Abstract
Ensuring digital resources are accessible to all users, including those with disabilities, is critical in today's digital landscape. The growing volume of online content has intensified the need for automated accessibility evaluations to ensure compliance with accessibility guidelines. Yet, existing automated tools are limited in scope, being unable to identify many types of accessibility barriers. Recent advances in AI, particularly large language models (LLMs), offer opportunities to expand the range of automated accessibility checks. This work explores the ability of LLMs to detect accessibility barriers related to web page headings. We developed targeted prompts to help LLMs identify them and evaluated the effectiveness of three models - Llama 3.1, GPT-4o, and GPT-4o mini - in multiple versions of a reference webpage, each featuring different heading-related barriers. Findings reveal that model performance depends on barrier type: Llama 3.1 stands out at detecting structural issues like heading appropriateness and hierarchy, GPT-4o is better at identifying accessible names and semantic substitutions, while GPT-4o mini is the most versatile, handling complex structural modifications and labelling. This study highlights LLM's potential in advancing web accessibility evaluation and bridging gaps in automated assessments.

Abstract
The alterations to the visual appearance of patients' breasts that occur due to breast cancer locoregional treatment can impact the self-esteem and satisfaction of the patients, affecting quality-of-life after treatment. As such, it is imperative that the patients are adequately informed of the potential aesthetic outcomes of treatment, to facilitate the choice of treatment and promote realistic expectations. As breast asymmetries are among the most notable effects of treatment, we propose a conditional generative adversarial network for manipulating the breast shape in torso images, applying it to simulate how the breasts' shape may change through surgical interventions. Experiments on a private breast dataset suggest that the proposed model outperforms the state-of-the-art in the realistic reconstruction of the torso of the patient while effectively manipulating the breasts.

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PurposeFresh food products remain one of the most challenging product categories for e-commerce managers. The literature emphasizes the importance of perceived freshness in explaining their purchase behavior. However, studies on online purchases of fresh food products are scarce, especially regarding repurchase intentions, and the role of perceived freshness in online settings has so far been disregarded. This research addresses this gap by examining the role of perceived freshness in the intention to repurchase fresh food products online.Design/methodology/approachGuided by the expectation confirmation theory (ECT) and the perceived risk theory, this study defined a set of hypotheses tested through structural equation modeling. Participants were consumers with previous experience in purchasing fresh food products online.FindingsThe findings indicate that the importance of sensory attributes negatively affected the perceived freshness of fresh food products purchased online, while the importance of non-sensory attributes had a non-significant impact. Expectations of freshness positively affected perceived freshness and confirmation of freshness, as suggested by ECT. The hypothesized positive effects of confirmation on satisfaction and of satisfaction on intention to repurchase fresh food products online were also supported. Finally, it was found that repurchase intention was negatively affected by perceived performance risk and financial risk.Originality/valueThis article contributes to the limited literature on online purchase of fresh food by focusing on perceived freshness as a determinant of repurchase intention.

Abstract
COVID-19 is an extremely contagious respiratory sickness instigated by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Common symptoms encompass fever, cough, fatigue, and breathing difficulties, often leading to hospitalization and fatalities in severe cases. CTCovid19 is a novel model tailored for COVID-19 detection, specifically honing in on a distinct deep learning structure, ResNet-50 trained with ImageNet serves as the foundational framework for our model. To enhance its capability to capture pertinent features related to COVID-19 patterns in Computed Tomography scans, the network underwent fine-tuning through layer adjustments and the addition of new ones. The model achieved accuracy rates that went from 97.0 % to 99.8 % across three widely recognized and documented datasets dedicated to COVID-19 detection. © 2024 The Authors