bluuebunny/crossref_metadata_2025_split huggingface dataset:
Beyond the stereotypes: Artificial Intelligence image generation and diversity in anesthesiology
Authors: Gisselbaek Mia , Minsart Laurens , Köselerli Ekin , Suppan Mélanie , Meco Basak Ceyda , Seidel Laurence , Albert Adelin , Barreto Chang Odmara L. , Saxena Sarah , Berger-Estilita Joana 
Published: October 2024

IntroductionArtificial Intelligence (AI) is increasingly being integrated into anesthesiology to enhance patient safety, improve efficiency, and streamline various aspects of practice.ObjectiveThis study aims to evaluate whether AI-generated images accurately depict the demographic racial and ethnic diversity observed in the Anesthesia workforce and to identify inherent social biases in these images.MethodsThis cross-sectional analysis was conducted from January to February 2024. Demographic data were collected from the American Society of Anesthesiologists (ASA) and the European Society of Anesthesiology and Intensive Care (ESAIC). Two AI text-to-image models, ChatGPT DALL-E 2 and Midjourney, generated images of anesthesiologists across various subspecialties. Three independent reviewers assessed and categorized each image based on sex, race/ethnicity, age, and emotional traits.ResultsA total of 1,200 images were analyzed. We found significant discrepancies between AI-generated images and actual demographic data. The models predominantly portrayed anesthesiologists as White, with ChatGPT DALL-E2 at 64.2% and Midjourney at 83.0%. Moreover, male gender was highly associated with White ethnicity by ChatGPT DALL-E2 (79.1%) and with non-White ethnicity by Midjourney (87%). Age distribution also varied significantly, with younger anesthesiologists underrepresented. The analysis also revealed predominant traits such as “masculine, ““attractive, “and “trustworthy” across various subspecialties.ConclusionAI models exhibited notable biases in gender, race/ethnicity, and age representation, failing to reflect the actual diversity within the anesthesiologist workforce. These biases highlight the need for more diverse training datasets and strategies to mitigate bias in AI-generated images to ensure accurate and inclusive representations in the medical field.
Exploring How Education Can Leverage Artificial Intelligence for Social Good
Authors: Leddy Marie , Mc Creanor Niall 
Published: October 2024

Artificial intelligence (AI) is rapidly transforming society and industries, presenting both opportunities and ethical challenges. AI enables machines to perform tasks traditionally done by humans, such as natural language processing, pattern recognition, decision-making, and problem-solving (Brookings, 2023). In education, AI enhances teaching methodologies, student assessment, and administrative tasks through tools like intelligent tutoring systems, adaptive learning platforms, and educational chatbots. These tools offer customised learning experiences, immediate feedback, and data-driven insights. This research aims to investigate how AI can be leveraged within education to promote social good by identifying how familiar educators and students are with AI tools, identify how educators and students perceive the role of AI in education and what are the current applications of AI technologies in educational settings and how widely are they used. Finally, discuss the opportunities and ethical considerations of integrating AI in education. AI technologies can address critical social challenges such as inequality, accessibility, and personalised learning. According to Luckin et al. (2016), "AI can provide tailored educational experiences that adapt to individual learning needs, thus promoting equity in education." This exploratory research begins with an overview of AI's role and tools in education, followed by a discussion of the challenges, opportunities, and ethical considerations associated with AI integration. Understandings are drawn from educator’s response to a questionnaire and a focus group with first year and final-year third level students. This qualitative data, analysed using NVivo software, reveals key themes and significant findings on effectively utilising AI in education.
Mercury-probe measurement of electron mobility in β-Ga2O3 using junction moderated dielectric relaxation
Authors: Li Jian V. , Kim Yunjo , Charnas Adam R. , Noesges Brenton A. , Evans Prescott E. , Asel Thaddeus J. , Neal Adam T. , Gorsak Cameron A. , Nair Hari P. , Mou Shin 
Published: October 2024

