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Multicomponent refractory alloys have the potential to operate in high-temperature environments. Alloys with heterogeneous/composite microstructure exhibit an optimal combination of high strength and ductility. The present work generates designed compositions using high-throughput computational and machine-learning (ML) models based on elements Mo-Nb-Ti-V-W-Zr manufactured utilizing vacuum arc melting. The experimentally observed phases were consistent with CALPHAD and Scheil simulations. ML models were used to predict the room temperature mechanical properties of the alloy and were validated with experimental mechanical data obtained from the three-point bending and compression tests. This work collectively showcases a data-driven, inverse design methodology that can effectively identify new promising multicomponent refractory alloys. 

With the technology trend of hardware and workload consolidation for embedded systems and the rapid development of edge computing, there has been increasing interest in supporting parallel real-time tasks to better utilize the multi-core platforms while meeting the stringent real-time constraints. For parallel real-time tasks, the federated scheduling paradigm, which assigns each parallel task a set of dedicated cores, achieves good theoretical bounds by ensuring exclusive use of processing resources to reduce interferences. However, because cores share the last-level cache and memory bandwidth resources, in practice tasks may still interfere with each other despite executing on dedicated cores. Such resource interferences due to concurrent accesses can be even more severe for embedded platforms or edge servers, where the computing power and cache/memory space are limited. To tackle this issue, in this work, we present a holistic resource allocation framework for parallel real-time tasks under federated scheduling. Under our proposed framework, in addition to dedicated cores, each parallel task is also assigned with dedicated cache and memory bandwidth resources. Further, we propose a holistic resource allocation algorithm that well balances the allocation between different resources to achieve good schedulability. Additionally, we provide a full implementation of our framework by extending the federated scheduling system with Intel’s Cache Allocation Technology and MemGuard. Finally, we demonstrate the practicality of our proposed framework via extensive numerical evaluations and empirical experiments using real benchmark programs. 

Flame-retardant and thermal management structures have attracted great attention due to the requirement of high-temperature exposure in industrial, aerospace, and thermal power fields, but the development of protective fire-retardant structures with complex shapes to fit arbitrary surfaces is still challenging. Herein, we reported a rotation-blade casting-assisted 3D printing process to fabricate nacre-inspired structures with exceptional mechanical and flame-retardant properties, and the related fundamental mechanisms are studied. 3-(Trimethoxysilyl)propyl methacrylate (TMSPMA) modified boron nitride nanoplatelets (BNs) were aligned by rotation-blade casting during the 3D printing process to build the “brick and mortar” architecture. The 3D printed structures are more lightweight, while having higher fracture toughness than the natural nacre, which is attributed to the crack deflection, aligned BN (a-BNs) bridging, and pull-outs reinforced structures by the covalent bonding between TMSPMA grafted a-BNs and polymer matrix. Thermal conductivity is enhanced by 25.5 times compared with pure polymer and 5.8 times of anisotropy due to the interconnection of a-BNs. 3D printed heat-exchange structures with vertically aligned BNs in complex shapes were demonstrated for efficient thermal control of high-power light-emitting diodes. 3D printed helmet and armor with a-BNs show exceptional mechanical and fire-retardant properties, demonstrating integrated mechanical and thermal protection.