Purpose -The purpose of this paper is to report the results of an exploratory investigation of the behavioral factors in relation to virtual knowledge sharing among Multimedia University students, Malaysia, based on the theory of reasoned action (TRA). Design/methodology/approach -A search and review of the existing literature was followed by an empirical test of the proposed model in the pilot study (number of participants: n ¼ 50) and the main study (n ¼ 250). Findings -Trust, anticipated reciprocal relationship and willingness to share knowledge as an individual's attitude; while identification and organizational culture acting as subjective norms, indirectly have an impact on individual's intention to share knowledge virtually. No positive relationship was discovered between the degree of competition and an individual's attitude to share knowledge; nor between collectivism and subjective norms.Research limitations/implications -The results may have been influenced by self-selection bias, as only one university was sampled.Practical implications -This study encourages academic researchers and service providers in educational institutions to focus on an individual's attitude and institutional subjective norms to comprehend students' behavior in virtual knowledge sharing and to improve the knowledge sharing activities among them, allowing scholars to benefit from better results in their routine academic tasks. Originality/value -The results indicate that trust, anticipated reciprocal relationship and willingness to share knowledge were significant predictors of an individual's intention to share knowledge indirectly through their attitude toward knowledge sharing. Therefore, lecturers interested in developing and sustaining knowledge exchange through virtual communities should develop strategies or mechanisms that encourage the interaction and strength of the relationships among students. Lecturers can encourage reciprocity by using extrinsic motivators such as assigning rewards for knowledge sharing activities among students. Also, lecturers can facilitate the factor of "trust" among student relationships by enhancing the norm of reciprocity.

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Cholangiocarcinoma, cancer of the bile ducts, is often diagnosed via magnetic resonance cholangiopancreatography (MRCP). Due to low resolution, noise and difficulty is actually seeing the tumor in the images, especially by examining only a single image, there has been very little development of automated systems for cholangiocarcinoma diagnosis. This paper presents a computer-aided diagnosis (CAD) system for the automated preliminary detection of the tumor using a single MRCP image. The multi-stage system employs algorithms and techniques that correspond to the radiological diagnosis characteristics employed by doctors. A popular artificial neural network, the multi-layer perceptron (MLP), is used for decision making to differentiate images with cholangiocarcinoma from those without. The test results achieved was 94% when differentiating only healthy and tumor images, and 88% in a robust multi-disease test where the system had to identify the tumor images from a large set of images containing common biliary diseases.

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This paper attempts to improve the diagnostic quality of magnetic resonance (MR) images through application of lossy compression as a noise-reducing filter. The amount of imaging noise present in MR images is compared with the amount of noise introduced by the compression, with particular attention given to the situation where the compression noise is a fraction of the imaging noise. A popular wavelet-based algorithm with good performance, Set Partitioning in Hierarchical Trees (SPIHT), was employed for the lossy compression. Tests were conducted with a number of MR patient images and corresponding phantom images. Different plausible ratios between imaging noise and compression noise (ICR) were considered, and the achievable compression gain through the controlled lossy compression was evaluated. Preliminary results show that at certain ICR's, it becomes virtually impossible to distinguish between the original and compressed-decompressed image. Radiologists presented with a blind test, in certain cases, showed preference to the compressed image rather than the original uncompressed ones, indicating that under controlled circumstances, lossy image compression can be used to improve the diagnostic quality of the MR images.

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