The Power of Posner: A Study of Prestige and Influence in the Federal Judiciary

Some judges have a disproportionate influence over the American judiciary; existing research has shown Judge Richard Posner is one of those judges. Our goal was to identify and determine how Judge Posner’s influence has changed over time. To measure and track his influence, we collected and compared citation and invocation data from three distinct time frames. While these measurements are imperfect, they can help illustrate the level of influence and prestige Judge Posner enjoys. The existing literature led us to expect Judge Posner’s early citation rates to be low. After several years on the bench, the citation rates for each opinion should rise dramatically. By contrast, Judge Posner’s citation rates are exceptionally high from the outset while more recent opinions actually have lower citation rates

Analysis of β-globin chromatin micro-environment using a novel 3C variant, 4Cv

Copyright: © 2010 Pink et al.Higher order chromatin folding is critical to a number of developmental processes, including the regulation of gene expression. Recently developed biochemical techniques such as RNA TRAP and chromosome conformation capture (3C) have provided us with the tools to probe chromosomal structures. These techniques have been applied to the β-globin locus, revealing a complex pattern of interactions with regions along the chromosome that the gene resides on. However, biochemical and microscopy data on the nature of β-globin interactions with other chromosomes is contradictory. Therefore we developed a novel 4C variant, Complete-genome 3C by vectorette amplification (4Cv), which allows an unbiased and quantitative method to examine chromosomal structure. We have used 4Cv to study the microenvironment of the β-globin locus in mice and show that a significant proportion of the interactions of β-globin are inter-chromosomal. Furthermore, our data show that in the liver, where the gene is active, β-globin is more likely to interact with other chromosomes, compared to the brain where the gene is silent and is more likely to interact with other regions along the same chromosome. Our data suggest that transcriptional activation of the β-globin locus leads to a change in nuclear position relative to the chromosome territory.Ryan Pink is supported by a grant from Action Medical Research; Daniel Caley is supported by a grant from The Dunhill Medical Trust; David Carter is supported by a grant from the British Society for Haematology

A unified model of the electrical power network

Traditionally, the different infrastructure layers, technologies and management activities associated with the design, control and protection operation of the Electrical Power Systems have been supported by numerous independent models of the real world network. As a result of increasing competition in this sector, however, the integration of technologies in the network and the coordination of complex management processes have become of vital importance for all electrical power companies. The aim of the research outlined in this paper is to develop a single network model which will unify the generation, transmission and distribution infrastructure layers and the various alternative implementation technologies. This 'unified model' approach can support ,for example, network fault, reliability and performance analysis. This paper introduces the basic network structures, describes an object-oriented modelling approach and outlines possible applications of the unified model

Update of an early warning fault detection method using artificial intelligence techniques

This presentation describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. In an earlier paper [11], a computer simulated medium length transmission line has been tested by the detector and the results clearly demonstrate the capability of the detector. Today’s presentation considers a case study illustrating the suitability of this AI Technique when applied to a distribution transformer. Furthermore, an evolutionary optimisation strategy to train ANNs is also briefly discussed in this presentation, together with a ‘crystal ball’ view of future developments in the operation and monitoring of transmission systems in the next millennium

Power system fault prediction using artificial neural networks

The medium term goal of the research reported in this paper was the development of a major in-house suite of strategic computer aided network simulation and decision support tools to improve the management of power systems. This paper describes a preliminary research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. To achieve this goal, an AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system . Simulation will normally take place using equivalent circuit representation. Artificial Neural Networks (ANNs) are used to construct a hierarchical feed-forward structure which is the most important component in the fault detector. Simulation of a transmission line (2-port circuit ) has already been carried out and preliminary results using this system are promising. This approach provided satisfactory results with accuracy of 95% or higher

Mixtures of Shifted Asymmetric Laplace Distributions

A mixture of shifted asymmetric Laplace distributions is introduced and used for clustering and classification. A variant of the EM algorithm is developed for parameter estimation by exploiting the relationship with the general inverse Gaussian distribution. This approach is mathematically elegant and relatively computationally straightforward. Our novel mixture modelling approach is demonstrated on both simulated and real data to illustrate clustering and classification applications. In these analyses, our mixture of shifted asymmetric Laplace distributions performs favourably when compared to the popular Gaussian approach. This work, which marks an important step in the non-Gaussian model-based clustering and classification direction, concludes with discussion as well as suggestions for future work

Modulation of inherent dynamical tendencies of the bisabolyl cation via preorganization in epi-isozizaene synthase.

The relative importance of preorganization, selective transition state stabilization and inherent reactivity are assessed through quantum chemical and docking calculations for a sesquiterpene synthase (epi-isozizaene synthase, EIZS). Inherent reactivity of the bisabolyl cation, both static and dynamic, appears to determine the pathway to product, although preorganization and selective binding of the final transition state structure in the multi-step carbocation cascade that forms epi-isozizaene appear to play important roles

Realization of an all-optical zero to π cross-phase modulation jump

We report on the experimental demonstration of an all-optical π cross-phase modulation jump. By performing a preselection, an optically induced unitary transformation, and then a postselection on the polarization degree of freedom, the phase of the output beam acquires either a zero or π phase shift (with no other possible values). The postselection results in optical loss in the output beam. An input state may be chosen near the resulting phase singularity, yielding a pi phase shift even for weak interaction strengths. The scheme is experimentally demonstrated using a coherently prepared dark state in a warm atomic cesium vapor

Early warning fault detection using artificial intelligent methods

This paper describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. A simulated medium length transmission line has been tested by the detector and the results demonstrate the capability of the detector. Furthermore, comments on an evolutionary technique as the optimisation strategy for ANNs are included in this paper