The Effect of Human RIghts on Criminal Evidentiary Processes: Convergence, Divergence or Realignment?

This article examines the contribution which the European Court of Human Rights has made to the development of common evidentiary processes across the common law and civil law systems of criminal procedure in Europe. It is argued that the continuing use of terms such as 'adversarial' and 'inquisitorial' to describe models of criminal proof and procedure has obscured the genuinely transformative nature of the Court's jurisprudence. It is shown that over a number of years the Court has been steadily developing a new model of proof that is better characterised as 'participatory' than as 'adversarial' or 'inquisitorial'. Instead of leading towards a convergence of existing 'adversarial' and 'inquisitorial' models of proof, this is more likely to lead towards a realignment of existing processes of proof which nonetheless allows plenty of scope for diverse application in different institutional and cultural settings.

Freeing order processes in Northern Ireland: Painfully slow

Greg Kelly and Dominic McSherry describe the timescales involved in achieving freeing orders for 23 children in Northern Ireland between 1996 and 1999. There was delay at all stages in the process. The delays were particularly pronounced in social services and social services/legal services processes. The authors conclude that if adoption is to be made available to more children from state care, current arrangements, including legislation, will need revision and reform.

Improved Principal Component Monitoring of Large-Scale Processes

This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.

Analysis of Extended Partial Least Squares for Monitoring Large-Scale Processes

This paper theoretically analysis the recently proposed "Extended Partial Least Squares" (EPLS) algorithm. After pointing out some conceptual deficiencies, a revised algorithm is introduced that covers the middle ground between Partial Least Squares and Principal Component Analysis. It maximises a covariance criterion between a cause and an effect variable set (partial least squares) and allows a complete reconstruction of the recorded data (principal component analysis). The new and conceptually simpler EPLS algorithm has successfully been applied in detecting and diagnosing various fault conditions, where the original EPLS algorithm did only offer fault detection.

Statistical Monitoring of Dynamic Multivariate Processes: Part I: Modeling Auto- and Cross-Correlation

The work in this paper is of particular significance since it considers the problem of modelling cross- and auto-correlation in statistical process monitoring. The presence of both types of correlation can lead to fault insensitivity or false alarms, although in published literature to date, only autocorrelation has been broadly considered. The proposed method, which uses a Kalman innovation model, effectively removes both correlations. The paper (and Part 2 [2]) has emerged from work supported by EPSRC grant GR/S84354/01 and is of direct relevance to problems in several application areas including chemical, electrical, and mechanical process monitoring.