Abstract
We demonstrate the junction-moderated dielectric relaxation method to measure the in-plane electron mobility in β-Ga2O3 epitaxial layers. Unlike the Hall technique and channel mobility measurement in field-effect transistors, this method does not require the deposition of permanent metal contacts. Rather, it measures the bias voltage and frequency dependence of the equivalent capacitance of the Mercury/β-Ga2O3/Mercury structure consisting of a Schottky contact, a quasi-neutral thin film semiconductor, and an Ohmic contact connected in series. The intrinsic dielectric relaxation of the bulk β-Ga2O3 semiconductor typically occurs at ∼1012 s−1, but when moderated by the Mercury/β-Ga2O3 Schottky junction, it manifests itself as an inflection in the capacitance-frequency characteristics at a much lower frequency of ∼106 s−1 within the range of most capacitance measuring instruments. Using carrier density and layer thickness determined from capacitance-voltage measurement, we extract the electron mobility of β-Ga2O3 from the junction-moderated dielectric relaxation frequency.
Assessing Wetland Health Through Decomposition in Degraded and Semi-Intact Wetlands in Uganda
Authors: Kibira Waliyyah Nabukeera , Aremu Ademola , Masaba Sowedi 
Published: October 2024

The assessment of ecological functions of wetland areas offers a more dependable measure of the health of these ecosystems. The study examined the decomposition process within two urban wetlands with different degree of disturbance as a means of evaluating their ecological health and functionality. The study also inspected the influence of various physicochemical parameters on rates of decomposition. 20g of Pennisetum purpureum, an increasingly common invasive species in natural ecosystems due to human activities, was harvested, air dried to a constant weight and utilised in litter bag decomposition experiments within the two wetlands. Invasive species such as Pennisetum purpureum often have similar impacts across different ecosystems, making them useful models for generalizing results. Changes in leaf mass over time and leaf breakdown rates using a single exponential decay model were determined. Wetland physicochemical characteristics (pH, temperature, TDS, dissolved oxygen & conductivity) were also measured and recorded for the two wetlands within the duration of the experiment. Pearson’s correlation test was conducted to validate the relationship between the physico-chemical parameters and decomposition rates of the two different wetlands. Results revealed a significant difference in mean decomposition rates, (p < 0.001) with the degraded wetland exhibiting a higher value (0.20 kd-1) compared to the semi-intact wetland (0.015 kd-1). The rates of decomposition were also found to positively correlate with temperature (r = 0.54), conductivity and pH, while showing a slight negative correlation with TDS and dissolved oxygen. The study concluded that rapid decomposition in degraded wetlands is primarily due to human disturbances that alter their physical characteristics indicating deteriorating ecological health inform of eutrophication, altered native plant growth, subsidence, decreased carbon storage, oxygen depletion and so forth. Additionally, the alternating periods of inundation within the wetlands, caused by the wet-dry cycles is a primary influencer of decomposition rates in wetlands especially within the tropical region
Histopathological spectrum of Cardiac Tuberculosis on autopsy: series of 11 cases
Authors: Ruchi Agarwal  , Kulwant Singh  , Swaran Kaur  , Deepti Agarwal  , Monika Gathwal  , Hooda Sunaina 
Published: October 2024

Background: Tuberculosis (TB) is leading cause of morbidity and mortality worldwide. However, this endemic disease rarely involves heart. Cardiac TB can involve any structure of heart with pericarditis is most frequent manifestation.
Methods: This retrospective study was conducted at Department of Pathology from January 2012 to December 2020. Autopsy records of all cases suggestive of tuberculosis in any part of heart were selected and slides were reviewed.
Results: A total of 11 cases of cardiac TB were recorded on autopsy, including 6 cases of isolated myocarditis, 2 cases of myopericarditis 2 cases of isolated pericarditis and one case of necrotizing arteritis in left coronary artery. Concomitant pulmonary TB was present in 72.7% cases.
Conclusion: This study highlights that in all patients of pulmonary tuberculosis with appearance of any cardiovascular sign or symptom, cardiac TB should be suspected as one of the differentials.
Application of FFBP neural network to the prediction of swirling flow
Authors: Lalmi Djemoui , Kifouche Abdessalam , Kaddour Abdelmadjied , Hadef Redjem , Khodja Khadija 
Published: October 2024

