Energy and transport in comparison: Immaterialisation,dematerialisation and decarbonisation in the EU15 between 1970 and 2000

This article compares the development of transport and energy use with a focus on carbon dioxide (CO₂) emissions in the EU15 countries between 1960 and 2000, and separately by each individual EU country between 1970 and 2000. Based on a review on the literature, immaterialisation can be defined as the reduction of energy intensity and transport intensity; dematerialisation can be defined as the reduction in carbon intensity of energy production and the carbon intensity of transport; decarbonisation can be defined as the reduction in (total and transport) carbon intensity of the whole economy. Although there is a clear pattern of reduction in energy intensity of the economy and carbon intensity of energy production, a similar pattern cannot be found in transport. Neither the transport intensity of the economy nor the carbon intensity of transport has been reduced. In particular, freight transport intensity has grown between 1985 and 2000. Data presented by country have shown even more variation. The EU15 countries were aggregated into six groups by cluster analysis to establish the different patterns on each of the three measures. It is concluded that the EU15 countries will have problems in achieving the EU White Paper target of decoupling transport growth from economic growth and the Kyoto target of reducing total CO₂ emissions by 8% from the 1990 level between 2008 and 2012. However, there are some weak signals suggesting a more sustainable passenger transport system.     

Fatty acid composition of erythrocyte,platelet, and serum lipids in strict vegans

The fatty acid composition of erythrocytes, platelets, and serum lipids was compared between subjects who had been eating a strict uncooked vegan diet (“living food”) for years and omnivore controls. The vegan diet contains equal amounts of fat but more monounsaturated and polyunsaturated and less saturated fatty acids than the mixed diet of the control group. In vegans, the proportion of linoleic acid was greater in all lipid fractions studied. Also, the levels of other n−6 fatty acids were greater, with the exception of arachidonic acid levels, which were similar in most fractions. In erythrocytes, platelets and serum phospholipid fractions, this increase was mainly at the expense of the n−3 fatty acids. The proportions of eicosapentaenoic and docosahexaenoic acid were only 29–36% and 49–52% of those in controls, respectively. In vegans the ratio of n−3 to n−6 fatty acids was only about half that in omnivores. In addition to the lower levels of n−3 fatty acids, the proportions of palmitic and stearic acids were lower in serum cholesteryl esters, triglycerides and free fatty acids of vegans. The proportion of oleic acid was slightly lower only in serum cholesteryl esters and erythrocyte phosphatidylserine. The results show that, in the long term, the vegan diet has little effect on the proportions of oleic and arachidonic acids, whereas the levels of n−3 fatty acids are depressed to very low levels with prolonged consumption of the high linoleic and oleic acid components of this diet.     

Design and application of a generic clinical decision support system for multiscale data

Medical research and clinical practice are currently being redefined by the constantly increasing amounts of multiscale patient data. New methods are needed to translate them into knowledge that is applicable in healthcare. Multiscale modeling has emerged as a way to describe systems that are the source of experimental data. Usually, a multiscale model is built by combining distinct models of several scales, integrating, e.g., genetic, molecular, structural, and neuropsychological models into a composite representation. We present a novel generic clinical decision support system, which models a patient's disease state statistically from heterogeneous multiscale data. Its goal is to aid in diagnostic work by analyzing all available patient data and highlighting the relevant information to the clinician. The system is evaluated by applying it to several medical datasets and demonstrated by implementing a novel clinical decision support tool for early prediction of Alzheimer's disease.     

Private Forest Owners Play Key Role in European Forest Derived Fuel Utilization

The AFO （activating private forest owners to increase forest fuel supply） project was set up to discover the best tools for activating an important fraction of the 12 million PFOs （private forest owners） in Europe to supply wood fuel, especially to small and medium-sized heating plants. It aimed to increase the utilization of Europe＇s vast forest fuel reserves. This was expected to benefit both the economy of the forest owners and rural areas and increase the use of renewable energy sources. The project was carried out between 2009 and 2012. The project operated in selected target regions that have a high proportion of private forest ownership （France, Slovenia, Latvia and the UK）. It started by analyzing the potential wood fuel supply and use in relation to current regional markets. After assessing through inquiries the potential wood fuel supply and demand and the parties involved, the next step was to initiate and support the formation of wood fuel supply clusters and organize supply chains. This was followed by the dissemination of best practice examples from Austria and Finland--countries with the most expertise and long traditions of forest fuel production. In the last phase of the project, results and approved activation methods were disseminated to all 27 EU （European Union） countries.     

