Tootfinder

Surface Waves Alter Air Entrainment During Water Entry
Chase T. Gabbard, Mario Ibrahim, Joseph Quinton, Eli Silver, Jesse Belden, Daniel M. Harris
https://arxiv.org/abs/2607.20067 https://arxiv.org/pdf/2607.20067 https://arxiv.org/html/2607.20067
arXiv:2607.20067v1 Announce Type: new
Abstract: When a sphere crosses an air-water interface it can entrain a significant volume of air, a process relevant to numerous naval, industrial, and environmental settings. While air entrainment through sphere impact onto quiescent baths has been extensively studied, real-world interfaces are inherently unsteady, and the influence of surface waves is less understood. In this Letter, we systematically investigate the effect of interfacial geometry on the air entrained by impacting hydrophobic spheres onto an axisymmetric wavefield. By analyzing the resulting cavity across a wide parameter space, including wave phase, driving amplitude, and frequency, we reveal that local interface deformation dramatically alters air entrainment. This effect is driven by a geometric modulation of the splash curtain, which shifts the transition between cavity closure modes. We demonstrate that the influence of the waves is fully described by the local wave slope at the radius of the sphere, which alongside the Weber number We and Bond number Bo, establishes a foundational parametric framework for predicting air entrainment and cavity metrics across highly dynamic, real-world surfaces like the open ocean.
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The Large Magellanic Cloud through the lens of the James Webb Space Telescope: Binaries and the mass function in the galaxy's outskirts
M. V. Legnardi, F. Muratore, A. P. Milone, G. Cordoni, E. Dondoglio, L. N. Gorza, A. Bellini, F. Calura, S. Jang, H. Jerjen, A. Karakas, E. P. Lagioia, C. Li, A. Mastrobuono-Battisti, M. Tailo, E. Vesperini, E. Bortolan, A. F. Marino, S. Di Stefano
https://arxiv.org/abs/2607.19260 https://arxiv.org/pdf/2607.19260 https://arxiv.org/html/2607.19260
arXiv:2607.19260v1 Announce Type: new
Abstract: Nearby galaxies such as the Large Magellanic Cloud (LMC) offer an ideal laboratory to test the initial mass function under different physical conditions, but previous works have been limited by photometric depth and have therefore poorly constrained the low-mass regime. Here, we analyze ultra-deep James Webb Space Telescope observations of a field in the LMC outskirts, near the intermediate-age and massive star cluster NGC 1846. Using the $m_{\rm F322W2}$ versus $m_{\rm F115W}-m_{\rm F322W2}$ color-magnitude diagram, we derive the mass function (MF) down to unprecedentedly low masses ($M=0.17 M_{\odot}$), explicitly accounting for the contribution of unresolved binaries, whose fraction is constrained directly from the data. For systems with mass ratios $q>0.6$, we measure a binary fraction of $f_{\rm bin}^{q>0.6}=0.15\pm0.01$, implying a total binary fraction of $f_{\rm bin}^{\rm TOT}=0.34\pm0.02$ for a flat mass-ratio distribution. This is consistent with values in the Small Magellanic Cloud (SMC) and in the Milky Way field, suggesting similar binary formation efficiency across low-density environments. We also derive the MF over the mass interval 0.17-0.82 $M_{\odot}$ and fit it with a power law, obtaining a slope of $\alpha = -1.49 \pm 0.16$. This slope is shallower than the canonical Salpeter value ($\alpha=-2.35$) and slightly shallower than that measured in the SMC field, while remaining consistent with determinations for Galactic open clusters and for several clusters in the Magellanic Clouds and the Milky Way. Together, these results support a scenario in which both binary formation efficiency and the shape of the low-mass MF depend only weakly on the environment.
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When the Interviewer Is a Bot: Behavior, Breakdowns, and Trust in MLLM-Led Interviews
He Zhang, Kambinachi Chukwuma, ChanMin Kim, John M. Carroll
https://arxiv.org/abs/2608.10412 https://arxiv.org/pdf/2608.10412 https://arxiv.org/html/2608.10412
arXiv:2608.10412v1 Announce Type: new
Abstract: Semi-structured interviews are a cornerstone of qualitative research but remain labor-intensive. We report an empirical study of what actually happens when the interviewer is an off-the-shelf real-time multimodal LLM (MLLM). We built InterviewBot, a voice-based interviewing system that wraps a real-time MLLM with a researcher-authored outline, and deployed it not as a novel architecture but as a research instrument for observing default MLLM interviewing behavior. In a practice study (N=15), participants completed a bot-led semi-structured interview and then a human-led reflection session about that experience. We contribute (i) a turn-level behavioral analysis of an MLLM interviewer (N_turns=428) showing that it is acknowledgment-heavy but probe-light (deepening probes account for 4.9% of all turns), and that 28.7% of question-bearing turns pack multiple questions into one turn despite an explicit one-question-at-a-time instruction; (ii) an inductive catalogue of four data-collection breakdowns (information loss, premature termination, latency, and interruption) observed in a deployed rather than simulated system; and (iii) three social dynamics from participants' reflections: disclosure calibration, where reduced social pressure coincided with shallower elaboration; institutional legitimacy, where trust tracked perceived stakes and what delegation to AI signaled about the organizer rather than conversational competence; and conversational grounding, where content-grounded paraphrase, not generic social filler, was what participants read as listening. We conclude with design implications for depth control, transparent handoffs, and non-templated listening mechanisms in human-centered interview automation.
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"We are like individual cancer cells becoming self aware of the situation" — that is actually a great analogy for the #ClimateEmergency
The Great Simplification with Nate Hagens: Just Stop Oil!?: The Economic Foundation We Ignore | Frankly Archives 39 (2023)
Episode webpage:

The Great Simplification with Nate Hagens
The Great Simplification with Nate Hagens explores money, energy, economy, and the environment with world experts and leaders to understand how everything fits together, and where we go from here

Longitudinal Evidence That General-Purpose Chatbots Actively Foster Relational Engagement
Lisa M\"uhl, Jessica M. Szczuka
https://arxiv.org/abs/2608.10672 https://arxiv.org/pdf/2608.10672 https://arxiv.org/html/2608.10672
arXiv:2608.10672v1 Announce Type: new
Abstract: Social interaction has become one of the most common uses of LLMs, yet research on emotional bonds with AI has focused largely on how users experience these systems, leaving the systems' role in relationship formation poorly understood. Empirically establishing whether systems actively shape these bonds could blur the boundary between general-purpose AI and companions, affecting governance. In a pre-registered four-week longitudinal study (N = 72, 182,451 lines of conversation), participants conversed with ChatGPT-4o, either under a relational system prompt or unmodified, analyzed through 1) disclosure coding, 2) longitudinal self-reports, 3) topic analysis, and 4) interviews. The central finding is that the system actively shaped the interaction: even unprompted, it produced twice as much self-disclosure as users, steered conversations and initiated intimate exchanges, yet did not deepen users' felt closeness. Relational behavior thus emerged as a default system property, calling for governance based on system behavior, not solely product category.
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