52 resultados para Dynamic response


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The three-dimensional transient wave response problem is presented for an infinite elastic medium weakened by a plane crack of infinite length and finite width. Tractions are applied suddenly to the crack, which simulates the case of impact loading. The integral transforms are utilized to reduce the problem to a standard Fredholm integral equation in the Laplace transform variable and sequentially invert the Laplace transforms of the stress components by numerical inversion method. The dynamic mode I stress intensity factors at the crack tip are obtained and some numerical results are presented in graphical form.

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The objective was to study dynamic changes of ovaries in rhesus macaques stimulated by gonadotropins to identify an indicator for predicting ovarian response to stimulation. Twenty-one cycling monkeys were given 36 IU/d recombinant human follicle-stimulat

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In this paper, we propose the dynamic P-V curve for modulator and P-I curve for laser diode, and present a simple approach to deriving the curves from the small-signal frequency responses measured using a microwave network analyzer. The linear response range, modulation efficiency, optimal driving conditions at different frequency can, therefore, be determined. It is demonstrated that the large-signal performance of electro-absorption (EA) modulator and the directly modulated semiconductor lasers can be predicted from the dynamic curved surface. Experiments show a good agreement between the evaluated characteristics and the measured large-signal performance.

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A novel silicon-on-insulator thermo-optic variable optical attenuator with isolated grooves based on a multimode interference coupler principle is fabricated by the inductive coupled plasma etching technology. The maximum fibre-to-fibre insertion loss is lower than 2.2 dB, the dynamic attenuation range is from 0 to 30 dB in the wavelength range 1500-1600 nm, and the maximum power consumption is only 140 mW. The response frequency of the fabricated variable optical attenuator is about 30 kHz. Compared to the variable optical attenuator without isolated grooves, the maximum power consumption decreases more than 220 mW, and the response frequency rises are more than 20 kHz.

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A mechanistic model is developed to present the photosynthetic response of phytoplankton to irradiance at the physiological level. The model is operated on photosynthetic units (PSU), and each PSU is assumed to have two states: reactive and activated. Light absorption that drives a reactive PSU into the activated state results from the effective absorption of the PSU. Transitions between the two states are asymmetrical in rate. A PSU in the reactive state becomes activated much faster than it recovers from the activated state to the reactive one. The turnover time for an activated PSU to transit into the reactive one is defined by the turnover time of the electron transport chain. The present model yields a photosynthesis-irradiance curve (PE-curve) in a hyperbola, which is described by three physiological parameters: effective cross-section (sigma (PSII)), turnover time of electron transport chain (tau) and number of PSUs (N). The PE-curve has an initial slope of sigma (PSII) x N, a half-saturated irradiance of 1/(sigma (PSII)), and a maximal photosynthetic rate of Nlc at the saturated irradiance. The PE-curve from the present model is comparable to the empirical function based on the target theory described by the Poisson distribution. (C) 2001 Academic Press.

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This study attempts to model alpine tundra vegetation dynamics in a tundra region in the Qinghai Province of China in response to global warming. We used Raster-based cellular automata and a Geographic Information System to study the spatial and temporal vegetation dynamics. The cellular automata model is implemented with IDRISI's Multi-Criteria Evaluation functionality to simulate the spatial patterns of vegetation change assuming certain scenarios of global mean temperature increase over time. The Vegetation Dynamic Simulation Model calculates a probability surface for each vegetation type, and then combines all vegetation types into a composite map, determined by the maximum likelihood that each vegetation type should distribute to each raster unit. With scenarios of global temperature increase of I to 3 degrees C, the vegetation types such as Dry Kobresia Meadow and Dry Potentilla Shrub that are adapted to warm and dry conditions tend to become more dominant in the study area.

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Synthesis efforts that identify patterns of ecosystem response to a suite of warming manipulations can make important contributions to climate change science. However, cross-study comparisons are impeded by the paucity of detailed analyses of how passive warming and other manipulations affect microclimate. Here we document the independent and combined effects of a common passive warming manipulation, open-top chambers (OTCs), and a simulated widespread land use, clipping, on microclimate on the Tibetan Plateau. OTCs consistently elevated growing season averaged mean daily air temperature by 1.0-2.0 degrees C, maximum daily air temperature by 2.1-7.3 degrees C and the diurnal air temperature range by 1.9-6.5 degrees C, with mixed effects on minimum daily air temperature, and mean daily soil temperature and moisture. These OTC effects on microclimate differ from reported effects of a common active warming method, infrared heating, which has more consistent effects on soil than on air temperature. There were significant interannual and intragrowing season differences in OTC effects on microclimate. For example, while OTCs had mixed effects on growing season averaged soil temperatures, OTCs consistently elevated soil temperature by approximately 1.0 degrees C early in the growing season. Nonadditive interactions between OTCs and clipping were also present: OTCs in clipped plots generally elevated air and soil temperatures more than OTCs in nonclipped plots. Moreover, site factors dynamically interacted with microclimate and with the efficacy of the OTC manipulations.These findings highlight the need to understand differential microclimate effects between warming methods, within warming method across ecosystem sites, within warming method crossed with other treatments, and within sites over various timescales. Methods, sites and scales are potential explanatory variables and covariables in climate warming experiments. Consideration of this variability among and between experimental warming studies will lead to greater understanding and better prediction of ecosystem response to anthropogenic climate warming.