Slippery Rock University of Pennsylvania

Accurately evaluating the impact of generative artificial intelligence (GAI) on firms' key core technological innovation is essential. Using enterprise-level data from China covering the period 2010 to 2022, this study applies a double/debiased machine learning (DML) framework together with mediation effect models to examine the effect of GAI on firms' key core technological innovation and its underlying mechanisms. The results show that GAI significantly promotes firms' key core technological innovation. Mechanism analysis reveals that this effect is primarily achieved by enhancing knowledge diversity and optimizing the labor skill structure. Heterogeneity analysis further indicates that GAI has a pronounced enabling effect on key core technological innovation in state-owned enterprises (SOEs), non-high-tech enterprises, and non-Specialized, Refined, Unique, and Innovative (SRUI) "Little Giant" firms, whereas no significant effect is observed for non-SOEs, high-tech enterprises, or SRUI "Little Giant" firms. These findings provide robust empirical evidence to guide firms with different characteristics in leveraging GAI to advance key core technological innovation and offer valuable insights for emerging economies seeking technological catch-up through intelligent transformation.

Metric dimension is a graph parameter that has been applied to robot navigation and finding low-dimensional vector embeddings. Throttling entails minimizing the sum of two available resources when solving certain graph problems. In this paper, we introduce throttling for metric dimension, edge metric dimension, and mixed metric dimension. In the context of vector embeddings, metric dimension throttling finds a low-dimensional, low-magnitude embedding with integer coordinates. We show that computing the throttling number is NP-hard for all three variants. We give formulas for the throttling numbers of special families of graphs, and characterize graphs with extremal throttling numbers. We also prove that the minimum possible throttling number of a graph of order n is Θ(logn/loglogn), while the minimum possible throttling number of a tree of order n is Θ(n^1/3) or Θ(n^1/2) depending on the variant of metric dimension.

This study explores how language teacher well-being, as an ecological phenomenon that includes layered (un)caring practices, is shaped through institutional discourses and mentoring relationships across three distinct contexts. Using a participatory multiple case study design, we analyze narrative and textual data from three mentoring pairs in which we participated as language teacher educators: one with a veteran professor in Kazakhstan and two with U.S.-based pre-service teachers. Drawing on discourse analytical principles of framing, interdiscursivity, and rescaling, we show how institutional documents shaped teacher and teacher educator roles, and impacted well-being as they navigated institutional expectations. Findings illustrate the parameters of our ability as teacher educators to support interdiscursive meaning-making and rescale mentoring practices amid institutional constraints. Institutional framings both hinder and enable teacher well-being, and mentoring relationships play a critical role in (re)negotiating these dynamics. We propose implications for mentoring practices that promote well-being and contribute to institutional transformation.

The species-area relationship (SAR) has long been used to predict extirpation rates from habitat loss, but these rates depend not only on habitat area but also on the surrounding landscape and species' habitat specialization. We collated global data from forest islands created by river damming and forest fragments resulting from clear-cut deforestation to examine the effects of matrix type (aquatic or terrestrial) and tree cover on avian SARs. Unlike oceanic islands, which are often millions of years old, anthropogenic forest islands provide a contemporary analog to forest fragments to understand matrix effects on SARs and serve as a baseline for worst-case scenarios of forest fragmentation. Our database comprises 50 datasets from 45 studies conducted in tropical and subtropical regions, totaling 1,954 bird species detected through 39,197 incidence records from 336 forest islands and 669 forest fragments. We found that bird extirpation rates were lower in fragments than on islands, especially for forest-dependent species compared to all species. Species losses were further reduced by increasing tree cover around forest remnants at local landscape scales of 300 m, highlighting the importance of small-scale conservation strategies. Moreover, even small forest fragments with greater nearby tree cover held high conservation value, emphasizing the crucial role of the surrounding landscape in mitigating avian extirpations from forest remnants. Beyond protecting forest remnants themselves, area-based conservation efforts would therefore be greatly enhanced by improving matrix quality and expanding tree cover in otherwise hostile landscapes.

BACKGROUND:Using the intelligence leadership model as a conceptual framework, this study examined the relationship between organization-level safety climate and two dimensions of worker safety behavior (worker safety participation and workers safety engagement) and how this relationship could be affected by workers perceived safety leadership traits of the person in charge. METHODS:A cross-sectional study was conducted with 105 oil and gas field workers from multiple contractor companies across eight sites in Pennsylvania and Ohio. Participants completed a comprehensive 53-item self-administered questionnaire designed to assess three constructs: safety leadership intelligence (emotional intelligence [six items], rational intelligence [four items], and spiritual intelligence [six items]), organization-level safety climate (16 items), and worker safety behavior (safety participation [five items] and safety engagement [four items]). Study hypotheses were evaluated using structural equation modeling analysis. FINDINGS:The results showed significant direct and indirect pathways between organization-level safety climate and worker safety behavior (worker safety participation and worker safety engagement), through safety intelligence leadership attributes (emotional, rational, and spiritual). Specifically, perceived person-in-charge's (PIC) leadership attributes related to emotional safety intelligence were found to both fully and partially mediate the relationship with worker safety participation and worker safety engagement respectively. CONCLUSIONS/APPLICATION TO PRACTICE:In high-risk, high-pressure environments, workers are more likely to engage in proactive safety behaviors when they perceive their leaders as empathetic, emotionally self-aware, and capable of fostering genuine interactions. This type of influence cannot be mandated by authority alone, nor achieved just by charisma; it must be earned through emotional connections.