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Abstract. The Vacuum-Assisted Resin Infusion Molding (VARIM) process is widely used in wind turbine blade manufacturing due to its cost-effectiveness and reliability. However, challenges such as prolonged curing cycles and defects caused by non-uniform cure remain persistent. To address these issues, multizone heating systems have been developed to enable independent temperature control across blade sections. Yet, optimizing the temperature profile in each zone is computationally intensive, requiring detailed modelling of curing kinetics and heat transfer mechanisms. To overcome these challenges, in this work, a machine learning (ML) based digital twin of the VARIM process was developed using a time-distributed long short-term memory (LSTM) network trained on data generated by a high-fidelity multiphysics solver. The model achieved a predictive accuracy of 96.7 % in replicating the resin curing behavior. Its time-distributed architecture effectively captures the spatial – temporal dependencies across multiple zones, allowing precise prediction of the degree-of-cure evolution. Paired with a gradient-free optimization algorithm, the digital twin reduced curing time by 12.5 % while improving cure uniformity. This AI-driven framework eliminates costly trial-and-error experimentation, and provides a scalable, adaptive solution for improving both quality and productivity in wind turbine blade manufacturing, with strong potential for extension to other composite manufacturing processes. 

Identifying thermodynamic signatures of electronic phases, such as superconductivity, is challenging in low-dimensional materials due to strong fluctuations and low probing volume. Spectroscopic methods are often used to identify new bulk phases, but their main measurable quantity—electronic energy gaps—is no longer an effective order parameter in low-dimensional and fluctuating systems. Combining angle-resolved photoemission with a domain-adversarial neural network, we report a data-driven method to identify thermodynamic phase transitions solely based on single-particle spectra. We demonstrate 97.6% accuracy in cuprate superconductor Bi2⁢Sr2⁢CaCu2⁢O8+𝛿 with strong superconducting fluctuations. This model notably compensates for the scarcity of experimental data by leveraging virtually inexhaustible simulated data. Further, its explainability reveals the crucial role of in-gap spectral weight in detecting phase fluctuations and thermodynamic transitions. Our work pinpoints the spectroscopic signatures of fluctuating orders and enables using spectroscopy for machine-learning-assisted material discovery for low-dimensional and strong coupling systems. 

Summary Global food production faces persistent threats from environmental challenges and pathogenic attacks, leading to significant yield losses. Conventional strategies to combat pathogens, such as fungicides and disease‐resistant breeding, are limited by environmental contamination and emergence of pathogen resistance. Herein, we engineered sunlight‐sensitive and biodegradable carbon dots (CDs) capable of generating reactive oxygen species (ROS), offering a novel and sustainable approach for plant protection. Our study demonstrates that CDs function as dual‐purpose materials: priming plant immune responses and serving as broad‐spectrum antifungal agents. Foliar application of CDs generated ROS under light, and the ROS could damage the plant cell wall and trigger cell wall‐mediated immunity. Immune activation enhanced plant resistance against pathogens without compromising photosynthetic efficiency or yield. Specifically, spray treatment with CDs at 240 mg/L (2 mL per plant) reduced the incidence of grey mould inN. benthamianaand tomato leaves by 44% and 12%, respectively, and late blight in tomato leaves by 31%. Moreover, CDs (480 mg/L, 1 mL) combined with continuous sunlight irradiation (simulated by xenon lamp, 9.4 × 10⁵lux) showed a broad‐spectrum antifungal activity. The inhibition ratios for mycelium growth were 66.5% forP. capsici, 8% forS. sclerotiorumand 100% forB. cinerea, respectively. Mechanistic studies revealed that CDs effectively inhibited mycelium growth by damaging hyphae and spore structures, thereby disrupting the propagation and vitality of pathogens. These findings suggest that CDs offer a promising, eco‐friendly strategy for sustainable crop protection, with potential for practical agricultural applications that maintain crop yields and minimize environmental impact.