Since ancient times, man has been seeking to improve in the field of transportation and energy consumption, as the combination of optimal energy consumption and not polluting the environment in exchange for obtaining a better return made him constantly searching for optimal methods in the field of anxiety and industry based on combustion. The swirl or vortex flow is one of the most important discoveries of the last century seeking to develop combustion, as research is still focused on it in many aspects, whether in terms of the shape of the rotational areas it forms. This study aims to determine the extent to which artificial intelligence can predict the characteristics of the Swirl flow. The model adopted experimental data of the Swirl flow, both descriptive and positional data as inputs and horizontal, vertical, and kinetic energy as outputs for several positions within the combustion chamber. The results indicate that the model can capture the spatial characteristics of the vortex flow field and the combined Learning of the relationship between the input and output parameters. The results of the velocity density distribution and the position of the vortex center of the vortex flow field agree well with the experimental results. The generated prediction model has good prediction accuracy based on previously observed datasets and can reconstruct the vortex flow field. In addition, it can perform inductive prediction on a previously unseen dataset and has some generalization ability. Finally, this study suggests that many potential architectures have applications based on the induction prediction model created here.
SDSS. IV. MaNGA: The Impact of the Acquisition of Gas with Opposite Angular Momentum on the Evolution of Galaxies
Authors: Beom Minje , Walterbos René A. M. , Bizyaev Dmitry 
Published: October 2024

Abstract
A gaseous counterrotating galaxy is a galaxy containing a gas component with opposite angular momentum to the main stellar disk. The counterrotating gas provides direct evidence for the accretion of external material, a key aspect in hierarchical galaxy evolution. We identified 303 gaseous counterrotators out of 9992 galaxies in MaNGA. The majority of the counterrotators are early types. This implies their formation is highly correlated with early-type galaxies, although it is still difficult to know if one leads to the other. To disentangle which of the galaxy characteristics within a morphological class were changed by the accretion of counterrotating gas, we carefully selected a comparison sample with similar fundamental galactic properties but corotation in gas. This comparison shows that gaseous counterrotation correlates with weak rotation in the stellar component, the high central concentration of star-forming regions, if present, and a higher fraction of central low ionization emission regions (cLIER) galaxies. The light distributions of the stellar components, dust and H i content (both low), and overall suppressed star formation rates are similar for both samples and seem typical for the morphological class. We claim that elliptical and about half of the lenticular counterrotators, those with weak rotation in the stellar component in the outskirts and central regions, likely have a major merger origin for the gas acquisition, and the other half of lenticulars, with stronger stellar rotation, may have a minor merger or pure gas accretion origin.
Digging into the Interior of Hot Cores with ALMA (DIHCA). IV. Fragmentation in High-mass Star-forming Clumps
Authors: Ishihara Kousuke , Sanhueza Patricio , Nakamura Fumitaka , Saito Masao , Chen Huei-Ru Vivien , Li Shanghuo , Olguin Fernando , Taniguchi Kotomi , Morii Kaho , Lu Xing , Luo Qiu-yi , Sakai Takeshi , Zhang Qizhou 
Published: October 2024

Abstract
Fragmentation contributes to the formation and evolution of stars. Observationally, high-mass stars are known to form multiple-star systems, preferentially in cluster environments. Theoretically, Jeans instability has been suggested to determine characteristic fragmentation scales, and thermal or turbulent motion in the parental gas clump mainly contributes to the instability. To search for such a characteristic fragmentation scale, we have analyzed Atacama Large Millimeter/submillimeter Array (ALMA) 1.33 mm continuum observations toward 30 high-mass star-forming clumps taken by the Digging into the Interior of Hot Cores with ALMA survey. We have identified 573 cores using the dendrogram algorithm and measured the separation of cores by using the Minimum Spanning Tree technique. The core separation corrected by projection effects has a distribution peaked around 5800 au. In order to remove biases produced by different distances and sensitivities, we further smooth the images to a common physical scale and perform completeness tests. Our careful analysis finds a characteristic fragmentation scale of ∼7000 au, comparable to the thermal Jeans length of the clumps. We conclude that thermal Jeans fragmentation plays a dominant role in determining the clump fragmentation in high-mass star-forming regions, without the need to invoke turbulent Jeans fragmentation.
Atmospheric Response for MeV γ Rays Observed with Balloon-borne Detectors
Authors: Karwin Christopher M. , Kierans Carolyn , Shih Albert Y. , Martinez Castellanos Israel , Lowell Alex , Siegert Thomas , Roberts Jarred , Gallego Savitri , Laviron Adrien , Zoglauer Andreas , Tomsick John A. , Boggs Steven E. ,   for the COSI Collaboration
Published: October 2024

