The near-threshold fatigue crack propagation of a CoCrFeMnNi high-entropy alloy was investigated after thermomechanical processing that produced deformation-dominated (AR, HT650) and recrystallized (HT10 0 0, HT110 0) states. The recrystallized HT10 0 0 and HT110 0 exhibited high fatigue thresholds of 10.9-11.4 MPa m1/2 , whereas the deformation-dominated states showed pronounced orientation dependence (AR LT: 7.9, AR TL: 4.6 MPa m1/2 ). DIC and crack-tip EBSD analyses indicated that recrystallization delayed strain localization and promoted distributed crack-tip deformation. Crack-path analysis revealed that near-threshold resistance could not be explained by visible tortuosity alone, but was governed by boundary intersection density, local interaction response, and intersection geometry. HT10 0 0 maintained higher interaction density and a more tortuous crack path than HT1100, despite comparable corrected deflection angles per intersection. TEM further showed that crack-tip deformation localized toward a crystallographically defined E3 twin boundary, indicating that twin boundaries act as local interaction sites rather than universal barriers. The superior resistance of HT10 0 0 thus arises from a favorable microstructural window in which a dislocation-reduced recrystallized matrix is coupled with an effective boundary interaction network. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
Given an exogenous binary treatment D, covariates X, and any outcome Y (binary, count, continuous, …), Ordinary Least Squares (OLS) of Y on D−E(D|X) is consistent to the “overlap-weight” average of heterogeneous effects, where E(D|X) is the propensity score (PS). When D is endogenous with an instrument Z, the Instrumental Variable Estimator (IVE) of Y on D with Z−E(Z|X) as the instrument yields an analogous finding. The conventional approach uses probit/logit for the PS or “instrument score (IS)” E(Z|X), which, however, may be misspecified. In this paper, we use Y−E(Y|X) instead of Y to make the IVE (and OLS) “double-debiasing”, and estimate the nuisance functions {E(Y|X),PS,IS} with machine learning methods. Since this “brute force” nonparametric approach may suffer from nonparametric dimensionality and computational issues, we also explore a middle ground (between the conventional and nonparametric approaches) using Y−E(Y|PS,IS) instead of Y and Y−E(Y|X).
Plant productivity is severely constrained by diverse pathogens, among which oomycetes represent some of the most destructive threats to global agriculture. These filamentous microorganisms cause devastating diseases, including potato late blight and downy mildew, leading to significant yield losses in major crops. Successful infection relies on the formation of haustoria through which oomycetes deliver numerous effector proteins that manipulate host cellular processes and suppress both pattern-triggered and effector-triggered immunity. To date, three major classes of oomycete effectors, including RXLR, Crinkler, and CHXC, along with a putative class YxSL [RK], have been identified in oomycetes. These effector molecules, along with the recently identified apoplastic effectors, play key roles in governing compatible and incompatible interactions and establishing disease in the host plant. Plants perceive these effectors by deploying multilayered immune strategies including plasma-membrane localized pattern-recognition receptors (PRRs) and intracellular NLR receptors that induce redox- and hormone-regulated defense pathways, and dynamic remodeling of transcriptional and metabolic networks. Understanding these effectors and how they manipulate host defense is a prerequisite for the generation of disease-resistant plants. In this review, we discuss the recent progress in the oomycete effectors, their secretion system, and their targets in the plant cells. By integrating pathogen strategies with host immune responses, we highlight how effector-mediated manipulation of plant signaling provides new opportunities for breeding and engineering broad-spectrum and durable resistance against oomycete pathogens.
This study examines the impact of product market threats on the cost of equity capital. Utilizing product market fluidity as a proxy for firm-level competitive threats, we find that heightened competition is associated with a lower cost of equity. We identify operating efficiency and investment efficiency as key channels through which this negative relationship operates. This negative association is more pronounced among firms with severe agency problems, suggesting that competition serves as an external governance mechanism. Furthermore, our results reveal that firm-level competition has an incremental negative effect on the cost of equity, even after controlling for industry-level competition. Overall, our findings highlight that product market competition encourages managerial stewardship, effectively reducing slack and enhancing operational efficiency.
The rapid advancement of generative AI is reshaping the digital creative industry. This study integrates user experience and flow theories to investigate consumers' willingness to pay (WTP) for AI art design tools (AIADT) through a survey of 309 Chinese users and PLS-SEM analysis. The results show that 14 out of 19 hypotheses were supported. Specifically, content experience, functionality experience, interactive experience, and emotional experience significantly enhance concentrated attention and perceived enjoyment. While perceived enjoyment directly drives satisfaction and WTP, concentrated attention only improves satisfaction without directly influencing WTP. Notably, technology discomfort and insecurity significantly inhibit WTP. These findings extend theoretical applications of user experience in the digital economy and reveal the dynamics of AI commercialization. In practice, developers should optimize modularity to lower technical barriers and enhance market competitiveness, supporting sustainable growth and business model innovation within the green digital economy.
