Residents’ satisfaction with post-disaster reconstruction in earthquake-stricken areas directly affects their quality of life, which cannot be ignored in post-disaster reconstruction. More than 10 years after the Wenchuan earthquake, we took ten randomly selected villages in the five areas hardest-hit by the Wenchuan earthquake as research objects and obtained 483 valid completed questionnaires. The villagers were randomly sampled and descriptive statistical analysis, factor analysis, and ordered logistic regression were used to explore the factors and relationships influencing villagers’ satisfaction with post-disaster reconstruction in Wenchuan earthquake-stricken areas. The results show that: 1) the more rural residents know about the post-disaster reconstruction, the greater their level of satisfaction; 2) the more the annual income of families increases after resettlement, the greater the satisfaction of rural residents with the post-disaster reconstruction; 3) six public factors, namely the village committee acts as, housing construction quality, public service, policy of benefiting farmers, cultural environment, and hardware environment, all significantly positively affect residents’ overall satisfaction with post-earthquake reconstruction. This study thus enriches the theory of residents’ satisfaction studies and the practice of post-earthquake reconstruction.@en

With the rise of global urbanization, the rural built environment has undergone tremendous changes. As such, the rural built environment impacts on residents’ daily travel behavior is getting more researchers’ attention. To date, most of the research focuses on urban areas in developed countries. To understand the state-of-the-art of interplay between the rural built environment and travel behaviors and to identify future research directions, this study adopts a science mapping approach to identify the relevant topics, authors, journals, and countries of the research done. This study proceeds through bibliometric retrieval of articles from 2005, followed by scientometric analysis and qualitative discussion. 37 documents are found to compare urban and rural domains, with 28 on the rural built environment. Research gaps and the research trends are discussed, of which the main themes are multi-dimensional correlation comparison of rural transportation service systems and emerging transportation modes, the influence of rural social and cultural factors on travel behavior, and low-carbon sustainable transportation. This review provides empirical foundation for current state-of-the-art and identifies the future research directions, specifically for rural built environment impact on travel behavior.@en

Accurately predicting the lifetime of lithium-ion batteries in early cycles is crucial for ensuring the safety and reliability, and accelerating the battery development cycle. However, most of existing studies presented poor prediction results for early prediction, due to the nonlinear battery capacity fade with negligible variation in early cycles. In this paper, to achieve an accurate early-cycle prediction of battery lifetime, a comprehensive machine learning (ML) based framework containing three modules, the feature extraction, feature selection, and machine learning based prediction, is proposed. First, by analysing the evolution pattern of various informative parameters, forty-two features are manually crafted based on the first-100-cycle charge-discharge raw data. Second, to manage feature irrelevancy and redundancy, four typical feature selection methods are adopted to generate an optimal lower-dimensional feature subset. Finally, the selected features are fed into six representative ML models to effectively predict the battery lifetime. Numerical experiments and paired t-test are conducted to statistically evaluate the performance of the proposed framework. Results show that the wrapper-based feature selection method outperforms other methods, and significantly improves the prediction performance of subsequent ML models. Both before and after wrapper feature selection, the elastic net, Gaussian process regression, and support vector machine present better performance than other complex ML prediction models. The support vector machine model combined with wrapper feature selection statistically presents the best result for battery lifetime prediction, with a root-of-mean-square-error of 115 cycles, and a R² of 0.90. Finally, when compared with an existing work, the root-of-mean-square-error is substantially decreased from 173 to 115 cycles, by using the proposed framework.

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