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Fatigue life of filled natural rubbers used in isolators is very dispersive owing to many uncertainties such as loads and temperature. Conventional crack initiation methods and crack propagation methods to determine fatigue life of rubbers are insufficiently to characterize the phenomenon; thus, probabilistic methods are desired in practical engineering. Based on the test data of uniaxial tensile fatigue experiment of rubbers, the cumulative distribution function and probability density function of residual fatigue life corresponding to two typical probability distribution models of three parameters Weibull distribution（3 PWD） and logarithmic normal distribution（LND） were derived and applied to the prediction of residual life reliability, in order to determine the minimum required under different probability distribution model of sample number and working condition number. The results showed that 3 PWD was more suitable to characterize the probability distribution of rubber fatigue life. The minimum values of the number of rubber fatigue test samples and the number of working conditions are distributed between 4 and 6. The log mean should be preferred for subsequent fatigue analysis when determining the median life. The research can provide guidance for the anti-fatigue design and test of rubber vibration isolators.

The persistent increase in forest pest outbreaks requires timely detection methods to monitor the disaster precisely. However, early detection is challenging due to insufficient temporal observation and subtle tree changes. This article proposed a novel framework that collaborates multi-source remote sensing data and uses a change detection algorithm to archive early detection of infestation caused by Dendrolimus tabulaeformis Tsai et Liu (D. tabulaeformis) attacks. First, all available Sentinel-2 images with less than 20% cloud cover were utilized. During periods with long intervals (>16 days) between Sentinel-2 images, Landsat-8 images with less than 20% cloud cover were downscaled to a spatial resolution of 10 m using a deep learning algorithm to meet the requirement for a high temporal frequency of clear observations. Second, the spectral index differences between healthy and infested trees were examined to address the challenge of detecting subtle changes in pest attacks. The Enhanced Vegetation Index (EVI) was selected for early defoliation detection. On this basis, the EWMACD (Exponentially Weighted Moving Average Change Detection) algorithm, which is sensitive to subtle changes, was enhanced to improve the capability of detecting early D. tabulaeformis attacks. The assessment showed that the overall accuracy of the change detection (F1 score) reached 0.86 during the early stage and 0.88 during the late stage. The temporal accuracy (Precision) was 84.1% during the early stage. The accuracy significantly improved compared to using a single remote sensing data source. This study presents a new framework capable of monitoring early forest defoliation caused by D. tabulaeformis attacks and offering opportunities for predicting future outbreaks and implementing preventive measures. [ABSTRACT FROM AUTHOR]

Understanding how resilient forests are to ecological engineering projects (EEPs) is essential to forest management and ecosystem health. Despite growing evidence that EEPs achieve increasing carbon stocks, whether such benefits can be sustainable and what are the consequences of EEPs on forest health remain unclear. This study aimed to investigate the long‐term effects of EEPs using forest resilience from aspects of resistance and recovery, by applying a change detection algorithm (breaks for additive seasonal and trend; BFAST) spatially on net ecosystem production (NEP) (proxy for carbon stocks) time series (1981–2019) in red soil hilly region (RSHR) of subtropical China. The spatial parameters (e.g., the number, magnitude, and time of changes) used to construct resilience metrics were generated based on BFAST‐derived breakpoints. These metrics were then utilized to analyze the dynamics of forest resilience in relation to EEPs factors in terms of plantation area, forest type, and stand age. Our results observed 92.77% of breakpoints in NEP after 2000, which corresponds well with the periods that multiple EEPs were conducted. NEP resilience showed great variability during 2001–2019, with a positive increasing trend in resistance (R2 = 0.72) and a continuous decline (R2 = 0.37) in recovery, indicating an unhealthy ecosystem in RSHR. Our findings revealed that forest resistance was strongly associated with plantation area (R = 0.71), and the presence of monoculture and young coniferous forest may be the potential factors for the decline in recovery. This suggested that forest resilience in RSHR is mainly modulated by large‐scale EEPs. [ABSTRACT FROM AUTHOR]