Teaching, learning and assessment in MSc. Databases units at University of Greenwich

In this paper we look at ways of delivering and assessing learning on database units offered on higher degree programmes (MSc) in the School of Computing and Mathematical Sciences at the University of Greenwich. Of critical importance is the teaching methods employed for verbal disposition, practical laboratory exercises and a careful evaluation of assessment methods and assessment tools in view of the fact that databases involve not only database design but also use of practical tools, such as database management systems (DBMSs) software, human designers, database administrators (DBA) and end users. Our goal is to clearly identify potential key success factors in delivering and assessing learning in both practical and theoretical aspects of database course units.

Recommendations on the nature of the passenger response time distribution to be used in the MSC 1033 assembly time analysis based on data derived from sea trials

The passenger response time distributions adopted by the International Maritime Organisation (IMO)in their assessment of the assembly time for passanger ships involves two key assumptions. The first is that the response time distribution assumes the form of a uniform random distribution and the second concerns the actual response times. These two assumptions are core to the validity of the IMO analysis but are not based on real data, being the recommendations of an IMO committee. In this paper, response time data collected from assembly trials conducted at sea on a real passanger vessel using actual passangers are presented and discussed. Unlike the IMO specified response time distributions, the data collected from these trials displays a log-normal distribution, similar to that found in land based environments. Based on this data, response time distributions for use in the IMO assesmbly for the day and night scenarios are suggested

Chiton phylogeny (Mollusca Polyplacophora) and the placement of the enigmatic species Choriplax grayi (H. Adams & Angas)

Shallow marine chitons (Mollusca:Polyplacophora:Chitonida) are widespread and well described from established morphoanatomical characters, yet key aspects of polyplacophoran phylogeny have remained unresolved. Several species, including Hemiarthrum setulosum Carpenter in Dall, 1876, and especially the rare and enigmatic Choriplax grayi (Adams & Angas, 1864), defy systematic placement. Choriplax is known from only a handful of specimens and its morphology is a mosaic of key taxonomic features from two different clades. Here, new molecular evidence provides robust support for its correct association with a third different clade: Choriplax is placed in the superfamily Mopalioidea. Hemiarthrum is included in Cryptoplacoidea, as predicted from morphological evidence. Our multigene analysis of standard nuclear and mitochondrial markers demonstrates that the topology of the order Chitonida is divided into four clades, which have also been recovered in previous studies: Mopalioidea is sister to Cryptoplacoidea, forming a clade Acanthochitonina. The family Callochitonidae is sister to Acanthochitonina. Chitonoidea is resolved as the earliest diverging group within Chitonida. Consideration of this unexpected result for Choriplax and our well-supported phylogeny has revealed differing patterns of shell reduction separating the two superfamilies within Acanthochitonina. As in many molluscs, shell reduction as well as the de novo development of key shell features has occurred using different mechanisms, in multiple lineages of chitons.

MSC-clustering and forward stepwise regression for virtual metrology in highly correlated input spaces

Increasingly semiconductor manufacturers are exploring opportunities for virtual metrology (VM) enabled process monitoring and control as a means of reducing non-value added metrology and achieving ever more demanding wafer fabrication tolerances. However, developing robust, reliable and interpretable VM models can be very challenging due to the highly correlated input space often associated with the underpinning data sets. A particularly pertinent example is etch rate prediction of plasma etch processes from multichannel optical emission spectroscopy data. This paper proposes a novel input-clustering based forward stepwise regression methodology for VM model building in such highly correlated input spaces. Max Separation Clustering (MSC) is employed as a pre-processing step to identify a reduced srt of well-conditioned, representative variables that can then be used as inputs to state-of-the-art model building techniques such as Forward Selection Regression (FSR), Ridge regression, LASSO and Forward Selection Ridge Regression (FCRR). The methodology is validated on a benchmark semiconductor plasma etch dataset and the results obtained are compared with those achieved when the state-of-art approaches are applied directly to the data without the MSC pre-processing step. Significant performance improvements are observed when MSC is combined with FSR (13%) and FSRR (8.5%), but not with Ridge Regression (-1%) or LASSO (-32%). The optimal VM results are obtained using the MSC-FSR and MSC-FSRR generated models. © 2012 IEEE.