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Metal micro/nanoparticle ink-based printed circuits have shown promise for promoting the scalable application of flexible electronics due to enabling superhigh metallic conductivity with cost-effective mass production. However, it is challenging to activate printed metal-particle patterns to approach the intrinsic conductivity without damaging the flexible substrate, especially for high melting-point metals. Here, we report a pressure-constrained sonication activation (PCSA) method of the printed flexible circuits for more than dozens of metal (covering melting points from room temperature to 3422 °C) and even nonmetallic inks, which is integrated with the large-scale roll-to-roll process. The PCSA-induced synergistic heat-softening and vibration-bonding effect of particles can enable multilayer circuit interconnection and join electronic components onto printed circuits without solder within 1 s at room temperature. We demonstrate PCSA-based applications of 3D flexible origami electronics, erasable and foldable double-sided electroluminescent displays, and custom-designed and large-area electronic textiles, thus indicating its potential for universality in flexible electronics., (© 2024. The Author(s).)

3D printing polymer or metal can achieve complicated structures while lacking multifunctional performance. Combined printing of polymer and metal is desirable and challenging due to their insurmountable mismatch in melting-point temperatures. Here, a novel volume-metallization 3D-printed polymer composite (VMPC) with bicontinuous phases for enabling coupled structure and function, which are prepared by infilling low-melting-point metal (LM) to controllable porous configuration is reported. Based on vacuum-assisted low-pressure conditions, LM is guided by atmospheric pressure action and overcomes surface tension to spread along the printed polymer pore channel, enabling the complete filling saturation of porous structures for enhanced tensile strength (up to 35.41 MPa), thermal (up to 25.29 Wm -1 K -1 ) and electrical (>10 6  S m -1 ) conductivities. The designed 3D-printed microstructure-oriented can achieve synergistic anisotropy in mechanics (1.67), thermal (27.2), and electrical (>10 12 ) conductivities. VMPC multifunction is demonstrated, including customized 3D electronics with elevated strength, electromagnetic wave-guided transport and signal amplification, heat dissipation device for chip temperature control, and storage components for thermoelectric generator energy conversion with light-heat-electricity., (© 2024 Wiley‐VCH GmbH.)

Nowadays, with the rapid growth of the internet of things (IoT), massive amounts of time series data are being generated. Time series data play an important role in scientific and technological research for conducting experiments and studies to obtain solid and convincing results. However, due to privacy restrictions, limited access to time series data is always an obstacle. Moreover, the limited available open source data are often not suitable because of a small quantity and insufficient dimensionality and complexity. Therefore, time series data generation has become an imperative and promising solution. In this paper, we provide an overview of classical and state-of-the-art time series data generation methods in IoT. We classify the time series data generation methods into four major categories: rule-based methods, simulation-model-based methods, traditional machine-learning-based methods, and deep-learning-based methods. For each category, we first illustrate its characteristics and then describe the principles and mechanisms of the methods. Finally, we summarize the challenges and future directions of time series data generation in IoT. The systematic classification and evaluation will be a valuable reference for researchers in the time series data generation field.

The healthcare industry faces serious problems with health data. Firstly, health data is fragmented and its quality needs to be improved. Data fragmentation means that it is difficult to integrate the patient data stored by multiple health service providers. The quality of these heterogeneous data also needs to be improved for better utilization. Secondly, data sharing among patients, healthcare service providers and medical researchers is inadequate. Thirdly, while sharing health data, patients' right to privacy must be protected, and patients should have authority over who can access their data. In traditional health data sharing system, because of centralized management, data can easily be stolen, manipulated. These systems also ignore patient's authority and privacy. Researchers have proposed some blockchain-based health data sharing solutions where blockchain is used for consensus management. Blockchain enables multiple parties who do not fully trust each other to exchange their data. However, the practice of smart contracts supporting these solutions has not been studied in detail. We propose CrowdMed-II, a health data management framework based on blockchain, which could address the above-mentioned problems of health data. We study the design of major smart contracts in our framework and propose two smart contract structures. We also introduce a novel search contract for searching patients in the framework. We evaluate their efficiency based on the execution costs on Ethereum. Our design improves on those previously proposed, lowering the computational costs of the framework. This allows the framework to operate at scale and is more feasible for widespread adoption., (© The Author(s) 2021.)

In this study, a medical knowledge graph is constructed from the electronic medical record text of knee osteoarthritis patients to support intelligent medical applications such as knowledge retrieval and decision support, and to promote the sharing of medical resources. After constructing the domain ontology of knee osteoarthritis and manually labeling, we trained a machine learning model to automatically perform entity recognition and entity relation extraction, and then used a graph database to construct the knowledge graph of knee osteoarthritis. The experiment proves that the knowledge graph is comprehensive and reliable, and the knowledge graph construction method proposed in this study is effective., (© Springer Nature Switzerland AG 2020.)

With the rapid development of mobile networks and smart terminals, mobile crowdsourcing has aroused the interest of relevant scholars and industries. In this paper, we propose a new solution to the problem of user selection in mobile crowdsourcing system. The existing user selection schemes mainly include: (1) find a subset of users to maximize crowdsourcing quality under a given budget constraint; (2) find a subset of users to minimize cost while meeting minimum crowdsourcing quality requirement. However, these solutions have deficiencies in selecting users to maximize the quality of service of the task and minimize costs. Inspired by the marginalism principle in economics, we wish to select a new user only when the marginal gain of the newly joined user is higher than the cost of payment and the marginal cost associated with integration. We modeled the scheme as a marginalism problem of mobile crowdsourcing user selection (MCUS-marginalism). We rigorously prove the MCUS-marginalism problem to be NP-hard, and propose a greedy random adaptive procedure with annealing randomness (GRASP-AR) to achieve maximize the gain and minimize the cost of the task. The effectiveness and efficiency of our proposed approaches are clearly verified by a large scale of experimental evaluations on both real-world and synthetic data sets.