Abstract
The notion of obligation is used in a variety of domains, ranging from legal frameworks to cybersecurity (e.g. in access control policies, to specify privacy guarantees in web services and mobile applications, to deal with data collection constraints in the Internet of Things). We define an axiomatic model of obligations as an extension of the category-based access control (CBAC) metamodel. An abstract notion of event is used to identify system states that trigger obligations or fulfill them. The category-based model of obligations inherits the generality of the CBAC metamodel, allowing us to specify obligations in all of the above-mentioned domains. Obligations can be defined on their own or linked to data collection or access control decisions. In the latter case, the access control policy should be compatible with the obligation policy to ensure that obligations can be fulfilled. We provide techniques to analyse the interplay between obligations and authorizations in order to ensure compatibility.

Abstract
The video game industry has grown to become one of the largest in the market, surpassing even the film industry over a decade ago (Statista in Video game industry revenue worldwide 2000-2020). However, the development of games designed with visually impaired players in mind is still almost non-existent when compared to the sheer number of games released yearly. NonVisual Pong is our approach to addressing this challenge, providing blind players with a way to engage in competitive fun through gaming. We took the original Pong game from 1972 and fully adapted it to be played using only a controller-no visual display required. Following the development process, we tested our implementation with experts, discovering that, overall, our game was easy to pick up, required no overly complex setup, and successfully delivered the intended experience. Players enjoyed a balanced challenge and immersion, facilitated by audio cues and the controller's vibrations.

Abstract
PurposeThis study examines how firms' alliance orientation impacts firm financial performance, varying across manufacturing and retail service industries and during the COVID-19 Crisis. Coopetition requires simultaneous competition and cooperation, sometimes competition-based coopetition, other times cooperation-based coopetition. In this study, alliance orientation was used as the observable construct, enabling us to interpret its implications within the broader literature on coopetition dynamics.Design/methodology/approachWe used a sample of 330 Portuguese and Spanish firms across different industries and employed an Ordinary Least Squares model. The study spans 2013-2022, encompassing pre-COVID-19 and during COVID-19 pandemic periods.FindingsResults show that alliance orientation positively influences financial performance in the retail services industry, particularly during COVID-19, where alliances mitigated the negative effects of firm age, sales growth opportunities and asset tangibility. No significant effect was observed in manufacturing firms, highlighting industry-specific dynamics.Originality/valueThe study offers threefold novelties. First, it assesses the impact of strategic alliance engagement on financial performance through an econometric model that considers the effect of strategic alliances on return on assets and includes control variables to express organizational complexity. Second, it highlights that the benefits of alliance strategies, which can enable coopetition dynamics, vary across industries. Third, it provides evidence that alliance orientation can be a strategic risk and crisis management mechanism, particularly during disruptive events such as the COVID-19 pandemic.

Abstract

Abstract
The search for sustainable solutions in the electrical sector has driven the use of biodegradable insulating fluids, such as esters, as alternatives to petroleum-derived mineral oils. This study experimentally evaluates the dielectric performance of two esters (one natural and one synthetic) under high-voltage transient stresses that simulate lightning strikes. A total of 40 standard lightning impulses (± 5 0 ~ k Vpeak) were applied to a prototype with an insulation system impregnated with each fluid. Partial discharge measurements were performed before and after the tests, in accordance with IEC 60270. In both cases, the apparent charge of partial discharge levels remained below 100 pC, the limit established for transformers rated up to 72.5 kV, indicating that there was no significant dielectric degradation. The results demonstrate that the esters tested offer good dielectric performance and provide additional environmental benefits, including biodegradability and a lower ecological impact. Therefore, esters are a technically viable and sustainable alternative for high-voltage insulation systems. © 2026 IEEE.

Abstract
Multi-label image classification models often inevitably learn on partially labeled datasets, where a considerable proportion of labels are missing. However, the popular PyTorch deep learning ecosystem is less compatible with training on partially labeled datasets, as many built-in functions like loss functions and metrics do not work correctly or raise errors when unknown labels are present. To this end, we present an original and easy-to-install Python package called mlcpl, which expands the PyTorch ecosystem to offer a friendly environment for learning with partially labeled datasets. The package provides a series of multi-label loss functions and metrics that are compatible with unknown labels. Seven recently proposed approaches are also implemented for the convenient use of cutting-edge techniques. In addition, eleven dataset loading functions, followed by three partial label simulation schemes, expedite the development of experiments. Furthermore, these functions are simple to use, have a PyTorch-like interface, and can collaborate well with other PyTorch components. Several examples of experiments with mlcpl are also provided for demonstration. We wish the release of this package could facilitate relevant academic research and real-world applications. The source code is available at https://github.com/ maxium0526/mlcpl.