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Abstract Motivated by recent experimental observations of opposite Chern numbers in R-type twisted MoTe₂and WSe₂homobilayers, we perform large-scale density-functional-theory calculations with machine learning force fields to investigate moiré band topology across a range of twist angles in both materials. We find that the Chern numbers of the moiré frontier bands change sign as a function of twist angle, and this change is driven by the competition between moiré ferroelectricity and piezoelectricity. Our large-scale calculations, enabled by machine learning methods, reveal crucial insights into interactions across different scales in twisted bilayer systems. The interplay between atomic-level relaxation effects and moiré-scale electrostatic potential variation opens new avenues for the design of intertwined topological and correlated states, including the possibility of mimicking higher Landau level physics in the absence of magnetic field. 

Reactive carbonyl and oxygen species (RCS/ROS), often generated as metabolic byproducts, particularly under conditions of pathology, can cause direct damage to proteins, lipids, and nucleic acids. Glyoxal oxidases (Gloxs) oxidize aldehydes to carboxylic acids, generating hydrogen peroxide (H₂O₂). Although best characterized for their roles in lignin degradation, Glox in plant fungal pathogens are known to contribute to virulence, however, the mechanism underlying such effects are unclear. Here, we show that Glox in the insect pathogenic fungus,Metarhizium acridum, is highly expressed in mycelia and during formation of infection structures (appressoria), with the enzyme localizing to the cell membrane.MaGloxtargeted gene disruption mutants showed RCS and ROS accumulation, resulting in cell toxicity, induction of apoptosis and increased autophagy, inhibiting normal fungal growth and development. The ability of theMaGloxmutant to scavenge RCS was significantly reduced, and the mutant exhibited increased susceptibility to aldehydes, oxidative and cell wall perturbing agents but not toward osmotic stress, with altered cell wall contents. The ΔMaGloxmutant was impaired in its ability to penetrate the host cuticle and evade host immune defense resulting in attenuated pathogenicity. Overexpression ofMaGloxpromoted fungal growth and conidial germination, increased tolerance to H₂O₂, but had little to other phenotypic effects. Transcriptomic analyses revealed downregulation of genes related to cell wall synthesis, conidiation, stress tolerance, and host cuticle penetration in the ΔMaGloxmutant. These findings demonstrate thatMaGlox-mediated scavenging of RCS is required for virulence, and contributes to normal fungal growth and development, stress resistance. 

Purpose This paper aims to present an unconditionally energy-stable scheme for approximating the convective heat transfer equation. Design/methodology/approach The scheme stems from the generalized positive auxiliary variable (gPAV) idea and exploits a special treatment for the convection term. The original convection term is replaced by its linear approximation plus a correction term, which is under the control of an auxiliary variable. The scheme entails the computation of two temperature fields within each time step, and the linear algebraic system resulting from the discretization involves a coefficient matrix that is updated periodically. This auxiliary variable is given by a well-defined explicit formula that guarantees the positivity of its computed value. Findings Compared with the semi-implicit scheme and the gPAV-based scheme without the treatment on the convection term, the current scheme can provide an expanded accuracy range and achieve more accurate simulations at large (or fairly large) time step sizes. Extensive numerical experiments have been presented to demonstrate the accuracy and stability performance of the scheme developed herein. Originality/value This study shows the unconditional discrete energy stability property of the current scheme, irrespective of the time step sizes. 

The Federal Highway Administration (FHWA) mandates biannual bridge inspections to assess the condition of all bridges in the United States. These inspections are recorded in the National Bridge Inventory (NBI) and the respective state’s databases to manage, study, and analyze the data. As FHWA specifications become more complex, inspections require more training and field time. Recently, element-level inspections were added, assigning a condition state to each minor element in the bridge. To address this new requirement, a machine-aided bridge inspection method was developed using artificial intelligence (AI) to assist inspectors. The proposed method focuses on the condition state assessment of cracking in reinforced concrete bridge deck elements. The deep learning-based workflow integrated with image classification and semantic segmentation methods is utilized to extract information from images and evaluate the condition state of cracks according to FHWA specifications. The new workflow uses a deep neural network to extract information required by the bridge inspection manual, enabling the determination of the condition state of cracks in the deck. The results of experimentation demonstrate the effectiveness of this workflow for this application. The method also balances the costs and risks associated with increasing levels of AI involvement, enabling inspectors to better manage their resources. This AI-based method can be implemented by asset owners, such as Departments of Transportation, to better serve communities.