(1) Background: Diabetic retinopathy, one of the most serious complications of diabetes, is the primary cause of blindness in developed countries. Therefore, the prediction of diabetic retinopathy has a positive impact on its early detection and treatment. The prediction of diabetic retinopathy based on high-dimensional and small-sample-structured datasets (such as biochemical data and physical data) was the problem to be solved in this study. (2) Methods: This study proposed the XGB-Stacking model with the foundation of XGBoost and stacking. First, a wrapped feature selection algorithm, XGBIBS (Improved Backward Search Based on XGBoost), was used to reduce data feature redundancy and improve the effect of a single ensemble learning classifier. Second, in view of the slight limitation of a single classifier, a stacking model fusion method, Sel-Stacking (Select-Stacking), which keeps Label-Proba as the input matrix of meta-classifier and determines the optimal combination of learners by a global search, was used in the XGB-Stacking model. (3) Results: XGBIBS greatly improved the prediction accuracy and the feature reduction rate of a single classifier. Compared to a single classifier, the accuracy of the Sel-Stacking model was improved to varying degrees. Experiments proved that the prediction model of XGB-Stacking based on the XGBIBS algorithm and the Sel-Stacking method made effective predictions on diabetes retinopathy. (4) Conclusion: The XGB-Stacking prediction model of diabetic retinopathy based on biochemical and physical data had outstanding performance. This is highly significant to improve the screening efficiency of diabetes retinopathy and reduce the cost of diagnosis.

Industrial lands are the most vulnerable urban typologies in areas undergoing urban regeneration. They are considered less adaptive to integrated residential typologies, and their legacies are threatened under fast gentrification. The goal of this paper is to explore a sustainable strategy to address the conflict between urban sprawl and industrial conservation in Alexandria, Sydney. Through the application of a sequential evolutionary simulation, the presented research proposes a potential mixed-use scheme to rejuvenate the existing industrial district of Alexandria in an integrative manner without necessitating its destruction. This paper provides a prototype of urban regeneration, optimised by a multi-objective evolutionary algorithm, that demonstrates the necessity of industrial integration in the pursuit of true mixed use urban typologies.

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