Abstract
Digital Twins (DTs) for the ocean are rapidly emerging as essential tools for understanding, forecasting, and managing environmental phenomena. However, most existing DT visualization solutions are tightly coupled to specific platforms and lack semantic coherence and interoperability-challenges that are particularly critical in federated and distributed DT systems. Furthermore, visualizing dynamic and spatio-temporal behaviors, such as oil spills, across multiple rendering environments remains a complex, platform-dependent task. In this paper, we present VChor, a domain-agnostic virtual choreography framework designed to address these limitations. Our approach integrates model-driven engineering, semantic web technologies, and platform-independent representations to support the declarative specification of behaviors and visual mappings. A single VChor instance describes spatio-temporal dynamics and associated actions, and can be interpreted by multiple visualization engines (e.g., Unity3D and CesiumJS) without the need for code recompilation or platform-specific programming. We demonstrate our approach through a real-world oil spill monitoring use case, developed in the context of the ILIAD H2020 project, and encapsulated within a modular Application Package. This package automates the generation, validation, and transformation of virtual choreographies from raw data to platform-specific outputs. The framework promotes interoperability, reusability, and scalability, while supporting FAIR principles in environmental Digital Twin workflows. The findings highlight VChor's potential to streamline scenario modeling, enable cross-platform visualization, and support decision-makers with accurate, flexible, and reusable visual representations of ocean dynamics.

Abstract
In recent years, the development of Decision Support Systems (DSS) has played an instrumental role in the advancement of offshore renewable energy projects, particularly within the blue energy sector. Notwithstanding the technological advancements that have been made, the acceleration of such projects continues to be impeded by significant obstacles related to stakeholder engagement, feasibility assessment, and policy compliance. The objective of this study is to propose a design for a DSS for accelerating the construction of blue offshore energy platforms. This is to address the aforementioned challenges by integrating insights from stakeholder feedback and innovation trends. A participatory action study was conducted through a workshop with a diverse group of experts (n=20), including policymakers, practitioners, researchers, and public entities involved in offshore energy projects. The evaluation facilitated the determination of the DSS's efficacy in addressing user requirements and the identification of areas for enhancement. This study proposes a model for integrating stakeholder insights into technological solutions for offshore energy installations, thus offers significant contributions to the domain of sustainable blue energy development.

Abstract
This paper presents a solution for visualizing oil spills at sea by combining satellite data with virtual choreographies. The system enables dynamic, interactive visualization of oil slicks, reflecting their shape, movement, and interaction with environmental factors like currents and wind. High resolution geospatial data supports a multiplatform experience with aerial and underwater perspectives. This approach promotes independence, interoperability, and multiplatform compatibility in environmental disaster monitoring. The results validate virtual choreographies as effective tools for immersive exploration and analysis, offering structured data narratives beyond passive visualization especially valuable for mixed reality applications.

Abstract
Creating content for digital video game is an expensive segment of the development process, and many techniques have been explored to automate it. Much of the generated content is graphical, ranging from textures and sprites to typographical elements and user interfaces. Numerous techniques have been explored to automate the generation of these assets, with recent advancements incorporating artificial intelligence methodologies, such as deep learning generative models. This study comprehensively surveys the literature from 2016 onward, focusing on using machine learning to generate image-based assets for video game development, reviewing the deep learning approaches employed, and analyzing the specific challenges found. Specifically, the deep learning approaches employed, the problems addressed within the domain, and the metrics used for evaluating the results. The study demonstrates a knowledge gap in generative methods for some types of video game assets. In addition, applicability and effectiveness of the most used evaluation metrics in the literature are studied. As future research prospects, with the increase in popularity of generative AI, the adoption of such techniques will be seen in automation processes.

Abstract