E-Health systems include applications of information communication technologies topromote healthcare services support, delivery and education. The success of an E-Health system is very much dependent on the success of EHR systems, as EHR forms the core of any E-Health system. Readiness assessment has been identified as an essential requirement for the success of EHR in terms of adoption rate and/or acceptance. Through a literature review of current E-Health readiness frameworks, it is observed that most studied components reflect healthcare providers' and organisational perspectives but there is an inconsistent coverage of the evaluation components. Further, an unclear measure of readiness levels poses another problem for E-Health readiness assessment. This paper presents an E-Health readiness assessment framework by integrating components of each reviewed framework and quantifying constructs (a graph-based approach) within the new framework.

This study examined the long-term water budget closures for 370 watersheds over Canada's landmass by using 30 years ' (1981-2010) data products recently produced for precipitation (P) gridded using climate station measurements, land surface evapotranspiration (ET), and water surface evaporation (E0) obtained by the Ecological Assimilation of Land and Climate Observations (EALCO) model, and observed streamflow (Q). The results show that 29%, 58%, and 83% of the watersheds were closed within 5%, 10%, and 20% of P, respectively. The positive and negative imbalances among the 370 watersheds are largely offset and the national scale average is À24 mm yr À1 , or 4.2% of P. Water budget closures have large variation across the landmass. Regions with sparse or less accurate monitoring of P such as the mountainous region and the Arctic exhibit the largest water imbalances. Further efforts on enhancing the climate observation networks, improving spatial models for P and ET estimates, and streamflow measurements are all likely critical for a better understanding of Canada's water budgets.

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The effectiveness of workload identification is one of the critical aspects in a monitoring instrument of mental state. In this field, the workload is usually recognised as binary classes. There are scarce studies towards multi-class workload identification because the challenge of the success of workload identification is much tough, even though one more workload class is added. Besides, most of the existing studies only utilized spectral power features from individual channels but ignoring abundant inter-channel features that represent the interactions between brain regions. In this study, we utilized features representing intra-channel information and inter-channel information to classify multiple classes of workload based on EEG. We comprehensively compared each category of features contributing to workload identification and elucidated the roles of feature fusion and feature selection for the workload identification. The results demonstrated that feature combination (83.12% in terms of accuracy) enhanced the classification performance compared to individual feature categories (i.e., band power features, 75.90%; connection features, 81.72%, in terms of accuracy). With the Fscore feature selection, the classification accuracy was further increased to 83.47%. When the features of graph metric were fused, the accuracy was reached to 84.34%. Our study provided comprehensive performance comparisons between methods and feature categories for the multi-class workload identification and demonstrated that feature selection and fusion played an important role in the enhancement of workload identification. These results could facilitate further studies of multi-class workload identification and practical application of workload identification.