Abstract
The atmospheric response for MeV γ rays (∼0.1–10 MeV) can be characterized in terms of two observed components. The first component is due to photons that reach the detector without scattering. The second component is due to photons that reach the detector after scattering one or more times. While the former can be determined in a straightforward manner, the latter is much more complex to quantify, as it requires tracking the transport of all source photons that are incident on Earth’s atmosphere. The scattered component can cause a significant energy-dependent distortion in the measured spectrum, which is important to account for when making balloon-borne observations. In this work, we simulate the full response for γ-ray transport in the atmosphere. We find that the scattered component becomes increasingly more significant toward lower energies, and at 0.1 MeV, it may increase the measured flux by as much as a factor of ∼2–4, depending on the photon index and off-axis angle of the source. This is particularly important for diffuse sources, whereas the effect from scattering can be significantly reduced for point sources observed with an imaging telescope.
Multistructured Accretion Flow of Sgr A*. II. Signatures of a Cool Accretion Disk in Hydrodynamic Simulations of Stellar Winds
Authors: Balakrishnan Mayura , Russell Christopher M. P. , Corrales Lia , Calderón Diego , Cuadra Jorge , Haggard Daryl , Markoff Sera , Neilsen Joey , Nowak Michael , Wang Q. Daniel , Baganoff Frederick 
Published: October 2024

Abstract
Hydrodynamic simulations of the stellar winds from Wolf–Rayet stars within the Galactic center can provide predictions for the X-ray spectrum of the supermassive black hole Sgr A*. Herein, we present results from updated smooth particle hydrodynamics simulations, building on the architecture of Cuadra et al. and Russell et al., and find that a “cold” (104 K) gas disk forms around Sgr A* with a simulation runtime of 3500 yr. This result is consistent with previous grid-based simulations, demonstrating that a cold disk can form regardless of numerical method. We examine the plasma scenarios arising from an environment with and without this cold disk, by generating synthetic spectra for comparison to the quiescent Fe Kα Sgr A* spectrum from Chandra HETGS, taken through the Chandra X-ray Visionary Program. We find that current and future X-ray missions are unlikely to distinguish between the kinematic signatures in the plasma in these two scenarios. Nonetheless, the stellar wind plasma model presents a good fit to the dispersed Chandra spectra within 1.″5 of Sgr A*. We compare our results to the radiatively inefficient accretion flow (RIAF) model fit to the HETGS spectrum presented in Paper I and find that the Bayesian model evidence does not strongly favor either model. With 9″ angular resolution and high spectral resolution of the X-IFU, NewAthena will offer a clearer differentiation between the RIAF plasma model and hydrodynamic simulations, but only a future X-ray mission with arcsecond resolution will significantly advance our understanding of Sgr A*’s accretion flow in X-rays.
Simulation-based Inference of Radio Millisecond Pulsars in Globular Clusters
Authors: Berteaud Joanna , Eckner Christopher , Calore Francesca , Clavel Maïca , Haggard Daryl 
Published: October 2024

Abstract
Millisecond pulsars (MSPs) are abundant in globular clusters (GCs), which offer favorable environments for their creation. While the advent of recent, powerful facilities led to a rapid increase in MSP discoveries in GCs through pulsation searches, detection biases persist. In this work, we investigate the ability of current and future detections in GCs to constrain the parameters of the MSP population in GCs through a careful study of their luminosity function. Parameters of interest are the number of MSPs hosted by a GC, as well as the mean and the width of their luminosity function, which are typically affected by large uncertainties. While, as we show, likelihood-based studies can lead to ill-behaved posteriors on the size of the MSP population, we introduce a novel, likelihood-free analysis, based on marginal neural ratio estimation, which consistently produces well-behaved posteriors. We focus on the GC Terzan 5 (or Ter 5), which currently counts 48 detected MSPs. We find that 158

