Stellar feedback is an essential step in the baryon cycle of galaxies, but it remains unconstrained beyond Cosmic Noon. We study the ionized gas kinematics, dynamical mass and gas-flow properties of a sample of 16 sub-$L^{\star}$ star-forming galaxies at $4\leq z\leq7.6$, using high-resolution JWST/NIRSpec observations. The emission lines are resolved, with velocity dispersions ($\sigma_{\rm gas}{\rm~(km~s^{-1})}\simeq38-96$) comparable to more massive galaxies at Cosmic Noon. From $\sigma_{\rm gas}$ and the galaxy size ($r_e=400-960~$pc), we estimate the dynamical mass to be $\log M_{\rm dyn}/M_{\odot}=9.25-10.25$. Stellar-to-dynamical mass ratios are low ($\log M_{\star}/M_{\rm dyn}\in[-0.5,-2]$) and decrease with increasing SFR surface density ($\Sigma_{\rm SFR}$). We estimate the gas surface densities assuming a star-formation law, but the gas masses do not balance the baryon-to-dynamical mass ratios, which would require a decrease in the star-formation efficiency. We find evidence of ionized outflows in five out of the sixteen galaxies, based on the need of broad components to reproduce the emission-line wings. We only observe outflows from galaxies undergoing recent bursts of star formation ${\rm SFR_{10}/SFR_{100}\geq1}$, with elevated$\Sigma_{\rm SFR}$ and low $M_{\star}/M_{\rm dyn}$. This links high gas surface densities to increased outflow incidence and lower $M_{\star}/M_{\rm dyn}$. With moderate outflow velocities ($v_{\rm flow}{\rm~(km~s^{-1})}=150-250$) and mass outflow rates ($\dot{M}_{\rm flow}/{\rm M_{\odot} yr^{-1}}=0.2-5$), these high-redshift galaxies appear more efficient at removing baryons than low-redshift galaxies with similar $M_{\star}$, showing mass loading-factors of $\dot{M}_{\rm flow}/{\rm SFR}=0.04-0.4$. For their given dynamical mass, the outflow velocities exceed the escape velocities, meaning that they may eventually enrich the Circumgalactic Medium.

The ability to discern subtle emotional cues is fundamental to human social intelligence. As artificial intelligence (AI) becomes increasingly common, AI's ability to recognize and respond to human emotions is crucial for effective human-AI interactions. In particular, whether such systems can match or surpass human experts remains to be seen. However, the emotional intelligence of AI, particularly multimodal large language models (MLLMs), remains largely unexplored. This study evaluates the emotion recognition abilities of MLLMs using the Reading the Mind in the Eyes Test (RMET) and its multiracial counterpart (MRMET), and compares their performance against human participants. Results show that, on average, MLLMs outperform humans in accurately identifying emotions across both tests. This trend persists even when comparing performance across low, medium, and expert-level performing groups. Yet when we aggregate independent human decisions to simulate collective intelligence, human groups significantly surpass the performance of aggregated MLLM predictions, highlighting the wisdom of the crowd. Moreover, a collaborative approach (augmented intelligence) that combines human and MLLM predictions achieves greater accuracy than either humans or MLLMs alone. These results suggest that while MLLMs exhibit strong emotion recognition at the individual level, the collective intelligence of humans and the synergistic potential of human-AI collaboration offer the most promising path toward effective emotional AI. We discuss the implications of these findings for the development of emotionally intelligent AI systems and future research directions.

Artificial Intelligence (AI) is increasingly being used for generating digital assets, such as programming codes and images. Games composed of various digital assets are thus expected to be influenced significantly by AI. Leveraging public data and AI disclosure statements of games, this paper shows that relatively more independent developers entered the market when generative AI became more publicly accessible, but their purposes of using AI are similar with non-independent developers. Game features associated with AI hint nuanced impacts of AI on independent developers.

