Eliminating poverty is the primary goal of sustainable development. China has eliminated absolute poverty in 2020, yet there is a chance that it could happen again. The poor population is mostly concentrated in ecologically fragile areas. We need to take more inclusive and effective initiatives to prevent the population in ecologically fragile areas from returning to poverty. In this study, a decision tree and logistic regression model were used to assess the risk of returning to poverty in Karst ecologically fragile areas. The data comes from 303 households in four counties in Guizhou and Guangxi. There are 12 main influencing factors identified, with the percentage of workforce numbers and loans having interactive effects. The results show that: (1) Poor resilience of livelihood assets, external shocks, and the effects of some support measures will be visible after a long period, leading to "transient" poverty and return to poverty. (2) Ecological environment management in ecologically fragile areas is very important to solve the problem of returning to poverty. (3) Appropriate loans can reduce poverty, especially when loans are used to cultivate a new excellent labor force. At the same time, it is necessary to evaluate farmers’ repayment ability reasonably and scientifically to reduce the risk of returning to poverty. The combination of ecological restoration and agricultural development is the key to solving ecological and social problems in Karst areas. Efforts should be made to improve the risk-resilience of farmers’ livelihood assets and the efficiency of livelihood assets utilization by implementing targeted support measures. This research provides a new approach to studying the mechanism of poverty recurrence, which is of great practical significance for consolidating the results of poverty eradication and realizing rural vitalization.

Precise prediction on brain age is urgently needed by many biomedical areas including mental rehabilitation prognosis as well as various medicine or treatment trials. People began to realize that contrasting physical (real) age and predicted brain age can help to highlight brain issues and evaluate if patients' brains are healthy or not. Such age prediction is often challenging for single model-based prediction, while the conditions of brains vary drastically over age. In this work, we present an age-adaptive ensemble model that is based on the combination of four different machine learning algorithms, including a support vector machine (SVR), a convolutional neural network (CNN) model, and the popular GoogLeNet and ResNet deep networks. The ensemble model proposed here is nonlinearly adaptive, where age is taken as a key factor in the nonlinear combination of various single-algorithmbased independent models. In our age-adaptive ensemble method, the weights of each model are learned automatically as nonlinear functions over age instead of fixed values, while brain age estimation is based on such an age-adaptive integration of various single models. The quality of the model is quantified by the mean absolute errors (MAE) and spearman correlation between the predicted age and the actual age, with the least MAE and the highest Spearman correlation representing the highest accuracy in age prediction. By testing on the Predictive Analysis Challenge 2019 (PAC 2019) dataset, our novel ensemble model has achieved a MAE down to 3.19, which is a significantly increased accuracy in this brain age competition. If deployed in the real world, our novel ensemble model having an improved accuracy could potentially help doctors to identify the risk of brain diseases more accurately and quickly, thus helping pharmaceutical companies develop drugs or treatments precisely, and potential offer a new powerful tool for researchers in the field of brain science.

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