Abstract
Breast cancer patients often face difficulties when choosing among diverse surgeries. To aid patients, this paper proposes ACID-GAN (Anatomically and Clinically Informed Deep Generative Adversarial Network), a conditional generative model for predicting post-operative breast cancer outcomes using deep learning. Built on Pix2Pix, the model incorporates clinical metadata, such as surgery type and cancer laterality, by introducing a dedicated encoder for semantic supervision. Further improvements include colour preservation and anatomically informed losses, as well as clinical supervision via segmentation and classification modules. Experiments on a private dataset demonstrate that the model produces realistic, context-aware predictions. The results demonstrate that the model presents a meaningful trade-off between generating precise, anatomically defined results and maintaining patient-specific appearance, such as skin tone and shape.

Abstract
We study the optimal online service for grocery retailers operating both physical and online stores. The challenge lies in optimizing the size of the online assortment and the delivery fees to maximize profitability across channels, while considering customer, operational, and market dynamics. Using transaction data from a major grocery retailer, we employ an alternative-specific conditional logit model to investigate how delivery fees, assortment size, network characteristics, and customer needs influence store choice and spending across physical and online channels. We develop a profitability model that incorporates online service variables, customer behavior, and operational costs, enabling us to explore optimal strategies under various conditions. By identifying favorable conditions for the online store and analyzing optimal service variables, we provide actionable insights for retailers. Our findings challenge common practices in omnichannel retail. We show that delivery fees should not merely cover costs but can be strategically set higher, particularly for retailers with strong offline presence. Additionally, while reducing fulfillment costs improves profitability, its impact is smaller than expected. Multichannel retailers can offset these costs by passing them on to customers, with minimal overall demand loss, as some customers opt to shop in physical stores rather than abandoning the retailer entirely. Lastly, maximizing the online assortment may not always be optimal, particularly if the operational inefficiencies and costs outweigh the value customers place on variety. Our methodological framework provides retailers the opportunity to align their online services with customer preferences and operational constraints and to leverage customer data in shaping their omnichannel strategies. © 2026 The Author(s)

Abstract
This work addresses the challenge of stabilizing a vessel-mounted parallel mechanism (3-RPU) to mitigate wave-induced roll and pitch motions during UAV-USV cooperative operations, a critical requirement for safe take-off and landing in dynamic maritime environments. A hierarchical control architecture is proposed to decouple high-level kinematic stabilization from low-level actuator dynamics, which are managed by high-precision closed-loop digital drivers. Multiple control strategies are evaluated ranging from classical reactive approaches, to model predictive control (MPC) frameworks, and finally, advanced deep reinforcement learning (DRL) methods: Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), trained and tested in a high-fidelity simulation environment modeling realistic ocean waves and vessel-fluid interactions. Among the controllers, the SAC-trained agent achieves the best performance, reducing wave-induced platform motion by up to 89.8%, compared to 77.2% for MPC, while also delivering 8.6 & times; lower inference time (0.161 vs 1.383 ms). The framework's feasibility is further validated through Hardware-in-the-Loop (HiL) testing, demonstrating an 85.8% stabilization efficiency and confirming a minimal simulation-to-reality gap. These results demonstrate that DRL can provide robust, low-latency motion compensation, while outperforming conventional methods in complex maritime scenarios.

Abstract
Breast cancer locoregional treatment includes a wide variety of procedures with diverse aesthetic outcomes. The aesthetic assessment of such procedures is typically subjective, hindering the fair comparison between their outcomes, and consequently restricting evidence-based improvements. Most objective evaluation tools were developed for conservative surgery, focusing on asymmetries while ignoring other relevant traits. To overcome these limitations, we propose SiameseOrdinalCLIP, an ordinal classification network based on image-text matching and pairwise ranking optimisation for the aesthetic evaluation of breast cancer treatment. Furthermore, we integrate a concept bottleneck module into the network for increased explainability. Experiments on a private dataset show that the proposed model surpasses the state-of-the-art aesthetic evaluation and ordinal classification networks.

Abstract
As technology continues to evolve, organizations face growing and complex challenges and opportunities that affect their ability to govern, manage and harness data as a key source of competitive advantage. Equally, data are considered a powerful and unique source of success for organizations, which in turn, can impact their decision-making capabilities and play a critical role in their success. Hence, this article aims to provide a detailed identification, analysis and discussion over the current data governance context and its existing frameworks, highlighting their commonalities, differences and gaps, including ones related to data governance relationship with Generative Artificial Intelligence (GenAI). This article conducts an extensive methodological and in-depth analysis over a set of sixteen data governance frameworks based on different key data governance attributes, denoting that although there are numerous frameworks, they hold weaknesses, limitations and challenges which prevent them from being capable of incorporating and governing the use and management of AI, particularly the demands originating from GenAI. Our findings provide and propose a new and enhanced data governance framework which integrates the best features and ideas from the existing ones and initiatives derived from the advancements and particularities of AI and GenAI models, systems, and overall usage.

Abstract
One of the main challenges in modern industrial environments is managing the large amount of physical documentation obtained during the production process. Companies increasingly seek to adopt paperless alternatives to promote production efficiency and reduce their industrial environmental impact. On the shop floor, each production line relies on standardised forms to verify parameters and conditions before and after production begins; however, the large volume of paper documentation generated from these records led to the need to develop a digital platform capable of streamlining and digitising forms, enhancing process sustainability and efficiency. The proposed interoperable web application provides various features that allow users to create, customise, submit and approve forms digitally. It also integrates automated notifications and alerts for specific situations, enabling more effective responses to the production process's momentary needs. By unifying all processes related to forms management within a digital infrastructure, this solution aligns with the current industrial paradigm, reducing reliance on paper, optimising workflow efficiency, and incorporating innovative and industrial advancements.