The vectorial four-wave mixing response of an individual strongly confined exciton-biexciton system with fine-structure splitting in a GaAs/AlGaAs quantum dot is measured by dual-polarization heterodyne spectral interferometry. The results are compared with theoretical predictions based on the optical Bloch equations. The system is described by a fourlevel scheme, which is a model system of the nonlinear excitonic response in low-dimensional semiconductors. We measure its coherence properties and determine the underlying dephasing mechanisms. An impact of the inhomogeneous broadening by spectral wandering on the coherent response is investigated. We further discuss the different four-wave mixing pathways, polarization selection rules, the time-resolved polarization state, the vectorial response in two-dimensional four-wave mixing and ensemble properties.

Online healthcare platforms allow physicians and patients to communicate in a timely manner. Yet little is known about how physicians’ online and offline activities affect each other and, consequently, the healthcare system. We collected data from both online and offline channels to study physicians’ online-offline behavior dynamics. We find that physicians’ online activities can lead to a higher service quantity in offline channels, whereas offline activities may reduce physicians’ online services because of resource constraints. We also find that the more offline patients that physicians serve, the more articles the physicians will likely share in online healthcare platforms. These findings are of great importance to practitioners and policy makers. Our work provides evidence that online healthcare platforms supplement offline services and thus lessen the concern that physicians’ participation in online healthcare platforms will negatively influence offline healthcare services. Our findings also indicate the need for the improvement of online-offline coordination and better system design.

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PurposeThe primary objective of this study was to recognize critical indicators in predicting blood glucose (BG) through data-driven methods and to compare the prediction performance of four tree-based ensemble models, i.e. bagging with tree regressors (bagging-decision tree [Bagging-DT]), AdaBoost with tree regressors (Adaboost-DT), random forest (RF) and gradient boosting decision tree (GBDT).Design/methodology/approachThis study proposed a majority voting feature selection method by combining lasso regression with the Akaike information criterion (AIC) (LR-AIC), lasso regression with the Bayesian information criterion (BIC) (LR-BIC) and RF to select indicators with excellent predictive performance from initial 38 indicators in 5,642 samples. The selected features were deployed to build the tree-based ensemble models. The 10-fold cross-validation (CV) method was used to evaluate the performance of each ensemble model.FindingsThe results of feature selection indicated that age, corpuscular hemoglobin concentration (CHC), red blood cell volume distribution width (RBCVDW), red blood cell volume and leucocyte count are five most important clinical/physical indicators in BG prediction. Furthermore, this study also found that the GBDT ensemble model combined with the proposed majority voting feature selection method is better than other three models with respect to prediction performance and stability.Practical implicationsThis study proposed a novel BG prediction framework for better predictive analytics in health care.Social implicationsThis study incorporated medical background and machine learning technology to reduce diabetes morbidity and formulate precise medical schemes.Originality/valueThe majority voting feature selection method combined with the GBDT ensemble model provides an effective decision-making tool for predicting BG and detecting diabetes risk in advance.

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Smart health is considered to be a new phase in the application of information and communication technologies (ICT) in healthcare that can improve its efficiency and sustainability. However, based on our literature review on the concept of smart health, there is a lack of a comprehensive perspective on the concept of smart health and a framework for how to link the drivers and outcomes of smart health. This paper aims to interweave the drivers and outcomes in a multi-dimensional framework under the input–process–output (IPO) logic of the “system view” so as to promote a deeper understanding of the model of smart health. In addition to the collection of studies, we used the modified Delphi method (MDM) to invite 10 experts from different fields, and the views of the panelists were analyzed and integrated through a three-round iterative process to reach a consensus on the elements included in the conceptual framework. The study revealed that smart health contains five drivers (community, technology, policy, service, and management) and eight outcomes (efficient, smart, sustainable, planned, trustworthy, safe, equitable, health-beneficial, and economic). They all represent a unique aspect of smart health. This paper expands the research horizon of smart health, shifting from a single technology to multiple perspectives, such as community and management, to guide the development of policies and plans in order to promote smart health.