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The purpose of this study is to test the relationship among green logistics, environmental degradation, and energy demand. The system generalized method of moment (system GMM) is used to study the data of Asian countries and four subregions in Asia. The estimation results show that logistics operation consumes nonrenewable energy such as fossil fuels, which has a negative impact on environmental sustainability and energy demand. The service quality and capability (LPITC) in the logistics performance index has a significant contribution to reducing carbon dioxide emissions. The logistics infrastructure (LPIIN) has significantly reduced the energy consumption in Asia, while the customs (LPIC) has significantly increased the energy demand. Other indicators of LPI also have a significant impact on the environment and energy in different subregions of Asia. Industrialization and urbanization both increase carbon emissions in Asia, while trade opening reduces carbon emissions. At the same time, these three variables have a positive impact on reducing energy consumption. The results show that logistics performance is significantly related to environmental degradation and energy, and renewable energy and green supply chain management can reduce the harmful effects of logistics activities on environment and energy. Therefore, Asian countries should give priority to environmental sustainability in supply chain management and encourage the application of green practices in logistics. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has triggered a new response involving public health surveillance. The popularity of personal wearable devices creates a new opportunity for tracking and precaution of spread of such infectious diseases. In this study, we propose a framework, which is based on the heart rate and sleep data collected from wearable devices, to predict the epidemic trend of COVID-19 in different countries and cities. In addition to a physiological anomaly detection algorithm defined based on data from wearable devices, an online neural network prediction modelling methodology combining both detected physiological anomaly rate and historical COVID-19 infection rate is explored. Four models are trained separately according to geographical segmentation, i.e., North China, Central China, South China, and South-Central Europe. The anonymised sensor data from approximately 1.3 million wearable device users are used for model verification. Our experiment's results indicate that the prediction models can be utilized to alert to an outbreak of COVID-19 in advance, which suggests there is potential for a health surveillance system utilising wearable device data. [ABSTRACT FROM AUTHOR]

The pandemics of COVID-19 triggered out an alarm on the public health surveillance. The popularity of wearable devices enables a new perspective for the precaution of the infectious diseases. In this study, we propose a framework, which is based on the heart rate and sleep data collected from the wearable devices, to predict the epidemic trend of COVID-19 in different countries and cities. On top of a physiological anomaly detection algorithm defined based on wearable device data, an online neural network prediction modelling methodology combining both detected physiological anomaly rate and historical COVID-19 infection rate is explored. 4 models are trained separately according to geographical segmentation, i.e., North China, Central China, South China, and South-Central Europe. The de-identified sensor data from about 1.3 million wearable device users are used for verification. Experiment results indicate that the prediction models can be utilized to alert the outbreak of COVID-19 in advance, which sheds light on a health surveillance system with wearable device. [ABSTRACT FROM AUTHOR]

The pandemics of COVID-19 triggered out an alarm on the public health surveillance. The popularity of wearable devices enables a new perspective for the precaution of the infectious diseases. In this study, we propose a framework, which is based on the heart rate and sleep data collected from the wearable devices, to predict the epidemic trend of COVID-19 in different countries and cities. On top of a physiological anomaly detection algorithm defined based on wearable device data, an online neural network prediction modelling methodology combining both detected physiological anomaly rate and historical COVID-19 infection rate is explored. 4 models are trained separately according to geographical segmentation, i.e., North China, Central China, South China, and South-Central Europe. The de-identified sensor data from about 1.3 million wearable device users are used for verification. Experiment results indicate that the prediction models can be utilized to alert the outbreak of COVID-19 in advance, which sheds light on a health surveillance system with wearable device. [ABSTRACT FROM AUTHOR]