Recent advances in the use of organic-inorganic hybrid perovskites for optoelectronics have been rapid, with reported power conversion efficiencies of up to 22 per cent for perovskite solar cells. Improvements in stability have also enabled testing over a timescale of thousands of hours. However, large-scale deployment of such cells will also require the ability to produce large-area, uniformly high-quality perovskite films. A key challenge is to overcome the substantial reduction in power conversion efficiency when a small device is scaled up: a reduction from over 20 per cent to about 10 per cent is found when a common aperture area of about 0.1 square centimetres is increased to more than 25 square centimetres. Here we report a new deposition route for methyl ammonium lead halide perovskite films that does not rely on use of a common solvent or vacuum: rather, it relies on the rapid conversion of amine complex precursors to perovskite films, followed by a pressure application step. The deposited perovskite films were free of pin-holes and highly uniform. Importantly, the new deposition approach can be performed in air at low temperatures, facilitating fabrication of large-area perovskite devices. We reached a certified power conversion efficiency of 12.1 per cent with an aperture area of 36.1 square centimetres for a mesoporous TiO-based perovskite solar module architecture.

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The Internet of medical things is an emerging information network technology, which can realize the automatic identification, monitoring and management of personnel, medical equipment, medicines, etc. through this network, and is an effective means to reduce medical errors and improve work efficiency. This article first studies the theory and method of health information and sports information collection, that is, the temperature sensor and the acceleration sensor are used to collect human body temperature and exercise steps, respectively, and then estimate the human health and sports. Second, the prototype system of health and sports information collection system is realized the system is divided into two parts: terminal node and client information management system. Finally, a component collaborative modeling and data analysis method for Internet of medical things is proposed. This method constructs different types of components according to different functions of the Internet of Things equipment, and designs a set of communication mechanisms between the components based on the Internet of Things network communication characteristics, and uses visual methods to model. The experimental results verify that the method of collecting and analyzing human motion information using a motion information collection system is feasible. Multiple methods should be integrated to obtain as much information as possible to make the human motion analysis more scientific and reasonable.INDEX TERMS Internet of medical things, motion information collection, collaborative modeling, data analysis, prototype.

With the development of financial technology (referred to as fintech), the risks faced by fintech companies have received increasing attention. This paper uses the Sentence Latent Dirichlet Allocation (Sent-LDA) topic model to comprehensively identify risk factors in the fintech industry based on textual risk factors disclosed in Form 10-K. Furthermore, this paper analyzes the importance of risk factors and the similarities of the risk factors for the whole fintech industry and different fintech sub-sectors from the perspectives of risk factor types and risk factor contents. In the empirical analysis, 53,452 risk factor headings of 34 fintech companies included in the KBW Nasdaq Financial Technology Index (KFTX) over the period 2015–2019 are collected. The empirical results show that 20 risk factors of the fintech industry are identified. However, the important risk factors vary differently among different fintech sub-sectors. For the analysis of risk factor similarity, mean values of similarity of risk factor types and the similarity of risk factor contents both increased from 2015 to 2019, which indicates that the risks faced by fintech companies are becoming increasingly similar. The mean value of similarity of risk factor contents is 42.13%, while the mean value of similarity of risk factor types is 80.93%. Thus, although the types of risk factors faced by different fintech companies are similar, the contents of risk factors disclosed by different companies are still quite different. The comprehensive identification of fintech risk factors lays an important foundation for the further measurement and management of risks in the fintech industry. In the feature, we will further make effective risk estimations of the fintech industry based on the identified fintech risk factors.

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