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Oxygen reduction electrocatalysts play important roles in metal-air batteries. Herein, Fe3C-TiN heterostructural quantum dots loaded on carbon nanotubes (FCTN@CNTs) are prepared as electrocatalysts for the oxygen reduction reaction (ORR) through a one-pot pyrolysis. The Fe3C-TiN quantum dots with a diameter of 2-5 nm show the unique characteristic of heterostructural interface. The as-prepared FCTN@CNTs display Pt/C comparable ORR performance (Eonset 1.06 and E1/2 0.95 V) in alkaline medium, which is ascribed to the heterostructural interface between TiN and Fe3C. Furthermore, the Al-air batteries with the FCTN@CNT catalyst display superior discharge performance, demonstrating good feasibility for practical application. This work provides an effective new method to synthesize affordable and efficient oxygen reduction reaction catalysts.

Oxygen reduction electrocatalysts play important roles in metal-air batteries. Herein, Fe

The SEIR model is widely used in simulating the spread of infectious diseases. COVID-19 virus is a very severe infectious disease. Some studies leverage the SEIR or SEIRD model to simulate the spread and estimate the number of infected and recovered people as time goes on. However, these models suffer from two key deficiencies: (i) conventional SEIRD does not update its model parameters w.r.t. time; (ii) it focuses on predicting the trend, instead of the actual number of infections in the future. In this paper, we propose a cascade SEIRD model. The model learns and updates its parameters every day. Moreover, it is able to predict the number of infection cases, recovered cases and deaths. Specifically, we leverage a machine learning like approach to dynamically estimate the parameters of infection rate, incubation rate, recovery rate and death rate, which can be updated by gradient descent algorithm. Once the nature of the parameters w.r.t. time is determined, ARIMA model is adopted to characterize the dynamics of the parameters and predict their future changes. To validate the effectiveness of the proposed cascade SEIRD model, we conduct experiments on five data sets of different scales of regions (China, Hubei, Wuhan, Shenzhen, US). Experimental results show that the proposed cascade SEIRD achieves the most accurate prediction and outperforms state-of-the-art techniques.

As promising alternatives to noble metal catalysts such as platinum-based electrocatalysts, Fe/N co–doped carbon (Fe–N–C) materials attract extensive attention because of their high activity and good durability. It is acknowledged that non–crystalline Fe–Nx moieties and crystalline iron–based nanoparticles as important active sites, largely determine the catalytic performance of Fe–N–C catalysts. However, the design and preparation of Fe–N–C catalyst still suffer from insufficient effective active sites because of the inevitable agglomeration phenomenon. In our work, from the perspective of minimizing catalyst size and prohibiting catalyst agglomeration, we put forward a co-operative protection strategy and successfully fabricate abundant Fe–Nx moieties and highly dispersed hyperfine Fe3C nanodots jointly decorated N–doped carbon framework (Fe–Nx/Fe3C@NC). By systematic characterization and analysis, we find the formation of these abundant active sites (Fe–Nx moieties and Fe3C nanodots) originates from the co-operative protection of different components in original precursors. As a result, the obtained product, Fe3C/Fe–Nx@NC hybrid displays high activity and robust durability towards oxygen reduction reaction (ORR) in both alkaline and acid medium. When employed as the cathode catalyst for Zn–air batteries, Fe3C/Fe–Nx@NC also exhibits comparable performance to that of commercial Pt/C catalyst. Clearly, our adopted strategy provides a good guidance on the preparation of high–performance transition metal–N–C–based catalysts for energy storage and conversion systems.

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.