Material and Methods: In this study, we have introduced a new technique to reduce the motion artifacts, based on data binning and low rank plus sparse (L+S) reconstruction method for DCE MRI. For Data binning, radial k-space data is acquired continuously using the golden-angle radial sampling pattern and grouped into various motion states or bins. The respiratory signal for binning is extracted directly from radially acquired k-space data. A compressed sensing- (CS-) based L+S matrix decomposition model is then used to reconstruct motion sorted DCE MR images. Undersampled free breathing 3D liver and abdominal DCE MR data sets are used to validate the proposed technique.

Results: The performance of the technique is compared with conventional L+S decomposition qualitatively along with the image sharpness and structural similarity index. Recovered images are visually sharper and have better similarity with reference images.

METHODS: Continuous raw PPG waveforms were blindly allocated into segments with an equal length (5s) without leveraging any pulse location information and were normalized with Z-score normalization methods. A 1-D-CNN was designed to automatically learn the intrinsic features of the PPG waveform, and perform the required classification. Several training hyperparameters (initial learning rate and gradient threshold) were varied to investigate the effect of these parameters on the performance of the network. Subsequently, this proposed network was trained and validated with 30 subjects, and then tested with eight subjects, with our local dataset. Moreover, two independent datasets downloaded from the PhysioNet MIMIC II database were used to evaluate the robustness of the proposed network.

RESULTS: A 13 layer 1-D-CNN model was designed. Within our local study dataset evaluation, the proposed network achieved a testing accuracy of 94.9%. The classification accuracy of two independent datasets also achieved satisfactory accuracy of 93.8% and 86.7% respectively. Our model achieved a comparable performance with most reported works, with the potential to show good generalization as the proposed network was evaluated with multiple cohorts (overall accuracy of 94.5%).

DISCUSSION: It is a set of various methodologies which are used to capture internal or external images of the human body and organs for clinical and diagnosis needs to examine human form for various kind of ailments. Computationally intelligent machine learning techniques and their application in medical imaging can play a significant role in expediting the diagnosis process and making it more precise.

OBJECTIVES: To apply and validate the Knowledge Sharing Behavior Scale (KSBS) as a measure of knowledge sharing behavior within the academic community.

SUBJECTS: Respondents (N = 447) were academics from arts and science streams in 10 local, public universities in Malaysia.

MEASURES: Data were collected using the 28-item KSBS that assessed four dimensions of knowledge sharing behavior namely written contributions, organizational communications, personal interactions, and communities of practice.

RESULTS: The exploratory factor analysis showed that the items loaded on the dimension constructs that they were supposed to represent, thus proving construct validity. A within-factor analysis revealed that each set of items representing their intended dimension loaded on only one construct, therefore establishing convergent validity. All four dimensions were not perfectly correlated with each other or organizational citizenship behavior, thereby proving discriminant validity. However, all four dimensions correlated with organizational commitment, thus confirming predictive validity. Furthermore, all four factors correlated with both tacit and explicit sharing, which confirmed their concurrent validity. All measures also possessed sufficient reliability (α > .70).