Canadian university students either of Chinese origin (CC) or non-Asian origin (NAC) and Chinese university students educated in Asia (AC) solved simple-arithmetic problems in the 4 basic operations (e.g., 3 + 4, 7 -3, 3 X 4, 12 •*• 3) and reported their solution strategies. They also completed a standardized test of more complex multistep arithmetic. For complex arithmetic, ACs outperformed both CCs and NACs. For simple arithmetic, however, ACs and CCs were equal and both performed better than NACs. The superior simple-arithmetic skills of CCs relative to NACs implies that extracurricular culture-specific factors rather than differences in formal education explain the simple-arithmetic advantage for Chinese relative to non-Asian North American adults. NAC's relatively poor simple-arithmetic performance resulted both from less efficient retrieval skills and greater use of procedural strategies. Nonetheless, all 3 groups reported using procedures for the larger simple subtraction and division problems, confirming the importance of procedural knowledge in skilled adults' performance of elementary mathematics.

To investigate effects of surface notation on basic numerical skills we examined number naming, magnitude selection, and simple arithmetic performed by adult Chinese-English bilinguals born and educated in China. Stimuli were presented using either arabic (7 + 8) or "mandarin" ( symbols and participants were cued to respond either in English or Chinese. The naming task demonstrated that the mandarin characters were as easy to identify as the arabic digits, but for both arithmetic and magnitude selection there were faster RTs and fewer errors overall with arabic notation. Arabic notation also produced smaller problem-size effects in arithmetic and a smaller split effect in magnitude selection relative to mandarin notation. These results suggest that retrieval processes in the arithmetic and selection tasks were more efficient with arabic than mandarin stimuli. Arithmetic RTs were substantially slower with English than Chinese responses given either arabic or mandarin stimuli, but the English-language cost was greater with mandarin stimuli. The form of the Notation × Language RT interaction is consistent with language-specific Chinese and English number-fact stores ("arithmecons") that were differentially accessible as a function of notation. Naming RTs also presented a significant Notation × Language interaction due mainly to slow RTs to produce English number names for mandarin stimuli. These Notation × Language interactions are not easily reconciled with the standard version of McCloskey's (1992) model of number processing, which holds that numeral reading and arithmetic performance are based on a single, abstract-semantic code regardless of input or output conditions. Instead, the results suggest that different Notation × Language combinations were mediated by independent associative paths that varied in strength and efficiency as a function of prior experience.Requests for reprints should be sent to

No abstract

Intense, large-scale forest fires are damaging and very challenging to control. Locations, where various types of fire behavior occur, vary depending on environmental factors. According to the burning site of forest fires and the degree of damage, this paper considers the classification and identification of surface fires and canopy fires. Deep learning-based forest fire detection uses convolutional neural networks to automatically extract multidimensional features of forest fire images with high detection accuracy. To accurately identify different forest fire types in complex backgrounds, an improved forest fire classification and detection model (FCDM) based on YOLOv5 is presented in this paper, which uses image-based data. By changing the YOLOv5 bounding box loss function to SIoU Loss and introducing directionality in the cost of the loss function to achieve faster convergence, the training and inference of the detection algorithm are greatly improved. The Convolutional Block Attention Module (CBAM) is introduced in the network to fuse channel attention and spatial attention to improve the classification recognition accuracy. The Path Aggregation Network (PANet) layer in the YOLOv5 algorithm is improved into a weighted Bi-directional Feature Pyramid Network (BiFPN) to fuse and filter forest fire features of different dimensions to improve the detection of different types of forest fires. The experimental results show that this improved forest fire classification and identification model outperforms the YOLOv5 algorithm in both detection performances. The mAP@0.5 of fire detection, surface fire detection, and canopy fire detection was improved by 3.9%, 4.0%, and 3.8%, respectively. Among them, the mAP@0.5 of surface fire reached 83.1%, and the canopy fire detection reached 90.6%. This indicates that the performance of our proposed improved model has been effectively improved and has some application prospects in forest fire classification and recognition.

No abstract