University of North Texas at Dallas

ABSTRACT Immigrants are increasingly vilified and constructed as threats to the nation‐state. Their linguistic practices too face parallel stigmatization. Within this context, Spanish is conflated with undocumented status. Against the backdrop of heightened anti‐immigrant sentiment and persistent challenges to Spanish language use, this study explores the role of language in the lives of Latina/o college students who immigrated to the United States during their K‐12 schooling and whose first language is Spanish. Drawing on focus group data from students at a Hispanic Serving Institution in California, we examine how participants make sense of and negotiate linguistic practices that serve as markers of their racialized identities. Our findings reveal that language operates as a site of constant negotiation, shaped by three dimensions of what we term raciolinguistic melancolía: sacrifice, linguistic marginalization, and linguistic negotiation. We contend that raciolinguistic melancolía positions language as an ungrievable loss that cannot be publicly mourned. Through this lens, Latina/o immigrant college students expose the melancholic structures of language enabling them to move past linguistic traumas as they learn to accept and unapologetically celebrate their linguistic identities despite painful experiences associated with racialization practices.

Preservatives, including antimicrobial compounds, are routinely added to home and personal care products such as dishwashing detergents to prevent microbial contamination during repeated consumer use. Traditional antimicrobial efficacy testing (T-AET) relies on colony counting methods, where microbial cocktails are inoculated into preservative-containing products and plated on agar to measure microbial survival. While considered a gold standard, T-AET is time-consuming, labor-intensive, costly, and prone to variability and error due to manual handling. To address these limitations, we developed a high-throughput, miniature, ATP-based, on-chip AET (O-AET) using a 384PillarPlate platform. Pseudomonas aeruginosa, Enterobacter cloacae, and Staphylococcus aureus were encapsulated in 3% agarose and exposed to test compounds and detergent formulations. ATP levels in viable microbes were quantified using the BacTiter-Glo™ luminescence assay to generate dose-response curves, IC50 values, and log reduction measurements. Two industrial antimicrobials, Acticide LG and Acticide MBR2, were evaluated, showing CV values below 8%. Nine detergent formulations from Colgate-Palmolive, including both fresh and aged samples (stored for 8 weeks at 40°C), were tested. Log reduction data from O-AET showed strong correlation with T-AET. Using a 3-log reduction cutoff, O-AET achieved 98% sensitivity, 44% specificity, and 71% overall predictivity. The lower specificity was partly attributed to limited true negative samples used and nutrient deficiencies. The O-AET enables significant reduction in reagent volume, rapid turnaround, and compatibility with standard plate readers, making it cost-effective and scalable. This ATP bioluminescence-based platform provides a robust, reproducible, and high-throughput alternative to T-AET, with high potential to streamline product development and preservative screening in consumer care applications.

Abstract. A variety of chemical and dynamical processes in the troposphere and stratosphere affect tropical total column ozone (TCO), the net effect of which may cause changes in surface UV radiation and impact human and ecosystem health. We use dynamical linear modeling to estimate tropical trends in TCO and partial column ozone (PCO) in the troposphere and three stratospheric layers to assess agreement between satellite observational composites and chemistry–climate model (CCM) simulations from two multi-model experiments (CCMI-1 and CCMI-2022). While both model experiments show tropical TCO increases over 2000–2021, CCMI–2022 trends (+2.5 DU) agree slightly better with observations than CCMI-1 (+1.6 DU). However, this overall agreement obscures multiple systematic differences in PCO trends between the models and observations across atmospheric layers. For example, since 2000 tropical tropospheric PCO increased significantly in CCMI-2022 (+1.5 DU) but not in CCMI–1 (+0.3 DU), largely explaining the difference in TCO trends. Also, despite nearly identical stratospheric PCO trends, CCMI-2022 trends are slightly more negative in the lower stratospheric (by ~0.5 DU), compensated by more positive middle/upper stratosphere trends compared to CCMI-1. Crucially, substantial differences exist across observational PCO trends, particularly in the troposphere and middle/upper stratosphere, and these disagreements limit the ability to evaluate CCM fidelity. Furthermore, while the inter-model correlation between late and early 21st century trends is suggestive of a potential emergent constraint on future ozone trends, the spread in observational trends precludes its observational implementation.

