Passive Intermodulation in Printed Lines: Effects of Trace Dimensions and Substrate

A comprehensive experimental study was performed to identify and discriminate mechanisms contributing to passive intermodulation (PIM) in microstrip transmission lines. The effects of strip length and width, and substrate materials on PIM performance of printed lines were investigated in the GSM900, DCS1800 and UMTS frequency bands. The major features of the experiment design, sample preparation and test setup are discussed in detail. The measurement results have demonstrated that the PIM level cumulatively grows on the longer microstrip lines and decreases on wider strips and, thus, indicated that the distributed resistive nonlinearity of the printed traces represents the dominant mechanism of intermodulation generation in the printed lines on PTFE-based substrates. © 2009 The Institution of Engineering and Technology.

Rule Britannia!: British Stock Market Returns, 1825-1870

This article presents a new series of monthly equity returns for the British stock market for the period 1825-1870. In addition to calculating capital appreciation and dividend yields, the article also estimates the effect of survivorship bias on returns. Three notable findings emerge from this study. First, stock market returns in the 1825-1870 period are broadly similar for Britain and the United States, although the British market is less risky. Second, real returns in the 1825-1870 period are higher than in subsequent epochs of British history. Third, unlike the modern era, dividends are the most important component of returns.

The trace amine associated receptor (TAAR6) gene is not associated with schizophrenia in the Irish Case-Control Study of Schizophrenia (ICCSS) sample

To replicate previous association between TAAR6 and schizophrenia, including our own finding in the Irish Study of High Density Schizophrenia Families (ISHDSF) sample, we genotyped 12 single nucleotide polymorphisms (SNPs) in the Irish Case-Control Study of Schizophrenia (ICCSS) sample. Only rs9389020 provided nominal evidence for association (p

'Rule of Law and Politics of Anti-Corruption Campaigns in a Post-Authoritarian State- The Nigerian Case'

This article evaluates the anti-corruption campaign instituted in Nigeria following on the post-authoritarian transition in the country, with specific focus on political corruption. The anti-corruption campaign is being prosecuted within a context where law is as critical a factor as politics. This article examines whether the judiciary, in view of its accountability deficit, can offer legitimacy to the campaign. How has its questionable credentials impacted on its involvement in the campaign to sanitise public life? What has been the impact of the judicial role on the rule of law? These are some of the important questions this article seeks to answer. The inquiry in this article demonstrates how the guardian institution of the rule of law faces an uphill task in the performance of that role in a post-authoritarian context.

A heterosynaptic learning rule for neural networks

In this article we intoduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre-and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean leaxning time increases with the number of patterns to be learned polynomially, indicating efficient learning.