The emergence of flash drought has attracted widespread attention due to its rapid onset. However, little is known about the recent evolution of flash droughts in terms of the speed of onset and the causes of such a rapid onset phase of flash droughts. Here, we present a comprehensive assessment of the onset development of flash droughts and the underlying mechanisms on a global scale. We find that 33.64−46.18% of flash droughts with 5-day onset of drying, and there is a significant increasing trend in the proportion of flash droughts with the 1-pentad onset time globally during the period 2000−2020. Flash droughts do not appear to be occurring more frequently in most global regions, just coming on faster. In addition, atmospheric aridity is likely to create a flash drought-prone environment, and the joint influence of soil moisture depletion and atmospheric aridity further accelerates the rapid onset of flash droughts.

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Knot detection is a challenging problem for the wood industry. Traditional methodologies depend heavily on the features selected manually and therefore were not always accurate due to the variety of knot appearances. This paper proposes an automated framework for addressing the aforementioned problem by using the state-of-the-art YOLO-v5 (the fifth version of You Only Look Once) detector. The features of surface knots were learned and extracted adaptively, and then the knot defects were identified accurately even though the knots vary in terms of color and texture. The proposed method was compared with YOLO-v3 SPP and Faster R-CNN on two datasets. Experimental results demonstrated that YOLO-v5 model achieved the best performance for detecting surface knot defects. F-Score on Dataset 1 was 91.7% and that of Dataset 2 was up to 97.7%. Moreover, YOLO-v5 has clear advantages in terms of training speed and the size of the weight file. These advantages made YOLO-v5 more suitable for the detection of surface knots on sawn timbers and potential for timber grading.

Based on the physiological nature of breast movement in exercising females, a sports bra made of fabric with dynamic moisture transfer properties was developed to improve female thermal comfort. This study aimed to investigate the effects of fabrics with dynamic moisture transfer properties on breast skin temperature, and the thermal physiological and psychological response of women while wearing the sports bra during exercise and recovery. Ten healthy women exercised in random order with two types of sports bra with or without the dynamic moisture transfer properties and then performed a 20-minute short-duration high-intensity exercise and rest to recover under thermoneutral conditions. Heart rate, body core temperature, skin temperature, body mass and thermal psychological subjective sensations were investigated during exercise and recovery. The results indicated that in the running state, the local breast skin temperatures of sports bra made of fabrics with dynamic moisture transfer properties (33.427 ± 0.087℃) are significantly lower than bras without these dynamic moisture transfer properties (33.964 ± 0.055℃) ( P < 0.01). During the exercise and recovery, the thermal psychological subjective sensation for the two types of fabrics were very similar, whereas the body mean skin temperature was revealed to undergo greater decreasing effects in sports bras made of fabrics with dynamic moisture transfer properties than those without the dynamic moisture transfer properties ( P < 0.05). These results provide novel information that usage of fabrics with dynamic moisture properties in sports bras could improve thermoregulation to benefit exercising women’s thermal comfort in terms of decreasing local breast skin temperature.