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In globalisation of business, Knowledge Management (KM) plays an important role in Supply Chain (SC) to create, build and maintain competitive advantage through utilisation of knowledge and through collaborative practices. Literature review have suggested the performance of KM adoption in SC may be affected by various influencing factors but it is always difficult for the practitioners to improve all aspects at the same time. The aim of this study is to identify Critical Success Factors (CSFs) of KM adoption in SC. This study presents a favourable method combining fuzzy set theory and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to segment the critical factors for successful KM adoption in SC. The empirical case study analysis of an Indian hydraulic valve manufacturing organisation is conducted to illustrate the use of the proposed framework for identifying the CSFs of KM adoption in SC. According to the results of the empirical study, six CSFs of KM adoption in SC are identified out of 25 influencing factors, these are top management support, communication and collaboration techniques, employee involvement, employee training and education, communication among the SC members and trustworthy teamwork to exchange knowledge within SC which will help to improve effectiveness and efficiency of KM adoption in SC. The decision makers can apply a phased implementation of these CSFs to ensure the effective KM adoption in SC under the constraints of available resources. This proposed method provides a more accurate, effective and systematic decision support tool for identifying CSFs of KM adoption in SC.

Phase Contrast Magnetic Resonance Image (PC-MRI) is an emerging noninvasive technique that contains pulsatile information by measuring the parameters of cerebrospinal fluid (CSF) flow. As CSF flow quantities are measured from the selected region on the images, the accuracy in the identification of the interested region is the most essential, and the examination requires a lot of time and experience to analyze and for accurate CSF flow assessment. In this study, a three-dimensional (3D)-Unet architecture, including pulsatile flow data as the third dimension, is proposed to address the issue. The dataset contains 2176 phase and rephase images from 57 slabs of 39 3-tesla PC-MRI subjects collected from the lower thoracic levels of control and Idiopathic Scoliosis (IS) patients. The procedure starts with labeling the CSF containing spaces in the spinal canal. In the preprocessing step, unequal cardiac cycle images (i.e., frame) and the numbers of MRIs in cases are adjusted by interpolation to align the temporal dimension of the dataset to an equal size. The five-fold cross-validation procedure is used to evaluate the 3D Attention-U-Net model after training and achieved an average weighted performance of 97% precision, 95% recall, 98% F1 score, and 95% area under curve. The success of the model is also measured using the CSF flow waveform quantities as well. The mean flow rates through the labeled and predicted CSF lumens have a significant correlation coefficient of 0.96, and the peak CSF flow rates have a coefficient of 0.65. To our knowledge, this is the first fully automatic 3D deep learning architecture implementation to segment spinal CSF-containing spaces that utilizes both spatial and pulsatile information in PC-MRI data. We expect that our work will attract future research on the use of PC-MRI temporal information for training deep models.

An automatic cortical gray matter segmentation from a three-dimensional brain images is a well-known problem in medical image processing. Determining the location of the cortical surface of the human brain is often a first step in brain visualization and analysis. Due to the complicated and convoluted nature of the cortex, the manual slice by slice segmentation is generally a difficult, inefficient and inaccurate process, which makes an automatic 3D cortex segmentation an important task. In this chapter, we review techniques for automatic 3D MR images segmentation including boundary- and region-based methods, statistical methods, fuzzy clustering and deformable models.