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Wearable electronics suffer from severe power shortage due to limited working time of Li‐ion batteries, and there is a desperate need to build a hybrid device including energy scavenging and storing units. However, previous attempts to integrate the two units are mainly based on simple external connections and assembly, so that maintaining small volume and low manufacturing cost becomes increasingly challenging. Here a convoluted power device is presented by hybridizing internally a solid Li‐ion battery (SLB) and a triboelectric nanogenerator (TENG), so that the two units are one inseparable entity. The fabricated device acts as a TENG that can deliver a peak output power of 7.4 mW under a loading resistance of 7 MΩ, while the device also acts as an SLB to store the obtained electric energy. The device can be mounted on a human shoe to sustainably operate a green light‐emitting diode, thus demonstrating potential for self‐powered wearable electronics.

Owing to drug synergy effects, drug combinations have become a new trend in combating complex diseases like cancer, HIV and cardiovascular diseases. However, conventional synergy quantification methods often depend on experimental dose–response data which are quite resource-demanding. In addition, these methods are unable to interpret the explicit synergy mechanism. In this review, we give representative examples of how systems biology modeling offers strategies toward better understanding of drug synergy, including the protein-protein interaction (PPI) network-based methods, pathway dynamic simulations, synergy network motif recognitions, integrative drug feature calculations, and “omic”-supported analyses. Although partially successful in drug synergy exploration and interpretation, more efforts should be put on a holistic understanding of drug-disease interactions, considering integrative pharmacology and toxicology factors. With a comprehensive and deep insight into the mechanism of drug synergy, systems biology opens a novel avenue for rational design of effective drug combinations.

We present a miniaturized microbubble generator via three-dimensional (3D) printing for potential use in gas–liquid chemical reactions. We design it based on venturi channel structures to enable continuous gas dispersion by turbulent interactions and fabricate it using 3D printing for its high design flexibility and fast manufacturing speed. By experiments using water and nitrogen, we discuss quantitatively the dependence of the formed microbubble characteristics on operating conditions and geometric parameters. In particular, predictive models of Sauter mean diameter of daughter bubbles are proposed to help select proper operating and design parameters in order to achieve the desired level of microbubbles. To further improve the performance, we explore series- and parallel-venturi configurations and find that the parallel-based assembly performs significantly better. Therefore, we prove that the 3D printed venturi microbubble generator has high potential for the flow chemistry community to implement gas–liquid reactions.

Mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. One critical challenge for these platforms and services is to understand user churn behavior in mobile games. Accurate churn prediction will benefit many stakeholders such as game developers, advertisers, and platform operators. In this paper, we present the first largescale churn prediction solution for mobile games. In view of the common limitations of the state-of-the-art methods built upon traditional machine learning models, we devise a novel semisupervised and inductive embedding model that jointly learns the prediction function and the embedding function for userapp relationships. We model these two functions by deep neural networks with a unique edge embedding technique that is able to capture both contextual information and relationship dynamics. We also design a novel attributed random walk technique that takes into consideration both topological adjacency and attribute similarities. To evaluate the performance of our solution, we collect real-world data from the Samsung Game Launcher platform that includes tens of thousands of games and hundreds of millions of user-app interactions. The experimental results with this data demonstrate the superiority of our proposed model against existing state-of-the-art methods.

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Traditional Chinese medicine (TCM) has shown significant efficacy in the treatment of cough variant asthma (CVA), a special type of asthma. However, there is shortage of explanations for relevant mechanism of treatment. As Zhengs differentiation is a critical concept in TCM, it is necessary to explain the mechanism of treatment of Zhengs. Based on TCM clinical cases, this study illustrated the mechanism of the treatment of three remarkably relevant Zhengs for CVA: “FengXieFanFei,” “FeiQiShiXuan”, and “QiDaoLuanJi.” To achieve this goal, five steps were carried out: (1) determining feature Zhengs and corresponding key herbs of CVA by analyses of clinical cases; (2) finding out potential targets of the key herbs and clustering them based on their functional annotations; (3) constructing an ingredient-herb network and an ingredient network; (4) identifying modules of the ingredient network; (5) illustrating the mechanism of the treatment by further mining the latent biological implications within each module. The systematic study reveals that the treatment of “FengXieFanFei,” “FeiQiShiXuan,” and “QiDaoLuanJi” has effects on the regulation of multiple bioprocesses by herbs containing different ingredients with functions of steroid metabolism regulation, airway inflammation, and ion conduction and transportation. This network-based systematic study will be a good way to boost the scientific understanding of mechanism of the treatment of Zhengs.