KNOWLEDGE SHARING AND COLLABORATION THROUGH SOCIAL MEDIA - THE CASE OF IBM

Intellectual capital is the single most important asset owned by any organization. Business continuity, innovation, and long-term sustainability of Small Medium Enterprises depend partly on accumulated organizational knowledge. Knowledge is hard to capture and manage due to its implicit nature. This paper seeks to investigate how Web 2.0 technologies are being used to overcome knowledge sharing and collaboration issues. The new web technologies, which are based on platforms, are referred to as emergent social software platforms (ESSP’s). The use of ESSP’s within a business enterprise to achieve business goals is known as enterprise 2.0 (E2.0). Central to this research is the proposed knowledge sharing cycle model, which has three main stages - internalization, externalization, and objectification. This model is adapted based on the findings of a case study of IBM Corporation. The findings indicate that ESSP’s can be used to support knowledge sharing practices and to help convert knowledge into its different forms

Spectral Efficiency Maximization of a Single Cell Massive MU-MIMO Down-Link TDD System by Appropriate Resource Allocation

This paper deals with the problem of maximizing the spectral efficiency in a massive multi-user MIMO downlink system, where a base station is equipped with a very large number of antennas and serves single-antenna users simultaneously in the same frequency band, and the beamforming training scheme is employed in the time-division duplex mode. An optimal resource allocation that jointly selects the training duration on uplink transmission, the training signal power on downlink transmission, the training signal power on uplink transmission, and the data signal power on downlink transmission is proposed in such a way that the spectral efficiency is maximized given the total energy budget. Since the spectral efficiency is the main concern of this work, and its calculation using the lower bound on the achievable rate is computationally very intensive, in this paper, we also derive approximate expressions for the lower bound of achievable downlink rate for the maximum ratio transmission (MRT) and zero-forcing (ZF) precoders. The computational simplicity and accuracy of the approximate expressions for the lower bound of achievable downlink rate are validated through simulations. By employing these approximate expressions, experiments are conducted to obtain the spectral efficiency of the massive MIMO downlink time-division duplexing system with the optimal resource allocation and that of the beamforming training scheme. It is shown that the spectral efficiency of the former system using the optimal resource allocation is superior to that yielded by the latter scheme in the cases of both MRT and ZF precoders

Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images

Abstract Background The imaging assessment of paraspinal muscle morphology and fatty infiltration has gained considerable attention in the past decades, with reports suggesting an association between muscle degenerative changes and low back pain (LBP). To date, qualitative and quantitative approaches have been used to assess paraspinal muscle composition. Though highly reliable, manual thresholding techniques are time consuming and not always feasible in a clinical setting. The tedious and rater-dependent nature of such manual thresholding techniques provides the impetus for the development of automated or semi-automated segmentation methods. The purpose of the present study was to develop and evaluate an automated thresholding algorithm for the assessment of paraspinal muscle composition. The reliability and validity of the muscle measurements using the new automated thresholding algorithm were investigated through repeated measurements and comparison with measurements from an established, highly reliable manual thresholding technique. Methods Magnetic resonance images of 30 patients with LBP were randomly selected cohort of patients participating in a project on commonly diagnosed lumbar pathologies in patients attending spine surgeon clinics. A series of T2-weighted MR images were used to train the algorithm; preprocessing techniques including adaptive histogram equalization method image adjustment scheme were used to enhance the quality and contrast of the images. All muscle measurements were repeated twice using a manual thresholding technique and the novel automated thresholding algorithm, from axial T2-weigthed images, at least 5 days apart. The rater was blinded to all earlier measurements. Inter-method agreement and intra-rater reliability for each measurement method were assessed. The study did not received external funding and the authors have no disclosures. Results There was excellent agreement between the two methods with inter-method reliability coefficients (intraclass correlation coefficients) varying from 0.79 to 0.99. Bland and Altman plots further confirmed the agreement between the two methods. Intra-rater reliability and standard error of measurements were comparable between methods, with reliability coefficient varying between 0.95 and 0.99 for the manual thresholding and 0.97–0.99 for the automated algorithm. Conclusion The proposed automated thresholding algorithm to assess paraspinal muscle size and composition measurements was highly reliable, with excellent agreement with the reference manual thresholding method