A multitude of JWST studies reveal a surprising over-abundance of over-massive accreting super-massive blackholes (SMBHs) -- leading to a deepening tension between theory and observation in the first billion years of cosmic time. Across X-ray to infrared wavelengths, models built off of pre-JWST predictions fail to easily reproduce observed AGN signatures (or lack thereof), driving uncertainty around the true nature of these sources. Using a sample of JWST AGN identified via their broadened Halpha emission and covered by the deepest X-ray surveys, we find neither any measurable X-ray emission nor any detection of high-ionization emission lines frequently associated with accreting SMBHs. We propose that these sources are accreting at or beyond the Eddington limit, which reduces the need for efficient production of heavy SMBH seeds at cosmic dawn. Using a theoretical model of super-Eddington accretion, we can produce the observed relative dearth of both X-ray and ultraviolet emission, as well as the high Balmer decrements, without the need for significant dust attenuation. This work indicates that super-Eddington accretion is easily achieved through-out the early Universe, and further study is required to determine what environments are required to trigger this mode of black hole growth.

Existing star-forming vs. active galactic nucleus (AGN) classification schemes using optical emission-line diagnostics mostly fail for low-metallicity and/or highly star-forming galaxies, missing AGN in typical $z\sim0$ dwarfs. To recover AGN in dwarfs with strong emission lines (SELs), we present a classification scheme optimizing the use of existing optical diagnostics. We use SDSS emission-line catalogs overlapping the volume- and mass-limited RESOLVE and ECO surveys to determine the AGN percentage in SEL dwarfs. Our photoionization grids show that the [O III]/H$\beta$ versus [S II]/H$\alpha$ diagram (SII plot) and [O III]/H$\beta$ versus [O I]/H$\alpha$ diagram (OI plot) are less metallicity sensitive and more successful in identifying dwarf AGN than the popular [O III]/H$\beta$ versus [N II]/H$\alpha$ diagnostic (NII plot or "BPT diagram"). We identify a new category of "star-forming AGN" (SF-AGN) classified as star-forming by the NII plot but as AGN by the SII and/or OI plots. Including SF-AGN, we find the $z\sim0$ AGN percentage in dwarfs with SELs to be $\sim$3-16\%, far exceeding most previous optical estimates ($\sim$1\%). The large range in our dwarf AGN percentage reflects differences in spectral fitting methodologies between catalogs. The highly complete nature of RESOLVE and ECO allows us to normalize strong emission-line galaxy statistics to the full galaxy population, reducing the dwarf AGN percentage to $\sim$0.6-3.0\%. The newly identified SF-AGN are mostly gas-rich dwarfs with halo mass < 10^{11.5} M_\odot, where highly efficient cosmic gas accretion is expected. Almost all SF-AGN also have low metallicities (Z $\lesssim 0.4$ Z$_\odot$), demonstrating the advantage of our method.

We present a new empirical framework modeling the metallicity and star-formation history (SFH) dependence of X-ray luminous (L > 10^{36} ergs s$^{-1}$) point-source population luminosity functions (XLFs) in normal galaxies. We expect the X-ray point-source populations are dominated by X-ray binaries (XRBs), with contributions from supernova remnants near the low luminosity end of our observations. Our framework is calibrated using the collective statistical power of 3,731 X-ray detected point-sources within 88 Chandra-observed galaxies at D < 40 Mpc that span broad ranges of metallicity ($Z \approx$ 0.03-2 $Z_\odot$), SFH, and morphology (dwarf irregulars, late-types, and early-types). Our best-fitting models indicate that the XLF normalization per unit stellar mass declines by $\approx$2-3 dex from 10 Myr to 10 Gyr, with a slower age decline for low-metallicity populations. The shape of the XLF for luminous X-ray sources (L < 10^{38} ergs s$^{-1}$) significantly steepens with increasing age and metallicity, while the lower-luminosity XLF appears to flatten with increasing age. Integration of our models provide predictions for X-ray scaling relations that agree very well with past results presented in the literature, including, e.g., the $L_{\rm X}$-SFR-$Z$ relation for high-mass XRBs (HMXBs) in young stellar populations as well as the $L_{\rm X}/M_\star$ ratio observed in early-type galaxies that harbor old populations of low-mass XRBs (LMXBs). The model framework and data sets presented in this paper further provide unique benchmarks that can be used for calibrating binary population synthesis models.

