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Abstract Theaus(Oryza sativaL.) varietal group comprises of aus, boro, ashina and rayada seasonal and/or field ecotypes, and exhibits unique stress tolerance traits, making it valuable for rice breeding. Despite its importance, the agro-morphological diversity and genetic control of yield traits inausrice remain poorly understood. To address this knowledge gap, we investigated the genetic structure of 181ausaccessions using 399,115 SNP markers and evaluated them for 11 morpho-agronomic traits. Through genome-wide association studies (GWAS), we aimed to identify key loci controlling yield and plant architectural traits. Our population genetic analysis unveiled six subpopulations with strong geographical patterns. Subpopulation-specific differences were observed in most phenotypic traits. Principal component analysis (PCA) of agronomic traits showed that principal component 1 (PC1) was primarily associated with panicle traits, plant height, and heading date, while PC2 and PC3 were linked to primary grain yield traits. GWAS using PC1 identifiedOsSAC1on Chromosome 7 as a significant gene influencing multiple agronomic traits. PC2-based GWAS highlighted the importance ofOsGLT1and OsPUP4/ Big Grain 3 in determining grain yield. Haplotype analysis of these genes in the 3,000 Rice Genome Panel revealed distinct genetic variations inausrice. In summary, this study offers valuable insights into the genetic structure and phenotypic diversity ofausrice accessions. We have identified significant loci associated with essential agronomic traits, withGLT1, PUP4, andSAC1genes emerging as key players in yield determination. 

We present a scrutiny on the state of the art and applicability of predictive methods for additive manufacturing (AM) of metals, alloys, and compositionally complex metallic materials, to provide insights from the computational models for AM process optimization. Our work emphasizes the importance of manufacturing parameters on the thermal profiles evinced during processing, and the fundamental insights offered by the models used to simulate metal AM mechanisms. We discuss the methods and assumptions necessary for an educated tradeoff between the efficacy and accuracy of the computational approaches that incorporate multi-physics required to mimic the associated fluid flow phenomena as well as the resulting microstructures. Finally, the current challenges in the existing approaches are summarized and future scopes identified.