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.
The Citizen CATE 2024 next-generation experiment placed 43 identical telescope and camera setups along the path of totality during the total solar eclipse (TSE) on 8 April 2024 to capture a 60-minute movie of the inner and middle solar corona in polarized visible light. The 2024 TSE path covered a large geographic swath of North America and we recruited and trained 36 teams of community participants ("citizen scientists") representative of the various communities along the path of totality. Afterwards, these teams retained the equipment in their communities for ongoing education and public engagement activities. Participants ranged from students (K12, undergraduate, and graduate), educators, and adult learners to amateur and professional astronomers. In addition to equipment for their communities, CATE 2024 teams received hands-on telescope training, educational and learning materials, and instruction on data analysis techniques. CATE 2024 used high-cadence, high-dynamic-range (HDR) polarimetric observations of the solar corona to characterize the physical processes that shape its heating, structure, and evolution at scales and sensitivities that cannot be studied outside of a TSE. Conventional eclipse observations do not span sufficient time to capture changing coronal topology, but the extended observation from CATE 2024 does. Analysis of the fully calibrated dataset will provide deeper insight and understanding into these critical physical processes. We present an overview of the CATE 2024 project, including how we engaged local communities along the path of totality, and the first look at CATE 2024 data products from the 2024 TSE.
Given the nonprofit sector's significant, ongoing challenges of filling vacant positions and retaining staff, this study examines the nonprofit job market through an analysis of online job announcements. Using Non-Negative Matrix Factorization topic modeling, a dataset of 13,685 job announcements was explored to find job-related themes. The analysis revealed nine topics that identify the core organizational functions for nonprofit organizations today as well as commonly stated requirements for nonprofit jobs. Findings also highlighted the cross-functional work of nonprofit employees. Practical implications are shared for organizations seeking to recruit and develop emerging leaders.
Contribution: This article describes an effort to quantitatively characterize the "complexity" of engineering programs across the United States using a burgeoning framework called curricular analytics. A new dataset is introduced to facilitate these analyses for researchers and practitioners, tied to an existing data-sharing agreement. Several avenues for future research using this dataset are outlined, such as connecting curricular data with student course-taking data. Background: As curriculum development and evaluation adopt a more data-driven approach to understanding how to best retain and graduate engineers, curricular analytics offers the engineering education community a method for uncovering the complexities of curricula that may be deterring students from the field. Curricular analytics involves representing program requirements as a network to assign a measure of complexity, called "structural complexity," which has been empirically shown to correlate with completion rates. Research question: How does the complexity of engineering programs vary across institutions, disciplines, and time? Methodology: The research question was approached using a quantitative research design. The sampling frame consisted of the multiple-institution database for investigating engineering longitudinal development (MIDFIELD), a data-sharing agreement encompassing 21 institutions (and still expanding). Plans of study were collected from 13 institutions for five disciplines-Mechanical, Electrical, Chemical, Industrial, and Civil-spanning a decade since their most recent record in MIDFIELD ( $n$ = 494). Then, network analysis was applied to calculate the structural complexity of the programs. The results were explored using descriptive statistics, boxplots, and plotting the complexities longitudinally. Findings: Across the years 2012-2022, a range of 307-372 was observed for structural complexity, with a mean of 325 and a median of 323. In the sample, Chemical Engineering was found to be the most structurally complex discipline, followed by Mechanical Engineering. The remaining disciplines were more tightly clustered together. Over time, chemical engineering and civil engineering marginally increased in complexity by 4%, whereas electrical and mechanical engineering decreased by 2% and 0.7%, respectively. Industrial engineering exhibited the most significant decrease of 11%.