Large language models (LLMs) offer promising opportunities for organizational research. However, their built-in moderation systems can create problems when researchers try to analyze harmful content, often refusing to follow certain instructions or producing overly cautious responses that undermine validity of the results. This is particularly problematic when analyzing organizational conflicts such as microaggressions or hate speech. This paper introduces an Elo rating-based method that significantly improves LLM performance for harmful content analysis In two datasets, one focused on microaggression detection and the other on hate speech, we find that our method outperforms traditional LLM prompting techniques and conventional machine learning models on key measures such as accuracy, precision, and F1 scores. Advantages include better reliability when analyzing harmful content, fewer false positives, and greater scalability for large-scale datasets. This approach supports organizational applications, including detecting workplace harassment, assessing toxic communication, and fostering safer and more inclusive work environments.

We present JWST/MIRI spectra from the Medium-Resolution Spectrometer of IZw18, a nearby dwarf galaxy with a metallicity of $\sim$3% Solar. Its proximity enables a detailed study of highly ionized gas that can be interpreted in the context of newly discovered high-redshift dwarf galaxies. We derive aperture spectra centered on eleven regions of interest; the spectra show very low extinction, A_V $\lesssim 0.1$, consistent with optical determinations. The gas is highly ionized; we have detected 10 fine-structure lines, including [OIV] 25.9 micron with an ionization potential (IP) of $\sim$ 55 eV, and [NeV] 14.3 micron with an IP of $\sim$ 97 eV. The ionization state of IZw18 falls at the extreme upper end of all of the line ratios we analyzed, but not coincident with galaxies containing an accreting massive black hole (active galactic nucleus). Comparison of the line ratios with state-of-the-art photoionization and shock models suggests that the high ionization state in IZw18 is not due to shocks. Rather it can be attributed to metal-poor stellar populations with a self-consistent contribution of X-ray binaries or ultra-luminous X-ray sources. It could also be partially due to a small number of hot low-metallicity Wolf-Rayet stars ionizing the gas; a small fraction (a few percent) of the ionization could come from an intermediate-mass black hole. Our spectroscopy also revealed four 14 micron continuum sources, $\gtrsim 30-100$ pc in diameter, three of which were not previously identified. Their properties are consistent with HII regions ionized by young star clusters.

In the contemporary landscape of computing education, the ubiquity of Generative Artificial Intelligence has significantly disrupted traditional assessment methods, rendering them obsolete and prompting educators to seek innovative alternatives. This research paper explores the challenges posed by Generative AI in the assessment domain and the persistent attempts to circumvent its impact. Despite various efforts to devise workarounds, the academic community is yet to find a comprehensive solution. Amidst this struggle, ungrading emerges as a potential yet under-appreciated solution to the assessment dilemma. Ungrading, a pedagogical approach that involves moving away from traditional grading systems, has faced resistance due to its perceived complexity and the reluctance of educators to depart from conventional assessment practices. However, as the inadequacies of current assessment methods become increasingly evident in the face of Generative AI, the time is ripe to reconsider and embrace ungrading.

The literature documents the effects of the pandemic on birthrate, birthweight, and pregnancy complications. This study contributes to this growing body of research by examining multiple facets of the phenomenon. Using the 2012-2022 hospital inpatient discharge data of New York, we implemented fixed-effects regression models and reported three key findings. First, birthrate was declining pre-pandemic by 1.11% annually. Second, we documented an additional 7.61% decline in birthrate with the onset of the pandemic in 2020. Notably, birthrate did not return to the pre-pandemic trajectory in subsequent years, indicating a persistent decline. Third, this post-pandemic decline was greater in vaginal delivery, with weak evidence of a drop in C-section. In our sample, C-section generates 61% more revenue than vaginal delivery. This raises the possibility that, in response to declining birthrate, healthcare providers have increased C-section rates to make up for lost revenues. While this hinted at upselling in the delivery room, further research is needed to draw definitive conclusions.

Dwarf AGN serve as the ideal systems for identifying intermediate mass black holes (IMBHs) down to the most elusive regimes ($\sim 10^3 - 10^4 M_{\odot}$). However, the ubiquitously metal-poor nature of dwarf galaxies gives rise to ultraluminous X-ray sources (ULXs) that can mimic the spectral signatures of IMBH excitation. We present a novel photoionization model suite that simultaneously incorporates IMBHs and ULXs in a metal-poor, highly star-forming environment. We account for changes in $M_{BH}$ according to formation seeding channels and metallicity, and changes in ULX populations with post-starburst age and metallicity. We find that broadband X-rays and UV emission lines are insensitive to $M_{BH}$ and largely unable to distinguish between ULXs and IMBHs. Many optical diagnostic diagrams cannot correctly identify dwarf AGN. The notable exceptions include He~II~$\lambda$4686 and [O~I]~$\lambda$6300, for which we redefine typical demarcations to account for ULX contributions. Emission lines in the mid-IR show the most promise in separating stellar, ULX, IMBH, and shock excitation while presenting sensitivity to $M_{BH}$ and $f_{\text{AGN}}$. Overall, our results expose the potential biases in identifying and characterizing dwarf AGN purely on strong line ratios and diagnostic diagrams rather than holistically evaluating the entire spectrum. As a proof of concept, we argue that recently discovered over-massive BHs in high-$z$ JWST AGN might not represent the overall BH population, with many galaxies in these samples potentially being falsely classified as purely star-forming.

