Objectives We examine the potential effects of gunshot detection technology longitudinally in Chicago through a synthetic control quasi-experiment. Methods Police districts receiving gunshot detection technology were compared to a synthetic control unit via a staggered difference-in-difference design. Across eleven unique gunshot detection technology deployment phases, the analyses produce results for aggregate, initial versus expanded, and phase-specific deployment effects across five gun violence outcome measures. Results Gunshot detection technology had no effect on fatal shootings, non-fatal shootings, general part I gun crimes, or shots fired calls for service. Gun recoveries significantly increased in the aggregate, initial, and expanded models, and in several individual phases relative to controls. Conclusions The results align with prior literature that has found a procedural benefit, but not a crime prevention benefit, of gunshot detection technology. Law enforcement agencies seeking crime prevention or reduction solutions may be better served by investing in other options.
Intrinsic water use efficiency (iWUE) at the leaf level measures water expenditures by terestrial plants during photosynthesis, yet its global spatiotemporal dynamics and responses to water stress remain poorly understood. Using machine-learning models and carbon isotope observations in C3 foliage, here we elucidate global patterns, trends, and water-stress responses of leaf iWUE. We find high iWUE in cold, arid regions and lower values in warm, humid areas. From 2001 to 2020, global iWUE increases at 0.2 ± 0.02 μmol mol-1 year-1, with strong biome specific differences. Grasslands exhibit the highest mean iWUE but the slowest increase, whereas evergreen broadleaf forests show the lowest iWUE yet the fastest increase. iWUE rises with increasing water stress, but the rate of growth diminishes as water stress intensifies. Vapor pressure deficit influence iWUE more broadly than soil moisture. The ecological optimality model reproduces the spatial patterns of leaf iWUE and identifies vapor pressure deficit as the dominant driver, but overestimates mean iWUE and its trend. Our findings suggest that increasing water stress may slow the rate of global iWUE increase as the climate continues to warm. Climate change is altering how plants balance carbon gain and water loss. This study maps global leaf-level water-use efficiency over the past two decades, showing it is highest in regions that are cold or dry, increasing worldwide, and strongly influenced by atmospheric dryness.
Text-to-SQL has emerged as a prominent research area, particularly with the rapid advancement of large language models (LLMs). By enabling users to query databases through natural language rather than SQL, this technology significantly lowers the barrier to data analysis. However, generating accurate SQL from natural language remains challenging due to ambiguity in user queries, the complexity of schema linking, limited generalization across SQL dialects, and the need for domain-specific understanding. In this study, we propose a Single-Agent Self-Refinement with Ensemble Voting (SSEV) pipeline built on PET-SQL that operates without ground-truth data, integrating self-refinement with Weighted Majority Voting (WMV) and its randomized variant (RWMA). Experimental results show that the SSEV achieves competitive performance across multiple benchmarks, attaining execution accuracies of 85.5% on Spider 1.0-Dev, 86.4% on Spider 1.0-Test, and 66.3% on BIRD-Dev. Building on insights from the SSEV pipeline, we further propose ReCAPAgent-SQL (Refinement-Critique-Act-Plan agent-based SQL framework) to address the growing complexity of enterprise databases and real-world Text-to-SQL tasks. The framework integrates multiple specialized agents for planning, external knowledge retrieval, critique, action generation, self-refinement, schema linking, and result validation, enabling iterative refinement of SQL predictions through agent collaboration. ReCAPAgent-SQL's WMA results achieve 31% execution accuracy on the first 100 queries of Spider 2.0-Lite, demonstrating significant improvements in handling real-world enterprise scenarios. Overall, our work facilitates the deployment of scalable Text-to-SQL systems in practical settings, supporting better data-driven decision-making at lower cost and with greater efficiency.
For fifty years, the Biglan dimensions have been frequently relied on by researchers interested in considering disciplinary differences as parts of their studies; however, applying a cultural lens draws attention to the possibility that over time, disciplinary cultures may have changed. This exploratory study examined faculty perceptions of their disciplinary cultures by adding an item set derived from common definitions and descriptors of Biglan’s dimensions onto the 2022 Faculty Survey of Student Engagement (FSSE). For 680 faculty at four-year colleges and universities, across 98 disciplines, results indicated that our items formed three scales of disciplinary cultures – Consensus, Pure Scholarship, and Life Systems – that align with Biglan’s dimensions. Faculty perceptions of their disciplinary cultures showed considerable variation across all three scales and disciplinary means and clusters do not necessarily align with how disciplines would be categorized using the Biglan dimensions. Our findings suggest the possibility that the cultures of some disciplines have evolved and that scholars should exercise caution in how they interpret dichotomous or categorical applications of Biglan’s dimensions to understanding faculty work.
As generative artificial intelligence (GenAI) becomes increasingly relevant in higher education, faculty face growing pressure to integrate it ethically and effectively into teaching. While students are already using GenAI and seek faculty guidance, course materials often lack clear policies or instructional support. This study surveyed faculty and analyzed syllabi at a large public Midwestern research university to examine how GenAI is addressed in instruction. Findings reveal that faculty generally view GenAI as a potentially useful learning tool but report limited institutional guidance and inconsistent classroom practices. Despite some departmental efforts to establish GenAI policies, consensus on best practices is lacking. These results underscore the need for clearer institutional frameworks and faculty development to support ethical, pedagogically sound use of GenAI.
