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  1. Free, publicly-accessible full text available August 1, 2025
  2. Spatio-temporal deep learning has drawn a lot of attention since many downstream real-world applications can benefit from accurate predictions. For example, accurate prediction of heavy rainfall events is essential for effective urban water usage, flooding warning, and mitigation. In this paper, we propose a strategy to leverage spatially connected real-world features to enhance prediction accuracy. Specifically, we leverage spatially connected real-world climate data to predict heavy rainfall risks in a broad range in our case study. We experimentally ascertain that our Trans-Graph Convolutional Network (TGCN) accurately predicts heavy rainfall risks and real estate trends, demonstrating the advantage of incorporating external spatially-connected real-world data to improve model performance, and it shows that this proposed study has a significant potential to enhance spatio-temporal prediction accuracy, aiding in efficient urban water usage, flooding risk warning, and fair housing in real estate. 
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    Free, publicly-accessible full text available March 25, 2025
  3. Abstract

    Forming metallurgical phases has a critical impact on the performance of dissimilar materials joints. Here, we shed light on the forming mechanism of equilibrium and non-equilibrium intermetallic compounds (IMCs) in dissimilar aluminum/steel joints with respect to processing history (e.g., the pressure and temperature profiles) and chemical composition, where the knowledge of free energy and atomic diffusion in the Al–Fe system was taken from first-principles phonon calculations and data available in the literature. We found that the metastable and ductile (judged by the presently predicted elastic constants) Al6Fe is a pressure (P) favored IMC observed in processes involving high pressures. The MoSi2-type Al2Fe is brittle and a strongP-favored IMC observed at high pressures. The stable, brittle η-Al5Fe2is the most observed IMC (followed by θ-Al13Fe4) in almost all processes, such as fusion/solid-state welding and additive manufacturing (AM), since η-Al5Fe2is temperature-favored, possessing high thermodynamic driving force of formation and the fastest atomic diffusivity among all Al–Fe IMCs. Notably, the ductile AlFe3, the less ductile AlFe, and most of the other IMCs can be formed during AM, making AM a superior process to achieve desired IMCs in dissimilar materials. In addition, the unknown configurations of Al2Fe and Al5Fe2were also examined by machine learning based datamining together with first-principles verifications and structure predictions. All the IMCs that are notP-favored can be identified using the conventional equilibrium phase diagram and the Scheil-Gulliver non-equilibrium simulations.

     
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  4. Abstract Background Brassica oleracea includes several morphologically diverse, economically important vegetable crops, such as the cauliflower and cabbage. However, genetic variants, especially large structural variants (SVs), that underlie the extreme morphological diversity of B. oleracea remain largely unexplored. Results Here we present high-quality chromosome-scale genome assemblies for two B. oleracea morphotypes, cauliflower and cabbage. Direct comparison of these two assemblies identifies ~ 120 K high-confidence SVs. Population analysis of 271 B. oleracea accessions using these SVs clearly separates different morphotypes, suggesting the association of SVs with B. oleracea intraspecific divergence. Genes affected by SVs selected between cauliflower and cabbage are enriched with functions related to response to stress and stimulus and meristem and flower development. Furthermore, genes affected by selected SVs and involved in the switch from vegetative to generative growth that defines curd initiation, inflorescence meristem proliferation for curd formation, maintenance and enlargement, are identified, providing insights into the regulatory network of curd development. Conclusions This study reveals the important roles of SVs in diversification of different morphotypes of B. oleracea , and the newly assembled genomes and the SVs provide rich resources for future research and breeding. 
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  5. A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS). However, an NLI that is trained in the general domain is hard to apply in the spatial domain due to the idiosyncrasy and expressiveness of the spatial questions. Inspired by the machine comprehension model, we propose a spatial comprehension model that is able to recognize the meaning of spatial entities based on the semantics of the context. The spatial semantics learned from the spatial comprehension model is then injected to the natural language question to ease the burden of capturing the spatial-specific semantics. With our spatial comprehension model and information injection, our NLI for the spatial domain, named SpatialNLI, is able to capture the semantic structure of the question and translate it to the corresponding syntax of an executable query accurately. We also experimentally ascertain that SpatialNLI outperforms state-of-the-art methods. 
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