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 onwards, 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. Additionally, 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. © 2018 IEEE.

Abstract
Canine hip dysplasia (CHD) screening relies on accurate positioning in the ventrodorsal hip extended (VDHE) view, as even mild pelvic rotation can affect CHD scoring and impact breeding decisions. This study aimed to assess the association between pelvic rotation and asymmetry in obturator foramina areas (AOFAs) and to develop a computer vision model for automated AOFA measurement. In the first part, 203 radiographs were analyzed to examine the relationship between pelvic rotation, assessed through asymmetry in iliac wing and obturator foramina widths (AOFWs), and AOFAs. A significant association was found between pelvic rotation and AOFA, with AOFW showing a stronger correlation (R-2 = 0.92, p < 0.01). AOFW rotation values were categorized into minimal (n = 71), moderate (n = 41), marked (n = 37), and extreme (n = 54) groups, corresponding to mean AOFA +/- standard deviation values of 33.28 +/- 27.25, 54.73 +/- 27.98, 85.85 +/- 41.31, and 160.68 +/- 64.20 mm(2), respectively. ANOVA and post hoc testing confirmed significant differences in AOFA across these groups (p < 0.01). In part two, the dataset was expanded to 312 images to develop the automated AOFA model, with 80% allocated for training, 10% for validation, and 10% for testing. On the 32 test images, the model achieved high segmentation accuracy (Dice score = 0.96; Intersection over Union = 0.93), closely aligning with examiner measurements. Paired t-tests indicated no significant differences between the examiner and model's outputs (p > 0.05), though the Bland-Altman analysis identified occasional discrepancies. The model demonstrated excellent reliability (ICC = 0.99) with a standard error of 17.18 mm(2). A threshold of 50.46 mm(2) enabled effective differentiation between acceptable and excessive pelvic rotation. With additional training data, further improvements in precision are expected, enhancing the model's clinical utility.

Abstract
Sensing will be a key technology in 6G networks, enabling a plethora of new sensing-enabled use cases. Some of the use cases require deployments over a wide physical area that needs to be sensed by multiple sensing sources at different locations. The efficient management of the sensing resources is pivotal for sustainable sensing-enabled mobile network designs. In this paper, we provide an example of such use case, and argue the energy consumption due to sensing has potential to scale to prohibitive levels. We then propose architectural enhancements to solve this problem, and discuss energy saving and energy efficient strategies in sensing, that can only be properly quantified and applied with the proposed architectural enhancements.

Abstract
Cybercrime is growing rapidly, and it is increasingly important to use advanced tools to combat it and support investigations. One of the battlefronts is the forensic investigation of mobile devices to analyze their misuse and recover information. Mobile devices present numerous challenges, including a rapidly changing environment, increasing diversity, and integration with the cloud/IoT. Therefore, it is essential to have a secure and reliable toolbox that allows an investigator to thwart, discover, and solve all problems related to mobile forensics while deciphering investigations, whether criminal, civil, corporate, or other. In this work, we propose an original and innovative instantiation of a structure in a forensic toolbox for mobile devices, corresponding to a set of different applications, methods, and best practice information aimed at improving and perfecting the investigative process of a digital investigator. To ensure scientific support for the construction of the toolbox, the Design Science Research (DSR) methodology was applied, which seeks to create new and unique artifacts, drawing on the strength and knowledge of science and context. The toolbox will help the forensic investigator overcome some of the challenges related to mobile devices, namely the lack of guidance, documentation, knowledge, and the ability to keep up with the fast-paced environment that characterizes the mobile industry and market. © 2024 by the authors. Licensee ESJ, Italy.

Abstract
Demand Driven Material Requirements Planning argues that production replenishment orders should be scheduled on the shop floor according to the buffers' on-hand inventory. However, the actual performance impact of this remains largely unknown. Using discrete event simulation, this study compares scheduling based on the on-hand inventory, with scheduling based on the inventory net flow position. Results of our study show that scheduling based on the former performs best, particularly when multiple production orders are simultaneously generated and progress independently on the shop floor. Our finds give hints that are important to both, industrial practice and software development for production planning and control.

Abstract
Cyber-resilience is a relatively recent concept that, in essence, adds risk management to the cybersecurity concept and extends the scope of its action to processes and people, in addition to the technological component. It aims to ensure that organizations, systems, and especially critical infrastructures of our society function properly regardless of their dependence on cybernetic resources that may be affected by adverse events. Considering that the ongoing digital transition increases the exposure of such infrastructures to physical and cyberspace threats, this article reports on an exploratory study supported by bibliographical research, which aimed to analyze recent scientific publications to determine the relevance of cyber-resilience in the context of national security and defense. Although the number of publications focused on cyber-resilience is still relatively reduced when compared to the number of publications related to cybersecurity, there is a growing interest in exploring cyber-resilience in areas such as international relations, internal security, and national defense, which are fundamental pillars of the security and defense of States. © 2024, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.