The role of micro computers on the construction site

The control needed in the management of a project was analysed with particular reference to the unique needs of the construction industry within the context of site management. This was explored further by analysing the various problems facing managers within the overall system and determining to what extent the organisation would benefit from an integrated mangement information system. Integration and management of information within the organisational units and the cycles of events that make up the main sub-system was suggested as the means of achieving this objective. A conceptual model of the flow of information was constructed within the whole process of project management by examining the type of information and documents which are generated for the production cycle of a project. This model was analysed with respect to the site managers' needs and the minimum requirements for an overall integrated system. The most tedious and time-consuming task facing the site manager is the determination of weekly production costs, calculation and preparation of interim certificates and valuation of variations occurring during the production stage and finally the settlement and preparation of supplier and sub-contractors' accounts. These areas where microcomputers could be of most help were identified and a number of packages were designed and implemented for various contractors. The gradual integration of stand-alone packages within the whole of the construction industry is a logical sequence to achieve integration of management system. The methods of doing this were analysed together with the resulting advantages and disadvantages.

PDNAsite:identification of DNA-binding site from protein sequence by incorporating spatial and sequence context

Protein-DNA interactions are involved in many fundamental biological processes essential for cellular function. Most of the existing computational approaches employed only the sequence context of the target residue for its prediction. In the present study, for each target residue, we applied both the spatial context and the sequence context to construct the feature space. Subsequently, Latent Semantic Analysis (LSA) was applied to remove the redundancies in the feature space. Finally, a predictor (PDNAsite) was developed through the integration of the support vector machines (SVM) classifier and ensemble learning. Results on the PDNA-62 and the PDNA-224 datasets demonstrate that features extracted from spatial context provide more information than those from sequence context and the combination of them gives more performance gain. An analysis of the number of binding sites in the spatial context of the target site indicates that the interactions between binding sites next to each other are important for protein-DNA recognition and their binding ability. The comparison between our proposed PDNAsite method and the existing methods indicate that PDNAsite outperforms most of the existing methods and is a useful tool for DNA-binding site identification. A web-server of our predictor (http://hlt.hitsz.edu.cn:8080/PDNAsite/) is made available for free public accessible to the biological research community.