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Abstract Zirconium carbide (ZrC), a high‐performance refractory ceramic, exhibits complex defect dynamics that critically influence its behavior in extreme environments. In this work, we employ density functional theory (DFT) simulations to determine the formation energies and migration barriers of four defect types—isolated carbon vacancies, divacancies, Frenkel pairs, and Schottky pairs—across various charge states. The calculated formation energies reveal that isolated carbon vacancies are the most energetically favorable (1.13 eV), followed by Frenkel pairs (3.29 eV), while divacancies (6.86 eV) and Schottky pairs (8.29 eV) require higher formation energies, indicating their lower intrinsic concentrations. Isolated carbon vacancies exhibit the highest migration barrier (4.11 eV) in ZrC, with a modest increase to 4.13 eV upon adding one electron to 64‐atom supercell and a decrease to 4.06 eV with two electrons/64‐atom supercell—reflecting charge redistribution that stabilizes the local environment and weakens nearby Zr–C bonds. In contrast, Frenkel and Schottky pairs show barrier increases with electron doping and decreases with holes (ranging from 3.26 to 3.44 eV and 3.37 to 3.73 eV, respectively), while divacancies display increases (carbon vacancies: 2.69 to 2.93 eV; zirconium vacancies: 3.60 to 3.69 eV) upon electron addition. These results reveal the defect‐specific impact of charge carriers on mobility in ZrC, offering key insights for optimizing its performance in extreme environments. 

As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal dynamics modeling on mobility networks is a challenging task particularly considering scenarios where open-world events drive mobility behavior deviated from the routines. While tremendous progress has been made to model high-level spatio-temporal regularities with deep learning, most, if not all of the existing methods are neither aware of the dynamic interactions among multiple transport modes on mobility networks, nor adaptive to unprecedented volatility brought by potential open-world events. In this paper, we are therefore motivated to improve the canonical spatio-temporal network (ST-Net) from two perspectives: (1) design a heterogeneous mobility information network (HMIN) to explicitly represent intermodality in multimodal mobility; (2) propose a memory-augmented dynamic filter generator (MDFG) to generate sequence-specific parameters in an on-the-fly fashion for various scenarios. The enhanced event-aware spatio-temporal network, namely EAST-Net, is evaluated on several real-world datasets with a wide variety and coverage of open-world events. Both quantitative and qualitative experimental results verify the superiority of our approach compared with the state-of-the-art baselines. What is more, experiments show generalization ability of EAST-Net to perform zero-shot inference over different open-world events that have not been seen. 

Abstract Ceramic processing through the combined use of pressure and water offers a promising approach to achieve accelerated mass transport between ceramic particles at reduced temperatures, providing a sustainable and low‐temperature method for ceramic synthesis and three‐dimensional printing. While previous studies have explored the roles of pressure and water in the fusion and densification of ceramic particles, the underlying mechanisms, especially for micro‐sized ceramic particles, are still debated. This paper aims to propose a potential mechanism for the fusion and densification of micro‐sized ceramic particles under the effect of pressure and water. Using a multi‐phase level‐set simulation model, our results suggest that stress‐assisted fracture and dissolution of interparticle contact points can be key factors driving the densification of micro‐sized ceramic particles in the presence of pressure and water. 

Sensitivity in polymer-bonded explosives (PBXs) relies on the presence of defects, such as cracks and voids, which create localized thermal energy, commonly known as hotspots, and initiate reactions through various localization phenomena. Our prior research has explored the use of internal gas pressure induced by thermite ignition to generate localized defects for PBX sensitization. However, further research is required to gain a more comprehensive understanding of the defect generation process resulting from internal gas pressure. This study investigates the process of defect generation in PBXs in response to internally induced gas pressure by applying controlled compressed gas to a fabricated cavity within the materials, simulating the gas pressure emitting from thermite. X-ray micro-computed tomography was employed to visualize the microstructure of the sample before and after gas injection. The experiments reveal the significance of gas pressure, cavity shape, temperature, and specimen compaction pressure in the defect generation. Numerical simulations using Abaqus/Standard were conducted to assess the defect generation in mock PBXs under varying gas pressures, cohesive properties, and binder thicknesses. The simulation results demonstrate the substantial influence of these properties on the ability to generate defects in mock PBXs. This study contributes to a better understanding of the factors influencing defect generation in mock PBXs. This knowledge is crucial for achieving precise control over defect generation, leading to improved ignition and detonation characteristics in PBXs. 

Abstract Direct ink writing (DIW) process is a facile additive manufacturing technology to fabricate three-dimensional (3D) objects with various materials. Its versatility has attracted considerable interest in academia and industry in recent years. As such, upsurging endeavors are invested in advancing the ink flow behaviors in order to optimize the process resolution and the printing quality. However, so far, the physical phenomena during the DIW process are not revealed in detail, leaving a research gap between the physical experiments and its underlying theories. Here, we present a comprehensive analytical study of non-Newtonian ink flow behavior during the DIW process. Different syringe-nozzle geometries are modeled for the comparative case studies. By using the computational fluid dynamics (CFD) simulation method, we reveal the shear-thinning property during the ink extrusion process. Besides, we study the viscosity, shear stress, and velocity fields, and analyze the advantages and drawbacks of each syringe-nozzle model. On the basis of these investigations and analyses, we propose an improved syringe-nozzle geometry for stable extrusion and high printing quality. A set of DIW printing experiments and rheological characterizations are carried out to verify the simulation studies. The results developed in this work offer an in-depth understanding of the ink flow behavior in the DIW process, providing valuable guidelines for optimizing the physical DIW configuration toward high-resolution printing and, consequently, improving the performance of DIW-printed objects. 

The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens’ lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus’s rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed.