree species investigated compared to the control: (1) directly through increased soil water availability, and (2) indirectly through increased soil nutrients and humus contents in the irrigated plots. Irrigation led to a full elimination of growth responses to climate for European larch and black pine, but not for Scots pine, which might become more sensitive to drought with increasing tree size in Valais. For the control trees, the response of the latewood increment to water availability in July/August has decreased in recent decades for all species, but increased in May for Scots pine only. The sudden irrigation stop caused a drop in radial growth to a lower level for Scots pine or similar level for larch compared to the control for up to ten years. However, both tree species were then able to adjust to the new conditions and subsequently grew with similar (Scots pine) or even higher growth rates (larch) than the control.
To estimate the impact of climate change on future forest development, the duration of manipulation experiments should be on longer time scales in order to capture adjustment processes and feedback mechanisms of forest ecosystems.
Growth and metabolism of CHO-cells in porous glass carriers / Lüllau, E. ; Biselli, M. ; Wandrey, C.
(1994)
Reconstructive surgery and tissue replacements like ureters or bladders reconstruction have been recently studied, taking into account growth and remodelling of cells since living cells are capable of growing, adapting, remodelling or degrading and restoring in order to deform and respond to stimuli. Hence, shapes of ureters or bladders and their microstructure change during growth and these changes strongly depend on external stimuli such as training. We present the mechanical stimulation of smooth muscle cells in a tubular fibrin-PVDFA scaffold and the modelling of the growth of tissue by stimuli. To this end, mechanotransduction was performed with a kyphoplasty balloon catheter that was guided through the lumen of the tubular structure. The bursting pressure was examined to compare the stability of the incubated tissue constructs. The results showed the significant changes on tissues with training by increasing the burst pressure as a characteristic mechanical property and the smooth muscle cells were more oriented with uniformly higher density. Besides, the computational growth models also exhibited the accurate tendencies of growth of the cells under different external stimuli. Such models may lead to design standards for the better layered tissue structure in reconstructing of tubular organs characterized as composite materials such as intestines, ureters and arteries.
This paper describes the results and methods used during the 8th Global Trajectory Optimization Competition (GTOC) of the DLR team. Trajectory optimization is crucial for most of the space missions and usually can be formulated as a global optimization problem. A lot of research has been done to different type of mission problems. The most demanding ones are low thrust transfers with e.g. gravity assist sequences. In that case the optimal control problem is combined with an integer problem. In most of the GTOCs we apply a filtering of the problem based on domain knowledge.
Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.
Handbook of space technology
(2009)