Kevin A. Pimbblet

We investigate the link between dust obscuration and star formation rate (SFR) estimators using Hα luminosities, LHα and radio luminosities, Lradio. While the Balmer Decrement (BD) is the standard tool for attenuating Hα, it can fail to account for optically thick dust regions from which Hα cannot escape. The ratio of SFRs estimated from radio luminosity and LHα corrected for dust attenuation using the BD can be used as a probe of the degree of optically thick dust affecting Hα. We explore this combining Evolutionary Map of the Universe (EMU) Early Science data at 887.5 MHz in the Galaxy and Mass Assembly (GAMA) G23 field with the GAMA data. We draw on dust estimates using both the BD and the dust mass, Md, demonstrating systematic underlying dependencies with Lradio/LHα. By combining the BD and Md, we quantify how they relate to Lradio/LHα and other galaxy properties. We use this approach to establish an Hα SFR (SFRHα) estimator that accounts for optically thick dust, demonstrating its consistency with conventional SFR indicators. Rather than proposing a replacement for standard correction methods, we aim to demonstrate the effectiveness of a dust geometry model that integrates both BD and Md parameters. This Hdust parameter provides a more physically complete representation of optically thick environments that traditional methods may overlook, and can produce SFR estimates consistent with traditional techniques.

Small-scale interactions such as minor mergers, flybys and harassment are considered common driving mechanisms that can both initiate and quench star-forming processes within post-starburst (PSB) galaxies. PSBs have been linked to disturbed morphologies and faint tidal features in numerous works. We aim to independently study these features in E+A galaxies found in the Galaxy and Mass Assembly Fourth Data Release (GAMA DR4). Using image stacking techniques, we derive the averaged radial surface brightness profiles of E+A image stacks alongside the Sérsic parameters that best fit these profiles. These E+A stacks consist of 57 images extracted from the Sloan Digital Sky Survey (SDSS) in g, r & i bands. The E+A stacks are shown to have brighter extended regions than similar non-PSB galaxies, which is indicative of faint tidal features that could be caused by small-scale interactions. Additionally, we make use of Dark Energy Spectroscopic Instrument (DESI) Legacy Survey imaging, which is deeper than SDSS, to further support our conclusions and find that the Legacy Survey images independently strengthen the evidence for excess light at extended radii relative to the comparison samples. We discuss these findings in the context of future surveys such as the Vera C. Rubin Legacy Survey of Space and Time (LSST) survey.

In achieving development objectives, the balance between innovation and protection is perceived as a lever or seesaw in AI policies, as in intellectual property law. However, innovation in the technology sector that uses online content without consent or attribution poses risks to both innovation and rights protection in the culture sector. Generative AI technologies have fundamentally altered the economic and social context for cultural production and practice. Communal cultural identity and meaning maintained through intangible cultural heritage, such as traditional knowledge, music, craft, and performance, are affected by increased opportunities for misrepresentation, misappropriation, and misattribution. Strengthening intellectual property protections and/or extending them to cultural heritage in sui generis regimes are insufficient to address these problems. This article analyzes African continental policy to understand how culture is represented in AI policy and what challenges are faced in addressing generative AI risks. The article suggests that to foster development that benefits the culture sector, “balance” could be better understood as a four-legged chair than a lever, supporting not only innovation and rights protection (including human rights to cultural participation) but also access to technologies and empowerment of creators. Examples from Burkina Faso are used to illustrate the need to supplement sui generis intellectual property rights protection for national cultural heritage with more direct support, such as subsidies or tailored marketing and capacity-building training programs.

The grading of trading cards and collectibles remains a labour-intensive and subjective task, traditionally dependent on expert visual inspection. This paper investigates the integration of Artificial Intelligence (AI) into automated grading systems, with a focus on balancing accuracy, scalability and interpretability. We compare conventional image-processing methods based on OpenCV with modern Convolutional Neural Network (CNN) models and propose a hybrid framework that incorporates YOLOv8 with Oriented Bounding Box (OBB) support for localised defect detection. The system operates through a six-stage pipeline encompassing centring, corner wear, edge integrity, surface quality and holistic scoring. To support reproducibility and consistency, we developed a robotic imaging platform capable of capturing standardised high-resolution images (6000 × 4000 pixels) under controlled lighting conditions, enhanced by Flat Field Correction (FFC) for illumination normalisation. Using this platform, we constructed a large-scale dataset comprising over 43,000 training samples after augmentation, covering a wide range of real-world card conditions. Experimental evaluation using localisation accuracy, region-level grading agreement and professional grading consistency metrics demonstrates that CNN-based classification approaches significantly outperform traditional OpenCV pipelines in predictive accuracy but lack spatial interpretability. In contrast, the proposed hybrid framework achieves improved defect localisation and grading consistency relative to CNN-only baselines, while providing transparent visual explanations through YOLO-OBB detections. The integration of deterministic image analysis with deep learning enables both quantitative subgrades and interpretable defect localisation, offering an explainable and scalable solution for automated collectible card grading.

Morphable Models (3DMMs) are a type of morphable model that takes 2D images as inputs and recreates the structure and physical appearance of 3D objects, especially human faces and bodies. 3DMM combines identity and expression blendshapes with a basic face mesh to create a detailed 3D model. The variability in the 3D Morphable models can be controlled by tuning diverse parameters. They are high-level image descriptors, such as shape, texture, illumination, and camera parameters. Previous research in 3D human reconstruction concentrated solely on global face structure or geometry, ignoring face semantic features such as age, gender, and facial landmarks characterizing facial boundaries, curves, dips, and wrinkles. In order to accommodate changes in these high-level facial characteristics, this work introduces a shape and appearance-aware 3D reconstruction system (named SARS by us), a c modular pipeline that extracts body and face information from a single image to properly rebuild the 3D model of the human full body.

We investigated the star formation history and stellar populations of a sample of 205 Type I quasar host galaxies (0.1 < z < 0.35) and compared them with normal (nonactive) galaxies of the same mass and redshift within the volume of the Galaxy and Mass Assembly redshift survey. We find that quasar host galaxies tend to be star-forming galaxies (∼80%) lying on the star-forming main sequence; the fraction of quasar host galaxies that are quiescent (∼20%) is lower than the fraction of quiescent galaxies in the comparison sample of normal galaxies (54%). We find that the mean star formation rate (SFR) of quasar host galaxies has increased over the past 100 Myr by a factor of 2–3, but these galaxies were star-forming at all times previously. Our data are more consistent with quasar activity originating together with an increase in the SFR of otherwise normal galaxies, similar to episodic star formation in normal spirals. We argue that this indicates that secular processes and minor mergers may be the favored triggers of nuclear activity in the local Universe.