Moving a perovskite into the black: The bandgap of the black α-phase FAPbI ₃ (where FA is formamidinium) is nearly ideal for solar cells, but it is unstable with respect to the photoinactive yellow δ-phase. Lu et al. found that a film of the yellow phase was converted to a highly crystalline black phase by vapor exposure to methylammonium thiocyanate at 100°C, and it retained this structure after 500 hours at 85°C. Solar cells fabricated with this material had a power conversion efficiency of more than 23%. After 500 hours under maximum power tracking and a period of dark recovery, 94% of the original efficiency was retained. Science , this issue p. eabb8985

Hand paralysis is one of the most common complications in stroke patients, which severely impacts their daily lives. This paper presents a wearable hand rehabilitation system that supports both mirror therapy and task-oriented therapy. A pair of gloves, i.e., a sensory glove and a motor glove, was designed and fabricated with a soft, flexible material, providing greater comfort and safety than conventional rigid rehabilitation devices. The sensory glove worn on the non-affected hand, which contains the force and flex sensors, is used to measure the gripping force and bending angle of each finger joint for motion detection. The motor glove, driven by micromotors, provides the affected hand with assisted driving-force to perform training tasks. Machine learning is employed to recognize the gestures from the sensory glove and to facilitate the rehabilitation tasks for the affected hand. The proposed system offers 16 kinds of finger gestures with an accuracy of 93.32%, allowing patients to conduct mirror therapy using fine-grained gestures for training a single finger and multiple fingers in coordination. A more sophisticated task-oriented rehabilitation with mirror therapy is also presented, which offers six types of training tasks with an average accuracy of 89.4% in real-time.

The dental disease is a common disease for a human. Screening and visual diagnosis which are currently performed in clinics possibly cost a lot in various manners. Along with the progress of the Internet of Things (IoT) and Articial Intelligence (AI), the internet-based intelligent system have shown great potential in applying home-based healthcare. Therefore, a smart dental Health-IoT system based on intelligent hardware, deep learning and mobile terminal is proposed in this paper, aiming at exploring the feasibility of its application on In-home dental healthcare. Moreover, a smart dental device is designed and developed in this study to perform the image acquisition of teeth. Based on the data set of 12, 600 clinical images collected by the proposed device from 10 private dental clinics, an automatic diagnosis model trained by MASK R-CNN is developed for the detection and classication of 7 different dental diseases including decayed tooth, dental plaque, uorosis and periodontal disease, with the diagnosis accuracy of them reaching up to 90%, along with high sensitivity and high specicity. Following the one-month test in 10 clinics, compared with that last month when the platform is not used, the mean diagnosis time reduces by 37.5% for each patient, helping explain the increase of the number of treated patients by 18.4%. Furthermore, application software (App) on mobile terminal for client-side and for dentist-side are implemented to provide service of pre-examination, consultation, appointment, and evaluation.

With the swift growth in commerce and transportation in the modern civilization, much attention has been paid to air quality monitoring, however existing monitoring systems are unable to provide sufficient spatial and temporal resolutions of the data with cost efficient and real time solutions. In this paper we have investigated the issues, infrastructure, computational complexity, and procedures of designing and implementing real-time air quality monitoring systems. To daze the defects of the existing monitoring systems and to decrease the overall cost, this paper devised a novel approach to implement the air quality monitoring system, employing the edge-computing based Internet-of-Things (IoT). In the proposed method, sensors gather the air quality data in real time and transmit it to the edge computing device that performs necessary processing and analysis. The complete infrastructure & prototype for evaluation is developed over the Arduino board and IBM Watson IoT platform. Our model is structured in such a way that it reduces the computational burden over sensing nodes (reduced to 70%) that is battery powered and balanced it with edge computing device that has its local data base and can be powered up directly as it is deployed indoor. Algorithms were employed to avoid temporary errors in low cost sensor, and to manage cross sensitivity problems. Automatic calibration is set up to ensure the accuracy of the sensors reporting, hence achieving data accuracy around 75–80% under different circumstances. In addition, a data transmission strategy is applied to minimize the redundant network traffic and power consumption. Our model acquires a power consumption reduction up to 23% with a significant low cost. Experimental evaluations were performed under different scenarios to validate the system’s effectiveness.

This paper presents a neuromorphic processing system with a spike-driven spiking neural network (SNN) processor design for always-on wearable electrocardiogram (ECG) classification. In the proposed system, the ECG signal is captured by level crossing (LC) sampling, achieving native temporal coding with single-bit data representation, which is directly fed into an SNN in an event-driven manner. A hardware-aware spatio-temporal backpropagation (STBP) is suggested as the training scheme to adapt to the LC-based data representation and to generate lightweight SNN models. Such a training scheme diminishes the firing rate of the network with little plenty of classification accuracy loss, thus reducing the switching activity of the circuits for low-power operation. A specialized SNN processor is designed with the spike-driven processing flow and hierarchical memory access scheme. Validated with field programmable gate arrays (FPGA) and evaluated in 40 nm CMOS technology for application-specific integrated circuit (ASIC) design, the SNN processor can achieve 98.22% classification accuracy on the MIT-BIH database for 5-category classification, with an energy efficiency of 0.75 $\mu$ J/classification.