−
104

+
294

MSPs should be hosted in this GC, but the uncertainty on this number remains large. We explore the performance of our new method on simulated Terzan 5-like data sets mimicking possible future observational outcomes. We find that significant improvement on the posteriors can be obtained by adding a reliable measurement of the diffuse radio emission of the GC to the analysis or by improving the detection threshold of current radio pulsation surveys by at least a factor of 2.
Multistructured Accretion Flow of Sgr A*. I. Examination of a Radiatively Inefficient Accretion Flow Model
Authors: Balakrishnan Mayura , Corrales Lia , Markoff Sera , Nowak Michael , Haggard Daryl , Wang Q. Daniel , Neilsen Joey , Russell Christopher M. P. , Calderón Diego , Cuadra Jorge , Baganoff Frederick 
Published: October 2024

Abstract
The extreme low-luminosity supermassive black hole Sgr A* provides a unique laboratory in which to test models of radiatively inefficient accretion flows (RIAFs). Previous fits to the quiescent Chandra ACIS-S spectrum found that a RIAF model with an equal inflow–outflow balance works well. In this work, we apply the RIAF model to the Chandra HETG-S spectrum obtained through the Chandra X-ray Visionary Program, which displays features suggestive of temperature and velocity structures within the plasma. A comprehensive forward model analysis accounting for the accretion flow geometry and HETG-S instrumental effects is required for a full interpretation of the quiescent Chandra HETG-S spectrum. We present a RIAF model that takes these effects into account. Our fits to the high-resolution grating spectrum indicate an inflow balanced by an outflow (s ∼ 1) alongside a temperature profile that appears shallower than what would be expected from a gravitational potential following 1/r. The data require that the abundance of iron relative to solar is Z
Fe < 0.32 Z
⊙ (90% credible interval), much lower than the 2 Z
⊙ metallicity measured in nearby late-type giants. While future missions like NewAthena will provide higher spectral resolution, source separation will continue to be a problem. Leveraging Chandra’s unparalleled spatial resolution, which is not expected to be surpassed for decades, remains essential for detailed investigations of the densely populated Galactic center in X-rays.
Reionization Parameter Inference from 3D Minkowski Functionals of the 21 cm Signals
Authors: Diao Kangning , Chen Zhaoting , Chen Xuelei , Mao Yi 
Published: October 2024

Abstract
The Minkowski functionals (MFs), a set of topological summary statistics, have emerged as a powerful tool for extracting non-Gaussian information. We investigate the prospect of constraining the reionization parameters using the MFs of the 21 cm brightness temperature field from the epoch of reionization (EOR). Realistic effects, including thermal noise, synthesized beam, and foreground avoidance, are applied to the mock observations from radio interferometric array experiments such as the Hydrogen Epoch of Reionization Array (HERA) and the Square Kilometre Array (SKA). We demonstrate that the MFs of the 21 cm signal measured with SKA-Low can be used to distinguish different reionization models, whereas the MF measurement with a HERA-like array cannot be made accurately enough. We further forecast the accuracies with which the MF measurements can place constraints on reionization parameters, using the standard Markov Chain Monte Carlo analysis for parameter inference based on forward modeling. We find that for SKA-Low observation, MFs provide unbiased estimations of the reionization parameters with accuracies comparable to the power spectrum (PS) analysis. Furthermore, joint constraints using both MFs and PS can improve the constraint accuracies by up to 30% compared to those with the PS alone. Nevertheless, the constraint accuracies can be degraded if the EOR window is shrunk with strong foreground avoidance. Our analysis demonstrates the promise of MFs as a set of summary statistics that extract complementary information from the 21 cm EOR field to the two-point statistics, which suggests a strong motivation for incorporating the MFs into the data analysis of future 21 cm observations.