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Rapid urbanization influences green infrastructure (GI) development in cities. The government plans to optimize GI in urban areas, which requires understanding GI spatiotemporal trends in urban areas and driving forces influencing their pattern. Traditional GIS-based methods, used to determine the greening potential of vacant land in urban areas, are incapable of predicting future scenarios based on the past trend. Therefore, we propose a heterogeneous ensemble technique to determine the spatial pattern of GI development in Jinan, China, based on driving biophysical and socioeconomic factors. Data-driven artificial neural networks (ANN) and random forests (RF) are selected as base learners, while support vector machine (SVM) is used as a meta classifier. Results showed that the stacking model ANN-RF-SVM achieved the best test accuracy (AUC 0.941) compared to the individual ANN, RF, and SVM algorithms. Land surface temperature, distance to water bodies, population density, and rainfall are found to be the most influencing factors regarding vacant land conversion to GI in Jinan.

Despite the growing recognition of the impact of affective factors on second/foreign language (L2) learning, there remains a paucity of knowledge regarding academic burnout in L2 learning. Moreover, the intricate interplay between L2 burnout, maladaptive emotion regulation strategies, and negative L2 emotions remains inadequately explored. Given the increasing acknowledgment of network analysis as an advanced and appropriate method for unraveling the complex relationships among psychological constructs in applied linguistics, the current study aimed to investigate the network structure of burnout, maladaptive emotion regulation strategies, and negative emotions among 841 Chinese undergraduates who were learning English as a foreign language (EFL). The results of the network analysis revealed that shame, emotional exhaustion, and avoidance emerged as the most central nodes within negative emotions, burnout, and maladaptive emotion regulation strategies, respectively; shame, emotional exhaustion, and avoidance were also the most powerful bridging nodes linking the aforementioned three constructs. Notably, the robust bridging association between emotional exhaustion and anxiety was observed. Overall, Chinese EFL students may experience high levels of burnout and negative emotions and apply counter-productive regulation strategies in English learning, but these reactions are intertwined rather than independent of each other. Students who are overwhelmed by anxiety and shame are more prone to burnout symptoms, while those dominated by anger are more likely to vent it out. Theoretical and pedagogical implications are discussed.