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Abstract Fatigue scattering caused by inherent geometrical defects in laser powder bed fusion (LPBF) imposes a great challenge for fabricating reliable load-bearing components. However, the lack of sufficient fatigue data and the limitation of runout conditions rationalize the need to bridge the gap between limited data and fatigue reliability. This work has developed two models based on censored linear regression (CR) and censored Gaussian process regression (CGP), respectively, to predict fatigue life scattering bounds at a given confidence for both as-built and heat-treated SS 316L samples. Furthermore, fatigue life reliability is modeled under different stress amplitudes with a CGP-based reliability model. 

An epitaxial NbN–Co VAN thin film was deposited on a MgO substrate with a cubic NbN phase, which presents ferromagnet properties with strong out-of-plane magnetic anisotropy. This hybrid metamaterial could find future applications in device design. 

With advancements in sensor technology, real-time monitoring of machine health conditions allows us to perform condition-based maintenance (CBM) for multi-unit systems. The maintenance decision of a unit is usually dependent on other units in a multi-unit system, inducing an exponentially large state space, which makes CBM of large multi-unit systems a very challenging engineering problem. In this work, we first propose two heuristic decision policies for multi-unit systems, namely the binary action policy and the $(n,N_1,N_2)$-policy. Then we propose a multi-step lookahead rollout approach using the two heuristic policies to solve the challenging CBM problem. By applying the binary action policy, we can effectively reduce the action space and thus reduce the computational load in the rollout, while the $(n,N_1,N_2)$-policy can be an excellent base policy for the rollout to improve upon. The theoretical gap between the proposed rollout approach and the optimal policy is also derived. The study further shows extensive experimentation to demonstrate the effectiveness of the proposed lookahead rollout approach for solving the CBM problem for small (3 and 5 units), medium (10 and 15 units), and large (20, 30, 40, and 50 units) scale systems. 

Plasma wakefield acceleration in the nonlinear blowout regime has achieved marked milestones in electron beam acceleration, demonstrating high acceleration gradients and energy efficiency while preserving excellent beam quality. However, this regime is deemed unsuitable for achieving positron acceleration of comparable results, which is vital for future compact electron–positron colliders. In this article, we find that an intense positron beam loaded at the back of beam-driven blowout cavity can self-consistently induce the focusing field and flatten the longitudinal wakefield, leading to stable, high-efficiency, and high-quality positron acceleration. This is achieved through the formation of an on-axis electron filament induced by positron beam load, which shapes the plasma wakefield in a distinct way compared to electron beam load in the blowout regime. Via a nonlinear analytic model and numerical simulations, we explain the novel beam loading effects of the interaction between the on-axis filament and the blowout cavity. High-fidelity simulations show that a high-charge positron beam can be accelerated with >20% energy transfer efficiency, ~1% energy spread, and ~1 mm·mrad normalized emittance, while considerably depleting the energy of the drive beam. The concept can also be extended to simultaneous acceleration of electron and positron beams and high transformer ratio positron acceleration as well. This development offers a new route for the application of plasma wakefield acceleration into particle physics. 

Abstract Bismuth ferrite (BiFeO₃) is a multiferroic material that exhibits both ferroelectricity and canted antiferromagnetism at room temperature, making it a unique candidate in the development of electric-field controllable magnetic devices. The magnetic moments in BiFeO₃are arranged into a spin cycloid, resulting in unique magnetic properties which are tied to the ferroelectric order. Previous understanding of this coupling has relied on average, mesoscale measurements. Using nitrogen vacancy-based diamond magnetometry, we observe the magnetic spin cycloid structure of BiFeO₃in real space. This structure is magnetoelectrically coupled through symmetry to the ferroelectric polarization and this relationship is maintained through electric field switching. Through a combination of in-plane and out-of-plane electrical switching, coupled with ab initio studies, we have discovered that the epitaxy from the substrate imposes a magnetoelastic anisotropy on the spin cycloid, which establishes preferred cycloid propagation directions. The energy landscape of the cycloid is shaped by both the ferroelectric degree of freedom and strain-induced anisotropy, restricting the spin spiral propagation vector to changes to specific switching events. 

Condition-based maintenance of multi-component systems is a prevalent engineering problem due to its effectiveness in reducing the operational and maintenance costs of the system. However, developing the exact optimal maintenance decisions for the large multi-component system is computationally challenging, even not feasible, due to the exponential growth in system state and action space size with the number of components in the system. To address the scalability issue in CBM of large multi-component systems, we propose a Component-Wise Markov Decision Process(CW-MDP) and an Adjusted Component-Wise Markov Decision Process (ACW-MDP) to obtain an approximation of the optimal system-level CBM decision policy for large systems with heterogeneous components. We propose using an extended single-component action space to model the impact of system-level setup cost on a component-level solution. The theoretical gap between the proposed approach and system-level optima is also derived. Additionally, theoretical convergence and the relationship between ACW-MDP and CW-MDP are derived. The study further shows extensive numerical studies to demonstrate the effectiveness of component-wise solutions for solving large multi-component systems. 

The unique redox properties and high oxygen capacity of nanostructured CeO₂demonstrate a wide range of applications, such as electrolytes for solid oxide fuel cells, gas sensors, and catalysis for automotive exhaust gas. Most CeO₂nanomaterials are prepared by chemical synthesis or hard templating methods. An effective way to obtain highly textured, small‐radius dimensions with high specific surface area remains challenging. Here, highly textured CeO₂nanostructures with various shapes ranging from nanowires to nanoporous thin films are successfully synthesized. Vertically aligned nanocomposites (VANs) of Sr₃Al₂O₆(SAO) and CeO₂are synthesized first while varying concentration ratio between them. Once the SAO is dissolved in water, the remaining CeO₂forms distinct nanostructures. The thermal stability of the nanostructured CeO₂is evaluated byin situheating XRD and thermal annealing tests. This method provides an alternative approach to preparing nanostructured CeO₂without toxic chemical solutions or complex micro/nanofabrication techniques. These results present a novel approach to prepare nanostructured CeO₂for future sensing and energy device applications.