No abstract

[1] North American Model (NAM) analysis data and the Weather Research and Forecasting (WRF) Advanced Research WRF (ARW) model version 2.2 are used to investigate the effect of a mesoscale convective system (MCS) in extratropical regions on the transport of water vapor in the upper troposphere and lower stratosphere (UTLS). In addition, physical mechanisms contributing most to the water vapor distribution in the UTLS and the amount of water vapor transported during the most active period of the convective system are examined. In an MCS occurring over the Midwest, the primary focus of the present study, simulated by WRF on 13-14 July 2006, hourly water vapor amount averaged near the system in the UTLS increased substantially during the time that convective system activity developed, and reached maximum values at the same time that the strongest convection and heaviest precipitation occurred at the surface. In the upper troposphere, large positive hourly water vapor tendencies were mainly due to vertical advection with highest rates at the time of highest rain rates. Water vapor tendencies due to microphysical processes tended to oppose the moistening due to advection. Near the tropopause and in the lower stratosphere, however, positive hourly water vapor tendencies were primarily due to microphysics and mixing within the MCS. Horizontal advection also transported some moisture in regions downstream from the MCS at most times, with the largest impacts later in the MCS lifetime. Around the tropopause, microphysical processes related to the presence of convectively injected ice appeared to be the largest contributor to moistening for this case. The results were not found to be sensitive to model microphysical schemes.Citation: Le, T. V., and W. A. Gallus Jr. (2012), Effect of an extratropical mesoscale convective system on water vapor transport in the upper troposphere/lower stratosphere: A modeling study,

No abstract

Falls are a very common unexpected accident that result in serious injuries such as broken bones, head injury. Detecting falls, taking fall patients to the emergency room, and sending notification to their family in time is very important. In this paper, we propose a method that combines face recognition and action recognition for fall detection. Specifically, we identify seven basic actions that take place in elderly daily life based on skeleton data extracted using YOLOv7-Pose model. Two deep models which are Spatial Temporal Graph Convolutional Network (ST-GCN), and Long Short-Term Memory (LSTM) are employed for action recognition on the skeleton data. The experimental results on our dataset show that ST-GCN model achieved an accuracy of 90% that is 7% higher than the LSTM model.

Fly ash is a solid residual by-product of coal combustion in thermal power plants and considered a problematic solid waste. To justify the application of fly ash in agriculture, the Pha Lai (Vietnam) thermal power plantderived fly ash (FA) was amended, along with farmyard manure (FYM) and NPK chemical fertilizer (NPK), to sandy soil in Quang Binh (Vietnam) for peanut (Arachis hypogaea L.) cultivation. The effect of FA amendment was investigated, based on the changes in soil properties and peanut yields. The results revealed that the FA amendment had positive benefits to soil properties and peanut yields. Especially, the amendment of 5% FA in combination with FYM and NPK increased notably the proportion of silt-sized particles, surface charges, pH, electrical conductivity, cation exchange capacity, contents of Ca 2+ and Mg 2+ , contents of total and available P and K, and hydraulic conductivity. Microbial populations, enzyme activities, and bacterial community diversity were considerably improved. More dominant species, viz. Paraburkholderia sacchari, Rheinheimera tangshanensis, and Betaproteobacteria bacterium, were observed. Moreover, the FA increased dry peanut grain yield from 0.19 to 2.3 t/ha (12.1-fold). It is recommended to utilize 5% FA along with FYM and NPK in peanut production for economically valuable and environmentally friendly disposal.