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 ''overlapweight'' 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 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.. 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).
The demand for electricity continues to increase with economic growth and increasing end-use electrification, particularly in the transport sector. Despite ongoing progress, achieving Sustainable Development Goal (SDG) 7 by 2030, which aims to provide affordable and clean energy for all, remains uneven and structurally constrained. Electric vehicle (EV)-integrated community microgrids (MGs), operating in grid-connected or islanded modes are a promising solution that combines distributed energy resources, local electricity generation options and mobile energy storage systems (ESSs) through EV fleets. However, the high penetration of renewable energy resources, EVs and battery ESSs introduces significant challenges to system stability, power flow and operational reliability. This review provides a comprehensive assessment of recent developments in EV-integrated community MGs, energy management, decentralized EV fleet coordination and bidirectional EV charging/discharging into the electricity network. It also provides a framework for integrating MGs, EVs, and renewables, including grid-to-vehicle (G2V) charging, bidirectional vehicle-to-grid (V2G) and vehicle-to-everything (V2X) applications, to enhance grid flexibility and ancillary services. It further discusses the optimization mechanisms, control frameworks and their applications within the MG environment. In addition, the review evaluates the contribution of EV-integrated community MGs toward achieving SDG 7 in sustainable community energy systems. The analysis also addresses existing technical, economic, social acceptance and regulatory challenges that hinder large-scale deployments. The study outlines future research directions and proposes a conceptual framework for researchers and policymakers, offering actionable pathways to accelerate the global clean energy transition and support the achievement of the UN 2030 Agenda for Sustainable Development.
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.
