Following the 2022 US Supreme Court decision Dobbs v Jackson, many states adopted early-term abortion (ETA) bans that prohibit abortions before the age of viability and often before sex determination is technologically feasible. We analyze the effect of ETA bans on the sex ratio at birth among first-generation Chinese, Korean, and Indian (CKI) immigrants using difference-in-differences and triple differences methodologies. While our estimates across higher parity children sometimes indicate a decrease in the sex ratio at birth, they are also generally small and statistically insignificant, which is consistent with continued access to sex selection under ETA bans. In contrast, we find that ETA bans are associated with a large and statistically significant increase in the sex ratio at birth among first-parity children to immigrant CKI women. As elevated sex ratios have not been previously documented on first-parity children, this may indicate that ETA bans shift sex-selective behavior to earlier parities by placing additional emphasis on having a boy now, before any further restrictions on abortions occur. Our estimates suggest that ETA bans caused 139 missing, first-born girls in 2023 alone.
Funders increasingly challenge INGOs in developing countries to provide evidence of their impact. While economic impact is often documented, the social impact on participants has received less attention. To understand INGOs' social impact, we conducted a case study of Heifer International's Values-Based Holistic Community Development program in Honduras. Based on interviews with 100 participants, our analysis shows that beneficiaries experience positive change across four dimensions: (1) self-identity, (2) communication competency, (3) state of leadership, and (4) civic and community engagement. The article emphasizes the importance of assessing INGOs' impact beyond economic gain and contributes to the literature on personal transformation.
This paper investigates the existence of traveling wave solutions for diffusive two-species Lotka–Volterra systems with delays in both the reaction and diffusion terms under partial monotonicity assumptions. The model incorporates small-memory effects in the homogeneous diffusion term, representing a modification of the random-walk interpretation underlying Fick’s law. We extend the partial (cross) monotone iteration method to systems satisfying a partial quasi-monotone condition through the construction of appropriate upper and lower solutions. Convergence of the iteration is established using Schauder’s fixed point theorem.
We investigate the existence of traveling wave solutions for the reaction-diffusion equation partial derivative u(x, t) /partial derivative t = Delta u ( x, t - tau(1) ) + f ( u(x, t), u(x, t - tau(2) ) ) , where tau(1) , tau(2 )> 0. This model is motivated by ecological applications in which migration rates incorporate historical effects and reproduction/death processes are subject to time delays at a given location. To address such systems under standard monotonicity assumptions, we extend the classical monotone iteration method. A key step involves a thorough investigation of the Green function associated with the functional equation x ''(t) - a x ' (t + r) - b x(t + r) = f(t), where a not equal 0 and b > 0. Building on the resulting framework, we construct quasi-upper and lower solutions for the Belousov-Zhabotinski equations, thereby demonstrating the existence of traveling waves when delays are sufficiently small
The rapid expansion of scholarly literature presents significant challenges in synthesizing comprehensive, high-quality academic surveys. Recent advancements in agentic systems offer considerable promise for automating tasks that traditionally require human expertise, including literature review, synthesis, and iterative refinement. However, existing automated survey-generation solutions often suffer from inadequate quality control, poor formatting, and limited adaptability to iterative feedback, which are core elements intrinsic to scholarly writing. To address these limitations, we introduce ARISE, an Agentic Rubric-guided Iterative Survey Engine designed for automated generation and continuous refinement of academic survey papers. ARISE employs a modular architecture composed of specialized large language model agents, each mirroring distinct scholarly roles such as topic expansion, citation curation, literature summarization, manuscript drafting, and peer-review-based evaluation. Central to ARISE is a rubric-guided iterative refinement loop in which multiple reviewer agents independently assess manuscript drafts using a structured, behaviorally anchored rubric, systematically enhancing the content through synthesized feedback. Evaluating ARISE against state-of-the-art automated systems and recent human-written surveys, our experimental results demonstrate superior performance, achieving an average rubric-aligned quality score of 92.48. ARISE consistently surpasses baseline methods across metrics of comprehensiveness, accuracy, formatting, and overall scholarly rigor. All code, evaluation rubrics, and generated outputs are provided openly at https://github.com/ziwang11112/ARISE
