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We study the scaling limit of the rank-one truncation of various beta ensemble generalizations of classical unitary/orthogonal random matrices: the circular beta ensemble, the real orthogonal beta ensemble, and the circular Jacobi beta ensemble. We derive the scaling limit of the normalized characteristic polynomials and the point process limit of the eigenvalues near the point 1. We also treat multiplicative rank one perturbations of our models. Our approach relies on a representation of truncated beta ensembles given by Killip-Kozhan [24], together with the random operator framework developed in [42, 43, 44] to study scaling limits of beta ensembles. 

Abstract Direct ink writing (DIW) using polymer‐particle composite inks is a new research area enabling a wide range of new functionalities. Despite extensive studies, there remains a need for a deeper understanding of how particle size and loading specifically influence printability, especially in the nano range. This work aims to systematically evaluate the effects of SiO₂nanoparticle size (26–847 nm) and loading on printability within a polydimethylsiloxane (PDMS) matrix. For the single‐layer printing process, which is influenced by the substrate properties, a 3D printing line analysis (3D‐PLA) is developed to monitor the top and side views of printed lines. It is found that line width varies with ink composition and substrate, while the line height decreases with solvent evaporation, indicating a strong confinement effect from the substrate. For multilayer structures, dual‐layer printing analysis (DLPA) is utilized to evaluate the printability. It is shown that DLPA is independent of the substrate and can be used to compare the printabilities from different inks. Both 3D‐PLA and DLPA can be correlated to the rheological behavior of the ink through ink rheology analysis (IRA). Finally, this research defined the design space for DIW by benchmarking the minimum and maximum particle loadings for printable composite inks. 

To obtain actionable information for humanitarian and other emergency responses, an accurate classification of news or events is critical. Daily news and social media are hard to classify based on conveyed information, especially when multiple categories of information are embedded. This research used large language models (LLMs) and traditional transformer-based models, such as BERT, to classify news and social media events using the example of the Sudan Conflict. A systematic evaluation framework was introduced to test GPT models using Zero-Shot prompting, Retrieval-Augmented Generation (RAG), and RAG with In-Context Learning (ICL) against standard and hyperparameter-tuned bert-based and bert-large models. BERT outperformed GPT in F1-score and accuracy for multi-label classification (MLC) while GPT outperformed BERT in accuracy for Single-Label classification from Multi-Label Ground Truth (SL-MLG). The results illustrate that a larger model size improves classification accuracy for both BERT and GPT, while BERT benefits from hyperparameter tuning and GPT benefits from its enhanced contextual comprehension capabilities. By addressing challenges such as overlapping semantic categories, task-specific adaptation, and a limited dataset, this study provides a deeper understanding of LLMs’ applicability in constrained, real-world scenarios, particularly in highlighting the potential for integrating NLP with other applications such as GIS in future conflict analyses. 

Abstract Two-dimensional carbides and nitrides, known as MXenes, are promising for water-processable coatings due to their excellent electrical, thermal, and optical properties. However, depositing hydrophilic MXene nanosheets onto inert or hydrophobic polymer surfaces requires plasma treatment or chemical modification. This study demonstrates a universal salt-assisted assembly method that produces ultra-thin, uniform MXene coatings with exceptional mechanical stability and washability on various polymers, including high-performance polymers for extreme temperatures. The salt in the Ti₃C₂T_xcolloidal suspension reduces surface charges, enabling electrostatically hydrophobized MXene deposition on polymers. A library of salts was used to optimize assembly kinetics and coating morphology. A 170 nm MXene coating can reduce radiation temperature by ~200 °C on a 300 °C PEEK substrate, while the coating on Kevlar fabric provides comfort in extreme conditions, including outer space and polar regions. 

ABSTRACT The aim of this paper is to systematically investigate merging and ensembling methods for spatially varying coefficient mixed effects models (SVCMEM) in order to carry out integrative learning of neuroimaging data obtained from multiple biomedical studies. The ”merged” approach involves training a single learning model using a comprehensive dataset that encompasses information from all the studies. Conversely, the ”ensemble” approach involves creating a weighted average of distinct learning models, each developed from an individual study. We systematically investigate the prediction accuracy of the merged and ensemble learners under the presence of different degrees of interstudy heterogeneity. Additionally, we establish asymptotic guidelines for making strategic decisions about when to employ either of these models in different scenarios, along with deriving optimal weights for the ensemble learner. To validate our theoretical results, we perform extensive simulation studies. The proposed methodology is also applied to 3 large-scale neuroimaging studies.