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In order to realize the full potential of wireless edge artificial intelligence (AI), very large and diverse datasets will often be required for energy-demanding model training on resource-constrained edge devices. This paper proposes a lead federated neuromorphic learning (LFNL) technique, which is a decentralized energy-efficient brain-inspired computing method based on spiking neural networks. The proposed technique will enable edge devices to exploit brain-like biophysiological structure to collaboratively train a global model while helping preserve privacy. Experimental results show that, under the situation of uneven dataset distribution among edge devices, LFNL achieves a comparable recognition accuracy to existing edge AI techniques, while substantially reducing data traffic by >3.5× and computational latency by >2.0×. Furthermore, LFNL significantly reduces energy consumption by >4.5× compared to standard federated learning with a slight accuracy loss up to 1.5%. Therefore, the proposed LFNL can facilitate the development of brain-inspired computing and edge AI.

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Effective screening of COVID-19 cases has been becoming extremely important to mitigate and stop the quick spread of the disease during the current period of COVID-19 pandemic worldwide. In this article, we consider radiology examination of using chest X-ray images, which is among the effective screening approaches for COVID-19 case detection. Given deep learning is an effective tool and framework for image analysis, there have been lots of studies for COVID-19 case detection by training deep learning models with X-ray images. Although some of them report good prediction results, their proposed deep learning models might suffer from overfitting, high variance, and generalization errors caused by noise and a limited number of datasets. Considering ensemble learning can overcome the shortcomings of deep learning by making predictions with multiple models instead of a single model, we propose EDL-COVID, an ensemble deep learning model employing deep learning and ensemble learning. The EDL-COVID model is generated by combining multiple snapshot models of COVID-Net, which has pioneered in an open-sourced COVID-19 case detection method with deep neural network processed chest X-ray images, by employing a proposed weighted averaging ensembling method that is aware of different sensitivities of deep learning models on different classes types. Experimental results show that EDL-COVID offers promising results for COVID-19 case detection with an accuracy of 95%, better than COVID-Net of 93.3%.

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Environmental pollution caused by lead toxicity causes harm to human health. Lead pollution in the environment mainly comes from the processes of mining, processing, production, use, and recovery of lead. China is the world's largest producer and consumer of refined lead. In this paper, the material flow analysis method is used to analyze the flow and direction of lead loss in four stages of lead production, manufacturing, use, and waste management in China from 1949 to 2017. The proportion coefficient of lead compounds in each stage of lead loss was determined. The categories and quantities of lead compounds discharged in each stage were calculated. The results show that in 2017, China emitted 2.1519 million tons of lead compounds. In the four stages of production, manufacturing, use, and waste management, 137.9 kilo tons, 209 kilo tons, 275 kilo tons, and 1.53 million tons were respectively discharged. The emissions in the production stage are PbS, PbO, PbSO4, PbO2, Pb2O3, and more. The emissions during the manufacturing phase are Pb, PbO, PbSO4, Pb2O3, Pb3O4, and more. The main emissions are Pb, PbO, Pb2O3, Pb3O4, and more. The main emissions in the waste management stage are PbS, Pb, PbO, PbSO4, PbO2, PbCO3, Pb2O3, Pb3O4, and more. Among them, the emissions of PbSO4, PbO, Pb, and PbO2 account for about 90%, which are the main environmental pollution emissions. The waste management stage is an important control source of lead compound emission and pollution. In view of these characteristics of the environmental pollution risk of lead compounds in China, the government should issue more targeted policies to control lead pollution.

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