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Extreme heat events pose great threats to public health and socioeconomic development, particularly in cities where the urban heat island (UHI) effect can exacerbate heat stress. The heat stress can be assessed by various heat indexes that vary in their weightings of air temperature and humidity. Utilizing the data from a global climate model, this study investigates the spatiotemporal pattern of the urban humid heat island measured by wet-bulb temperature (Tw), Humidex, and Heat Index (HI) and assesses its effect on the number of heat stressed days and the population humid heat exposure. Different heat indexes generate distinct spatiotemporal patterns of urban humid heat island and result in various number of additional heat-stressed days in cities, with the largest disparities observed in arid regions. The urban effect decreases the number of heat-stressed days in arid climate by 0.5 days when measured by Tw but Humidex and HI show an increase. This discrepancy arises from the differing reliance of these indexes on air temperature and humidity, with wet-bulb temperature exhibiting a greater dependence on humidity and HI showing the greatest sensitivity to air temperature. After accounting for the urbanization effect, the global population exposure to humid heat measured by Tw, Humidex, and HI increase by 11.2%, 46.4%, 72.1%, respectively. We highlight that it is crucial to exercise caution when interpreting assessment of humid heat using different heat indexes due to disparities in their spatial patterns of the urban humid heat island.

Human action recognition is a challenging field in recent years. Many traditional signal processing and machine learning methods are gradually trying to be applied in this field. This paper uses a hidden Markov model based on mixed Gaussian to solve the problem of human action recognition. The model treats the observed human actions as samples which conform to the Gaussian mixture model, and each Gaussian mixture model is determined by a state variable. The training of the model is the process that obtain the model parameters through the expectation maximization algorithm. The simulation results show that the Hidden Markov Model based on the mixed Gaussian distribution can perform well in human action recognition.