Thermochemical interpretation of one-dimensional seismic reference models for the upper mantle: Evidence for bias due to heterogeneity

A 1-D reference model for the mantle that is physically meaningful would be invaluable both in geodynamic modelling and for an accurate interpretation of 3-D seismic tomography. However, previous studies have shown that it is difficult to reconcile the simplest possible 1-D physical model—1300°C adiabatic pyrolite—with seismic observations. We therefore generate a set of alternative 1-D thermal and chemical mantle models, down to 900 km depth, and compare their properties with seismic data. We use several different body and surface wave data sets that provide complementary constraints on mantle structure. To assess the agreement between our models and seismic data, we take into account the large uncertainties in both the elastic/anelastic parameters of the constituent minerals, and the thermodynamic procedures for calculating seismic velocities. These uncertainties translate into substantial differences in seismic structure. However, in spite of such differences, subtle trends remain. We find that models which attain (1) higher velocity gradients between 250 and 350 km; (2) higher velocity gradients in the lower transition zone; and (3) higher average velocities immediately beneath the 660-discontinuity, than 1300°C adiabatic pyrolite—either via a temporary shift to lower temperatures, and/or a change to a seismically faster chemical composition—provide a significantly better fit to the seismic data than adiabatic pyrolite. This is compatible with recent thermochemical dynamic models by Tackley et al. in which average thermal structure is smooth and monotonous, but average chemical structure deviates substantially from pyrolite above, in, and below the transition zone. Our results suggest that 1-D seismic reference models are being systematically biased by a complex 3-D chemical structure. This bias should be taken into account when attempting quantitative interpretation of seismic anomalies, since those very anomalies contribute to the 1-D average signa

Progressive Transient Photon Beams

In this work we introduce a novel algorithm for transient rendering in participating media. Our method is consistent, robust, and is able to generate animations of time-resolved light transport featuring complex caustic light paths in media. We base our method on the observation that the spatial continuity provides an increased coverage of the temporal domain, and generalize photon beams to transient-state. We extend the beam steady-state radiance estimates to include the temporal domain. Then, we develop a progressive version of spatio-temporal density estimations, that converges to the correct solution with finite memory requirements by iteratively averaging several realizations of independent renders with a progressively reduced kernel bandwidth. We derive the optimal convergence rates accounting for space and time kernels, and demonstrate our method against previous consistent transient rendering methods for participating media

Actual and potential impact of air pollution on Italian forests: results from the long-term national forest monitoring networks under the ICP Forests

Actual and potential pressure and impacts of air pollution have been summarized by using the dataseries of the Italian forest monitoring networks (CONECOFOR), mostly on the basis of evaluations carried out within the LIFE project SMART4Action. Trends in air pollution shows only few important reductions (e.g.: sulphate and ozone). The impacts on forest health status, increments and standing volumes, plant diversity, soil and nutrient are discussed. Evidences of risk are also reported, mainly due to N deposition, on all the response factors

General model with experimental validation of electrical resonant frequency tuning of electromagnetic vibration energy harvesters

This paper presents a general model and its experimental validation for electrically tunable electromagnetic energy harvesters. Electrical tuning relies on the adjustment of the electrical load so that the maximum output power of the energy harvester occurs at a frequency which is different from the mechanical resonant frequency of the energy harvester. Theoretical analysis shows that for this approach to be feasible the electromagnetic vibration energy harvester’s coupling factor must be maximized so that its resonant frequency can be tuned with the minimum decrease of output power. Two different-sized electromagnetic energy harvesters were built and tested to validate the model. Experimentally, the micro-scale energy harvester has a coupling factor of 0.0035 and an untuned resonant frequency of 70.05 Hz. When excited at 30 mg, it was tuned by 0.23 Hz by changing its capacitive load from 0 to 4000 nF; its effective tuning range is 0.15 Hz for a capacitive load variation from 0 to 1500 nF. The macro-scale energy harvester has a coupling factor of 552.25 and an untuned resonant frequency of 95.1 Hz and 95.5 Hz when excited at 10 mg and 25 mg, respectively. When excited at 10 mg, it was tuned by 3.8 Hz by changing its capacitive load from 0 to 1400 nF; it has an effective tuning range of 3.5 Hz for a capacitive load variation from 0 to 1200 nF. When excited at 25 mg, its resonant frequency was tuned by 4.2 Hz by changing its capacitive load from 0 to 1400 nF; it has an effective tuning range of about 5 Hz. Experimental results were found to agree with the theoretical analysis to within 10%

adhesion of functional layer on polymeric substrates for optoelectronic applications

The use of plastic film substrates for organic electronic devices promises to enable new applications, such as flexible displays. Plastic substrates have several distinct advantages, such as ruggedness, robustness, ultra lightness, conformability and impact resistance over glass substrates, which are primarily used in flat panel displays (FPDs) today. However, high transparency, proper surface roughness, low gas permeability and high transparent electrode conductivity of the plastic substrate are required for commercial applications. Polyesters, both amorphous and semicrystalline, are a promising class of commercial polymer for optoelectronic applications. Surface modification of polyester films was performed via chemical solution determining hydrolysis or oxidation. Hydrolysis was carried out by means of sodium hydroxide solution and oxidation by using standard clean 1 (SC-1) of RCA procedure [1]. For this work we have used commercial polymer films of 100µm in thickness: AryLite™ [2], supplied by Ferrania Imaging Technologies S.p.A. and characterised by very high glass transition temperature, Mylar™ (Polyethylene Terephthalate PET) and Teonex™ (Polyethylene Naphthalate PEN) both supplied by Dupont. More over, a bioriented and semicrystalline PET have been used. The aim of this study is modifying the polymer surface to improve the adhesion between organic-inorganic layer. It was found that the NaOH and SC-1 treatment cause a decrease of contact angles. In the present study we have deposited a thin films of amorphous hydrogenated silicon (a-Si:H) and its oxide (SiO2) on a new high temperature polymer substrate, AryLite™, by plasma enhanced chemical vapour deposition (PECVD) [3], with a radio frequency plasma system

Uncertainty in Simulating Wheat Yields Under Climate Change

Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1,3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking

О перспективе извлечения йода из продукта утилизации окислителя ракетного топлива

Crop models are essential tools for assessing the threat of climate change to local and global food production. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degree C of further temperature increase and become more variable over space and time