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Stone matrix asphalt and asphalt concrete mixture with 13.2 mm nominal maximum aggregate size (named SMA13 and AC13, respectively) are widely used in the surface course of asphalt pavement in China. Generally, the pavement performance of SMA13 is superior to that of AC13, while the cost of the former is significantly higher than that of the latter. The objective of this paper was to develop a new hot mix asphalt (named SMAC13) whose performance and cost are between SMA13 and AC13. A boundary sieve size (BSS) of 2.36 mm was selected between fine and coarse aggregates. Based on the union set of aggregate gradation ranges of SMA13 and AC13, the family of gradation curves in the forms of S shapes were designed in terms of the BSS passing rate. According to the evaluation of the skeleton interlock of coarse aggregate of the gradation curve family, the aggregate gradation range of SMAC13 was determined. Also, the performance of three kinds of asphalt mixtures were compared through laboratory tests. The results indicated that SMA13 shows the best rutting resistance, followed by SMAC13 then AC13, while in terms of low-temperature performance in resistance to cracking, the sequence is SMAC13, AC13, and SMA13. The sequence of water stability is AC13, SMAC13, and SMA13. Furthermore, the cost of SMAC13 is 25% less than that of SMA13. Therefore, SMAC13 can be used as an alternative for the surface course of asphalt pavement in terms of performance and cost. [ABSTRACT FROM AUTHOR]

This study examines the impact of bio-organic fertilizers on the physiological characteristics and leaf nutrient content of "Qi-Nan" agarwood seedlings. The aim is to provide a theoretical foundation for the efficient cultivation of "Qi-Nan". The experiment was conducted under field conditions using a Randomized complete block design. The control group received no fertilization, while the three fertilizer-treated groups received vermicompost, sheep manure, or microbial organic fertilizer. The results indicate that the application of bio-organic fertilizer significantly promoted the growth of plant height and diameter, increased the contents of soluble proteins, soluble sugar, chlorophyll, and leaf nutrients; and decreased the contents of free proline, peroxidase, malondialdehyde, and other stress indices. Redundancy analysis showed that the main factors affecting the seedlings height and diameter were free proline, chlorophyll, total nitrogen, and total potassium. Principal component analysis and membership function analysis revealed significant variations in growth, physiological characteristics, and nutrient content among the three fertilizer groups and the control group, with vermicompost > microbial organic fertilizer > sheep manure > no fertilization. In conclusion, biological organic fertilizer significantly promotes the growth of "Qi-Nan" seedlings, and vermicompost is the most effective fertilizer in this experiment. [ABSTRACT FROM AUTHOR]

Learning discriminative features is of vital importance for automatic facial expression recognition (FER) in the wild. In this article, we propose a novel Slide-Patch and Whole-Face Attention model with SE blocks (SPWFA-SE), which jointly perceives the discriminative locality characteristics and informative global features of the face for effective FER. Specifically, the well-designed slide patches are proposed to extract local features. Different from the existing methods, our slide patches not only can maintain the information at the edge area of patches, but also do not need to detect facial landmarks. Moreover, to make the model adaptively focus on the distinguishable regions, an attention module is proposed in the patch level to learn the weight of each patch. Furthermore, squeeze-and-excitation blocks are explored in the channel level to learn the weight of each channel. As such, the proposed multi-level feature extraction and attention mechanisms can enhance the representative ability of the learned features. Extensive experiments on five challenging datasets demonstrate that our method can achieve state-of-the-art performance. Cross database experiments on another three databases show the superior generalization performance of our model. Furthermore, complexity analysis results show that our model contains fewer parameters with fast training advantages than other competing models. [ABSTRACT FROM AUTHOR]