Paving the Way for Digital Transformation: Investigate Customer Experiences of Using Mobile Apps

Background: Mobile apps have become a critical channel for retailers to interact with their customers and digitalize shopping behaviors in the customer journeys. In the transition of digital transformation, convenience stores have launched mobile apps to interact with and particularly collecting data from their customers. It is critical to investigate customer experiences in using mobile apps because it paves the way for successful digital transformation. This paper aims to investigate the customer experiences of convenience stores’ mobile apps from the online reviews. Method: This study proposed a mobile apps quality model as the theoretical framework to empirically test the online reviews of mobile apps. Large volumes of online reviews generated by customers provide important strategic values for business and service design for mobile apps. This paper collected 40,521 online reviews of two leading convenience stores in Taiwan and analyzed with text analysis and qualitative analysis. Results: Applying text analysis and qualitative analysis, this paper identified the critical quality attributes of mobile apps in the convenience stores that need to be improved. In addition, software quality is the major concern that 7-11 Open Point needs to improve, followed by service quality and information quality respectively. Software quality is also the major concern that FamilyMart app needs to improve the customer experiences, followed by service quality and information quality. Moreover, customer dissatisfaction primarily resulted from problems in software quality and information quality. Instead, convenience, ease of use, and practicability of mobile apps lead to customer satisfaction. Conclusion: The results demonstrate that software quality, information quality, and service quality are critical dimensions in affecting customer experiences in using mobile apps. Although different mobile apps may have different priorities of quality attributes that are needed to be improved, these improvements of mobile apps help to enhance customer experiences and accelerate digital transformation of the convenience stores

Machine Learning-Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes : Prediction Model Development Study

Publisher Copyright: © Mukkesh Kumar, Li Ting Ang, Cindy Ho, Shu E Soh, Kok Hian Tan, Jerry Kok Yen Chan, Keith M Godfrey, Shiao-Yng Chan, Yap Seng Chong, Johan G Eriksson, Mengling Feng, Neerja KarnaniBackground: The increasing prevalence of gestational diabetes mellitus (GDM) is concerning as women with GDM are at high risk of type 2 diabetes (T2D) later in life. The magnitude of this risk highlights the importance of early intervention to prevent the progression of GDM to T2D. Rates of postpartum screening are suboptimal, often as low as 13% in Asian countries. The lack of preventive care through structured postpartum screening in several health care systems and low public awareness are key barriers to postpartum diabetes screening. Objective: In this study, we developed a machine learning model for early prediction of postpartum T2D following routine antenatal GDM screening. The early prediction of postpartum T2D during prenatal care would enable the implementation of effective strategies for diabetes prevention interventions. To our best knowledge, this is the first study that uses machine learning for postpartum T2D risk assessment in antenatal populations of Asian origin. Methods: Prospective multiethnic data (Chinese, Malay, and Indian ethnicities) from 561 pregnancies in Singapore's most deeply phenotyped mother-offspring cohort study-Growing Up in Singapore Towards healthy Outcomes-were used for predictive modeling. The feature variables included were demographics, medical or obstetric history, physical measures, lifestyle information, and GDM diagnosis. Shapley values were combined with CatBoost tree ensembles to perform feature selection. Our game theoretical approach for predictive analytics enables population subtyping and pattern discovery for data-driven precision care. The predictive models were trained using 4 machine learning algorithms: logistic regression, support vector machine, CatBoost gradient boosting, and artificial neural network. We used 5-fold stratified cross-validation to preserve the same proportion of T2D cases in each fold. Grid search pipelines were built to evaluate the best performing hyperparameters. Results: A high performance prediction model for postpartum T2D comprising of 2 midgestation features-midpregnancy BMI after gestational weight gain and diagnosis of GDM-was developed (BMI_GDM CatBoost model: AUC=0.86, 95% CI 0.72-0.99). Prepregnancy BMI alone was inadequate in predicting postpartum T2D risk (ppBMI CatBoost model: AUC=0.62, 95% CI 0.39-0.86). A 2-hour postprandial glucose test (BMI_2hour CatBoost model: AUC=0.86, 95% CI 0.76-0.96) showed a stronger postpartum T2D risk prediction effect compared to fasting glucose test (BMI_Fasting CatBoost model: AUC=0.76, 95% CI 0.61-0.91). The BMI_GDM model was also robust when using a modified 2-point International Association of the Diabetes and Pregnancy Study Groups (IADPSG) 2018 criteria for GDM diagnosis (BMI_GDM2 CatBoost model: AUC=0.84, 95% CI 0.72-0.97). Total gestational weight gain was inversely associated with postpartum T2D outcome, independent of prepregnancy BMI and diagnosis of GDM (P = .02; OR 0.88, 95% CI 0.79-0.98). Conclusions: Midgestation weight gain effects, combined with the metabolic derangements underlying GDM during pregnancy, signal future T2D risk in Singaporean women. Further studies will be required to examine the influence of metabolic adaptations in pregnancy on postpartum maternal metabolic health outcomes. The state-of-the-art machine learning model can be leveraged as a rapid risk stratification tool during prenatal care.Peer reviewe

SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES). II. Structural Properties and Near-infrared Morphologies of Faint Submillimeter Galaxies

We present structural parameters and morphological properties of faint 450 μm selected submillimeter galaxies (SMGs) from the JCMT Large Program, STUDIES, in the COSMOS-CANDELS region. Their properties are compared to an 850 μm selected and a matched star-forming samples. We investigate stellar structures of 169 faint 450 μm sources (S 450 = 2.8–29.6 mJy; S/N > 4) at z 2 mJy) and more extended than the star-forming galaxies in the same redshift range. For the stellar mass and SFR-matched sample at z sime 1 and z sime 2, the size differences are marginal between faint SMGs and the matched galaxies. Moreover, faint SMGs have similar Sérsic indices and projected axis ratios as star-forming galaxies with the same stellar mass and SFR. Both SMGs and the matched galaxies show high fractions (~70%) of disturbed features at z sime 2, and the fractions depend on the SFRs. These suggest that their star formation activity is related to galaxy merging and the stellar structures of SMGs are similar to those of star-forming galaxies. We show that the depths of submillimeter surveys are approaching the lower luminosity end of star-forming galaxies, allowing us to detect galaxies on the main sequence

Hubba: hub objects analyzer—a framework of interactome hubs identification for network biology

One major task in the post-genome era is to reconstruct proteomic and genomic interacting networks using high-throughput experiment data. To identify essential nodes/hubs in these interactomes is a way to decipher the critical keys inside biochemical pathways or complex networks. These essential nodes/hubs may serve as potential drug-targets for developing novel therapy of human diseases, such as cancer or infectious disease caused by emerging pathogens. Hub Objects Analyzer (Hubba) is a web-based service for exploring important nodes in an interactome network generated from specific small- or large-scale experimental methods based on graph theory. Two characteristic analysis algorithms, Maximum Neighborhood Component (MNC) and Density of Maximum Neighborhood Component (DMNC) are developed for exploring and identifying hubs/essential nodes from interactome networks. Users can submit their own interaction data in PSI format (Proteomics Standards Initiative, version 2.5 and 1.0), tab format and tab with weight values. User will get an email notification of the calculation complete in minutes or hours, depending on the size of submitted dataset. Hubba result includes a rank given by a composite index, a manifest graph of network to show the relationship amid these hubs, and links for retrieving output files. This proposed method (DMNC || MNC) can be applied to discover some unrecognized hubs from previous dataset. For example, most of the Hubba high-ranked hubs (80% in top 10 hub list, and >70% in top 40 hub list) from the yeast protein interactome data (Y2H experiment) are reported as essential proteins. Since the analysis methods of Hubba are based on topology, it can also be used on other kinds of networks to explore the essential nodes, like networks in yeast, rat, mouse and human. The website of Hubba is freely available at http://hub.iis.sinica.edu.tw/Hubba

Prime Focus Spectrograph (PFS) for the Subaru Telescope: Overview, recent progress, and future perspectives

PFS (Prime Focus Spectrograph), a next generation facility instrument on the 8.2-meter Subaru Telescope, is a very wide-field, massively multiplexed, optical and near-infrared spectrograph. Exploiting the Subaru prime focus, 2394 reconfigurable fibers will be distributed over the 1.3 deg field of view. The spectrograph has been designed with 3 arms of blue, red, and near-infrared cameras to simultaneously observe spectra from 380nm to 1260nm in one exposure at a resolution of ~1.6-2.7A. An international collaboration is developing this instrument under the initiative of Kavli IPMU. The project is now going into the construction phase aiming at undertaking system integration in 2017-2018 and subsequently carrying out engineering operations in 2018-2019. This article gives an overview of the instrument, current project status and future paths forward.Comment: 17 pages, 10 figures. Proceeding of SPIE Astronomical Telescopes and Instrumentation 201