Over the years, several Bridges papers have delved into the concept of danceability of a knot diagram. Inspired by dancing on non-orientable surfaces, in this paper, we expand danceability to twisted virtual knot diagrams. This paper is accompanied by a Math-Dance video which can be found at this https URL.

As astronomy enters an era defined by global telescope networks, petabyte-scale surveys, and powerful computational tools, the longstanding goals of astronomy education, particularly introductory ``ASTRO101'', but equally encompassing both higher and lower level courses, warrant fresh examination. In June 2024, the AstroEdUNC meeting at UNC--Chapel Hill convened 100 astronomers, education researchers, and practitioners to synthesise community perspectives on the purpose, content, and delivery of astronomy education. Beginning with historical vignettes, the meeting's deliberations were organised into six interrelated themes: (1) Context, highlighting astronomy's evolution from classical charting to multi-messenger discovery and its role as a connective thread across STEM and the humanities; (2) Content, exploring how curricula can balance essential concepts with authentic investigations and leverage open-source and AI-augmented resources; (3) Skills, arguing that astronomy should foreground scientific literacy, computational fluency, and communication through genuine data-driven inquiry; (4) Engagement, advocating for active-learning strategies, formative assessment, and culturally inclusive narratives; (5) Beyond the Classroom, emphasising scaffolding, universal-design practices, and K--12/community partnerships; and (6) Astronomy Education Research, outlining priority areas for assessing knowledge, attitudes, and long-term outcomes. We provide concrete recommendations for future astronomy education research development, underscoring the need for approaches to education that are authentic while meeting the learning and life goal needs of the students, a vibrant community of practice and robust researcher--practitioner partnerships to ensure that introductory astronomy is pertinent, applicable and inspiring to a broad student population.

Kriging is a widely recognized method for making spatial predictions. On the sphere, popular methods such as ordinary kriging assume that the spatial process is intrinsically homogeneous. However, intrinsic homogeneity is too strict in many cases. This research uses intrinsic random function (IRF) theory to relax the homogeneity assumption. A key component of modeling IRF processes is estimating the degree of non-homogeneity. A graphical approach is proposed to accomplish this estimation. With the ability to estimate non-homogeneity, an IRF universal kriging procedure can be developed. Results from simulation studies are provided to demonstrate the advantage of using IRF universal kriging as opposed to ordinary kriging when the underlying process is not intrinsically homogeneous.

We present a methodology for modeling the joint ionizing impact due to a "simple X-ray population" (SXP) and its corresponding simple stellar population (SSP), where "simple" refers to a single age and metallicity population. We construct composite spectral energy distributions (SEDs) including contributions from ultra-luminous X-ray sources (ULXs) and stars, with physically meaningful and consistent consideration of the relative contributions of each component as a function of instantaneous burst age and stellar metallicity. These composite SEDs are used as input for photoionization modeling with Cloudy, from which we produce a grid for the time- and metallicity-dependent nebular emission from these composite populations. We make the results from the photoionization simulations publicly available. We find that the addition of the SXP prolongs the high-energy ionizing output from the population, and correspondingly increases the intensity of nebular lines such as He II $\lambda$1640,4686, [Ne V] $\lambda$3426,14.3$\mu$m, and [O IV] 25.9$\mu$m by factors of at least two relative to models without an SXP spectral component. This effect is most pronounced for instantaneous bursts of star formation on timescales $>$ 10 Myr and at low metallicities ($\sim$ 0.1 $Z_{\odot}$), due to the imposed time- and metallicity-dependent behavior of the SXP relative to the SSP. We propose nebular emission line diagnostics accessible with JWST suitable for inferring the presence of a composite SXP + SSP, and discuss how the ionization signatures compare to models for sources such as intermediate mass black holes.

