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A key open question in the study of layered superconducting nickelate films is the role that hydrogen incorporation into the lattice plays in the appearance of the superconducting state. Due to the challenges of stabilizing highly crystalline square planar nickelate films, films are prepared by the deposition of a more stable parent compound which is then transformed into the target phaseviaa topotactic reaction with a strongly reducing agent such as CaH₂. Recent studies, both experimental and theoretical, have introduced the possibility that the incorporation of hydrogen from the reducing agent into the nickelate lattice may be critical for the superconductivity. In this work, we use secondary ion mass spectrometry to examine superconducting La_1−xX_xNiO₂/ SrTiO₃(X= Ca and Sr) and Nd₆Ni₅O₁₂/ NdGaO₃films, along with non-superconducting NdNiO₂/ SrTiO₃and (Nd,Sr)NiO₂/ SrTiO₃. We find no evidence for extensive hydrogen incorporation across a broad range of samples, including both superconducting and non-superconducting films. Theoretical calculations indicate that hydrogen incorporation is broadly energetically unfavorable in these systems, supporting our conclusion that extensive hydrogen incorporation is not generally required to achieve a superconducting state in layered square-planar nickelates.

 

Science learning reforms require shifting epistemic and power structures so students and teachers build knowledge together within the context of meaningful questions and problems. Curricular customization allows teachers to preserve these reform-oriented goals while adapting for their specific contexts and students. This paper presents three cases from professional learning communities (PLCs) who followed the same curriculum customization model, but with different equity goals: supporting bi/multilingual learners’ ownership of learning, increasing the relevance of curriculum for students, and encouraging more student voices and multiple perspectives. Together, these cases highlight how the collaborative customization model facilitated productive tensions that lead to teacher learning 

Lightning channel morphology depends on the thunderstorm cloud charge structure, which in turn is influenced by the thunderstorm dynamics. In this paper, based on three‐dimensional radiation source localization data from the Lightning Mapping Array and radar‐based data, our analysis shows that the overall morphology and detailed morphology of the lightning channel correspond to different eddy dissipation rate (EDR) characteristics. Lightning with complex channel morphology occurs in regions with large EDRs. In single lightning events, channels that extend directly within a certain height range without significant bifurcation and turning tend to propagate in the direction of decreasing EDRs, while channel bifurcations and turns usually occur in regions with large radial velocity gradients and large EDRs. This study shows the relationship between channel morphology and thunderstorm dynamics and provides a new method for the direct application of channel‐level localization data to understand thunderstorm dynamics characteristics.

 

In this paper, we present the design, optimization, and implementation of a sub-wavelength grating (SWG) multi-mode interference coupler (MMI) on the silicon nitride photonic integrated circuit (PIC) platform with a significantly enhanced bandwidth compared to the conventional MMI. We extend the SWG MMI theory, previously presented for the silicon-on-insulator platform, to the Si₃N₄/SiO₂platform. Our approach involves an initial parameter optimization for a non-paired design, followed by a shift to a paired design that offers a smaller footprint and a broader bandwidth. The optimized SWG MMI exhibits a 1 dB bandwidth of 300 nm for both the insertion loss and power imbalance, making it a significant addition to silicon nitride photonics.

 

Chilling stress threatens plant growth and development, particularly affecting membrane fluidity and cellular integrity. Understanding plant membrane responses to chilling stress is important for unraveling the molecular mechanisms of stress tolerance. Whereas core transcriptional responses to chilling stress and stress tolerance are conserved across species, the associated changes in membrane lipids appear to be less conserved, as which lipids are affected by chilling stress varies by species. Here, we investigated changes in gene expression and membrane lipids in response to chilling stress during one 24 h cycle in chilling-tolerant foxtail millet (Setaria italica), and chilling-sensitive sorghum (Sorghum bicolor) and Urochloa (browntop signal grass, Urochloa fusca, lipids only), leveraging their evolutionary relatedness and differing levels of chilling stress tolerance. We show that most chilling-induced lipid changes are conserved across the three species, while we observed distinct, time-specific responses in chilling-tolerant foxtail millet, indicating the presence of a finely orchestrated adaptive mechanism. We detected rhythmicity in lipid responses to chilling stress in the three grasses, which were also present in Arabidopsis thaliana, suggesting the conservation of rhythmic patterns across species and highlighting the importance of accounting for time of day. When integrating lipid datasets with gene expression profiles, we identified potential candidate genes that showed corresponding transcriptional changes in response to chilling stress, providing insights into the differences in regulatory mechanisms between chilling-sensitive sorghum and chilling-tolerant foxtail millet.

 

Uncertainty decomposition refers to the task of decomposing the total uncertainty of a predictive model into aleatoric (data) uncertainty, resulting from inherent randomness in the data-generating process, and epistemic (model) uncertainty, resulting from missing information in the model’s training data. In large language models (LLMs) specifically, identifying sources of uncertainty is an important step toward improving reliability, trustworthiness, and interpretability, but remains an important open research question. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling, which can be applied to any pre-trained LLM. Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions. We show that, when aleatoric uncertainty arises from ambiguity or under-specification in LLM inputs, this approach makes it possible to factor an (un-clarified) LLM’s predictions into separate aleatoric and epistemic terms, using a decomposition similar to the one employed by Bayesian neural networks. Empirical evaluations demonstrate that input clarification ensembling provides accurate and reliable uncertainty quantification on several language processing tasks.