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.

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Abstract
This paper discusses some recent developments in collaborative healthcare research considering settings where human clinicians collaborate through or interact with artificial intelligence (AI)-enabled systems to enhance clinical diagnosis, treatment procedures, and decision-making practices. Through a detailed examination of the potential gaps, implications, and challenges for health professionals and patients, this work explores typical AI-based collaborative clinical workflows and infrastructures that involve tasks such as patient data analysis, medical imaging, and event prediction. A brief synopsis of published research reveals inherent sociotechnical barriers concerning interoperability, data scarcity, bias amplification, trust, and transparency. It also highlights risks related to inadequate model and interface design, the oversimplification of clinical processes (e.g., lack of shared situational awareness), institutional misalignment (e.g., cultural norms and practices shaping how clinicians coordinate their efforts and make decisions based on AI recommendations), and commercial data manipulation that threatens patient care. © 2025 IEEE.

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[No abstract available]

Abstract
Interest in artificial intelligence (AI)-driven crowd work has increased during the last few years as a line of inquiry that expands upon prior research on microtasking to represent a means of scaling up complex tasks through AI mediation. Despite the increasing attention to the macrotask phenomenon in crowdsourcing, there is a need to understand the processes, elements, and constraints underlying the infrastructural and behavioral aspects in such form of crowd work when involving collaboration. To this end, this paper provides a first attempt to characterize some of the research conducted in this direction to identify important paths for an agenda comprising key drivers, challenges, and prospects for integrating human-centered AI in collaborative crowdsourcing environments.