156,650 research outputs found

    Current issue in corporate waqf in Malaysia

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    Corporate waqf is still new for the contemporary Islamic studies in Malaysia. There are limited resources and guidance to explain about corporate waqf. The purposes of this study are to explore the issues, concept and development of corporate waqf. Current structure for corporate waqf also being explored as part of case studies. This paper provides several perspectives and suggestions to this issue. The methodology for this study is secondary data approach by using the data analysis from the related journal and paper. The subject for this study is Selangor Muamalat. This paper come out with the concept and development of the contemporary waqf focused on corporate waqf and there are five current issues identified in the contemporary waqf. Next, seven proposed action plans are suggested to cover the issues. Lastly, the structure of Selangor Muamalat is analyses by focusing on the management structure, financial and operational framework and the Shariah consideration towards Selangor Muamalat structure

    Service Knowledge Capture and Reuse

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    The keynote will start with the need for service knowledge capture and reuse for industrial product-service systems. A novel approach to capture the service damage knowledge about individual component will be presented with experimental results. The technique uses active thermography and image processing approaches for the assessment. The paper will also give an overview of other non-destructive inspection techniques for service damage assessment. A robotic system will be described to automate the damage image capture. The keynote will then propose ways to reuse the knowledge to predict remaining life of the component and feedback to design and manufacturing

    Inspection of Bradford Local Education Authority

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    Inspection report Waltham Forest Local Education Authority, September 2002

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    Improving aircraft maintenance, repair, and overhaul: A novel text mining approach

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    Aircraft Maintenance, Repair and Overhaul (MRO) feedback commonly includes an engineer’s complex text-based inspection report. Capturing and normalizing the content of these textual descriptions is vital to cost and quality benchmarking, and provides information to facilitate continuous improvement of MRO process and analytics. As data analysis and mining tools requires highly normalized data, raw textual data is inadequate. This paper offers a textual-mining solution to efficiently analyse bulk textual feedback data. Despite replacement of the same parts and/or sub-parts, the actual service cost for the same repair is often distinctly different from similar previously jobs. Regular expression algorithms were incorporated with an aircraft MRO glossary dictionary in order to help provide additional information concerning the reason for cost variation. Professional terms and conventions were included within the dictionary to avoid ambiguity and improve the outcome of the result. Testing results show that most descriptive inspection reports can be appropriately interpreted, allowing extraction of highly normalized data. This additional normalized data strongly supports data analysis and data mining, whilst also increasing the accuracy of future quotation costing. This solution has been effectively used by a large aircraft MRO agency with positive results

    Sympiler: Transforming Sparse Matrix Codes by Decoupling Symbolic Analysis

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    Sympiler is a domain-specific code generator that optimizes sparse matrix computations by decoupling the symbolic analysis phase from the numerical manipulation stage in sparse codes. The computation patterns in sparse numerical methods are guided by the input sparsity structure and the sparse algorithm itself. In many real-world simulations, the sparsity pattern changes little or not at all. Sympiler takes advantage of these properties to symbolically analyze sparse codes at compile-time and to apply inspector-guided transformations that enable applying low-level transformations to sparse codes. As a result, the Sympiler-generated code outperforms highly-optimized matrix factorization codes from commonly-used specialized libraries, obtaining average speedups over Eigen and CHOLMOD of 3.8X and 1.5X respectively.Comment: 12 page

    Use of data in special schools by school improvement partners (SIPs) : a guidance document

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