Abstract The origin of a binary‐mixing of DM and EM1 sources for the Cenozoic intraplate volcanism in eastern Northeast China is yet unclear. Seismic attenuation imaging is a tool that can shed light on this question. Here we present the first map of teleseismic P‐wave attenuation across Northeast China. Three hundred eighty‐two teleseismic direct‐P phases are analyzed using a time‐domain waveform matching method and a 2‐D relative attenuation map is inverted by applying a Hierarchical Bayesian method. Strong attenuation is observed beneath Jingpohu volcano. The estimated asthenospheric Qp is abnormally low (Qp < 70), indicating a high‐temperature (∼1500°C) asthenosphere containing plausible partial melting (>4%) underneath Jingpohu volcano, presumably induced by hot mantle upwelling. Meanwhile, the hot asthenosphere could induce the melting of the bottom of the lithospheric mantle, and we infer that this lithospheric melting combined with the ascending asthenospheric melt may feed the Jingpohu volcanism, explaining the mixed‐source geochemical signature.

Herein, we introducea powerful alkene difunctionalizationprocesswhere anomeric amides (i.e., N-halogenated-O-activated hydroxylamines) react directly with olefins,without the use of catalysts or additives, to yield the corresponding N-haloalkyl-O-activated hydroxylamines.These multifunctional hydroxylamines (MFHAs), containing both alkylhalide and O-activated hydroxylamine moieties, areconvenient building blocks/electrophilic aminating reagents for thesynthesis of structurally complex N-unprotected secondaryamines and various N-heterocycles (i.e., N-alkyl/N-aryl aziridines, pyrrolidines,oxazolidinones and tetrahydroquinolines). Both activated and unactivatedalkenes (including cyclic and acyclic olefins, dienes, and enynes)are effectively converted to the corresponding difunctionalized hydroxylaminederivatives with excellent atom economy. The versatility of MFHAswas demonstrated through the synthesis of various nitrogen-containingmolecules. Density functional theory (DFT) calculations and moleculardynamics simulations, together with mechanistic experiments, indicatethat the reaction proceeds through a radical chain addition mechanisminitiated by a polar-to-radical crossover step.

The article examines how visual communication practices in academic marketing shape stakeholder perception when universities deploy digital assets across websites and short-form video platforms. Relevance is driven by intensified competition for attention, heightened sensitivity to credibility signals in digitally mediated interactions, and expanding use of AI-supported creative production. Novelty lies in integrating evidence on visual design richness and processing outcomes, institutional visual identity effects, and the trust consequences of AI-related disclosures into a single analytic account of stakeholder judgment formation. The study aims to explain how specific categories of digital assets—identity systems, still imagery, motion graphics, short videos, and AI-generated creative—alter perceived credibility, institutional reputation, and behavioral intentions among prospective students, current students, alums, donors, and partners. The work applies analytical synthesis of recent peer-reviewed research and open research outputs, using comparative analysis and structured source review. The conclusion formulates design and governance implications for academic marketing teams seeking higher persuasion efficiency under trust constraints.

User Interface (UI) testing is crucial for quality assurance of industrial mobile applications, and yet it remains labor-intensive and challenging to automate effectively. Recent advances in Vision-Language Models (VLMs) present a promising solution for automating GUI testing by mapping natural language instructions to pixel-level actions, significantly reducing the manual effort required for writing test scripts and even designing test cases. While numerous VLMs have been proposed and evaluated for GUI testing, they often fail to meet two critical industrial requirements: (1) effectiveness when handling complex, multi-step workflows in industrial applications, and (2) efficiency for large-scale, high-frequency testing environments typical in industrial settings. Toward addressing the preceding industrial requirements, in this paper, we report our experiences in developing and deploying RePeek, a novel approach employing a unified three-stage pipeline for both training and inference, enables a VLM to explicitly detect and reason over discrete GUI elements, thereby overcoming the limitations of pixel-based reasoning for both efficiency and effectiveness improvements. In the first stage, RePeek integrates a lightweight UI-element detector named OmniParser to decompose UI screenshots into a structured element list. In the second stage, RePeek adopts the vision encoder of the VLM to generate the embedding for each element. In the third stage, RePeek fuses these element embeddings with the textual instruction to reason and perform classification directly on the UI elements, empowering efficient small models to achieve superior performance against expensive large models. Comprehensive evaluations on public benchmarks and deployment at WeChat show that RePeek consistently achieves superior accuracy and efficiency compared to state-of-the-art VLMs. Specifically, RePeek enables a fine-tuned Qwen2.5-VL-3B model to outperform a 72B model with 75% less training data, validating the effectiveness of incorporating domain knowledge into VLM-based GUI testing. We conclude by summarizing three key lessons from developing and deploying RePeek, offering insights for both researchers and practitioners working on industrial-strength UI testing.