Characterization of random rough surfaces from scattered intensities by neural networks

Abstract

Optical scatterometry, a non-invasive characterization method, is used to infer the statistical properties of random rough surfaces. The Gaussian model with rms-roughness [sgrave] and correlation length σ is considered in this paper but the employed technique is applicable to any representation of random rough surfaces. Surfaces with wide ranges of Λ and σ, up to 5 wavelengths (λ), are characterized with neural networks. Two models are used: self-organizing map (SOM) for rough classification and multi-layer perceptron (MLP) for quantitative estimation with nonlinear regression. Models infer Λ and σ from scattering, thus involving the inverse problem. The intensities are calculated with the exact electromagnetic theory, which enables a wide range of parameters. The most widely known neural network model in practise is SOM, which we use to organize samples into discrete classes with resolution ΔΛ = Δσ = 0.5λ. The more advanced MLP model is trained for optimal behaviour by providing it with known parts of input (scattering) and output (surface parameters). We show that a small amount of data is sufficient for an excellent accuracy on the order of 0.3λ and 0.15λ for estimating Λ and σ, respectively.     

Synergistic effect of plasma‐modified halloysite nanotubes and carbon black in natural rubber—butadiene rubber blend

Halloysite nanotubes (HNTs) were investigated concerning their suitability for rubber reinforcement. As they have geometrical similarity with carbon nanotubes, they were expected to impart a significant reinforcement effect on the rubber compounds but the dispersion of the nanofillers is difficult. In this work, HNTs were surface‐modified by plasma polymerization to change their surface polarity and chemistry and used in a natural rubber/butadiene rubber blend in the presence of carbon black. The aim of the treatment was to improve the rubber–filler interaction and the dispersion of the fillers. A thiophene modification of HNTs improved stress–strain properties more than a pyrrole treatment. The surface modification resulted in a higher bound rubber content and lower Payne effect indicating better filler–polymer interaction. Scanning electron microscopy measurements showed an increased compatibility of elastomers and fillers. As visualized by transmission electron microscopy, the thiophene‐modified HNTs formed a special type of clusters with carbon black particles, which was ultimately reflected in the final mechanical properties of the nanocomposites. The addition of HNTs increased loss angle. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Comparison of Three Particle Number Concentration Calibration Standards Through Calibration of a Single CPC in a Wide Particle Size Range

We carried out a set of experiments to compare three particle number concentration standards (NCSs) by calibrating the same condensation particle counter (CPC) unit (Model 3772, TSI Inc., Shoreview, MN, USA). The standards were, in the order of operation size range, the primary NCS of the National Institute of Advanced Industrial Science and Technology (AIST, Japan), the Single Charged Aerosol Reference (SCAR) (Finland), and the Inkjet Aerosol Generator (IAG) of AIST. The results obtained with the 3 standards were found to agree at all overlapping particle sizes within the uncertainty limits. The relative expanded uncertainties varied between 0.6% and 2.6%, depending on the size and standard, while the overall agreement between the standards was within 0.5%. The observed consistency of the results is an important step toward establishing internationally coherent particle NCSs. As a result, the CPC 3772 was successfully calibrated in a particularly wide size range, approximately from 10 nm to 10 μm. The results indicate that the CPC can be considered as a practical tool for calibrating particle number concentration up to 1 μm. In general, the particle number concentration can be measured up to 2.5 μm without a significant decrease of the detection efficiency. By attaching an appropriate size-classifying inlet, the CPC could be used even for measuring the total number concentration for particles smaller than 2.5 μm, in parallel with the PM_2.5 mass measurement. Above this particle diameter, the detection efficiency gradually decreased and reached 50% at about 10 μm.

Copyright 2012 American Association for Aerosol Research     

Comparison of algorithms for standard median filtering

Standard median filtering that is searched repeatedly for a median from a sample set which changes only slightly between the subsequent searches is discussed. Several well-known methods for solving this running median problem are reviewed, the (asymptotical) time complexities of the methods are analyzed, and simple variants are proposed which are especially suited for small sample sets, a frequent situation. Although the discussion is restricted to the one-dimensional case, the ideas are easily extended to higher dimensions     

Mathematical simulation of interdendritic solidification of low-Alloyed and stainless steels

A mathematical model for simulating the interdendritic solidification of low-alloyed and stainless steels is presented based on thermodynamic and diffusion calculations made in one volume element in the mushy zone. The method involves the determination of stable phases (ferrite, austenite, liquid, and FeMnS) as well as their fractions and compositions at any temperature during solidification. The model was tested by comparing calculations with experiments, and reasonable agreement was demonstrated. The model also includes a routine for predicting the liquid undercooling caused by solute accumulation ahead of the dendrite tips.