Assessing a Knowledge Management (KM) Project Selection Framework

Knowledge management (KM) is becoming recognized as a valuable tool for establishing and maintaining competitive advantage. Decision superiority, the ultimate goal of KM, is only possible through the effective and efficient use of knowledge. But, to effectively and efficiently create and use KM, it is important to carefully select KM projects. This research assesses the usefulness of a KM project selection framework that was developed by U.S. Air Force Captain William Bower (2001) and refined by 1st Lt Jeffrey Phillips (2003) referred to here as the Bower-Phillips decision framework. To assess the usability of the framework, a case study was undertaken at an Air Force organization to identify and address potential knowledge management opportunities. The framework was found to be useful in identifying possible KM projects

SATL Based Lesson for Teaching Grignard Reagents in Synthetic Organic Chemistry

Synthesizing new products from raw materials has been very popular aspects of research in organic chemistry. Traditionally, Grignard reagent has been very vital component of such synthetic procedures. Hence learning of various issues concerning with applications of Grignard reactions in synthetic organic chemistry is vital for enhancing the students creative capability. In this paper we will illustrate the uses of SATL methodology, which is recently getting popular [1- 3], in an SATL-based model lesson concerning teaching and learning of synthetic organic reactions related to Grignard reagents

Evaluation of a method for fusing LIDAR data and multispectral images for building detection

In this paper, we describe the evaluation of a method for building detection by the Dempster-Shafer fusion of LIDAR data and multispectral images. For that purpose, ground truth was digitised for two test sites with quite different characteristics. Using these data sets, the heuristic model for the probability mass assignments of the method is validated, and rules for the tuning of the parameters of this model are discussed. Further we evaluate the contributions of the individual cues used in the classification process to the quality of the classification results. Our results show the degree to which the overall correctness of the results can be improved by fusing LIDAR data with multispectral images

Application of FT-IR spectroscopy to analyze casein in cow milk

AbstractInfrared spectrometry is, at present, the most frequently applied methodology for the composition analysis of milk and dairy products. The determination of fat, protein and lactose has been described during recent decades. The introduction of Fourier Transform Infra-red (FT-IR) technology in combination with the application of multi-dimensional procedures (i.e. principal component regression, partial least squares) has improved this methodology, opening new perspectives for the simultaneous and routinely determination of many new parameters such as casein, urea, specific sugars, etc.. The aim of our study was to develop on MilkoScanTM FT 120 (Foss Electric, Hillerod, Denmark) a calibration curve for the analysis of casein in cow milk and to execute a preliminary validation. The calibration curve was developed on 89 individual milk samples collected from 4 dairy herds in the Grana-Padano cheese district. In order to obtain a higher variability of milk protein content and composition, in each herd m..