Activation functions are crucial in deep learning networks, given that the nonlinear ability of activation functions endows deep neural networks with real artificial intelligence. Nonlinear nonmonotonic activation functions, such as rectified linear units, Tan hyperbolic (tanh), Sigmoid, Swish, Mish, and Logish, perform well in deep learning models; however, only a few of them are widely used in mostly all applications due to their existing inconsistencies. Inspired by the MB-C-BSIF method, this study proposes Smish, a novel nonlinear activation function, expressed as $f\left(x\right)=x·tanh\left[\ln \left(1+sigmoid\left(x\right)\right)\right]$, which could overcome other activation functions with good properties. Logarithmic operations are first used to reduce the range of sigmoid(x). The value is then calculated using the tanh operator. Inputs are ultimately used to multiply the previous value, thus exhibiting negative output regularization. Experiments show that Smish tends to operate more efficiently than Logish, Mish, and other activation functions on EfficientNet models with open datasets. Moreover, we evaluated the performance of Smish in various deep learning models and the parameters of its function $f\left(x\right)=\alpha x·tanh\left[\ln \left(1+sigmoid\left(\beta x\right)\right)\right]$, and where α = 1 and β = 1, Smish was found to exhibit the highest accuracy. The experimental results show that with Smish, the EfficientNetB3 network exhibits a Top-1 accuracy of 84.1% on the CIFAR-10 dataset; the EfficientNetB5 network has a Top-1 accuracy of 99.89% on the MNIST dataset; and the EfficientnetB7 network has a Top-1 accuracy of 91.14% on the SVHN dataset. These values are superior to those obtained using other state-of-the-art activation functions, which shows that Smish is more suitable for complex deep learning models.

The tree sway frequency is an important part of the dynamic properties of trees. In order to obtain trees sway frequency in wind, a method of tracking and measuring the sway frequency of leafless deciduous trees by adaptive tracking window based on MOSSE was proposed. Firstly, an adaptive tracking window is constructed for the observed target. Secondly, the tracking method based on Minimum Output Sum Of Squared Error Filter (MOSSE) is used to track tree sway. Thirdly, Fast Fourier transform was used to analyze the horizontal sway velocity of the target area on the trees, and the sway frequency was determined. Finally, comparing the power spectral densities (PSDs) of the x axis acceleration measured by the accelerometer and PSDs of the x axis velocity measured by the video, the fundamental sway frequency measured by the accelerometer is equal to the fundamental sway frequency measured by video. The results show that the video-based method can be used successfully for measuring the sway frequency of leafless deciduous trees.

Wood-rotting fungi’s degradation of wood not only facilitates the eco-friendly treatment of organic materials, decreasing environmental pollution, but also supplies crucial components for producing biomass energy, thereby reducing dependence on fossil fuels. The ABC gene family, widely distributed in wood-rotting fungi, plays a crucial role in the metabolism of lignin, cellulose, and hemicellulose. Trametes gibbosa, as a representative species of wood-rotting fungi, exhibits robust capabilities in wood degradation. To investigate the function of the ABC gene family in wood degradation by T. gibbosa, we conducted a genome-wide analysis of T. gibbosa’s ABC gene family. We identified a total of 12 Tg-ABCs classified into four subfamilies (ABCA, ABCB, ABCC, and ABCG). These subfamilies likely play significant roles in wood degradation. Scaffold localization and collinearity analysis results show that Tg-ABCs are dispersed on scaffolds and there is no duplication of gene sequences in the Tg-ABCs in the genome sequence of T. gibbosa. Phylogenetic and collinearity analyses of T. gibbosa along with four other wood-rotting fungi show that T. gibbosa shares a closer phylogenetic relationship with its same-genus fungus (Trametes versicolor), followed by Ganoderma leucocontextum, Laetiporus sulphureus, and Phlebia centrifuga in descending order of phylogenetic proximity. In addition, we conducted quantitative analyses of Tg-ABCs from T. gibbosa cultivated in both woody and non-woody environments for 10, 15, 20, 25, 30, and 35 days using an RT-qPCR analysis. The results reveal a significant difference in the expression levels of Tg-ABCs between woody and non-woody environments, suggesting an active involvement of the ABC gene family in wood degradation. During the wood degradation period of T. gibbosa, spanning from 10 to 35 days, the relative expression levels of most Tg-ABCs exhibited a trend of increasing, decreasing, and then increasing again. Additionally, at 20 and 35 days of wood degradation by T. gibbosa, the relative expression levels of Tg-ABCs peak, suggesting that at these time points, Tg-ABCs exert the most significant impact on the degradation of poplar wood by T. gibbosa. This study systematically reveals the biological characteristics of the ABC gene family in T. gibbosa and their response to woody environments. It establishes the foundation for a more profound comprehension of the wood-degradation mechanism of the ABC gene family and provides strong support for the development of more efficient wood-degradation strategies.