Onderzoekingen over het aantonen van aardappel-yN-virus met behulp van toetsplanten

Studies were conducted to determine some of the physical and biological properties of a new strain of virus Y, called virus Y N. Different methods of preserving the virus in vitro and conditions affecting local lesion formation on detached leaves of the test plants Solanum demissum hybrid ' A6 ' and Solanum demissum 'Y' were studied. It was shown that detached leaves of these test plants produced also local lesions after inoculation with the potato viruses Y 0, Y C, A, 'aucubabont,' and with rattle and tobacco mosaic viruses. The effect of temperature on lesion formation was more obvious than the effect of light. The optimum incubation temperatures for virus Y and A were 24°-25°C and 19°-20°C, respectively. The suitability of the host ' A6 ' as a test plant for virus Y Nwas studied with infected potato plants at different stages of development. In leaves and parts of stems of plants with secondary infection, virus Y Nwas detected reliably. In tubers of those plants, virus Y Ncould reliably be detected directly after early harvesting with cut tubers as inoculum. In young tubers of primarily infected plants virus Y Ncould be detected shortly after harvesting if the period between inoculation and lifting was long. In tubers stored for a long period after harvesting, the virus could not reliably be detected. Testing the sprouts of the tubers for the presence of virus Y Nwas alway consistent

In Search of Optimal Linkage Trees

Linkage-learning Evolutionary Algorithms (EAs) use linkage learning to construct a linkage model, which is exploited to solve problems efficiently by taking into account important linkages, i.e. dependencies between problem variables, during variation. It has been shown that when this linkage model is aligned correctly with the structure of the problem, these EAs are capable of solving problems efficiently by performing variation based on this linkage model [2]. The Linkage Tree Genetic Algorithm (LTGA) uses a Linkage Tree (LT) as a linkage model to identify the problem's structure hierarchically, enabling it to solve various problems very efficiently. Understanding the reasons for LTGA's excellent performance is highly valuable as LTGA is also able to efficiently solve problems for which a tree-like linkage model seems inappropriate. This brings us to ask what in fact makes a linkage model ideal for LTGA to be used

Process Simulation and Control Optimization of a Blast Furnace Using Classical Thermodynamics Combined to a Direct Search Algorithm

Several numerical approaches have been proposed in the literature to simulate the behavior of modern blast furnaces: finite volume methods, data-mining models, heat and mass balance models, and classical thermodynamic simulations. Despite this, there is actually no efficient method for evaluating quickly optimal operating parameters of a blast furnace as a function of the iron ore composition, which takes into account all potential chemical reactions that could occur in the system. In the current study, we propose a global simulation strategy of a blast furnace, the 5-unit process simulation. It is based on classical thermodynamic calculations coupled to a direct search algorithm to optimize process parameters. These parameters include the minimum required metallurgical coke consumption as well as the optimal blast chemical composition and the total charge that simultaneously satisfy the overall heat and mass balances of the system. Moreover, a Gibbs free energy function for metallurgical coke is parameterized in the current study and used to fine-tune the simulation of the blast furnace. Optimal operating conditions and predicted output stream properties calculated by the proposed thermodynamic simulation strategy are compared with reference data found in the literature and have proven the validity and high precision of this simulation

The phase of iron catalyst nanoparticles during carbon nanotube growth

We study the Fe-catalyzed chemical vapor deposition of carbon nanotubes by complementary in situ grazing-incidence X-ray diffraction, in situ X-ray reflectivity, and environmental transmission electron microscopy. We find that typical oxide supported Fe catalyst films form widely varying mixtures of bcc and fcc phased Fe nanoparticles upon reduction, which we ascribe to variations in minor commonly present carbon contamination levels. Depending on the as-formed phase composition, different growth modes occur upon hydrocarbon exposure: For γ-rich Fe nanoparticle distributions, metallic Fe is the active catalyst phase, implying that carbide formation is not a prerequisite for nanotube growth. For α-rich catalyst mixtures, Fe3C formation more readily occurs and constitutes part of the nanotube growth process. We propose that this behavior can be rationalized in terms of kinetically accessible pathways, which we discuss in the context of the bulk iron–carbon phase diagram with the inclusion of phase equilibrium lines for metastable Fe3C. Our results indicate that kinetic effects dominate the complex catalyst phase evolution during realistic CNT growth recipes.S.H. acknowledges funding from ERC grant InsituNANO (No. 279342). We acknowledge the European Synchrotron Radiation Facility (ESRF) for provision of synchrotron radiation facilities. We acknowledge the use of facilities within the LeRoy Eyring Center for Solid State Science at Arizona State University. C.T.W. and C.S.E. acknowledge funding from the EC project Technotubes. A.D.G. acknowledges funding from the Marshall Aid Commemoration Commission and the National Science Foundation. R.S.W. acknowledges funding from EPSRC (Doctoral training award) and B.C.B. acknowledges a Research Fellowship at Hughes Hall, Cambridge.This is the accepted manuscript. The final version is available from ACS at http://pubs.acs.org/doi/abs/10.1021/cm301402g