Marketing scholars have underscored the importance of conceptual articles in providing theoretical foundations and new perspectives to the field. This paper supports the argument by employing two network-based measures - the number of citations and the disruption score - and comparing them for conceptual and empirical research. With the aid of a large language model, we classify conceptual and empirical articles published in a substantial set of marketing journals. The findings reveal that conceptual research is not only more frequently cited but also has a greater disruptive impact on the field of marketing than empirical research. Our paper contributes to the understanding of how marketing articles advance knowledge through developmental approaches.

We present a search for the decay B^0 --> pi^0 pi^0 by the BABAR experiment at the PEP-II asymmetric-energy B-factory at SLAC. Using approximately 88 million B bar{B} pairs collected between 1999 and 2002, we place a 90% confidence level upper limit on the branching fraction of B(B^0 --> pi^0 pi^0) < 3.6 times 10^{-6}. This result is preliminary.

Nearby blue compact dwarf galaxies (BCDs) share similar properties with objects from the Epoch of Reionization revealed by JWST, in terms of low stellar mass, low metallicity and high specific star-formation rate. Thus, they represent ideal local laboratories for detailed multi-wavelength studies to understand their properties and the mechanisms shaping them. We report the first JWST MIRI/MRS observations of the BCD SBS 0335-052 E, analyzing MIR emission lines tracing different levels of ionization (e.g., [NeII], [SIV], [NeIII], [OIV], [NeV]) of the ionized gas. SBS 0335-052 E MIR emission is characterized by a bright point source, located in one of the youngest and most embedded stellar clusters ($t\sim3$ Myr, $A_V\sim15$), and underlying extended high-ionization emission (i.e., [OIV], [NeV]) from the surroundings of the older and less dusty stellar clusters (t&lt; 20 Myr, $A_V\sim8$). From the comparison with state-of-the-art models, we can exclude shocks, X-ray binaries, and old stellar populations as the main sources of the high ionization. Interestingly, a 4-16% contribution of a $\sim10^5$ M$_\odot$ intermediate massive black hole (IMBH) is needed to justify the strong [NeV]/[NeII] and would be consistent with optical/UV line ratios from previous studies. However, even IMBH models cannot explain the strongest [OIV]/[NeIII]. Also, star-forming models (regardless of including X-ray binaries) struggle to reproduce even the lower ionization line ratios (e.g., [SIV]/[NeII]) typically observed in BCDs. Overall, while current models suggest the need to account for an accreting IMBH in this high-$z$ analog, limitations still exist in predicting high-ionization emission lines (I.P. &gt;54 eV) when modeling these low-metallicity environments, thus other sources of ionization cannot be fully ruled out.

Current observational facilities have yet to conclusively detect $10^3 - 10^4 M_{\odot}$ intermediate mass black holes (IMBHs) that fill in the evolutionary gap between early universe seed black holes and $z \sim 0$ supermassive black holes. Dwarf galaxies present an opportunity to reveal active IMBHs amidst persistent star formation. We introduce photoionization simulations tailored to address key physical uncertainties: coincident vs. non-coincident mixing of IMBH and starlight excitation, open vs. closed surrounding gas cloud geometries, and different AGN SED shapes. We examine possible AGN emission line diagnostics in the optical and mid-IR, and find that the diagnostics are often degenerate with respect to the investigated physical uncertainties. In spite of these setbacks, and in contrast to recent work, we are able to show that [O III]/H$\beta$ typically remains bright for dwarf AGN powered by IMBHs down to $10^3 M_{\odot}$. Dwarf AGN are predicted to have inconsistent star-forming and Seyfert/LINER classifications using the most common optical diagnostics. In the mid-IR, [O IV] 25.9$\mu$m and [Ar II] 6.98$\mu$m are less sensitive to physical uncertainties than are optical diagnostics. Based on these emission lines, we provide several mid-IR emission line diagnostic diagrams with demarcations for separating starbursts and AGN with varying levels of activity. The diagrams are valid over a wide range of ionization parameters and metallicities out to $z\sim0.1$, so will prove useful for future JWST observations of local dwarf AGN in the search for IMBHs. We make our photoionization simulation suite freely available.