Genotypic diversity in Puccinia striiformis f. sp. tritici, the cause of wheat stripe rust, in western Canada

Non-Peer Reviewe

Wheat stripe rust in Saskatchewan and Alberta: race dynamics and pathogen population

Non-Peer Reviewe

InAs-AlSb quantum wells in tilted magnetic fields

InAs-AlSb quantum wells are investigated by transport experiments in magnetic fields tilted with respect to the sample normal. Using the coincidence method we find for magnetic fields up to 28 T that the spin splitting can be as large as 5 times the Landau splitting. We find a value of the g-factor of about 13. For small even-integer filling factors the corresponding minima in the Shubnikov-de Haas oscillations cannot be tuned into maxima for arbitrary tilt angles. This indicates the anti-crossing of neighboring Landau and spin levels. Furthermore we find for particular tilt angles a crossover from even-integer dominated Shubnikov-de Haas minima to odd-integer minima as a function of magnetic field

Using synchrotron techniques to understand plant diseases stripe rust and wheat

Non-Peer Reviewe

Research Notes: New races of cyst nematode

Physiological strains of soybean cyst nematode (SCN) were first reported by Ross (1962). Later a new biotype was observed in Arkansas (Riggs et al ., 1968). The SCN pathotypes then known were classified into four races based on their ability to reproduce on a set of soybean differentials (Golden et al. , 1970)

Research Notes: Genotype response to soybean cyst nematodes in different soil sources

Soybean cyst nematode was first observed in North Carolina as a pest on soybeans in 1954. Within a few years, it was reported from Virginia, Tennessee, Arkansas, Missouri and Illinois. This necessitated the screening of the soybean germplasm, which lead to the discovery of the following lines carrying resistance to SCN

Three-Omega Thermal-Conductivity Measurements with Curved Heater Geometries

The three-omega method, a powerful technique to measure the thermal conductivity of nanometer-thick films and the interfaces between them, has historically employed straight conductive wires to act as both heaters and thermometers. When investigating stochastically prepared samples such as two-dimensional materials and nanomembranes, residue and excess material can make it difficult to fit the required millimeter-long straight wire on the sample surface. There are currently no available criteria for how diverting three-omega heater wires around obstacles affects the validity of the thermal measurement. In this Letter, we quantify the effect of wire curvature by performing three-omega experiments with a wide range of frequencies using both curved and straight heater geometries on SiO$_2$/Si samples. When the heating wire is curved, we find that the measured Si substrate thermal conductivity changes by only 0.2%. Similarly, we find that wire curvature has no significant effect on the determination of the thermal resistance of a $\sim$65 nm SiO$_2$ layer, even for the sharpest corners considered here, for which the largest measured ratio of the thermal penetration depth of the applied thermal wave to radius of curvature of the heating wire is 4.3. This result provides useful design criteria for three-omega experiments by setting a lower bound for the maximum ratio of thermal penetration depth to wire radius of curvature.Comment: 4 pages, 3 figure

Fault detection in power transformers using random neural networks

This paper discuss the application of artificial neural network-based algorithms to identify different types of faults in a power transformer, particularly using DGA (Dissolved Gas Analysis) test. The analysis of Random Neural Network (RNN) using Levenberg-Marquardt (LM) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms has been done using the data of dissolved gases of power transformers collected from Punjab State Transmission Corporation Ltd.(PSTCL), Ludhiana, India. Sorting of the preprocessed data have been done using dimensionality reduction technique, i.e., principal component analysis. The sorted data is used as inputs to the Random Neural Networks (RNN) classifier. It has been seen from the results obtained  that BFGS has better performance for the diagnosis of fault in transformer as compared to LM

Research Notes : Screening for cyst nematode resistance in soybean breeding

As the soybean cyst nematode (SCN), Heterodera glycines Ichinohe, has become a serious pest of soybeans in the USA, development of resistant culti-vars has received greater attention. This necessitates screening of large numbers of plant progenies to locate SCN-resistant isolates in segregating generations. Ross and Brim (1957) used a double-row method to detect SCN-resistant strains of soybeans