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Breast cancer is the leading cancer affecting women globally. Despite deep learning models making significant strides in diagnosing and treating this disease, ensuring fair outcomes across diverse populations presents a challenge, particularly when certain demographic groups are underrepresented in training datasets. Addressing the fairness of AI models across varied demographic backgrounds is crucial. This study analyzes demographic representation within the publicly accessible Emory Breast Imaging Dataset (EMBED), which includes de-identified mammography and clinical data. We spotlight the data disparities among racial and ethnic groups and assess the biases in mammography image classification models trained on this dataset, specifically ResNet-50 and Swin Transformer V2. Our evaluation of classification accuracies across these groups reveals significant variations in model performance, highlighting concerns regarding the fairness of AI diagnostic tools. This paper emphasizes the imperative need for fairness in AI and suggests directions for future research aimed at increasing the inclusiveness and dependability of these technologies in healthcare settings. Code is available at: https://github.com/kuanhuang0624/EMBEDFairModels. 

ABSTRACT We present an RR Lyrae (RRL) catalogue based on the combination of the third data release of the Zwicky Transient Facility (ZTF DR3) and Gaia EDR3. We use a multistep classification pipeline relying on the Fourier decomposition fitting to the multiband ZTF light curves and random forest classification. The resulting catalogue contains 71 755 RRLs with period and light-curve parameter measurements and has a completeness of 0.92 and a purity of 0.92 with respect to the Specific Objects Study Gaia DR2 RRLs. The catalogue covers the Northern sky with declination ≥−28°, its completeness is ≳0.8 for heliocentric distance ≤80 kpc, and the most distant RRL is at 132 kpc. Compared with several other RRL catalogues covering the Northern sky, our catalogue has more RRLs around the Galactic halo and is more complete at low-Galactic latitude areas. Analysing the spatial distribution of RRL in the catalogue reveals the previously known major overdensities of the Galactic halo, such as the Virgo overdensity and the Hercules–Aquila Cloud, with some evidence of an association between the two. We also analyse the Oosterhoff fraction differences throughout the halo, comparing it with the density distribution, finding increasing Oosterhoff I fraction at the elliptical radii between 16 and 32 kpc and some evidence of different Oosterhoff fractions across various halo substructures. 

2D particle surfactants are attractive for the formation of highly stable emulsions and use as templates to prepare composite structures with performance properties dependent on the composition. Cobalt oxide nanosheets (CONs) are a relatively understudied class of 2D particle surfactants that can be produced by the chemical exfoliation of lithium cobalt oxide, a transition metal oxide known for excellent gas-sensing, catalytic, and electrochemical properties. Here, we report a simple method to access CONs stabilized oil-in-water Pickering emulsions and use these as templates to prepare particles with a core of polymer and shell of CONs. Salt-flocculated CONs produce emulsions with droplets of hydrophobic monomer ( e.g. , styrene) in water that are stable for at least 24 hours, and suspension free radical polymerization is used to produce CON-armored particles. Characterization by X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and thermal gravimetric analysis (TGA) confirmed the presence of CONs on the surface of the polymer core. We then demonstrated the CON-armored polymer particles can activate the oxidant peroxymonosulfate (PMS) for the degradation of bisphenol A (BPA). Freshly prepared and artificially aged CON-armored particles showed full degradation of BPA in less than an hour and no decrease in activity was observed after two uses. CON-armored particles combine high surface area of the nanosheets with the ease of recoverability of the particles. 

ABSTRACT We report the result of searching for globular clusters (GCs) around 55 Milky Way (MW) satellite dwarf galaxies within the distance of 450 kpc from the Galactic Centre except for the Large and Small Magellanic Clouds and the Sagittarius dwarf. For each dwarf, we analyse the stellar distribution of sources in Gaia DR2, selected by magnitude, proper motion, and source morphology. Using the kernel density estimation of stellar number counts, we identify 11 possible GC candidates. Cross-matched with existing imaging data, all 11 objects are known either GCs or galaxies and only Fornax GC 1–6 among them are associated with the targeted dwarf galaxy. Using simulated GCs, we calculate the GC detection limit $$M_{\rm V}^{\rm lim}$$ that spans the range from $$M_{\rm V}^{\rm lim}\sim -7$$ for distant dwarfs to $$M_{\rm V}^{\rm lim}\sim 0$$ for nearby systems. Assuming a Gaussian GC luminosity function, we compute that the completeness of the GC search is above 90 per cent for most dwarf galaxies. We construct the 90 per cent credible intervals/upper limits on the GC specific frequency SN of the MW dwarf galaxies: 12 < SN < 47 for Fornax, SN < 20 for the dwarfs with −12 < MV < −10, SN < 30 for the dwarfs with −10 < MV < −7, and SN < 90 for the dwarfs with MV > −7. Based on SN, we derive the probability of galaxies hosting GCs given their luminosity, finding that the probability of galaxies fainter than MV = −9 to host GCs is lower than 0.1.