Structural deformations analysis by means of Kalman-filtering

The surveillance of engineering structures like dams is an interdisciplinary task and mainly focused on the assessment of stability and reliability of the objects to be monitored. To show the co-operation of the disciplines involved in a comprehensible manner, it is suitable to use system analysis approaches. Structural deformations analysis by means of system analysis is explained in the following with an example of a dam. The determination of the dam deformations is demonstrated by an integration of computed and measured data by using Kalman Filtering

Impact of Dendritic Size and Dendritic Topology on Burst Firing in Pyramidal Cells

Neurons display a wide range of intrinsic firing patterns. A particularly relevant pattern for neuronal signaling and synaptic plasticity is burst firing, the generation of clusters of action potentials with short interspike intervals. Besides ion-channel composition, dendritic morphology appears to be an important factor modulating firing pattern. However, the underlying mechanisms are poorly understood, and the impact of morphology on burst firing remains insufficiently known. Dendritic morphology is not fixed but can undergo significant changes in many pathological conditions. Using computational models of neocortical pyramidal cells, we here show that not only the total length of the apical dendrite but also the topological structure of its branching pattern markedly influences inter- and intraburst spike intervals and even determines whether or not a cell exhibits burst firing. We found that there is only a range of dendritic sizes that supports burst firing, and that this range is modulated by dendritic topology. Either reducing or enlarging the dendritic tree, or merely modifying its topological structure without changing total dendritic length, can transform a cell's firing pattern from bursting to tonic firing. Interestingly, the results are largely independent of whether the cells are stimulated by current injection at the soma or by synapses distributed over the dendritic tree. By means of a novel measure called mean electrotonic path length, we show that the influence of dendritic morphology on burst firing is attributable to the effect both dendritic size and dendritic topology have, not on somatic input conductance, but on the average spatial extent of the dendritic tree and the spatiotemporal dynamics of the dendritic membrane potential. Our results suggest that alterations in size or topology of pyramidal cell morphology, such as observed in Alzheimer's disease, mental retardation, epilepsy, and chronic stress, could change neuronal burst firing and thus ultimately affect information processing and cognition

Root Growth of Black Walnut Trees Related to Soil Temperature, Soil Water Potential, and Leaf Water Potential

The effects of soil temperature, soil water potential, and leaf water potential were evaluated on root length growth, number of growing roots, and suberization for three mature black walnut (Juglans nigra L.) trees in an oak-hickory forest in central Missouri. Rhizotrons were used to access the root systems. When only roots growing in wet soils were included, growth began at a soil temperature of 4°C and increased slowly to 13°C, with a more rapid increase above that. Growth rate peaked at 17° to 19°C and the number of growing roots peaked at 21°C, the highest temperature observed. When roots in dry soil were the only ones included in the analysis, root growth was low with a peak at 17°C. Root growth and number of growing roots decreased sharply as soil water potential decreased from 0 to -0.5 MPa, and approached zero as soil water potential went from -0.5 to -1.0 MPa. At low and high soil temperatures suberization rate was high, and at intermediate temperatures it was low. Suberization rate was low at high water potentials and increased as the soil dried. Root Growth of Black Walnut Trees Related to Soil Temperature, Soil Water Potential, and Leaf Water Potential - ResearchGate. Available from: http://www.researchgate.net/publication/233553247_Root_Growth_of_Black_Walnut_Trees_Related_to_Soil_Temperature_Soil_Water_Potential_and_Leaf_Water_Potential [accessed Jul 7, 2015]

Additive tree biomass equations for Betula platyphylla Suk. plantations in Northeast China

International audienceAbstractKey messageA new system of additive tree biomass equations was developed for juvenile white birch plantations based on tree diameter at breast height (DBH) and tree height (HT). Compared with previous equations developed for natural white birch forests, the new system included one more biomass component and provided more accurate predictions.ContextAccurate estimates of tree component and total biomass are necessary for evaluating alternative forest management strategies for biomass feedstock, carbon sequestration, and products. Previous biomass equations developed for white birch trees in natural stands provided substantially biased predictions for white birch plantations.AimsA new system of additive tree biomass equations was developed for juvenile white birch plantations in the northeastern China.MethodsWith destructive biomass sampling data from 501 trees sampled from white birch provenance and family trails at ages 7, 9, 10, and 13 in three provinces, a system of nonlinear additive tree biomass equations based on DBH and tree height was developed using the nonlinear seemingly unrelated regressions (NSUR) approach.ResultsCompared with previously published equations developed for natural white birch forests, the new system provided more accurate predictions of white birch tree component and aboveground and total biomass, especially of branch, foliage, and root biomass.ConclusionThe new system extended the applicability of biomass equations to white birch plantations in the northeastern China

Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model–data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6  ×  105 km2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO2, decreased precipitation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region