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Abstract Predictive Maintenance (PdM) emerges as a critical task of Industry 4.0, driving operational efficiency, minimizing downtime, and reducing maintenance costs. However, real-world industrial environments present unsolved challenges, especially in predicting simultaneous and correlated faults under evolving conditions. Traditional batch-based and deep learning approaches for simultaneous fault prediction often fall short due to their assumptions of static data distributions and high computational demands, making them unsuitable for dynamic, resource-constrained systems. In response, we propose OEMLHAT (Online Ensemble of Multi-Label Hoeffding Adaptive Trees), a novel model tailored for real-time, multi-label fault prediction in non-stationary industrial settings. OEMLHAT introduces a scalable online ensemble architecture that integrates online bagging, dynamic feature subspacing, and adaptive output weighting. This design allows it to efficiently handle concept drift, high-dimensional input spaces, and label sparsity, key bottlenecks in existing PdM solutions. Experimental results on three public multi-label PdM case studies demonstrate substantial improvements in predictive performance of OEMLHAT over previous batch-based and online proposals for multi-label classification, particularly with an average improvement in micro-averaged F1-score of 18.49% over the second most-accurate batch-based proposal and of 8.56% in the case of the second best online model. By addressing a critical gap in online multi-label learning for PdM, this work provides a robust and interpretable solution for next-generation industrial monitoring systems for fault detection, particularly for rare and concurrent failures. 

Substituting the central proline residue in a collagen mimetic peptide with δ-oxaproline affords a faster-folding analogue with equivalent triple helix stability. 

Global warming is expected to significantly impact the soil fungal and bacterial microbiomes, yet the predominant ecological response of microbial taxa—whether an increase, decrease, or no change—remains unclear. It is also unknown whether microbial taxa from different evolutionary lineages exhibit common patterns and what factors drive these changes. Here, we analyzed three mid‐term (> 5 years) warming experiments across contrasting dryland and temperate‐boreal ecosystems, encompassing over 500 topsoil samples collected across multiple time points. We found that warming altered the relative abundance of microbial taxa, with both increases and decreases over time. For instance, the relative abundance of bacterial and fungal taxa responding to warming (increase or decrease) accounted for 35.9% and 42.9% in the dryland ecosystem, respectively. Notably, taxa within the same phylum exhibited divergent responses to warming. These ecological shifts were linked to factors such as photosynthetic cover and fungal lifestyle, both of which influence soil functions. Overall, our findings indicate that soil warming can reshape a significant fraction of the microbial community across ecosystems, potentially driving changes in soil functions. 

One of the most striking signatures of Weyl fermions in solid-state systems is their surface Fermi arcs. Fermi arcs can also be localized at internal twin boundaries where two Weyl materials of opposite chirality meet. In this work, we derive constraints on the topology and connectivity of these “internal Fermi arcs.” We show that internal Fermi arcs can exhibit transport signatures, and we propose two probes: quantum oscillations and a quantized chiral magnetic current. We propose merohedrally twinned B20 materials as candidates to host internal Fermi arcs, verified through both model and calculations. Our theoretical investigation sheds light on the topological features and motivates experimental studies on the intriguing physics of internal Fermi arcs. 

Gene expression and complex phenotypes are determined by the activity of cis-regulatory elements. However, an understanding of how extant genetic variants affect cis regulation remains limited. Here, we investigated the consequences of cis-regulatory diversity using single-cell genomics of more than 0.7 million nuclei across 172Zea mays(maize) inbreds. Our analyses pinpointed cis-regulatory elements distinct to domesticated maize and revealed how historical transposon activity has shaped the cis-regulatory landscape. Leveraging population genetics principles, we fine-mapped about 22,000 chromatin accessibility–associated genetic variants with widespread cell type–specific effects. Variants in TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTOR–binding sites were the most prevalent determinants of chromatin accessibility. Finally, integrating chromatin accessibility–associated variants, organismal trait variation, and population differentiation revealed how local adaptation has rewired regulatory networks in unique cellular contexts to alter maize flowering.