A hybrid evaluation approach and guidance for mHealth education applications

© Springer International Publishing AG 2018. Mobile health education applications (MHEAs) are used to support different users. However, although these applications are increasing in number, there is no effective evaluation framework to measure their usability and thus save effort and time for their many user groups. This paper outlines a useful framework for evaluating MHEAs, together with particular evaluation metrics: an efficient hybrid of selected heuristic evaluation (HE) and usability evaluation (UE) factors to enable the determination of the usefulness and usability of MHEAs. We also propose a guidance tool to help stakeholders choose the most suitable MHEA. The outcome of this framework is envisioned as meeting the requirements of different users, in addition to enhancing the development of MHEAs using software engineering approaches by creating new and more effective evaluation techniques. Finally, we present qualitative and quantitative results for the framework when used with MHEAs

New tricks for KDEL receptors

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Export intention in developing countries: A configuration approach to managerial success factors

What drives small and medium‐sized enterprises (SMEs) to enter export markets? This study introduces a configuration approach using a fuzzy‐set qualitative comparative analysis to show that, contrary to previous findings, export intention does not depend on any one managerial characteristic, but rather on the interplay of these attributes. Using a sample of 180 Algerian non‐exporters, findings suggest that firms’ export intention is determined by two distinct combinations of managerial attributes, namely: (export perception*entrepreneurial orientation) and (export perception*international orientation*foreign knowledge*Age). In line with the emerging resource orchestration view, such findings hold significant implications for both theory and practice

Class II ADP-ribosylation factors are required for efficient secretion of Dengue viruses

This article is available open access through the publisher’s website.Identification and characterization of virus-host interactions are very important steps toward a better understanding of the molecular mechanisms responsible for disease progression and pathogenesis. To date, very few cellular factors involved in the life cycle of flaviviruses, which are important human pathogens, have been described. In this study, we demonstrate a crucial role for class II Arf proteins (Arf4 and Arf5) in the dengue flavivirus life cycle. We show that simultaneous depletion of Arf4 and Arf5 blocks recombinant subviral particle secretion for all four dengue serotypes. Immunostaining analysis suggests that class II Arf proteins are required at an early pre-Golgi step for dengue virus secretion. Using a horseradish peroxidase protein fused to a signal peptide, we show that class II Arfs act specifically on dengue virus secretion without altering the secretion of proteins through the constitutive secretory pathway. Co-immunoprecipitation data demonstrate that the dengue prM glycoprotein interacts with class II Arf proteins but not through its C-terminal VXPX motif. Finally, experiments performed with replication-competent dengue and yellow fever viruses demonstrate that the depletion of class II Arfs inhibits virus secretion, thus confirming their implication in the virus life cycle, although data obtained with West Nile virus pointed out the differences in virus-host interactions among flaviviruses. Our findings shed new light on a molecular mechanism used by dengue viruses during the late stages of the life cycle and demonstrate a novel function for class II Arf proteins.Research Fund for Control of Infectious Diseases of Hong Kong and BNP Paribas Corporate and Investment Banking

New control for two mass positioning system using nominal characteristic trajectory following controller

In this study, a nominal characteristic trajectory following (NCTF) controller for point-to-point (PTP) positioning system is introduced for two mass systems and its performance is evaluated.The NCTF controller consists of a nominal characteristic trajectory (NCT) and a compensator. The objective of the NCTF controller is to make the object motion follow the NCT and end at its origin.Therefore, the NCT is used as an intended object motion and the compensator is used to make the motion of the controlled object follow the NCT.The NCTF controller is designed based on a simple open-loop experiment of the object and no information except the NCT is necessary for controller design. The effectiveness of the NCTF controller is evaluated and discussed through simulations.The effect of the design parameters on the robustness of the NCTF controller to inertia and friction variations is evaluated and the influence of saturation on the positioning performance is examined

Identifying Asthma genetic signature patterns by mining Gene Expression BIG Datasets using Image Filtering Algorithms

Asthma is a treatable but incurable chronic inflammatory disease affecting more than 14% of the UAE population. Asthma is still a clinical dilemma as there is no proper clinical definition of asthma, unknown definitive underlying mechanisms, no objective prognostic tool nor bedside noninvasive diagnostic test to predict complication or exacerbation. Big Data in the form of publicly available transcriptomics can be a valuable source to decipher complex diseases like asthma. Such an approach is hindered by technical variations between different studies that may mask the real biological variations and meaningful, robust findings. A large number of datasets of gene expression microarray images need a powerful tool to properly translate the image intensities into truly differential expressed genes between conditioned examined from the noise. Here we used a novel bioinformatic method based on the coefficient of variance to filter nonvariant probes with stringent image analysis processing between asthmatic and healthy to increase the power of identifying accurate signals hidden within the heterogeneous nature of asthma. Our analysis identified important signaling pathways members, namely NFKB and TGFB pathways, to be differentially expressed between severe asthma and healthy controls. Those vital pathways represent potential targets for future asthma treatment and can serve as reliable biomarkers for asthma severity. Proper image analysis for the publicly available microarray transcriptomics data increased its usefulness to decipher asthma and identify genuine differentially expressed genes that can be validated across different datasets