Real-time and continuous monitoring of physiological signals is essential for mobile health, which is becoming a popular tool for efficient and convenient medical services. Here, an active pulse sensing system that can detect the weak vibration patterns of the human radial artery is constructed with a sandwich-structure piezoelectret that has high equivalent piezoelectricity. The high precision and stability of the system result in possible medical assessment applications, including the capability to identify common heart problems (such as arrhythmia); the feasibility to conduct pulse palpation measurements similar to well-trained doctors in Traditional Chinese Medicine; and the possibility to measure and read blood pressure.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adfm.201803413.imitate the TCM practice for health assessments without well-trained, real doctors. Previously, human pulse waves have been measured using sensors based on different detection mechanisms, such as optics, [16,17] image processing, [15,18,19] acoustics, [20] and pressure means, [21][22][23][24][25][26][27][28][29] etc. Among these, active pressure sensors based on the working principles of piezoelectricity [21][22][23][24] or triboelectricity [7,8] are the more intuitive and sensitive method to detect pulse waves, as these sensors can accurately and directly reflect the weak vibration of the radial artery to better imitate the pulse diagnosis in TCM. Several vertical contact-separation and single-electrode triboelectric pressure sensors have been published to detect human motion and physiological signals, with advantages of thin, flexible, and excellent sensitivity. [30] However, vertical contact-separation devices are composed of two separated parts, which increases the difficulty of assembly and operation in practice. Single-electrode triboelectric sensors have been proposed to address this issue but the exposed residual charges on the sensor surface can be easily leaked. Piezoelectret is flexible, lightweight, and have large and stable equivalent piezoelectric coefficient for high sensitivity. [25] Furthermore, sensors based on piezoelectret materials can alleviate the aforementioned issues in triboelectric sensors to detect physiological signals.In this work, we use an active and flexible pulse wave sensing system based on a fluorinated ethylene propylene (FEP)/Ecoflex/FEP sandwich-structured piezoelectret for piezoelectriclike detections. [25,26,31] Several key features are accomplished from the prototype sensing systems: 1) excellent precision and stability capable of differentiating and classifying pulses from different volunteers coupled with the help of big data analyses; 2) the identification of a common heart problem (arrhythmia) from volunteers who were previously diagnosed in hospitals equipped with advanced and bulky electrocardiogram (ECG) setups; 3) the feasibility in recording and revealing the blood pressure using the pulse sensing system instead of a ...

Flexible and wearable devices with the capabilities of both detecting and generating mechanical stimulations are critical for applications in human–machine interfaces, such as augmented reality (AR) and virtual reality (VR). Herein, a flexible patch based on a sandwiched piezoelectret structure is demonstrated to have a high equivalent piezoelectric coefficient of d 33 at 4050 pC/N to selectively perform either the actuating or sensing function. As an actuator, mechanical vibrations with a peak output force of more than 20 mN have been produced, similar to those from the vibration mode of a modern cell phone, and can be easily sensed by human skin. As a sensor, both the pressure detection limit of 1.84 Pa for sensing resolution and excellent stability of less than 1% variations in 6000 cycles have been achieved. The design principle together with the sensing and driving characteristics can be further developed and extended to other soft matters and flexible devices.

Persistent luminescence phosphors are a novel group of promising luminescent materials with afterglow properties after the stoppage of excitation. In the past decade, persistent luminescence nanoparticles (PLNPs) with intriguing optical properties have attracted a wide range of attention in various areas. Especially in recent years, the development and applications in biomedical fields have been widely explored. Owing to the efficient elimination of the autofluorescence interferences from biotissues and the ultra-long near-infrared afterglow emission, many researches have focused on the manipulation of PLNPs in biosensing, cell tracking, bioimaging and cancer therapy. These achievements stimulated the growing interest in designing new types of PLNPs with desired superior characteristics and multiple functions. In this review, we summarize the works on synthesis methods, bioapplications, biomembrane modification and biosafety of PLNPs and highlight the recent advances in biosensing, imaging and imaging-guided therapy. We further discuss the new types of PLNPs as a newly emerged class of functional biomaterials for multiple applications. Finally, the remaining problems and challenges are discussed with suggestions and prospects for potential future directions in the biomedical applications.

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Black rot, Black measles, Leaf blight and Mites of grape are four common grape leaf diseases that seriously affect grape yield. However, the existing research lacks a realtime detecting method for grape leaf diseases, which cannot guarantee the healthy growth of grape plants. In this article, a real-time detector for grape leaf diseases based on improved deep convolutional neural networks is proposed. This article first expands the grape leaf disease images through digital image processing technology, constructing the grape leaf disease dataset (GLDD). Based on GLDD and the Faster R-CNN detection algorithm, a deep-learning-based Faster DR-IACNN model with higher feature extraction capability is presented for detecting grape leaf diseases by introducing the Inception-v1 module, Inception-ResNet-v2 module and SE-blocks. The experimental results show that the detection model Faster DR-IACNN achieves a precision of 81.1% mAP on GLDD, and the detection speed reaches 15.01 FPS. This research indicates that the real-time detector Faster DR-IACNN based on deep learning provides a feasible solution for the diagnosis of grape leaf diseases and provides guidance for the detection of other plant diseases.

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We study on-line strategies for solving problems with hybrid algorithms. There is a problem Q and w basic algorithms for solving Q. For some λ ≤ w, we have a computer with λ disjoint memory areas, each of which can be used to run a basic algorithm and store its intermediate results. In the worst case, only one basic algorithm can solve Q in finite time, and all the other basic algorithms run forever without solving Q. To solve Q with a hybrid algorithm constructed from the basic algorithms, we run a basic algorithm for some time, then switch to another, and continue this process until Q is solved. The goal is to solve Q in the least amount of time. Using competitive ratios to measure the efficiency of a hybrid algorithm, we construct an optimal deterministic hybrid algorithm and an efficient randomized hybrid algorithm. This resolves an open question on searching with multiple robots posed by Baeza-Yates, Culberson and Rawlins. We also prove that our randomized algorithm is optimal for λ = 1, settling a conjecture of Kao, Reif and Tate.