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Additional file 2: Table S1. Summary of the 306 worldwide wheat accessions. Table S2. Comparison of the eight plant architecture traits between two environments. Table S3. Detailed information of all significantly associated SNPs for the investigated traits> 5). Table S4. The 330 identified loci associated with investigated traits in this study. Table S5. Overlap between the known genes/QTLs and 330 identified loci in this study. Table S6. XP-CLR scores for the known genes. Table S7. The haplotypes of four major loci for the length of the four internodes in 831 Chinese wheat accessions. Table S8. The phenotypic data for the varieties from the seven continents/regions. Table S9. The phenotypic data of the haplotypes of the four major loci for the length of the four internodes. Table S10. The distribution of haplotypes of the four internodes in 306 worldwide accessions. Table S11. The genotypic data of all the varieties in the first pedigree. Table S12. The genotypic data of all the varieties in the second pedigree. Table S13. The interaction between environments and haplotypes for the length of the four internodes. Table S14. The interaction between environments and haplotypes for the length of the four internodes. Table S15. The 432 wheat varieties used for the analysis of the evolutionary relationship of the haplotypes. Table S16. The haplotype combinations for the length of the four internodes in 306 worldwide wheat accessions. Table S17. The haplotype combinations for the length of the four internodes in 306 worldwide wheat accessions. Table S18. The distribution of haplotype combinations for the length of the four internodes in 831 Chinese wheat accessions. Table S19. The phenotypic data of the four major haplotypes of TraesCS1A02G064800 in 306 worldwide wheat accessions. Table S20. The allele distribution of the SNP used in the RILs in the 306 worldwide wheat accessions. Table S21. The phenotypic data of RILs.

Additional file 3. Review history.

Computational complexity has been the bottleneck for applying physically based simulations in large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessment. To overcome the issue of long computational time and accelerate the prediction process, this paper proposes that the prediction of maximum water depth can be considered an image‐to‐image translation problem in which water depth rasters are generated using the information learned from data instead of by conducting simulations. The proposed data‐driven urban pluvial flood approach is based on a deep convolutional neural network trained using flood simulation data obtained from three catchments and 18 hyetographs. Multiple tests to assess the accuracy and validity of the proposed approach were conducted with both design and real hyetographs. The results show that flood prediction based on neural networks use only 0.5% of the time compared with that of physically based models, with promising accuracy and generalizability. The proposed neural network can also potentially be applied to different but relevant problems, including flood analysis for flood‐safe urban layout planning., Journal of Flood Risk Management, 14 (1)

Digital architecture has been a fast-evolving domain since the first computer program was written to imitate the technique of drawings. During the last few decades, various computational modeling tools have been developed to equip architects with the ability to computationally solve complex design problems and produce novel building geometries. However, the rapid propagation of programmable design tools has raised a critical situation where researchers are celebrating the productivity of computing power while losing a proper understanding of what computation is about. This brings digital architecture to a deadlock where most of our state-of-the-art computational models in architecture nowadays are not fundamentally advanced compared with their early beginnings decades ago. This situation motivates this work to build a comprehensive theoretical understanding regarding the capacity and limitations of our modeling techniques, and to explore a way out of the current dilemma. Towards this goal, this work emphasizes that computational modeling in architecture is not about geometric modeling which evaluates the capacity of models by the geometrical complexity of the designs, rather, it has always been a branch of artificial intelligence which helps architects to develop new ideas and concepts by the means of intelligent customized computer programs. Following this aspect, this work first reviews the state-of-the-art computational models as well as their counterparts in artificial intelligence. The review shows that, on a technical level, computational modeling in architecture faces two fundamental obstacles – the combinatorial explosion and the frame problem – resulting a phenomenon that most problem-solving programs in architecture succeeded in small-scale research had failed in larger-scale applications. These obstacles are credited for the conceptual assumption that having a faithful representation of the target is a necessity for dealing with the target. The modeling paradigm that follows this assumption is named as simulation. This work then reveals that the contemporary artificial intelligence challenges the paradigm of simulation by giving up the explicit representation of an object and shifting towards the object’s contextual relations to other objects. For contemporary artificial intelligence, any object can be the context of any object. Hence, the computational process no longer represents specific problem content nor the problem-solving process. This allows contemporary artificial intelligence to bypass the mentioned obstacles and use the same technique to solve problems in different fields. This new data-driven context-based modeling paradigm is named as synthesis. Finally, this work applies the idea of synthesis to architectural modeling, using a series of experiments as demonstrations. These experiments cover a wide range of topics, including urban-scale physics, urban dynamics, and ontologies for representations. The experiments show that by giving up reasoning we outperform the conventional modeling techniques. This derives an important implication that, as now all problems can be solved by the same principle, the new challenge ahead of us is no longer how to solve problems, but what questions to ask. This corresponds to the changing role of computers from the tools for computation, to the machines for automation, and finally to the infrastructures for communication.