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
Service-based architectures provide substantial benefits, yet service orchestration remains a challenge, particularly for newcomers. While various resources on orchestration techniques exist, they often lack clarity and standardization, making best practices difficult to implement and limiting their adoption within the software industry. To address this gap, we analyzed existing literature and tools to identify common orchestration practices. Based on our findings, we define three key orchestration resource optimization patterns: Preemptive Scheduling, Service Balancing, and Garbage Collection. Preemptive Scheduling allows the allocation of sufficient resources for services of higher priority in stressful situations, while Service Balancing enables a restructuring of the nodes to allow better resource usage. To end, Garbage Collection creates cleanup mechanisms to better understand the system’s resource usage and optimize it. These patterns serve as foundational elements for improving orchestration practices and fostering broader adoption in service-based architectures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
[No abstract available]

Abstract
Spectral imaging is a broad term that refers to the use of a spectroscopy technique to analyze sample surfaces, collecting and representing spatially referenced signals. Depending on the technique utilized, it allows the user to reveal features and properties of objects that are invisible to the human eye, such as chemical or molecular composition. However, the interpretability and interaction with the results are often limited to screen visualization of two-dimensional representations. To surpass such limitations, augmented reality emerges as a promising technology, assisted by recent developments in the integration of spectral imaging datasets onto three-dimensional models. Building on this context, this work explores the integration of spectral imaging with augmented reality, aiming to create an immersive toolset to increase the interpretability and interactivity of the results of spectral imaging analysis. The procedure follows a two-step approach, starting from the integration of spectral maps onto a three-dimensional models, and proceeding with the development of an interactive interface to allow immersive visualization and interaction with the results. The approach and tool developed present the opportunity for a user-centric extension of reality, enabling more intuitive and comprehensive analyses with the potential to drive advancements in various research domains.

Abstract
Li-ion batteries are widely used in electric vehicles, grid storage, and portable electronics. Battery Management Systems play a crucial role in ensuring the safety, efficiency, and longevity of Li-ion batteries. Accurate battery modelling is essential for effective battery management functionality, enabling precise state of charge/ state of health estimation, as well as protection against hazardous conditions such as overcharging or overheating. This article explores system identification techniques for battery modelling using a piecewise LTI approach where separate LTI models are identified for different state of charge intervals. A modified Thevenin circuit is employed, where the open-circuit voltage is represented by a capacitor that models the bulk charge storage. The capacitance of this element is dependent on the state of charge, reflecting the nonlinear nature of the battery's charge storage mechanism. Additionally, parallel resistor-capacitor networks capture transient voltage recovery dynamics. The identification process estimates the battery parameters from experimental data, and the resulting piecewise models are interpolated using cubic splines to construct a linear parameter-varying (LPV) representation of the system. The proposed methodology was validated through experimental results, demonstrating its effectiveness in enhancing battery management performance. Namely, (i) the model accurately captures the battery's voltage response with minimal error. (ii) the LPV model obtained by fitting splines to the estimated parameters demonstrates a level of accuracy comparable to that of the piecewise LTI model. (iii) the model robustness was validated through a continuous discharge test, showing strong agreement with experimental data and, therefore, demonstrating its reliability in real-world operating conditions. These results highlight the potential of the proposed methodology in improving battery management systems. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
[No abstract available]